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Emotional Well-Being: What It Is and Why It Matters

• COMMENTARY / OPINIONS
• Published: 15 November 2022
• Volume 4 , pages 10–20, ( 2023 )

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• Crystal L. Park 1 ,
• Laura D. Kubzansky 2 ,
• Sandra M. Chafouleas 1 ,
• Richard J. Davidson 3 ,
• Dacher Keltner 4 ,
• Parisa Parsafar 5 ,
• Yeates Conwell 6 ,
• Michelle Y. Martin 7 ,
• Janel Hanmer 8 &
• Kuan Hong Wang 6  

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Psychological aspects of well-being are increasingly recognized and studied as fundamental components of healthy human functioning. However, this body of work is fragmented, with many different conceptualizations and terms being used (e.g., subjective well-being, psychological well-being). We describe the development of a provisional conceptualization of this form of well-being, here termed emotional well-being (EWB), leveraging prior conceptual and theoretical approaches. Our developmental process included review of related concepts and definitions from multiple disciplines, engagement with subject matter experts, consideration of essential properties across definitions, and concept mapping. Our conceptualization provides insight into key strengths and gaps in existing perspectives on this form of well-being, setting a foundation for evaluating assessment approaches, enhancing our understanding of the causes and consequences of EWB, and, ultimately, developing effective intervention strategies that promote EWB. We argue that this foundation is essential for developing a more cohesive and informative body of work on EWB.

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Acknowledgements

We are very grateful to the Taxonomy Working Group and the broader members of the networks for their input on our thinking and writing. Members of the Taxonomy Working Group who contributed to this manuscript are Feng Vankee Lin, Elizabeth Necka, Lisbeth Nielsen, Caroline G. Richter, and Janine Simmons.

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Crystal L. Park & Sandra M. Chafouleas

Department of Social and Behavioral Sciences and Lee Kum Sheung Center for Health and Happiness, Harvard TH Chan School of Public Health, Boston, MA, USA

Laura D. Kubzansky

Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA

Richard J. Davidson

Department of Psychology, University of California at Berkeley, Berkeley, CA, USA

Dacher Keltner

Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA

Parisa Parsafar

University of Rochester School of Medicine and Dentistry, Rochester, NY, USA

Yeates Conwell & Kuan Hong Wang

Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA

Michelle Y. Martin

Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

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This research was supported by EWB Network grants from the National Institutes of Health: U24 AT011310-01; U24 AT011281; U24 AT011289; U24 AG072699; U24 AG072701; U24 HD107562-01.

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Park, C.L., Kubzansky, L.D., Chafouleas, S.M. et al. Emotional Well-Being: What It Is and Why It Matters. Affec Sci 4 , 10–20 (2023). https://doi.org/10.1007/s42761-022-00163-0

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A systematic review of workplace triggers of emotions in the healthcare environment, the emotions experienced, and the impact on patient safety

• Raabia Sattar 1 ,
• Rebecca Lawton 1 ,
• Gillian Janes 2 ,
• Mai Elshehaly 3 ,
• Jane Heyhoe 1 ,
• Isabel Hague 1 &
• Chloe Grindey 1  

BMC Health Services Research volume  24 , Article number:  603 ( 2024 ) Cite this article

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Healthcare staff deliver patient care in emotionally charged settings and experience a wide range of emotions as part of their work. These emotions and emotional contexts can impact the quality and safety of care. Despite the growing acknowledgement of the important role of emotion, we know very little about what triggers emotion within healthcare environments or the impact this has on patient safety.

To systematically review studies to explore the workplace triggers of emotions within the healthcare environment, the emotions experienced in response to these triggers, and the impact of triggers and emotions on patient safety.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, four electronic databases were searched (MEDLINE, PsychInfo, Scopus, and CINAHL) to identify relevant literature. Studies were then selected and data synthesized in two stages. A quality assessment of the included studies at stage 2 was undertaken.

In stage 1 , 90 studies were included from which seven categories of triggers of emotions in the healthcare work environment were identified, namely: patient and family factors, patient safety events and their repercussions, workplace toxicity, traumatic events, work overload, team working and lack of supervisory support. Specific emotions experienced in response to these triggers (e.g., frustration, guilt, anxiety) were then categorised into four types: immediate, feeling states, reflective, and longer-term emotional sequelae. In stage 2 , 13 studies that explored the impact of triggers or emotions on patient safety processes/outcomes were included.

The various triggers of emotion and the types of emotion experienced that have been identified in this review can be used as a framework for further work examining the role of emotion in patient safety. The findings from this review suggest that certain types of emotions (including fear, anger, and guilt) were more frequently experienced in response to particular categories of triggers and that healthcare staff's experiences of negative emotions can have negative effects on patient care, and ultimately, patient safety. This provides a basis for developing and tailoring strategies, interventions, and support mechanisms for dealing with and regulating emotions in the healthcare work environment.

Peer Review reports

Healthcare is delivered in emotionally charged settings [ 1 ]. Worried patients present with complex health issues, and anxious relatives need information and support, and safe care is reliant on clinical judgement and effective multi-disciplinary teamwork within a time-pressured resource-limited, complex system. Working in this environment, healthcare staff experience a range of emotions (e.g., anxiety, anger, joy, sadness, pride, and guilt) which can impact the safety of the care delivered [ 1 , 2 , 3 , 4 , 5 ]. Clinical judgement often involves weighing up risk based on incomplete information and uncertain outcomes. Research outside healthcare [ 6 , 7 , 8 , 9 ] suggests that if, while making these decisions, healthcare staff experience strong emotions, this can influence their decisions and behavior.

Research focusing on the role of emotion in patient safety is still limited [ 1 , 2 , 10 ] and fragmented [ 11 ]. This may in part be because emotion research is complex. For example, the experience and influence of emotion can be approached and interpreted from a range of perspectives including cognitive and social psychology, cognitive neuroscience, and sociology. There is also a lack of consensus on what is meant by ‘emotion’. In decision-making research, ‘emotion’ has been distinguished from ‘affect’ [ 11 ]. In response to stimuli or situations, ‘emotion’ is viewed as a slower, more reflective process, whilst ‘affect’ is an instantaneous and automatic reaction. Other research has focused on identifying and examining different types of affect, such as ‘anticipatory affect’—an immediate, strong visceral state in response to stimuli e.g. anger (Knutson, 2008) and ‘anticipated affect’ – considering how current actions might make you feel in the future e.g. regret [ 12 ].

Research often lacks a clear distinction between the different types of feeling states being examined, and as such, it is difficult to build robust evidence of the processes involved and the role they each play in judgement and associated behaviour [ 11 ]. Furthermore, the emotions experienced by healthcare staff can be both positive and negative and can influence the delivery of safe care in positive and negative ways. Until more recently, the focus has tended to be on the impact of negative emotions, including their role in diagnostic accuracy [ 13 , 14 ], time spent on history taking, examinations, and treatment decisions [ 15 , 16 ], and the instigation of verbal checking during procedures [ 4 ]. More attention is now being given to the role of positive emotions in the workplace such as their effect on reasoning [ 17 ], and engagement and teamwork [ 18 ].

There are many potential triggers (e.g. physical, circumstantial, tangible, and psycho-social aspects of the immediate clinical work environment and the broader organisation) that generate a feeling state via reactions or interactions of emotion in the workplace. Research exploring some of the triggers of emotions within a healthcare environment has found that involvement in care that has gone wrong [ 4 ], and interactions with patients can elicit negative emotions [ 13 , 14 , 15 , 16 ] and that triggers of emotion can be at a clinical, hospital and system level [ 15 ]. Only a limited number of these studies have also explored how the emotions experienced by healthcare staff impact patient care [ 15 , 16 ]. While emotion has a direct effect on patient care, it can also indirectly influence patient safety. Burnout, sickness absence, and turnover are impacted by emotion [ 19 , 20 , 21 ] and, in turn, are associated with healthcare organisations’ ability to provide safe care [ 21 , 22 , 23 ]. Due to the multifaceted approaches to research in this area, it is currently unclear what contexts and settings elicit emotions in healthcare staff, how these make healthcare staff feel, and the influence these feelings may have on decisions and actions relevant to providing safe patient care. There is therefore a need to synthesise the current evidence to help develop an in-depth understanding of the triggers of emotions experienced by healthcare staff in the work environment, the emotions experienced and the impact these may have on patient safety.

The protocol was pre-registered on Prospero (ID: CRD42021298970).

This systematic review aimed to identify gaps in the evidence by answering these research questions:

What triggers emotions in the healthcare work environment?

What are the emotions experienced in response to these triggers?

Are certain emotions more often experienced in the context of particular triggers?

What impact do different triggers/ emotions have on patient safety processes and outcomes?

Search strategy and databases

Four electronic databases (MEDLINE, PsychInfo, Scopus, and CINAHL)( were systematically searched in March 2020 and updated in January 2022. Only studies published since 2000 were sought as this was when the Institute of Medicine’s seminal report, ‘To Err is Human’’ [ 24 ] was published promoting a widespread focus on patient safety. The search strategy had three main foci (patient safety, emotions, and healthcare staff). Previous systematic reviews examining any of these topics in combination; patient safety [ 25 ] and healthcare staff [ 26 ]were used to guide search strategy development. As a foundation to develop the search terms in relation to emotions, the six basic emotions (fear, anger, joy, sadness, disgust, and surprise) described by Ekman [ 27 ] were included, with synonyms for emotion. This resulted in a search strategy that combined all three concepts (Available in Appendix 1 ). The reference lists of all included studies were hand-searched.

Eligibility criteria

Studies were included if they were: published post-2000, original empirical research (either quantitative, qualitative, or mixed-methods), published in English, conducted in any healthcare environment, and included healthcare staff as participants. Studies were excluded if they; focused on healthcare staff’s non-work related emotions, included healthcare students/staff who were not involved in direct patient care (e.g. administrative staff), or if the primary focus was on longer-term emotional states (e.g. burnout and emotional exhaustion) with no reference to specific emotions.

This review had two stages:

Stage 1: The first stage addressed the first three research questions and identified studies focused on triggers of emotions in the healthcare work environment and the specific emotions experienced by healthcare staff in response to these.

Stage 2: The second stage examined the fourth research question and identified the impact of triggers and/or emotions on patient safety outcomes and processes. The studies included in stage 1 were further screened and considered at this stage if they included either (i.) triggers of emotions and their relationship with patient safety, (ii.) emotions experienced and their relationship with patient safety outcomes or processes (iii.) triggers, emotions and the relationship with patient safety outcomes or processes.

Study selection

PRISMA guidelines [ 28 ] for study selection were followed. The study selection process is described below. Throughout each stage, all decisions and any uncertainty or discrepancies were discussed by the review team to achieve consensus.

Stage 1: Title and abstract then full-text screening, was conducted by IH & CG independently and then discussed together. RL reviewed a random 10% at the abstract review stage and all included full-text articles.

Stage 2: RS independently conducted abstract and title screening for all included studies. A random 10% of these were each independently screened by two reviewers (JH&RL). Full texts were obtained for all studies deemed potentially eligible for inclusion. All full texts were screened by RS. JH &RL double-screened half each. A final set of studies meeting all the eligibility criteria was identified for data extraction.

Assessment of the methodological quality of included studies was carried out using the 16-item quality assessment tool (QuADS) [ 25 ] which is appropriate for studies using different methodological approaches. Quality assessment was undertaken independently for two studies by three reviewers (RS, GJ&JH) and scores were discussed to check for consistency. RS&GJ completed a quality assessment for the remaining studies and discussed scores to check for consistency. No studies were discarded based on low scoring.

Data extraction

Stage 1: A data extraction form developed in Microsoft Excel by IH&CG and agreed with the wider review team was used to extract: the study title, triggers of emotions in the healthcare work environment, and emotions experienced in response to these triggers. Two reviewers extracted these data (IH&CG), conferring at intervals throughout the extraction process to ensure consistency. Due to the large number of studies at this stage and our aim to take a broader approach to explore triggers and the associated emotions, we did not extract data related to study characteristics. We categorised both the types of triggers and emotions (drawing on existing theory and wider team expertise) to advance knowledge by providing an initial framework for further testing. The detailed process for categorisation of the emotions and triggers is described in supplementary Appendix 2 .

Stage 2: A data extraction form developed and agreed upon by authors was used to extract: information on the study population, setting, design and methods used, key findings, conclusions, recommendations, triggers of emotions, emotions experienced, and impact on patient safety. CG&IH completed data extraction for included studies. This was cross-checked by RS and discussed with all reviewers.

