research article on sedentary lifestyle

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• Korean J Fam Med
• v.41(6); 2020 Nov

Sedentary Lifestyle: Overview of Updated Evidence of Potential Health Risks

Jung ha park.

1 Department of Family Medicine, Jeju National University Hospital, Jeju, Korea

Ji Hyun Moon

2 Department of Family Medicine, Jeju National University School of Medicine, Jeju, Korea

Hyeon Ju Kim

Mi hee kong, yun hwan oh.

One-third of the global population aged 15 years and older engages in insufficient physical activities, which affects health. However, the health risks posed by sedentary behaviors are not well known. The mean daily duration of sedentary behavior is 8.3 hours among the Korean population and 7.7 hours among the American adult population. Sedentary lifestyles are spreading worldwide because of a lack of available spaces for exercise, increased occupational sedentary behaviors such as office work, and the increased penetration of television and video devices. Consequently, the associated health problems are on the rise. A sedentary lifestyle affects the human body through various mechanisms. Sedentary behaviors reduce lipoprotein lipase activity, muscle glucose, protein transporter activities, impair lipid metabolism, and diminish carbohydrate metabolism. Furthermore, it decreases cardiac output and systemic blood flow while activating the sympathetic nervous system, ultimately reducing insulin sensitivity and vascular function. It also alters the insulin-like growth factor axis and the circulation levels of sex hormones, which elevates the incidence of hormone-related cancers. Increased sedentary time impairs the gravitostat, the body’s weight homeostat, and weight gain, adiposity, and elevated chronic inflammation caused by sedentary behavior are risk factors for cancer. Sedentary behaviors have wide-ranging adverse impacts on the human body including increased all-cause mortality, cardiovascular disease mortality, cancer risk, and risks of metabolic disorders such as diabetes mellitus, hypertension, and dyslipidemia; musculoskeletal disorders such as arthralgia and osteoporosis; depression; and, cognitive impairment. Therefore, reducing sedentary behaviors and increasing physical activity are both important to promote public health.

INTRODUCTION

1. epidemiology.

Approximately 31% of the global population aged ≥15 years engages in insufficient physical activity, and it is known to contribute to the death of approximately 3.2 million people every year [ 1 ]. In South Korea, the physical activity rate is on the decline among adults aged ≥19 years, irrespective of the type of activity, including aerobic exercise, walking, and muscle training. Therefore, in 2017, the rates of aerobic exercise, walking, and muscle training in the Korean adult population were 48.5%, 39.0%, and 21.6%, respectively, with the majority of the Korean population engaging in physical inactivity [ 2 ]. In addition to physical inactivity, sedentary behavior is also a serious problem, and a substantial number of people engage in it for prolonged periods. For instance, Americans spend 55% of their waking time (7.7 hours a day) engaged in sedentary behaviors whereas Europeans spend 40% of their leisure time (2.7 hours a day) watching television [ 3 ]. Similar patterns have been observed in Koreans, who have been reported to demonstrate long sedentary times. According to Korea Health Statistics of 2018, adults in Korea aged ≥19 years engage in 8.3 hours of sedentary time. Only 8.9% of the adult population engaged in <4 hours of sedentary time whereas 20.6% of the adults were involved in >12 hours of sedentary time [ 4 ].

2. Causes of Physical Inactivity and Sedentary Lifestyles

A poor participation in physical activity is speculated to be influenced by multiple factors. Some environmental factors include traffic congestion, air pollution, shortage of parks or pedestrian walkways, and a lack of sports or leisure facilities [ 1 ]. Television viewing, video viewing, and cell phone usage are positively correlated with an increasingly sedentary lifestyle [ 5 ]. Sedentary behaviors are projected to continue to rise on the basis of this socio-cultural background.

Sedentary lifestyles have a major impact on the overall health of the global population. Many people worldwide engage in sedentary lifestyles, and the prevalence of relevant non-communicable diseases is on the rise. It is well known that insufficient physical activity, that is, physical inactivity, has a detrimental effect on health. Physical inactivity is the fourth leading risk factor for global mortality, accounting for 6% of global mortality [ 6 ]. Despite the fact that sedentary behavior poses a comparable risk to health and contributes to the prevalence of various diseases, most physical activity-related education in clinical practice is focused on improving the physical activity levels, with less emphasis on lowering the sedentary behavior. In addition to understanding and informing patients about the health impact of a sedentary lifestyle, healthcare providers of various fields, including clinicians, should reflect upon its significance in policies. This study examined the effects of a sedentary lifestyle on health and the lifestyle-related improvements to be made to promote healthy living.

OVERVIEW OF SEDENTARY LIFESTYLE

1. the concept of a sedentary lifestyle.

Sedentary behavior is defined as any waking behavior such as sitting or leaning with an energy expenditure of 1.5 metabolic equivalent task (MET) or less [ 7 ]. This definition, proposed by the Sedentary Behavior Research Network in 2012, is currently the most widely used definition of sedentary behavior. Some examples of sedentary behavior include television viewing, playing video games, using a computer, sitting at school or work, and sitting while commuting ( Figure 1 ) [ 8 ]. According to the 2011 Compendium of Physical Activities, MET is defined as the ratio of work metabolic rate to the standard resting metabolic rate (RMR) of 1 kcal/(kg/h). One MET is the RMR or energy cost for a person at rest. When classified quantitatively based on their intensities, physical activities can be classified into 1.0–1.5 METs (sedentary behavior), 1.6–2.9 METs (light intensity), 3–5.9 (moderate intensity), and ≥6 METs (vigorous intensity) ( Figure 2 ) [ 9 ].

Examples of sedentary behavior. (A) Playing a video game. (B) Watching television. (C) Using a computer. (D) Reading a book.

Examples of moderate to vigorous physical activity. (A) Riding a bicycle. (B) Running.

A sedentary lifestyle increases all-cause mortality and the risks for cardiovascular diseases (CVD), diabetes mellitus (DM), hypertension (HTN), and cancers (breast, colon, colorectal, endometrial, and epithelial ovarian cancer). This has been consistently documented in the literature [ 3 , 10 , 11 ]. There is no disagreement on the fact that prolonged total sedentary behavior times are associated with poor disease outcomes. However, the patterns of sedentary time may differ even within the same total amount of time, and not much is known about the particular patterns of prolonged sedentary time that pose more significant health hazards (for example, continuous sedentary behavior without a break or intermittent sedentary behavior) [ 12 ]. One study reported that even if the total sedentary time was equal, having short sedentary bouts and engaging in physical activities intermittently can have relative health benefits. The total sedentary time and moderate-to-vigorous physical activity (MVPA) have been reported to be negatively correlated, where the waist circumference (standardized β, -0.16; 95% confidence interval [CI], -0.31 to -0.02; P=0.026), body mass index (β, -0.19; 95% CI, -0.35 to -0.02; P=0.026), triglyceride level (β, -0.18; 95% CI, -0.34 to -0.02; P=0.029), and 2-hour postprandial plasma glucose level (β, -0.18; 95% CI, -0.34 to -0.02; P=0.025) decreased with increasing the number of breaks in the sedentary time [ 13 ]. Furthermore, when the sedentary time was interrupted with light- or moderate-intensity physical activity, the systolic and diastolic blood pressures dropped by 2–3 mm Hg whereas interrupting the sedentary time with light-intensity physical activity (LIPA) or simple muscle training in patients with diabetes (88% of the population had HTN) decreased the systolic pressure by 14–16 mm Hg and the diastolic pressure by 8–10 mm Hg [ 14 ].

2. Physiological Features

The exact mechanisms of the various adverse effects of sedentary behavior on the human body are currently unknown. However, several hypotheses have been proposed for the overall understanding of the impact of sedentary behavior on the human body, which are described below.

Sedentary lifestyles are associated with metabolic dysfunctions, such as elevated plasma triglycerides and high-density lipoprotein (HDL) cholesterol and reduced insulin sensitivity [ 15 , 16 ]. Lipoprotein lipase (LPL) is a protein that interacts at the cellular level, and a low LPL concentration is known to decrease the plasma HDL cholesterol level, while affecting the prevalence of severe HTN, diabetes-induced dyslipidemia, metabolic disorders caused by aging, metabolic syndrome, and coronary artery diseases. Moreover, LPL activity is diminished by physical inactivity. Additionally, physical inactivity inhibits LPL activity in skeletal muscles and rapidly signals for impaired lipid metabolism. In an experiment based on a rat model, the reduction of LPL activity in rats that engaged in light walking was only about 10% of the LPL activity in rats that were only placed in their cages [ 17 ]. The fact that muscle LPL activity is highly sensitive to physical inactivity and low-intensity muscular contractile activity can serve as evidence supporting the theory that sedentary behavior is a risk factor for various metabolic disorders [ 18 ].

Physical inactivity reduces bone mineral density [ 19 ]. In a study on healthy adult men and women, 12 weeks of bed rest decreased the mineral density of the lumbar spine, femoral neck, and greater trochanter by 1%–4% [ 19 ]. The balance between bone resorption and bone deposition mediates the relationship between sedentary behavior and the reduction of bone mineral density. According to some studies, bed rest elevates bone resorption markers and does not influence bone formation markers [ 20 - 22 ].

Some studies have provided limited evidence that sedentary behavior has a negative impact on vascular health. A study on healthy women reported that 56 days of head-down bed rest decreased the endothelium-dependent vasodilation while increasing the endothelial cell damage. Such alterations in vascular function were prevented through aerobic exercise and muscle training [ 23 ].

SEDENTARY LIFESTYLES AND HEALTH RISKS

1. sedentary lifestyles, mortality, and morbidity (cardiovascular diseases and other causes).

A sedentary lifestyle is strongly associated with CVD, DM, cancer, and premature mortality. The total daily sedentary time and television viewing time were correlated with an increased all-cause mortality risk [ 24 ]. In a study analyzing the mortality rates of people with >10 hours and <5 hours of sitting times a day, the sitting time was significantly correlated with all-cause mortality (odds ratio [OR], 1.16; 95% CI, 1.04– 1.29; P<0.05) [ 25 ]. In a study that examined the correlation between the television viewing time and all-cause mortality, the people who watched television for ≥6 hours a day had a two-fold higher all-cause mortality risk compared to the people who watched television for <2 hours a day (hazard ratio [HR], 1.98; 95% CI, 1.25–3.15) [ 26 ] whereas the people who watched television for ≥4 hours a day had a 1.5 times higher all-cause mortality risk compared to the people who watched TV for <2 hours a day (HR, 1.48; 95% CI, 1.19–1.83) [ 27 ].

Sedentary time (sitting time, television or screen viewing time, leisure time while sitting in a day) is independently associated with allcause mortality, CVD incidence or mortality, incidence or mortality of certain cancers (breast, colon, colorectal, endometrial, and epithelial ovarian cancer), and type 2 DM. In particular, the adverse effect of sedentary time was more evident among people who engaged in little physical activity compared to those who engaged in frequent physical activity. The relative risk (RR) for all-cause mortality was 30% higher with high physical activity (HR, 1.16; 95% CI, 0.84–1.59) compared to that with low physical activity (HR, 1.46; 95% CI, 1.22–1.75) [ 28 ].

2. Sedentary Lifestyles and Metabolic Diseases

1) diabetes mellitus.

The fact that the prevalence of type 2 DM increases with increasing sedentary time has been consistently documented in various studies (HR, 1.91; 95% CI, 1.64–2.22) [ 28 ].

