physical activity and mental health literature review

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• Volume 57, Issue 18
• Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews
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• http://orcid.org/0000-0002-7227-2406 Ben Singh 1 ,
• Timothy Olds 1 ,
• Rachel Curtis 1 ,
• http://orcid.org/0000-0003-3057-0963 Dorothea Dumuid 1 ,
• Rosa Virgara 1 ,
• Amanda Watson 1 ,
• Kimberley Szeto 1 ,
• Edward O'Connor 1 ,
• Ty Ferguson 1 ,
• Emily Eglitis 1 ,
• Aaron Miatke 1 ,
• Catherine EM Simpson 1 ,
• Carol Maher 2
• 1 Allied Health & Human Performance , University of South Australia , Adelaide , South Australia , Australia
• 2 Health and Use of Time (HUT) Group , University of South Australia , Adelaide , South Australia , Australia
• Correspondence to Dr Ben Singh, University of South Australia, Adelaide, South Australia, Australia; ben.singh{at}unisa.edu.au

Objective To synthesise the evidence on the effects of physical activity on symptoms of depression, anxiety and psychological distress in adult populations.

Design Umbrella review.

Data sources Twelve electronic databases were searched for eligible studies published from inception to 1 January 2022.

Eligibility criteria for selecting studies Systematic reviews with meta-analyses of randomised controlled trials designed to increase physical activity in an adult population and that assessed depression, anxiety or psychological distress were eligible. Study selection was undertaken in duplicate by two independent reviewers.

Results Ninety-seven reviews (1039 trials and 128 119 participants) were included. Populations included healthy adults, people with mental health disorders and people with various chronic diseases. Most reviews (n=77) had a critically low A MeaSurement Tool to Assess systematic Reviews score. Physical activity had medium effects on depression (median effect size=−0.43, IQR=−0.66 to –0.27), anxiety (median effect size=−0.42, IQR=−0.66 to –0.26) and psychological distress (effect size=−0.60, 95% CI −0.78 to –0.42), compared with usual care across all populations. The largest benefits were seen in people with depression, HIV and kidney disease, in pregnant and postpartum women, and in healthy individuals. Higher intensity physical activity was associated with greater improvements in symptoms. Effectiveness of physical activity interventions diminished with longer duration interventions.

Conclusion and relevance Physical activity is highly beneficial for improving symptoms of depression, anxiety and distress across a wide range of adult populations, including the general population, people with diagnosed mental health disorders and people with chronic disease. Physical activity should be a mainstay approach in the management of depression, anxiety and psychological distress.

PROSPERO registration number CRD42021292710.

• physical activity
• stress, physiological

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bjsports-2022-106195

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Introduction

Mental health disorders are among the leading causes of the global health-related burden, with substantial individual and societal costs. 1 2 In 2019, one in eight people (970 million) worldwide were affected by a mental health disorder 3 and almost one in two (44%) will experience a mental health disorder in their lifetime. 4 The annual global costs of mental health disorders have been estimated at $2.5 trillion (USD), which is projected to increase to $6 trillion (USD) by 2030. 5 Depression is the leading cause of mental health-related disease burden, 6 while anxiety is the most prevalent mental health disorder. 3 Additionally, the COVID-19 pandemic has been associated with increased rates of psychological distress, with prevalence ranging between 35% and 38% worldwide. 7–9

The role of lifestyle management approaches, such as exercise, sleep hygiene and a healthy diet, varies between clinical practice guidelines in different countries. In US clinical guidelines, 10 psychotherapy or pharmacotherapy is recommended as the initial treatment approaches, with lifestyle approaches considered as ‘complementary alternative treatments’ where psychotherapy and pharmacotherapy are ‘ineffective or unacceptable’. In other countries such as Australia, lifestyle management is recommended as the first-line treatment approach, 11 12 though in practice, pharmacotherapy is often provided first.

There have been hundreds of research trials examining the effects of physical activity (PA) on depression, anxiety and psychological distress, many of which suggest that PA may have similar effects to psychotherapy and pharmacotherapy (and with numerous advantages over psychotherapy and pharmacotherapy, in terms of cost, side-effects and ancillary health benefits). 13–18 Despite the evidence for the benefits of PA, it has not been widely adopted therapeutically. Patient resistance, the difficulty of prescribing and monitoring PA in clinical settings, as well as the huge volume of largely incommensurable studies, have probably impeded a wider take-up in practice. 13 14 17

Meta-reviews are systematic reviews of systematic reviews, offering a way of synthesising a vast evidence base. While there have been several meta-reviews of PA for depression, anxiety and psychological distress, 17 19–24 they have focused on specific population subgroups, particular conditions (eg, depression only) or on particular forms of PA. We set out to undertake the most comprehensive synthesis to date of evidence regarding the effects of all modes of PA on symptoms of depression, anxiety and psychological distress in adult populations.

Protocol and registration

The protocol for this systematic umbrella review was prospectively registered on PROSPERO and results are reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 25 guidelines.

Selection criteria and search strategy

The population, intervention, comparison, outcomes and study type (PICOS) framework was used to develop the inclusion criteria as follows: population: any adult population (aged ≥18 years); intervention: interventions designed to increase PA. The following definition of PA was used: ‘any bodily movement produced by the contraction of skeletal muscles that results in a substantial increase in caloric requirements over resting energy expenditure’. 26 Reviews were eligible irrespective of PA modality, supervision, delivery (eg, in-person or online) or dose (frequency, intensity and duration). Reviews were ineligible if they included any randomised control trials (RCTs) of non-PA interventions, if PA was combined with another intervention (eg, diet) or if they evaluated single bouts of acute exercise. Comparator: reviews were eligible if ≥75% of the included RCTs involved either usual care, waitlist, nothing an equal attention intervention or a lower/lesser PA intervention (eg, a supervised exercise intervention vs printed PA materials). During study selection, it became apparent that the comparator inclusion/exclusion criteria needed elaboration. After careful consideration and discussion, we decided to exclude reviews where >25% of component RCTs compared PA to pharmaceutical interventions or compared two types of equal dose exercise (eg, resistance vs aerobic exercise) without a non-PA comparison, since the inclusion of such reviews would limit our ability to evaluate the effectiveness of PA per se. Outcomes: any self-report or clinician-rated assessment of depression, anxiety or psychological distress symptoms. Study type: systematic reviews with meta-analyses of RCTs only, which included meta-analyses of the outcomes of interest.

Twelve databases were searched (CINAHL, Cochrane, Embase, MEDLINE, Emcare, ProQuest Health and Medical Complete, ProQuest Nursing and Allied Health Source, PsycINFO, Scopus, Sport Discus, EBSCOhost and Web of Science) using subject heading, keyword and Medical Subject Headings (MeSH) term searches for ‘systematic review’, ‘meta-analysis’, ‘physical activity’, ‘exercise’, ‘anxiety’, ‘depression’ and ‘psychological distress’ (see online supplemental eTable 1 for the full search strategy). Database searches were limited to peer-reviewed journal articles published in English language from inception to 1 January 2022.

Supplemental material

Data management and extraction.

Search results were imported into EndNote V.x9 (Clarivate, Philadelphia) where duplicates were removed, then exported into Covidence (Veritas Health Innovation, Melbourne, Australia). Title/abstract and full-text screening, data extraction and risk of bias scoring were completed in duplicate by two independent reviewers (BS and AM, AW, CEMS, DD, EE, EO, KS, RC, RV or TF), with disagreements resolved by team discussion.

Data were extracted in duplicate by two independent reviewers (BS and AM, AW, CEMS, DD, EE, EO, KS, RC, RV or TF) using a standardised extraction form, 27 28 and discrepancies were resolved by team discussion. The risk of bias of the included reviews was assessed by two independent reviewers (BS and AM, AW, CEMS, DD, EE, EO, KS, RC, RV or TF) in duplicate using the A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) tool. 29 The AMSTAR-2 tool involves 16 items, with each item scored as yes, partial yes or no. Seven items are considered ‘critical’ and nine ‘non-critical’. 29 The critical domains are protocol registration, adequacy of search strategy, justification for excluding individual studies, risk of bias assessment, appropriateness of meta-analysis methods, use of risk of bias during interpretation and assessment of publication bias. Reviews were rated as ‘high confidence’ (0 critical weakness and <3 non-critical weaknesses), ‘moderate’ (one critical weakness and <3 non-critical weaknesses), ‘low’ (>1 critical weakness and <3 non-critical weaknesses) or ‘critically low’ (>1 critical weakness and ≥3 non-critical weaknesses). 29

Umbrella review synthesis methods

The overlap in component RCTs that were included across all eligible reviews was assessed using the Corrected Covered Area (CCA) method. 30 A CCA of 100% indicates that every review included in our umbrella review comprised the same component RCTs, while a CCA of 0% indicates that every review in our umbrella review included entirely unique RCTs. The following cut-offs were used to quantify the CCA: 0%–5%=‘slight overlap’; 6%–10%=‘moderate’; 11%–15%=‘high’ and >15%=‘very high’ overlap. 30 Publication bias was assessed by creating a funnel plot and observing the presence of asymmetries or missing sections. 31

Meta-analysis results from each review were presented using forest plots. Separate forest plots were created for meta-analyses reporting standardised (eg, standardised mean difference, SMD) and unstandardised effect sizes (eg, mean difference). For meta-analyses that reported standardised effect sizes, we undertook subgroup analyses for clinical status and intervention characteristics. Meta-analysis results were summarised using medians and IQRs

The Oxford Centre for Evidence-Based Medicine levels of evidence and grades for recommendations 32 were used to classify the overall level of evidence as grade A: consistent level 1 studies (ie, systematic reviews of RCTs or individual RCTs); B: consistent level 2 (ie, systematic reviews of cohort studies or individual cohort studies) or level 3 studies (ie, systematic reviews of case–control studies or individual case–control studies) or extrapolations from level 1 studies; C: level 4 studies (ie, case series) or extrapolations from level 2 or 3 studies or D: level 5 (ie, expert opinion without explicit critical appraisal) evidence or troublingly inconsistent or inconclusive studies of any level. 32

Of the 1280 records identified, 97 were eligible. They included 1039 unique (component) RCTs and the CCA was 0.6%, indicating slight overlap (see online supplemental eFigure 1 for PRISMA flowchart, including reasons for exclusions). Evaluation of funnel plots indicated no evidence of publication bias ( online supplemental eFigure 2 ).

An overview of all reviews’ characteristics is shown in online supplemental eTable 2 . There was a total of >128 119 participants (n=1 33 did not report the number of participants). Mean participant age ranged from 29 to 86 (median=55) years, and most reviews (n=83, 86%) involved female and male participants. An overview of all populations and PA modalities is shown in table 1 . Fifteen reviews specifically involved individuals with depression 33–41 and three involved individuals with anxiety. 42–44 Most reviews involved various PA modes (n=70) and most (n=77) had a critically low AMSTAR-2 score (low: n=10; high: n=10, online supplemental eTable 3 ).

• View inline

Overview of all populations, conditions and physical activity modes of the included reviews

Meta-analysis results: depression

Results from 72 meta-analyses based on SMD (n=875 component RCTs, >62 040 participants) showed a medium effect in favour of PA for reducing depression and depressive symptoms (median SMD=−0.43, IQR=−0.66 to –0.27, figure 1 ).

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Results of meta-analyses that assessed symptoms of depression using standardised mean differences (negative values represent a reduction in symptoms).

MD effect size for each instrument was: profile of mood states: −7.68 (1 review), Beck Depression Inventory: −5.53 (IQR=−6.24 to –4.81), The Edinburgh Postnatal Depression Scale: −2.97 (IQR=−3.49 to –2.44), self-rating scale: −3.99 (one review), Brief Symptom Inventory 18: −3.02 (one review), Centre for Epidemiological Studies Depression: −0.36 (IQR=−1.25 to 0.02), Montgomery-Asberg Depression Rating Scale: −1.80 and Hospital Anxiety and Depression Scale: −1.26 (IQR=−1.41 to –1.18, online supplemental eFigure 3 and online supplemental eTable 4 ).

Grade of recommendation : (A) Consistent level 1 studies.

Results from 28 meta-analyses using SMD (171 component RCTs, >10 952 participants) showed a medium effect of PA for reducing anxiety (median SMD=−0.42, IQR=−0.66 to –0.26, figure 2 ).

Results of meta-analyses that assessed symptoms of anxiety using standardised mean differences (negative values represent a reduction in symptoms).

MD effect sizes for each instrument were: The State-Trait Anxiety Inventory: −3.61 (IQR=−6.01 to –1.66), Brief Symptom Inventory-18: −5.45 (1 review), Self-rating scale: −4.57 (1 review), Hospital Anxiety and Depression Scale: −1.26 (IQR=−1.26 to –0.79, online supplemental eTable 4 and online supplemental eFigure 5 ).

Grade of recommendation: (A) Consistent level 1 studies.

Psychological distress

One systematic review 45 reported SMD results for psychological distress (six component RCTs, 508 participants), while another systematic review 46 reported MD results (one component RCT, 39 participants). Results showed a medium effect in favour of PA, compared with usual care (SMD=−0.60, 95% CI −0.78 to –0.42). For MD, findings showed no significant effect (MD=−0.30, 95% CI −5.55, 4.95, one review, one component RCT, 39 participants).

Grade of recommendation: (B) Consistent level 2 or 3 studies or extrapolations from level 1 studies.

Subgroup analyses: clinical status

Seventeen reviews provided data on patients with cancer, 45 47–62 and 16 on people with depression or depressive symptoms. 10 33 39 63–75 PA was effective in reducing depressive symptoms across all conditions (median SMD range: –0.85 (kidney disease), –0.16 (cardiovascular disease)). The largest effects were found in kidney disease, HIV, chronic obstructive pulmonary disease, generally healthy adults and individuals diagnosed with depression ( table 2 ).

Summary data on the effects of physical activity interventions on depression for a range of clinical conditions, including the number of reviews, studies and participants covered; and the 25th percentile, median and 75th percentile for standardised mean differences

PA was generally effective for reducing anxiety across disease conditions, with median SMDs ranging from –1.23 (HIV) to –0.16 (multiple sclerosis). However, the evidence base was limited except for cancer and anxiety disorders ( table 3 ).

Summary data on the effects of physical activity interventions on anxiety for a range of clinical conditions, including the number of reviews, studies and participants covered; and the 25th percentile, median and 75th percentile for standardised mean differences

Exercise mode

Eighteen reviews 33 34 37 39 42 51 57 58 60 61 72–74 76–80 provided analyses by exercise mode (310 component RCTs, >14 496 participants, online supplemental eFigure 6 ). All modes were effective, and median effect sizes (SMDs) were similar across modes: –0.64 (IQR=–0.86 to–0.19) for strength-based interventions (nine reviews); –0.47 (IQR=–0.64 to–0.29) for mixed-mode interventions (12 reviews); –0.46 (IQR=–0.77 to–0.33) for stretching, yoga and other mind–body modalities (11 reviews) and –0.45 (IQR=–0.79 to–0.37) for aerobic exercise (15 reviews).

Fifteen reviews 44 45 48 51 58 60 61 78 79 81–86 reported analyses by exercise mode (115 component RCTs, >5451 participants, online supplemental eFigure 7 ). All modes were effective, with median SMDs of –0.23 (IQR=–0.37 to –0.08) for strength-based interventions (two reviews); –0.35 (IQR=–0.86 to –0.23) for mixed modes (four reviews); –0.42 (IQR=–0.78 to –0.16) for stretching, yoga, and other mind-body modalities (seven reviews) and –0.29 (IQR=–0.54, –0.16) for aerobic exercise (six reviews).

Exercise intensity

Five reviews 21 42 58 73 74 reported analyses by exercise intensity (63 component RCTs, >2776 participants, online supplemental eFigure 8 ). Low, moderate and high-intensity exercise interventions had a median SMD of –0.22 (IQR=–0.50 to –0.12), –0.56 (IQR=–1.03 to –0.33) and –0.70 (IQR=–1.25 to –0.24), respectively.

Two reviews 58 84 reported analyses by exercise intensity (23 component RCTs, online supplemental eFigure 9 ). All intensities were effective. The single review for low-intensity exercise had a median SMD of –0.26; the one for moderate-intensity exercise –0.47, and the two for high-intensity exercise –0.44 (IQR=–0.49 to –0.13).

Intervention duration

Twelve reviews 38 42 56 57 60 61 65 68 69 78 80 reported analyses by intervention duration (166 component RCTs, 15 669 participants, online supplemental eFigure 10 ). All durations were effective, but effectiveness declined as intervention duration increased. The median SMDs for short (≤12 weeks, 12 reviews), medium (12–23 weeks, 11 reviews) and long duration (≥24 weeks, 4 reviews) interventions were –0.84 (IQR=–1.50 to –0.48), –0.46 (IQR=–0.53 to –0.25) and –0.28 (IQR=–1.15 to –0.17), respectively.

Four reviews 56 60 61 78 reported analyses by intervention duration (38 component RCTs, 2325 participants, online supplemental eFigure 11 ). Median SMDs for short (12 weeks) and median-duration (12–23 weeks) interventions were –0.55 (IQR=–0.83 to –0.27) and –0.47 (IQR=–0.72 to –0.08), respectively. The single review reporting on longer interventions (≥24 weeks) reported a median SMD of –0.15.

Weekly duration

Four reviews 42 44 57 58 presented analyses by weekly session duration (68 component RCTs, >5016 participants, online supplemental eFigure 12 ). The median SMD for ≤150 min/week and >150 min/week was –0.58 (IQR=–0.77 to –0.30) and –0.29 (IQR=–0.40 to –0.07), respectively.

One review 58 provided analyses by weekly session duration (17 component RCTs, online supplemental eFigure 13 ). The median SMDs for <150 min/week and ≥150 min/week were –1.23 and –0.99, respectively.

Session frequency

Three reviews 42 76 78 (36 component RCTs, >232 participants) reported on session frequency. High-frequency (5–7 sessions per week), moderate-frequency (4–5 per week) and low-frequency (<4 per week) interventions had a median SMD of –0.76 (IQR=–1.20 to –0.32), –1.12 (–1.39 to –0.85) and –0.47 (IQR=–0.59 to–0.35), respectively ( online supplemental eFigure 14 ).

One review 78 compared session frequency, with SMDs of –0.50, –0.96 and –0.52 for 2–3, 4–5 and 6–7 session per week, respectively ( online supplemental eFigure 13 ).

Session duration

Three reviews 42 50 78 presented analyses on session duration ( online supplemental content 17 ). Long (≥60 min, SMD=–0.57, IQR –0.85 to –0.35) and medium (30–60 min, SMD=–0.60, IQR –0.78 to –0.41) session durations had similar benefits. The sole study of short sessions (<30 min) had a SMD of 0.01 ( online supplemental eFigure 15 ).

One review 78 reported on the effects of session duration ( online supplemental content 16 ). There was no difference between long (SMD=–0.63) and short (SMD=–0.83) sessions ( online supplemental eFigure 13 ).

This is the first ever study to compile the extensive base of evidence regarding the effects of PA on depression, anxiety and psychological distress. We identified 97 systematic reviews, reporting the findings of 1039 unique RCTs, involving 128 119 participants. Findings suggest that PA interventions are effective in improving symptoms of depression and anxiety. Improvements were observed across all clinical populations, though the magnitude of effect varied across different clinical populations. The greatest benefits were seen in people with depression, pregnant and postpartum women, apparently healthy individuals and individuals diagnosed with HIV or kidney disease. All PA modes were effective, and higher intensity exercise was associated with greater improvements for depression and anxiety. Longer duration interventions had smaller effects compared with short and mid-duration, though the longest duration interventions still had positive effects.

PA was effective at reducing depression and anxiety across all clinical conditions, though the magnitude of the benefit varied between clinical groups. The larger effect sizes observed in clinical populations may reflect that these populations experience above-average symptoms of depression and anxiety and have low PA levels, and, therefore, have a greater scope for improvement compared with non-clinical populations. 17

All PA modes were beneficial, including aerobic, resistance, mixed-mode exercise and yoga. It is likely that the beneficial effects of PA on depression and anxiety are due to a combination of various psychological, neurophysiological and social mechanisms. 87 Different modes of PA stimulate different physiological 88 and psychosocial effects, 88–90 and this was supported by our findings (eg, resistance exercise had the largest effects on depression, while Yoga and other mind–body exercises were most effective for reducing anxiety). Furthermore, our findings showed that moderate-intensity and high-intensity PA modes were more effective than lower intensities. PA improves depression though various neuromolecular mechanisms including increased expression of neurotrophic factors, increased availability of serotonin and norepinephrine, regulation of hypothalamic–pituitary–adrenal axis activity and reduced systemic inflammation. 91 92 Therefore, low-intensity PA may be insufficient for stimulating the neurological and hormonal changes that are associated with larger improvements in depression and anxiety. 87 Overall, our findings add further support to public health guidelines, which recommend multimodal, moderate and vigorous intensity PA.

Our findings that longer duration interventions were less effective than shorter interventions may seem counter intuitive. It is possible that this finding reflects a decline in adherence with longer interventions. Furthermore, due to a lack of blinding of participants in PA trials, participants may have expected to have improved symptoms. It is possible that after experiencing short-term improvements in depression or anxiety, the expectancy effect may diminish over longer periods of time. An alternative explanation is that the longer interventions might not provide sufficient progression of PA dose, leading to a reduction in their effectiveness. Furthermore, it was somewhat surprising that smaller weekly duration interventions demonstrated larger effects than higher weekly duration. This is the opposite to the dose–benefit relationship observed for exercise and physical health outcomes. 93 It is possible that shorter duration interventions are easier for participants to comply with, whereas longer weekly duration interventions are more burdensome and that may be impacting the psychological benefits. It is a useful message that interventions do not need to provide high doses of PA for improvements in depression.

The key strength of this study was that it is the first umbrella review to evaluate the effects of all types of PA on depression, anxiety and psychological distress in all adult populations. We included only the highest level of evidence: meta-analyses of RCTs and applied stringent criteria regarding the design of the component RCTs to ensure that effects could be confidently attributed to PA rather than other intervention components. Additionally, there was only slight overlap in the component RCTs, increasing our confidence in the findings.

A limitation of the review is that most evidence focused on mild-to-moderate depression, with fewer reviews addressing anxiety and psychological distress, preventing us from reaching firm conclusions in the subgroup analyses for these outcomes. Furthermore, most (n=77) of the included reviews were rated as ‘critically low’, based on the AMSTAR-2 quality rating.

Clinical implications

PA is effective for managing symptoms of depression and anxiety across numerous populations, including the general population, people with mental illnesses and various other clinical populations. While the benefit of exercise for depression and anxiety is generally recognised, it is often overlooked in the management of these conditions. Furthermore, many people with depression and anxiety have comorbidities, and PA is beneficial for their mental health and disease management. This underscores the need for PA to be a mainstay approach for managing depression and anxiety.

All modes of PA are effective, with moderate-to-high intensities more effective than low intensity. Larger benefits are achieved from shorter interventions, which has health service delivery cost implications–suggesting that benefits can be obtained following short-term interventions, and intensive long-term interventions are not necessarily required to achieve therapeutic benefit. The effect size reductions in symptoms of depression (−0.43) and anxiety (−0.42) are comparable to or slightly greater than the effects observed for psychotherapy and pharmacotherapy (SMD range=−0.22 to −0.37). 94–97 Future research to understand the relative effectiveness of PA compared with (and in combination with) other treatments is needed to confirm these findings.

In conclusion, PA is effective for improving depression and anxiety across a very wide range of populations. All PA modes are effective, and higher intensity is associated with greater benefit. The findings from this umbrella review underscore the need for PA, including structured exercise interventions, as a mainstay approach for managing depression and anxiety.

What is already known

Previous research trials suggest that physical activity may have similar effects to psychotherapy and pharmacotherapy for patients with depression, anxiety or psychological distress.

Studies have evaluated different forms of physical activity, in varying dosages, in different population subgroups, and using different comparator groups, making it difficult for clinicians to understand the body of evidence for physical activity in the management of mental health disorders.

What are the new findings

Results showed that physical activity is effective for reducing mild-to-moderate symptoms of depression, anxiety and psychological distress (median effect size range=−0.42 to –0.60), compared with usual care across all populations.

Our findings underscore the important role of physical activity in the management of mild-to-moderate symptoms of depression, anxiety and psychological distress.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Twitter @bensinghphd

Correction notice This article has been corrected since it published Online First. The article type has been changed to systematic review.

Contributors BS, TO and CM conceived the idea for the review. BS, RC, DD, RV, AW, KS, EOC, TF, EE, AM and CEMS conducted search, study selection, data extraction and quality assessment. BS, TO and CM drafted the initial manuscript. RC, DD, RV, AW, KS, EOC, TF, EE, AM and CEMS contributed to writing the manuscript. All authors reviewed and approved the final manuscript.

Funding DD is supported by the Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship APP1162166 and by the Centre of Research Excellence in Driving Global Investment in Adolescent Health funded by NHMRC APP1171981. AM is supported by the Centre of Research Excellence in Driving Global Investment in Adolescent Health funded by NHMRC APP1171981. Dr Maher is supported by a Medical Research Future Fund Emerging Leader Grant (GNT1193862).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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The Association of Physical (in)Activity with Mental Health. Differences between Elder and Younger Populations: A Systematic Literature Review

Laia maynou.

1 Health Policy Department, London School of Economics and Political Science, London WC2A 2AE, UK; [email protected]

2 Centre for Research in Health and Economics (CRES-UPF) Mercè Rodoreda Building, Universitat Pompeu Fabra, 08005 Barcelona, Spain

Helena M. Hernández-Pizarro

3 Tecnocampus, Universitat Pompeu Fabra, 08302 Mataró, Spain

María Errea Rodríguez

4 Freelance Researcher, 31007 Pamplona, Spain; [email protected]

Associated Data

Not applicable.

Background: Physical activity is associated with mental health benefits. This systematic literature review summarises extant evidence regarding this association, and explores differences observed between populations over sixty-five years and those younger than sixty-five. Methods: We reviewed articles and grey literature reporting at least one measure of physical activity and at least one mental disorder, in people of all ages. Results: From the 2263 abstracts screened, we extracted twenty-seven articles and synthesized the evidence regarding the association between physical (in)activity and one or more mental health outcome measures. We confirmed that physical activity is beneficial for mental health. However, the evidence was mostly based on self-reported physical activity and mental health measures. Only one study compared younger and elder populations, finding that increasing the level of physical activity improved mental health for middle aged and elder women (no association was observed for younger women). Studies including only the elderly found a restricted mental health improvement due to physical activity. Conclusions: We found inverse associations between levels of physical activity and mental health problems. However, more evidence regarding the effect of ageing when measuring associations between physical activity and mental health is needed. By doing so, prescription of physical activity could be more accurately targeted.

1. Introduction

Over a third of the world’s population is currently affected by a mental health condition, or will be during their lives [ 1 ]. A recent report from the European Union (EU) Health Programme 2014–2020 estimates that the overall one-year prevalence of mental health disorders is around 38% [ 2 ]. Indeed, these types of disorders are the third biggest cause of disability-adjusted life years (DALY) in Europe [ 3 ]. Mental health is defined by the World Health Organization as “ a state of well-being in which every individual realises his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community ” [ 4 ]. Several factors influence mental health. Lifestyle aspects such as physical (in)activity [ 5 ], unhealthy diets, alcohol and drug consumption [ 6 ], social context [ 7 ], work life [ 8 ], or family background [ 9 ] have been shown to impact on mental health in different contexts.

This paper focuses on the relationship between mental health (MH) and physical activity (PA). Physical activity (PA) does not only include sports and active forms of recreation (e.g., dancing), but also refers to mobility (walking and cycling), work-related activities and household tasks [ 5 ]. PA can improve physical health, self-esteem and quality of life which, in turn, enhances well-being and mental health [ 10 ]. Numerous health organisations (CDC, WHO, Health and Human Services) have outlined the benefits of physical activity, including a reduction in the risk of suffering mental health problems. Consequently, recommendations have been made on the minimum amount of activity that should be undertaken for all age groups [ 5 ]. Yet, despite the apparent benefits, 25% of all adults and 75% of teenagers (individuals aged between 11 and 17 years old) do not achieve these recommendations [ 5 ]. Physical inactivity has been defined by the WHO as a global public health problem, “partly due to people being less active during leisure time and an increase in sedentary behaviour during occupational and recreational activities” [ 11 ].

Evidence has acknowledged beneficial effects of PA on MH for the elderly [ 12 ], as well as for younger populations [ 13 ]. However, extant literature shows poor adherence rates to the prescription of PA. This non-adherence is more prominent among patients with MH [ 14 ] as well as with an increase in age [ 15 ], or for people presenting chronic diseases [ 16 , 17 ]. Some studies also suggested that the effect of PA on MH is stronger for elder populations than for younger adults [ 18 ]. Despite this evidence, it is rare to find papers looking at heterogeneous effects by age exploring this association between PA and MH. Additional problems found with the currently available evidence are that the studies are: (i) mostly based on self-reported measures of both PA and MH, which can lead to potential biases (e.g., [ 19 ]), (ii) when evaluating the association of PA with self-reported MH, do not analyse or distinguish respondents’ levels of self-reported MH, but treat them as a continuum (e.g., [ 20 ]); (iii) based on small sample sizes (e.g., [ 21 ]); (iv) based on cross-sectional data (e.g., [ 22 ]); or (v) purely descriptive (e.g., [ 23 ]). All of these are limitations that impede the quality of the current generation of evidence.