Categorising the patient safety outcomes and processes

The wide range of patient safety processes and outcomes ( n  = 50) from the included studies, meant it was necessary to reduce the data. Therefore, categories of outcomes were developed to allow the relationship between triggers/emotions and patient safety to be explored. The first step in the categorisation process involved a team of 8 patient safety researchers using a sorting process in which they were provided with 50 cards each describing a patient safety process/outcome extracted from the studies Working independently they grouped these 50 cards and gave each group a title. A large group discussion with all 8 patient safety researchers followed this, resulting in 7 categories. We then presented these categories and the items each contained, to a large group of patient safety researchers, healthcare staff, and patients ( n  = 16), at an inter-disciplinary meeting. This resulted in a final set of five categories representing patient safety processes: altered interaction with patients, disengagement with the job, negative consequences for work performance, defensive practice, being more cautious, negative impact on team relationships, and reduced staff confidence (see appendix 2 for further detail) and the sixth, patient safety outcomes.

Quality assessment

There was a very high level of agreement between RS & GJ regarding the quality assessment. The quality of studies was variable, with total scores ranging from 79 to 48% across the studies. There was limited discussion of relevant theories related to emotions and patient safety, and few studies provided a rationale for the choice of data collection tools. There was also limited evidence to suggest stakeholders had been considered in the research design and limited – or often no justification for analytical methods used. A detailed quality assessment table is available in Appendix 3 .

After duplicates were removed; the search resulted in 8,432 articles for initial review which were downloaded into the reference management software Endnote (see PRISMA flow diagram in (Fig.  1 ). Stage 1: 90 studies met the inclusion criteria, investigating triggers of emotions in the healthcare work environment and the emotions experienced by healthcare staff in response to these.

PRISMA flow diagram

Research question 1: What triggers emotions in the healthcare work environment?

The following categories of triggers were identified:

(1) Patient and family factors ( included patient aggression, challenging patient behaviours, patient violence, patient hostility, and interactions with patients family)*

(2) Patient safety events and their repercussions (including adverse events, errors, medical errors, and surgical complications)*

(3) Workplace toxicity (including workplace bullying, and staff hostility)*

(4) Traumatic events with negative outcomes for patients (including patient deaths/suicide, patient deterioration, and critical incidents).

(5) Work overload (including work pressures and poor staffing levels).

(6) Team working and lack of supervisory support (including teamwork and the lack of appropriate managerial support).

*The most common triggers investigated and reported in the literature

Research question 2: What are the emotions experienced in response to these triggers?

In response to the triggers described above, healthcare staff experienced four main types ofemotions:

1. Immediate: an instantaneous, visceral emotional response to a trigger ( e.g. fear, anxiety, anger, comfort, satisfaction, joy ).

2. Feeling states: short-lived, more mindful and conscious cognitive-based responses to a trigger (e.g. include feeling disoriented, confused, helpless, inadequate, alone).

3. Reflective/self-conscious: Mindful and conscious cognitive-based response after exposure to a trigger and following time to reflect on how others may perceive them (e.g. moral distress, guilt, pride).

4. Sequelae: chronic and longer-term mental health states that arise as a result of repeated exposure to a trigger and experiencing the emotions in response to that trigger over time (e.g. chronic depression, fatigue, distress, PTSD symptoms).

Research question 3: Are certain emotions more often experienced in the context of particular triggers?

The frequency of the emotions experienced across the studies in response to the categories of triggers is illustrated using a heat map (Fig. 2 ) developed by a data visualisation expert (MA). Below is a summary of the most frequently experienced emotions by healthcare staff in response to the categories of triggers across the studies.

Patient and family factors: Immediate emotional responses including most commonly anger, frustration, etc.

Patient safety events & their repercussions: Reflective/self–conscious emotions including guilt and regret

Workplace toxicity: Immediate emotional responses including fear and anxiety

Traumatic events with negative outcomes for patients: Reflective/self–conscious emotions including guilt and regret

Work overload: Immediate emotional responses including anxiety and worry

Team working & supervisory support: Immediate emotional responses including anxiety and worry

A heat map displaying the triggers of emotions experienced by healthcare staff and the emotions experienced in response to these. (The darker the colour on the heat map represents a higher frequency of that emotion being experienced across the studies)

Stage 2 Research question 4. What impact do different triggers/ emotions have on patient safety processes or outcomes?

Thirteen publications [ 15 , 16 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] addressed this research question and were included at this stage.

All 13 studies [ 15 , 16 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] described the following patient safety processes/outcomes as being impacted by either the triggers of emotions or the emotions experienced; altered interaction with patients, disengagement with the job, negative consequences for work performance, defensive practice, being more cautious, negative impact on team relationships and reduced staff confidence and patient safety outcomes.

Depending on the nature of the studies, some explored only one of the patient safety processes/outcomes, whereas others focused on several. Eight studies used quantitative methods [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], three qualitative methods [ 15 , 38 , 39 ], and two mixed-methods designs [ 16 , 40 ] to explore these relationships. Twelve studies were conducted in hospital settings [ 15 , 16 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], and one was conducted in hospital and community healthcare settings [ 40 ].

The impact of triggers of emotion on patient safety processes or outcomes

Of the 13 studies [ 15 , 16 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], 10 [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 38 , 39 , 40 ] included an exploration of or commented on the impact of triggers of emotion on patient safety processes/outcomes. None had a direct focus on exploring the relationship between the triggers and patient safety processes/outcomes; rather some stated this as one of multiple aims, whereas others reported any associations as part of the broader study findings. The relationship between specific triggers and patient safety processes/outcomes is displayed in Table  1 .

The most commonly described patient safety processes/outcomes following exposure to the triggers were 'disengagement with the job' [ 30 , 31 , 35 , 40 , 41 ] and ‘being more cautious’ [ 31 , 32 , 33 , 34 , 35 ]. There was increased disengagement with the job after experiencing workplace bullying, medical errors, and workplace violence. This included dissatisfaction and a desire to change jobs or leave healthcare practice. Involvement in medical errors, surgical complications, and workplace bullying also resulted in staff being more cautious. For example, they reported paying more attention to detail, keeping better patient records, and increased information-seeking from colleagues.

Within four studies, triggers were described as having a ‘negative impact on team relationships’ [ 30 , 34 , 36 , 39 ] where being exposed to workplace bullying resulted in communication problems amongst staff and conflicts with co-workers. In one case being involved in a patient safety incident resulted in staff feeling uncomfortable within their team [ 36 ]. Workplace bullying and being involved in a patient safety incident also resulted in ‘Negative consequences for work performance’ in four studies [ 30 , 34 , 36 , 40 ] which included delays in care delivery and being unable to provide quality care.

Exposure to triggers was also linked to ‘defensive practice’ [ 29 , 32 , 36 ]. There was an increase in defensive practice (e.g. ordering more tests, keeping errors to self, and avoiding risks) as a result of triggers such as workplace bullying and medical errors. 'Patient safety outcomes' were only described in one study, where it was perceived that there may be an increased risk of patient safety incidents such as increased medical errors, patient falls, adverse events, or patient mortality as a result of experiencing workplace bullying [ 30 ].

The impact of emotions on patient safety processes or outcomes

Only three studies [ 15 , 16 , 37 ] included an exploration of the impact of emotions on either patient safety processes [ 15 , 16 ] or outcomes [ 37 ]. These studies directly explored the relationship between the emotions experienced in response to triggers and patient safety processes/outcomes (see Table  2 ).

In response to the emotions experienced by healthcare staff, ‘negative consequences for work performance’ were described[15, 16} as staff feeling unable to provide quality care or a delay or failure to provide appropriate examination/treatment was reported. Emotions also influenced defensive practices [ 16 ] such as risk avoidance, and provision of unnecessary treatment, and emotions were described as an influencing factor for overprescribing. Emotions were also found to influence physical restraint in mental health settings, where a positive correlation was reported between staff experiencing anger (as a result of patient aggression) and the approval of physical restraint [ 37 ].

The type of patients also influenced the emotions experienced by staff, which in turn altered the interaction with patients [ 15 , 16 ]. Isbell et al. [ 16 ] found that encounters with angry and mental health patients elicited highly negative emotions such as fear and frustration, where staff spent less time with the patient and acted less compassionately. Increased interaction including expediting patient care and spending more time with the patient was associated with encounters with positive patients who elicited positive emotions (happiness, satisfaction)in staff. Isbell et al. [ 15 ] also found that patients with psychiatric conditions elicited negative emotions, which resulted in reduced patient interaction and potential for diagnostic error.

Whilst these studies [ 15 , 16 , 37 ] highlight that emotions may impact patient safety processes and/or outcomes, it was not always possible to ascertain the impact of specific emotions. Studies by Isbell et al. [ 15 , 16 ] illustrate how negative emotions elicited by patients have a negative impact on patient safety processes, whereas positive emotions resulting from patient behaviours have the potential to enhance patient care. However, it is difficult to disentangle the effect of specific emotions, due to a lack of evidence regarding the link between individual emotions and patient safety processes and/or outcomes as studies have not attempted to explore this. Only Jalil et al. [ 37 ] focused specifically on anger as an emotion and its impact on restraint practices, where higher levels of anger were correlated with greater approval of restraint of mental health patients.

Summary of main aims and findings

This review has identified and categorised the triggers of emotions in the healthcare work environment and the types of emotions experienced by healthcare staff in response to those triggers. It has also established the types of emotions more often experienced in the context of particular triggers, and the impact that different triggers and emotions may have on patient safety processes or outcomes. The most frequently reported triggers within the literature were 'patient and family factors', 'patient safety events and their repercussions', and ‘workplace toxicity’ , and the most frequently cited emotions were ‘ anger, frustration, rage, irritation, annoyance’ and ‘ guilt, regret and self-blame’ . These emotions were all negative in nature, which may reflect a bias in the research literature.

The studies that focused on the triggers did not directly set out to assess the impact of triggers of emotions on patient safety processes or outcomes, but the reporting of this link in study findings did enable knowledge to be gained about this. Studies that did focus on emotions and patient safety directly explored the relationship between the emotions experienced in response to triggers and patient safety processes and outcomes. Previous literature [ 41 , 42 , 43 ] supports the link between the categories of patient safety processes identified within this review ( including reduced staff confidence, disengagement with the job, and defensive practice ) and patient care and or/patient safety, suggesting these processes may serve as mechanisms to influence patient safety. Only three studies were identified that focused on the impact of emotions experienced by healthcare staff within the work environment on patient safety processes/outcomes [ 15 , 16 , 37 ]. These studies highlight that a majority of emotional responses experienced by healthcare staff are negative and have the potential to result in negative work performance ( including being unable to provide quality care ), increased defensive practice, and negative patient safety outcomes (increased approval of physical restraint ). In only one study [ 15 ], positive emotions were reported which resulted in positive outcomes including expediting patient care and spending more time with the patient.

The findings of this review support previous calls to acknowledge the importance of emotions and their impact on safe care [ 1 , 2 , 4 , 5 , 44 ], however, research in this area is still limited and fragmented [ 15 , 16 ]. Except for one study [ 41 ], it was not possible to ascertain the association between specific emotions and patient safety processes, and even for this study (a cross-sectional survey), causal relationships were not demonstrated. Nevertheless, the findings do suggest that negative emotions elicited by patients within healthcare staff have a negative impact on the described patient safety processes and positive emotions have a positive impact on these processes. Earlier work by Croskerry et al. [ 10 ] highlighted the importance of bringing attention to the notion that healthcare providers are not immune to emotional influences, and must therefore focus on not allowing their emotional experiences to negatively influence the care they provide.

Within this review, patients were described as the most common trigger eliciting emotions and subsequently influencing patient safety processes and outcomes. Although one study did also identify hospital and system-level factors as triggers [ 15 ], these were not explored in patient safety processes. As well as patients, many other factors within the healthcare work environment were identified in the first stage of this review as influencing the emotions staff experienced. However, how emotional responses to such triggers affect patient safety processes and outcomes is currently unclear and warrants further research. The studies reviewed here focused on the intrapersonal effects of emotion. Researchers have recently highlighted the need for further work to understand the social aspects of emotion [ 45 , 46 , 47 ]. The Emotions as Social Information (EASI) model [ 45 ] posits that many of our decisions and actions cannot be explained solely by individual thought processes, but are often due to social interaction which involves observing and responding to the emotional displays of others, providing a potentially useful framework for further exploration.

Workplace violence and patient aggression were identified in this review as triggers of emotions in the healthcare work environment. Research evidence suggests that gender plays a role in determining recipients who are subjected to workplace violence and the type of violence they may experience. Male healthcare staff report experiencing a higher prevalence of workplace violence compared to their female counterparts [ 48 , 49 ]. Gender influenced the types of violence experienced by healthcare staff, where in general, female healthcare staff experienced more verbal violence, and male healthcare staff experienced more physical violence [ 48 ]. Different risk factors for workplace violence have been reported for males and females. For male healthcare staff, lower income levels and managers were at a higher risk of workplace violence, whereas longer working hours were associated with a higher risk of workplace violence among female healthcare staff [ 49 ].