In an assessment of DM risk considering both sedentary time and physical activity, the DM risk increased with the increasing daily sedentary time (HR, 1.13; 95% CI, 1.04–1.22; P<0.001), and the effect was not offset by the level of physical activity (HR, 1.11; 95% CI, 1.01–1.19; P<0.001). The risk for CVD also increased with the increasing daily sedentary time (HR, 1.29; 95% CI, 1.27–1.30; P<0.001), and although the physical activity level slightly offset this effect, sedentary time still significantly increased the risk (HR, 1.11; 95% CI, 1.01–1.19; P<0.001). This shows that the level of physical activity does not influence the impact of prolonged sedentary time on the risk for CVD and DM.

A few biological mechanisms can explain the impact of the total daily sedentary time on CVD and DM risk. Prolonged sitting is known to affect the content and activity of muscle glucose transporter proteins. An animal study observed that prolonged muscle inactivity reduces the LPL activity, which regulates blood lipid concentration and carbohydrate metabolism through cellular pathways that differ from the normal motor response; however, additional verification is required by human studies [ 29 ].

2) Hypertension

A sedentary lifestyle affects blood pressure through various mechanisms, and subsequently changes the blood pressure by altering the cardiac output and total peripheral vascular resistance. A prolonged sedentary time reduces the metabolic demands and systemic blood flow, and by stimulating the sympathetic nervous system, it decreases insulin sensitivity and vascular function while increasing the oxidative stress and promoting the low-grade inflammatory cascade [ 14 ]. A study reported a direct association between sedentary behavior and a high risk of HTN (HR, 1.48; 95% CI, 1.01–2.18; P for trend=0.03). Among sedentary behaviors, non-interactive sedentary behaviors (watching television, sleeping) have been reported to further escalate the risk for HTN compared to interactive sedentary behaviors (driving, using a computer) [ 30 ].

3) Dyslipidemia

Sedentary behaviors induce metabolic dysfunction characterized by elevated blood triglyceride levels, reduced HDL-cholesterol levels, and diminished insulin sensitivity [ 17 ]. A study reported that sedentary behaviors increased the rate of newly diagnosed dyslipidemias in women (OR, 1.17; 95% CI, 1.00–1.36) and increased the risk for dyslipidemia in both men and women (men: OR, 1.21; 95% CI, 1.02–1.44) (women: OR, 1.24; 95% CI, 1.04–1.48) [ 31 ]. In contrast, MVPA was negatively associated with blood triglyceride levels (β, -0.18; 95% CI, -0.36 to -0.01; P=0.038) [ 32 ].

Sedentary time is known to have significant correlations with waist circumference and clustered metabolic risk scores independent of MVPA. The waist circumference increased by 3.1 cm with a 10% increase in the sedentary time [ 32 ]. Obese patients tend to move less; therefore, increasing the activity levels can be utilized as a strategy in obesity treatment [ 33 ]. While this is a widely known fact, the underlying mechanism remains unknown. A study in 2020 reported that the reason for weight gain is a prolonged sedentary time [ 34 ]. According to a Swedish study that compared an experimental group which wore a heavy 11-kg vest for 8 hours a day and the control group which wore a light 1-kg vest for 8 hours a day, the experimental group had a weight loss of 1.6 kg whereas the control group lost 0.3 kg three weeks later. An animal study shed light on an energy balance system known as the “gravitostat” that maintains a consistent body weight [ 35 ]. This regulation occurs partially due to an influence on appetite where the system requires a personal weighing machine for the proper functioning of this regulation. This Swedish study found that humans also feature a similar built-in scale. An individual’s scale measures lower values with prolonged sitting, which explains why sitting is associated with obesity and poor health. A heavy vest can increase the score on this, thereby inducing weight loss [ 34 ].

3. Sedentary Lifestyles and Cancer Risk

Sedentary behavior is also closely related to the prevalence of cancer. According to a study that investigated the correlation between sedentary behavior and cancer prevalence, the cancer risk was 13% higher in the group with the longest sedentary time compared to that with the shortest sedentary time [ 28 ], and another study reported that sedentary time increased the overall cancer risk by 20% [ 36 ].

Prolonged sitting increases colorectal, endometrial, ovarian, and prostate cancer risks, and it has been reported to increase cancer mortality particularly in women [ 37 ]. There was a significant correlation between cancer mortality and the incidences of breast, colorectal, endometrial, and epithelial ovarian cancers [ 28 ]. An increased total sitting time was positively correlated with colon cancer (RR, 1.24; 95% CI, 1.03–1.50) and endometrial cancer (RR, 1.32; 95% CI, 1.08–1.61) [ 36 ]. Additionally, television viewing time was also positively correlated with colon cancer (RR, 1.54; 95% CI, 1.19–1.98) and endometrial cancer (RR, 1.66; 95% CI, 1.21–2.28) [ 36 ]. Occupational sitting time was positively correlated with only colon cancer (RR, 1.24; 95% CI, 1.09–1.41) [ 36 ].

Sedentary behavior leads to metabolic dysfunctions such as hyperglycemia, hyperinsulinemia, insulin resistance, perturbation of insulin-like growth factor axis, and changes in the circulation levels of sex hormones. Altered circulation levels of sex hormones can be linked to hormone-related cancers such as breast and endometrial cancers [ 38 ]. Additionally, sedentary behavior induces low-grade chronic systemic inflammation, and sedentary time is associated with inflammation-related markers such as C-reactive protein (β, 0.18±0.06; P=0.002), interleukin 6 (β, 0.24±0.06; P<0.001), leptin (β, 0.15±0.04; P<0.001), and the leptin: adiponectin ratio (β, 0.21±0.05; P<0.001) [ 39 ]. Chronic inflammation can trigger cancer growth [ 40 ]. Adiposity can also mediate the relationship between sedentary behavior and cancer, and obesity is a risk factor for several cancers [ 8 ].

4. Sedentary Lifestyles and Osteoporosis

Sedentary behavior is known to show a negative association with the bone mineral density of the total femur and all hip sub-regions irrespective of MVPA, and the bone mineral density (g/cm2) of the total femur had a marked negative correlation with the sedentary time (β, -0.16; 95% CI, -0.24 to -0.08) in adult women [ 41 ]. Bone mineral density was correlated with the duration and not the frequency of sedentary behavior. In men, sedentary behavior was not markedly correlated with the bone mineral density of the hip and spine [ 41 ].

5. Sedentary Lifestyles and Musculoskeletal Diseases

A prolonged sedentary time was correlated with chronic knee pain. In an analysis of the correlation between chronic knee pain and the total daily sedentary time (<5, 5–7, 8–10, >10 hours), the results claimed that the incidence of chronic knee pain was higher in individuals with longer sedentary times (P for trend=0.02) [ 42 ]. In particular, a sedentary time >10 hours a day was markedly correlated with chronic knee pain (adjusted OR, 1.28; 95% CI, 1.02–1.61; P=0.03) [ 42 ]. People who engaged in greater physical activity had less chronic knee pain (adjusted OR, 0.78; 95% CI, 0.67–0.91; P=0.00), but women with >10 hours of sedentary time while engaging in greater physical activity were highly likely to experience chronic knee pain (adjusted OR, 1.19; 95% CI, 1.02–1.39; P=0.03). The study recommends individuals to shorten their sedentary times to <10 hours a day [ 42 ].

6. Sedentary Lifestyles and Other Diseases

1) depression.

Mentally passive sedentary behaviors such as television viewing (RR, 1.18; 95% CI, 1.07–1.30), sitting, listening to music, and talking while sitting were positively correlated with depression risks (RR, 1.17; 95% CI, 1.08–1.27). In contrast, mentally active sedentary behaviors such as reading books or newspapers, driving, attending a meeting, or knitting or sewing were not markedly correlated with depression risk (RR, 0.98; 95% CI, 0.83–1.15) [ 43 ]. Using a computer, which is a mentally active sedentary behavior, was not correlated with depression risk in one study (RR, 0.99; 95% CI, 0.79–1.23) [ 43 ] but was positively correlated with depression risk in another study (RR, 1.22; 95% CI, 1.10–1.34) [ 44 ], and thus its correlation with depression remains controversial. The mechanism underlying the correlation between sedentary behavior and depression may involve the following: sedentary behaviors may increase the risk for depression by blocking direct communication and lowering social interactions, or by reducing the available time to engage in physical activities that help to prevent and treat depression [ 43 ].

2) Cognitive function

The relationship between sedentary behavior and cognitive function is uncertain. A systematic review found marked alterations of cognition (improved in two studies45,46) and impaired in two studies47,48)) in some studies but no changes in cognitive function in some studies.49) However, the only long-term study included in that systematic review suggested that a less-sedentary lifestyle and less sedentary work have benefits related to cognitive function.46) It is believed that replacing the sedentary time with physical activity can help improve the cognitive function. In a randomized clinical trial that analyzed the cognitive changes after 30 minutes of sedentary behavior with other activities for 6 months in older adults with little physical activity, replacing the sedentary time with MVPA and sleep significantly improved cognitive functions, and replacing it with LIPA did not lead to statistically significant changes [ 46 ].

SEDENTARY LIFESTYLES AND PHYSICAL ACTIVITY

1. discrepant health effects of sedentary lifestyle and physical activity.

Past studies have observed that a prolonged sedentary lifestyle leads to poor health outcomes irrespective of physical activity. A sedentary lifestyle was independently correlated with mortality and was not compensated for by physical activity [ 10 ]. The time spent in front of a screen was positively correlated with the presence of metabolic syndrome, independent of the level of physical activity (OR, 3.30; 95% CI, 2.04–5.34) [ 50 ].

2. The Attenuative Effect of Physical Activity on Sedentary Lifestyles

A few recent studies have reported that increasing physical activity can offset the adverse impacts of sedentary behavior. In particular, the offset effect was more evident in people with little physical activity.

A meta-analysis reported that mortality was not elevated in the people engaging in high levels of moderate-intensity physical activity (60– 75 minutes of moderate-intensity physical activity a day) even when they had >8 hours of sedentary time a day. There was no difference in mortality between the most active people (>35.5 MET-h/wk) with <4 hours of sedentary time a day and equally active people (>35.5 MET-h/wk) with >8 hours of sedentary time a day (HR, 1.04; 95% CI, 0.99–1.10). However, television viewing for >3 hours a day increased the mortality regardless of physical activity, and the people who watched television for ≥5 hours a day showed markedly high mortalities (HR, 1.16; 95% CI, 1.05–1.28) [ 51 ].

In one study, sitting time showed a dose-response with all-cause mortality and CVD mortality risk in the least active group (<150 MVPA min/wk) [ 51 ]. In contrast, the group with at least 8 hours of sedentary time a day showed a higher mortality than the group with less than 4 hours of sedentary time a day (HR, 1.52; 95% CI, 1.13–2.03). However, the group who met the essential MVPA criterion (150–299 MVPA min/ wk) or engaged in more physical activity did not show a consistent trend in the relationship between increased sitting time and CVD and all-cause mortalities.

Similarly, a study showed that a sedentary time of over 9 hours per day in the low physical activity group (<600 METs-min/wk) had a significant association with an increased CVD risk (OR, 1.29; 95% CI, 1.04–1.62). In the group with more physical activity, sedentary time was not significantly associated with CVD risk [ 52 ].

In other words, while increased sedentary time increases the mortality among people who engage in little physical activity, adequate physical activity seems to offset the impact of increased sedentary time on mortality [ 53 ].