The aim of this paper is to explore and summarise published evidence regarding the association of PA with MH outcomes, and explore heterogeneous effects for elder and younger populations. Specific objectives are to assess whether: (i) there are differences in the association of PA with MH between the elder and younger populations; (ii) there are differences in the association of PA with MH according to the type of PA measured (objective vs. subjective); and (iii) there are differences in the association of PA with MH according to the type of MH measured (objective vs. subjective)—with a focus on clinically relevant symptoms when MH is subjective.

Hence, there are two main contributions from this review. First, we look for heterogeneous effects in the literature by age (below and above 65 years old). Second, we distinguish between objective and subjective measures of both PA and MH; moreover, subjective self-reported measures are distinguished by the use of validated scales. In addition, we have also identified that previous reviews lead to weak findings because they include papers based on (i) clinically irrelevant MH problems and (ii) descriptive non-robust statistical analysis. Thus, our goal is achieved by conducting a systematic literature review, focusing on studies conducting some type of econometric analysis, excluding studies that are purely descriptive, and selecting evidence based on clinically relevant MH problems. A clinically relevant MH problem is defined by validated score cut-offs for certain instruments used in the measurement of self-reported MH problems; e.g., a score over 10 points in the CES-D questionnaire is used as an indicator of clinically relevant depression symptoms in Ball et al. [ 24 ], according to a previous validation study [ 25 ]. As summarized in Figure 1 , weak evidence is excluded, minimizing the risk of biased results. Imposing strong inclusion criteria (clinical characteristics and methodological restrictions) ensures better comparability among the selected studies, guaranteeing the robustness of our findings. Our selection criteria make it more likely that people self-reporting MH problems resemble clinically diagnosed patients than in the alterative situation where we might include all those other papers that use self-reported MH measures scores as a continuum, or that do not use a cut-off score. Additionally, restricting inclusion to papers using econometric methods means that the included papers provide information that can be used to establish an association of PA with MH, something that would not be possible using papers conducting purely descriptive analyses.

Evidence on MH and PA.

This is the first systematic review that has filtered these analyses identifying the specific association of these practices with MH for the population that either has a clinical diagnosis of MH or has clinically relevant symptoms of MH (objectively measured).

The paper is organised as follows. In Section 2 we present the methodology, in Section 3 , the results, Section 4 presents the discussion, and finally, Section 5 , concludes.

2. Material and Methods

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 26 ]. The framework of this systematic review according to PICO [ 27 ] was: Population: people with mental health disorders, either diagnosed, or clinically relevant when self-reported; Intervention: Physical Activity of any type, objective or self-reported; Comparison: Elder and younger populations; Outcomes: effect of physical activity over mental health.

2.1. Search Strategy

We conducted our search using PubMed/Medline and EconLit as our main databases for this systematic literature review. Other sources were also consulted to complete the search with papers that were identified after reviewing some of the included records.

We applied the PICO/PECO method to structure [ 28 ] and combine keywords regarding MH (that included “Mental health”, “Mental disorder”, “Depress*”, “Anxiety”, “Psychiatr*”), as well as keywords for PA (“Physical activity”, “physical inactivity”, “Physical exertion”, “exercise”, “physical exercise”, “sport”, and “physical education”) and econometric methods (“Quantitative studies”, “quantitative analys*”, “regression”, “econometric*”, “association”, “cross-section*”, “longitudinal analys*”, “panel data analys*”, “causality*”). We excluded descriptive studies (using the words “descriptive analys*”, “ANOVA”, and “correlations measures”).

We combined these words using an algorithm and the boolean terms OR, AND, and NOT. Our PubMed/Medline and EconLit search strategy, focused on finding those records containing the following terms in titles or abstracts, is provided as Supplementary Materials .

The search strategy for PubMed/Medline, EconLit and additional sources is presented in Figure 2 below. Reference lists of primary research reports were cross-checked in an attempt to identify additional studies.

PRISMA flow diagram. * Other reasons for exclusion at the eligibility stage included: systematic reviews or meta-analyses (n = 48), studies of small sample size (n = 9), studies that were work in progress (n = 8), guidelines (n = 6), indirect influence of PA with MH measured only (n = 4), pilot studies (n = 3), MH outcome does not use cut-off for clinically relevant symptoms (n = 2), descriptive studies (n = 1), associations based on beliefs (n = 1), convenience sample (n = 1), not in English or Spanish (n = 1), qualitative studies (n = 1), and specific population with risk of selection bias (n = 1).

2.2. Eligibility Criteria

We limited records to any academic articles or grey literature published since 2000 available in full-text format, assessing the association of PA (whether this was objectively or subjectively measured) with MH (objectively measured, or population has at least clinically relevant symptoms). We sought studies that used econometric analysis methods (i.e., regression analysis) to establish an association of PA with MH. Papers with a different objective, and purely descriptive papers—even if they were pursuing this objective—were excluded. Studies were also excluded when investigating only symptoms of mental disorders. We did not filter for age groups in order to capture publications for all age groups, allowing comparisons by age groups. Meta-analyses, systematic reviews, methodological papers, congress proceedings, meeting abstracts and case studies were excluded from the search. We also excluded papers that presented a high risk-of-bias. All identified reasons for exclusion are detailed in the PRISMA flow diagram ( Figure 2 ).

We consider objective and subjective measures for both PA and MH (if subjective, only those measures that are clinically relevant). Objective measures of PA are those recorded by an external technology (e.g., accelerometer recording number of steps or time spent performing the exercise) or by an exercise supervisor (e.g., coach). PA that is manually reported by the individual, for example, through a questionnaire or interview, is considered a self-reported type of PA. Regarding MH, measures are considered self-reported MH when they are not measured through a medical diagnosis. Only medical diagnoses are considered objective measures of MH. Subjective measures considered for PA and MH can be distinguished between those measured based on validated scales (e.g., IPAQ questionnaire, the only validated scale found for PA in this review, or GHQ-12 or PHQ-8, amongst others, for MH) and non-validated scales (e.g., questions for PA such as “How often are you physically active or perform exercise during your leisure time? (excluding domestic work)”), and questions for MH such as “Have you ever been diagnosed with depression?” Note that there exist other validated scales for measuring objective PA, such as the GPAQ questionnaire. However, there were no studies using the GPAQ questionnaire that satisfied the inclusion criteria specified for the objective of this review.

2.3. Study Selection and Data Extraction

After completing the search in each database, all references were imported into Zotero, the bibliographic software programme in which the study selection was conducted. The study selection included the screening of titles and abstracts in a first stage, and full-texts in a second stage, conducting a forward and backward search. The search and study selection were conducted in January 2021 by two researchers (M.E and L.M) independently from each other. Any doubts or disagreements between the two researchers were discussed with a third researcher (H.M.H.-P.). The methodology followed for data extraction was reviewed and approved by all authors. It was not necessary to contact any of the authors of the papers included in this review for completion of missing relevant information from the article.

2.4. Risk of Bias Assessment

We followed the method developed by Parmar et al. [ 29 ] for assessing the risk-of-bias of our included records. This includes seven key domains: selection bias, ecological fallacy, confounding bias, reporting bias, time bias, measurement error in exposure indicator, and measurement error in health outcome. For each publication, we rated each of the abovementioned domains: a score of 1 is given for a low risk of bias, 2 for a moderate risk and 3 for a high risk. Then, we computed the overall rating as follows: 1 (strong) was given if none of its domains were rated as weak, 2 (moderate) if up to two domains were rated as weak, or 3 (weak) if three or more domains were rated as weak.

2.5. Synthesis of Results

Data extraction from the selected papers focused on the following fields: authors and year of publication, type of study (RCT, cohort with follow-up, cross-sectional), study’s objective, sample size (and % of MH patients), age range (and mean age of the study sample), PA measure (self-reported (validated scale or not)/objective (programme)), MH problem assessed, MH Patient reported outcome (PRO) measure (self-reported (validated scale or not)/objective (clinical diagnosis)), results of the study (regarding the association of PA with MH only -all other results unrelated with these objective were not extracted-), and overall effect found for the association of PA with MH. These fields were used to construct our summary result table ( Table 1 ).

Characteristics and summary findings of the included studies.

Notes. CRS: Clinically Relevant Symptoms; PA: Physical Activity; MH: Mental Health; SD: Standard Deviation; IQ range: Inter-quartile range; OR: Odds Ratio; AOR: Adjusted Odds Ratio; 95% CI: 95% Confidence Interval; p : p -value; RCT: Randomised Controlled Trial; PRO: Patient Reported Outcomes; CES-D: Center for Epidemiologic Studies Depression scale; GMS: Geriatrics Mental Status scale; IPAQ: International Physical Activity Questionnaire; VPA: Vigorous Physical Activity; MPA: Moderate Physical Activity; MVPA: Moderate-Vigorous Physical Activity; GHQ: General Health Questionnaire; BDI-II: Beck Depression Inventory scale II; PHQ: The Self-reported Patient Health Questionnaire Depression Scale; POMS: Profile of Mood States; GDS: Geriatric Depression Scale; EPDS: Edinburgh Postnatal Depression Scale; SDS: Self-rating Depression Scale; SAS: Self-rating Anxiety Scale; SCL-5: Short version of the Hopkins Symptom Check List Five-item scale; MADRS: Montgomery–Åsberg Depression Rating Scale; MCS: Mental Component Summary Score; HADS-A: Hospital Anxiety Depression Scale [Anxiety]; HADS-D: Hospital Anxiety Depression Scale [Depression]; SF-36: Short Form 36 Health Survey; BRFSS: Behavioral Risk Factor Surveillance System; BMI: Body Mass Index; CPM: Counts per minute; ST: Screen Time; BRFSS: Behavioral Risk Factor Surveillance System; MDD: Major Depressive Disorder. § Study includes as confounding factors for some type of MH treatment, either directly or indirectly; ¥ no-specification on the association of PA with MH by age groups or global analyses for the adult population only (<65). Θ Elderly papers focused on +65 population or that report specific results for this subgroup. * Population studied aged 60–101. A positive (+) overall effect represents a worsening in MH or an increase in the risk of MH due to physical inactivity. A negative (−) overall effect represents an improvement in MH or a reduction in the risk of MH as a result of PA.

Next, we classified studies in clusters according to the different criteria categories: age (we used 3 categories: all ages, <65 and 65+), PA type of measure (3 categories: objective, subjective validated scale, and subjective non-validated scale), and MH type of measure (3 categories: objective, subjective validated scale, and subjective non-validated scale). Thus, we ended up with 27 potential clusters. The result of this classification is summarised in Table 1 and in the Main Results section.

We limited our synthesis to studies that reported results of the association of PA with MH for a minimum sample size in each group of individuals. Our minimum study sample size requirements were (i) a minimum of 10% individuals from the total study sample with self-reported/diagnosed MH in samples with less than 100 individuals, or (ii) in samples of more than 100 individuals, a minimum of 5% of individuals with self-reported/diagnosed MH. Moreover, population-based studies representative of the general population were preferred. Alternatively, a minimum power of 0.80 and significance of 0.05 were required for a study to be able to detect group differences. Studies needed to report estimates, p -values and 95% confidence intervals from the econometric model. We only considered those studies that were moderate or strong at the quality and risk of bias assessment. The weakest studies were excluded.

We considered an association of PA with MH existed when the paper showed significant results in the econometric analyses ( p -value < 0.05). When a paper explored the association of PA with MH for different population subgroups (e.g., age groups), when available, we extracted the specific information for the overall population as well as the information on the association found for each subgroup. We considered there was no association when the paper reported that no effect of PA on MH was found, or when the differences found were not statistically significant.

Finally, we summarised and interpreted our analysis according to the available combinations of PA and MH measures (PA and MH objective and/or subjective), and for the different age groups identified. This ensured the provision of the most complete interpretation of the selected studies’ results for this review.

3.1. Study Selection

Our search strategy identified 2268 potential studies from PubMed (2249), EconLit (13) and other sources (6). After removing duplicates, 2263 abstracts remained for title and abstract screening. We excluded 2125 abstracts and selected 138 for full-text screening. Among these, only 1 paper was selected from the EconLit search, 136 were selected from the search at PubMed, and 1 paper was manually retrieved from Google scholar given our awareness of the study and its relevance. We finally extracted information from 29 papers, and excluded 2 papers after performing the risk-of-bias check. A Prisma 2009 flow diagram representing the study selection process has been presented in Figure 1 .

Table 1 summarises the characteristics of the 27 studies finally included in this systematic review. Table 2 summarises the studies’ objectives and their results, including a column with the overall effect (positive, negative or none) that can be concluded after reviewing each study.

Evaluation of studies according to Parmar et al. (2016) [ 29 ] Risk of Bias and quality assessment.

* Study is a RCT reporting good sampling methods and appropriate design.

The majority of the reviewed studies found a negative association of PA with MH, and positive for physical inactivity studies. There are two studies [ 30 , 31 ] that found no association of PA with MH, and a third that found association with depression symptoms, but no association for patients with anxiety [ 32 ].

3.2. Quality Assessment and the Risk of Bias

We use the Parmar et al. [ 29 ] scale for risk of bias and quality assessment. We evaluate the qualities of all included studies in the qualitative synthesis, based on a set of seven questions. Some of these questions needed to be adapted for this paper. For example, for RCTs, because representativity does not apply, we evaluate selection bias by analysing the appropriateness of the sampling methods (e.g., the study reports good power of the sampling methods reported, large-enough sample sizes, blinding, randomisation methods). Regarding confounding bias assessment, we consider stronger those studies that included an indicator of individuals taking a MH treatment as a control variable. For time bias, we consider that the longer the distance (in years) between the timeframe analysed and the time of publication, the higher the risk of time bias. A higher risk of measurement error in the exposure variable or in the MH measurement is assumed for self-reported types of PA and MH, respectively, especially when the PA/MH measurement instrument used was not a validated scale.

Each item of the bias scale was rated into one of three categories according to its risk of bias—low (for strong studies with little risk of bias), moderate, or high (weak studies with high risk of bias). When converting these elements into an overall bias score for each paper, the overall assessment of two studies was “weak”, and these were automatically excluded for the review (not shown in the summary of studies in Table 1 ). Among the included studies, there were fifteen strong studies (having no “weak” ratings) and twelve of moderate quality (with maximum one “weak” rating).

3.3. Main Results

Of the 27 studies included in this review, 14 (51.8%) were cross-sectional studies, 2 RCTs, and 11 follow-ups of a cohort. Different PA measures were assessed: 6 programmes of PA reporting an objective measure of PA [ 30 , 31 , 36 , 44 , 46 , 47 ], and the remaining 21 offering conclusions from studies based on self-reported PA. The included studies were critically assessed and their main focus was on finding an association of different types of PA with objective/clinically relevant symptoms for MH [ 31 , 39 , 41 , 46 , 47 ]. We identified in Table 1 the number of studies that used objective or self-reported, and validated vs. not validated, measures of PA and MH. Most studies used self-reported validated MH measures (85%), while for PA 48% used non-validated self-reported measures, 30% used validated measures and 22% used objective measures. There were only 4 studies presenting results for the elderly, and they all used validated self-reported MH measures. There was only a minority of 5 studies that, when measuring the association of PA with MH, accounted (as a confounding factor) for some type of MH treatment [ 31 , 39 , 41 , 46 , 47 ].

3.3.1. Differences in the Association of PA with MH between Elder and Younger Populations

Among the 27 studies included, 14 included elder populations of 65 years or over [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 39 , 42 , 44 , 45 , 51 , 54 ]. However, only 2 studies [ 17 , 40 ] included as covariates the interaction between age groups and PA to facilitate the comparison across age groups of PA on MH. In particular, Griffiths et al. [ 40 ] found a lower risk of mental ill-health for mid-life (AOR = 0.81 (0.66–0.99) for ≥60 MET hours/week) and older women (OR = 0.77 (0.55–1.07) for ≥60 MET hours/week) who reported increased levels of physical activity than those who did not increase physical activity.

There were 9 studies that [ 32 , 33 , 35 , 36 , 39 , 44 , 49 , 51 , 54 ] included age as a control variable, but the analysis was performed in a way that did not allow any conclusions to be made regarding the differences in the association of PA with MH between the elder and the younger populations. One study, Hamer et al. [ 18 ], observed slightly stronger associations of PA with self-reported MH in participants >60years of age or with chronic conditions.

The study of Bishwajit et al. [ 35 ] is based on self-reported PA while Karg et al. [ 46 ] is based on objectively measured PA (programme). Bishwajit et al. [ 35 ] found, for the population aged 50 and older (mean age ~ 60, SD ~9), that those who reported never engaging in self-reported moderate or vigorous PA had ORs clearly higher for diagnosed depression than those who engaged in moderate or vigorous PA. Karg et al. [ 46 ], who focused on a middle-aged population (mean age = 42, SD = 12.5), found support for previous findings suggesting positive effects of physical activity and particularly bouldering in depressed individuals. This study also controlled for participants’ current therapeutic treatment, in addition to the prescribed PA. Comparing ORs for both studies, the effect is higher in the bouldering therapy programme [ 46 ]. One should take into account that their population is also slightly younger.

3.3.2. Differences in the Association of PA with MH between Self-Reported and Objective Types of MH

There were only 2 studies out of the 27 that included a population with a clinically diagnosed mental health disorder [ 35 , 46 ]. The remaining twenty-five studies assessed self-reported mental health using validated scales, with the most frequent the GHQ-12 (n = 4 studies) ahead of the GDS-15 (n = 3), CES-D (n = 3), PHQ-9 (n = 2), and the SF-36 (n = 2). All of the papers selected for this review which analysed the association of PA with MH based on self-reported MH used a MH cut-off score, meaning their MH measures should be considered as the probable presence of a MH problem. As is common in the literature, when an individual scores above that cut-off, this individual was considered to have clinically relevant symptoms of a MH disorder. We also included 2 studies [ 38 , 53 ] that, even though they used a validated MH scale, did not use a specific cut-off but rather performed analysis by categories of severity of symptoms.

Among the 25 studies based on populations’ clinically relevant symptoms of MH as identified by self-reported mental health measures, 18 studies concluded that there was an unconditional, negative association between PA levels and MH prevalence, fifteen of them indicating PA is beneficial for MH, and three indicating physical inactivity worsens MH; 1 study reported differences between PA but only for depression, not for anxiety [ 32 ]; 1 study found that PA is especially beneficial for the MH of the elder population. Finally, 1 study found a beneficial but only for women [ 31 ]. Two studies did not find an association of PA with MH [ 30 , 31 ].

3.3.3. Differences in the Association of PA with MH for Self-Reported and Objective Types of PA

Among the 27 studies reviewed and analysed, four assessed impact on MH with an objective measure of PA [ 30 , 31 , 36 , 46 ]. Objective measures of PA included supervised exercise programmes, accelerometer/activity monitor, and bouldering psychotherapy. The majority of studies (N = 21, 77.7%) assessed the association with MH using a self-reported measure of PA. The most repeated instrument for self-recording PA time was the IPAQ questionnaire (n = 3), while all other studies used different questions to assess time dedicated to PA. Among the studies using a self-reported measure of PA, 3 assessed physical inactivity [ 44 , 49 , 50 ], and all them found it was associated with adverse MH.

Of the 4 studies using objective PA, 2 found that higher PA was associated with lower levels of poor MH [ 36 , 46 ], and 2 found no effect, one of which studied an elderly population [ 31 ] and the other a population of post-partum women [ 30 ]. Within the self-reported PA measures, more PA led to better MH in 18 studies, and more physical inactivity led to worse MH in three studies. Fourteen studies conclude that this effect of PA is persistent without restrictions. A similar effect was identified but with some restrictions, for PA, in four studies. Some techniques were found to be more effective than others [ 37 ], or MH might be effective for depression but not for anxiety [ 29 ], or it showed effectiveness especially on a subgroup of the population (e.g., aged > 60 or with chronic conditions, as in one of the studies [ 18 ], or for women only [ 33 ]). Two studies conclude there was no association of PA with MH [ 30 , 31 ].

4. Discussion

This systematic review aimed to present and rigorously assess the evidence available on the association of PA with MH and differences by (i) age groups (elder and younger populations), (ii) type of MH (self-reported and objectively measured), and (iii) type of PA measure (objective vs. self-reported) in order to identify literature gaps, document the current leading-edge knowledge, and open a discussion regarding the direction in which further research should move. Our review results indicate that physical activity is beneficial for mental health. However, the evidence was mostly based on self-reported physical activity and mental health measures, and did not allow to really compare results between younger adults and adults aged 65 or over.

Given the number of abstracts captured by our search strategy, one could think that there exists an extensive literature on the association between PA and MH outcomes. However, a large number of studies were excluded (N = 65 excluded records at the full-text screening phase, representing 47.1% of all the full-text screened records, as stated in Figure 1 ) because they included in their analytic samples individuals who had low MH symptoms as well as those with probable MH issues [ 55 ], despite the differences between these two populations. Failing to account for this weakness reduces the validity and precision of previous reviews.

Imposing this strong inclusion criterion is based on medical literature. There is evidence suggesting people clinically diagnosed with MH and people who self-report to be suffering from MH are very different [ 56 , 57 ]. In addition, despite the validity of the instruments that could be used to identify self-reporting people with clinically relevant symptoms of MH, most published papers ignore the fact that these two populations (reaching or not the cut-off) are different, and treat them without making a distinction (e.g., [ 58 , 59 , 60 ] and many others). Different cut-offs are recommended, specific for each scale or instrument, to assess the severity of MH disorders, and to distinguish people who would be very likely to be diagnosed with a MH disorder from those with less severe MH symptoms. Although for some instruments the cut-off points are still unclear [ 61 ], there are now many instruments that have been validated and for which high degrees of sensitivity have been demonstrated [ 62 , 63 ]. In consequence, studies like Zang et al. [ 54 ] have demonstrated significantly different associations of PA with MH for individuals with self-reported but not-clinically relevant symptoms, and for those with clinically relevant symptoms. In spite of evidence to support the validated cutoffs used to screen for MH problems linked to clinically relevant symptoms [ 57 ], the papers we exclude from our study ignore them. Instead, they treat MH outcome scores as continuous variables when exploring the effect of PA on self-reported MH, or on mental health scores that are not confirmed by a clinician [ 64 ]. These studies group all of the participants who self-report an MH problem in the same category as those with a clinical diagnosis of MH, which is imprecise and weak.

Literature has also found consistently that moderate-to-vigorous intensity physical activity improves MH of the mentally-ill [ 35 , 36 , 37 , 65 ]. Physical activity could, indeed, be an effective measure for both preventing and treating MH. While psychotropic medications are still the main treatment for most MH disorders [ 66 ] a growing body of scientific evidence strongly supports the role of exercise in the treatment regime [ 67 ]. For example, Zhang and Yen [ 54 ] used econometric models to demonstrate that physical activity remedies the depressive symptoms amongst individuals suffering from mild and moderate depression. Although Lordan et al. [ 68 ] confirmed these results and added that the impact is even greater for women, their study was based upon a population with MH symptoms, with no screening indicator for the clinical relevance of such symptoms. Physical activity remedying depressive symptoms has also been analysed through different categories such as green spaces, group exercise, the elderly, youth, gender and countries/regions. However, the association of these with populations suffering from MH problems, or with, at least, probable MH problems is still uncertain.

This is the first systematic review, to our knowledge, assessing the association of PA with MH combining studies of populations with diagnosed MH and those with self-reported MH and clinically relevant symptoms. We believe including the subsample of people with probable MH is important given their proximity to MH diagnosis.

Indeed, if we compare the results observed in those studies using patients clinically diagnosed with MH against results from studies using self-reported clinically relevant MH measures, we observe similar findings. In particular, the two studies using populations of patients with a MH diagnosis, and twenty-one out of the twenty-five studies using self-reported clinically relevant MH measures, found a negative and unconditional association between levels of PA with levels of MH, indicating that PA is beneficial for MH (and (in)PA worsens MH). The remaining four studies using self-reported MH measures found a conditional association, for example, that some therapies would work better than others to help patients with MH.

In addition to this, we designed our study selection and search strategy to ensure that we captured populations of all ages within our studies. Therefore, we were able to use age as a comparison factor, putting our focus on the differences of the association of PA with MH between elder and younger populations. Although our review provides evidence indicating PA is beneficial for MH, we observe that the intensity of such a relationship varies by the type of PA and MH measured, as well as by age. The number of studies offering a cut-off distinguishing clinically relevant symptoms from less important symptoms of MH is small compared to the number of studies that use MH self-reported outcome measures without making this distinction. These findings suggest that more evidence is needed regarding (1) the association of physical activities with mental health for people of different ages; and (2) in people with probable MH, ignoring the similarity between this population and the population with a clinical diagnosis on MH. Another gap we identify in the literature is the lack of longitudinal studies, with most studies analysing cross-sectional data or short-term follow-ups. This creates a barrier to establishing causality in the analyses of the association between PA and MH. We, thus encourage further studies to use validated cut-offs to provide analyses of people with possible MH in the future.

Our paper has some limitations. First, we did not include papers published before 2000. However, the time constraint decision lies on the fact that most of the MH measures including a cut-off to distinguish for clinically relevant symptoms were validated after the year 2000. Hence, we considered that a search focused on 2000 and onwards papers would be more accurate. Second, our analysis is not purely based on clinically diagnosed MH, the most accurate measure of MH, given the low number of published studies with clinically diagnosed populations (n = 2). Thus, we also included papers based on population with clinically relevant symptoms of MH. Yet, these clinically relevant symptom cut-offs were created specifically to indicate the high probability of individuals to be diagnosed in the early future, which mitigates, somewhat, this limitation.

5. Conclusions

We found inverse associations between PA and MH. However, research designs are often weak, based mostly on self-reported measures of PA and MH, and effects are small to moderate. Effect by age seems to be scarce when measuring the differences in the association of PA with MH. More studies are required to provide an accurate estimate of the association of PA with MH, using more robust methods which can be externally verified for different populations. In order to better target and effectively prescribe PA, more evidence comparing elder and younger populations, and the specific populations with probable MH, is required.

Acknowledgments

The project leading to these results has received funding from “la Caixa” Foundation, under agreement LCF/PR/CE07/50610001. Helena belongs to AGAUR Consolidated Research Group 2017 SGR 1059 and received financial support from “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya”. We gratefully acknowledge the contribution of Marlene Herisson and Marc Saez in a first version of the manuscript. We would also like to thank Alba Pardo for her valuable comments.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph18094771/s1 , Supplementary tables of the data extraction process and risk-of-bias assessment can be provided upon request.

Author Contributions

All authors contributed to the design of the search strategy and approved it. M.E.R. and L.M. conducted the studies’ selection and data extraction. H.M.H.-P. acted as a third reviewer when there was a disagreement between M.E.R. and L.M. on the studies’ selection and data extraction processes. M.E.R. conducted the analyses. The content of the data extraction and analyses were approved by L.M. and H.M.H.-P. All authors were involved in the writing and review of the manuscript. All authors have read and agreed to the published version of the manuscript.

This project was funded by “la Caixa” Foundation agreement LCF/PR/CE07/50610001. H.M.H.-P. received financial support from “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” grant number AGAUR Consolidated Research Group 2017 SGR 1059. The APC was funded by London School of Economics and Political Science.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Improving Mental Health through Physical Activity: A Narrative Literature Review

• Victor M. Tringali
• Ryan C. Thoms

MBA, MS, CSCS

• Victor M. Tringali , Department of Human Resources, University of Virginia, Charlottesville, VA; School of Medicine, Department of Public Health Sciences, University of Virginia, Charlottlesville, VA, United States
• Ryan C. Thoms , Department of Kinesiology, School of Education and Human Development, Charlottesville, VA, United States
• Page/Article: 146–153
• DOI: 10.5334/paah.108
• Accepted on 23 Jun 2021
• Published on 5 Aug 2021
• Peer Reviewed

• Open access
• Published: 28 February 2022

Systematic review and meta-analysis of the effects of exercise on depression in adolescents

• Xiang Wang 1 ,
• Zhi-dong Cai 1 ,
• Wan-ting Jiang 1 ,
• Yan-yan Fang 1 ,
• Wen-xin Sun 1 &
• Xing Wang   ORCID: orcid.org/0000-0002-3230-3482 1  

Child and Adolescent Psychiatry and Mental Health volume  16 , Article number:  16 ( 2022 ) Cite this article

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Depression is widespread among adolescents and seriously endangers their quality of life and academic performance. Developing strategies for adolescent depression has important public health implications. No systematic review on the effectiveness of physical exercise for adolescents aged 12–18 years with depression or depressive symptoms has previously been conducted. This study aims to systematically evaluate the effect of physical exercise on adolescent depression in the hope of developing optimum physical exercise programs.

Nine major databases at home and abroad were searched to retrieve randomized controlled trials (RCTs) on exercise interventions among adolescents with depression or depressive symptoms. The retrieval period started from the founding date of each database to May 1, 2021. The methodological quality of the included articles was evaluated using the modified PEDro scale. A meta-analysis, subgroup analysis, sensitivity analysis, and publication bias tests were then conducted.