As experiencing workplace violence and patient aggression have been found to have a negative impact on the delivery of patient care, this is a topic area that warrants further research. The majority of emotions identified in response to the triggers in this review were negative in nature. Within the few studies where positive emotions were mentioned, experiencing these as a result of a positive patient encounter was associated with increased interaction with patients, where healthcare staff perceived they were more engaged and provided expedited care [ 15 , 16 ]. This finding is congruent with limited previous Research that suggests positive emotions may improve patient safety and patient care; positive affect led medical students to identify lung cancer in patients more quickly [ 50 ] and resulted in correctly diagnosing patients with liver disease sooner [ 51 ]. However, positive emotional responses may also have the opposite effect e.g. over-testing and over-treating patients, or reducing staff belief that the patient has a serious illness, resulting in adverse outcomes [ 16 ]. Greater understanding is required to articulate conditions and triggers of positive emotions and when these might support patient safety or cause harm [ 44 ].

Limitations

There was heterogeneity within the included studies and the primary aim of most studies was not to answer the research questions posed here. To answer our research questions, it was necessary to include articles where the study aims addressed only one of the concepts of interest or where only limited associations between triggers of emotion or the emotions experienced in response to triggers and patient safety processes or outcomes were made. Moreover, it is important to recognise that there is likely to be some bias in the research literature, meaning that the triggers of emotion we identified from the current published research and the emotions experienced in response to these cannot be assumed to accurately represent the routine experiences of healthcare staff. Also worthy of note is that the majority of studies focus on negative triggers of emotions or the negative emotions experienced which may also lead to reporting bias. We acknowledge that we did not search studies before the year 2000.

Implications for future research and practice

The triggers of emotion and types of emotion experienced that have been identified in this review can be used as a framework for further work examining the role of emotion in patient safety. Developing validated measures of the triggers of emotions, and the types of emotions experienced by healthcare staff in the work environment will facilitate this and is urgently needed. The findings also suggest that particular types of emotion were more frequently experienced in response to particular categories of triggers and that healthcare staff’s experiences of negative emotions have negative effects on patient care and ultimately, patient safety. This provides a basis for developing and tailoring strategies, interventions, and support mechanisms for dealing with either short-term or long-term consequences, and regulation of emotions in the healthcare work environment. For example, healthcare staff can be offered some time out from their clinical duties to take a brief pause when immediate and short-term emotional reactions are experienced. They may also be provided with one-to-one peer support to help healthcare staff experience a more reflective, self-conscious emotional response. It also highlights the possibility of preparing healthcare staff for likely emotional reactions in particular clinical situations to assist them in being more mindful of the possible impact on the safety of the care they provide. The limited research currently available suggests that emotions influence patient safety processes/outcomes. Further research is needed to explore this relationship further. For example, studies that focused exclusively on more amorphous emotional concepts like burnout were excluded. However, in some of the included studies, these longer-term emotional responses were identified in addition to the immediate, short-term, and reflective emotions. Further research needs to explore longer-term emotional responses such as PTSD, burnout, and work satisfaction, the associated triggers, and the impact on patient safety.

It is important to raise awareness of the potential impact of emotional triggers and the emotions experienced in response to these on patient safety through training and education for healthcare staff. As suggested by previous authors, we recommend that emotional awareness and regulation skills, both of which can be developed and enhanced using emotional intelligence training interventions [ 52 , 53 ] are included in healthcare staff training [ 44 , 54 , 55 , 56 ]. Future work should also distinguish between specific types of emotional responses rather than broadly classifying these as negative and positive, and explore how these influence patient safety. The findings also have potential implications for health equity given that the evidence indicates certain types of patients (e.g. angry and mental health patients) are more likely to provoke negative emotions, and such emotions can result in a negative impact on patient care and safety. This may suggest that such patient groups may receive poorer quality of care due to social factors beyond their control and is an area that requires further research.

Conclusions

Healthcare staff are exposed to many emotional triggers within their work environment including patient safety events, traumatic events, work overload, workplace toxicity, lack of supervisory support, and patient and family factors. In response, healthcare staff experience emotions ranging from anger and guilt to longer-term burnout and PTSD symptoms. Both triggers and the emotional responses to these are perceived to negatively impact patient care and safety, although robust empirical evidence is lacking.

Availability of data and materials

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

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Acknowledgements

The authors would like to thank all those were involved in supporting this review including the Workforce Engagement and Wellbeing Theme members within the Patient Safety Translational Research Centre.

This research is funded by the National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC) and Yorkshire and Humber ARC. This research has also been supported by the Yorkshire and Humber Patient Safety Research Collaboration (PSRC). The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care.

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RS, RL, GJ, and JH contributed towards the development of the ideas for this review. RS built the search strategy and this was checked by RL and JH. RS performed the searches in all databases. Data extraction for stage 1 was carried out by CG and IH and checked by RS, RL, JH and GJ. Data extraction for stage 2 was carried out by RS and checked by RL, JH, and JH. Data analysis was led by RS . ME developed the infographic for the results and supported with the interpretation of the data. RS wrote a first draft of the manuscript.  All authors provided input and recommendations at all stages of the study and revised the draft manuscript. All authors read, contributed towards and approved the final manuscript.

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Sattar, R., Lawton, R., Janes, G. et al. A systematic review of workplace triggers of emotions in the healthcare environment, the emotions experienced, and the impact on patient safety. BMC Health Serv Res 24 , 603 (2024). https://doi.org/10.1186/s12913-024-11011-1

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Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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Additional file 1: figure s1.

. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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The Effects of Mental Health Interventions on Labor Market Outcomes in Low- and Middle-Income Countries

Mental health conditions are prevalent but rarely treated in low- and middle-income countries (LMICs). Little is known about how these conditions affect economic participation. This paper shows that treating mental health conditions substantially improves recipients’ capacity to work in these contexts. First, we perform a systematic review and meta-analysis of all randomized controlled trials (RCTs) ever conducted that evaluate treatments for mental ill-health and measure economic outcomes in LMICs. On average, treating common mental disorders like depression with psychotherapy improves an aggregate of labor market outcomes made up of employment, time spent working, capacity to work and job search by 0.16 standard deviations. Treating severe mental disorders, like schizophrenia, improves the aggregate by 0.30 standard deviations, but effects are noisily estimated. Second, we build a new dataset, pooling all available microdata from RCTs using the most common trial design: studies of psychotherapy in LMICs that treated depression and measured days participants were unable to work in the past month. We observe comparable treatment effects on mental health and work outcomes in this sub-sample of highly similar studies. We also show evidence consistent with mental health being the mechanism through which psychotherapy improves work outcomes.

The three authors listed first (Crick Lund, Kate Orkin, and Marc Witte) are jointly the first author. This study was funded by the Wellspring Philanthropic Fund. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

Vikram Patel acknowledges research support from the NIMH, Wellcome Trust, Grand Challenges Canada and the Medical Research Council. He also receives funding from the Lone Star Prize and serves as a consultant to Modern Health and Johnson & Johnson.

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Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

Alexander j. scott.

a Keele University, School of Psychology, Keele, UK

Thomas L. Webb

c Department of Psychology, The University of Sheffield, UK

Marrissa Martyn-St James

b School of Health and Related Research (ScHARR), The University of Sheffield, UK

Georgina Rowse

d Clinical Psychology Unit, Department of Psychology, The University of Sheffield, UK

Scott Weich

Associated data.

All data and analysis code are freely available on the Open Science Framework under a creative commons 4.0 license (for access, see [ 73 ]).

The extent to which sleep is causally related to mental health is unclear. One way to test the causal link is to evaluate the extent to which interventions that improve sleep quality also improve mental health. We conducted a meta-analysis of randomised controlled trials that reported the effects of an intervention that improved sleep on composite mental health, as well as on seven specific mental health difficulties. 65 trials comprising 72 interventions and N  = 8608 participants were included. Improving sleep led to a significant medium-sized effect on composite mental health ( g+  = −0.53), depression ( g+  = −0.63), anxiety ( g+  = −0.51), and rumination ( g+  = −0.49), as well as significant small-to-medium sized effects on stress ( g+  = −0.42), and finally small significant effects on positive psychosis symptoms ( g+  = −0.26). We also found a dose response relationship, in that greater improvements in sleep quality led to greater improvements in mental health. Our findings suggest that sleep is causally related to the experience of mental health difficulties. Future research might consider how interventions that improve sleep could be incorporated into mental health services, as well as the mechanisms of action that explain how sleep exerts an effect on mental health.

Does improving sleep lead to better mental health? A meta-analysis of randomised controlled trials

Problems sleeping are common. A review of several hundred epidemiological studies [ 1 ] concluded that nearly one-third of the general population experience symptoms of insomnia (defined as difficulties falling asleep and/or staying asleep), between 4% and 26% experience excessive sleepiness, and between 2% and 4% experience obstructive sleep apnoea. Additionally, a recent study of over 2000 participants reported that the prevalence of ‘general sleep disturbances’ was 32% [ 2 ] and Chattu et al. concluded on the basis of a large systematic review of the evidence that public and health professionals need to be more aware of the adverse effects of poor sleep [ 3 ]. Mental health problems are also common, with around 17% of adults experiencing mental health difficulties of varying severities [ 4 ], and evidence from large nationally representative studies suggesting that mental health difficulties are on the increase [ 5 ]. Sleep and mental health are, therefore, global public health challenges in their own right, with each having substantive impacts on both individuals and society [ 3 , 6 , 7 ]. However, problems sleeping and mental health difficulties are also intrinsically linked [ 8 , 9 ]. It was previously assumed that mental health difficulties led to problems sleeping [ 10 , 11 ]; however, the reverse may also be true [ 12 ], such that poor sleep contributes to the onset, recurrence, and maintenance of mental health difficulties [ [13]∗ , [14] , [15]∗ , [16] , [17] ]. Therefore, the extent to which there is a causal relation between (poor) sleep and (worse) mental health and the possibility that interventions designed to improve sleep might be able to reduce mental health difficulties warrants investigation.

Evidence on the relationship between sleep and mental health

The association between sleep and mental health is well documented [ 9 , 13 , [18] , [19] , [20] , [21] , [22] , [23]∗ ]. For example, people with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of depression and anxiety, respectively [ 24 ]. Furthermore, a meta-analysis of 21 longitudinal studies reported that people with insomnia at baseline had a two-fold risk of developing depression at follow-up compared with people who did not experience insomnia [ 13 ]. Although research most commonly studies the associations between insomnia and depression and anxiety, there is also evidence that problems sleeping are associated with a variety of mental health difficulties. For example, poor sleep has also been associated with post-traumatic stress [ 25 ], eating disorders [ 26 ], and psychosis spectrum experiences such as delusions and hallucinations [ 23 , 27 ]. Studies have also found that specific sleep disorders, such as sleep apnoea [ 28 ], circadian rhythm disruption [ 29 ], restless leg syndrome [ 30 ], excessive daytime sleepiness and narcolepsy [ 31 , 32 ], sleepwalking [ 33 ], and nightmares [ 34 ] are all more prevalent in those experiencing mental health difficulties.

Unfortunately, most research on the association between sleep and mental health is observational in design. While informative, inferring causation from such studies is difficult. For example, cross-sectional designs tell us that variables are associated in some way, but they cannot say whether one variable precedes the other in a causal chain [ 35 ]. Longitudinal designs provide stronger evidence, but are prone to residual confounding [ [36] , [37] , [38] ] and other forms of bias that limit causal inference [ [39] , [40] , [41] , [42] , [43] ]. The best evidence is provided by studies that randomly allocate participants to experimental and control conditions to minimise the effects of potential confounds [ 44 , 45 ]. Therefore, to establish whether sleeping problems are causally associated with mental health difficulties, it is necessary to experimentally manipulate sleep to see whether changes in sleep lead to changes in mental health over time (i.e., the interventionist approach to causation, [ 46 ]).

Many RCTs have examined the effect of interventions designed to improve sleep (typically cognitive behavioural therapy for insomnia, CBTi), on mental health (typically depression and anxiety). There have also been attempts to meta-analyse some of these RCTs and quantify their effects on mental health outcomes [ [47]∗ , [48] , [49] , [50] ]. However, even these meta-analyses do not permit robust conclusions as to the causal impact of sleep on mental health outcomes for several reasons. First, previous reviews have included studies that did not successfully manipulate sleep (i.e., the intervention did not improve sleep relative to controls). It is not possible to conclude whether sleep is causally linked to mental health if the experimental manipulation of sleep is unsuccessful [ 51 ]. Indeed, these studies simply tell us that it can sometimes be difficult to improve sleep in the first place. Second, reviews have tended to examine the effect of interventions targeting sleep on mental health at the first post-intervention time point. This is problematic for two reasons; 1) there is no temporal lag between the measurement of sleep and measurement of mental health (a key tenet of causal inference); and 2) effects are limited to the short-term where they are likely to be strongest. Third, the focus of previous reviews has been limited to depression and anxiety only, and typically limited to CBTi interventions. Therefore, the effect of improving sleep on other mental health outcomes, using different approaches to intervention, is limited. Finally, to date there has been no or limited attempts to investigate variables that influence – or moderate – the impact of interventions that improve sleep on mental health. It is crucial that the impact of such variables is systematically examined to understand whether the effect of improving sleep on mental health differs across populations, settings, and study designs.