A study analyzed the correlation between all-cause mortality and net uncompensated sedentary behavior metabolic equivalent hours (USMh=MET/h [sedentary time]–MET/h [MVPA time]), which was computed by subtracting METs for MVPA from METs for sedentary behavior throughout a day. USMh was independently associated with all-cause mortality when it was greater than 7 MET/h, and for television viewing, when it was greater than 3 MET/h. The mean increase in mortality per USMh was 1% (RR, 1.01; 95% CI, 1.00–1.02; P=0.01), and the mean increase in mortality per USMh for TV watching was 7% (RR, 1.07; 95% CI, 1.04–1.10; P<0.001). In other words, physical activity as well as sedentary time should be assessed, and therefore, USMh was revealed to be a more practical index for assessing sedentary behavior [ 54 ].

In the people with the least daily activity (≤17 min/d MVPA), replacing 30 minutes of the sitting time each day with light physical activity reduced the mortality risk by 14% (HR, 0.86; 95% CI, 0.81–0.89), and replacing it with MVPA reduced the mortality risk by 45% (HR, 0.55; 95% CI, 0.47–0.62). However, in the people with the highest daily activity (MVPA >38 min/d), replacing the sitting time with LIPA or MVPA was not linked with a reduced mortality risk [ 55 ].

Replacing the sedentary behavior with physical activity also has an impact on cancer-related mortality. A recently published study showed that sedentary behavior was independently associated with cancer mortality risk, where a higher sedentary time led to a greater cancer mortality risk. In this study, the individuals in the top 1/3 of the sedentary group showed a substantially higher cancer mortality risk than those in the bottom 1/3 (adjusted HR, 1.52; 95% CI, 1.01–2.27) [ 56 ]. However, replacing 30 minutes of sedentary time with LIPA reduced the cancer mortality by 8% (HR, 0.92; 95% CI, 0.86–0.97) and replacing it with MVPA reduced it by 31% (HR, 0.69; 95% CI, 0.48–0.97) [ 56 ].

CLINICAL PEARLS FOR SEDENTARY INDIVIDUALS

While the various countries have their own guidelines for physical activity and sedentary behavior, overall, the recommendations are similar.

1. Recommendations in the United States

Although the 2018 Advisory Committee revealed that sedentary behavior is strongly correlated with all-cause and CVD mortalities in adults, the evidence was insufficient to offer advice on the recommended daily sedentary time and duration of physical activity. It could not determine the recommended daily sedentary time and frequency of physical activity for adults or adolescents because the risks associated with sedentary behavior are related to the amount of MVPA.

It is advisable for inactive people not engaging in moderate physical activity (MPA) to lower their sedentary behavior and replace their sedentary behavior with LIPA. However, LIPA alone is insufficient to obtain health benefits; they will be able to reduce their health risk by gradually increasing their physical activities to MPA or beyond. Inactive people who engage in insufficient physical activity that does not meet the criterion of 150–300 minutes of MPA per week would be able to obtain health benefits by increasing their MPA slightly and reap even greater health benefits by reducing their sedentary behaviors. Active people who engage in sufficient physical activity (150–300 minutes of MPA per week) would gain more benefits by lowering their sedentary behaviors. Highly active people who engage in more than 300 minutes of MPA per week are recommended to maintain or improve their levels of physical activity by participating in a variety of activities [ 57 ].

2. Recommendations in Australia

The Australian Government Department of Health presented age-specific recommendations for physical activity and sedentary behavior. According to the Australian physical activity-sedentary behavior guidelines, individuals are recommended to minimize their sitting times, including sitting during work, commuting, and breaks, and to avoid sitting for prolonged periods as much as possible [ 58 ]. Infants and children aged less than 5 years are advised to not be bound in a stroller, car seat, or high chair for more than 1 hour at a time. While they engage in sedentary behaviors, they are recommended to spend time reading books, singing, solving puzzles, and talking with their caregivers as compared to watching television or a DVD (digital video disc), playing on the computer, or playing other video games [ 59 ]. For children between the ages of 5 and 17, the sedentary recreational screen time should be limited to 2 hours a day, and they are advised to engage in positive social interactions and experiences. Older adults aged 65 years and more are advised to remain active as much as possible every day [ 60 ]. The recommended amount of physical activity for adults is 150–300 minutes of MPA or 75–150 minutes of vigorous physical activity or an equivalent MVPA per week.

3. Recommendations in Korea

The guidelines for physical activity for the Korean population published by the Department of Health Promotion at the Ministry of Health and Welfare in October 2013 recommend that people limit their sedentary leisure time (e.g., computer, smartphone, and television) to 2 hours a day and engage in a low level of physical activity. Children and adolescents are recommended to develop an active lifestyle overall, including sports, physical education, walking, and cycling at home and school. If older adults and people with chronic diseases are unable to engage in the recommended physical activities, these groups of people are advised to engage in physical activities to the extents permitted by their situations [ 61 ].

Since the beginning of the coronavirus pandemic, social distancing has become important, and engaging in physical activity in the postcorona era is difficult. Therefore, a study of the problems of sedentary lifestyle is considered more valuable at this point.

A sedentary lifestyle has an array of adverse health effects, including elevated all-cause mortality, CVD mortality, cancer risk, risks for metabolic diseases such as DM, HTN, dyslipidemia, and musculoskeletal diseases such as knee pain and osteoporosis.

It is indisputable that the negative health impacts intensify with increases in the total daily sedentary times. For this reason, it is important to reduce the sedentary time as much as possible.

The findings of studies determining the worst type of sedentary behavior varied across studies. Studies observed better health outcomes with a short sedentary bout with intermittent physical activity, with light physical activity or simple muscle training, intermittent interruptions of sedentary behavior during work, and rest with physical activity.

Health outcomes also vary depending on the type of sedentary behavior and watching television led to the worst outcomes. This may be attributable to the fact that television watching is a passive sedentary behavior and that people often consume snacks while watching television. Therefore, among the various types of sedentary behaviors, individuals should refrain from watching television as much as possible, and snacking should be minimized while watching television.

Even if the total daily sedentary time cannot be reduced for unavoidable reasons, it is advisable to engage in sufficient exercise equivalent to or more than 150–300 minutes of MPA per week, as studies found that physical activity could offset the adverse effects of sedentary behavior. If sufficient exercise cannot be performed, individuals should at least perform light physical activity, as opposed to not engaging in physical activity at all as health benefits can be obtained even with light physical activity, albeit insufficient; they should further try to increase their physical activity levels as their situations permit.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

• Open access
• Published: 26 November 2020

New global guidelines on sedentary behaviour and health for adults: broadening the behavioural targets

• Paddy C. Dempsey   ORCID: orcid.org/0000-0002-1714-6087 1 , 2 , 3 ,
• Stuart J. H. Biddle 4 ,
• Matthew P. Buman 5 ,
• Sebastien Chastin 6 , 7 ,
• Ulf Ekelund 8 , 9 ,
• Christine M. Friedenreich 10 , 11 ,
• Peter T. Katzmarzyk 12 ,
• Michael F. Leitzmann 13 ,
• Emmanuel Stamatakis 14 ,
• Hidde P. van der Ploeg 15 ,
• Juana Willumsen 16 &
• Fiona Bull 16  

International Journal of Behavioral Nutrition and Physical Activity volume  17 , Article number:  151 ( 2020 ) Cite this article

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This article has been updated

In 2018, the World Health Organisation (WHO) commenced a program of work to update the 2010 Global Recommendations on Physical Activity for Health, for the first-time providing population-based guidelines on sedentary behaviour. This paper briefly summarizes and highlights the scientific evidence behind the new sedentary behaviour guidelines for all adults and discusses its strengths and limitations, including evidence gaps/research needs and potential implications for public health practice.

An overview of the scope and methods used to update the evidence is provided, along with quality assessment and grading methods for the eligible new systematic reviews. The literature search update was conducted for WHO by an external team and reviewers used the AMSTAR 2 (Assessment of Multiple Systematic Reviews) tool for critical appraisal of the systematic reviews under consideration for inclusion. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to rate the certainty (i.e. very low to high) of the evidence.

The updated systematic review identified 22 new reviews published from 2017 up to August 2019, 14 of which were incorporated into the final evidence profiles. Overall, there was moderate certainty evidence that higher amounts of sedentary behaviour increase the risk for all-cause, cardiovascular disease (CVD) and cancer mortality, as well as incidence of CVD, cancer, and type 2 diabetes. However, evidence was deemed insufficient at present to set quantified (time-based) recommendations for sedentary time. Moderate certainty evidence also showed that associations between sedentary behaviour and all-cause, CVD and cancer mortality vary by level of moderate-to-vigorous physical activity (MVPA), which underpinned additional guidance around MVPA in the context of high sedentary time. Finally, there was insufficient or low-certainty systematic review evidence on the type or domain of sedentary behaviour, or the frequency and/or duration of bouts or breaks in sedentary behaviour, to make specific recommendations for the health outcomes examined.

Conclusions

The WHO 2020 guidelines are based on the latest evidence on sedentary behaviour and health, along with interactions between sedentary behaviour and MVPA, and support implementing public health programmes and policies aimed at increasing MVPA and limiting sedentary behaviour. Important evidence gaps and research opportunities are identified.

Introduction

Sedentary behaviour is defined as any waking behaviour characterized by an energy expenditure ≤1.5 metabolic equivalents (METs), while in a sitting, reclining, or lying posture [ 1 ]. Most desk-based office work, driving or riding in a car, and watching television are examples of sedentary behaviours and can also apply to those unable to stand, such as wheelchair users. In most research studies to date involving ambulatory individuals, sedentary behaviour is typically operationalized as total daily sitting time, television viewing, or low counts on an accelerometer or activity monitor. Sedentary behaviours are considered conceptually distinct from physical inactivity, with the latter referring to performing insufficient amounts of moderate-to-vigorous physical activity (MVPA) to meet current physical activity recommendations. Indeed, it is possible to meet or exceed the public health guidelines for MVPA, and yet also spend most waking hours sedentary. At present, most published population-based estimates of sedentary behaviour are limited to high-income countries, with data on global trends in adults remaining scant. Accelerometer-based estimates from a recent review, derived from large or population-representative studies, indicate that adults spend approximately 8.2 h/day (range 4.9–11.9 h/day) sedentary [ 2 ].

Research on sedentary behaviour is relatively recent compared to that of physical activity. Indeed, much of the evidence on the detrimental health effects associated with sedentary behaviour has rapidly accumulated within the past decade. However, there have been notable developments, and the evidence-base is now at a level where dose-response relationships between sedentary behaviour and multiple health outcomes are being systematically examined, along with the interplay between sedentary behaviour and MVPA [ 3 , 4 , 5 , 6 , 7 ]. Given its high prevalence and increasing concern of the potential impact on public health, a growing number of countries are interested in and developing recommendations on sedentary behaviour at varying levels of specificity, either by incorporating them into their physical activity guidelines or by issuing specific sedentary behaviour guidelines (e.g. [ 8 , 9 , 10 , 11 , 12 ]).

In 2018, the World Health Organisation (WHO) was requested to update the 2010 Global Recommendations on Physical Activity for Health based on the latest available science, including sedentary behaviour, as part of global efforts to support countries to implement recommendations set out in the Global Action Plan on Physical Activity 2018–2030 and achieve a 15% reduction in physical inactivity by 2030 [ 13 ]. WHO commenced this program of work and convened an international group of public health scientists and practitioners to serve on the Guideline Development Group (GDG) [ 14 ]. The purpose of this paper is to briefly summarize and highlight the scientific evidence that underpinned the new sedentary behaviour guidelines and its strengths and limitations. We also discuss the public health importance and practical implications of these new guidelines and outline several important evidence gaps and future research directions.