Fifteen articles, involving 19 comparisons, with a sample size of 1331, were included. Physical exercise significantly reduced adolescent depression (standardized mean difference [SMD] = − 0.64, 95% CI − 0.89, − 0.39, p < 0.01), with a moderate effect size, in both adolescents with depression (SMD = -0.57, 95% CI − 0.90, − 0.23, p < 0.01) and adolescents with depressive symptoms (SMD = − 0.67, 95% CI − 1.00, − 0.33, p < 0.01). In subgroups of different depression categories (depression or depressive symptoms), aerobic exercise was the main form of exercise for the treatment of adolescents with depression. For adolescents with depression, interventions lasting 6 weeks, 30 min/time, and 4 times/week had optimum results. The effects of aerobic exercise and resistance + aerobic exercise in the subgroup of adolescents with depressive symptoms were significant, while the effect of physical and mental exercise (yoga) was not significant. For adolescents with depressive symptoms, aerobic exercise lasting 8 weeks, 75–120 min/time, and 3 times/week had optimum results. Physical exercise with moderate intensity is a better choice for adolescents with depression and depressive symptoms.

Conclusions

Physical exercise has a positive effect on the improvement of depression in adolescents.

The protocol for this study was registered with INPLASY (202170013). DOI number is 10.37766/inplasy2021.7.0013. Registration Date:2021.7.06.

The mental health of adolescents has become an increasingly serious public health problem worldwide [ 1 ]. Depression is a common mental illness in adolescents, with a prevalence of about 4.5% [ 2 ]. Depression seriously endangers adolescents’ physical and mental health, academic performance, and interpersonal relationships [ 3 ]. In severe cases, these adolescents may even commit suicide [ 4 ]. In recent years, the incidence of depression in China has continued to rise, and adolescents account for a prominent proportion of patients in the clinic [ 5 , 6 ]. When adolescents with depressive symptoms or negative emotions do not receive timely intervention, they risk developing depression [ 7 ]. On August 31, 2020, China’s National Health Commission released the “Working Plan for Exploring Special Services for the Prevention and Treatment of Depression” [ 8 ]. The plan stated that high schools should add depression screening to student health examinations, as results show that many students have depressive symptoms. Effective strategies to reduce depressive symptoms in adolescents are needed.

The effect of exercise on depression has become a research hotspot in recent years [ 9 , 10 ]. Cross-sectional studies over the past 30 years have suggested that low physical activity is an important risk factor for the development of depression [ 11 , 12 ]. Prospective cohort studies have suggested that regular exercise reduces the risk of developing depression [ 13 , 14 ]. Human and animal experiments showed that exercise can exert an antidepressant effect by increasing mitochondrial activity in brain neurons, stimulating the secretion of monoamine neurotransmitters, increasing the concentration of neurotrophic factors, inhibiting the overexpression of inflammatory factors, and regulating the expression of microRNAs [ 15 ]. It can also reduce depressive symptoms by improving self-efficacy, reducing negative emotions, and stimulating positive behaviors in depressed patients [ 16 ]. RCTs have also shown that structured exercise programs can effectively alleviate depressive symptoms, and there is a dose–response relationship [ 17 , 18 ]. Studies have found that moderate- and high-intensity physical exercise can have a positive effect in the treatment of mild and moderate depression [ 19 ]. For adults, depression is usually regarded as the mental health problem most likely to be positively affected by exercise [ 20 ].

Antidepressant medication may be associated with side effects such as weight gain, sleep disturbance, and reproductive dysfunction which can be disturbing for adolescents [ 21 ]. Psychotherapy is more resource-intensive and is associated with perceived stigma from attending the therapist [ 22 ]. Comparatively, physical exercise is more cost-effective. It is convenient to implement within the community and can potentially have wider reach and participation [ 23 ]. There is less research on exercise interventions to treat depression in adolescents compared to adults, especially examining the moderating effects of exercise-related variables (e.g., exercise type, exercise program duration, exercise session duration, exercise intensity, and exercise frequency) [ 24 ]. Although RCTs in children and young people have shown that physical exercise can relieve depression and depressive symptoms [ 25 , 26 ], the dose–response relationship remains unclear. Given this, this study aimed to systematically summarize the effect of physical exercise on adolescent depression and to clarify the dose–response relationship between physical exercise and depressive symptoms in adolescents.

The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Fig.  1 ) [ 27 ].

Article screening flow chart

Inclusion criteria

Based on the PICOS (participants, interventions, comparisons, outcomes, study design) process used in evidence-based medicine [ 28 ], the inclusion criteria were as follows: (1) Participants: adolescents aged 12–18 diagnosed with depression (based on the Diagnostic and Statistical Manual of Mental Disorders [DSM-IV/5] or International Classification of Diseases [ICD-10]) or assessed to have depressive symptoms. (2) Intervention: the experiment group received a structured exercise program (aerobic exercise, resistance + aerobic exercise, or physical and mental exercise such as yoga [ 29 ]) compared with the control group [ 30 ]; if there were multiple experimental groups in the study, only the group with exercise intervention was included; if there had been multiple independent experiment groups in the same article, it was counted as multiple independent comparisons as well. (3) Comparison: The control group had no exercise intervention. (4) Outcomes: An internationally recognized depressive symptom-related scale was used, and its score was used as the study outcome. (5) Study design: randomized controlled trials (RCTs); original peer-reviewed Chinese or English papers.

Exclusion criteria

The exclusion criteria were as follows: (1) Participants: Any identified physical or non-depressive mental illness (such as cancer, diabetes, overweight/obesity, or anxiety disorders). Studies which did not provide any information on the participants’ characteristics were excluded. (2) Interventions: Use of combined interventions, such as exercise combined with music therapy or cognitive training; lack of description of the physical exercise in the intervention design (type; program duration; session duration; intensity; or frequency). (3) Comparisons: Lack of a control group or control interventions that significantly increased cardiovascular activity. (4) Outcome Measures: Depressive symptom scores not evaluated pre- and post-intervention; Data could not be extracted or original data could not be obtained by contacting the corresponding author. (5) Study design: Scores on the modified PEDro scale of less than 4.

Literature retrieval strategy

Nine databases were searched, comprising PubMed, Web of Science, The Cochrane Library, Embase, MEDLINE, China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), VIP database, and Wanfang Database. The search was conducted from database inception to May 1, 2021.

The search strategy involved a combination of subject terms and free words, and was finalized after repeated checks. Chinese search terms included: adolescents, junior high school students, high school students; exercise, aerobic exercise, resistance exercise, high-intensity interval, physical and mental exercise, yoga, dance, aerobics, running, walking; depression, depression symptoms, depressive symptoms, negative emotions; randomized controlled trials. As an example of the English search terms, the PubMed search strategy was as follows:

#1 Adolescent [MeSH Terms] OR Adolescents [Title/Abstract] OR Adolescence [Title/Abstract] OR Teenager [Title/Abstract] OR Teenagers [Title/Abstract] OR Teen [Title/Abstract] OR Teens [Title/Abstract] OR Youth [Title/Abstract] OR Youths [Title/Abstract];

#2 Exercise [MeSH Terms] OR Exercise [Title/Abstract] OR Aerobic Exercise [Title/Abstract] OR Resistance Exercise [Title/Abstract] OR High-Intensity Interval Training [Title/Abstract] OR Mind–Body Exercise [Title/Abstract];

#3 Depression [MeSH Terms] OR Depressive Disorder [Title/Abstract] OR Depressive Symptom [Title/Abstract] OR Emotional Depression [Title/Abstract] OR Negative Emotion [Title/Abstract];

#4 randomized controlled trial [MeSH Terms].

#5 #1 and #2 and #3 and #4.

Literature screening, data extraction, and quality evaluation

Literature screening.

EndNote X9 software was used to remove the duplicates from the search results. Thereafter, two authors (both of whom were experienced researchers in the field) independently screened the literature according to the inclusion and exclusion criteria. First, the titles and abstracts were read to preliminarily screen the articles, and the articles that did not meet the inclusion and exclusion criteria were deleted and recorded. Second, the full text of the remaining articles was downloaded, read, and reviewed to re-screen the articles. If there was a disagreement between both authors, a third author would review and decide whether to include the study.

Data extraction

Two authors used a pre-designed data extraction form to extract the following data and record it: (1) Basic article information: first author’s name, study country, and year of publication. (2) Basic information: depression type (depression or depressive symptoms), age, sex ratio, and sample size. (3) Physical exercise variable (e.g., exercise type, exercise program duration, exercise session duration, exercise intensity, and exercise frequency).

Quality evaluation

Two authors used a modified version of the PEDro scale [ 31 ] to evaluate the methodological quality of the included studies. If there was a disagreement, a third author evaluated the issue, which was discussed until a consensus was reached. The 10 items on the scale include "eligibility criteria", "random allocation", "allocation concealment", "baseline similarity between groups", "exercise intensity control", "blinded outcome evaluation", and "dropout rate < 15%", "intention-to-treat analysis", "Statistical analysis comparing groups", "point and variability measures". If the relevant standard was clearly met, the item was scored as 1 point; if the relevant standard was not clearly met or not mentioned, the item was scored as 0. The highest score that could be achieved was 10 points, so < 4, 4–5, 6–8, and 9–10 indicated low, medium, good, and high quality, respectively.

Statistical analysis

The statistical analysis was conducted in Stata 16.0 software. The outcome variables were continuous, and mean ± standard deviations (SD) were extracted for each included comparison. There were no significant differences in the outcome variables between the groups in each comparison at baseline. At the end of the experiment, we chose scale scores of both the intervention group and the control group as the effect size, which reflects the intervention effect. Due to the use of multiple depression scales among the included articles, standardized mean difference (SMD) was used as the effect size for analysis, with 0.2, 0.5, and 0.8 indicating small, moderate, and large effect sizes, respectively [ 32 ]. Heterogeneity was quantified by I 2 (with 75%, 50%, and 25% indicating high, medium, and low degrees of inter-study heterogeneity, respectively [ 33 ]) and Cochran’s Q test p value. If there is publication bias among the included articles, the trim and fill method was used to correct for asymmetry.

Literature retrieval results

As shown in Fig.  1 , a total of 4792 articles were obtained by searching PubMed (n = 413), MEDLINE (n = 142), Web of Science (n = 1442), Embase (n = 729), The Cochrane Library (n = 458), CNKI (n = 557), CBM (n = 428), VIP database (n = 153), and Wanfang Database (n = 470). Additionally, 3 articles were obtained by searching the references of the included articles. After deduplication, 3774 articles were obtained. After preliminary screening, 355 articles were obtained. After re-screening the articles by reading the full texts and excluding articles due to unsuitable study design (not an RCT) intervention/control groups, research objective, or outcome measures, 19 articles were obtained. After excluding articles with unavailable data or low-quality articles, 15 articles were included in the meta-analysis.

Characteristics of included literature

As shown in Table 1 , 15 articles were included, with 19 comparisons. The publication year was 1982 to 2017. There were 1331 participants, ranging from 24 to 209 per article. The mean age of the participants was 15.90 ± 1.23 years old. The comparisons were conducted in 8 countries: the United States (n = 8) [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ], Iran (n = 3) [ 41 , 42 ], Germany (n = 2) [ 43 ], Australia (n = 2) [ 44 ], the United Kingdom (n = 1) [ 45 ], South Korea (n = 1) [ 46 ], Chile (n = 1) [ 47 ], and Colombia (n = 1) [ 48 ]. Regarding depression type, 6 comparisons were on depression and 13 were on depressive symptoms. Regarding recruitment, 9 comparisons involved participants recruited from the following special organizations: inpatient department (n = 3), mental health center (n = 2), community outpatient clinic (n = 1), juvenile detention center (n = 2), and school for young offenders (n = 1). Additionally, 10 comparisons involved exercise interventions carried out in school (5 in junior high school and 5 in high school).

The exercise programs mainly involved aerobic exercise, resistance + aerobic exercise, or yoga (though one article involved whole-body muscle vibration). The total exercise program duration for adolescents with depression ranged from 6 to 12 weeks, the exercise session duration ranged from 30 to 45 min, exercise intensity was moderate intensity (1 comparison) and optional intensity (1 comparison), and the exercise frequency ranged from 3 to 4 times per week. The total exercise program duration for adolescents with depressive symptoms ranged from 6 to 40 weeks, the exercise session duration ranged from 8 to 120 min, exercise intensity was moderate intensity (2 comparisons) and self-selected intensity (2 comparisons), and the frequency ranged from 2 to 3 times per week.

Nine comparisons (depression: 3; depressive symptoms: 6) showed that there was no significant difference in depression scores between the exercise and control groups at the end of the study, while 10 comparisons (depression: 3; depressive symptoms: 7) showed significant differences. Five comparisons (depression: 4; depressive symptoms: 1) included a follow-up period after the experiment. Two of these comparisons (depression: 1; depressive symptoms: 1) showed that the depression scores during the follow-up period were not significantly different between the exercise and control groups, while 3 comparisons (depression: 3) showed significant differences. There were no adverse events among the included studies. The mean dropout rate of the exercise group was 8.33% and that of the control group was 9.21%, with no significant difference (t = -0.18, p = 0.86).

Methodological quality evaluation

As shown in Table 2 , the PEDro score among the 15 included articles was 5–8 points. There were 4 medium and 11 high-quality articles, with a mean of 6 points. The overall research quality was good. All articles mentioned “eligibility criteria”, “random allocation”, “baseline similarity between groups”, “statistical analysis comparing groups”, and “point and variability measures”. Additionally, 4 articles mentioned “exercise intensity control”, 1 article mentioned “blinded outcome evaluation”, 2 articles mentioned using “intention-to-treat analysis”, and 7 articles mentioned “dropout rate < 15%” (Table 2 ).

Meta-analysis of the impact of exercise on depression in adolescents

Meta-analysis results.

A total of 19 studies included I 2  = 75.05%, combined effect size SMD = − 0.64, 95% CI (− 0.89, − 0.39, P < 0.01). The results showed that post-intervention, subjects in the intervention group showed more significant reduction in depressive symptoms than the control group, with a moderate effect size. As shown in Fig.  2 , adolescents with depression I 2  = 20.35%, combined effect size SMD = -0.57, 95% CI (− 0.90, − 0.23, P < 0.01), there is low heterogeneity. As shown in Fig.  3 , adolescents with depressive symptoms I 2  = 83.62%, combined effect size SMD = − 0.67, 95% CI (− 0.90, − 0.23), P < 0.01, there is a high degree of heterogeneity.

Forest plot of the effect of exercise on adolescents with depression

Forest plot of the effect of exercise on adolescents with depressive symptoms

Sensitivity analysis

In order to explore whether the heterogeneity between studies is caused by a single study, Stata 16.0 software was used for sensitivity analysis [ 49 ]. As shown in Figs.  4 and 5 , the effect size of the depression group and the depressive symptom group were not significantly changed after eliminating single studies one by one, indicating that the study results were relatively stable.

Sensitivity analysis of the effect of exercise on depression in adolescents

Sensitivity analysis of the effect of exercise on depressive symptoms in adolescents

Subgroup analysis

As shown in Table 3 , to further explore the source of heterogeneity, a subgroup analysis of potential moderating variables was performed. Regarding exercise type, the effect of aerobic exercise among adolescents with depression was significant. The effects of aerobic exercise and resistance + aerobic exercise among adolescents with depressive symptoms were also significant, while the effect of yoga was not significant. There was a high degree of heterogeneity in the aerobic exercise and resistance + aerobic exercise subgroups among adolescents with depressive symptoms. Regarding program duration, the effect of continuous intervention for 6 weeks in adolescents with depression was significant, while the effect of continuous intervention for 9–12 weeks was not significant. For adolescents with depression symptoms, the effect of continuous intervention for 8 weeks was significant, while the effects of continuous intervention for 10–12 and > 12 weeks were not significant. There was a high degree of heterogeneity in the > 8-week subgroup among adolescents with depressive symptoms. Regarding exercise session duration, the effect of 30 min of exercise in adolescents with depression was significant, while 45 min was not significant. For adolescents with depressive symptoms, the effect of 75–120 min was significant, but the effect of 30–45 min was not significant. There was a high degree of heterogeneity in the 30–45 and 75–120 min subgroups among adolescents with depressive symptoms. Regarding exercise frequency, 3–4 times/week among adolescents with depression was significant, and 4 times/week was better. The effect of 3 times/week in adolescents with depressive symptoms was significant, while 2 times/week was not. The effect of exercise 3 times/week among adolescents with depressive symptoms was highly heterogeneous. Moderate and high intensity exercise had a significant effect on depressive symptoms among adolescents with moderate intensity interventions producing the greater benefit.

Publication bias

The Egger regression method was used to assess publication bias regarding the included articles [ 50 ]. Egger’s test results showed that there was no publication bias in the depression group (t = − 1.42, P = 0.23, 95% CI − 6.45, 2.09). In the depressive symptom group, the result showed likelihood of publication bias (t = − 3.12, P = 0.01, 95% CI − 6.87, − 1.19). The reason for publication bias is that more positive results than negative results were included [ 51 ]. The trim and fill method [ 52 ] was used to identify and correct the asymmetry caused by publication bias. The results show that no sample needed to be corrected or recalculated among the experimental samples. The random-effects model calculates the point estimate of the combined RR and 95% CI was − 0.65 (− 0.87, -0.58) after trim-and-fill, and the effect size RR difference before and after trim-and-fill did not change significantly, suggesting that publication bias has little effect on the results, and the meta-analysis results are relatively robust.

This study showed that exercise has a moderate effect on alleviating depressive symptoms in adolescents, which is consistent with the results from a previous meta-analysis [ 53 ]. A previous meta-analysis on the use of exercise to treat depression in children and adolescents also showed that exercise had a small-to-moderate effect [ 21 ], but due to the heterogeneity among the patients, the authors stated that there was insufficient evidence to prove the benefits of exercise. Additionally, two meta-analyses of young people (4–25 years old) found that exercise has a moderate-to-large effect [ 54 , 55 ]. However, in addition to RCTs, these two meta-analyses included quasi-experimental and observational studies, and the overall methodological quality was low. In addition, previous meta-analyses covered participants with a wide range of ages [ 21 , 50 , 51 ]. Given the biological and psychological differences between children and adolescents [ 56 ], this may have had a confusing effect on the summary results. Therefore, unlike these previous meta-analyses, our meta-analysis only included adolescents aged 12–18 years who had been diagnosed with depression or had been assessed to have significant depressive symptoms. We also excluded studies which recruited individuals with comorbid diseases closely related to depression. Furthermore, we explored the moderating effects of exercise-related variables in order to better assess the dose–response relationship of exercise. The sensitivity analysis and publication bias test suggested that the results were highly stable.

In this meta-analysis, 5 comparisons involved follow-up results, 3 of which suggested that exercise had a sustained benefit (at about 6 months) after the intervention. First, Carter et al. found that the depressive symptoms at week 24 in the 6-week aerobics exercise group were significantly lower than those in the control group [ 41 ]. Second, the two comparisons by Wunram et al. (one involving aerobic exercise and the other involving whole-body muscle vibration) both found that there was a significant difference at week 26 in depression scores between the exercise and control groups. The depression remission rate in both the exercise groups (67.8%) was significantly higher than that in the control group (26.8%) [ 39 ]. The authors explained that this may be related to the participants maintaining regular exercise after the intervention [ 57 ]. Due to the limited number of follow-up studies, short timeframes, and lack of continuous measurement, larger follow-up studies are still needed to further explore the sustainability of the effectiveness of exercise interventions to reduce depression in adolescents.

In addition, research has shown that 80% of depressed adolescents refuse to be treated again due to side effects or stigma after the first psychological or drug treatment [ 58 ]. In contrast, none of our included articles reported any adverse events during the interventions. A meta-analysis of the effectiveness of treatment for depression in adolescents found that the dropout rates regarding psychological and drug therapy among adolescents were approximately 23% and 45%, respectively [ 59 ]. In contrast, in our meta-analysis, the mean dropout rates were 8.33% and 9.21% in the exercise and control groups, respectively, with no significant difference (t = − 0.18, p = 0.86). Given that exercise has similar benefits to psychological and drug therapy, the high compliance among adolescents with exercise, and the many benefits of exercise in the growth and development stages [ 60 ], the use of exercise to prevent and treat depression in adolescents is feasible and acceptable.

At present, most studies report that the cause of depression is related to the dysfunction of neurotransmitters such as serotonin (5-hydroxytryptamine), dopamine, and norepinephrine [ 61 ]. In rats, swimming exercises over 10 weeks significantly increased the levels of serotonin, dopamine, and norepinephrine in the hippocampus [ 62 ]. Voluntary running exercises for 8 weeks significantly increased the levels of dopamine and its metabolites in the prefrontal cortex and striatum of rats [ 63 ]. After a 6-week jogging intervention for patients with depression, the plasma levels of gamma-aminobutyric acid (GABA) increased and the depression symptoms decreased [ 64 ]. Six months of moderate-intensity aerobic exercise significantly increased the prefrontal cortex gray matter volume in patients with depression (decreased gray matter volume is a key physiological sign of depression [ 65 ]), which was directly proportional to the amount of exercise [ 66 ]. In addition, exercise among patients with depression upregulated brain-derived neurotrophic factor (BDNF), which stimulates and mediates neurogenesis and regulates depressive behavior [ 67 ], in the hippocampus and cortex, promoted hippocampal neurogenesis, and significantly enhanced synaptic plasticity [ 68 ]. In addition, exercise alleviated hypothalamic–pituitary–adrenal feedback regulation obstacles by modulating cortisol and IL-6 levels, and thereby improved depression [ 69 ].

Current research generally indicates that for people with depressive symptoms, physical exercise is as effective as antidepressant drugs and psychotherapy and, for patients with depression, exercise can be used as a supplement to traditional therapy [ 70 , 71 , 72 ]. Walking every day for 10 days, with an 80% maximum heart rate (HRmax), significantly reduced the Bech–Rafaelsen Mania Scale (BRMS) score of patients with major depression [ 73 ]. Cycling at 70–80% HRmax for 12 weeks reduced the symptoms of individuals with depressive symptoms, and improved maximum oxygen uptake and visuospatial memory [ 74 ]. Aerobic exercises in physical education classes significantly reduced adolescents’ impulsivity, anxiety, drug abuse [ 36 , 38 ]. Studies have found that the effect of physical exercise on depression is influenced by the severity of depression, and is significantly negatively correlated with the level of depression [ 75 , 76 ]. This is because patients with depression usually have a low mood for more than 2 weeks, which is accompanied by a decrease in hippocampus volume, structural changes in the prefrontal cortex, cingulate gyrus, and temporal lobe, as well as cognitive dysfunction [ 77 ]. The symptoms of general depression are usually mild, often manifested as lack of happiness, low self-esteem, pessimism, loneliness, and other negative emotions [ 78 ]. A single exercise session among individuals with depressive symptoms can significantly improve self-efficacy, thereby reducing negative emotions [ 79 ], while regular exercise for > 3 months can effectively reshape the central nervous system organization of patients with depression [ 80 ]. Participants' physical health may also be an important factor affecting the effectiveness of the intervention. Adolescents with mental or physical diseases such as obesity [ 81 ], chronic fatigue syndrome [ 82 ], attention deficit hyperactivity disorder [ 83 ], may differ from ordinary adolescents in their exercise tolerance. They are more likely to adhere to moderate- than to high-intensity exercise [ 84 ].

Exercise type may be the moderating factor that affects the effect of the intervention. At present, aerobic exercise is the most important type of exercise to treat depression [ 85 ]. Aerobic exercise increases the levels of vascular endothelial growth factor (VEGF) and insulin-like growth factor 1 (IGF-1) in the mouse brain, and increases the volume of the subventricular and subgranular zones in the hippocampal dentate gyrus, promoting the differentiation of hippocampal neurons [ 86 ]. In addition, aerobic exercise activates central nervous system neuroactive substances and BDNF in the brain [ 87 ]. Jeong et al. found that 12 weeks of aerobic dance in adolescents increased the plasma concentration of serotonin and decreased the concentration of dopamine, suggesting that the stability of the sympathetic nervous system increased [ 42 ]. Roshan et al. found that 6 weeks of aerobic walking in water significantly increased the 3-methoxy-4-hydroxyphenylglycol (MHPG) sulfate value in the urine of adolescents, and it was significantly negatively correlated with the HAMD score, suggesting that aerobic exercise reduced depressive symptoms in adolescents [ 37 ]. In addition, compared with aerobic exercise alone, resistance + aerobic exercise can produce complementary neurobiological and other physiological effects [ 88 ]. Regarding the two resistance + aerobic exercises included in our meta-analysis, Hilyer et al. found that the BDI score was significantly lower after a 20-week resistance + aerobic exercise program than that of the control group [ 32 ]. Second, Costigan et al. found that resistance + aerobic exercise slightly improved subjective well-being among adolescents, but not depressive symptoms [ 40 ]. As our meta-analysis only included 2 articles on resistance + aerobic exercise, its effect on depression in adolescents needs to be verified by multiple additional RCTs. In addition, due to the inherent listlessness of patients with major depression, it is sometimes difficult to motivate them to take active exercise[ 89 ]. Whole-body muscle vibration training was chosen as an auxiliary or supplementary exercise method. This kind of exercise can be performed on a high level of physical activity even with a low motivation to exercise[ 90 ]. Hyperactive HPA axis in patients with major depression is one of the important reasons for its onset[ 91 ]. There is some evidence that whole-body muscle vibration training can have a positive effect on maintaining stable cortisol secretion in adolescents with severe depression and reducing the activity of the HPA [ 43 ]. Due to the limited number of studies in this literature, vibration training currently needs stronger empirical evidence to investigate the effects of this approach in alleviating depression in adolescents.

In addition, our meta-analysis found that yoga did not significantly impact depression in adolescents. Yoga, as a physical and mental exercise based on body posture exercises, has been found to reduce anxiety and stress [ 92 ]. Yoga can improve negative emotions by regulating the hypothalamus–pituitary–adrenal axis and sympathetic nervous system, increasing thalamic GABA levels and reducing cortisol levels [ 93 ]. Of the 4 included yoga articles (satyananda yoga:1; kripalu yoga:2; self-designed yoga:1), only 1 study has a significant difference compared to the control group. Although these studies mentioned the type of yoga selected, they did not describe the specific intervention details during the implementation process. We can only roughly know that yoga elements include posture, breathing, relaxation and meditation. Among them, Kripalu Yoga is often described as "dynamic meditation"[ 94 ]. During practice, students need to pay more attention to the individual psychological feelings brought by yoga postures[ 95 ]. Therefore, students are required to maintain a gentle and introspective attitude throughout the practice. Each pose of Kripalu Yoga needs to be maintained for a long time in order to fully release the pent-up emotions[ 96 ]. Kripalu Yoga has achieved significant intervention effects in one study, which may be related to its emphasis on obeying the wisdom of the body[ 40 ]. The reason for the insignificant effect of yoga intervention may lie in the characteristics of yoga exercise, the duration of yoga experiment and experiment control. Adolescent males often resist participating in low-intensity exercise such as yoga and tend to choose more intense exercise types, so gender factors may impact the effect of yoga interventions [ 97 ]. For male students who do not like yoga, they would choose to use the word "active" to describe the purpose they want to pursue in their physical exercise. In yoga exercises, they feel more restrained [ 98 ]. As a complementary therapy for physical and psychological disorders, yoga has been extensively studied in adults [ 99 ]. Long-term follow-up showed that yoga led to delayed transformation, leading to improvement in long-term self-control in emotion, though the short-term effect was not significant [ 36 ]. In addition, the current practice of yoga among young people may limit its effectiveness due to the lack of specific standards for quality control of yoga implementation. Longer post-intervention follow-up studies on yoga interventions should be conducted, and the implementation processes should be clearly reported, so as to provide the best advice for young people on using yoga to relieve depression.

Exercise program duration, session duration, frequency, and intensity may moderate the effects of exercise. One study showed that there is an inverted U-shaped relationship between the exercise program duration and mental health symptom relief in adolescents [ 100 ]. Another study showed that maintaining regular exercise for 6–8 weeks significantly reduced negative emotions in adolescents, but with the prolongation of the program, the benefits did not significantly improve [ 101 ]. International public health physical activity guidelines stipulate that at least 150 min of moderate-intensity exercise should be performed every week to maintain health [ 102 ]. Usually, there is a positive dose–response relationships between exercise duration/frequency and depressive symptoms improvement [ 103 ]. In depressed rats, a single 30-min session of wheel running reduced the serum corticosterone concentration compared to 20 min [ 104 ]. In addition, 8-week high-frequency (3–5 sessions/week) aerobic exercise significantly increased serotonin and amygdala norepinephrine in the hippocampus of the brain of patients with depression compared to low-frequency (1 session/week) aerobic exercise [ 105 ]. Moreover, high-frequency exercise accelerated the serum BDNF peak, which promoted adaptation of central neurotransmitter release and was more effective at reducing depressive symptoms [ 106 ]. For adolescents, the shorter the effective time of physical exercise, the easier it is to improve the motivation of the adolescents to participate in physical exercise [ 107 ]. Reduced energy level is a characteristic symptom in depressed patients, and long-term continuous exercise may be too demanding for them. Furthermore, a meta-analysis on exercise durations/frequencies showed that exercise that lasted ≤ 45 min/session reduced depression symptoms more than > 45 min/session, and ≥ 4 sessions/week had a greater effect than 2–3 sessions/week [ 108 ].