The present review: an interventionist approach to causation

The present review sought to address these issues to provide an accurate and robust estimate of the effect of changes in sleep quality (i.e., as a result of an intervention) on changes in mental health. To test this empirically, we identified randomised controlled trials that successfully manipulated sleep in an intervention group relative to controls, and then measured mental health at a later follow-up point. We did not limit the scope of interventions to CBTi, or the measures of mental health to solely depression and/or anxiety. Instead, we included any intervention designed to improve sleep that produced a statistically significant effect on sleep quality relative to controls and examined the effect of that improvement in sleep on any subsequent mental health outcome. To better isolate the effect of improved sleep on mental health, we excluded interventions that included specific elements targeting mental health (e.g., CBT elements for depression). Given the (potentially) high degree of heterogeneity between studies that this approach might create, we examined the effect of different study characteristics and outcomes using moderation analyses. Our primary hypothesis is that interventions that significantly improve sleep will lead to significantly improved mental health at follow-up.

Eligibility criteria

To be included in the present review, studies needed to 1) be a randomized controlled trial that tested an intervention designed to improve sleep; 2) produce a statistically significant effect on sleep quality when compared to a control group or an alternative treatment, 3) report a measure of mental health subsequent to the measure of sleep quality, 4) report sufficient data to compute an effect size representing the impact of the intervention on both sleep quality and mental health, 5) be written in English, or translatable using available resources. In order to reliably and validly assess the independent contribution of changes in sleep on mental health outcomes among adult populations, studies were excluded if 1) the intervention contained elements that specifically target a mental health problem in addition to elements that target sleep; or 2) recruited children and young people (i.e., <18 years of age).

Search strategy

First, we searched MEDLINE (1946 to present), Embase (1974 to present), PsycINFO (1967 to present), and The Cochrane Library (1898 to present) using the Cochrane Highly Sensitive Search Strategy (i.e., HSSS, [ 52 ]) to identify RCTs that included terms relating to sleep quality and/or sleep disorders, and mental health (see Table 1 for a list of the search terms and Supplementary Material 1 for an example search strategy). Second, the reference lists of extant reviews of the relationship between sleep and mental health were searched for any potential articles. Third, a search for any unpublished or ongoing studies was conducted by searching online databases including White Rose Online, The National Research Register, WHO approved clinical trial databases (e.g., ISRCTN), and PROSPERO. Searches were originally conducted in May 2019 and then updated in February 2021.

Table 1

Search terms used to identify RCT's that examined the effect of improving sleep on mental health.

Notes : HSSS for RCTs = highly sensitive search strategy for randomised controlled trials, OCD = obsessive compulsive disorder, PTSD = post-traumatic stress disorder.

Data management and study selection

We followed PRISMA guidelines [ 53 ] when selecting studies. The first phase of screening removed duplicate records and records that were clearly ineligible based on the title and/or abstract. The second phase of screening cross-referenced full-text versions of articles against the inclusion criteria, with eligible records included in the present review, and ineligible records excluded along with reasons for exclusion. Records were screened by two members of the review team, and a sub-sample of 10% of each reviewer's records were second checked by the other reviewer, with almost perfect agreement between the reviewers ( kappa  = 1.00 and 0.99).

Data extraction

Data was extracted from included studies using a standardized form and an accompanying manual detailing each variable for extraction. In addition to extracting statistical data to compute effect sizes, data pertaining to source characteristics of included studies (e.g., publication status, year, impact factor), characteristics of the sample (e.g., age, type of mental health problem), the study (e.g., the nature of the comparison group, length of follow-up), and the intervention (e.g., intervention type, mode of delivery) was also extracted.

Outcomes and prioritization

Measuring improvements in sleep.

The concept of ‘improved sleep’ is multifaceted and can mean different things to different people [ [54] , [55] , [56] ]. Consequently, one challenge for the proposed review was to ensure that included studies assessed a similar notion of improved sleep so that they could be meaningfully combined using a single metric. Therefore, we specified that primary studies reported a measure that reflected the overall quality of sleep experienced by participants. The concept of sleep quality can also be subjective [ 54 ]; however, broadly speaking, sleep quality consists of sleep continuity (e.g., sleep onset, sleep maintenance, and number of awakenings) and daytime impact (e.g., the extent to which the person feels refreshed on waking and throughout the day, see [ 54 , 57 ]). We used the following hierarchy to decide which outcome measure(s) to use to estimate an effect size (in descending order of prioritization); 1) self-report measures of global sleep quality (e.g., the Pittsburgh Sleep Quality Index); 2) outcomes specific to a given sleep disorder that assess sleep continuity and impact on daily life (e.g., the Insomnia Severity Index); and 3) individual components of self-reported sleep continuity aggregated to form a single composite effect size (e.g., the average effect of intervention on sleep onset latency (SoL) and wake after sleep onset (WASO)).

Measuring mental health

We examined the effect of improving sleep on 1) composite mental health (which included all mental health outcomes reported across studies, see Table 2 for outcomes), and 2) specific mental health difficulties in isolation (e.g., depression separately from other mental health outcomes). We computed the between-group effect of improving sleep on each mental health outcome reported by the study at the furthest follow-up point available. This strategy provides a stringent test of the effect of improving sleep on mental health outcomes in the sense that any changes need to have been maintained over time. In line with previous reviews [ 58 ], these effect sizes were then averaged to form a ‘composite’ measure of mental health. As with the measures of sleep quality, we prioritized self-report measures of mental health rather than observer-rated measures, as arguably it is the subjective experience of mental health problems that is most important [ 59 ].

Table 2

Summary of studies included in the review.

Note : ∗ p  < 0.05, ∗∗ p  < 0.01, ∗∗∗ p  < 0.001. CBTi = cognitive behavioural therapy for insomnia, dx = diagnosis, IRT = image rehearsal therapy, MH = mental health, n e  = number of participants in intervention group, n c  = number of participants in the control group, PTSD = post-traumatic stress disorder, TaU = treatment as usual, WLC = wait list control. ab Subscript indicates that the study reports multiple eligible interventions in the same study, in these situations both interventions were included as separate studies in the analysis and the control was halved accordingly.

Risk of bias

Risk of bias was assessed using the risk of bias assessment criteria developed by the Cochrane Collaboration [ 60 ]. RCTs were classified as being at overall risk of bias according to three of the six domains – 1) allocation concealment, 2) blinding of outcome assessment and 3) completeness of outcome data (attrition). RCTs judged as being at low risk of bias for all three domains were judged at overall low risk of bias. RCTs judged as being at high risk for any of the three domains were judged as overall high risk of bias. RCTs judged as a mix of low and unclear risk on these three domains, or all unclear were judged as unclear with respect to risk of bias.

Estimating effect sizes

Hedges g and the associated standard error were estimated using the means and standard deviations reported by each of the primary studies. Where means and standard deviations were not reported, effect sizes were estimated by converting relevant summary statistics into Hedges g . Where studies reported multiple outcome measures for the same/similar constructs (e.g., several measures of depression), effect sizes were computed for each outcome and then meta-analysed in their own right to form one overall effect.

Meta-analytic approach

All analyses were conducted in R [ 61 ], using the ‘ esc ’ [ 62 ], ‘ meta’ [ 63 ], ‘ metafor’ [ 64 ], ‘ dmetar ’ [ 65 ], and ‘ robvis ’ [ 66 ] packages. The pooled, sample-weighted, average effect size was computed using a random effects model as effect sizes between studies are likely to vary considerably [ 67 ]. Following Cohen's recommendations [ 68 ], g  = 0.20 was taken to represent a ‘small’ effect size, g  = 0.50 a ‘medium’ effect size and g  = 0.80 a ‘large’ effect size. The I 2 statistic was used to assess heterogeneity of effect sizes across the included studies and was interpreted according to the classifications suggested by Higgins et al. [ 69 ], where I 2  = 25% indicates low heterogeneity, I 2  = 50% indicates moderate heterogeneity, and I 2  = 75% indicates high heterogeneity. Publication bias was assessed via visual inspection of a funnel plot and Egger's test [ 70 ]. Additionally, Orwin's formula [ 71 ] was used to determine the fail-safe n . Finally, outliers were defined as any effect size for which the confidence intervals did not overlap with the confidence interval of the pooled effect [ 72 ]. We conducted a sensitivity analysis examining the effect of outliers for each outcome by rerunning the analysis with any outlying effect sizes removed.

Subgroup analyses

Moderation analysis was conducted to identify variables that were associated with the effect of improving sleep on mental health outcomes. A minimum of three studies representing each moderator level category was required in order to conduct moderation analysis. For categorical variables, the analysis was based on a mixed effects model, in that the pooling of effect sizes within each moderator level was based on a random effects model, while the comparison of effect sizes between levels was based on a fixed effects model. The Q statistic was then used to assess whether effect sizes were significantly different between moderator levels. For continuous variables, sample-weighted meta-regression was used to investigate the impact of the moderator on mental health effect sizes.

Data availability statement

Study selection.

Fig. 1 shows the flow of records through the review. Systemic searches of the published and grey literature retrieved a total of 21,733 records, which was reduced to 15,139 after duplicates were removed. Of these records, 14,687 (97%) were excluded in the first stage of screening, leaving 452 full-text records to be screened. Of these records, 387 (86%) were cross-referenced against the review eligibility criteria and excluded (see Fig. 1 for a breakdown of reasons and Supplementary Materials 2 for a list of the studies excluded at this stage), leaving 65 records for inclusion in the meta-analysis.

PRISMA diagram showing the flow of studies through the review.

Study characteristics

Table 2 describes key characteristics of the included studies. The 65 studies provided 72 comparisons between an intervention that successfully improved sleep quality vs. a control group.

Participants

A total of N  = 8608 participants took part across the 72 interventions. 38 of the comparisons (53%) included participants with a comorbid physical or mental health problem, while 31 (43%) reported no comorbid health problems, and 3 (4%) reported insufficient detail to make a judgement. Of the 38 comparisons including participants with comorbid health problems, 18 (47%) reported mental health diagnoses, and 20 (53%) had physical health problems.

Outcome measures

The majority of comparisons (61, 85%) reported a measure of depression, but 33 (46%) reported a measure of anxiety, 6 (8%) reported a measure of stress, 5 (7%) reported measures of psychosis spectrum experiences (e.g., total, positive, and negative symptoms), 9 (13%) reported a measure of general mood, 2 (3%) reported post-traumatic stress disorder outcomes, 2 (3%) reported measures of suicidal ideation, 4 (6%) reported rumination outcomes, and 1 (2%) reported a measure of psychological burnout.

Interventions and comparisons

Most interventions were multi-component CBTi (53, 74%), but interventions also involved acupuncture (7, 10%), pharmacological treatments (2, 3%), sleep hygiene alone (2, 3%), sleep restriction alone (2, 3%), Tai Chi (2, 3%), CBT for nightmares (1, 2%), herbal remedies (1, 2%), walking (1, 2%), and yoga (1, 2%). Interventions were most often compared against an active control group (34, 47%), but were also compared to waitlist control groups (25, 35%), and groups receiving treatment as usual (13, 18%). On average participants’ mental health was followed-up 20.5 weeks post-intervention (median = 12 weeks post-intervention), with the earliest follow-up being 4-weeks post-intervention, and the furthest follow-up 156-weeks (three years) post intervention.

Manipulation check: did sleep quality improve significantly in the intervention group relative to controls?

Before we examined the effect of improving sleep quality on subsequent mental health, we confirmed that studies included in the review successfully improved sleep quality. The interventions had large and statistically significant effects on sleep quality at the earliest follow-up point reported ( g +  = −1.07, 95% CI = −1.26 to −0.88, p  < 0.001), although heterogeneity between studies was substantial ( I 2  = 79%, Q  = 331.93, p  < 0.001). After twelve outlying effect sizes were removed, the effect of the interventions on sleep quality remained large and statistically significant ( g +  = −0.97, 95% CI = −1.07 to −0.88, p  < 0.001), and heterogeneity was reduced to moderate levels ( I 2  = 43%, Q  = 102.32, p  < 0.001). These findings suggest that the primary studies included in the present review successfully manipulated sleep quality, even after accounting for outliers.

What effect do improvements in sleep quality have on mental health?

Table 3 presents the effect of improving sleep quality on composite mental health outcomes, and on measures of depression, anxiety, stress, psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout.

Table 3

The effect of improving sleep on mental health outcomes.