The guideline development process followed WHO protocols [ 15 ] and included establishing a guideline development group (GDG) who met in July 2019 to review and finalise the scope and agree on the methods. Full details of the procedures for identifying and grading the evidence are described in detail elsewhere [ 14 , 16 ]. The evidence-base around sedentary behaviour and health outcomes in youth are detailed separately [ 17 ].

Briefly, the critical outcomes that were examined and the set of PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) questions are shown in Table  1 . Literature searches were undertaken to update the most recent and relevant systematic reviews for the critical outcomes only and not the important outcomes (see Table 1 ), which for sedentary behaviours and adults were identified to be the comprehensive syntheses of evidence undertaken by the 2018 Physical Activity Guidelines Advisory Committee (PAGAC) Scientific Report from the United States [ 9 ].

The literature search update was conducted for WHO by an external team and reviewers used the AMSTAR 2 (Assessment of Multiple Systematic Reviews) tool for critical appraisal of the systematic reviews under consideration for inclusion [ 18 ]. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to rate the certainty (i.e. very low to high) of the evidence for each PI/ECO (see Additional File  1 ) [ 19 , 20 ].

The GDG considered both the evidence reported by the PAGAC and new reviews identified to make recommendations on health outcomes and sedentary behaviour for all adults and older adults. Other considerations such as values, preferences and risks and identified evidence gaps were also appraised. Observational evidence from reviews including more well-conducted longitudinal studies were upgraded to better reflect the increased certainty in findings and improved inferences about the causal structure of associations between sedentary behaviour and health outcomes from such studies. Greater emphasis was given to evidence provided by reviews graded moderate or above, and to those reviews providing evidence from studies using measures of total sedentary or sitting time, or device-based measures of sedentary time, where available.

The PAGAC report provided systematic-review-level evidence published from 2011 to 2016 on sedentary behaviour and all-cause ( n  = 9), cardiovascular disease (CVD; n  = 5) and cancer ( n  = 5) mortality, and type 2 diabetes ( n  = 5), weight status ( n  = 2), CVD ( n  = 5) and cancer ( n  = 8) incidence in adults. PAGAC applied a modified evidence grading protocol which is fully described elsewhere [ 9 , 21 , 22 ].

The updated search for systematic reviews identified 22 potential new reviews published from 2017 to August 2019. Of these, 17 reviews met inclusion criteria (five were excluded because their study exposures or design were out-of-scope) and a further three reviews were excluded because they had critically low credibility ratings (Table  2 ). The 13 remaining reviews provided updated evidence on all-cause, CVD and cancer mortality, type 2 diabetes, CVD and cancer incidence, and adiposity. For a detailed summary of the grading of meta-analyses and systematic reviews that contributed new evidence to inform the GDG conclusions, see the WHO report [ 16 ] and Web Annex Evidence Profiles (Tables B. 2 .a-e).

All-cause and cause-specific mortality (Web Annex, Table B. 2 .a)

A recent high certainty harmonised meta-analysis (eight prospective studies; n  = 36,383) in middle aged and older adults (mean age 62.6 years; 72.8% women) showed a non-linear positive dose-response relationship between accelerometer-measured sedentary time and all-cause mortality [HRs and 95% CI per quartile of higher sedentary time relative to the least sedentary referent, after adjustment for potential confounders including time spent in MVPA: 1.28 (1.09 to 1.51), 1.71 (1.36 to 2.15), and 2.63 (1.94 to 3.56)], with mortality risks increasing gradually from approximately 7.5 to 9 h/day and becoming more pronounced from > 9.5 h/day [ 5 ]. Another recent comprehensive dose-response meta-analysis of over a million participants [ 7 ] also found non-linear positive associations for total sedentary behaviour (mostly self-reported) with all-cause mortality (RR per 1 h/day = 1.01 (95% CI = 1.00–1.01) for ≤8 h/day and 1.04 (95% CI = 1.03–1.05) for > 8 h/day of exposure) and cardiovascular disease mortality (RR = 1.01 (95% CI = 0.99–1.02) for ≤6 h/day and RR = 1.04 (95% CI = 1.03–1.04) for > 6 h/day), although associations with cancer mortality were not statistically significant, after adjustment for physical activity.

A new harmonized meta-analysis (CVD mortality, 9 studies, n  = 850,060; Cancer mortality, 8 studies, n  = 777,696) provided high certainty evidence on whether associations between self-reported sitting (and TV viewing) varied among different strata of MVPA for CVD and cancer mortality [ 3 ]. Significant dose–response associations (9–32% higher risk) were shown for sitting time and CVD mortality in the inactive, lowest quartile of MVPA (~ 5 min/day). More specifically, the hazard of cardiovascular disease mortality was 32% higher in those who sat > 8 h/day compared with the reference group (< 4 h/day). The results were less pronounced but remained statistically significant compared with the reference group for the second (HR = 1.11, 95% CI = 1.03–1.20) and third quartiles (HR = 1.14, 95% CI = 1.03–1.26) of MVPA, but this association was mitigated in the most active quartile (~ 60–75 min/day). This review also found that associations for sedentary behaviour and cancer mortality were generally weaker, although a 6–21% higher dose-related risk was observed with higher sitting time (particularly > 8 h/day) among those who were in the lowest quartile of MVPA (~ 5 min/day), with hazard ratio’s largely attenuated in the highest quartile of MVPA. These new findings build upon previous harmonized meta-analyses [ 4 ] and show overall that the associations of sedentary behaviour and risk for all-cause, CVD and cancer mortality appear to be more pronounced at lower levels of MVPA than at higher levels, but that higher levels of MVPA (i.e. about 60–75 min/day) can largely mitigate the increased risks of sedentary behaviour.

Type 2 diabetes, CVD, and cancer incidence (Web Annex, Table B. 2 .b-d)

Two new moderate certainty reviews [including eleven prospective studies ( n  = 400,292) and five prospective studies ( n  = 4575) with two duplicates, and slightly different foci in terms of exposure and study inclusion criteria], examined associations of total daily sitting time [ 24 ] and total sedentary behaviour (mostly self-reported) [ 7 ] with type 2 diabetes incidence. Small linear associations were observed for increments of 1 h/d in total sedentary behaviour (RR = 1.01, 95% CI = 1.00, 1.01) [ 7 ], while higher levels of total sitting time were also associated with increased risk of diabetes incidence (HR = 1.11, 95% CI = 1.01, 1.19) after adjustment for physical activity [ 24 ]. Bailey et al. also found an increase in incident CVD risk (HR = 1.29 (95% CI, 1.27 to 1.30) with total sitting, which was attenuated following statistical adjustment for physical activity (HR = 1.14 (95% CI, 1.04 to 1.23)) [ 24 ]. Four new reviews [ 25 , 26 , 31 , 36 ] reporting on sedentary behaviour and cancer incidence all had very low to low certainty ratings, mostly attributable to a lack of adjustment for confounding variables, indirectness in terms of diversity of outcome assessment, and high statistical heterogeneity. However, evidence from three meta-analyses [ 37 , 38 , 39 ] previously summarized [ 9 ] provided moderate certainty evidence for an association between sedentary behaviour and type cancer incidence (endometrial, colon, and lung cancers).

Adiposity (Web Annex, Table B. 2 .e)

Only two new reviews for adiposity [ 6 , 40 ] were identified. Both were of very low certainty and included mostly cross-sectional studies with higher risk of bias [i.e. thirteen cross sectional studies and one prospective study ( n  = 13,395) and six cross-sectional studies ( n  = 4774)]. The results reported were consistent with previous reviews [ 9 ] and showed limited, heterogeneous, and/or low certainty evidence for a small association between sedentary behaviour and adiposity markers (i.e. BMI and waist circumference), and low/insufficient certainty evidence for a dose-response relationship.

Overall conclusions, extrapolation to sub-populations, and WHO guidelines

Table  3 provides a summary of the relationships and level of evidence for each health outcome examined with sedentary behaviour, which was largely consistent and complementary between the systematic reviews. Overall, there was moderate certainty systematic review evidence for a direct association (i.e. an ‘independent’ association after adjustment for potential confounders, including MVPA) between higher amounts of sedentary behaviour and increased risk of all-cause, CVD and cancer mortality, as well as CVD, cancer, and type 2 diabetes incidence; however, evidence was limited and of low certainty for adiposity markers. Moderate certainty evidence also supported a non-linear dose-response relationship between sedentary behaviour and all-cause, CVD and cancer mortality, and incident CVD. This evidence provided sufficient support for new recommendations to limit sedentary time and replace it with activity of any intensity to reduce health risks (see Table  4 ) and the benefits of limiting sedentary behaviour were deemed to outweigh the risks. However, given the considerable variations in how sedentary behaviour was assessed (via self-reported sitting time, television viewing time, or device-based (accelerometer) assessments) and reported in studies, it was concluded there was insufficient evidence to set quantified (time-based) recommendations (Table 3 ). It was also considered probable that specific thresholds for sedentary time were likely to vary across health outcomes, by levels of MVPA (see below), and among population sub-groups.

There was moderate certainty evidence that the associations between sedentary behaviour and all-cause, CVD and cancer mortality vary by level of MVPA when modelled in joint or stratified analyses. In other words, higher amounts of MVPA (i.e. about 60–75 min/day) can attenuate the detrimental association between sedentary behaviour and health outcomes. This underpinned additional guidance around increased levels of MVPA in the context of high levels of sedentary time to help reduce the risks (see Table 4 ).

In addition to overall volume of sedentary behaviour, the patterns by which sedentary behaviour is accrued were reviewed. However, consistent with previously summarized evidence syntheses [ 9 ], there was insufficient or low-certainty systematic review evidence to make recommendations on the frequency and/or duration of bouts or breaks in sedentary behaviour. The possibility that some types or domains of sedentary behaviour may be more detrimental to health than others, both in terms of their direct associations and their potential to displace time spent in more healthful physical activity, was also considered. For example, some studies report stronger associations with sedentary behaviour reported as TV viewing compared with total sitting time [ 7 ]. This may be due to differences in the behaviours themselves, differential measurement error/validity, or differences in residual/unmeasured confounding associated with the self-report measures and instruments, but further research is still needed to untangle this. Similarly, some misclassification may occur from device-based measures of sedentary time as many of these device placements (e.g. wrist, waist) do not currently distinguish between positions (e.g. lying, sitting, and standing still). At present, there is insufficient/low certainty review-level evidence directly comparing associations between different types/domains of sedentary behaviour to make any conclusions or recommendations.

Direct evidence on sedentary behaviours and health outcomes in people living with disabilities or chronic conditions, or pregnant or postpartum women, remains sparse. However, the evidence reviewed in the general adult population, including the benefit of undertaking more MVPA to help counteract the potential risks of high levels of sedentary behaviour, was considered applicable (assuming no contraindications) and therefore extrapolated to inform guidelines on sedentary behaviour for these specified sub-populations [ 41 , 42 ] for a common set of critical health outcomes, with a downgrading of certainty to reflect indirectness (see Table 4 ). In extrapolating this evidence to people living with disabilities, it was recognized that certain population groups, such as wheelchair users, unavoidably sit for long periods of time and sitting may therefore be the norm. For these groups, sedentary behaviour is best defined based on the low energy expenditure component, rather than the postural component (e.g. moving in a power chair or being pushed in a wheelchair). The final guidelines on sedentary behaviour for all adults and older adults are detailed in Table 4 .