In addition, there are a total of 6 comparisons in this article detailing the control of exercise intensity in the experiment. For adolescents with depression, moderate intensity and self-selected intensity may be better exercise options for them. Since only one comparison of each level of intensity was identified, further experiments are needed to strengthen any specific conclusions that can be drawn. For adolescents with depressive symptoms, a subgroup analysis found that moderate intensity and high intensity have a good effect on reducing their depressive symptoms. Previous research showed that moderate- and high-intensity exercise has a stronger effect on depression than low-intensity exercise [ 109 ]. The American Sports Medicine Association recommends 60–80% HRmax intensity to treat depression [ 110 ]. Exercise intensity is positively associated with BDNF and plasma endorphin release [ 111 ]. However, one study found that compared to high-intensity interval training, moderate-intensity continuous aerobic exercise reduced the levels of inflammatory factors such as TNF-α, IL-6, and IL-1β [ 112 ]. Given the low self-esteem and self-efficacy of depressed patients, moderate-intensity exercise is currently used more frequently [ 113 ]. Compared to self-selected intensity, exercise at a prescribed intensity usually leads to a poorer emotional experience [ 114 ]. The single included article on self-selected exercise intensity among adolescents with severe depression found that there was no significant difference in depression scores between the exercise and control groups after 6 weeks of intervention, but the exercise group had a significantly higher improvement at the 6-month follow-up. Further studies to examine the effectiveness of self-selected physical activity intensity on depression in adolescents are needed. As the suitable exercise intensity differs between adolescents and adults, it is necessary to further explore the effect and acceptability of different intensities on depression in adolescents in order to find the optimum intensity.

This meta-analysis has several limitations. (1) Only published Chinese and English articles were included, so the comprehensiveness of the search was limited. (2) Only two of the included articles fully described allocation concealment and only one mentioned blinded outcome assessment. (3) All the articles used subjective self-reported outcomes, with a lack of objective evaluation (such as biomarkers). (4) There was a high degree of heterogeneity among the studies of adolescents with depressive symptoms. (5) There was a lack of uniform standards and controls for exercise intensity variables. (6) The physical activity level of the subjects may affect the intervention effect, while this variable was not found or included when compiling relevant data. (7) The optimal exercise program in this research was based on the summary of current evidence. More RCTs are needed in the future to further discuss the intervention effects of different variables.

This study shows that physical exercise, as an alternative or complementary treatment, has a positive effect on alleviating depression in adolescents, with a moderate effect size. Based on the current evidence, for adolescents with depression lasting for 6 weeks, a physical exercise program of 30 min/time, 4 times/week, and aerobic exercise is better. For adolescents with depressive symptoms lasting for 8 weeks, 75–120 min/time of exercise 3 times/week, and aerobic exercise is better. Physical exercise of moderate intensity is a better choice for adolescents with depression and depressive symptoms. In the future, empirical research should involve long-term, high-quality RCTs, and increased follow-up to explore the sustained benefits of physical exercise. The forms of exercise intervention among adolescents should be further enriched, and the control of the intensity of physical exercise should be strengthened as well.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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This work was supported by the Key Laboratory Project of Shanghai Science and Technology Commission (11DZ2261100).

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Wang, X., Cai, Zd., Jiang, Wt. et al. Systematic review and meta-analysis of the effects of exercise on depression in adolescents. Child Adolesc Psychiatry Ment Health 16 , 16 (2022). https://doi.org/10.1186/s13034-022-00453-2

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Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews

• Pawel Posadzki 1 , 2 ,
• Dawid Pieper   ORCID: orcid.org/0000-0002-0715-5182 3 ,
• Ram Bajpai 4 ,
• Hubert Makaruk 5 ,
• Nadja Könsgen 3 ,
• Annika Lena Neuhaus 3 &
• Monika Semwal 6  

BMC Public Health volume  20 , Article number:  1724 ( 2020 ) Cite this article

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Sedentary lifestyle is a major risk factor for noncommunicable diseases such as cardiovascular diseases, cancer and diabetes. It has been estimated that approximately 3.2 million deaths each year are attributable to insufficient levels of physical activity. We evaluated the available evidence from Cochrane systematic reviews (CSRs) on the effectiveness of exercise/physical activity for various health outcomes.

Overview and meta-analysis. The Cochrane Library was searched from 01.01.2000 to issue 1, 2019. No language restrictions were imposed. Only CSRs of randomised controlled trials (RCTs) were included. Both healthy individuals, those at risk of a disease, and medically compromised patients of any age and gender were eligible. We evaluated any type of exercise or physical activity interventions; against any types of controls; and measuring any type of health-related outcome measures. The AMSTAR-2 tool for assessing the methodological quality of the included studies was utilised.

Hundred and fifty CSRs met the inclusion criteria. There were 54 different conditions. Majority of CSRs were of high methodological quality. Hundred and thirty CSRs employed meta-analytic techniques and 20 did not. Limitations for studies were the most common reasons for downgrading the quality of the evidence. Based on 10 CSRs and 187 RCTs with 27,671 participants, there was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% confidence intervals (CI) 0.78 to 0.96]; I 2  = 26.6%, [prediction interval (PI) 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]. Data from 15 CSRs and 408 RCTs with 32,984 participants showed a small improvement in quality of life (QOL) standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]. Subgroup analyses by the type of condition showed that the magnitude of effect size was the largest among patients with mental health conditions.

There is a plethora of CSRs evaluating the effectiveness of physical activity/exercise. The evidence suggests that physical activity/exercise reduces mortality rates and improves QOL with minimal or no safety concerns.

Trial registration

Registered in PROSPERO ( CRD42019120295 ) on 10th January 2019.

Peer Review reports

The World Health Organization (WHO) defines physical activity “as any bodily movement produced by skeletal muscles that requires energy expenditure” [ 1 ]. Therefore, physical activity is not only limited to sports but also includes walking, running, swimming, gymnastics, dance, ball games, and martial arts, for example. In the last years, several organizations have published or updated their guidelines on physical activity. For example, the Physical Activity Guidelines for Americans, 2nd edition, provides information and guidance on the types and amounts of physical activity that provide substantial health benefits [ 2 ]. The evidence about the health benefits of regular physical activity is well established and so are the risks of sedentary behaviour [ 2 ]. Exercise is dose dependent, meaning that people who achieve cumulative levels several times higher than the current recommended minimum level have a significant reduction in the risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events [ 3 ]. Benefits of physical activity have been reported for numerous outcomes such as mortality [ 4 , 5 ], cognitive and physical decline [ 5 , 6 , 7 ], glycaemic control [ 8 , 9 ], pain and disability [ 10 , 11 ], muscle and bone strength [ 12 ], depressive symptoms [ 13 ], and functional mobility and well-being [ 14 , 15 ]. Overall benefits of exercise apply to all bodily systems including immunological [ 16 ], musculoskeletal [ 17 ], respiratory [ 18 ], and hormonal [ 19 ]. Specifically for the cardiovascular system, exercise increases fatty acid oxidation, cardiac output, vascular smooth muscle relaxation, endothelial nitric oxide synthase expression and nitric oxide availability, improves plasma lipid profiles [ 15 ] while at the same time reducing resting heart rate and blood pressure, aortic valve calcification, and vascular resistance [ 20 ].

However, the degree of all the above-highlighted benefits vary considerably depending on individual fitness levels, types of populations, age groups and the intensity of different physical activities/exercises [ 21 ]. The majority of guidelines in different countries recommend a goal of 150 min/week of moderate-intensity aerobic physical activity (or equivalent of 75 min of vigorous-intensity) [ 22 ] with differences for cardiovascular disease [ 23 ] or obesity prevention [ 24 ] or age groups [ 25 ].

There is a plethora of systematic reviews published by the Cochrane Library critically evaluating the effectiveness of physical activity/exercise for various health outcomes. Cochrane systematic reviews (CSRs) are known to be a source of high-quality evidence. Thus, it is not only timely but relevant to evaluate the current knowledge, and determine the quality of the evidence-base, and the magnitude of the effect sizes given the negative lifestyle changes and rising physical inactivity-related burden of diseases. This overview will identify the breadth and scope to which CSRs have appraised the evidence for exercise on health outcomes; and this will help in directing future guidelines and identifying current gaps in the literature.

The objectives of this research were to a. answer the following research questions: in children, adolescents and adults (both healthy and medically compromised) what are the effects (and adverse effects) of exercise/physical activity in improving various health outcomes (e.g., pain, function, quality of life) reported in CSRs; b. estimate the magnitude of the effects by pooling the results quantitatively; c. evaluate the strength and quality of the existing evidence; and d. create recommendations for future researchers, patients, and clinicians.

Our overview was registered with PROSPERO (CRD42019120295) on 10th January 2019. The Cochrane Handbook for Systematic Reviews of interventions and Preferred Reporting Items for Overviews of Reviews were adhered to while writing and reporting this overview [ 26 , 27 ].

Search strategy and selection criteria

We followed the practical guidance for conducting overviews of reviews of health care interventions [ 28 ] and searched the Cochrane Database of Systematic Reviews (CDSR), 2019, Issue 1, on the Cochrane Library for relevant papers using the search strategy: (health) and (exercise or activity or physical). The decision to seek CSRs only was based on three main aspects. First, high quality (CSRs are considered to be the ‘gold methodological standard’) [ 29 , 30 , 31 ]. Second, data saturation (enough high-quality evidence to reach meaningful conclusions based on CSRs only). Third, including non-CSRs would have heavily increased the issue of overlapping reviews (also affecting data robustness and credibility of conclusions). One reviewer carried out the searches. The study screening and selection process were performed independently by two reviewers. We imported all identified references into reference manager software EndNote (X8). Any disagreements were resolved by discussion between the authors with third overview author acting as an arbiter, if necessary.

We included CSRs of randomised controlled trials (RCTs) involving both healthy individuals and medically compromised patients of any age and gender. Only CSRs assessing exercise or physical activity as a stand-alone intervention were included. This included interventions that could initially be taught by a professional or involve ongoing supervision (the WHO definition). Complex interventions e.g., assessing both exercise/physical activity and behavioural changes were excluded if the health effects of the interventions could not have been attributed to exercise distinctly.

Any types of controls were admissible. Reviews evaluating any type of health-related outcome measures were deemed eligible. However, we excluded protocols or/and CSRs that have been withdrawn from the Cochrane Library as well as reviews with no included studies.

Data analysis

Three authors (HM, ALN, NK) independently extracted relevant information from all the included studies using a custom-made data collection form. The methodological quality of SRs included was independently evaluated by same reviewers using the AMSTAR-2 tool [ 32 ]. Any disagreements on data extraction or CSR quality were resolved by discussion. The entire dataset was validated by three authors (PP, MS, DP) and any discrepant opinions were settled through discussions.

The results of CSRs are presented in a narrative fashion using descriptive tables. Where feasible, we presented outcome measures across CSRs. Data from the subset of homogeneous outcomes were pooled quantitatively using the approach previously described by Bellou et al. and Posadzki et al. [ 33 , 34 ]. For mortality and quality of life (QOL) outcomes, the number of participants and RCTs involved in the meta-analysis, summary effect sizes [with 95% confidence intervals (CI)] using random-effects model were calculated. For binary outcomes, we considered relative risks (RRs) as surrogate measures of the corresponding odds ratio (OR) or risk ratio/hazard ratio (HR). To stabilise the variance and normalise the distributions, we transformed RRs into their natural logarithms before pooling the data (a variation was allowed, however, it did not change interpretation of results) [ 35 ]. The standard error (SE) of the natural logarithm of RR was derived from the corresponding CIs, which was either provided in the study or calculated with standard formulas [ 36 ]. Binary outcomes reported as risk difference (RD) were also meta-analysed if two more estimates were available. For continuous outcomes, we only meta-analysed estimates that were available as standardised mean difference (SMD), and estimates reported with mean differences (MD) for QOL were presented separately in a supplementary Table  9 . To estimate the overall effect size, each study was weighted by the reciprocal of its variance. Random-effects meta-analysis, using DerSimonian and Laird method [ 37 ] was applied to individual CSR estimates to obtain a pooled summary estimate for RR or SMD. The 95% prediction interval (PI) was also calculated (where ≥3 studies were available), which further accounts for between-study heterogeneity and estimates the uncertainty around the effect that would be anticipated in a new study evaluating that same association. I -squared statistic was used to measure between study heterogeneity; and its various thresholds (small, substantial and considerable) were interpreted considering the size and direction of effects and the p -value from Cochran’s Q test ( p  < 0.1 considered as significance) [ 38 ]. Wherever possible, we calculated the median effect size (with interquartile range [IQR]) of each CSR to interpret the direction and magnitude of the effect size. Sub-group analyses are planned for type and intensity of the intervention; age group; gender; type and/or severity of the condition, risk of bias in RCTs, and the overall quality of the evidence (Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria). To assess overlap we calculated the corrected covered area (CCA) [ 39 ]. All statistical analyses were conducted on Stata statistical software version 15.2 (StataCorp LLC, College Station, Texas, USA).

The searches generated 280 potentially relevant CRSs. After removing of duplicates and screening, a total of 150 CSRs met our eligibility criteria [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 ] (Fig.  1 ). Reviews were published between September 2002 and December 2018. A total of 130 CSRs employed meta-analytic techniques and 20 did not. The total number of RCTs in the CSRs amounted to 2888; with 485,110 participants (mean = 3234, SD = 13,272). The age ranged from 3 to 87 and gender distribution was inestimable. The main characteristics of included reviews are summarised in supplementary Table  1 . Supplementary Table  2 summarises the effects of physical activity/exercise on health outcomes. Conclusions from CSRs are listed in supplementary Table  3 . Adverse effects are listed in supplementary Table  4 . Supplementary Table  5 presents summary of withdrawals/non-adherence. The methodological quality of CSRs is presented in supplementary Table  6 . Supplementary Table  7 summarises studies assessed at low risk of bias (by the authors of CSRs). GRADE-ings of the review’s main comparison are listed in supplementary Table  8 .

Study selection process

There were 54 separate populations/conditions, considerable range of interventions and comparators, co-interventions, and outcome measures. For detailed description of interventions, please refer to the supplementary tables . Most commonly measured outcomes were - function 112 (75%), QOL 83 (55%), AEs 70 (47%), pain 41 (27%), mortality 28 (19%), strength 30 (20%), costs 47 (31%), disability 14 (9%), and mental health in 35 (23%) CSRs.

There was a 13% reduction in mortality rates risk ratio (RR) 0.87 [95% CI 0.78 to 0.96]; I 2  = 26.6%, [PI 0.70, 1.07], median effect size (MES) = 0.93 [interquartile range (IQR) 0.81, 1.00]; 10 CSRs, 187 RCTs, 27,671 participants) following exercise when compared with various controls (Table 1 ). This reduction was smaller in ‘other groups’ of patients when compared to cardiovascular diseases (CVD) patients - RR 0.97 [95% CI 0.65, 1.45] versus 0.85 [0.76, 0.96] respectively. The effects of exercise were not intensity or frequency dependent. Sessions more than 3 times per week exerted a smaller reduction in mortality as compared with sessions of less than 3 times per week RR 0.87 [95% CI 0.78, 0.98] versus 0.63 [0.39, 1.00]. Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low ROB exerted smaller reductions in mortality when compared to RCTs at an unclear or high ROB, RR 0.90 [95% CI 0.78, 1.02] versus 0.72 [0.42, 1.22] versus 0.86 [0.69, 1.06] respectively. CSRs with moderate quality of evidence (GRADE), showed slightly smaller reductions in mortality when compared with CSRs that relied on very low to low quality evidence RR 0.88 [95% CI 0.79, 0.98] versus 0.70 [0.47, 1.04].

Exercise also showed an improvement in QOL, standardised mean difference (SMD) 0.18 [95% CI 0.08, 0.28]; I 2  = 74.3%; PI -0.18, 0.53], MES = 0.20 [IQR 0.07, 0.39]; 15 CSRs, 408 RCTs, 32,984 participants) when compared with various controls (Table 2 ). These improvements were greater observed for health related QOL when compared to overall QOL SMD 0.30 [95% CI 0.21, 0.39] vs 0.06 [− 0.08, 0.20] respectively. Again, the effects of exercise were duration and frequency dependent. For instance, sessions of more than 90 mins exerted a greater improvement in QOL as compared with sessions up to 90 min SMD 0.24 [95% CI 0.11, 0.37] versus 0.22 [− 0.30, 0.74]. Subgroup analyses by the type of condition showed that the magnitude of effect was the largest among patients with mental health conditions, followed by CVD and cancer. Physical activity exerted negative effects on QOL in patients with respiratory conditions (2 CSRs, 20 RCTs with 601 patients; SMD -0.97 [95% CI -1.43, 0.57]; I 2  = 87.8%; MES = -0.46 [IQR-0.97, 0.05]). Subgroup analyses by risk of bias (ROB) in RCTs showed that RCTs at low or unclear ROB exerted greater improvements in QOL when compared to RCTs at a high ROB SMD 0.21 [95% CI 0.10, 0.31] versus 0.17 [0.03, 0.31]. Analogically, CSRs with moderate to high quality of evidence showed slightly greater improvements in QOL when compared with CSRs that relied on very low to low quality evidence SMD 0.19 [95% CI 0.05, 0.33] versus 0.15 [− 0.02, 0.32]. Please also see supplementary Table  9 more studies reporting QOL outcomes as mean difference (not quantitatively synthesised herein).

Adverse events (AEs) were reported in 100 (66.6%) CSRs; and not reported in 50 (33.3%). The number of AEs ranged from 0 to 84 in the CSRs. The number was inestimable in 83 (55.3%) CSRs. Ten (6.6%) reported no occurrence of AEs. Mild AEs were reported in 28 (18.6%) CSRs, moderate in 9 (6%) and serious/severe in 20 (13.3%). There were 10 deaths and in majority of instances, the causality was not attributed to exercise. For this outcome, we were unable to pool the data as effect sizes were too heterogeneous (Table 3 ).

In 38 CSRs, the total number of trials reporting withdrawals/non-adherence was inestimable. There were different ways of reporting it such as adherence or attrition (high in 23.3% of CSRs) as well as various effect estimates including %, range, total numbers, MD, RD, RR, OR, mean and SD. The overall pooled estimates are reported in Table 3 .

Of all 16 domains of the AMSTAR-2 tool, 1876 (78.1%) scored ‘yes’, 76 (3.1%) ‘partial yes’; 375 (15.6%) ‘no’, and ‘not applicable’ in 25 (1%) CSRs. Ninety-six CSRs (64%) were scored as ‘no’ on reporting sources of funding for the studies followed by 88 (58.6%) failing to explain the selection of study designs for inclusion. One CSR (0.6%) each were judged as ‘no’ for reporting any potential sources of conflict of interest, including any funding for conducting the review as well for performing study selection in duplicate.

In 102 (68%) CSRs, there was predominantly a high risk of bias in RCTs. In 9 (6%) studies, this was reported as a range, e.g., low or unclear or low to high. Two CSRs used different terminology i.e., moderate methodological quality; and the risk of bias was inestimable in one CSR. Sixteen (10.6%) CSRs did not identify any studies (RCTs) at low risk of random sequence generation, 28 (18.6%) allocation concealment, 28 (18.6%) performance bias, 84 (54%) detection bias, 35 (23.3%) attrition bias, 18 (12%) reporting bias, and 29 (19.3%) other bias.

In 114 (76%) CSRs, limitation of studies was the main reason for downgrading the quality of the evidence followed by imprecision in 98 (65.3%) and inconsistency in 68 (45.3%). Publication bias was the least frequent reason for downgrading in 26 (17.3%) CSRs. Ninety-one (60.7%) CSRs reached equivocal conclusions, 49 (32.7%) reviews reached positive conclusions and 10 (6.7%) reached negative conclusions (as judged by the authors of CSRs).

In this systematic review of CSRs, we found a large body of evidence on the beneficial effects of physical activity/exercise on health outcomes in a wide range of heterogeneous populations. Our data shows a 13% reduction in mortality rates among 27,671 participants, and a small improvement in QOL and health-related QOL following various modes of physical activity/exercises. This means that both healthy individuals and medically compromised patients can significantly improve function, physical and mental health; or reduce pain and disability by exercising more [ 190 ]. In line with previous findings [ 191 , 192 , 193 , 194 ], where a dose-specific reduction in mortality has been found, our data shows a greater reduction in mortality in studies with longer follow-up (> 12 months) as compared to those with shorter follow-up (< 12 months). Interestingly, we found a consistent pattern in the findings, the higher the quality of evidence and the lower the risk of bias in primary studies, the smaller reductions in mortality. This pattern is observational in nature and cannot be over-generalised; however this might mean less certainty in the estimates measured. Furthermore, we found that the magnitude of the effect size was the largest among patients with mental health conditions. A possible mechanism of action may involve elevated levels of brain-derived neurotrophic factor or beta-endorphins [ 195 ].

We found the issue of poor reporting or underreporting of adherence/withdrawals in over a quarter of CSRs (25.3%). This is crucial both for improving the accuracy of the estimates at the RCT level as well as maintaining high levels of physical activity and associated health benefits at the population level.

Even the most promising interventions are not entirely risk-free; and some minor AEs such as post-exercise pain and soreness or discomfort related to physical activity/exercise have been reported. These were typically transient; resolved within a few days; and comparable between exercise and various control groups. However worryingly, the issue of poor reporting or underreporting of AEs has been observed in one third of the CSRs. Transparent reporting of AEs is crucial for identifying patients at risk and mitigating any potential negative or unintended consequences of the interventions.

High risk of bias of the RCTs evaluated was evident in more than two thirds of the CSRs. For example, more than half of reviews identified high risk of detection bias as a major source of bias suggesting that lack of blinding is still an issue in trials of behavioural interventions. Other shortcomings included insufficiently described randomisation and allocation concealment methods and often poor outcome reporting. This highlights the methodological challenges in RCTs of exercise and the need to counterbalance those with the underlying aim of strengthening internal and external validity of these trials.

Overall, high risk of bias in the primary trials was the main reason for downgrading the quality of the evidence using the GRADE criteria. Imprecision was frequently an issue, meaning the effective sample size was often small; studies were underpowered to detect the between-group differences. Pooling too heterogeneous results often resulted in inconsistent findings and inability to draw any meaningful conclusions. Indirectness and publication bias were lesser common reasons for downgrading. However, with regards to the latter, the generally accepted minimum number of 10 studies needed for quantitatively estimate the funnel plot asymmetry was not present in 69 (46%) CSRs.

Strengths of this research are the inclusion of large number of ‘gold standard’ systematic reviews, robust screening, data extractions and critical methodological appraisal. Nevertheless, some weaknesses need to be highlighted when interpreting findings of this overview. For instance, some of these CSRs analysed the same primary studies (RCTs) but, arrived at slightly different conclusions. Using, the Pieper et al. [ 39 ] formula, the amount of overlap ranged from 0.01% for AEs to 0.2% for adherence, which indicates slight overlap. All CSRs are vulnerable to publication bias [ 196 ] - hence the conclusions generated by them may be false-positive. Also, exercise was sometimes part of a complex intervention; and the effects of physical activity could not be distinguished from co-interventions. Often there were confounding effects of diet, educational, behavioural or lifestyle interventions; selection, and measurement bias were inevitably inherited in this overview too. Also, including CSRs only might lead to selection bias; and excluding reviews published before 2000 might limit the overall completeness and applicability of the evidence. A future update should consider these limitations, and in particular also including non-CSRs.

Conclusions

Trialists must improve the quality of primary studies. At the same time, strict compliance with the reporting standards should be enforced. Authors of CSRs should better explain eligibility criteria and report sources of funding for the primary studies. There are still insufficient physical activity trends worldwide amongst all age groups; and scalable interventions aimed at increasing physical activity levels should be prioritized [ 197 ]. Hence, policymakers and practitioners need to design and implement comprehensive and coordinated strategies aimed at targeting physical activity programs/interventions, health promotion and disease prevention campaigns at local, regional, national, and international levels [ 198 ].

Availability of data and materials

Data sharing is not applicable to this article as no raw data were analysed during the current study. All information in this article is based on published systematic reviews.

Abbreviations

Adverse events

Cardiovascular diseases

Cochrane Database of Systematic Reviews

Cochrane systematic reviews

Confidence interval

Grading of Recommendations Assessment, Development and Evaluation

Hazard ratio

Interquartile range

Mean difference

Prediction interval

Quality of life

Randomised controlled trials

Relative risk

Risk difference

Risk of bias

Standard error

Standardised mean difference

World Health Organization

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Additional file 1:.

Supplementary Table 1. Main characteristics of included Cochrane systematic reviews evaluating the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 2. Additional information from Cochrane systematic reviews of the effects of physical activity/exercise on health outcomes ( n  = 150). Supplementary Table 3. Conclusions from Cochrane systematic reviews “quote”. Supplementary Table 4 . AEs reported in Cochrane systematic reviews. Supplementary Table 5. Summary of withdrawals/non-adherence. Supplementary Table 6. Methodological quality assessment of the included Cochrane reviews with AMSTAR-2. Supplementary Table 7. Number of studies assessed as low risk of bias per domain. Supplementary Table 8. GRADE for the review’s main comparison. Supplementary Table 9. Studies reporting quality of life outcomes as mean difference.

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Posadzki, P., Pieper, D., Bajpai, R. et al. Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews. BMC Public Health 20 , 1724 (2020). https://doi.org/10.1186/s12889-020-09855-3

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The Impact of Typical School Provision of Physical Education, Physical Activity and Sports on Adolescent Mental Health and Wellbeing: A Systematic Literature Review

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• Padraic Rocliffe   ORCID: orcid.org/0000-0001-7216-4504 1 ,
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Typical school provision of physical education, physical activity and sports, which is reflective of the school’s response to the national curriculum, available resources and school ethos, may impact adolescent mental health and wellbeing. Systematic literature reviews have not yet considered this impact. The Web of Science, SPORTDiscus, PsychINFO, ERIC and MEDLINE databases were searched for relevant literature (2000–2022) pertaining to adolescents aged 12–18 years in secondary schools. Twenty studies met the inclusion criteria, including thirteen interventions, five cross-sectional and two longitudinal studies. Included studies contributed 108 reported effects, that examined depression, anxiety, self-esteem, self-efficacy, wellbeing, life satisfaction and positive mental health. Anxiety was the most frequently reported outcome, with 59% of the reported findings found to be non-significant, 24% significantly positive, 12% significantly negative and 6% reporting a negative trend but with no test of significance. Evidence supported the impact of physical education on adolescent mental health and wellbeing. Significantly positive effects were linked to interventions with minor modifications to typical provision such as the integration of teacher workshops and/or implementation of curriculum models. This suggests the importance of supplementing typical school provision of physical education to positively influence future impact.

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Introduction

Physical activity predicts adolescent health (Rollo et al., 2020 ; Telama et al., 2005 ). The international guidelines for physical activity recommend an average of sixty- minutes of moderate to vigorous physical activity weekly for adolescent populations (World Health Organization, 2020 ). Despite this, it is estimated that more than 80% of adolescents fail to meet these specifications (World Health Organization, 2022a , 2022b , 2022c ). Adolescents with lower levels of physical activity are more susceptible to developing issues with their mental health and wellbeing that track from adolescence into adulthood (Shlack et al., 2021 ). While evidence exists to support the positive impact of physical activity outside of school (Biddle & Asare, 2011 ; Conn, 2010 ; Stubbs et al., 2017 ), international health experts have emphasized the requirement for further physical activity promotion strategies to increase adolescent physical activity for mental health and wellbeing and to reduce worldwide health costs (Teychenne et al., 2020 ; Bouchard et al., 2006 ; Morton et al., 2016 ). The authors sought to address this research gap by systematically reviewing the literature that examines typical school provision of physical education, physical activity and sports as a strategy to impact adolescent mental health and wellbeing.

Schools are considered an ideal setting to enhance adolescent mental health and wellbeing by providing a variety of physical activity opportunities, including provision of school physical education, physical activity and sports (ISPAH, 2020 ; Morton et al., 2016 ; Pate et al., 2006 ). International policymakers have increasingly acknowledged the potential importance of health enhancing schools (World Health Organization, 2022a , 2022b , 2022c ). Subsequent policy documents from leading health promoting institutions advocate for schools to strengthen their capacity as primary vehicles to promote health for living, learning and working (World Health Organization, ISPAH, 2020 ). However, it is estimated that approximately 20% of the primary source of physical activity for adolescents occurs outside of schools (Ding et al., 2016 ). Of concern, is that in the potential absence of typical school provision of physical education, physical activity and sports, adolescents would not acquire the necessary exposure to skills for physical activity for health and wellbeing across a lifetime (Pulimeno et al., 2020 ; World Health Organization, 2022a , 2022b , 2022c ).