Notes : ∗∗∗ p  < 0.001, ∗ p  < 0.05, PANSS = Positive and Negative Symptoms Scale, PTSD = Post Traumatic Stress Disorder.

Composite mental health

On average, the 72 interventions that successfully improved sleep quality had a statistically significant, medium-sized effect on subsequent composite mental health outcomes, ( g +  = −0.53, 95% CI = −0.68 to −0.38, p  < 0.001); however, there was substantial heterogeneity between the effect sizes, ( I 2  = 76%, Q  = 291.94, p  < 0.001). After re-running the analysis with eleven outlying effect sizes removed, the effect of improving sleep on composite mental health outcomes was small-to-medium sized but still statistically significant, ( g +  = −0.42, 95% CI = −0.49 to −0.34, p  < 0.001) and now relatively homogeneous ( I 2  = 20%, Q  = 75.24, p  = 0.0888). See Fig. 2 for a forest plot.

Forest plot showing the effect of improving sleep on composite mental health outcomes.

Interventions that successfully improved sleep quality had a statistically significant, medium-sized effect on depression across 61 comparisons, ( g +  = −0.63, 95% CI = −0.83 to −0.43, p  < 0.001); however, once again, there was substantial heterogeneity, ( I 2  = 81%, Q  = 322.09, p  < 0.001). After re-running the analysis with nine outlying effect sizes removed, the effect of improving sleep on depression remained medium-sized, ( g +  = −0.47, 95% CI = −0.57 to −0.37, p  < 0.001), with moderate heterogeneity, ( I 2  = 32%, Q  = 74.86, p  = 0.0164). See Fig. 3 for a forest plot.

Forest plot showing the effect of improving sleep on depression.

Interventions that successfully improved sleep quality had a statistically significant, small-to-medium sized effect on anxiety across 35 comparisons, ( g +  = −0.50, 95% CI = −0.76 to −0.24, p  < 0.001), with substantial levels of heterogeneity, ( I 2  = 82%, Q  = 187.02, p  < 0.001). After re-running the analysis with four outlying effect sizes removed, the effect improving sleep on anxiety outcomes was small-to-medium sized, but still statistically significant, ( g +  = −0.38, 95% CI = −0.49 to −0.27, p  < 0.001), with lower levels of heterogeneity, ( I 2  = 43%, Q  = 52.49, p  = 0.0067). See Fig. 4 for a forest plot.

Forest plot showing the effect of improving sleep on anxiety.

Interventions that successfully improved sleep quality had a statistically significant, small-to-medium sized effect on stress ( g +  = −0.42, 95% CI = −0.79 to −0.05, p  = 0.033), across six comparisons. There were moderate levels of heterogeneity ( I 2  = 55%, Q  = 11.05, p  = 0.05), but there were no outlying effect sizes. See Fig. 5 for a forest plot.

Forest Plot Showing the Effect of Improving Sleep on Stress, Suicidal Ideation, PTSD, and rumination.

Psychosis spectrum experiences

Interventions that successfully improved sleep quality had a small effect on total symptoms as indicated by the PANSS ( g +  = −0.17, 95% CI = −0.53 to 0.19, p  = 0.18) across three comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 0.41, p  = 0.813). Interventions that successfully improved sleep quality had a small effect on positive symptoms ( g +  = −0.26, 95% CI = −0.43 to −0.08, p  = 0.014) across five comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 1.71, p  = 0.788). Finally, interventions that successfully improved sleep quality had a small effect on negative symptoms ( g +  = −0.28, 95% CI = −3.22 to 2.65, p  = 0.436) across k  = 2 comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 1, p  = 0.318). See Fig. 6 for a forest plot.

Forest plot showing the effect of improving sleep on psychosis spectrum outcomes.

Suicidal ideation

Interventions that successfully improved sleep quality had a small, adverse effect on suicidal ideation ( g +  = 0.10, 95% CI = −3.74 to 3.94, p  = 0.804) across two comparisons. There were low levels of heterogeneity ( I 2  = 20%, Q  = 1.25, p  = 0.263) and no outlying effect sizes. See Fig. 5 for a forest plot.

Post-traumatic stress disorder (PTSD)

Interventions that successfully improved sleep quality had a medium-to-large effect on PTSD ( g +  = −0.72, 95% CI = −2.90 to 1.46, p  = 0.149) across two comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 0.59, p  = 0.442). See Fig. 5 for a forest plot.

Interventions that successfully improved sleep quality had a statistically significant, medium sized effect on rumination ( g +  = −0.49, 95% CI = −0.93 to −0.04, p  = 0.041) across four comparisons, with moderate heterogeneity ( I 2  = 36%, Q  = 4.65, p  = 0.1991). See Fig. 5 for a forest plot.

Only one study reported the effect of improving sleep on burnout finding almost zero effect ( g  = −0.03, CI = −0.58 to 0.52, p  = 0.917).

Moderators of the effect of improving sleep quality on composite mental health outcomes

Table 4 presents the findings of analyses evaluating categorical moderators of the effect of improving sleep quality on composite mental health outcomes and Table 5 presents analyses evaluating continuous moderators using meta-regression. Studies that found significant effects of the intervention on sleep quality reported larger effects on subsequent composite mental health, ( g  = −0.53, 95% CI = −0.68 to −0.38, p  < 0.001), than studies that did not find a significant effect of the intervention on sleep quality, ( g  = −0.12, 95% CI = −0.24 to 0.01, p  = 0.0522), a difference that was statistically significant, ( Q  = 17.59, p  < 0.001). This finding strengthens the notion that improvements in sleep are behind improvements in mental health. The effect of improving sleep on mental health was larger in studies with shorter follow-up periods, (i.e., <6 months, g + = −0.60), than in studies with longer follow-ups, (i.e., 6 months, g + = −0.18, Q  = 10.75, p  < 0.01). Furthermore, interventions that were delivered face-to-face by a clinician or therapist were associated with significantly larger effects on mental health, ( g + = −0.63), than those that were self-administered by participants, ( g + = −0.34, Q  = 4.50, p  < 0.05). Finally, there was significant variation in the size of the effect between countries ( Q  = 53.69, p  < 0.001). No other statistically significant categorical moderator effects were found. Regarding continuous moderators, meta-regression revealed a statistically significant dose–response effect for the association between the effect of interventions on sleep quality and the effect on subsequent mental health outcomes ( B  = 0.77, 95% CI = 0.52 to 1.02, p  < 0.001), suggesting that greater improvements in sleep led to greater improvements in mental health. No other continuous variables significantly moderated the effect of improving sleep on mental health.

Table 4

Categorical moderators of the effect of improving sleep on composite mental health outcomes.

Notes : CBTi = cognitive behavioural therapy for insomnia, MH = Mental Health, PH = Physical Health, TaU = treatment as usual, WLC = wait list control.

∗ p  < 0.05, ∗∗ p  < 0.01, ∗∗∗ p  < 0.001.

Table 5

Continuous moderators of the effect of improving sleep on composite mental health outcomes.

Post-hoc moderation analysis

Is the smaller effect of improving sleep on mental health at longer follow-ups associated with smaller effects on sleep quality.

We conducted further (unplanned) post-hoc analysis to investigate whether the smaller effect of improving sleep on mental health at longer follow-ups was accompanied by a reduction in the improvements to sleep quality. Studies reporting the effect of the intervention at shorter follow-ups reported larger improvements in sleep quality, ( g  = −1.03, 95% CI = −1.27 to −0.78, p  < 0.001), than those reporting longer follow-ups ( g  = −0.44, 95% CI = −0.62 to −0.27, p  < 0.001), a difference that was statistically significant, ( Q  = 14.38, p  < 0.001). This suggests that the smaller effect of improving sleep on mental health at longer follow-ups might be driven by a smaller effect of the interventions on sleep quality at longer follow-ups.

Can some of the effect of improved mental health be explained by CBTi modules that target processes associated with mental health?

Finally, although the present review excluded interventions that specifically and directly targeted mental health, some CBTi protocols include modules that might target similar processes associated with some mental health difficulties (rumination around sleep, catastrophizing over the effect of poor sleep etc.). Therefore, we compared CBTi interventions with modules that could target processes associated with mental health vs. interventions that did not include these modules (e.g., sleep restriction alone, sleep hygiene alone, herbal tea, and pharmacological intervention). There were no significant differences in the effect of improved sleep quality on mental health between CBTi interventions including modules addressing processes associated with mental health ( g  = −0.44, 95% CI = −0.59 to −0.29, p  < 0.001), relative to those that did not ( g  = −0.48, 95% CI = −0.65 to −0.32, p  < 0.001, Q  = 2.51, p  = 0.285). This finding suggests that it is the beneficial effect of improved sleep quality that confers improvements in mental health rather than the inclusion of modules that target processes associated with mental health commonly seen in CBTi protocols.

Risk of bias assessments

Fig. 7 summarizes the weighted assessment of risk of bias. Individual risk of bias judgements for included studies are presented in Supplementary Material 3 . Ten studies (15%) were judged as having low risk of bias, 29 studies (45%) were judged as high risk of bias, and 26 studies (40%) were judged as unclear. The methodological quality of the included studies was not associated with the effect of improving sleep on composite mental health outcomes, Q  = 0.72, p  = 0.395.

Weighted risk of bias summary plot.

Publication bias

A funnel plot of the effect of improving sleep quality on composite outcomes revealed asymmetry in the effect sizes (Egger's regression = −1.09, 95% CI = −1.91 to −0.28, p  < 0.05, see Fig. 8 ). Duval and Tweedie's [ 74 ] trim and fill procedure was therefore used to address the asymmetry. Ten studies were imputed resulting in a statistically significant, small-to-medium sized adjusted effect of improving sleep on composite mental health outcomes ( g +  = −0.35, 95% CI = −0.55 to −0.16, p  < 0.001). Orwin's failsafe n test suggested that an additional 4101 comparisons producing null effects would be needed to reduce the average effect of improving sleep on composite outcomes to zero. Taken together these results suggest that the effect of improving sleep on composite mental health is robust to possible publication bias.

Contrast enhanced funnel plot for the effect of improving sleep on composite mental health (solid grey markers) with imputed studies (hollow markers).

The present review used meta-analysis to synthesize the effect of 72 interventions that improved sleep quality relative to a control condition on subsequent mental health. The findings revealed that improving sleep quality had, on average, a medium-sized effect on mental health, including clear evidence that improving sleep reduced depression, anxiety, and stress. A dearth of primary studies of other mental health difficulties (e.g., psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout) mean that it is premature to draw definitive conclusions in these areas. It was also notable that we found a dose–response relationship between improvements in sleep quality and subsequent mental health, such that greater improvements in sleep led to greater improvements in mental health. Although there was some evidence of publication bias, the effects remained robust to correction. Taken together, the findings suggest that improving sleep leads to better mental health, therefore providing strong evidence that sleep plays a causal role in the experience of mental health difficulties.

Sleep as a transdiagnostic treatment target

The present findings support the idea that targeting sleep promotes mental health across a range of populations and experiences. The effect of improving sleep quality on composite mental health was medium-sized and statistically significant, regardless of the presence of physical and/or mental health comorbidities. This finding is particularly important given the healthcare challenges associated with multimorbidity [ 75 ] and mental and physical health problems often co-occur [ [76] , [77] , [78] , [79] ], something that appears to be increasing [ 80 ]. Consequently, it is important that the benefits of improving sleep on mental health occur even in the presence of comorbid health complaints, as was reported in the present research. Improving sleep has also been shown to improve aspects of physical health, including fatigue [ 81 ], chronic pain [ 82 , 83 ], and overall health related quality of life [ 84 ] and could reduce the cost of healthcare. For example, offering a digital CBTi intervention (Sleepio) to primary care patients was associated with an average saving of £70.44 per intervention user [ 85 ], and cost savings following sleep intervention have also been specifically reported in people with comorbid mental health difficulties such as depression [ 86 ].

Another finding to suggest that targeting sleep could promote mental health across a range of populations and experiences, is that we found no difference in the effect of improving sleep quality on mental health between those with clinically defined mental health difficulties and those with non-clinical experiences or between those recruited from clinical vs. community settings, with both groups receiving significant benefits of improved sleep on mental health. This suggests that improving sleep could prove helpful across a range of mental health severities, thus broadening the possible impact of sleep interventions within healthcare services. Finally, there is growing evidence that sleep disturbances predict the development of mental health difficulties in the future. For example, shorter and more variable sleep has been shown to be longitudinally associated with more severe hallucinations and delusional ideation in those at high-risk of psychosis [ 87 ]. The present research found that improving sleep has a significant beneficial impact on future mental health in those with non-clinical experiences, raising the possibility that delivering interventions that improve sleep early might limit the risk of developing (or exacerbating) substantive mental health difficulties. Indeed, less severe mild-to-moderate presentations of mental health difficulties can develop over time into more severe mental health diagnoses [ 88 , 89 ], therefore improving sleep might be one tool that can be used in combination with others to limit the risk of transition.