In the past decade, there has been a rapid accumulation of evidence on the prospective associations between sedentary behaviour and several critical public health outcomes (see Table 3 ). With increasing concerns around low prevalence of physical activity and rising levels of sedentary behaviour among many populations worldwide, important public health gains could be made by limiting excessive sedentary behaviour and replacing it with more physical activity of light, moderate or vigorous intensity. As such, the incorporation of new evidence-based recommendations on sedentary behaviour for all adults and older adults within the 2020 WHO guidelines marks an important step forward, while complementing and extending existing physical activity guidelines.

Although the evidence reviewed shows that sedentary behaviour is clearly related to several health outcomes, there remains some imprecision and uncertainty in the characteristics of the specific dose-response curves, which in turn has made it difficult to provide specific quantitative public health recommendations. As researchers and policy makers enter this new era of joint physical activity and sedentary behaviour recommendations, three important evidence and practice gaps warrant further contextualisation and discussion:

1. No specific or quantified (time-based) threshold for sedentary time: Identifying whether there is a threshold of sedentary behaviour which is associated with increased health risk is of public health and policy relevance. Although there was some consistency in the positive dose-response relationships between sedentary time and mortality outcomes (but less so for type 2 diabetes and cancer incidence or adiposity) to support clear statements to limit sedentary time overall (see Table 3 ), there was insufficient evidence to identify a specific time-based threshold. Limitations in the evidence included the variations in how sedentary time has been measured or reported across all the major reviews, which complicates the identification of specific quantitative thresholds. Moreover, the evidence reviewed showed that thresholds of increased risks for sedentary time are likely to vary depending upon the level of MVPA (as described in the second sedentary behaviour recommendation; also see point 2 ), by health outcome, and among different population sub-groups (e.g. defined by age, sex, race/ethnicity, socioeconomic status, or weight status, etc). Future reviews that include well-harmonised and/or device-based measures of total sedentary time in ethnically and culturally diverse populations could help inform more specific quantifications around the amounts of sedentary time that significantly increase health risks. However, evidence to inform such sedentary guidance is complex and will also need to be considered within the context of its inter-relationships with physical activity of various types/intensities.

2. The feasibility of achieving or exceeding the upper limits of MVPA guidelines to reduce the detrimental effects of “high levels” of sedentary behaviour: The new evidence reviewed demonstrating that high levels of MVPA (i.e. > 300 min/week) can largely offset the mortality risks associated with sedentary behaviour is good news for those who are highly sedentary, but who are also able to achieve high levels of MVPA. This evidence emphasizes the role of MVPA in offsetting the potential harms associated with excessive sedentary time (see Table 4 ). However, achieving such high levels of MVPA may be a challenge for large segments of the population, as illustrated by the high proportion of people not meeting the lower limit for MVPA guidelines [ 43 , 44 ]. Dual sedentary behaviour guidelines therefore emphasize and support added flexibility in options, through encouraging the use of multiple approaches or strategies to reduce risk. These could include lowering total sedentary time (and likely increasing light-intensity activity), increasing MVPA time or, ideally, some combination of both strategies. This integration concept is illustrated elegantly by the heat map developed by the PAGAC [ 9 ], which shows conceptually that many combinations of less sedentary time and more MVPA can be associated with a reduced risk of all-cause mortality (see Fig.  1 ) – evidence which has now been extended to include CVD and cancer mortality outcomes [ 3 ]. Importantly, the new WHO guidelines point to the importance of attending to both physical activity (i.e. of light, moderate, and vigorous intensity) and sedentary time to try to optimize the “balance” of these behaviours for better health. The dual sedentary recommendations also promote more inclusivity towards a broader variety of sub-populations – including large portions of the population who are physically inactive or obese, or who have chronic conditions or disabilities – for whom achieving high levels of MVPA may be challenging.

Joint associations of sedentary (sitting) time and MVPA with risk of all-cause mortality based on data by Ekelund et al. [ 4 ] – now also broadly applicable for risk of CVD and cancer mortality [ 3 ]. Orange and yellow shading represents transitional decreases in risk. For context, data analysis ranges for all-cause mortality [ 4 ] were based on four levels of self-reported sedentary time (< 4, 4–6, 6–8, > 8 h/day) and MVPA ( ∼ 5, 25–35, 50–65, 60–75 min/day), but specific scales are intentionally left blank and could vary considerably for either device-based measures (e.g. hip or thigh accelerometry), by different health outcomes (e.g. type 2 diabetes, adiposity), or by different sub-populations (e.g. frail/elderly adults, people living with some chronic conditions or disabilities). Heat map adapted from the PAGAC [ 9 ] report

3. No specific recommendations on how to break up sedentary behaviour: Information on sedentary break and bout accumulation patterns in relation to health outcomes represents a promising and potentially powerful public health messaging tool. However, operationalising and analysing sedentary break and bout accumulation pattern data, distinct from the volume of time spent in sedentary and active behaviours, requires more clarity and detailed interrogation. There was insufficient or low-certainty systematic review evidence on the frequency and/or duration of bouts or breaks in sedentary behaviour with health outcomes to make specific recommendations. A lack of evidence to date is likely due in part to the reliance on device-based measures, and some heterogeneity/limitations in how bouts and breaks in sedentary time are summarized. Some emerging but limited evidence is available from short to medium duration randomized controlled trials (RCTs) and prospective cohort studies with clinical endpoints. However, it should also be noted that most of the review-level evidence is based on cross-sectional [ 45 ] or acute laboratory-based [ 46 ] studies, or free-living short to medium duration intervention studies with behavioural or surrogate disease risk biomarkers as primary outcomes [ 47 , 48 , 49 ].

Key limitations/gaps in the evidence base and future directions

Several limitations and gaps in the current evidence base were identified and future research recommendations generated from this work, some of which are also broadly covered in a separate paper [ 50 ]. Key future directions and opportunities are detailed below. More research in all of these areas would lead to more specificity and generalisability in guidelines, such as what types/domains of sedentary behaviours to limit most, the role of standing in replacing sitting, which types/domains of sedentary behaviours may be neutral or even health-promoting, and specific daily or weekly time-based thresholds/ranges above which there are important health risks. Advances in measurement tools and analytics, as well as better data harmonization or pooling across studies, should support and provide important insights towards these areas. However, it is important that such research is paralleled by greater efforts to improve global surveillance and data collection across a diversity of sub-populations and low- and middle-income countries (LMICs), including those from more disadvantaged/vulnerable backgrounds, where evidence remains particularly scant.

1. Measurement and analytics: Most of the evidence reviewed by the GDG was derived from self-reported sitting and TV viewing time, with less evidence from device-based measures of sedentary behaviour or sitting per se. There is a need to develop and incorporate better field-based measurement methods to adequately quantify time spent in sedentary behaviours. Such methods should quantify both postural and energy expenditure components, consistent with the accepted sedentary behaviour definition, and ideally also capture more detailed information on the type and domain of sedentary behaviour (e.g. occupational, sedentary screen time, active/passive sedentary behaviours, upper and lower-body fidgeting, etc). To capture all such elements will likely require combinations of both self-report and device-based methods, including possible use of wearable cameras. Moreover, since sedentary time, light-intensity physical activity, MVPA and sleep all co-exist and interact within a finite 24-h day or energy total, analytical methods that better account for the relative time- and energy-use compositions of these behaviours will provide more useful insights into correlates and behavioural associations with health outcomes [ 51 , 52 , 53 ]. Similarly, better analytical methods to identify, standardize, and summarize information on sedentary breaks and bout accumulation patterns (distinct from total sedentary volume or other time-uses) will provide more useful information when examining associations with prospective health outcomes.

2. Outcomes, mechanisms, and context: More prospective evidence is needed on a broader range of health and psycho-biological outcomes. The bulk of the literature so far, including the evidence synthesis for this review, relies on mortality or cardiometabolic outcomes. Similar to physical activity, future guidelines should ideally be based on evidence from a more comprehensive suite of important health or health-related outcomes, including: specific cancers; mental health (including affective responses); cognitive/brain health; musculoskeletal health and falls; social outcomes; and quality of life. More experimental and etiological research that informs biological mechanisms (both acute and longer term) and potential causal pathways linking sedentary behaviour with health outcomes will also be highly informative [ 54 ]. Examples include: experimental evidence on direct effects of exposures to different types, postures, or patterns of sedentary behaviour; residual/unmeasured confounding issues (e.g. socioeconomic status, diet, occupation type, mental health, functional status, cancer screening); untangling issues around reverse or bi-directional causality and mediation (e.g. for type 2 diabetes and adiposity); effect modification by key demographic and personal characteristics (e.g. sex, age, race/ethnicity, chronic conditions, disabilities, socioeconomic status, occupation type, adiposity, and cardiorespiratory fitness); and interactions between sedentary time and physical activity across the intensity spectrum (e.g. light, moderate, and vigorous).

3. Examining options for limiting sedentary behaviour in RCTs: Building on previous points, research that encapsulates the full 24-h range of behaviours (e.g. sleep, sedentary time, light-intensity physical activity, and MVPA) will inform sedentary behaviour guidelines by providing guidance on how to replace sedentary time optimally. In particular, the positive or negative consequences of replacing sedentary time with additional sleep duration, standing, and various other activities within the wide range of light-intensity activities, and for whom, is of interest. Ideally, these questions should be examined within the context of high-quality RCTs targeting a variety of sub-populations, using harmonizable measures of these behaviours. This research will allow specific replacement effects to be more reliably ascertained through later individual-level participant data analyses and meta-analytic approaches [ 47 ]. Finally, a better understanding of the key biological or modifiable determinants (or “drivers”) of sedentary behaviour (a behaviour which can often be habitual and socially/environmentally reinforced) will also be crucial in informing the design, targeting, and implementation of the above RCTs, as well as more effective, evidence-based interventions and policies to change sedentary time [ 55 ].

4. An uneven evidence base generated in high-income countries: To date, most evidence on sedentary behaviour (and physical activity) has been built on studies examining populations predominately from western high-income countries. Therefore, the prevalence, context, and generalisability of different amounts, types, and patterns of sedentary behaviour – particularly in terms of determinants, health impacts, and targeted interventions – remains less clear for populations in LMICs. A global perspective and more high-quality empirical data in a diversity of sub-populations and LMICs is therefore needed. This need is particularly pressing since LMICs are already experiencing economic and societal transitions that are expected to lead to rapid urbanisation and more sedentary jobs/societies. Indeed, populations in LMICs are already experiencing higher global disease burden or differential disease patterns compared to high-income countries [ 56 , 57 ]. It seems likely that some underlying institutional, social, and cultural practices between countries will mean that sedentary and active living are viewed or influenced in different ways [ 58 ], such as leisure or occupational sedentary time representing higher social status or active transport representing poverty. These unique determinants/macrolevel drivers and potential differential health impacts of sedentary behaviour require more detailed data and understanding across a wider range of countries, with a focus on disadvantaged populations and LMICs. Recent global surveillance data, though somewhat limited, indicates that self-reported sedentary time varies substantially between high- and low-income countries [ 59 ], with high-income countries reported sedentary time almost double that of low income countries (4.9 vs 2.7 h/day). Moreover, the contextual patterns in which sedentary behaviours occur also vary by indices of socioeconomic status and markers of social disadvantage. For example, in high-income countries, occupational sedentary time tends to be higher among those with higher educational attainment or income, whereas TV viewing levels are often higher among those in lower socioeconomic positions [ 60 ]. Understanding and addressing potential social and cultural inequities in the determinants, prevalence, and health impacts of different sedentary behaviours, and appropriate opportunities and strategies to intervene, is therefore an important area in need of more research.