The International Society of Physical Activity for Health (ISPAH) recognizes the importance of physical activity for the promotion of public health by placing a key focus on the concept of a “whole of school approach” to physical activity (ISPAH, 2020 ). Opportunities for physical activity in school range from timetabled physical education provision to co-curricular physical activity and sports. Physical education includes “teaching students a structured curriculum to help them acquire the skills, knowledge and dispositions necessary to be “wise consumers” of physical activity” (Johnson & Turner, 2016 p3; SHAPE America, 2016 ). Furthermore, physical education is advocated as a primary requirement in all but 23% of countries globally (SHAPE America, 2016 ; Hardman et al., 2014 ). For the purpose of this study, the nomenclature of school physical activity refers to any skeletal muscle-driven movement that involves energy expenditure, including extracurricular activities, active recess and active classroom breaks (Caspersen et al., 1985 p126). School sports are underpinned by the ethos of participating in or preparing for school competitions beyond the curriculum, such as track and field, net and invasion games (Bailey, 2005 ). Previous empirical evidence points to the beneficial properties associated with physical activity outside of school on one’s mental health and wellbeing (McMahon et al., 2017 ; Monshouwer et al., 2013 ; Schuch & Vancampfort, 2021 ). Therefore, International health experts hypothesize that the impact of typical school provision of physical education, physical activity and sports as a supplementary strategy, may reduce ever-growing health costs and risk factors associated with poor mental health and wellbeing.

The World Health Organization ( 2018 p17) recognizes schools as integral investments for “lifelong health, active lifestyles, prevention of NCD’s and mental health disorders.” NCD’s are defined as non-communicable diseases e.g., diabetes and heart disease. Mental health disorders are “characterized by a clinically significant disturbance in an individual’s cognition, emotional regulation, or behavior” (World Health Organization, 2022a , 2022b , 2022c p1). Mental health disorders are increasingly more prevalent and as such are considered the second leading risk factor for worldwide burden of illness (Kassebaum et al., 2017 ) and account for 45% of the burden of disease in adolescent populations (Gore et al., 2011 ). Much of the evidence pertaining to adolescent mental health is concentrated on negative mental health outcomes, such as depression and anxiety (Murphy et al., 2020 ). Depression is projected to be a leading risk factor for disability worldwide (Kessler & Bromet, 2013 ) and is estimated to impact 322 million people globally (World Health Organization, 2017 ). As such, the sale of antidepressants is the fourth most prescribed drug worldwide, accounting for $9.9 billion annually (Cruz, 2012 ). The onset of anxiety is most prevalent in adolescents (Kessler et al., 2007 ) and is the sixth leading risk factor for disability (Zimmermann et al., 2020 ). Anxiety is estimated to impact 265 million people globally (World Health Organization, 2017 ). More commonly discussed now is the concept of wellbeing, which is considered a subgroup of mental health and pertains to positive mental health outcomes, such as life satisfaction and positive mental health (Dienlin & Johannes, 2020 ). However, the onset of poor mental health in adolescence is now becoming well documented and is substantially more prevalent than any other health disorders (Collishaw, 2009 ; Vos & Begg, 1999 ). Thus, strategies to combat poor mental health during this phase of life are imperative.

Although schools are recognized as ideal settings to promote physical activity, reduce poor mental health and increase adolescent wellbeing (ISPAH, 2020 ), a paucity of evidence exists that synthesizes the impact of typical school provision of physical education, physical activity and sports. Worldwide adoption of school physical education curricula, along with physical activity and sports policies to promote physical activity, is a public health initiative. However, little evidence exists to identify the impact of provision or if the modification to enhance its impact is required. Therefore, a review of the literature that evaluates the impact of typical school provision of physical education, physical activity and sports on adolescent mental health and wellbeing is timely.

Current Study

Typical school provision of physical education in tandem with opportunities for physical activity and sports, has the potential to effectively respond to the current challenges associated with physical inactivity in adolescents and may have a considerable impact on adolescent mental health and wellbeing. In the context of the current study, “typical” refers to what occurs in the majority of schools with no significant departure from the norm. Provision refers to the underpinning structures and activities involved in providing the physical education curriculum, and opportunities for physical activity and sports participation in secondary schools. The extent and nature of the provision is reflective of the school’s response to the national curriculum, available resources and school ethos. Some evidence regarding the specific nature of typical school provision exists, however, no review of this evidence has been completed to date. Therefore, the present research study is underpinned by the following research questions: (1) How is typical school provision of physical education, physical activity and sports related to adolescent mental health and wellbeing? (2) Is typical school provision of physical education, physical activity and sports impactful on adolescent mental health and wellbeing? (3) Are there robust examples of best practices in schools that positively impact adolescent mental health and wellbeing? (4) Does typical school provision of physical education, physical activity and sports have a greater impact on girls or boys’ mental health and wellbeing? Accordingly, for the purposes of this systematic literature review, a narrative synthesis was applied to the current body of evidence, summarizing the key characteristics that appear to be the most pertinent to impacting adolescent mental health and wellbeing.

Reporting in this review was underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (Page et al., 2021 ; Shamseer et al., 2015 ). The review was registered with the International Prospective Register of Systematic Reviews on May 21st, 2021 (ID number CRD42021201202) (Booth et al., 2012 ).

Study Eligibility Criteria

Eligible articles included male and/or female adolescent participants with a mean age of between 12 and 18 years up. If an article had participants with a mean age of below 12 or above 18 years, a breakdown for the specific target population was required in the results section. Eligible articles also included “typical” school provision of physical education, physical activity and sports as an exposure (see earlier definition). Articles that only defined/measured school physical activity and/or sports but not physical education were excluded. Eligible studies included quantitative and/or qualitative measures of mental health and wellbeing with one or more of the following outcome variables: depression, anxiety, self-esteem, self-efficacy, wellbeing, life satisfaction and positive mental health. Articles needed to be peer reviewed and published in English between 2000 and 2022. Systematic literature reviews and meta-analyses were excluded. In intervention studies, control and/or intervention groups pre and post baseline results were utilized (provided they had not received an intervention that caused significant or deliberate change to usual practice). Articles that reported on studies including minor modifications to typical school provision of physical education, physical activity and sports were included e.g., additional time, emphasis on physical activity intensity or teacher support workshops. The setting for the physical education, physical activity and sports exposures had to be in secondary schools (i.e., post-primary, high school), within school time and extended pre and post school physical activity and sports opportunities. The setting for the outcome measures was in and/or outside secondary schools. Outcomes measured within the physical education class only were excluded.

Sources, Search Strategies and Selection Processes

A systematic search of five electronic databases was performed in May 2021: Web of Science, SPORTDiscus, PsychINFO, ERIC and MEDLINE. Search strategies were completed in collaboration with a university library technician from inception to December 2021. Keyword search terms included: “school”, “provision”, “physical education”, “physical activity”, “sport”, “adolescents”, wellbeing”, “ depression ”, “ anxiety ”, “ self-esteem ”, “ positive mental health ” and “ life satisfaction ”. A comprehensive copy of the search strategy is provided (Online Resource 1). Articles were imported to Rayyan Intelligent Systematic Review online platform where they were stored throughout the screening process (Ouzanni et al., 2016 ). Duplicates were removed. Screening of titles and abstracts were independently assessed for eligibility by three review authors (PR, LW, AB). Subsequently, full text articles were assessed for eligibility by five review authors (PR, AB, MA et al.). A 10% inter reviewer reliability was incorporated into stage 1 and stage 2 of the screening process which established agreement among reviewers. Disagreements were resolved by consensus. A supplementary search was conducted in November 2022 via (1) updates across five databases (2) screening reference lists of eligible articles (3) contacting leading experts in the field.

Quality Assessment and Data Extraction

The tools used to assess the quality of the included articles were (a) Downs and Black checklist and (b) Critical Appraisal Skills Program checklist (Downs & Black, 1998 ; Critical Appraisal Skills Programme, 2018 ). The Downs and Black checklist has been validated as a tool for assessing experimental and non-experimental quantitative study designs in the physical activity and health field (Eime et al., 2013 ; Nugent et al., 2021 ). The modified checklist included 22 items that were categorized into 5 subscales: reporting (10), external validity (1), internal validity—bias (4), internal validity—confounding (6) and power (1). Items were scored as 1 (compliance) or 0 (non-compliance). Study quality was assessed out of a total of 23 points (distribution of principal confounders were awarded 2 points). Aligning with the methodology outlined by Woods et al., ( 2021 p4) we “calculated the total percentage of criteria met per study, based on the criteria applicable to the type of study design.” Criteria that was not applicable were scored NA. The Critical Appraisal Skills Program checklist is widely utilized as a tool for assessing qualitative study designs. The modified checklist included 10 items and was assessed out of 10 points (1 for compliance, 0 for non-compliance). Articles were assessed independently by two review authors (PR, BOK) via Covidence software and disagreements were resolved through consensus.

Data were extracted through the use of a customised data extraction table via Covidence, by three review authors (PR, MA, LGG). A 10% inter reviewer reliability was incorporated which established agreement among reviewers. Disagreements were resolved through consensus. The data extraction table included study descriptives, population demographics and data that reported the relationship between the exposure and outcome variables. Authors of articles were contacted to obtain omitted details where necessary.

Data Synthesis

Outcome data were tabulated to determine the impact of typical school provision of physical education, physical activity and sports on seven key outcome areas: depression, anxiety, self-esteem, self-efficacy, wellbeing, life satisfaction and positive mental health. Detailed descriptions of each outcome are provided in Table 1 . The potential effects of typical school provision of physical education, physical activity and sports on each outcome investigated was established by two independent reviewers (PR, MA) using the method described by Panter et al. ( 2019 ). The main reported effects were assessed and extracted for all specified outcomes within each article, based on four levels of effects; significantly positive; significantly negative; inconclusive/no effect or no significant test. Many articles tested multiple outcomes, therefore, the overall evidence of impact was expressed as a percentage of the four effects within each article (i.e., significantly positive; significantly negative; inconclusive/no effect or no significant test). An article was deemed significantly positive when 50% or more of the reported effects were significant and in positive direction, significantly negative when 50% or more of the reported effects were significant and in a negative direction and inconclusive/no effect when 50% of more of the reported effects were non-significant or when results were mixed (both positive and negative). An article was deemed to have no significant test when the reported effects were not supported with a test of significance. Where no test of significance was applied, the direction of the effect was required i.e., positive/negative direction.

Article Identification

The search strategy yielded 3,234 peer reviewed articles (Web of Science = 1,550; SPORTDiscus = 477; PsychINFO = 357; ERIC = 290; MEDLINE = 560). A total of 2,073 articles remained after removing duplicates. Upon completing stage 1 screening of title and abstracts, 210 articles remained for full text review. Upon completion of stage 2 screening of full text articles, 14 articles were included for analysis. The most common reasons for excluding articles at stage 2 screening were non-targeted outcomes (n = 71), and population (n = 68). A supplementary search of the literature yielded an additional six articles. See Fig.  1 for the study flowchart.

PRISMA Flowchart of the Study Selection Process

Study Design and Location

Of the 20 articles included in this review, 13 were interventions (Baena-Extremera et al., 2012 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska etal., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Lang et al., 2016 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2022 ; Ruiz-Ariza et al., 2019 ), five were cross-sectional (Barney et al. 2019 ; Bertillis et al., 2018 ; Jochimek & Lada, 2019 ; Park et al., 2020 ; Uchoa et al., 2020 ) and two were longitudinal (Mastagli et al., 2020 ; Mendez-Gimenez et al., 2019 ). Twelve studies were conducted in European countries (Baena-Extremera et al., 2012 ; Bertillis et al., 2018 ; Escarti et al., 2010 ; Jochimek & Lada, 2019 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lang et al., 2016 ; Luna et al., 2019 ; Mastagli et al., 2020 ; Mendez-Gimenez et al., 2022 ; Mendez-Gimenez et al., 2019 ; Ruiz-Ariza et al., 2019 ), three in the USA (Barney et al. 2019 ; Felver et al., 2015 ; Khalsa et al., 2012 ), two in Brazil (Lima et al., 2022 ; Uchoa et al., 2020 ) and one in Australia (Costigan et al., 2016 ), Taiwan (Lai et al., 2009 ) and South Korea (Park et al., 2020 ). All articles were published in 2009 or later with sixteen of the twenty published in 2015 or later.

The number of schools sampled in each article ranged from 1 to 400, with a combined total of 453 schools and a mean of 22 schools per article. Sample sizes ranged from 30 to 28,451 participants, with a combined total of 34,250 and a mean of 1712 participants per article. The mean age of the included participants ranged from 13 to 17 years. Eighteen articles had a mixed gender sample.

Articles typically reported on the physical education curriculum with 19 of the 20 articles reporting this as a primary exposure (Baena-Extremera et al., 2012 ; Barney et al. 2019 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Jochimek & Lada, 2019 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Lang et al., 2016 ; Luna et al., 2019 ; Mastagli et al., 2020 ; Mendez-Gimenez et al., 2022 ; Mendez-Gimenez et al., 2019 ; Park et al., 2020 ; Ruiz-Ariza et al., 2019 ; Uchoa et al., 2020 ). For the purpose of this study, physical education curriculum is described as a standard physical education class in accordance with the national physical education curriculum of the specified country or state. Additional typical school provision of physical education and physical activity exposures included teaching skills, lesson planning, long-term planning, grading, prerequisites climate (Bertillis et al., 2018 ) and active recess (Costigan et al., 2016 ). Interventions with a modification to typical provision of physical education included the implementation of an adventure education programs (Baena-Extremera et al., 2012 ; Koszalka-Silska et al., 2021 ), resistance and aerobic exercise program (Costigan et al., 2016 ), the use of a range of curriculum models (Escarti et al., 2010 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2022 ), additional class time, physical education teacher workshops (Lima et al., 2022 ), high intensity interval training (Ruiz-Ariza et al., 2019 ) and the infusion of self-esteem building activities (Lai et al., 2009 ). Of the 12 included interventions, the experimental group exposure was deemed outside the realms of typical provision in four (Felver et al., 2015 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Lang et al., 2016 ) and therefore, just the control groups were included. There were zero sports exposures found in this review.

A description of the outcomes is reported in Table 1 . Depression was examined in three articles (Felver et al., 2015 ; Khalsa et al., 2012 ; Lima et al., 2022 ), anxiety in nine articles (Barney et al., 2019 ; Costigan et al., 2016 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Lima et al., 2022 ; Lang et al., 2016 ; Luna et al., 2019 ; Mastagli et al., 2020 ; Park et al., 2020 ), self-esteem in six articles (Baena-Extremera et al., 2012 ; Jochimek & Lada, 2019 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Uchoa et al., 2020 ), self-efficacy in two articles (Bertillis et al., 2018 ; Escarti et al., 2010 ), wellbeing in three articles (Costigan et al., 2016 ; Luna et al., 2019 ; Ruiz-Ariza et al., 2019 ), life satisfaction in three articles (Khalsa et al., 2012 ; Mendez-Gimenez et al., 2022 ; Mendez-Gimenez et al., 2019 ) and positive mental health in three articles (Felver et al., 2015 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2019 ). All 20 articles used quantitative methods (e.g., surveys, many with recognised external validity) to measure each outcome variable (Baena-Extremera et al., 2012 ; Barney et al. 2019 ; Bertillis et al., 2018 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Jochimek & Lada, 2019 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Lang et al., 2016 ; Luna et al., 2019 ; Mastagli et al., 2020 ; Mendez-Gimenez et al., 2022 ; Mendez-Gimenez et al., 2019 ; Park et al., 2020 ; Ruiz-Ariza et al., 2019 ; Uchoa et al., 2020 ). Qualitative methods were also utilized in two articles (Barney et al. 2019 ; Escarti et al., 2010 ). Where possible, all data were expressed in the context of main reported effects.

Quality Assessment

All 20 articles were assessed for quality using a modified Downs and Black checklist for quantitative studies by two reviewers (PR, BOK) (Downs & Black, 1998 ; Nugent et al., 2021 ). Three articles were given a rating of ‘excellent’ (85–100%), six articles were given a rating of ‘good’ (70–84%), nine articles were given a rating of fair (55–69%) and two articles were given a rating of ‘poor’ (< 55%). The mean quality assessment score was 69% (fair). None of the articles demonstrated external validity by ensuring the sample was representative of the entire population from which they were recruited. Two articles provided a power calculation to demonstrate an adequate sample size (see Table 2 ). Two articles were mixed method studies, therefore, were also assessed for quality using the Critical Appraisal Skills Program by two review authors (PR, BOK) (Critical Appraisal Skills Programme, 2018 ). Both articles had a rating of ‘good’ (70–84%). The mean quality assessment score was 80% (good).

Summary of Findings

This section provides an overview of the main findings presented in Table 3 . Included articles (n = 20) contributed a total of 108 reported effects between typical school provision of physical education, physical activity and sports and adolescent mental health and wellbeing. The evidence indicated that 26% of the overall reported effects were significantly positive (n = 28 effects), 65% were non-significant (n = 70 effects), 7% were significantly negative (n = 8 effects), and 2% had no significant test applied (n = 2 effects). Of the reported effects that indicated no significant test, both demonstrated a negative direction.

The impact summary of the reported effects within each article indicated that 15% were significantly positive (n = 3 articles) (Baena-Extremera et al., 2012 ; Mastagli et al., 2020 ; Ruiz-Ariza et al., 2019 ), 70% were inconclusive/no effect (n = 14 articles) (Bertillis et al., 2018 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Jochimek & Lada, 2019 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2019 ; Park et al., 2020 ; Uchoa et al., 2020 ), 10% were significantly negative (n = 2 articles) (Barney et al. 2019 ; Mendez-Gimenez et al., 2022 ) and 5% demonstrated a negative direction but with no test of significance (n = 1 article) (Lang et al., 2016 ).

When analyzed by study design, the overall frequency of reported effects showed that 72% (n = 78 effects, n = 13 articles) occurred in intervention studies with a mean quality assessment score of 66.5% (fair) (Baena-Extremera et al., 2012 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Lang et al., 2016 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2022 ; Ruiz-Ariza et al., 2019 ), 17% (n = 18 effects, n = 5 articles) in cross-sectional studies with a quality assessment score of 78.33% (good) (Barney et al., 2019 ; Bertillis et al., 2018 ; Jochimek & Lada, 2019 ; Park et al., 2020 ; Uchoa et al., 2020 ) and 11% (n = 12 effects, n = 2 articles) in longitudinal studies with a quality assessment score of 62.5% (fair) (Mastagli et al., 2020 ; Mendez-Gimenez et al., 2019 ).

The bulk of significantly positive effects (64%, n = 18 effects) and non-significant effects (77%, n = 54 effects) were reported most frequently in intervention studies. Longitudinal studies had the highest percentage (although infrequent) of significantly positive effects (50%, n = 6 effects) and cross-sectional studies had the highest percentage (although infrequent) of significantly negative effects (22%, n = 4 effects). Of the 13 intervention studies included, the impact summary indicated that 15% (n = 2 articles) were significantly positive (Baena-Extremera et al., 2012 ; Ruiz-Ariza et al., 2019 ), 69% (n = 9 articles) were inconclusive/no effect (Costigan et al., 2016 ; Escarti et al., 2010 ; Felver et al., 2015 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lai et al., 2009 ; Lima et al., 2022 ; Luna et al., 2019 ; Mendez-Gimenez et al., 2019 ; Park et al., 2020 ; Uchoa et al., 2020 ), 8% (n = 1 article) was significantly negative (Mendez-Gimenez et al., 2022 ) and 8% (n = 1 article) demonstrated a negative direction but with test of significance (Lang et al., 2016 ). Of the five cross-sectional studies, 80% (n = 4 articles) were inconclusive/no effect (Bertillis et al., 2018 ; Jochimek & Lada, 2019 ; Park et al., 2020 ; Uchoa et al., 2020 ), while 20% (n = 1 article) was significantly negative (Barney et al. 2019 ). Of the two longitudinal studies, 50% (n = 1 article) was significantly positive (Mastagli et al., 2020 ) while 50% (n = 1 article) was inconclusive/no effect (Mendez-Gimenez et al., 2019 ). Table 3 provides an in-depth analysis of each outcome.

Depression was identified in 15% of articles (n = 3), representing 13% (n = 14) of the total reported effects with a mean quality assessment score of 69% (fair). Of this, 21% (n = 3 effects) were significantly positive and 79% (n = 11 effects) were non-significant. There were zero significantly negative effects when looking at the impact of typical school provision of physical education, physical activity and sports on depression. The evidence was most prevalent in intervention studies (100%, n = 3 articles) (Felver et al., 2015 ; Khalsa et al., 2012 ; Lima et al., 2022 ). There were no cross-sectional or longitudinal study designs for this outcome.

Anxiety was identified in 45% of articles (n = 9), representing 33% (n = 36) of the total reported effects with a mean quality assessment score of 74% (good). Of this, 22% (n = 8 effects) were significantly positive, 61% (n = 22 effects) were non-significant, 11% (n = 4 effects) were significantly negative and 6% (n = 2 effects) indicated a negative direction but with no test of significance. The bulk of the evidence was most prevalent in intervention studies (66%, n = 6 articles) (Costigan et al., 2016 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Lang et al., 2016 ; Lima et al., 2022 ; Luna et al., 2019 ), then cross-sectional studies (22%, n = 2 articles) (Barney et al. 2019 ; Park et al., 2020 ) and longitudinal studies (11%, n = 1 articles) (Mastagli et al., 2020 ). When analyzed by study design, the frequency of reported effects indicated that 53% (n = 19 effects) occurred in intervention studies, 22% (n = 8 effects) in cross-sectional studies and 25% (n = 9 effects) in longitudinal studies. Of the intervention studies, the reported effects indicated that 5% (n = 1 effect) was significantly positive, 84% (n = 16 effects) were non-significant and 11% (n = 2 effects) indicated a positive direction but with no test of significance. There were zero significantly negative effects associated with anxiety in intervention studies. Of the cross-sectional studies, the reported effects indicated that 25% (n = 2 effects) were significantly positive, 25% (n = 2 effects) were non-significant and 50% (n = 4 effects) were significantly negative. Of the longitudinal studies, the reported effects indicated that 56% (n = 5 effects) were significantly positive and 44% (n = 4 effects) were non-significant. There were zero significantly negative effects in the longitudinal study on anxiety.

Self-Esteem

Self-esteem was identified in 35% of articles (n = 7), representing 18% (n = 19) of the total reported effects with a mean quality assessment score of 66% (fair). Of this, 32% (n = 6 effects) were significantly positive and 68% (n = 13 effects) were non-significant. There were zero significantly negative effects when looking at the impact of typical school provision of physical education, physical activity and sports on self-esteem. The bulk of the evidence was most prevalent in intervention studies (71%, n = 5 articles) (Baena-Extremera et al., 2012 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalks-Silska et al., 2021 ; Lai et al., 2009 ) and cross-sectional studies (29%, n = 2 articles) (Bertillis et al., 2018 ; Uchoa et al., 2020 ). There were no longitudinal study designs for this outcome. When analyzed by study design, the frequency of reported effects indicated that 74% (n = 14 effects) occurred in intervention studies and 26% (n = 5 effects) in cross-sectional studies. Of the intervention studies, the reported effects indicated that 36% (n = 5 effects) were significantly positive and 64% (n = 9 effects) were non-significant. Of the cross-sectional studies, the reported effects indicated that 40% (n = 2 effects) were significantly positive and 60% (n = 3 effects) were non-significant.

Self-Efficacy

Self-efficacy was identified in 11% of articles (n = 2), representing 17% (n = 17) of the total reported effects with a mean quality assessment score of 66% (fair). Of this, 29% (n = 5 effects) were significantly positive and 71% (n = 12 effects). There were zero significantly negative effects when looking at the impact of typical school provision of physical education, physical activity and sports on self-efficacy. The bulk of the evidence was most divided by intervention studies (50%, n = 1 article) (Escarti et al., 2010 ) and cross-sectional studies (50%, n = 1 articles) (Bertillis et al., 2018 ). There were no longitudinal studies for this outcome. When analyzed by study design, the frequency of reported effects indicated that 71% (n = 12 effects) occurred in the intervention study and 29% (n = 5 effects) in the cross-sectional study. In the intervention study, the reported effects indicated that 33% (n = 4 effects) were significantly positive and 66% (n = 8 effects) were non-significant. In the cross-sectional study, the reported effects indicated that 20% (n = 1 effects) were significantly positive and 80% (n = 4 effects) were non-significant.

Wellbeing was identified in 15% of articles (n = 3), representing 6% (n = 7) of the total reported effects with a mean quality assessment score of 71% (good). Of this, 57% (n = 4 effects) were significantly positive and 43% (n = 3 effects) were non-significant. There were zero significantly negative effects when looking at the impact of typical school provision of physical education, physical activity and sports on wellbeing. The evidence was most prevalent in intervention studies (100%, n = 3 articles) (Costigan et al., 2016 ; Luna et al., 2019 ; Ruiz-Ariza et al., 2019 ). There were no cross-sectional or longitudinal study designs for this outcome.

Life Satisfaction

Life satisfaction was identified in 15% of articles (n = 3), representing 7% (n = 8) of the total reported effects with a mean quality assessment score of 62% (fair). Of this, 12.5% (n = 1 effects) were significantly positive, 37.5% (n = 3 effects) were non-significant and 50% (n = 4 effects) were significantly negative. The evidence was most prevalent in intervention studies (66%, n = 2 articles) (Khalsa et al., 2012 ; Mendez-Gimenez et al., 2022 ) and longitudinal studies (33%, n = 1 article) (Mendez-Gimenez et al., 2019 ). There were no cross-sectional study designs for this outcome. When analyzed by study design, the frequency of reported effects indicated that 87.5% (n = 7 effects) occurred in intervention studies and 12.5% (n = 1 effects) occurred in longitudinal studies. Of the intervention studies, the reported effects indicated that 43% (n = 3 effects) were non-significant and 57% (n = 4 effects) were significantly negative. There were zero significantly positive effects associated with life satisfaction intervention studies. Of the longitudinal studies, the reported effects indicated that 100% (n = 1 effect) was significantly positive.

Positive Mental Health

Positive mental health was identified in 15% of articles (n = 3), representing 6% (n = 7) of the total reported effects with a mean quality assessment score of 60% (fair). Of this, 29% (n = 2 effects) were significantly positive and 71% (n = 5 effects) were non-significant. There were zero significantly negative effects when looking at the impact of typical school provision of physical education, physical activity and sports on positive mental health. The bulk of the evidence was most prevalent in intervention studies (66%, n = 2 articles) (Felver et al., 2015 ; Luna et al., 2019 ) and longitudinal studies (33%, n = 1 article) (Mendez-Gimenez et al., 2019 ). There were no cross-sectional study designs for this outcome. When analyzed by study design, the frequency of reported effects indicated that 71% (n = 5 effects) occurred in intervention studies and 29% (n = 2 effects) in longitudinal studies. Of the intervention studies, the reported effects indicated that 40% (n = 2 effects) were significantly positive and 60% (n = 3 effects) were non-significant. Of the longitudinal studies, the reported effects indicated that 100% (n = 2 effects) were non-significant. Figures 2 , 3 and 4 illustrates the frequency of reported effects by outcome, study design and exposure.

Frequency of Reported Effects by Outcome

Frequency of Reported Effects by Study Design

Frequency of Reported Effects by School Exposure

Increasing adolescent physical activity for the betterment of adolescent mental health and wellbeing is a global public health priority. This is evident in the promotion of policy and investment in typical school provision of physical education, physical activity and sports (ISPAH, 2020 ; World Health Organization, 2018 ; European Parliament, 2016 ; Kohl et al., 2013 ; Australian Department of Health, 2021 ). While some previous empirical evidence investigates the effects of physical activity outside of schools on a range of mental health and wellbeing indicators (Biddle & Asare, 2011 ), further school-based health promoting strategies are required (Teychenne et al., 2020 ; Bouchard et al., 2006 ; Morton et al., 2016 ). The authors sought to address this gap by systematically reviewing, organizing and evaluating the extant literature pertaining to typical school provision of physical education, physical activity and sports on adolescent mental health and wellbeing. Twenty articles, the bulk of which were published from 2015 onwards, illuminating the recent nature of inquiry into this research area, with an average rating of fair quality, were rigorously evaluated. The overarching evidence indicates that there are grade related increases in negative mental health outcomes e.g., depression and anxiety and decreases in positive indicators of wellbeing e.g., self-esteem, life satisfaction and positive mental health that are in need of intervention. While worldwide adoption of policy advocates for a whole of school approach to physical activity for adolescent health (ISPAH), much of the evidence presented in this study pertains to individual components of provision that cohere to contribute to a whole school approach. Therefore, the results indicate that it may be worthwhile for future research in this field to holistically consider the additive impact of physical education, physical activity and sports provision in schools. Intervention studies indicated non-significant effects between baseline and follow up for control groups, if there were no modifications to supplement typical provision, even relative minor modifications. This suggests that modifications to existing provision may be required to have a positive impact or at least to slow the grade related decline of adolescent mental health and wellbeing. Where possible, robust examples of best practices that implemented modifications to typical provision and had a positive impact on adolescent mental health and wellbeing are illuminated.