Strengths and limitations

The present review has several strengths. First, it provides a comprehensive and up-to-date search of RCTs examining the effect of improving sleep on a variety of subsequent mental health outcomes. Indeed, with 65 RCTs and N  = 8608 participants, the present review is one of the largest studies of the effect of improving sleep on mental health to date. Second, the review was specifically designed to test the causal association between sleep and mental health (i.e., RCTs only, successful sleep improvement required, temporal lag between measures etc.). To our knowledge, the review is the first to adopt this approach in the field of sleep and mental health, although the general approach has been used in other fields [ 90 ]. Finally, we provide an analysis of possible moderators of the effect of improving sleep on mental health, identifying several key moderators of the effect.

However, there are limitations that must be considered when interpreting the findings. First, relatively few studies examined the effect of improving sleep over the long term. Those that did report longer follow-ups generally found smaller effects (although still statistically significant), most likely due to the diminishing effects of interventions on sleep quality over time [ 91 ]. Consequently, it is important that interventions targeting sleep quality as a route to improving mental health seek to maintain their beneficial effects. Second, there were few primary studies for some of the outcomes included in this review. Consequently, in lieu of more studies reporting these outcomes, the inferences that we can make for mental health outcomes other than depression and anxiety are more limited. Third, although the intention of the present review was to include a broad range of sleep disturbances, most of the analyses are based on CBT interventions for insomnia. This might be due to the relationship between insomnia and mental health being the one that is historically most studied. However, it may be that our focus on sleep quality precluded some studies that do not focus on insomnia from inclusion. For example, different sleep disorders have different conceptualisations of improvement that might not include sleep quality. For example, the timing of sleep is particularly important in circadian rhythm disorders and daytime sleepiness is a key outcome in sleep apnoea research. Future research might consider examining the effect of improving specific sleep disorders on mental health by conceptualising improvements using sleep disorder specific outcomes.

Future directions

The present review highlighted several areas for future research in terms of both research and theory, and the implementation of findings in practice. First, given that mental health was measured on average around 20.5 weeks post-intervention in the primary studies, and that the effect of improving sleep on mental health significantly reduced over time, future research should examine the effect of improving sleep on mental health over the longer term. Second, although not uncommon, the majority of RCTs included in the present review were at high risk, or unclear risk or bias. Consequently, in addition to studying the effect of improving sleep over the longer term, on a range of mental health difficulties beyond depression and anxiety, we need more research at lower risk of methodological bias.

Finally, although the present research provides evidence for a causal association between sleep and mental health, it is less clear how sleep affects mental health. One potential mechanism is whether and how people regulate their emotions (e.g., in response to negative events). Indeed, evidence suggests that poor sleep can amplify the adverse effect of negative life events [ 92 , 93 ], dull the beneficial impact of positive events [ 94 ], and is associated with more frequent use of emotion regulation strategies that might be detrimental to good mental health [ 95 ]. By extension, although we are unaware of RCTs testing the effect of improved sleep on emotion regulation, changes in sleep are prospectively associated with changes in aspects of emotion regulation [ 96 , 97 ], while experimentally induced sleep deprivation is adversely linked to poorer emotion regulation [ 96 , 97 ]. Contemporary perspectives on emotion regulation (e.g., the action control perspective), draw on research on how people regulate their behaviour, to propose that regulating emotions involves three tasks, 1) identifying the need to regulate, 2) deciding whether and how to regulate, and 3) enacting a regulation strategy [ 98 ]. We propose that poor sleep quality has the potential to adversely affect anyone (or all) of the three tasks involved in effectively regulating emotions, which might go some way toward explaining the relationship between poor sleep and mental health. Therefore, we would recommend that future research includes measures of aspects of emotion regulation (e.g., the Difficulties in Emotion Regulation Scale, [ 99 ]) within experimental and longitudinal designs to elucidate possible mechanisms by which improvements in sleep benefit mental health.

In terms of practice and implementation, evidence on the effect of sleep on mental health also supports calls for routine screening and treatment of problems with sleep. Both the Royal Society for Public Health (RSPH) and the Mental Health Foundation (MHF) recommend that primary health care training should include awareness of, and skills in assessing, sleep problems [ 100 , 101 ]. Despite this and a growing body of evidence, there has been little progress to date [ 102 ]. This may reflect under-appreciation of the importance of sleep [ 103 ] and lack of training and skills in assessing and managing sleep problems [ [104] , [105] , [106] , [107] , [108] ], as well as limited time and resources [ 103 , 109 ]. Therefore, a profitable next step might be to explore barriers and facilitators to assessing sleep and delivering effective interventions in specific care settings, from both the patient and clinician perspective. Indeed, the present review also highlighted a dearth of trials that tested the effect of improving sleep on mental health outcomes in ‘real world’ settings (e.g., within existing clinical and community health services). Although some researchers are taking important steps in this area [ [110] , [111] , [112] ], there is a clear need for more trials of interventions in clinical services so that the effectiveness and implementation of such interventions in routine care can be better understood.

Conclusions

Taken together, the present research supports the view that sleep is causally related to the experience of mental health difficulties, and therefore that sleep represents a viable treatment target that can confer significant benefits to mental health, as it has been found to do for physical health. We found that improving sleep was associated with better mental health regardless of the severity of mental health difficulty (i.e., clinical vs. non-clinical) or the presence of comorbid health conditions. Poor sleep is almost ubiquitous within mental health services [ 102 , 108 , 113 , 114 ], is causally related to the experience of mental health difficulties, and represents a potential treatment target [ 105 , 115 , 116 ]. Consequently, equipping health professionals with greater knowledge and resources to support sleep is an essential next step. Future research should consider how interventions that improve sleep could be better incorporated into routine mental health care, as well as the possible mechanisms of action that might explain how sleep exerts its effects on mental health.

Research agenda

To fully harness the effect of improved sleep on mental health, it is important that future research:

• 1. Explores the barriers and possible solutions to incorporating interventions that improve sleep into mental health care services.
• 2. Tests the effect of improving sleep on mental health outcomes beyond depression and anxiety, and over the long term, using designs at low risk of methodological bias.
• 3. Investigates the possible mechanisms of action that might explain how sleep exerts its effects on the experience of mental health difficulties.

Practice points

• • Sleep is causally related to the experience of mental health difficulties and represents a viable transdiagnostic treatment target for those experiencing mental health difficulties.
• • Improving sleep has beneficial effects on the experience of mental health difficulties, regardless of the severity of those difficulties, or the presence of comorbid health conditions.
• • Healthcare professionals aiming to improve mental health (particularly depression, anxiety, and stress) should consider interventions designed to improve sleep, particularly cognitive behavioral therapy for insomnia where the evidence base is strongest.

This research was funded by the National Institute for Health Research under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG- 0817-20027). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Acknowledgements

We thank Anthea Sutton (Information Resources Group Manager, The University of Sheffield) for her help developing the systematic search strategy and managing the records.

∗ The most important references are denoted by an asterisk.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.smrv.2021.101556 .

Appendix ASupplementary data

The following are the Supplementary data to this article:

Multimedia component 1

Multimedia component 2

Multimedia component 3

References ∗

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• Published: 27 January 2021

The effects of social isolation on well-being and life satisfaction during pandemic

• Ruta Clair   ORCID: orcid.org/0000-0001-9828-9911 1 ,
• Maya Gordon 1 ,
• Matthew Kroon 1 &
• Carolyn Reilly 1  

Humanities and Social Sciences Communications volume  8 , Article number:  28 ( 2021 ) Cite this article

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The SARS-CoV-2 pandemic placed many locations under ‘stay at home” orders and adults simultaneously underwent a form of social isolation that is unprecedented in the modern world. Perceived social isolation can have a significant effect on health and well-being. Further, one can live with others and still experience perceived social isolation. However, there is limited research on psychological well-being during a pandemic. In addition, much of the research is limited to older adult samples. This study examined the effects of perceived social isolation in adults across the age span. Specifically, this study documented the prevalence of social isolation during the COVID-19 pandemic as well as the various factors that contribute to individuals of all ages feeling more or less isolated while they are required to maintain physical distancing for an extended period of time. Survey data was collected from 309 adults who ranged in age from 18 to 84. The measure consisted of a 42 item survey from the Revised UCLA Loneliness Scale, Measures of Social Isolation (Zavaleta et al., 2017 ), and items specifically about the pandemic and demographics. Items included both Likert scale items and open-ended questions. A “snowball” data collection process was used to build the sample. While the entire sample reported at least some perceived social isolation, young adults reported the highest levels of isolation, χ 2 (2) = 27.36, p  < 0.001. Perceived social isolation was associated with poor life satisfaction across all domains, as well as work-related stress, and lower trust of institutions. Higher levels of substance use as a coping strategy was also related to higher perceived social isolation. Respondents reporting higher levels of subjective personal risk for COVID-19 also reported higher perceived social isolation. The experience of perceived social isolation has significant negative consequences related to psychological well-being.

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Introduction.

In March 2020, the World Health Organization declared the COVID-19 outbreak a global pandemic, prompting most governors in the United States to issue stay-at-home orders in an effort to minimize the spread of COVID-19. This was after several months of similar quarantine orders in countries throughout Asia and Europe. As a result, a unique situation arose, in which most of the world’s population was confined to their homes, with only medical staff and other essential workers being allowed to leave their homes on a regular basis. Several studies of previous quarantine episodes have shown that psychological stress reactions may emerge from the experience of physical and social isolation (Brooks et al., 2020 ). In addition to the stress that might arise with social isolation or being restricted to your home, there is also the stress of worrying about contracting COVID-19 and losing loved ones to the disease (Brooks et al., 2020 ; Smith and Lim, 2020 ). For many families, this stress is compounded by the challenge of working from home while also caring for children whose schools had been closed in an effort to slow the spread of the disease. While the effects of social isolation has been reported in the literature, little is known about the effects of social isolation during a global pandemic (Galea et al., 2020 ; Smith and Lim, 2020 ; Usher et al., 2020 ).

Social isolation is a multi-dimensional construct that can be defined as the inadequate quantity and/or quality of interactions with other people, including those interactions that occur at the individual, group, and/or community level (Nicholson, 2012 ; Smith and Lim, 2020 ; Umberson and Karas Montez, 2010 ; Zavaleta et al., 2017 ). Some measures of social isolation focus on external isolation which refers to the frequency of contact or interactions with other people. Other measures focus on internal or perceived social isolation which refers to the person’s perceptions of loneliness, trust, and satisfaction with their relationships. This distinction is important because a person can have the subjective experience of being isolated even when they have frequent contact with other people and conversely they may not feel isolated even when their contact with others is limited (Hughes et al., 2004 ).

When considering the effects of social isolation, it is important to note that the majority of the existing research has focused on the elderly population (Nyqvist et al., 2016 ). This is likely because older adulthood is a time when external isolation is more likely due to various circumstances such as retirement, and limited physical mobility (Umberson and Karas Montez, 2010 ). During the COVID-19 pandemic the need for physical distancing due to virus mitigation efforts has exacerbated the isolation of many older adults (Berg-Weger and Morley, 2020 ; Smith et al., 2020 ) and has exposed younger adults to a similar experience (Brooks et al., 2020 ; Smith and Lim, 2020 ). Notably, a few studies have found that young adults report higher levels of loneliness (perceived social isolation) even though their social networks are larger (Child and Lawton, 2019 ; Nyqvist et al., 2016 ; Smith and Lim, 2020 ); thus indicating that age may be an important factor to consider in determining how long-term distancing due to COVID-19 will influence people’s perceptions of being socially isolated.

The general pattern in this research is that increased social isolation is associated with decreased life satisfaction, higher levels of depression, and lower levels of psychological well-being (Cacioppo and Cacioppo, 2014 ; Coutin and Knapp, 2017 ; Dahlberg and McKee, 2018 ; Harasemiw et al., 2018 ; Lee and Cagle, 2018 ; Usher et al., 2020 ). Individuals who experience high levels of social isolation may engage in self-protective thinking that can lead to a negative outlook impacting the way individuals interact with others (Cacioppo and Cacioppo, 2014 ). Further, restricting social networks and experiencing elevated levels of social isolation act as mediators that result in elevated negative mood and lower satisfaction with life factors (Harasemiw et al., 2018 ; Zheng et al., 2020 ). The relationship between well-being and feelings of control and satisfaction with one’s environment are related to psychological health (Zheng et al., 2020 ). Dissatisfaction with one’s home, resource scarcity such as food and self-care products, and job instability contribute to social isolation and poor well-being (Zavaleta et al., 2017 ).