Policy and practice implications and opportunities

From a practical perspective, policy makers should view the new WHO sedentary behaviour guidelines as complementary and reinforcing to physical activity guidelines and public health endeavours to reduce the risk of non-communicable diseases. The benefits of MVPA are well-established, and these new sedentary recommendations show that substantial health gains also exist from promoting all adults to limit high levels of sedentary time, and to replace sedentary behaviours with physical activity of any intensity. These recommendation support new opportunities for more comprehensive messages and policies to help improve health, such as “move more” and “sit less” (or limit sedentary behaviour in non-ambulatory individuals), and even without specific thresholds, such messages are intrinsically synergistic from a public health perspective.

These new sedentary behaviour recommendations should be disseminated to key audiences and across multiple settings, broadening the potential options for health promotion and non-communicable disease prevention/management initiatives. Indeed, as emphasized in the Global Action Plan on Physical Activity 2018–2030 [ 13 ], there are multiple ways to be more active, multiple policy choices, settings and opportunities, and multiple benefits. The same is true of sedentary behaviour; thus, a combined emphasis on policies and initiatives to change both physical activity and sedentary behaviours is needed to help move the physical activity agenda forward, and importantly scale-up more action at the national and global level.

The synergies between physical activity and sedentary behaviour calls for both interdisciplinary and intersectoral population health action. However, this should be supported by and will require a deeper understanding of the complexity of sedentary behaviour (e.g. from measurement/operationalization, to determinants, and health impacts) and its inter-relationships with other behaviours under different contexts. These inherent complexities suggest we should be working with different fields, expertise, and constituencies beyond public health – such as urban planners, employers, educators, and the sport sector – to create more “activity-friendly” communities and social/built environments. A solutions-oriented approach, as well as a focus on different behavioural settings (e.g. neighbourhoods, schools, domestic/homes, workplaces, and transport/commuting) and creating stronger partnerships with communities, healthcare, employers, businesses, and government, transport and industry sectors, should also be emphasised [ 55 , 61 ]. Most importantly, public health efforts will need to move beyond short-term implementation and impact, and more towards achieving system embeddedness, co-benefits, and translation at scale into policy and practice [ 62 , 63 ]. Moreover, the appropriateness and efficacy of all such approaches and policies will need to be tested in a diversity of populations – including those from disadvantaged backgrounds or LMICs, those living with chronic diseases or disabilities, and across different cultural backgrounds.

The WHO 2020 guidelines on physical activity and sedentary behaviour provide new guidance on sedentary behaviour and its interrelationships with physical activity. They provide a broader, mutually reinforcing set of behavioural targets to help improve population health. Identified evidence gaps underscore the need for further research, especially among certain sub-populations and in diverse contexts including more LMICs. An important challenge will now be to identify effective, sustainable, and scalable approaches for limiting sedentary behaviour and increasing physical activity among those who need it most, particularly more vulnerable populations. These new sedentary behaviour guidelines should provoke more targeted research in this area and be a catalyst for more system-wide policies, programs, and initiatives to help improve global health.

Availability of data and materials

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Change history

10 december 2020.

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Acknowledgements

Systematic reviews of evidence prepared for 2018 US Physical Activity Guidelines Advisory Committee Scientific Report to the Secretary of Health and Human Services were updated thanks to additional literature searches conducted by Kyle Sprow (National Cancer Institute, National Institutes of Health, Maryland, USA). Summaries of evidence and GRADE tables were prepared by Carrie Patnode and Michelle Henninger (The Kaiser Foundation Hospitals, Center for Health Research, Portland, Oregon, USA).

The Public Health Agency of Canada and the Government of Norway provided financial support to update the WHO guidelines on physical activity and sedentary behaviour. PCD is supported by a National Health and Medical Research Council (NHMRC) of Australia Fellowship (#1142685) and the UK Medical Research Council [MC_UU_12015/3]. ES is funded by a NHMRC of Australia Senior Research Fellowship (#1110526) and an Investigator Grant (#1194510), and PAL Technologies (Scotland) who develop equipment measuring sedentary behaviour. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.

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Dempsey, P.C., Biddle, S.J.H., Buman, M.P. et al. New global guidelines on sedentary behaviour and health for adults: broadening the behavioural targets. Int J Behav Nutr Phys Act 17 , 151 (2020). https://doi.org/10.1186/s12966-020-01044-0

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The association of physical activity and sedentary behaviour on health-related quality of life: a cross-sectional study from the physical activity at work (PAW) trial

• Katika Akksilp 1 , 2 ,
• Falk Müller-Riemenschneider 1 , 3 ,
• Yot Teerawattananon 1 , 2 &
• Cynthia Chen 1 , 3  

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Introduction

Physical inactivity and sedentary behaviour independently increase morbidity and negatively affect quality of life. This study evaluates the associations between physical activity and sedentary behaviour with health-related quality of life, including the five dimensions of quality of life (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression).

This cross-sectional study analysed baseline data from Thailand's Physical Activity at Work (PAW) trial. Physical activity data were collected using the ActiGraph™ accelerometer, worn on the right hip for a minimum of three ten-hour workdays. Accelerometer data were then used to categorise participants into: (i) not-sedentary and physically active (the Reference Group), (ii) not-sedentary but inactive, (iii) sedentary but active, and (iv) sedentary and inactive. We employed the EuroQol-5 dimensions questionnaire with five scoring levels (EQ-5D-5L) to measure health-related quality of life. The Thai EQ-5D-5D valuation was utilised to convert the EQ-5D profile into utility index scores (EQ-5D values). Tobit regression models were used to analyse EQ-5D value differences. Moreover, the odds of having problems in each EQ-5D dimension were compared between categories.

277 valid participant data were included. Older age (P = 0.007), higher education (P < 0.001), and higher prevalence of cardiovascular disease (P = 0.032) were observed in participants who were sedentary and physically inactive compared to other groups. We found − 0.0503 (95% CI: − 0.0946–− 0.00597) lower EQ-5D value and 1.39 (95% CI: 1.07–1.79) higher odds of reporting pain or discomfort problems in the sedentary and physically inactive group compared to the Reference Group. We also found 2.12 (95%CI: 1.14–5.40) higher odds of reporting usual activity problems in the not-sedentary but physically inactive group than in the Reference Group.

We found further evidence of the potential benefit of higher physical activity levels and lower sedentary time for higher quality of life among healthy office workers in Thailand. Further research with larger cohorts and longitudinal data is needed to establish a stronger foundation for interventions and economic evaluations targeting physical activity promotion to improve quality of life.

Spending more time being sedentary, such as prolonged sitting or lying down, and less physical activity is associated with a greater risk of non-communicable diseases and all-cause mortality [ 1 , 2 , 3 ]. The World Health Organisation recommends that adults spend at least 150–300 min in moderate-intensity physical activity or 75–150 min in vigorous-intensity physical activity, or equivalence, per week and spend less time sedentary [ 4 ]. Many studies reported a negative association between health outcomes and sedentary behaviour [ 5 ]. Spending more time in physical activity provides a moderate protective effect against depression and a small protective effect against anxiety [ 6 ]. A recent review of Cochrane systematic reviews of randomised trials also concluded that exercise reduces mortality rates and improves quality of life among various populations, including children and adolescents, adults with and without underlying conditions such as heart diseases and mental illnesses, and the ageing population [ 7 ].

Moreover, recent studies in sedentary behaviour research introduce different constructs between physical activity and sedentary behaviour, indicating how “ sedentary behaviour may be more than just physical inactivity” [ 8 ]. For instance, individuals devoting 30 min daily to exercise, thus meeting the criteria for being physically active, can still be deemed highly sedentary due to prolonged periods spent seated in front of monitors throughout the remainder of the day [ 9 ]. Evidence also shows independent, negative effects of sedentary behaviour on health [ 10 , 11 ], increasing the risks of various diseases such as cardiovascular disease and type 2 diabetes [ 12 , 13 ].

Health-related quality of life (HRQoL) reflects an individual’s physical, mental, and social well-being and can be measured using several tools [ 14 ]. One of the most widely used measures is the EuroQol-5 Dimension (EQ-5D, five-level version) [ 15 ]. The significant importance of incorporating EQ-5D HRQoL data collection and analysis in research is the use in cost-utility analysis for health technology assessment, which can provide context-specific evidence for policy evaluation and sustainability of implementation [ 16 ]. Globally, studies have reported positive associations between higher physical activity levels and HRQoL [ 17 ]. However, very few studies included the sedentary behaviour domain to explore the correlations on HRQoL [ 18 ].

While overall EQ-5D HRQoL provides the foundation for health technology assessment and priority setting in public health investment, a better understanding of EQ-5D dimensions can give valuable comprehension of HRQoL problems within the targeted population [ 16 ]. However, few studies have explored the effects of both physical activity and sedentary behaviour on each HRQoL dimension. A recent study found positive associations between self-report active and not-sedentary lifestyles on all HRQoL domains in adults during a COVID-19 outbreak [ 19 ]. Other studies focusing on older adults also reported parallel results where higher physical activity levels and lower sedentary time are correlated with better HRQoL in all dimensions [ 20 , 21 , 22 ]. Moreover, EQ-5D dimensions affect the overall HRQoL differently across countries. For example, the mobility dimension showed the greatest impact on utility decrement in the Thai, Korean, Japanese, Indonesian, and Canadian populations. In contrast, pain or discomfort and anxiety or depression were most significant for the Dutch and English populations [ 23 ].

In Thailand, more than 70% of all deaths are attributed to non-communicable diseases [ 24 ]. Physical inactivity alone contributes to 2.4% of all deaths in the country [ 25 ], where around 31% of Thai adults do not meet the recommended physical activity level [ 26 ]. Moreover, Thais spend a significant portion of their day, approximately 14 h, being sedentary [ 26 ]. In the Thai ageing population, a nationwide survey found that no regular exercise had the highest odds ratio of poor quality of life compared to hearing or sleeping difficulty or poor financial status [ 27 ]. Other studies reported that leisure, household, and work-related activities were associated with higher HRQoL [ 28 , 29 ]. In addition, a cross-sectional survey found that performing, at least, three weekly exercise sessions improves HRQoL in Thai adults [ 30 ].

Nevertheless, there has not been any study in Thailand that evaluates the association between sedentary behaviour on overall or domains related to HRQoL. This is despite the increasing studies on physical activity and sedentary behaviour in recent years [ 31 ]. Furthermore, a recent scoping review of physical activity and sedentary behaviour research in Thailand encouraged researchers to use accelerometer data for more robust evidence since 94% of the studies used self-report data [ 31 ]. Thus, we used baseline data from the PAW study, a cluster-randomised control trial including 282 office workers in Thailand, which incorporated accelerometer-data measurement of physical activity and sedentary behaviour and self-report HRQoL using the EQ-5D-5L questionnaire [ 32 ]. This cross-sectional analysis evaluates the associations between physical activity and sedentary behaviour on HRQoL, including the five dimensions of HRQoL.