Impacts on Depression and Anxiety

A large proportion of the extant evidence indicated a mix of non-significant and significantly positive findings when examining the impact of typical school provision of physical education, physical activity and sports on depression. Zero significantly negative effects were reported. The relationship between typical school provision of physical education, physical activity and sports and depression in adolescents was investigated in just three research articles (intervention studies), confirming the paucity of evidence in this field of study. Of the significantly positive effects, a randomized control trial that integrated a minor modification to existing provision of physical education, consisting of a teacher workshop to update teachers on pedagogical and health related topics, lowered in 93% of adolescents, the risk of developing high depressive symptomology (Lima et al., 2022 ). Clinical studies suggest the prevention of depressive symptomology in adolescence, which is an indicator for depression, has the potential to serve as a protective mechanism that tracks into adulthood and positively impacts future quality of life (Copeland et al., 2009 ; Fergusson et al. 2005 ; Kim-Cohen et al. 2003 ; Bardone et al., 1998 ). Therefore, public health policies that subtly integrate health related teacher workshops into the typical physical education class should be considered as a potential factor that could positively impact mental health and wellbeing indicators, such as depression in adolescents. Of the non-significant effects, the current study reported no differences between baseline and follow up in control groups when examining the effect of typical provision of physical education on depression, without the implementation of an intervention with a minor modification (Felver et al., 2015 ; Khalsa et al., 2012 ). Prevalence of depression in adolescent populations has been found to worsen across grade level (Dooley et al., 2018 ). Therefore, future research should consider investigating the additive impact of alternative components of provision, such as physical activity and sports, to supplement physical education. It is conceivable that these may serve as integral strategies to positively impact adolescent depression.

In the context of anxiety, alternative exposures, such as physical education class assessment, were examined (Mastagli et al., 2020 ). Over a 4-month period, formative assessment (ongoing informal evaluation to monitor student learning e.g., essay or quiz) was found to demonstrate significantly positive effects when compared to summative assessment (formal evaluation of student learning e.g., end of year exam). These findings are consistent with existing literature that illuminate the implications associated with summative assessment in physical education for adolescents (Lentillon-Kaestner et al., 2018 ). Given that positive experiences with physical activity in adolescents track into adulthood (Telama et al., 2005 ), physical educators may consider the use of formative assessment techniques to positively influence future impact. Indeed, future research may consider the effects of a hybrid approach to classroom assessment (formative and summative) on adolescent anxiety. The current study also found significantly positive associations with total weekly physical education classes in a sample of 28,541 adolescents (Park et al., 2020 ). Implementation of physical education classes on two or more occasions weekly was found to be associated with lower levels of stress for both boys and girls which is consistent with previous qualitative research (Howard. 2011 ). In contrast, there were non-significant findings for levels of stress in boys and girls where provision consisted of just one day of physical education class weekly. This suggests that additional physical education classes are pertinent to impacting adolescent mental health and wellbeing. It must be noted that the current study also found significantly negative effects for stress across grade-level when exposed to typical provision of physical education. This was also noted by Barney et al., ( 2019 ) in their qualitative findings and is consistent with the national study of youth mental health in the Republic of Ireland consisting of 10,459 adolescents (Dooley et al., 2018 ). The study reported a grade-level increase in the severe range for anxiety, once more indicating the need to investigate the supplementation of physical education with additional components of physical activity provision in schools (e.g., active recess, active classroom breaks and active transport to schools). Similar to the results on depression, non-significant differences were found across an array of typical physical education control groups, in intervention studies, for social, general and somatic anxiety. Interventions consisted of basketball, team competition and traditional teaching models (Khalsa et al., 2012 ; Lima et al., 2022 ; Luna et al., 2019 ). Intervention studies with a minor modification to typical physical education provision, such as additional time, teacher workshops and use of the sport education curriculum model, also demonstrated non-significant effects, on social anxiety (Lima et al., 2022 ; Luna et al., 2019 ). These findings are consistent with literature regarding social relationship variables (Mendez-Gimenez et al., 2015 ; Cuevas et al., 2015 ). However, they contradict previous research that demonstrates positive effects (Kao 2019 ; Wallhead et al., 2014 ). Further investigation to analyze the effects of alternative components of provision on social anxiety is warranted. It must also be noted that while some intervention studies with minor modifications demonstrated non-significant effects on social anxiety, the intervention group almost always slowed the grade related increase in comparison to the control group. Slowing the grade related increase of negative mental health and wellbeing indicators, such as anxiety, is a relatively new phenomenon in this field of research and pertains to many of the non-significant intervention effects in this review. However, further research is required to understand and strengthen these findings. It is noteworthy that while there was one significantly positive effect for typical physical education provision on social anxiety (Kiliziene et al., 2018 ), the exposure beyond the national curriculum was not specified, which was common throughout this review. Active recess (PA), aerobic and resistance exercise (PE) were found to be non-significant contributors to reducing psychological distress (Costigan et al., 2016 ). While this is inconsistent with research examining the impact of physical activity outside of school on psychological distress (Hung et al., 2013 ; Perales et al., 2014 ; Zhao et al., 2013 ), further research investigating the effects of physical activity inside school as an additional strategic arm to reduce mental ill health in adolescent populations is required. Currently, there exists little evidence on differences in depression and anxiety across gender when exposed to typical school provision of physical education, physical activity and sports which should also be considered.

Impacts on Self-Esteem and Self-Efficacy

There were zero significantly negative effects associated with typical school provision of physical education, physical activity and sports on adolescent self-esteem and self-efficacy. Regarding self-esteem, an intervention that integrated a minor modification to existing provision consisting of an adventure education program was found to have significantly positive effects in comparison to a typical physical education control program of volleyball, football and athletics (Baena-Extremera et al., 2012 ). Comparatively, the current review found a similar intervention study with an adventure education experimental group exposure to demonstrate non-significant effects (Koszalka-Silska et al., 2021 ). These findings are consistent with the most recent systematic literature review in this research area, consisting of 16 articles that found mixed effects regarding the role of adventure education programs in increasing adolescent self-esteem (West & Crompton., 2001 ). The current study found an intervention that infused a minor modification to existing physical education provision consisting of self-esteem related “athlete spirit”, “knowing myself” and “I am the best” activities, to have significantly positive effects on physical and family self-esteem in comparison to a typical physical education class consisting of athletics, gymnastics, and badminton (Lai et al., 2009 ). This is consistent with findings that allude to the effective components associated with self-esteem enhancing programs (Dalgas-Pelish, 2006 ; Park & Park, 2015 ). The results illuminate the unique impact this may have to positively influence adolescent mental health and wellbeing outcomes, such as self-esteem, when incorporated into typical school provision of physical education. However, further research incorporating generalizability measures would be useful. When comparing boys with girls, this investigation found physical education participation to serve as a protective mechanism against low self-esteem for boys in comparison to their female counterparts (Uchoa et al., 2020 ). This may be because often the objective of exercising is often underpinned by weight loss for female adolescents (Cowley et al., 2021 ). As such, physical activity may be thought of as important by female adolescents, but not in a school environment. Thus, renewed strategies to promote self-esteem in girls via school physical activity would be beneficial. Research based upon longitudinal study designs that examine cause and effect, rather than cross-sectional study designs, such as in this review, are worthy of consideration. Similar to the depression and anxiety results, the evidence suggests a grade related decline in levels of self-esteem during adolescents that is likely to track into adulthood. This points to the importance of self-esteem promoting strategies, e.g., motivating teaching styles, during this phase of life (Dooley et al., 2018 ; Kiviruusu et al., 2015 ; Huang 2010 ). Once more, it is evident that the bulk of the intervention-based evidence in the current study presents non-significant effects for typical physical education provision in control groups without any modification to existing provision (Lai et al, 2009 ; Baena-Extremera et al., 2012 ; Costigan et al., 2016 ; Escarti et al., 2010 ; Khalsa et al., 2012 ; Kiliziene et al., 2018 ; Koszalka-Silska et al., 2021 ; Lima et al., 2022 ; Luna et al., 2019 ). This suggests that the power generated from physical education alone does not appear to be sufficient to improve adolescent mental health and wellbeing. Therefore, future research that considers strategies to successfully implement a whole of school, systems-based approach to supplement the impact of physical education provision on adolescent mental health and wellbeing (ISPAH, 2020 ) is worthy of consideration. In view of the absence of school physical activity and sports throughout the bulk of the articles presented in this review, programs that endeavor to include such exposures in unison with physical education may be impactful.

The relationship with adolescent self-efficacy was examined in just two articles (Bertillis et al., 2018 ; Escarti et al., 2010 ), illuminating the lack of evidence in this research area. An intervention study that integrated the teaching personal and social responsibility curriculum model into typical provision of physical education had significantly positive effects for perceived self-efficacy (Escarti et al., 2010 ). This is consistent with previous empirical evidence that suggest that through learning personal and social responsibility, one cultivates more optimistic attitudes regarding ability to manage social resources and strengthen one’s self-efficacy beliefs (Pajares & Urdan 2006 ; Vecchio et al., 2007 ). Therefore, the use of the teaching personal and social responsibility curriculum model in physical education class may be a viable strategy to enhance adolescent self-efficacy. However, longitudinal effects are in need of further investigation. Like many of the outcome variables discussed in this review, there were no differences between baseline and follow up in the control groups when examining the effect of typical school provision of physical education on self-efficacy without the implementation of a minor modification (Escarti et al., 2010 ). A cross-sectional study with alternative typical provision of physical education exposures, such as teaching skills, lesson planning and long-term planning, were also found to have non-significant effects in a diverse sample e.g., adolescents with disabilities, high grades, and low grades (Bertillis et al., 2018 ). This is inconsistent with studies that indicate the potential relationship between teaching style and student–teacher interactions on adolescent self-efficacy (Wentzel & Miele, 2016 ; Chen et al., 2022 ; Gunzenhauser et al., 2013 ). However, it must be noted that a paucity of empirical evidence exists that illuminates the impact of teachers on self-efficacy in adolescents with disabilities. Evidence suggests the pertinent role of teachers to promote “student persistence in demanding physical challenges, future health behavior and participation and higher self-efficacy for future success” (Usher & Pajares, 2008 , Bertillis et al., 2018 p2; Gao et al., 2008 ; Feltz & Magyar, 2006 ). Future studies that include randomized controlled trials with a minor modification to existing provision to examine cause-effect relationships between intervention and outcome rather than a cross-sectional study design may be beneficial. In contrast, however, this review reported a positive interaction between the physical education classroom climate and adolescent self-efficacy (Bertillis et al., 2018 ). This suggests that the physical education classroom climate “may serve as an indicator of how students experience their learning environment” (Bertillis et al., 2018 p 10) and that a cohesive learning environment is important to facilitate positive learning experiences for adolescents (Haegele & Sutherland, 2015 ). Currently, little evidence examines differences in self-efficacy across gender when exposed to typical school provision of physical education, physical activity and sports which should also be considered.

Impacts on Wellbeing, Positive Mental Health and Life Satisfaction

The extant evidence indicated a mix of non-significant, significantly positive and significantly negative effects when examining the impact of typical school provision of physical education, physical activity and sports on wellbeing, positive mental health and life satisfaction. Of all the outcomes reported in this review, typical school provision of physical education, physical activity and sports had the greatest impact on the bulk of these variables with the evidence reporting 57% significantly positive effects when examining wellbeing and 29% significantly positive effects when examining positive mental health. However, this relationship was investigated in a total of just eight articles across all three variables, indicating the scarcity of evidence in this research area. In the context of adolescent wellbeing, the data was spread across three intervention studies with varying degrees of minor modifications to existing provision. While a randomized controlled trial consisting of 8–10 min of high intensity interval training (shuttle runs, jumping jacks, bodyweight exercises) implemented into physical education classes over an eight-week period indicated a positive relationship that slowed the grade related decline of wellbeing, in comparison to a typical physical education control group, the effects were non-significant (Costigan et al., 2016 ). However, a follow up randomized control trial that further explored the possibility of this beneficial effect by infusing 16 min of high intensity interval training into the beginning of typical physical education classes over 12 weeks, found significantly positive effects on adolescent wellbeing, in comparison to a static stretching control group (Ruiz-Ariza et al., 2019 ). Future studies that generalize these findings to a larger, representative sample would be beneficial. Currently, there is no evidence to suggest the negative impact of high intensity interval training on adolescent mental health and wellbeing (Teychenne et al., 2020 ). Therefore, maximizing opportunities to engage in physical activity, such as high intensity interval training at the beginning of physical education class, could be considered when strategizing to positively impact adolescent mental health and wellbeing. Interestingly, a quasi-experiment study that embedded a sport education curriculum model into the physical education framework had significantly positive effects on adolescent wellbeing (Luna et al., 2019 ). This is explained via a systematic literature review of 14 articles that examined the impact of the sports education curriculum model and found significant associations with adolescent intrinsic motivation (Tendinha et al., 2021 ). It is noteworthy that previous literature suggests a close association between motivation in school and adolescent wellbeing (Beiswenger and Grolnick 2010 ). Therefore, “a motivating school context, enabled by the implementation of the sport education model” may be a viable strategy to impact adolescent wellbeing (Luna et al., 2019 p8).

Similar to wellbeing, a sport education model embedded within typical physical education class was found to positively affect indicators of adolescent positive mental health, such as positive and negative affect (Luna et al., 2019 ; Diaz-Garcia et al., 2020 ). However, the dearth of evidence in this research area is clear. Considering that “mood and affect in school-aged youth have been shown to predict” psychological functioning (Felver et al., 2015 p8; Weinstein & Mermelstein, 2013 ; Rothon et al., 2009 ), interventions with minor modification to existing provision, such as the inclusion of a sports education framework, should be considered as a conceivable strategy to impact adolescent mental health and wellbeing. However, further investigation is required to identify if the short-term effects of implementing a sport education model translate to improved long term outcomes. Consistent with the findings of this review, articles that examined typical provision of physical education, without the implementation of an intervention with a minor modification, yielded non-significant findings on positive and negative affect (Felver et al., 2015 ; Mendez-Gimenez et al., 2019 ). This notwithstanding, evidence in the literature suggests the positive effects of physical activity outside of school on components of positive mental health, such as positive and negative affect (Buecker et al., 2021 ). Therefore, alternative modes of school sport and physical activity, such as active classroom breaks, active recess and extra-curricular activities, should be strongly considered to support the provision of physical education classes. Similar to many of the outcome variables discussed in this review, the wider evidence suggests a grade-related decline in adolescent positive mental health that tracks into adulthood (Guo et al., 2018 ). This points to the importance of identifying positive mental health strategies during this phase of life.

A review of literature consisting of 141 empirical articles by Proctor et al. ( 2009 ), showed a decrease in life satisfaction in adolescence that tracks into adulthood. This is consistent with this current review that reported significantly negative effects across grade-level for life satisfaction when exposed to a sport education model, embedded within typical physical education class, over a school year (Mendez-Gimenez et al., 2022 ). Considering that the evidence indicates “a near universal decrease in life satisfaction during adolescence,” strategies to combat this decline are important during this phase of life (Orben et al., 2022 p1; Marquez & Long, 2021 ). This current study reported that during a full year of physical education, adolescents with high emotional intelligence were found to have higher levels of life satisfaction in comparison to adolescents with low emotional intelligence (Mendez-Gimenez et al., 2019 ). Positive relations between emotional intelligence and life satisfaction emerge frequently in the literature (Cazan & Nastasa, 2015 ; Barreiro 2014 ). Therefore, fostering a physical education environment that enables adolescents to recognize, control and appreciate their own emotions during classes could be an effective strategy to impact adolescent life satisfaction. In the context of healthcare costs, it must be noted that low life satisfaction is a significant and independent predictor of higher healthcare utilization, pertaining to the highest cost category (top 5%) in comparison to those with high life satisfaction (Goel et al., 2018 ). With adolescence considered to be a life phase in which future mental health and wellbeing patterns for adulthood are laid down, public health policies that enhance adolescent life satisfaction are an important factor in the promotion of adolescent wellbeing. Currently, there is little research available that examines differences in life satisfaction across gender when exposed to typical school provision of physical education, physical activity and sports, which is a common trend across many of the outcome measures discussed in this review.

Strengths and Limitations

This systematic literature review is the first to examine how the provision of typical school offerings of physical education, physical activity and sports impact adolescent mental health and wellbeing. In addition, a wide assessment of the literature was undertaken. This was enabled via the development of a comprehensive search strategy. Bias was reduced through the use of multiple reviewers at each phase of the study and allowed for a thorough examination of the literature. The investigation of an array of mental health and wellbeing outcomes enabled a comprehensive review of current standings and opportunities for future research. The data included quantitative and qualitative methods and a comprehensive supplementary search of the literature was conducted. The overall integrity and rigor of this systematic literature review was enhanced via the use of quality assessment tools that were appropriately regarded.

Heterogeneity associated with the article’s methodologies, nature of the outcomes, modes of analysis and a general lack of consensus on the constructs of mental health and wellbeing, presented difficulties in synthesising the data. The data was collected via self-report methods. Generalisability of the findings to low-income countries is problematic due to a large proportion of the participants residing in high income countries (Hamadeh et al., 2022 ). The exposure within the intervention studies’ control groups were often not reported. The methodological quality of the data was fair, indicating the dearth of high-quality methodological designs in this research area. Grey literature was not included. Regarding data analysis, some articles had a higher frequency of reported effects which were not taken into account. Lastly, seldom was the interaction effect of typical school provision of physical education, physical activity and sports investigated.

A review of the literature that examines the impact of typical school provision of physical education, physical activity and sports, as a strategy to enhance adolescent mental health and wellbeing and reduce global health costs, is a public health priority. The bulk of the evidence, however, pertained to physical education provision only, with a distinct paucity of data examining the additive impact of school physical activity and sports. In the context of the typical school provision of physical education framework, the current study illuminated a range of robust examples of best practices, including minor modifications to typical provision that positively impact adolescent mental health and wellbeing. The integration of physical education teacher workshops into the curriculum to update teachers on pedagogical and health related topics, short bouts of high intensity internal training, extended days dedicated to physical education weekly (≥ 2 days), formative assessment techniques, adventure education programs, self-esteem promoting programs, sport education/teaching personal and social understanding curriculum models and classroom climates that endorses high emotional intelligence, are viable strategies to enhance adolescent mental health and wellbeing. Many of the significantly positive effects implemented an intervention with a minor modification to typical provision, while control groups without a minor modification were often ineffective. This was a consistent finding across the outcome measures. These findings indicate the need to supplement typical school provision of physical education with alternative components of provision as noted above or via school provision of physical activity and sports. Indeed, the evidence suggests that the developmental benefits generated from physical education alone do not appear to be enough to enhance adolescent mental health and wellbeing. In addition, there were very few significantly negative effects in this review and while some of the evidence was found to be non-significant, the concept of slowing the grade related increase of negative mental health outcomes and decrease in positive indicators of wellbeing was clear and should be further examined. Future research should also consider gender differences when evaluating the impact of typical school provision of physical education, physical activity and sports as gender differences are often neglected in this research area.

Change history

24 august 2023.

A Correction to this paper has been published: https://doi.org/10.1007/s40894-023-00225-9

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Acknowledgements

We gratefully acknowledge the Editor of the Adolescent Research Review and associated reviewers for the expertise and extensive feedback provided. We would also like to acknowledge Dr Frank Nugent and Dr John Murphy for their contributions to the manuscript. The research leading to these results is in receipt of support funding from the Government of Ireland, Irish Research Council Postgraduate Scholarship Scheme.

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PR conceived of the study, participated in its design and coordination, performed the analysis and drafted the manuscript; MA participated in the design and interpretation of the data; BOK conceived of the study, participated in its design and coordination and interpretation of the data; LW participated in the design and interpretation of the data; AB participated in the design and interpretation of the data; LGG participated in the design and interpretation of the data and performed the analysis; FC participated in the design and interpretation of the data; MS participated in the design and interpretation of the data; IS conceived of the study, participated in its design and coordination, performed the analysis and drafted the manuscript; PMM conceived of the study and participated in its design and coordination; CMD conceived of the study, participated in its design and coordination, performed the analysis and drafted the manuscript. All authors read and approved the final manuscript.

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Rocliffe, P., Adamakis, M., O’Keeffe, B.T. et al. The Impact of Typical School Provision of Physical Education, Physical Activity and Sports on Adolescent Mental Health and Wellbeing: A Systematic Literature Review. Adolescent Res Rev (2023). https://doi.org/10.1007/s40894-023-00220-0

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Systematic review article, mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions.

• 1 Faculty of Business and Communications, INTI International University, Nilai, Negeri Sembilan, Malaysia
• 2 Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
• 3 Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
• 4 Faculty of Education and Liberal Arts, INTI International University, Nilai, Negeri Sembilan, Malaysia
• 5 Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
• 6 Faculty of Liberal Arts, Shinawatra University, Pathumthani, Thailand
• 7 College of Education, University of the Philippines, Diliman, Philippines

Introduction: This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.

Methods: It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.

Results: The co-citation analysis identified three major clusters: “Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity,” “EEG Neurofeedback and Cognitive Performance Enhancement,” and “Treatment of ADHD Using Neurofeedback.” The co-word analysis highlighted four key clusters: “Neurofeedback in Mental Health Research,” “Brain-Computer Interfaces for Stroke Rehabilitation,” “Neurofeedback for ADHD in Youth,” and “Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.

Discussion: This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.

Introduction

Neurofeedback is also known as EEG biofeedback and brainwave biofeedback ( Hellrung et al., 2022 ). The primary objective of neurofeedback is to modify brain electrical activity, which is the basis for emotional and behavioral processes in the body ( Mirifar et al., 2022 ). It combines electroencephalogram (EEG) capabilities with advances in computer technology and operant conditioning ( Swingle and Psych, 2010 ). Neurofeedback enables the brain to self-identify and adjust or self-regulate its electrical activity through the use of specific treatment procedures that either reward (strengthen) or inhibit (weaken) specific brainwave patterns ( Ninaus et al., 2015 ). Participants can learn to interrupt dysfunctional neurological patterns and create more stable brainwave patterns. A remarkable embodiment of neurofeedback principles can be seen in brain-computer interfaces (BCIs) for motor rehabilitation, particularly after stroke ( Sebastián-Romagosa et al., 2020 ). Remsik et al. (2021) describe the use of brain-computer interfaces (BCIs) for stroke rehabilitation through neurofeedback based on operant conditioning. This approach allows stroke survivors to purposefully control their brain’s sensorimotor rhythms by providing real-time feedback when they generate the desired brain activity. This method facilitates neurological recovery and can significantly improve motor function by reinforcing beneficial neural patterns, helping patients re-learn motor skills damaged by stroke. These advances highlight neurofeedback’s uniqueness and potency as a rehabilitation method, diverging fundamentally from conventional self-regulation and cognitive-behavioral interventions by offering specificity, direct targeting of brain function, and immediate, personalized feedback.

Neurofeedback distinguishes itself from conventional self-regulation and cognitive-behavioral techniques by directly focusing on and altering brain activity. Unlike traditional techniques that primarily aim to adjust thoughts, emotions, or behaviors through subjective means ( Zabihiyeganeh et al., 2019 ; Stran et al., 2020 ), neurofeedback utilizes a range of imaging modalities including real-time EEG, fMRI, MEG, and NIRS to provide objective, individualized insights into brain function, offering a more precise and data-driven approach to understanding and modifying neural activity ( Emmert et al., 2016 ; Marzbani et al., 2016 ; Kvamme et al., 2022 ; Wu et al., 2022 ; Yagi et al., 2022 ; Flanagan and Saikia, 2023 ; Lieberman et al., 2023 ). This level of specificity enables the focused training of specific brain areas and frequencies that are associated with certain functions or illnesses. This sets it apart from the more general effectiveness of conventional methods ( Hammond, 2007 ). Furthermore, neurofeedback’s immediate feedback loop permits real-time self-modulation of brain activity ( Marzbani et al., 2016 ), contrasting with the delayed feedback or the required conscious efforts associated with conventional therapies. Neurofeedback’s training paradigm is uniquely thorough compared to traditional procedures since it requires several sessions to induce enduring changes in brain function, rather than focusing on short-term effects. Hence, neurofeedback provides a specific, direct technique for improving brain activity and attaining therapeutic aims, substantially distinct from the broader, more generic tactics applied by traditional self-regulation and cognitive-behavioral therapies.

The effects of neurofeedback on cognitive function, with a focus on memory, are based on the principle of operant conditioning and involve informing the subject in real time about the workings of their organism to motivate them to change their behavior ( Pérez-Elvira et al., 2021 ). Neurofeedback is founded on two fundamental principles. First, the EEG accurately reflects observable mental states; the second reason is that these states can be educated ( Thompson and Thompson, 2003 ). The neurofeedback method aims to accomplish two primary goals. The first involves altering a specific brainwave frequency in a region of the participant’s brain that has been linked to their current emotional or behavioral issue ( Marzbani et al., 2016 ). The second objective is to improve the stability and communication of neural networks across the brain and between or within its hemispheres ( Sitaram et al., 2017 ). Neurofeedback restores the brain’s rhythm, timing, frequency, and synchronization, allowing the brain to better coordinate perception, movement, and conscious experience ( Farmer, 2002 ).

EEG neurofeedback systems utilize both operant conditioning and classical (associative) learning principles in the context of motor rehabilitation. Operant conditioning is employed to reinforce desired brain activity patterns associated with motor function. For example, when a patient generates specific brainwave patterns indicative of motor planning or execution, they may receive positive feedback such as auditory or visual cues, encouraging them to continue producing those patterns. Classical (associative) learning is utilized to establish connections between movement-related cues or mental imagery and positive outcomes. For instance, patients might be trained to associate imagining the movement of their limbs with successful motor execution or reduced pain, facilitating motor relearning and rehabilitation. By combining these learning principles, EEG neurofeedback systems can effectively engage both voluntary behavior modification and reflexive response associations, enhancing motor rehabilitation outcomes for patients.

Vernon et al. (2003) asserted that prior research suggests neurofeedback may be effective in treating a variety of early childhood disorders. Including attention-deficit/hyperactivity disorder (ADHD), Asperger’s disorder, learning disability, obsessive-compulsive disorder (OCD), and autism spectrum disorder (ASD) ( McVoy et al., 2019 ; Naeimian et al., 2020 ; Direito et al., 2021 ; Riesco-Matías et al., 2021 ; Zafarmand et al., 2022 ). Several randomized clinical studies on the use of neurofeedback techniques for ADHD have demonstrated the efficacy of neurofeedback ( Gevensleben et al., 2009 ; Sonuga-Barke et al., 2013 ; Micoulaud-Franchi et al., 2014 ; Cortese et al., 2016 ; Young et al., 2017 ). Because autistic children frequently exhibit symptoms of attention deficit and hyperactivity, these findings have prompted research into neurofeedback as an alternative treatment for autism ( Klöbl et al., 2023 ). Neurofeedback therapy has also been shown in studies to be effective and beneficial in the treatment of a variety of mental disorders, including anxiety, depression ( Wang et al., 2022 ), sleep disorders ( Kolken et al., 2023 ), headaches ( Arina et al., 2022 ), migraines ( Hashemipour and Isfahani Asl, 2022 ), and other emotional issues ( Zotev et al., 2011 ). It has also been shown to be effective in treating people with organic brain disorders such as cerebral palsy, and seizures ( Nigro, 2019 ). Other studies have shown that neurofeedback has the potential to improve optimal performance in high-level musical performers ( Egner and Gruzelier, 2003 ), dance performance ( Raymond et al., 2005 ), and sports performance ( Xiang et al., 2018 ; de Brito et al., 2022 ).

Literature review

Bibliometric analyses have been useful in identifying key research trends and mapping the intellectual structure of neurofeedback-related research. For instance, Rong et al. (2022) and Yao et al. (2022) conducted bibliometric analyses on ASD and quantitative EEG research in neuropsychiatric disorders, revealing the most influential authors, institutions, and countries in the field as well as the most frequently studied brain regions and EEG features. These analyses shed light on the global research status and trends in autism spectrum disorder (ASD) and electroencephalogram (EEG), as well as how neurofeedback can be used as a treatment option, providing valuable insights for researchers and practitioners. In addition, bibliometric evaluations of the publication history and influence of neurofeedback research have been conducted. Onganlar et al. (2021) conducted a comprehensive analysis of neurofeedback articles published between 1975 and 2020, providing a historical overview of publication trends, citation patterns, and research topics. Using bibliometrics and content analysis based on natural language processing, Wang et al. (2022) investigated changes in depression and radiology-related publications, revealing the evolution of research focus in these fields. These analyses provide historical context and emphasize the dynamic nature of neurofeedback research.

Meta-analyses have also been conducted to systematically evaluate the effects of neurofeedback on particular outcomes, in addition to bibliometric analyses. A meta-analysis conducted by Yeh et al. (2022) examined the effects of neurofeedback training on working memory and episodic memory in healthy populations, providing evidence for the cognitive benefits of neurofeedback. In addition, meta-analyses have been conducted to evaluate the efficacy of neurofeedback in treating ADHD, with studies by Arns et al. (2020) , Chiu et al. (2022) , and Cortese et al. (2016) revealing promising results for improving inattention, impulsivity, and hyperactivity in individuals with ADHD. These meta-analyses provide valuable evidence regarding the potential therapeutic benefits of neurofeedback in specific populations. In addition, empirical research has investigated the efficacy of neurofeedback in treating various neuropsychiatric conditions. For example, Arns et al. (2009) conducted a meta-analysis on the efficacy of neurofeedback for ADHD and found significant improvements in core ADHD symptoms. Russo et al. (2022) conducted a meta-analysis on neurofeedback for anxiety spectrum disorders, revealing promising results for anxiety symptom reduction. These empirical studies shed light on the clinical applications of neurofeedback and support its potential as a treatment for neuropsychiatric disorders.