Although there are fewer studies with young and middle aged adults, there is some evidence of a similar pattern of greater isolation being associated with negative psychological outcomes for this population (Bergin and Pakenham, 2015 ; Elphinstone, 2018 ; Liu et al., 2019 ; Nicholson, 2012 ; Smith and Lim, 2020 ; Usher et al., 2020 ). There is also considerable evidence that social isolation can have a detrimental impact on physical health (Holt-Lunstad et al., 2010 ; Steptoe et al., 2013 ). In a meta-analysis of 148 studies examining connections between social relationships and risk of mortality, Holt-Lunstad et al. ( 2010 ) concluded that the influence of social relationships on the risk for death is comparable to the risk caused by other factors like smoking and alcohol use, and greater than the risk associated with obesity and lack of exercise. Likewise, other researchers have highlighted the detrimental impact of social isolation and loneliness on various illnesses, including cardiovascular, inflammatory, neuroendocrine, and cognitive disorders (Bhatti and Haq, 2017 ; Xia and Li, 2018 ). Understanding behavioral factors related to positive and negative copings is essential in providing health guidance to adult populations.

Feelings of belonging and social connection are related to life satisfaction in older adults (Hawton et al., 2011 ; Mellor et al., 2008 ; Nicholson, 2012 ; Victor et al., 2000 ; Xia and Li, 2018 ). While physical distancing initiatives were implemented to save lives by reducing the spread of COVID-19, these results suggest that social isolation can have a negative impact on both mental and physical health that may linger beyond the mitigation orders (Berg-Weger and Morley, 2020 ; Brooks et al., 2020 ; Cava et al., 2005 ; Smith et al., 2020 ; Usher et al., 2020 ). It is therefore important that we document the prevalence of social isolation during the COVID-19 pandemic as well as the various factors that contribute to individuals of all ages feeling more or less isolated, while they are required to maintain physical distancing for an extended period of time. It was hypothesized that perceived social isolation would not be limited to an older adult population. Further, it was hypothesized that perceived social isolation would be related to individual’s coping with the pandemic. Finally, it was hypothesized that the experience of social isolation would act as a mediator to life satisfaction and basic trust in institutions for individuals across the adult lifespan. The current study was designed to examine the following research questions:

Are there age differences in participants’ perceived social isolation?

Do factors like time spent under required distancing and worry about personal risk for illness have an association with perceived social isolation?

Is perceived social isolation due to quarantine and pandemic mitigation efforts related to life satisfaction?

Is there an association between perceived social isolation and trust of institutions?

Is there a difference in basic stressors and coping during the pandemic for individuals experiencing varying levels of perceived social isolation?

Participants

Participants were adults age 18 years and above. Individuals younger than 18 years were not eligible to participate in the study. There were no limitations on occupation, education, or time under mandatory “stay at home” orders. The researchers sought a sample of adults that was diverse by age, occupation, and ethnicity. The researchers sought a broad sample that would allow researchers to conduct a descriptive quantitative survey study examining factors related to perceived social isolation during the first months of the COVID-19 mitigation efforts.

Participants were asked to complete a 42-item electronic survey that consisted of both Likert-type items and open-ended questions. There were 20 Likert scale items, 3 items on a 3-point scale (1 = Hardly ever to 3 = Often) and 17 items on a 5-point scale (1 = Not at all satisfied to 4 = very satisfied, 0 = I don’t know), 11 multiple choice items, one of which had an available short response answer, and 11 short answer items.

Items were selected from Measures of Social Isolation (Zavaleta et al., 2017 ) that included 27 items to measure feelings of social isolation through the proxy variables of stress, trust, and life satisfaction. Trust was measured for government, business, and media. Life satisfaction examined overall feelings of satisfaction as well as satisfaction with resources such as food, housing, work, and relationships. Three items related to social isolation were chosen from the Revised UCLA Loneliness Scale. Hughes et al. ( 2004 ) reported that these three items showed good psychometric validity and reliability for the construct of Loneliness.

There were a further 12 items from the authors specifically about circumstances regarding COVID-19 at the time of the survey. Participants answered questions about the length of time spent distancing from others, level of compliance with local regulations, primary news sources, whether physical distancing was voluntary or mandatory, how many people are in their household, work availability, methods of communication, feelings of personal risk of contracting COVID-19, possible changes in behavior, coping methods, stressors, and whether there are children over the age of 18 staying in the home.

This study was submitted to the Cabrini University Institutional Review Board and approval was obtained in March 2020. Researchers recruited a sample of people that varied by age, gender, and ethnicity by identifying potential participants across academic and non-academic settings using professional contact lists. A “snowball” approach to data gathering was used. The researchers sent the survey to a broad group of adults and requested that the participants send the survey to others they felt would be interested in taking part in research. Recipients received an email that contained a description of the purpose of the study and how the data would be used. Included at the end of the email was a link to the online survey that first presented the study’s consent form. Participants acknowledged informed consent and agreed to participate by opening and completing the survey.

At the end of the survey, participants were given the opportunity to supply an email to participate in a longitudinal study which consists of completing surveys at later dates. In addition, the sample was asked to forward the survey to their contacts who might be interested. Overall, the study took ~10 min to complete.

Demographics

Participants were 309 adults who ranged in age from 18 to 84 ( M  = 38.54, s  = 18.27). Data was collected beginning in 2020 from late March until early April. At the time of data collection distancing mandates were in place for 64.7% and voluntary for 34.6% of the sample, while 0.6% lived in places which had not yet outlined any pandemic mitigation policies. The average length of time distancing was slightly more than 2 weeks ( M  = 14.91 days, s  = 4.5) with 30 days as the longest reported time.

The sample identified mostly as female (80.3%), with males (17.8%) and those who preferred not to answer (1.9%) representing smaller numbers. The majority of the sample identified as Caucasian (71.5%). Other ethnic identities reported by participants included Hispanic/Latinx, African-American/Black, Asian/East Asian, Jewish/Jewish White-Passing, Multiracial/Multiethnic, and Country of Origin (Table 1 ). Individuals resided in the United States and Europe.

The majority of the sample lived in households with others (Fig. 1 ). More than one-third (36.7%) lived with one other person, 19.7% lived with two others, and 21% lived with three other people. People living alone comprised 12.1% of the sample. When asked about the presence of children under 18 years of age in the home, 20.5% answered yes.

Figure shows how many additional individuals live in the participant’s household in March 2020.

The highest level of education attained ranged from completion of lower secondary school (0.3%) to doctoral level (6.8%). Two thirds of the sample consisted of individuals with a Bachelor’s degree or above (Table 2 ).

Participants were asked to provide their occupation. The largest group identified themselves as professionals (26.5%), while 38.6% reported their field of work (Table 3 ). Students comprised 23.1% of the sample, while 11.1% reported that they were retired. Some of the occupations reported by the sample included nurses and physicians, lawyers, psychologists, teachers, mental health professionals, retail sales, government work, homemakers, artists across types of media, financial analysts, hairdresser, and veterinary support personnel. One person indicated that they were unemployed prior to the pandemic.

Social isolation and demographics

Spearman’s rank-order correlations were used to examine relationships between the three Likert scale items from the Revised UCLA Loneliness Scale that measure social isolation. Feeling isolated from others was significantly correlated with lacking companionship ( r s = 0.45, p  < 0.001) and feeling left out ( r s = 0.43, p  < 0.001). The items related to lacking companionship and feeling left out were also significantly correlated ( r s = 0.39, p  < 0.001).

Kruskal–Wallis tests were conducted to determine if the variables of time in required distancing and age were each related to the three levels of social isolation (hardly, sometimes, often). There were no significant findings between perceived social isolation and length of time in required distancing, χ 2 (2) = 0.024, p  = 0.98.

A significant relationship was found between perceived social isolation and age, χ 2 (2) = 27.36, p  < 0.001). Subsequently, pairwise comparisons were performed using Dunn’s procedure with a Bonferroni correction for multiple comparisons. Adjusted p values are presented. Post hoc analysis revealed statistically significant differences in age between those with high levels of social isolation (Mdn = 25) and some social isolation (Mdn = 31) ( p  = <0.001) and low isolation (Mdn = 46) ( p  = 0.002). Higher levels of social isolation were associated with younger age.

Age was then grouped (18–29, 30–49, 50–69, 70+) and a significant relationship was found between social isolation and age, χ 2 (3) = 13.78, p  = 0.003). Post hoc analysis revealed statistically significant differences in perceived social isolation across age groups. The youngest adults (age 18–29) reported significantly higher social isolation (Mdn = 2.4) than the two oldest groups (50–69 year olds: Mdn = 1.6, p  = 004); age 70 and above: Mdn = 1.57), p  = 0.01). The difference between the youngest adults and the next youngest (30–49) was not significant ( p  = 0.09).

When asked if participants feel personally at risk for contracting SARS-CoV-2 61.2% reported that they feel at risk. A Mann–Whitney U test was conducted to compare social isolation experienced by those who reported feeling at risk and those who did not feel at risk. Individuals who feel at risk for infection reported more social isolation (Mdn = 2.0) than those that do not feel at risk (Mdn = 1.75), U  = 9377, z  = −2.43, p  = 0.015.

Social isolation and life satisfaction

The relationship between level of social isolation and overall life satisfaction were examined using Kruskal–Wallis tests as the measure consisted of Likert-type items (Table 4 ).

Overall life satisfaction was significantly lower for those who reported greater social isolation ( χ 2 (2) = 50.56, p  < 0.001). Post hoc analysis revealed statistically significant differences in life satisfaction scores between those with high levels of social isolation (Mdn = 2.82) and some social isolation (Mdn = 3.04) ( p  ≤ 0.001) and between high and low isolation (Mdn = 3.47) ( p  ≤ 0.001), but not between high levels of social isolation and some social isolation ( p  = 0.09).

The pandemic added concern about access to resources such as food and 68% of the sample reported stress related to availability of resources. A significant relationship was found between social isolation and satisfaction with access to food, χ 2 (2) = 21.92, p  < 0.001). Individuals reporting high levels of social isolation were the least satisfied with their food situation. Statistical difference were evident between high social isolation (Mdn = 3.28) and some social isolation (Mdn = 3.46) ( p  = 0.003) and between high and low isolation (Mdn = 3.69) ( p  < 0.001). Reporting higher levels of social isolation is associated with lower satisfaction with food.

As a result of stay at home orders, many participants were spending more time in their residences than prior to the pandemic. A significant relationship was found between social isolation and housing satisfaction, χ 2 (2) = 10.33, p  = 0.006). Post hoc analysis revealed statistically a significant difference in housing satisfaction between those with high levels of social isolation (Mdn = 3.49) and low social isolation (Mdn = 3.75) ( p  = 0.006). Higher levels of social isolation is associated with lower levels of satisfaction with housing.

Work life changed for many participants and 22% of participants reported job loss as a result of the pandemic. A significant relationship was found between social isolation and work satisfaction, χ 2 (2) = 21.40, p  < 0.001). Post hoc analysis revealed individuals reporting high social isolation reported much lower satisfaction with work (Mdn = 2.53) than did those reporting low social isolation (Mdn = 3.27) ( p  < 0.001) and moderate social isolation (Mdn = 3.03) ( p  = 0.003).

Social isolation and trust of institutions

The relationship between social isolation and connection to community was measured using a Kruskal–Wallis test. A significant relationship was found between feelings of social isolation and connection to community ( χ 2 (2) = 13.97, p  = 0.001. Post hoc analysis revealed a statistically significant difference in connection to community such that the group reporting higher social isolation (Mdn = 2.27, p  = 0.001) reports less connection to their community than the group reporting low social isolation (Mdn = 2.93).

A significant relationship was found between social isolation and trust of central government institutions, χ 2 (2) = 10.46, p  = 0.005). Post hoc analysis revealed a statistically significant difference in trust of central government between individuals reporting low social isolation (Mdn = 2.91) and those reporting high social isolation (Mdn = 2.32) ( p  = 0.008) and moderate social isolation (Mdn = 2.48) ( p  = 0.03). There was less trust of central government for the group reporting high social isolation. However, distrust of central government did not extend to local government institutions. There was no significant difference in trust of local government for low, moderate, and high social isolation groups, χ 2 (2) = 5.92, p  = 0.052.

Trust levels of business was significantly different between groups that differed in feelings of social isolation, χ 2 (2) = 9.58, p  = 0.008). Post hoc analysis revealed more trust of business institutions for the low social isolation group (Mdn = 3.10) compared to the group reporting high social isolation (Mdn = 2.62) ( p  = 0.007).

Sixty-seven participants reported loss of a job as a result of COVID-19. A Mann–Whitney U test was conducted to compare social isolation experienced by those who had lost their job to those who had not. Individuals who experienced job loss reported more social isolation (Mdn = 2.26) than those that did not lose their job (Mdn = 1.80), U  = 5819.5, z  = −3.66 , p  < 0.001.