This is a sub-study of the PAW cluster-randomised trial with multi-component intervention, including individual (pedometer and individual-based weekly lottery reward), social (team movement breaks and team-based weekly lottery reward), organisational (leaders’ involvements), and environmental level (posters) to reduce sedentary time and increase physical activity in Thai office workers. Detailed protocol [ 32 ] and the main results of the trial [ 33 ] are available online. Eighteen offices in the Ministry of Public Health, Thailand, were recruited between July to September 2020. The recruitment criteria included: i) aged at least 18 years old, ii) were not pregnant, iii) had no physical limitation to perform team movement breaks. The baseline data were collected by the PAW-study research team, which consisted of trained research staffs and programme managers, at participants’ office buildings between July and October 2020 before participants were randomised into the 6-month active intervention and control group.

Physical activity and sedentary behaviour

Participants were requested to wear the ActiGraph™ wGT3X-BT triaxial accelerometer (ActiGraph, Pensacola, Florida, USA) on the right waist as much as possible (except for bathing, swimming, or diving) for ten days. A validity wear time criterion of more than 10 h per day for at least three workdays was used. Participants with insufficient wear time were asked to re-wear the accelerometers [ 32 ]. We used the ActiLife software (Version 6.13.4) to extract count data from the accelerometer and then R package ‘PhysicalActivity’ to categorise the tri-axial accelerometer data into time spent in sedentary behaviour (150 and below counts per minute), light physical activity (151 to 2689 counts per minute), moderate physical activity (2690 to 6167 counts per minute) and vigorous physical activity (6168 and above counts per minute), according to Freedson cutpoints and a validation study [ 34 , 35 , 36 ]. The daily mean time spent in sedentary behaviour and moderate-to-vigorous physical activity were then calculated. Next, participants were grouped into ‘sedentary’ or ‘not-sedentary’, using a cut-off of nine hours per day or more spent in sedentary behaviour [ 37 ]. ‘Physically active’ was defined as having at least 150 min spent in moderate physical activity or 75 min spent in vigorous physical activity per week [ 4 ]. Finally, we categorised participants into four categories: (i) not-sedentary and physically active (the Reference Group), (ii) not-sedentary but physically inactive, (iii) sedentary but physically active, and (iv) sedentary and physically inactive.

• Health-related quality of life

This study measured HRQoL using the EuroQol-5 Dimension questionnaire with five scoring options (EQ-5D-5L). The five dimensions included mobility, self-care, usual activities, pain or discomfort, and anxiety or depression [ 38 ]. Different health profiles from the questionnaire were summarised into utility index scores (EQ-5D values) using the Thai valuation study [ 23 ]. Moreover, to analyse between-group differences in each dimension, the five scoring options (no problem, mild-, moderate-, severe problem, and unable to perform tasks) were categorised into either “having no problems” (including only if the answer was ‘no problem’) or “having problems” (including ‘mild-, moderate-, severe problems, and unable to perform tasks) [ 16 , 20 ].

Age, sex, education, smoking history, and underlying cardiovascular disease data were collected using the Thai National Statistical Office's health survey [ 32 , 39 ]. Although the survey has been commonly utilised in previous studies [ 40 , 41 , 42 , 43 ], no validation study was conducted. Nevertheless, we employed the questionnaire because it has been extensively used in health-related research in Thailand, allowing us to compare our results with findings from other studies effectively. The total duration of each interview was approximately 30 min per participant.

Education, smoking history, and underlying cardiovascular diseases data were categorised as binary data, with education classified as above Bachelor's degree or below, smoking history categorised as ever or never smoked, and underlying conditions categorised as having any cardiovascular-related disease (including diabetes mellitus, hypertension, dyslipidemia, or any heart disease) or having none. A physical examination was done to collect body-mass index data. We measured height to the nearest 0.1 cm and weight to the nearest 0.1 kg to calculate participants' body-mass index (weight (kg.)/height (m.) 2 ). Participants were classified as obese and not obese, using the Asian body-mass index cut point of 25 kg/m 2 [ 44 ].

Statistical analysis

Participant characteristics were summarised and compared between participants’ physical activity and sedentary behaviour categories using mean (SD) with t-test for continuous variables, and count (percentage) with Pearson’s chi-squared test for categorical variables. In addition, descriptive analyses, such as mean (SD), median of EQ-5D values, the proportion of participants without problems, and data visualisation, were also implemented to comprehensively understand participants’ cross-sectional overall HRQoL and quality of life by dimensions [ 16 ].

The primary analysis used Tobit regression models to account for ceiling values [ 45 ] and examine differences in EQ-5D values between each participant category compared to the Reference Group. Four different models were performed: (i) the unadjusted model, (ii) adjusted for sex, age, smoking, and obesity, (iii) adjusted further for education, and (iv) finally adjusted further for underlying cardiovascular diseases. In addition, by stratifying the time spent in physical activity and sedentary behaviour into working and leisure hours, a supplementary analysis using Tobit regression models with continuous exposure variables was conducted to explore their associations on EQ-5D values.

The odds of reporting “having problems” in each EQ-5D dimension were estimated by comparing each participant’s physical activity and sedentary behaviour categories to the Reference Group, using the unadjusted and the fully adjusted logistic regression models (adjusted for sex, age, smoking, obesity, and underlying cardiovascular diseases).

Sensitivity analyses were performed using distinct binary variables for sedentary behaviour (sedentary vs not-sedentary participants, without the physical activity component) and physical activity (physically inactive vs active participants without the sedentary behaviour component) as exposure variables in both Tobit and logistic regression analyses. All statistical data analyses were performed using Stata software version 14.2, with a significance level of 5%.

Participant characteristics

Of 282 PAW participants, 277 (98.2%) valid accelerometer-measured data were collected. Participant characteristics are presented in Table 1 across the four categories: (i) the Reference Group ( n  = 97), (ii) not-sedentary but physically inactive ( n  = 91), (iii) sedentary but physically active ( n  = 36), and (iv) sedentary and physically inactive ( n  = 53). Demographic data were not balanced among categories, especially for age (P = 0.007), sex (P < 0.001), education status (P < 0.001), and underlying cardiovascular diseases (P = 0.032). Older age, higher education, and higher prevalence of cardiovascular disease were observed in participants who were sedentary and physically inactive (Table 1 ).

Overall, participants had high EQ-5D values (mean = 0.910, SD = 0.102). Participants who were sedentary and physically inactive had the lowest mean EQ-5D values of 0.887 (SD = 0.122). In contrast, participants in the Reference Group had the highest mean EQ-5D values of 0.923 (SD = 0.111). Significant mean differences in EQ-5D values were observed among participant categories (P = 0.001). Participants who were sedentary and physically inactive were more likely to have problems with mobility (43.4% vs 29.9%), usual activity (28.3% vs 14.4%), and pain or discomfort (69.5% vs 49.5%) compared to the Reference Group, though not statistically significant (Table 1 , Fig.  1 ).

a Health-related quality of life dimensions was collected using EQ-5D-5L interviewer-administered questionnaire. Participants were categorised into either ‘Having problem’ (1) or ‘Having no problem’ (0) in each dimension. b Sedentary refers to spending at least nine hours per day in sedentary behaviours, while not-sedentary refers to spending less than nine hours per day in sedentary activities. c physically inactive refers to participants who did not meet the current physical activity guideline (≥150 minutes moderate-intensity or >75 minutes vigorous-intensity equivalent physical activity per week), while active refers to participants who met the guideline

Health-related quality of life dimensions a compared between physical activity and sedentary behaviour categories.

Differences in EQ-5D values

From the Tobit regression analyses, all categories of participants had lower EQ-5D values than the Reference Group. Sedentary and physically inactive participants had -0.0503 (95%CI: − 0.0946 to − 0.00597) lower EQ-5D values than the Reference Group. All other findings from the Tobit regression analyses were non-significant.

Sensitivity analyses showed consistent findings. When comparing distinctly physically inactive (n = 144) to active participants (n = 133), excluding the sedentary data component, inactive participants had − 0.0326 (95%CI: − 0.0634 to − 0.00187) lower EQ-5D values in the unadjusted model (Additional file 1 : Table S1). Similarly, a lower EQ-5D value was observed in the sedentary (n = 89) compared to the not-sedentary participants (n = 188), although the differences did not reach statistical significance (Additional file 1 : Table S2).

Parallel results were also observed when stratifying physical activity and sedentary behaviour levels in working and leisure hours and using them as continuous variables. Spending one more hour in moderate-to-vigorous physical activity was associated with 0.0520 (95%CI: 0.000792 – 0.103) increase in EQ-5D value in waking hours in the unadjusted model. A lower EQ-5D value was associated with higher sedentary time, whereas a higher EQ-5D value was associated with higher physical activity levels in both working and leisure hours, although without statistical significance. Nevertheless, we observed a higher magnitude of associations during leisure hours than during working hours for sedentary time and time spent in light physical activity on EQ-5D value (Additional file 1 : Table S3).

Differences in EQ-5D dimensions

Considering all the five dimensions of the EQ-5D-5L, participants who were not-sedentary but physically inactive, sedentary but active, and sedentary and inactive had higher odds of reporting problems in each EQ-5D dimension than the Reference Group (Table 3 ). The sedentary and physically inactive participants had the highest odds ratio of having pain or discomfort problems (OR 1.39 compared to the Reference Group, with 95%CI: 1.07 to 1.79, from the adjusted model) (Table 3 ). Moreover, not-sedentary but physically inactive participants had 2.49 (95%CI: 1.14 to 5.40) higher odds of having problems conducting usual activity than the Reference Group.

Compared distinctly between physically inactive and active participants without the sedentary data component in the sensitivity analysis, more inactive participants reported having problems than the active participants in all dimensions (Additional file 1 : Fig. S1). Without statistical significance, the physically inactive group observed 1.89 (95%CI: 0.982 to 3.62) higher odds of having usual activity problems (Additional file 1 : Table S4). Accordingly, sedentary participants had higher odds ratios compared to not-sedentary participants (Additional file 1 : Fig. S2), with 1.72 (95%CI: 1.01 to 2.94) higher odds of having pain or discomfort problems than not-sedentary participants (Additional file 1 : Table S5).

This cross-sectional study used accelerometer-measured physical activity and sedentary behaviour data from the PAW cluster-randomised trial [ 33 ] to evaluate their associations with EQ-5D-5L HRQoL. The results showed that participants who were sedentary and physically inactive had lower EQ-5D values than those who were not-sedentary and active. Moreover, higher odds of reporting problems in the usual activity and pain or discomfort dimensions were found in participants who were either sedentary or physically inactive compared to not-sedentary and active.

The findings parallel previous studies that reported a positive impact of physical activity on HRQoL in adult populations in different countries [ 46 , 47 ] . A recent study reported a significant association between insufficient physical activity and lower physical HRQoL. However, the study did not find significant associations between physical activity on the mental dimension of HRQoL, or sedentary behaviour on any HRQoL domain [ 5 ]. Another recent review of systematic reviews indicated that physical activity improves HRQoL and well-being, with the most robust evidence in older adults and strong evidence in the adult population [ 17 ]. Regarding the negative impact of sedentary behaviour on HRQoL, a systematic review reported that higher levels of sedentary behaviours are related to lower physical HRQoL, while unclear evidence was found in mental and social HRQoL domains [ 18 ]. From our analyses, all categories of participants (sedentary and physically inactive, sedentary but active, and non-sedentary but inactive) had lower EQ-5D values than not-sedentary and active, based on the current recommendations from physical activity guidelines [ 4 , 10 ]. The lowest EQ-5D value was the participants who were sedentary and inactive. The results indicated that a higher HRQoL is related to both higher physical activity and lower sedentary behaviour levels in adult office workers. Our results align with another recent study concludingthat an extended period of sedentary time could diminish the mitigating impact of moderate-to-vigorous physical activity on the vulnerability to the risk of poor HRQoL [ 48 ]. The rationale behind the correlations found in our study might be based on the negative impact on health of sedentary behaviour and the lack of physical activity, attributing to various non-communicable diseases [ 1 , 2 , 3 , 12 ] and also psychiatric conditions [ 6 , 49 , 50 ]. On the other hand, this might be due to the reverse causation, where experiences of pain or discomfort impede individuals from exercise and enhance sedentary behaviours.