Additionally, the meta-analyses conducted by Cervera et al. (2018) , Nojima et al. (2022) , and Vavoulis et al. (2023) jointly emphasize the effectiveness of Brain-Computer Interface (BCI) systems in improving motor recovery after a stroke. BCIs have shown notable enhancements in motor performance by enabling the regulation of sensorimotor rhythms through neurofeedback. These benefits are measured using assessments like the Fugl-Meyer Assessment. The use of BCIs in rehabilitation not only provides a platform for neuroplasticity but also suggests the possibility of functional and structural brain healing. Despite encouraging findings, these studies highlight the demand for more research to enhance BCI technology, optimize training methods, and test the clinical efficacy through bigger, more varied study populations, hoping to secure BCI’s place in the future of neurorehabilitation.

Present study

The purpose of this investigation is to provide a thorough understanding of the neurofeedback research literature. To the best of the authors’ knowledge, no prior bibliometric study in this area has been conducted. Our study aims to supplement Onganlar et al. (2021) overview of bibliometric analysis because their study only focuses on publication trends, citation patterns, and research topics over time. This study, on the other hand, focuses on examining neurofeedback literature using a co-citation and co-word approach. By utilizing these two bibliometric analyses, this study fills a void by providing insights into past, present, and future research directions. As a result of the specific bibliometric analyses, the following research objectives emerge:

1. To assess significant historical research on neurofeedback using co-citation analysis.

2. To assess emerging trends in neurofeedback using co-word analysis.

Bibliometric approach

Bibliometric techniques are useful for examining the connections between scientific papers and identifying trends and patterns in the evolution of research disciplines ( Wider et al., 2023a ). Co-citation analysis is the process of identifying two or more documents that were cited in the reference section of a third paper ( Bronk et al., 2023 ). This analysis of co-citation connections across publications enables researchers to identify clusters of frequently cited works related to specific research topics or subdomains ( Li et al., 2023 ). These classifications provide insights into a research field’s intellectual foundation, the evolution of research themes, and the long-term impact of significant works ( Donthu et al., 2021 ; Wider et al., 2023b ). Furthermore, co-citation analysis can aid in the identification of prominent authors, institutions, and journals that have contributed to the advancement of a research field ( Gao et al., 2022 ).

Co-word analysis, on the other hand, entails detecting terms or phrases that appear together in the titles, abstracts, or keywords of academic papers ( Dhiman et al., 2023 ). Researchers can uncover clusters of interconnected research subjects, themes, or ideas by studying the co-occurrence patterns of these terms ( Lim et al., 2022 ; Zakaria et al., 2023 ). These clusters provide useful information about a research domain’s academic interests and intellectual organization. Furthermore, co-word analysis can help identify emerging research topics and trends, as well as track the evolution of research themes over time ( Liu et al., 2021 ; Wider et al., 2023c ).

Researchers can investigate the historical, current, and potential future trends in neurofeedback by using bibliometric techniques such as co-citation and co-word analysis. Neurofeedback research has shown potential benefits in improving cognitive performance, treating neurological disorders, and addressing mental health issues ( Loriette et al., 2021 ). Bibliometric analysis can help identify the most important or highly cited works related to these applications. Researchers can identify the most influential works in neurofeedback research and track their evolution over time by examining co-citation patterns and co-occurring terms. Furthermore, co-word analysis can help identify emerging subjects or trends in neurofeedback research, such as its potential as a powerful therapeutic tool. In conclusion, bibliometric techniques assist researchers in gaining a thorough understanding of the potential benefits of neurofeedback, its progression over time, and its possible future trajectory.

Search string

The search string used in this bibliometric investigation is detailed in Table 1 . The topic search (TS) feature of the Web of Science (WOS) database was used to limit terms to titles, abstracts, and keywords. The search term “neurofeedback” covered articles from 1989 to 2023. The search took place on April 6, 2023. The WOS database is well-known for its high quality and comprehensiveness, making it an excellent choice for bibliometric research. It is the world’s oldest, most widely used, and most trustworthy research publication and citation database, providing selective, balanced, and comprehensive coverage of the world’s leading research from over 34,000 journals ( Birkle et al., 2020 ). Eugene Garfield founded Web of Science in 1964 as the Science Citation Index, and it has since expanded its scope to cover a wide range of disciplines.

Table 1 . Search string, inclusion, and exclusion criteria.

The search was performed in the WOS Database, a large academic database that indexes conference proceedings, scientific journals, and books. The “Search Field” section outlines the parameters that confined the search to the subject area, encompassing the title, abstract, and keywords of a publication. The search period was extended until April 6th, 2023 in order to include all available publications in the results. To ensure data integrity, all publications were checked for inconsistencies and duplicates prior to conducting the bibliometric analysis ( Linnenluecke et al., 2020 ). Because the citation topics were set to “ALL,” the search results included all publications’ topics, regardless of their specific research focus. Articles, reviews, editorials, and conference proceedings were all included in the “ALL” document type. The search was restricted to publications written in English, which is a widely used language in scientific communication. This restriction ensured that the findings were accessible to a wide range of readers and researchers. Table 1 shows the inclusion and exclusion criteria for this review. Based on these criteria, the screening process retained 3,626 articles ( Figure 1 ). The article selection process was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology ( Page et al., 2021 ).

Figure 1 . Flowchart illustrating the search results following the modified PRISMA standard.

Results and discussion

Publication trends and descriptive analysis.

The Web of Science (WOS) database revealed 63,195 citations linked to the selected studies ( N  = 3,626), with a reduction to 33,1,234 when self-citations were excluded. These articles had an H-index of 111 and an average citation count of 17.43 per paper. The body of 3,626 articles reflects a growing interest in neurofeedback research. Although the inaugural paper on neurofeedback appeared in 1989, it wasn’t until 1994 that significant scholarly contributions were noted. Post-1994, publication frequency has surged exponentially. Growth was modest before the 21st century, but from 2000 to 2021, there was a marked escalation in the number of publications, soaring from 10 in 2000 to 392 in 2021, representing a substantial increase over two decades. In 2022, however, there was a slight dip in publications, decreasing to 326. It is anticipated that scholarly focus on neurofeedback will continue to ascend in the forthcoming years. Figure 2 illustrates the trajectory of published articles, and their citation counts from 1989 to 2023.

Figure 2 . Number of articles and citations from 1989 to April 6, 2023.

Co-citation analysis

In our co-citation analysis, we set a citation threshold of 86, meaning that only references cited together 86 times or more were included. This methodology led to the identification of 60 references that met or exceeded this co-citation frequency threshold, ensuring that our analysis concentrated on the most significant and relevant themes within the scientific literature. This threshold was determined through a series of tests aimed at ensuring the clusters identified were stable and accurately represented relevant themes. The optimal threshold was established after experimenting with various levels, specifically 54, 55, 57, 58, 59, 61, and 63. Table 2 displays the top 10 co-cited references with the highest total link strength. The study by Arns et al. (2009) received 284 citations, followed by DeCharms et al. (2005) with 228 citations, and Zoefel et al. (2011) with 200 citations. Figure 3 presents a network analysis of neurofeedback research, based on the cited references.

Table 2 . Top 10 documents with the highest co-citation and total link strength.

Figure 3 . Co-citation analysis.

Through the examination of co-citations, it becomes evident that there are three distinct clusters, each centered around a specific theme. These clusters represent groups of related items that share a common theme. Related articles are organized into clusters, indicated by nodes of matching colors ( Dong et al., 2023 ). Below is the description of each cluster and its corresponding label.

• Cluster 1 (red) is comprised of 22 publications titled “ Real-Time fMRI neurofeedback and self-regulation of brain activity .” Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for understanding and modulating brain activity, with significant implications for mental health and cognitive enhancement ( Martz et al., 2020 ; Direito et al., 2021 ). This collection of research articles looks into the methodologies, applications, and challenges of real-time fMRI neurofeedback, with a particular emphasis on brain activity self-regulation. Birbaumer et al. (2013) and Sitaram et al. (2017) provide comprehensive reviews of neurofeedback science, highlighting the potential of closed-loop brain training in treating a variety of neurological and psychiatric disorders. These studies highlight the significance of learning how to regulate brain metabolism as well as the potential of neurofeedback as a non-invasive intervention. Caria et al. (2007 , 2010) investigate the regulation of anterior insular cortex activity and show that volitional control over this area modifies responses to aversive stimuli. These findings could help us understand and treat anxiety and other emotional disorders. Similarly, DeCharms et al. (2004 , 2005) demonstrate that learned regulation of spatially localized brain activation can lead to improved pain perception control. Shibata et al. (2011) present a novel approach to perceptual learning based on decoded fMRI neurofeedback, demonstrating that learning can be induced without the need for stimulus presentation. This study demonstrates the potential of real-time fMRI neurofeedback for improving cognitive performance across multiple domains. Young et al. (2014 , 2017) investigate the use of real-time fMRI neurofeedback in the treatment of major depressive disorder, demonstrating that training amygdala activity can result in significant improvements in symptoms and autobiographical memory recall. These findings highlight neurofeedback’s therapeutic potential for mental health conditions. Sulzer et al. (2013) , Weiskopf (2012) and Weiskopf et al. (2003 , 2004) investigate the methodologies and exemplary data associated with real-time fMRI neurofeedback, emphasizing the potential for physiological self-regulation of regional brain activity. This research focuses on the technical aspects and challenges of this rapidly evolving field. Finally, Zotev et al. (2011) investigate amygdala activation self-regulation, bolstering the potential of real-time fMRI neurofeedback in treating emotional disorders and improving emotional control. In summary, this cluster demonstrates the efficacy and potential of real-time fMRI neurofeedback in understanding and modulating brain activity, with important implications for mental health, cognitive enhancement, and the future of neuroscience.

• Cluster 2 (green) contains 21 publications titled “ EEG-Neurofeedback and Cognitive Performance Enhancement .” The studies in this cluster are concerned with the effects of EEG-neurofeedback on cognitive performance as well as the methodologies involved. EEG-neurofeedback, a type of biofeedback, entails measuring and providing real-time feedback on EEG activity to help people learn self-regulation of brain activity and improve cognitive performance ( Marzbani et al., 2016 ; Ramalingam et al., 2023 ). This has been explored in healthy participants ( Gruzelier, 2014a ) and those with neurological disorders such as epilepsy ( Sterman and Egner, 2006 ). Gruzelier (2014a , b) provides comprehensive reviews on performance optimization using EEG-neurofeedback, with an emphasis on methodological and theoretical considerations. Gruzelier (2014a) emphasizes the beneficial effects on cognition and affect in healthy participants, whereas Gruzelier (2014b) discusses the importance of effective protocols as well as the role of individual differences. Several studies have been conducted to examine the effect of neurofeedback training on specific EEG frequency bands. Both Zoefel et al. (2011) and Hanslmayr et al. (2005) show that increasing upper alpha power via neurofeedback improves cognitive performance. Klimesch (1999) also discovered that EEG alpha and theta oscillations reflect cognitive and memory performance. Egner and Gruzelier (2001 , 2004) , on the other hand, concentrate on the low beta band components, reporting frequency-specific effects on attention and event-related brain potentials. Vernon et al. (2003) investigate the effect of different neurofeedback protocols on cognitive performance, whereas Delorme and Makeig (2004) present EEGLAB, an open-source toolbox for analyzing single-trial EEG dynamics, including independent component analysis. These tools are critical for researchers to analyze and comprehend the underlying neural processes associated with neurofeedback. Egner and Gruzelier (2003) demonstrate that slow-wave EEG modulation improves musical performance, addressing the ecological validity of neurofeedback. This study emphasizes the practicality of neurofeedback training. Finally, Vernon (2005) assesses the evidence for neurofeedback training’s ability to improve performance and emphasizes the need for additional research in order to draw firm conclusions. In summary, the studies in this cluster investigate the ability of EEG-neurofeedback to improve cognitive performance across multiple domains. They emphasize the relevance of specific frequency bands and the ecological validity of neurofeedback training, as well as methodological considerations, effective protocols, and individual differences.

• Cluster 3 (blue) contains 17 publications with the title “ Treatment of attention-deficit/hyperactivity disorder (ADHD) using neurofeedback .” This article collection looks into the efficacy, outcomes, and potential of neurofeedback as an alternative or complementary approach to managing ADHD symptoms in children and adolescents. The comparison of neurofeedback to traditional pharmacological treatments, such as methylphenidate, is a recurring theme in these articles ( Fuchs et al., 2003 ; Sonuga-Barke et al., 2013 ). This cluster includes multiple meta-analysis that synthesize findings from various studies to provide a comprehensive understanding of neurofeedback’s effectiveness ( Arns et al., 2009 ; Cortese et al., 2016 ; Van Doren et al., 2019 ). These meta-analysis show that neurofeedback training has a positive effect on ADHD symptoms and that the effects last. Several articles investigate specific neurofeedback techniques, such as slow cortical potential training ( Heinrich et al., 2004 ; Strehl et al., 2006 ), and investigate the underlying neurophysiological effects of these treatments ( Gevensleben et al., 2009 ). These studies contribute to a better understanding of how neurofeedback alters brain function in ADHD patients to improve attention, impulsivity, and hyperactivity. Furthermore, some articles provide critical assessments of neurofeedback research and discuss the difficulties in determining its efficacy ( Arns et al., 2014 ). They emphasize the importance of methodologically rigorous studies and long-term follow-ups in order to establish neurofeedback’s clinical utility as a viable ADHD treatment option. This cluster exemplifies the growing interest in neurofeedback as a non-pharmacological treatment for ADHD, highlighting both its potential benefits and limitations. This cluster contributes to a more comprehensive understanding of neurofeedback’s role in managing ADHD symptoms, as well as the importance of ongoing research in this area.

Table 3 presents a summary of co-citation analysis on neurofeedback with the clusters’ number, color, labels, number of publications and representative publications.

Table 3 . Co-citation clusters on neurofeedback.

Co-occurrence of keyword

In our co-word analysis, we identified a total of 63 keywords, with a minimum occurrence threshold set at 61. This threshold was crucial in ensuring that our analysis focused on the most frequently occurring and relevant keywords within our dataset, thereby highlighting key trends and areas of focus in the scientific literature. To determine the most effective threshold for our analysis, we conducted a series of tests using various levels, specifically 62, 64, 65, 66, and 67. This rigorous testing process helped us to identify a threshold that accurately captures the core themes and facilitates a stable and meaningful analysis of the relationships between keywords in our study. The co-word analysis revealed that the most frequently used keyword was “neurofeedback” (1,684 occurrences), followed by “EEG” (617 occurrences) and “ADHD” (378 occurrences). Table 4 displays the top 15 co-word analysis keywords.

Table 4 . The 15 most frequent keywords in the keyword co-occurrence analysis.

Following that, Figure 4 depicts the network structure of keyword co-occurrence. The diagram depicts four distinct clusters that appear to be related. Each cluster was examined and expanded upon as follows:

• Cluster 1 (Red): This cluster comprises a total of 19 keywords and is titled “neurofeedback and mental health research.” This cluster demonstrates the growing importance of neurofeedback techniques, such as EEG-neurofeedback and biofeedback, in the assessment and treatment of mental health disorders such as anxiety, depression, and attention-related issues ( Young et al., 2017 ; Hey, 2020 ; Taschereau-Dumouchel et al., 2022 ). Keywords such as “alpha,” “oscillations,” and “power” highlight the emphasis on specific brainwave patterns and their potential role in the manifestation and treatment of these disorders ( Klimesch, 1999 ; Egner and Gruzelier, 2003 ; Hanslmayr et al., 2005 ; Zoefel et al., 2011 ; Perera et al., 2022 ). Furthermore, the cluster emphasizes the importance of working memory and cognitive performance, indicating the growing interest in using neurofeedback training to improve overall brain function. Researchers are increasingly interested in using neurofeedback to improve cognitive performance, memory, and attention in healthy individuals, in addition to addressing mental health issues ( Egner and Gruzelier, 2001 ; Gruzelier, 2014a ; Marlats et al., 2020 ; Da Silva and De Souza, 2021 ; Moradi et al., 2022 ). Based on this cluster, future trends in neurofeedback research are expected to explore the connections between brain oscillations, mental health, and cognitive performance. Researchers may develop more targeted neurofeedback protocols to address specific disorders or enhance specific cognitive abilities as our understanding of the brain’s intricate processes grows. Furthermore, advances in EEG and biofeedback technology may result in more accessible and personalized neurofeedback training methods, allowing a broader range of people to benefit from these interventions. Finally, the cluster formed around these keywords reflects neurofeedback’s growing importance in the research and treatment of mental health disorders and cognitive enhancement. Future trends in this field are likely to focus on improving neurofeedback training methods and making these interventions more accessible to a larger population.

• Cluster 2 (green): There are 17 keywords in this cluster. Based on the keywords, a cluster reveals a significant research focus on the “ development and application of brain-computer interfaces (BCIs) for stroke patient rehabilitation .” This cluster is concerned with the use of BCIs and their underlying mechanisms, such as electroencephalography (EEG) and transcranial magnetic stimulation (TMS), for rehabilitation purposes. BCIs allow direct communication between the brain and external devices, allowing neural activity to be translated into actionable commands ( Shih et al., 2012 ; Kosal and Putney, 2022 ). One critical application of this technology is in the field of motor recovery and rehabilitation, particularly for people who have had a stroke ( Cervera et al., 2018 ; Fu et al., 2023 ). In this context, the study looks into the use of motor imagery techniques, which involve mental rehearsal of motor actions without physical execution, in conjunction with BCIs ( Vavoulis et al., 2023 ). EEG detects neural activity associated with motor imagery, and by modulating this activity, stroke patients can regain control of their motor functions ( Liao et al., 2023 ). TMS is also used as a non-invasive brain stimulation method to facilitate cortical reorganization and improve the efficacy of rehabilitation ( Naro and Calabrò, 2022 ). Furthermore, the cluster emphasizes the significance of feedback and classification systems in the development of effective BCI-based rehabilitation programs ( Gao et al., 2022 ). These systems enable the accurate interpretation and real-time adjustment of the user’s neural activity, allowing for a more personalized and adaptive approach to rehabilitation. Future trends in this cluster are likely to focus on refining and expanding BCI technologies for stroke rehabilitation, with an emphasis on increasing the accuracy and reliability of classification and communication systems ( Al-Qazzaz et al., 2023 ). Furthermore, the incorporation of machine learning and artificial intelligence techniques may aid in the development of more sophisticated and adaptive BCIs ( Barnova et al., 2023 ). Ultimately, these advances could lead to more effective and personalized rehabilitation interventions, significantly improving the quality of life and recovery outcomes for stroke patients.

• Cluster 3 (Blue): There are 13 keywords in this cluster. Based on the keywords, one possible cluster is “ neurofeedback for ADHD in children and adolescents .” This cluster demonstrates a strong emphasis on understanding and treating ADHD symptoms through the use of EEG biofeedback and slow cortical potentials as therapeutic modalities. The keywords “ADHD,” “adolescents,” “attention-deficit/hyperactivity disorder,” “children,” “deficit hyperactivity disorder,” and “deficit/hyperactivity disorder” highlight the population and condition under investigation. Keywords such as “EEG biofeedback,” “slow cortical potentials,” “therapy,” and “symptoms,” on the other hand, indicate the research’s methodological and therapeutic aspects. The terms “efficacy,” “meta-analysis,” and “hyperactivity” indicate a growing interest in assessing the efficacy of these therapeutic approaches in managing ADHD symptoms, particularly hyperactivity. The presence of “meta-analysis” within this cluster indicates that researchers are synthesizing the findings from multiple studies to obtain a more comprehensive understanding of the efficacy of these interventions ( Micoulaud-Franchi et al., 2014 ; Cortese et al., 2016 ; Van Doren et al., 2019 ; Riesco-Matías et al., 2021 ; Louthrenoo et al., 2022 ; Kuznetsova et al., 2023 ). Based on this cluster, future trends in ADHD research and treatment may include a greater focus on neurofeedback techniques such as EEG biofeedback and slow cortical potentials to improve the efficacy of ADHD interventions for children and adolescents ( Choudhury et al., 2023 ). Researchers could concentrate on developing personalized neurofeedback protocols that are tailored to individual needs in order to improve treatment outcomes ( Ma et al., 2023 ; Zhang et al., 2023 ). Furthermore, there may be an increased interest in researching the long-term effects of these therapies, as well as their potential to reduce or even eliminate the need for pharmacological interventions in some cases ( Sibley et al., 2023 ). Moreover, the integration of new technologies and methods, such as machine learning and real-time brain imaging, could help improve the accuracy and effectiveness of neurofeedback interventions ( Haugg et al., 2020 ; Singh et al., 2022 ; Taschereau-Dumouchel et al., 2022 ). This would allow for more targeted targeting of brain regions and neural networks linked to ADHD symptoms. Overall, this cluster points to a future trend in ADHD research that focuses on the development and optimization of novel, non-invasive, and personalized neurofeedback therapies for children and adolescents.

• Cluster 4 (Yellow): There are 12 keywords in this cluster. One possible cluster based on the keywords is “ neural mechanisms of emotion and self-regulation using advanced neuroimaging .” The cluster of keywords reflects a strong focus on brain function and connectivity research, particularly concerning emotional regulation and self-regulation processes. This cluster indicates a growing interest in studying the neural underpinnings of emotion regulation and self-regulation using advanced neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) and real-time fMRI ( Zhu et al., 2019 ; Mathiak and Keller, 2021 ; Taschereau-Dumouchel et al., 2022 ; Zotev et al., 2023 ). This cluster’s connections show an interaction between brain regions, particularly the amygdala and the prefrontal cortex, in modulating emotional responses and self-regulation processes ( Lowe et al., 2020 ; Drigas and Mitsea, 2021 ; Janet et al., 2023 ). The amygdala is well-known for its role in emotion processing, particularly fear and anxiety ( Šimić et al., 2021 ), whereas the prefrontal cortex is associated with higher-order cognitive functions and executive control ( Friedman and Robbins, 2022 ). This cluster’s functional connectivity research emphasizes the importance of interactions between these regions in emotion management and self-regulation. More in-depth studies of the dynamic interactions between various brain regions associated with emotion regulation and self-regulation are likely in the future ( Yang et al., 2020 ). This could include creating more advanced real-time fMRI techniques and analysis methods to better understand the temporal and spatial patterns of brain activation and connectivity during these processes ( Warbrick, 2022 ). Furthermore, researchers may investigate the potential of neurofeedback and other neuromodulation techniques to improve emotion regulation and self-regulation by targeting specific brain regions and networks ( Barreiros et al., 2019 ; Melnikov, 2021 ; James and Duarte, 2023 ). This could result in the development of novel therapeutic interventions for people suffering from emotional dysregulation, anxiety, depression, or other mental health issues. Furthermore, interdisciplinary research that integrates insights from psychology, psychiatry, and neuroscience may benefit the field by generating a more comprehensive understanding of the neural mechanisms underlying emotion regulation and self-regulation.

Figure 4 . Co-word analysis of neurofeedback research.

The co-word analysis of neurofeedback research is summarized in Table 5 , providing information on cluster number, color, labels, number of keywords, and representative keywords.

Table 5 . Co-word analysis on neurofeedback research.

Implications

This bibliometric study has multiple key clinical implications. Attention deficit hyperactivity disorder is a growing secondary pandemic in the developed world and has been partially exacerbated by the increasing amount of gadget use and consequent Internet and smartphone addiction issues that have emerged. There is hence higher recourse to “urge surfing” using mobile devices, which presents a double whammy for ADHD sufferers. Neurofeedback training has previously been regarded to be in its infancy, but this bibliometric study suggests through the network of keywords and authors that there is much literature of reasonable quality that can be referred to inform the creation of research-grounded, structured protocols as a promising new frontier of treatment for ADHD.

It is essential to acknowledge the significant contributions of neurofeedback to research, highlighting its role as a valuable tool for monitoring brain activity in real-time. Compared to other brain imaging modalities such as fMRI and PET, neurofeedback—often based on EEG—is particularly advantageous due to its non-invasiveness, affordability, and high temporal resolution. These characteristics make it well-suited for providing real-time feedback during neurofeedback interventions, allowing for immediate adjustment and optimization of treatment protocols. This gives it an edge and acts as an essential tool for monitoring real-time brain activity during neurofeedback interventions. Consequently, it enhances our understanding of the mechanisms involved in ADHD treatments which allows researchers and clinicians to customize interventions and evaluate the effectiveness of treatments with accuracy by analyzing variations in brainwave patterns.

Furthermore, the incorporation of transcranial magnetic stimulation (TMS) as a metric of results enhances our comprehension of neurofeedback interventions. TMS acts as a biomarker for enhanced motor function and offers valuable neurophysiological information about corticomuscular excitability. This information deepens our comprehension of the neural processes involved in neurofeedback interventions and can be used to complement behavioral outcomes. The utilization of both neuroimaging and neuromodulatory techniques in neurofeedback research demonstrates its multidisciplinary nature and its ability to improve treatment outcomes for ADHD and potentially other neurological disorders.

In addition to treating ADHD, neurofeedback and biofeedback show great promise in the emerging clinical fields of performance enhancement, especially in sports and occupational psychiatry. The use of neurofeedback will increase clinicians’ repertoire as they can then provide care options that are not invasive, that do not involve the ethical dilemmas of using psychopharmacology and consequent maleficence via unacceptable side effect profiles, while potentially inducing lasting changes in brainwave structure rather than merely symptomatic relief.

Limitations and conclusion

In conclusion, this bibliometric study demonstrates that there is high potential to grow for neurofeedback and biofeedback as a branch of medical practice. There is already much evidence extant for the role of neurofeedback in stroke and rehabilitation medicine. It now appears to show promise too in the emerging fields of ADHD and performance enhancement, as well as being suitable as a non-invasive treatment modality for general mental health wellness. This bodes well as we move into an age of personalized and precision medicine, where we do not offer one-size-fits-all solutions that offer a broad-based but non-specific treatment for primary and tertiary prevention of mental health disorders.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

WW: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. JM: Conceptualization, Resources, Validation, Visualization, Writing – review & editing. BC: Conceptualization, Resources, Visualization, Writing – review & editing. NP: Writing – review & editing. MF: Methodology, Validation, Visualization, Writing – review & editing. LU: Funding acquisition, Writing – review & editing. LJ: Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research is supported by the Postgraduate Research Grant (UMSGreat; No: GUG0207-1/2018), Universiti Malaysia Sabah.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Keywords: neurofeedback, bibliometrics analysis, web of science, human health, co-word analysis, co-citation analysis

Citation: Wider W, Mutang JA, Chua BS, Pang NTP, Jiang L, Fauzi MA and Udang LN (2024) Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions. Front. Hum. Neurosci . 18:1339444. doi: 10.3389/fnhum.2024.1339444

Received: 22 November 2023; Accepted: 23 April 2024; Published: 10 May 2024.

Reviewed by:

Copyright © 2024 Wider, Mutang, Chua, Pang, Jiang, Fauzi and Udang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jasmine Adela Mutang, [email protected] ; Lester Naces Udang, [email protected]

• Systematic Review
• Open access
• Published: 13 May 2024

Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review

• Fereshteh Baygi 1   na1 ,
• Sussi Friis Buhl 1   na1 ,
• Trine Thilsing 1 ,
• Jens Søndergaard 1 &
• Jesper Bo Nielsen 1  

BMC Geriatrics volume  24 , Article number:  421 ( 2024 ) Cite this article

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Sarcopenia and sarcopenic obesity (SO) are age-related syndromes that may compromise physical and mental health among older adults. The Nordic countries differ from other regions on prevalence of disease, life-style behavior, and life expectancy, which may impact prevalence of sarcopenia and SO. Therefore, the aim of this study is to review the available evidence and gaps within this field in the Nordic countries.

PubMed, Embase, and Web of science (WOS) were searched up to February 2023. In addition, grey literature and reference lists of included studies were searched. Two independent researcher assessed papers and extracted data.

Thirty-three studies out of 6,363 searched studies were included in this scoping review. Overall prevalence of sarcopenia varied from 0.9 to 58.5%. A wide prevalence range was still present for community-dwelling older adults when definition criteria and setting were considered. The prevalence of SO ranged from 4 to 11%, according to the only study on this field. Based on the included studies, potential risk factors for sarcopenia include malnutrition, low physical activity, specific diseases (e.g., diabetes), inflammation, polypharmacy, and aging, whereas increased levels of physical activity and improved dietary intake may reduce the risk of sarcopenia. The few available interventions for sarcopenia were mainly focused on resistance training with/without nutritional supplements (e.g., protein, vitamin D).

The findings of our study revealed inadequate research on SO but an increasing trend in the number of studies on sarcopenia. However, most of the included studies had descriptive cross-sectional design, small sample size, and applied different diagnostic criteria. Therefore, larger well-designed cohort studies that adhere to uniform recent guidelines are required to capture a full picture of these two age-related medical conditions in Nordic countries, and plan for prevention/treatment accordingly.

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The number of older adults with age-related disorders is expected to increase worldwide [ 1 , 2 ]. Sarcopenia and sarcopenic obesity (SO) are both age-related syndromes that may compromise the physical and mental health of older adults and increase their need for health care services in old age [ 3 , 4 ], and this may challenge the sustainability of health care systems economically and by shortage of health care personnel [ 5 ].