Stress related to caring for an elderly family member was identified by 12% of the sample. A Mann–Whitney U test was conducted to compare social isolation experienced by those who reported that caring for an elderly family member is a stressor to those who had not. There was no significant finding, U  = 4483, z  = −1.28, p  = 0.20. Similarly, there was no significant effect for caring for a child, U  = 3568.5, z  = −0.48, p  = 0.63.

Coping strategies

Participants were asked to check off whether they were using virtual communication, exercise, going outdoors, and/or substances in order to cope with the challenges of distancing during pandemic. A Mann–Whitney U test was conducted to compare social isolation experienced by those who used substances as a coping strategy and those that did not. Individuals who reported substance use reported more social isolation (Mdn = 2.12) than those that did not (Mdn = 1.80), U  = 6724, z  = −2.01, p  = 0.04.

There was no significant difference on Mann–Whitney U test for social isolation between those individuals who went outdoors to cope with pandemic versus those that did not, U  = 5416, z  = −0.72, p  = 0.47. Similarly, there was no difference in social isolation between those individuals who used exercise as a coping tool and those that did not. Finally, there was no difference in social isolation between those that used virtual communication tools and those that did not, U  = 7839.5, z  = −0.56, p  = 0.58. The only coping strategy which was significantly associated with social isolation was substance use.

While research has explored the subjective experience of social isolation, the novel experience of mass physical distancing as a result of the SARS-CoV-2 pandemic suggests that social isolation is a significant factor in the public health crisis. The experience of social isolation has been examined in older populations but less often in middle-age and younger adults (Brooks et al., 2020 ; Smith and Lim, 2020 ). Perceived social isolation is related to numerous negative outcomes related to both physical and mental health (Bhatti and Haq, 2017 ; Holt-Lunstad et al., 2010 , Victor et al., 2000 ; Xia and Li, 2018 ). Our findings indicate that younger adults in their 20s reported more social isolation than did those individuals aged 50 and older during physical distancing. This supports the findings of Nyqvist et al. ( 2016 ) that found teenagers and young adults in Finland reported greater loneliness than did older adults.

The experience of social isolation is related to a reduction in life satisfaction. Previous research has shown that feelings of social connection are related to general life satisfaction in older adults (Hawton et al., 2011 , Hughes et al., 2004 , Mellor et al., 2008 ; Victor et al., 2000 , Xia and Li, 2018 ). These findings indicate that perceived social isolation can be a significant mediator in life satisfaction and well-being across the adult lifespan during a global health crisis. Individuals reporting higher levels of social isolation experience less satisfaction with the conditions in their home.

During mandated “stay-at-home” conditions, the experience of work changed for many people. For many adults work is an essential aspect of identity and life satisfaction. The experience of individuals reporting elevated social isolation was also related to lower satisfaction with work. This study included a wide span of occupations involving both individuals required to work from home and essential workers continuing to work outside the home. Further, ~22% of the sample ( n  = 67) reported job loss as a stressor related to the SARS-CoV-2 pandemic and reported elevated social isolation. As institutions and businesses consider whether remote work is an economically viable alternative to face-to-face offices once physical distancing mandates are ended, the needs of workers for social interaction should be considered.

Further, individuals reporting higher social isolation also indicated less connection to their community and lower satisfaction with environmental factors such as housing and food. Findings indicate that higher perceived social isolation is associated with broad dissatisfaction across social and life domains and perceptions of personal risk from COVID-19. This supports research that identified a relationship between social isolation and health-related quality of life outcomes (Hawton et al., 2011 , Victor et al., 2000 ). Perceptions of elevated social isolation are related to lower life satisfaction in functional and social domains.

Perceived social isolation is likewise related to trust of some institutions. While there was no effect for local government, individuals with higher perceived social isolation reported less trust of central government and of business. There is an association between higher levels of perceived social isolation and less connection to the community, lower life satisfaction, and less trust of large-scale institutions such as central government and businesses. As a result, the individuals who need the most support may be the most suspicious of the effectiveness of those institutions.

Coping strategies related to exercise, time spent outdoors, and virtual communication were not related to social isolation. However, individuals who reported using substances as a coping strategy reported significantly higher social isolation than did the group who did not indicate substance use as a coping strategy. Perceived social isolation was associated with negative coping rather than positive coping. This study shows that clinicians and health care providers should ask about coping strategies in order to provide effective supports for individuals.

There are several limitations that may limit the generalizability of the findings. The study is heavily female and this may have an effect on findings. In addition, the majority of the sample has a post-secondary degree and, as such, this study may not accurately reflect the broad experience of individuals during pandemic. Further, it cannot be ruled out that individuals reporting high levels of perceived social isolation may have experienced some social isolation prior to the pandemic.

Conclusions

In conclusion, this study suggests that perceived social isolation is a significant element of health-related quality of life during pandemic. Perceived social isolation is not just an issue for older adults. Indeed, young adults appear to be suffering greatly from the distancing required to reduce the spread of SARS-CoV-2. The experience of social isolation is associated with poor life satisfaction across domains, work-related stress, lower trust of institutions such as central government and business, perceived personal risk for COVID-19, and higher levels of use of substances as a coping strategy. Measuring the degree of perceived social isolation is an important addition to wellness assessments. Stress and social isolation can impact health and immune function and so reducing perceived social isolation is essential during a time when individuals require strong immune function to fight off a novel virus. Further, it is anticipated that these widespread effects may linger as the uncertainty of the virus continues. As a result, we plan to follow participants for at least a year to examine the impact of SARS-CoV-2 on the well-being of adults.

Data availability

The dataset generated during and analyzed during the current study is not publicly available due to ethical restrictions and privacy agreements between the authors and participants.

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Clair, R., Gordon, M., Kroon, M. et al. The effects of social isolation on well-being and life satisfaction during pandemic. Humanit Soc Sci Commun 8 , 28 (2021). https://doi.org/10.1057/s41599-021-00710-3

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High‑quality health services through Medicare

Boosting access to essential health services

Building a better healthcare system

The Government is investing $2.8 billion to continue its commitment to strengthen Medicare. This includes the $1.2 billion package to address pressures facing the health system, which provides:

• $882.2 million to support older Australians avoid hospital admission, be discharged from hospital earlier and improve their transition out of hospital to other appropriate care.
• $227 million to deliver a further 29 Medicare Urgent Care Clinics and boost support for regional and remote clinics. This will increase the total number of clinics across Australia to 87. Since commencing last year, existing clinics have already provided almost 400,000 bulk‑billed visits.
• $90 million to address health workforce shortages by making it simpler and quicker for international health practitioners to work in Australia.

Rohan’s daughter Zoya has been  off school with a runny nose and a cough. By 6pm, she is lethargic and has a fever.

Rohan is concerned because his regular GP is now closed. Instead of waiting for hours at the emergency department, he takes Zoya to a Medicare Urgent Care Clinic, without having to make an appointment. 

During the bulk billed visit, Zoya is diagnosed with an infection by the doctor and prescribed appropriate medication. Rohan and Zoya leave within an hour of arrival. Zoya makes a full recovery.

Improving health outcomes

Almost half of Australians live with a chronic condition. This Budget will provide $141.1 million for research and services for people living with chronic conditions, including bowel and skin cancer, diabetes and dementia.

To improve health outcomes, the Government is providing:

• Support for Australians to enjoy healthier, more active lives by investing $132.7 million in sport participation and performance programs.
• $825.7 million to ensure Australians can continue to access testing for and vaccinations against COVID‑19. The Government is also ensuring continued access to oral antiviral medicines on the Pharmaceutical Benefits Scheme.
• $41.6 million over two years to continue funding for alcohol and other drug treatment and support services, including the Good Sports alcohol management program for community sporting clubs.

The Government is allocating an additional $411.6 million (for a total $1.6 billion over 13 years) through the Medical Research Future Fund to continue existing research and introduce two new research missions for low‑survival cancers and reducing health inequities.

Improving access to medicines

The Government is investing $3.4 billion for new and amended listings to the Pharmaceutical Benefits Scheme, which means eligible patients can save on treatment costs.

By expanding the Closing the Gap Pharmaceutical Benefits Scheme Co‑payment Program, eligible First Nations patients will have free or cheaper access to all Pharmaceutical Benefits Scheme medicines.

Australians will benefit from $141.1 million to support and expand the National Immunisation Program.

Mental health support

The Government’s $888.1 million mental health package over eight years will help people get the care they need, while relieving pressure on the Better Access initiative and making it easier to access services.

A free, low‑intensity digital service will be established to address the gap for people with mild mental health concerns. From 1 January 2026, Australians will be able to access the service without a referral and receive timely, high‑quality mental health support. Once fully established, 150,000 people are expected to make use of this service each year.

The Government is improving access to free mental health services through a network of walk‑in Medicare Mental Health Centres, built on the established Head to Health network. The upgraded national network of 61 Medicare Mental Health Centres will open by 30 June 2026. They will provide clinical services for adults with moderate‑to‑severe mental health needs.

For Australians with complex mental health needs, funding will be provided for Primary Health Networks to partner with GPs to deliver multidisciplinary, wraparound support services and care coordination.

Improving the aged care system

Providing quality care

The Budget provides $2.2 billion to deliver aged care reforms and continue implementing recommendations from the Royal Commission into Aged Care Quality and Safety.

The new Aged Care Act will put the rights and needs of older people at the centre of the aged care system. The new Act will provide the framework for fundamental change within the aged care sector.

More Home Care Packages

The Government is investing $531.4 million to release an additional 24,100 Home Care Packages in 2024–25. This will help reduce average wait times and enable people to age at home if they prefer to do so.

Improving aged care regulation

Funding of $110.9 million over four years will support an increase in the Aged Care Quality and Safety Commission’s regulatory capabilities.

The Government is investing $1.2 billion in critical digital systems to support the introduction of the new Aged Care Act and contemporary IT systems.

The My Aged Care Contact Centre will receive $37 million to reduce call‑waiting times for people seeking information and access to aged care.

Higher wages for aged care workers

The Government has committed to fund the Fair Work Commission decision to increase the award wage for direct and indirect aged care workers once the final determination is made. This will build on the $11.3 billion already allocated to support the interim 15 per cent wage increase for aged care workers.

The Government is providing $87.2 million for workforce initiatives to attract nurses and other workers into aged care.

Reforming the disability sector

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Getting the National Disability Insurance Scheme (NDIS) back on track

A further $468.7 million is being provided to support people with disability and get the NDIS back on track. This includes:

• $214 million over two years to fight fraud and to co‑design NDIS reforms with people with disability, announced earlier this year
• $160.7 million to upgrade the NDIS Quality and Safeguards Commission’s information technology
• $45.5 million to establish a NDIS Evidence Advisory Committee
• $20 million to start consultation and design on reforms to help NDIS participants and people with disability navigate services.

This builds on $732.9 million provided in the 2023–24 Budget.

In December 2023, National Cabinet agreed to work together to improve the experience of participants and restore the original intent of the Scheme to support people with permanent and significant disability, within a broader ecosystem of supports. This builds on an earlier decision by National Cabinet to ensure Scheme sustainability and achieve an 8 per cent growth target by 1 July 2026, with further moderation as the NDIS matures.

Improving employment for people with disability

A $227.6 million investment will support a new specialised disability employment program to replace the existing Disability Employment Services program by 1 July 2025. This includes a modern digital platform for providers and participants. These reforms will support more people with disability into sustainable work, through a program with greater flexibility, increased individual supports, and better service quality. Eligibility will be expanded to include volunteers outside the income support system and those with less than eight hours per week work capacity.

Delivering essential services

Investing in reliability and security

Strengthening resourcing for Services Australia

The Government is delivering safer and more efficient government services for all Australians.

A $1.8 billion provision will support delivery of customer and payment services. This includes funding for frontline and service delivery staff to manage claims, respond to natural disasters and improve the cyber security environment. The Government is providing $314.1 million over two years to strengthen safety and security at Services Australia centres.

The Government is investing $580.3 million over four years and $139.6 million per year ongoing to sustain the myGov platform and identify potential enhancements. A further $50 million will improve usability, safety and security of the myGov platform and ensure Services Australia can support people to protect their information and privacy.

Strengthening the Australian Taxation Office (ATO) against fraud

There will be $187.4 million to better protect taxpayer data and Commonwealth revenue against fraudulent attacks on the tax and superannuation systems. Funding will upgrade the ATO’s information and communications technologies and increase fraud prevention capabilities to manage increasing risk, prevent revenue loss, and support victims of fraud and cyber crime.

Looking after our veterans

Veterans’ claims processing is prioritised with an additional $186 million for staffing resources and $8.4 million to improve case management and protect against cyber risk. The Government will provide $222 million to harmonise veterans’ compensation and rehabilitation legislation.

A further $48.4 million will be available for Veterans’ Home Care and Community Nursing programs and $10.2 million to provide access to funded medical treatment for ill and injured veterans while their claims for liability are processed.

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