Sarcopenia is characterized by low muscle mass in combination with low muscle strength [ 4 ]. SO is characterized by the co-existence of obesity (excessive adipose tissue) and sarcopenia [ 3 ]. Sarcopenia and SO are both associated with physical disability, risk of falls, morbidity, reduced quality of life and early mortality [ 4 , 6 , 7 , 8 , 9 ]. In SO the consequences of sarcopenia and obesity are combined and maximized [ 4 , 6 , 7 , 8 ].

Etiology of sarcopenia and SO is multifactorial and closely linked to multimorbidity [ 3 , 7 , 8 , 9 , 10 ]. Nevertheless, lifestyle and behavioral components particularly diet and physical activity, are important interrelated factors that potentially can be modified. Physical inactivity and sedentary behavior may accelerate age-related loss of muscle mass, reduce energy expenditure, and increase risk of obesity [ 3 , 11 ]. In addition, weight cycling (the fluctuations in weight following dieting and regain) and an unbalanced diet (particularly inadequate protein intake) may accelerate loss of muscle mass and increase severity of sarcopenia and SO in older adults [ 3 , 12 ]. International guideline for the treatment of sarcopenia emphasizes the importance of resistance training potentially in combination with nutritional supplementation to improve muscle mass and physical function [ 13 ]. Similar therapeutic approach is suggested for treatment of SO [ 14 ]. However, more research is needed to confirm optimal treatment of SO [ 14 ].

According to a recently published meta-analysis the global prevalence of sarcopenia ranged from 10 to 27% in populations of older adults ≥ 60 years [ 15 ]. Further the global prevalence of SO among older adults was 11% [ 8 ]. So, sarcopenia and SO are prevalent conditions, with multiple negative health outcomes and should be given special attention [ 16 ]. Despite the large burden on patients and health care systems, the awareness of the importance of skeletal muscle maintenance in obesity is low among clinicians and scientists [ 3 , 16 ].

A recent meta-analysis on publication trends revealed that despite an increase in global research on sarcopenia, the Nordic countries were only limitedly represented [ 6 ]. Nordic countries may differ from other regions on aspects associated with the prevalence and trajectory of sarcopenia and SO and challenge the representativeness of research findings from other parts of the world. These include a different prevalence pattern of noncommunicable diseases [ 17 ], different life-style behavior and life-style associated risk factors [ 15 , 18 ], and higher life expectancy [ 18 ].

The Nordic countries including Sweden, Finland, Iceland, Norway, Denmark, and three autonomous areas (Åland Islands, Greenland and Faroe Islands) share common elements of social and economic policies such as a comprehensive publicly financed health care system [ 18 , 19 ]. Additionally, these countries have a strong tradition of collaboration including a common vision of a socially sustainable region by promoting equal health and inclusive participation in society for older adults [ 20 ]. Therefore, more insight into the etiology, prevalence, and risk factors for sarcopenia and SO among older adults is a prerequisite for the development and implementation of effective strategies to prevent and treat these complex geriatric conditions in this geographic region. So, the aim of this study is to conduct a scoping review to systematically identify and map the available evidence while also addressing knowledge gaps and exploring the following research questions: (1) What are the prevalence of sarcopenia and SO in older adults living in the Nordic countries? (2) Which risk factors or contributing conditions are involved in the development of sarcopenia and SO in the Nordic Countries? (3) Which interventions to prevent or counteract negative health outcomes of sarcopenia and SO have been tested or implemented among older adults living in the Nordic countries?

Identification of relevant studies

The development and reporting of this review were done by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [ 21 ].

The literature search was developed to target three main areas: Sarcopenia, sarcopenic obesity, and aging (See Appendix 1 for full search strategy). All studies published before the end of February 2023 were included in this scoping review. The optimal sensitivity of search was obtained by simultaneous search of the following databases: PubMed, Embase, and Web of science (WOS). Additionally, a detailed search for grey literature was performed in relevant databases (e.g., Research Portal Denmark, Libris, Oria, Research.fi). Besides, reference lists of the included studies were reviewed to identify eligible studies. Duplicates and non-peer reviewed evidence (e.g., PhD thesis) were excluded but if the latter contained published articles of relevance, these were included. If more than one publication on similar outcomes (e.g., prevalence) were based on a single study, just one publication was included. Data were extracted from large studies with combined data from several countries only when findings were presented separately for the Nordic countries.

Inclusion and exclusion criteria

The inclusion criteria were as follow : Broad selection criteria were used to be comprehensive: (1) studies with any outcome (e.g., prevalence, risk factors, etc.) to address our research questions on sarcopenia and SO, (2) studies on subjects with age ≥ 60 years in any type of settings (e.g., community, nursing homes, general practice, hospital, outpatients, homecare, etc.), (3) studies using any definition of sarcopenia and SO without restriction for criteria and cutoff values, (4) all type of study designs (e.g., randomized control trials, cohort studies, cross-sectional, etc.), (5) studies should be conducted in the Nordic countries The exclusion criteria are as follow : (1) studies without relevant outcome to sarcopenia or SO, (2) studies without sufficient information to determine eligibility.

Study selection and data extraction

Two independent researchers screened literature and conducted data extraction. Any discrepancies between them were resolved through discussion.

First, duplicates were removed by using EndNote 20.6 software, then titles and abstracts were screened to narrow down the list of potentially eligible studies. Finally, the full text review was done to examine in detail the studies that were not excluded in first step. For more clarification, the reasons for the exclusion were recorded (Fig.  1 ).

PRISMA diagram for searching resources

The following information was extracted: (1) study characteristics (e.g., first author’s name, country, year of publication), (2) characteristics of the target population (e.g., age, sex), (3) study design, setting, intervention duration and follow-up time (if applicable), measurements, tools, criteria, and results.

Study selection

A combined total of 6,358 studies were identified through the initial electronic database and grey literature searches. An additional five articles were identified through other sources (citation searching). After removing duplication, 3,464 articles remained. A total of 3107 articles were excluded based on screening titles and abstracts. Out of the remaining 357 studies, 324 were excluded after the full-text review. Finally, 33 studies met our inclusion criteria and were included in this current scoping review [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] (Fig.  1 ).

Study characteristics

Table  1 summarized characteristics of the included studies.

The number of documents showed an increasing trend between 2020 and 2021. A peak in the number of publications was observed in 2021 (24.2% of all documents). All the studies were conducted across four (Denmark, Norway, Sweden, and Finland) out of the five Nordic countries and three autonomous areas. The highest contribution in this field was made by Sweden ( n  = 12).

Most studies were conducted in community-dwelling settings [ 22 , 23 , 24 , 28 , 30 , 31 , 35 , 36 , 38 , 39 , 40 , 42 , 45 , 46 , 47 , 48 , 49 , 54 ]. Seven studies included patients with acute diseases (hospital-setting) [ 26 , 27 , 33 , 37 , 50 , 51 , 52 ], while four studies included patients with chronic conditions (out-patient setting) [ 25 , 32 , 41 , 44 ], and one study including nursing-home residents [ 34 ]. In terms of study design, most of the studies were observation studies with a cross-sectional or longitudinal design ( 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ), while three studies [ 32 , 35 , 46 ] applied interventions. It appears, however, that one study [ 32 ] out of the above three interventions is sub-project conducted within the framework of larger intervention program. Sample size ranged from 49 in a cross-sectional case control study [ 52 ] to 3334 in a cohort study [ 30 ].

Five studies were among males only [ 22 , 24 , 36 , 45 , 53 ] and three studies included females only [ 38 , 47 , 54 ]. The rest of the studies had a mixed sample. Top subject area was sarcopenia (31 out of the 33 included studies), and on this subject, publications were categorized into the following research areas (with some studies addressing more areas): prevalence [ 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 33 , 35 , 36 , 37 , 40 , 42 , 44 , 45 , 47 , 49 , 50 , 51 , 52 , 53 , 54 ], risk factors [ 24 , 27 , 28 , 30 , 31 , 34 , 38 , 40 , 42 , 44 , 47 , 49 , 50 , 51 ], and effectiveness of interventions on sarcopenia or indicator of sarcopenia [ 32 , 35 , 46 ].

In most studies sarcopenia was defined according to the criteria set by the European Working Group on Sarcopenia in Older People in the updated version from 2019 (EWGSOP2) ( n  = 15) or the original version from 2010 (EWGSOP) ( n  = 14). However, in some studies multiple criteria such as EWGSOP, EWGSOP2, and National Institutes of Health Sarcopenia Project definition (FNIH) were applied [ 27 , 39 , 43 ], and in other studies alternative criteria were used [ 26 , 33 , 35 , 45 , 57 ].

Different assessment methods of muscle mass including Dual energy X-ray absorptiometry (DXA) [ 22 , 24 , 25 , 27 , 29 , 30 , 32 , 33 , 38 , 39 , 40 , 41 , 45 , 46 , 47 , 52 , 53 , 54 ], Bioelectrical Impedance Analysis (BIA) [ 28 , 31 , 34 , 44 , 48 , 49 ], Bioimpedance Spectroscopy (BIS) [ 35 , 42 , 43 ], Computed Tomography (CT) [ 33 ], and Computed Tomography Angiogram (CTA) [ 26 ] were used in the included studies.

SO were defined by the co-existence of sarcopenia with obesity. Studies on SO used the EWGSOP2 criteria [ 39 ], or the EWGSOP2 criteria for hand grip strength only (probable sarcopenia) [ 23 ] in combination with obesity estimated from BMI cut points [ 23 , 39 ], waist circumference [ 23 , 39 ], and fat mass percentage [ 39 ]. Lastly, one study used measures of body composition measures that reflect adiposity as estimates of SO [ 48 ].

Four studies reported the prevalence of “probable sarcopenia” [ 23 , 30 , 36 , 45 ], while two studies reported the prevalence of sarcopenia and comorbidities (e.g., osteopenia, pre-frailty, malnutrition) [ 33 , 40 ].

Narrative synthesis

Due to the heterogeneity of the studies in definition of sarcopenia, settings, and sample size, the overall reported prevalence was variable and ranged from 0.9% [ 54 ] to 58.5% [ 26 ]. However, according to the most commonly used criteria (EWGSOP2) the highest (46%) and lowest (1%) prevalence of sarcopenia was reported in Sweden among inpatients in geriatric care [ 27 ], and community-dwelling older adults [ 30 ], respectively.

Prevalence of sarcopenia according to population and definition criteria is illustrated in Table  2 . Higher prevalence rates of sarcopenia were found in females compared to males among community-dwelling older adults [ 49 ] and in older adults acutely admitted to hospital [ 51 ]. Further, acutely admitted female patients also presented with more severe sarcopenia compared to male patients [ 51 ].

Frequency of sarcopenia was higher (9.1–40.0%) in patients with diabetes (with and without complications of charcot osteoarthropathy), compared to age-matched healthy adults [ 52 ].

The prevalence of “probable sarcopenia” ranged between 20.4% (reduced muscle strength only) and 38.1% (fulfilling one of the following criteria: reduced muscle strength, reduced muscle mass, or low physical function) in Finnish community-dwelling adults [ 23 , 36 ], while longitudinal studies on Swedish community-dwelling old (70 years) and very old adults (≥ 85 years) the prevalence of “probable sarcopenia” (reduced muscle strength only) ranged from 1.8 to 73%, respectively [ 30 , 45 ]. Lastly, in a Swedish study among nursing home residents the prevalence of probable sarcopenia was 44% (evaluated by an impaired chair stand test) [ 34 ].

Prevalence of Osteosarcopenia (sarcopenia and osteoporosis) was 1.5% [ 36 ], and the prevalence of co-occurrence of all three following conditions: pre-frail, malnutrition, and sarcopenia was 7% [ 34 ].

We only identified two studies with prevalence of SO [ 39 ] and probable SO [ 23 ]. The prevalence of SO in a Swedish population was 4% and 11% in females and males, respectively, while the prevalence of probable SO among Finnish community-dwelling ranged between 5.8% and 12.6%, depending on the criteria to define the obesity (e.g., BMI, waist circumference, etc.) [ 23 ].

Several studies investigated aspects of etiology and risk factors for sarcopenia [ 24 , 27 , 28 , 30 , 31 , 34 , 36 , 38 , 40 , 42 , 43 , 44 , 47 , 49 , 50 , 51 ] and one study focused on SO [ 49 ]. Higher physical activity was associated with a decreased likelihood of sarcopenia [ 30 ]. In addition, adhering to world health organization (WHO) guidlines for physical activity and the Nordic nutritional recommendations for protein intake was positively associated with greater physical function and lower fat mass in older female community-dwellers [ 38 ]. In older adults who are physically active, eating a healthy diet (based on the frequency of intake of favorable food like fish, fruits, vegetables, and whole grains versus unfavorable foods like red/processed meats, desserts/sweets/sugar-sweetened beverages, and fried potatoes) was associated with lower risk of sarcopenia [ 28 ]. Further, among older adults who already meet the physical activity guidelines, additional engagement in muscle-strengthening activities was associated with a lower sarcopenia risk score and improved muscle mass and chair rise time [ 31 ].

Associations between sarcopenia, risk of sarcopenia and malnutrition or nutritional status was identified in geriatric patients [ 27 , 51 ], older patients with hip fracture [ 50 ], nursing home residents [ 34 ] and in community-dwelling older adults [ 49 ]. Moreover, the importance of nutritional intake was investigated in the following studies [ 24 , 36 , 47 ]. A study among community-dwelling men revealed an inverse association between total energy intake, protein intake (total, plant, and fish protein), intake of dietary fibers, fat (total and unsaturated), and vitamin D with sarcopenia status [ 36 ]. In a cohort of 71-year-old men a dietary pattern characterized by high consumption of fruit, vegetables, poultry, rice and pasta was associated with lower prevalence of sarcopenia after 16 years [ 24 ]. A longitudinal Finnish study on sarcopenia indices among postmenopausal older women, showed that lower adherence to the Mediterranean (focuses on high consumption of olive oil) or Baltic Sea (focuses on the dietary fat quality and low-fat milk intake) diets resulted in higher loss of lean mass over a 3-year period [ 47 ]. Further, a higher adherence to the Baltic Sea diet was associated with greater lean mass and better physical function, and higher adherence to the Mediterranean diet was associated with greater muscle quality [ 47 ].

In a study of patients with hip fracture age, polypharmacy, and low albumin levels was associated with sarcopenia [ 50 ]. Exocrine pancreatic insufficiency was an independent risk factor for sarcopenia [ 44 ]. This study also revealed that sarcopenia was associated with reduced quality of life, physical function, and increased risk of hospitalization [ 44 ]. In a longitudinal study of community-dwelling adults (+ 75 years) at risk of sarcopenia, high physical function, muscle strength, muscle mass and low BMI predicted better physical function and reduced need for care after four years [ 42 ]. Furthermore, in community-dwelling adults with sarcopenia, muscle mass, muscle strength and physical function are independent predictors of all-cause mortality. As a result, they have been proposed by researchers as targets for the prevention of sarcopenia-related over-mortality [ 43 ]. Lastly, community-dwelling older adults with sarcopenia had lower bone mineral density compared to those without sarcopenia and they were more likely to develop osteoporosis (Osteosarcopenia) [ 40 ].

Regarding SO risk factors, a longitudinal study among community-dwelling older adults in Finland found that SO (operationalized by measures of adiposity) were associated with poorer physical function after ten years [ 48 ].

Our literature search identified three randomized controlled trials investigating the effectiveness of interventions to prevent or counteract sarcopenia in older adults of Norway, Finland, and Sweden, respectively [ 32 , 35 , 46 ]. The Norwegian study [ 32 ] was a double-blinded randomized controlled trial (RCT). The study included those who were at risk of developing sarcopenia, including patients with chronic obstructive pulmonary disease (COPD) or individuals who showed diagnostic indications of sarcopenia. Participants received either vitamin D 3 or placebo supplementation for 28 weeks. Additionally, resistance training sessions were provided to all participants from weeks 14 to 27. Vitamin D supplementation did not significantly affect response to resistance training in older adults at risk of sarcopenia with or without COPD [ 32 ].

Furthermore, a RCT among pre-sarcopenic Swedish older adults investigated the effectiveness of three weekly sessions of instructor-led progressive resistance training in combination with a non-mandatory daily nutritional supplement (175 kcal, 19 g protein) compared to control group. The 10 weeks intervention resulted in significant between group improvements of physical function and a significant improvement in body composition in the intervention group [ 46 ].

Another intervention study revealed that a 12-month intervention with two daily nutritional supplements (each containing 20 g whey protein) did not attenuate the deterioration of physical function and muscle mass in sarcopenic older community-dwelling adults compared to isocaloric placebo supplements or no supplementation. All participants were given instructions on home-based exercises, importance of dietary protein and vitamin D supplementation [ 35 ].

Based on our broad literature search 33 studies were identified that concerned sarcopenia and SO and met the inclusion criteria. However, research on SO was very limited with only three studies identified. Narrative synthesis of the included studies revealed that the most reported classification tool for sarcopenia in Nordic countries was the EWGSOP2. Moreover, some studies estimated sarcopenia using EWGSOP. The overall prevalence of sarcopenia in Nordic countries according to EWGSOP2 ranged between 1% and 46% [ 25 , 28 ]. The prevalence of SO, however, was reported only in one study in Sweden (4–11%) [ 39 ]. Even though the previous systematic reviews and meta-analysis have reported the prevalence of sarcopenia and SO in different regions and settings (e.g., community-dwelling, nursing home, etc.) [ 8 , 15 , 55 , 56 ], this current scoping review is to the best of our knowledge the first study that provides an overview of research on sarcopenia and SO in the Nordic countries.

Based on our findings from 24 studies, there were large variability in prevalence of sarcopenia in studies conducted in the Nordic countries. We think that the wide variation in estimated prevalence of sarcopenia in our scoping review might be due to a different definition/diagnostic criterion (e.g., EWGSOP, EWGSOP2, FNIH), methodology to measure muscle mass (DXA, BIA, CT), and heterogeneity in characteristics of the study population (e.g., setting, age, medical conditions, co-occurrence of multiple risk factors). A previous study on prevalence of sarcopenia in Swedish older people showed significant differences between prevalence of sarcopenia based on EWGSOP2 and EWGSOP1 [ 29 ]. Therefore, researchers stressed that prevalence is more dependent on cut-offs than on the operational definition [ 29 , 57 ]. Further, we know that various international sarcopenia working groups have issued expert consensus and such diagnostic criteria are being updated [ 4 , 58 ]. Since the revision of criteria focuses primarily on the adjustment of cut-off values, the main reason for differences in prevalence even when using an updated version of one diagnosis criteria is modification in cut-off values. For instance, if the cut-off value for gait speed was increased by 0.2 m/s, the prevalence of sarcopenia may increase by 8.5% [ 57 ]. Meaning that even a small change in cut-off value can have a big impact on how sarcopenia is diagnosed. Besides when we take definition criteria into account (Table  2 ), the prevalence of sarcopenia is still variable in the population of community-dwelling adults for instance. We believe it is basically because studies have applied different assessment tools and tests to identify older adults with low muscle mass and muscle strength, although using the same definition criteria (Table  1 ). Previous studies have illustrated that choice of methodology to assess muscle strength (e.g., hand grip strength, chair rise) [ 59 ] and muscle mass (e.g., DXA, BIA, anthropometry) [ 60 , 61 , 62 ] in older adults may impact findings and this variability may explain some of the variability in our findings. So, adherence to the latest uniform diagnostic criteria for future studies is recommended to simplify the comparison of findings within the same country, across countries, and regions. Moreover, we suggest that medical community particularly GPs to come to an agreement on assessment methods for muscle mass and muscle strength and the use of one set of definition criteria for sarcopenia.

In previous meta-analyses [ 15 ], sub-group analyses based on region and classification tool, revealed that the prevalence of sarcopenia was higher in European studies using EWGSOP (12%) compared to rest of the studies using Asian Working Group for Sarcopenia (AWGS), FNIH, and EWGSOP (3%) [ 15 ]. In our scoping review, we also found a high prevalence of sarcopenia in Nordic countries. Longevity and life expectancy is higher in the Nordic countries compared to estimates for rest of the world [ 18 ], which means that in this region many people reach old age, and consequently they are more likely to be diagnosed with sarcopenia as an age-related disorder. Therefore, the authors of this current scoping review emphasis the importance of preventive strategies targeted major risk factors and effective interventions to limit the consequences of sarcopenia in the Nordic populations. Besides, we think that the health care system in the Nordic countries should be better equipped with the necessary healthcare resources for both a timely diagnosis and dealing with this major age-related issue in the years to come. However, due to the limitations regarding the timely diagnosis, we highly recommend a comprehensive approach including establishment of support services, implement educational programs, offer training for health care professionals, and engage the community.

Many countries have conducted research on SO [ 7 , 39 , 63 , 64 , 65 ]. Based on our findings, however, among the Nordic countries only Sweden and Finland have investigated the prevalence of probable SO and SO [ 23 , 29 ]. Besides, we only found one study investigating the association between body adiposity and physical function over time [ 54 ]. We did not find any literature on risk factors or interventions among older adults with SO in this region. Therefore, we call on medical and research community in Nordic countries to attach importance to screening of SO in elderly people to capture a full picture of this public health risk to aging society and allocate healthcare resources accordingly.

In terms of risk factors for sarcopenia, our study revealed that malnutrition, low levels of physical activity, specific diseases (e.g., diabetes, osteoporosis), inflammation, polypharmacy (multiple medicines), BMI, and ageing are potential risk factor for sarcopenia in populations of the Nordic region. However, evidence on risk factors derived mainly from cross-sectional associations [ 27 , 28 , 30 , 31 , 34 , 40 , 44 , 49 , 50 , 51 ], and only to a limited extend from longitudinal studies [ 24 , 38 , 43 , 47 ]. Therefore, the associations between risk factors and sarcopenia should be interpreted with caution due to the possibility of reverse causality and confounding affecting the results. Moreover, our findings on risk factors mainly came from community-dwelling older adults, and only to a limited extend hospital and nursing home settings. We think that risk factors may vary depending on population characteristics (e.g., age, sex, health condition) and setting (e.g., hospital, nursing home, community). Therefore, we encourage researchers of the Nordic countries to perform well-designed prospective cohort studies in different settings to enhance the possibility to establish causal inference as well as understanding degree and direction of changes over time.

A recently published meta-analyses revealed a higher risk of having polypharmacy in Europe among individuals with sarcopenia compared to people without this condition [ 66 ]. A nationwide register-based study in Swedish population also showed that the prevalence of polypharmacy has increased in Sweden over the last decade [ 67 ]. Sarcopenia itself is associated with morbidity (identified by specific disease or inflammatory markers) and different health-related outcomes (e.g., disability) [ 7 ]; therefore, future research should investigate whether polypharmacy is a major factor to sarcopenia development [ 66 ]. Although we lack information on polypharmacy in Nordic countries other than Sweden, we encourage researchers in this region to examine the above research gap in their future studies.

According to previous studies physiological changes in ageing include systemic low-grade inflammation which results in insulin resistance, affect protein metabolism and leads to increased muscle wasting [ 68 ]. Acute and chronic disease may increase the inflammatory response and accelerate age-related loss of muscle mass and increase risk of sarcopenia [ 68 , 69 ]. Hence, we think that special attention should be made by health care professionals particularly GPs to older adults with acute or chronic conditions to limit the risk of sarcopenia.

Literature from the Nordic countries also indicated that higher levels of physical activity and different dietary patterns (e.g., higher protein intake, fruit, vegetables, fibers) were associated with reduced risk of sarcopenia or improvement in indicators of sarcopenia. There was a large heterogeneity in the studied aspect which makes direct comparison of studies difficult. Nevertheless, according to findings from a recent systematic review of meta-analyses on sarcopenia the identified risk factors are in alignment with previously identified risk factors globally [ 70 ]. Other potential lifestyle-related risk factors suggested from the above meta-analysis included smoking and extreme sleep duration. However, we did not identify studies investigating these health behaviors in the Nordic populations. Therefore, high-quality cohort studies are needed to deeply understand such associations with the risk of sarcopenia.

In this current review, we only found three intervention studies in Nordic countries. However, two of them were sub-projects of big intervention programs, meaning that such studies were not designed explicitly for the prevention/treatment of sarcopenia. Therefore, explicit intervention studies on sarcopenia in this region is recommended.

We believe that on a global level, research on sarcopenia will carry on with nutrition, exercise, and understanding of molecular mechanisms. Furthermore, examining the link between sarcopenia and other medical conditions/diseases would be the next step [ 6 ]. In the Nordic countries, however, already performed studies have a basic and descriptive design, so that, well-designed research and advanced analyses are lacking. Hence, we recommend conducting large well-designed and adequately powered studies to (a) explore the scale of this age-related health issue on country and regional level, (b) investigate the patterns of physical activity and sedentary behavior to understand if this should be a target in older adults with SO and sarcopenia, (c) determine whether elderly populations are suffering from nutritional deficiency or are at risk of malnutrition. The latest can support further studies to assess the impact of combined physical activity and dietary intake, which are still lacking globally [ 6 ].

A previous systematic review on therapeutic strategies for SO revealed that exercise-based interventions (e.g., resistance training) reduced total adiposity and consequently improved body composition. However, evidence of other therapeutic strategies (e.g., nutritional supplementation) was limited due to scarcity of data and lack of unique definition for SO [ 69 ]. Therefore, authors suggested that more research should be done to clarify optimal treatment options for various age-groups and not only for older adults [ 14 ].

In our scoping review, the included studies, did not provide a status of either SO or the prevention/treatment methods in this region. We believe that SO is practically neglected in clinical practice and research as well, and this is mainly because it is difficult to separate it from general obesity. The consequence of lacking knowledge in this research area is that when older adults with SO are recommended weight loss- a frequently used strategy for management of general obesity- this may accelerate the loss of muscle mass and increase the severity of the sarcopenia [ 3 ]. Consequently, we think that this issue may have adverse effects both on patients (e.g., decreasing quality of their life) and on the health care system (e.g., increasing the health care demands) of this region. Therefore, we encourage researchers to perform cohort studies to understand the epidemiology and etiological basis of SO, which are poorly understood even on a global scale [ 8 ]. We think that the consensus definition on SO from the European Society for Clinical Nutrition and Metabolism (ESPEN) and European Association for the Study of Obesity (EASO) which was published in 2022 [ 3 ], can positively affect the ability to define studies on prevalence and prevention of SO. Besides, we recommend conducting further research to find the optimal treatment for SO and reduce its adverse consequences both at individual and society levels. Additionally, we think that the concepts of sarcopenia and SO might be somehow unfamiliar to health care personnel. Therefore, it is highly recommended that more information be provided to bring their attention to the significance of prevention, timely diagnosis, and treatment of these two aging disorders.

Strengths and limitations of the study

This is the first study providing an overview of available evidence on sarcopenia and SO among older adults in the Nordic countries. These countries have important similarities in welfare sectors and on a population level and we believe that our findings will be a significant benefit for researchers and health care providers to understand the knowledge gaps and plan for future studies in this geographical region. However, the current scoping review has limitations. This review was limited to studies among individuals more than 60 years old which may limit the overview of available research in this field, as well as understanding risk factors, confounders for prevention, and the potential for early detection of these two diseases in younger age population. The included cross-sectional studies in our review cannot provide information on causality of the associations.

Sarcopenia and SO are generally prevalent syndromes among older adults in Nordic countries, even though the prevalence of them varies according to the criteria for definition, population, and setting. Research among older adults with SO was very limited in this region. Besides, studies on risk factors were primarily cross-sectional and only few intervention studies were identified. Therefore, we encourage researchers performing well-designed studies (e.g., prospective cohorts) to understand the epidemiology and etiological basis of these two age-related disorders. For the next step, implementation of interventions targeting risk factors (e.g., combined physical activity and dietary intake) and evaluating of their impact on prevention or treatment of sarcopenia and SO is recommended. Furthermore, for the comprehensive advancement of muscle health in older adults, we recommend implementing interventions directed at health care personnel and encouraging more collaboration among clinicians, professional societies, researchers, and policy makers to ensure comprehensive and effective approach to health care initiatives.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

sarcopenic obesity

Web of science

Preferred Reporting Items for Systematic Reviews and Meta-analyses

European Working Group on Sarcopenia in Older People in the updated version from 2019

National Institutes of Health Sarcopenia Project definition

Dual energy X-ray absorptiometry

Bioelectrical Impedance Analysis

Bioimpedance Spectroscopy

Computed Tomography

Computed Tomography Angiogram

World Health Organization

General Practitioner

Randomized Controlled Trial

Chronic Obstructive Pulmonary Disease

European Association for the Study of Obesity

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Fereshteh Baygi, Sussi Friis Buhl contributed equally to this work.

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Research Unit of General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark

Fereshteh Baygi, Sussi Friis Buhl, Trine Thilsing, Jens Søndergaard & Jesper Bo Nielsen

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FB conceived and designed the review, participated in literature review, data extraction, interpretation of the results and wrote the manuscript. SFB designed the review, participated in literature review, data extraction, and revised the manuscript. TT, JBN and JS contributed to the conception of the study and revised the manuscript critically. All the authors approved the final manuscript.

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Baygi, F., Buhl, S.F., Thilsing, T. et al. Sarcopenia and sarcopenic obesity among older adults in the nordic countries: a scoping review. BMC Geriatr 24 , 421 (2024). https://doi.org/10.1186/s12877-024-04970-x

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Received : 12 November 2023

Accepted : 12 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s12877-024-04970-x

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