Furthermore, the positive associations of higher physical activity levels on HRQoL were observed in both working and leisure hours (Additional file 1 : Table S3). This contrasts with the concept of the physical activity paradox, where emerging evidence reports a negative impact of occupational physical activity on health [ 51 , 52 , 53 ]. A plausible rationale for this observation could be that our participants were office workers who did not engage in strenuous physical tasks. On the other hand, studies supporting the physical activity paradox defined higher-risk categories as ‘heavy physical work (or labour)’, ‘carrying heavy burdens…’, and ‘activities that could significantly elevate heart rate during working hours’ [ 51 , 52 , 54 ]. Hence, our participants, categorised as sedentary workers, may not face the same detrimental effects from increased physical activity during their working hours.

To date, only a limited number of studies have analysed the associations between sedentary and physical activity levels on different HRQoL dimensions. Such analysis provides insightful information and shows how the focused explanatory variables associate differently with each HRQoL dimension in different populations [ 16 ]. Previous studies in ageing populations reported moderate-to-strong associations between increased physical activity levels and improved HRQoL across all dimensions [ 21 , 22 , 55 ]. Studies which included both physical activity and sedentary levels reported comparable results. In the U.S. adults, a study reported significant correlations between the poor physical health and activity limitation domains with both lower moderate-to-vigorous physical activity and higher sedentary behaviour levels, while all domains correlated with moderate-to-vigorous physical activity level alone [ 48 ]. On the other hand, another study in the ageing Korean population found significant associations across all dimensions, with the highest odds ratio in problems performing usual activities [ 20 ]. These pieces of evidence aligned with our study among healthy adult office workers, where higher sedentary and lower physical activity levels were associated with higher odds of having problems in HRQoL dimensions, particularly in the pain or discomfort dimension (Table 3 ). The rationale behind our findings might be due to the connection between high sitting time and pain, as reported in previous studies [ 56 , 57 ]. Another possible explanation could be attributed to the highest occurrence of pain or discomfort problems, as compared to other domains, in our population of healthy office workers. This contributed to a greater power to detect statistical significance among participant categories (Table 2 – 3 ).

Strengths and limitations

Major strengths of the study were the higher accuracy of the physical activity and sedentary data due to standard tri-axial accelerometer-measure data collection [ 35 , 58 ], the specific Thai value set for analysing the EQ-5D values tailored for the Thai population [ 23 ], and the Tobit regression model to account for the ceiling effect of HRQoL in our healthy population [ 45 ]. Moreover, delving into different HRQoL domains generates ideas on how, in Thai office workers, being physically inactive and sedentary might be associated more with pain or discomfort, and less with other domains, such as anxiety or depression (Table 3 ). Nevertheless, there are several limitations of the study. First, this was secondary data analysis. The PAW study was initially designed to evaluate the effectiveness of a complex intervention in reducing sedentary time in office workers [ 32 ]. As such, this analysis, focusing on HRQoL, was not powered to detect statistical differences, resulting in lower generalisability of the associations. The second limitation is the cross-sectional design, preventing the determination of causality. Future studies with longitudinal data to estimate the causation of physical activity and sedentary behaviour on HRQoL are needed. For instance, a recent study in the Korean population reported that HRQoL in the early ageing population was affected by the change in physical activity level over an 8-year follow-up [ 59 ]. Another limitation is the inability to judge the importance or imply concrete meanings of the between-group EQ-5D values difference without further information. This is because populations with different health states have different scales for EQ-5D values [ 16 ]. The idea of Minimal Importance Differences calculation to determine the smallest difference in EQ-5D value that the population perceive as important has been discussed without consensus [ 60 ]. Nevertheless, the implication of EQ-5D values lies in future cost-utility analyses of the same population [ 16 ]. The last limitation is that the generalisability might be low because different countries have different contexts and also use different EQ-5D valuations. Similar studies in other countries should be conducted to understand the generalisability of the findings.

This study underscored the importance of promoting physical activity along with reducing sedentary behaviour to enhance Thai office workers’ quality of life across different domains. Further research, incorporating larger cohorts and longitudinal data, is essential to establish a stronger foundation for interventions and economic evaluations targeting sedentary reduction and physical activity promotion for quality of life improvement in Thailand and beyond.

Availability of data and materials

The research team will have exclusive rights to the de-identified data for 24 months after the trial is completed. After that, the data and full protocol will be publicly accessible on the HITAP website.

Abbreviations

Body mass index

: EuroQol’s-5 dimension

: Health-related quality of life

: Physical activity at work

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Acknowledgements

We thank Saudamini Dabak, Kewalin Chomrenoo, Nachawish Kittibovorndit, and Sopitta Arsirasatian for the helpful discussions and administrative support contributing to this work.

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In addition to disseminating our research findings to the funder of this study, the Ministry of Public Health, we will disseminate our findings to other countries, the study participants and the research community. We also followed the authorship guidelines of the International Committee of Medical Journal Editing (ICMJE).

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The study is funded by sin-tax through the Thai Health Promotion Foundation (address: 99/8 Soi Ngamduplee Thungmahamek, Sathorn, Bangkok, Thailand 10120, Tel: (66) 2-343-1500, Fax: (66)-2-343-1501, email: [email protected]). HITAP's International Unit is supported by the International Decision Support Initiative (iDSI) to provide technical assistance on health intervention and technology assessment to governments in low and middle-income countries. KA is supported by Department of Medical Services scholarship and the Singapore Ministry of Health’s National Medical Research Council (MOH-HSRGMH18may-0001). The funders had no role in study design, data collection or analysis, preparation of the manuscript or decision to publish.

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All authors contributed to the study design, data management, and analysis. KA was the Principal Investigator. Physical activity data management expertise, health-related quality of life-related data analysis insight, and overall statistical data analysis expertise were provided by FMR, YT, and CC, respectively. All authors have reviewed the manuscript draft, have read, and approved the final version.

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Additional file 1. table s1..

The difference in EQ-5D value between physically inactive compared to active participants. Table S2. The difference in EQ-5D value between sedentary compared to not sedentary participants. Table S3. Tobit regression analysis of EQ-5D values with different exposures. Table S4. Odds of having problems in each of the EeuroQol-5 Dimensions between physically inactive compared to active participants. Table S5. Odds of having problems in each of the EeuroQol-5 Dimensions between sedentary compared to not-sedentary participants. Figure S1. Participants reporting problems in each of the EeuroQol-5 Dimensions compared between physically inactive and active participants. Figure S2. Participants reporting problems in each of the EeuroQol-5 Dimensions compared between sedentary and not-sedentary participants.

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Akksilp, K., Müller-Riemenschneider, F., Teerawattananon, Y. et al. The association of physical activity and sedentary behaviour on health-related quality of life: a cross-sectional study from the physical activity at work (PAW) trial. JASSB 2 , 22 (2023). https://doi.org/10.1186/s44167-023-00031-7

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Exploring adults’ experiences of sedentary behaviour and participation in non-workplace interventions designed to reduce sedentary behaviour: a thematic synthesis of qualitative studies

• G. H. Rawlings 1 ,
• R. K. Williams 2 ,
• D. J. Clarke 2 ,
• C. English 3 ,
• C. Fitzsimons 5 ,
• I. Holloway 4 ,
• R. Lawton 6 ,
• G. Mead 7 ,
• A. Patel 8 &
• A. Forster 2  

BMC Public Health volume  19 , Article number:  1099 ( 2019 ) Cite this article

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Sedentary behaviour is any waking behaviour characterised by an energy expenditure of ≤1.5 metabolic equivalent of task while in a sitting or reclining posture. Prolonged bouts of sedentary behaviour have been associated with negative health outcomes in all age groups. We examined qualitative research investigating perceptions and experiences of sedentary behaviour and of participation in non-workplace interventions designed to reduce sedentary behaviour in adult populations.

A systematic search of seven databases (MEDLINE, AMED, Cochrane, PsychINFO, SPORTDiscus, CINAHL and Web of Science) was conducted in September 2017. Studies were assessed for methodological quality and a thematic synthesis was conducted. Prospero database ID: CRD42017083436.

Thirty individual studies capturing the experiences of 918 individuals were included. Eleven studies examined experiences and/or perceptions of sedentary behaviour in older adults (typically ≥60 years); ten studies focused on sedentary behaviour in people experiencing a clinical condition, four explored influences on sedentary behaviour in adults living in socio-economically disadvantaged communities, two examined university students’ experiences of sedentary behaviour, two on those of working-age adults, and one focused on cultural influences on sedentary behaviour. Three analytical themes were identified: 1) the impact of different life stages on sedentary behaviour 2) lifestyle factors influencing sedentary behaviour and 3) barriers and facilitators to changing sedentary behaviour.

Conclusions

Sedentary behaviour is multifaceted and influenced by a complex interaction between individual, environmental and socio-cultural factors. Micro and macro pressures are experienced at different life stages and in the context of illness; these shape individuals’ beliefs and behaviour related to sedentariness. Knowledge of sedentary behaviour and the associated health consequences appears limited in adult populations, therefore there is a need for provision of accessible information about ways in which sedentary behaviour reduction can be integrated in people’s daily lives. Interventions targeting a reduction in sedentary behaviour need to consider the multiple influences on sedentariness when designing and implementing interventions.

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Introduction

Over the last decade, sedentary behaviour has emerged as an important public health issue. Sedentary behaviour has become the focus of research, clinical and policy interest. Evidence supporting the detrimental effects of prolonged sedentary time on health and wellbeing in individuals of all ages is rapidly growing [ 1 , 2 , 3 ]. In the general population, sedentary behaviour has been associated with an increased risk of a range of health problems including, cardiovascular conditions [ 4 ], mood disorders [ 5 ] and all-cause mortality [ 6 ]. Some clinical populations, for example stroke survivors [ 7 ] or those living with frailty [ 8 ], are more prone to engage in long, uninterrupted bouts of sedentariness. This is likely to contribute to increased risk of adverse health outcomes and limit the potential of rehabilitation therapies.

Sedentary behaviour is defined as ‘any waking behaviour characterised by an energy expenditure of ≤1.5 metabolic equivalents of task while in a sitting, reclining or lying posture’ (p.5). Sedentary behaviour is distinct from physical inactivity, which is defined as insufficient physical activity levels to meet current recommendations (150 min of moderate - vigorous physical activity a week) [ 9 ]. Previous systematic reviews related to sedentary behaviour have primarily focused on measurement of, determinants of and the health-related effects of sedentary behaviour, focused on interventions designed to reduce sedentary behaviour [ 10 , 11 , 12 ] or on whether physical activity is effective for offsetting the negative effects of sedentary behaviour [ 13 ]. These reviews explore intrapersonal, social, environmental, physical environmental and policy correlates of sedentary behaviour [ 14 ], and the relationship between sedentary behaviour and health-related quality of life. Systematic reviews of qualitative data are becoming more commonplace and have explored adults’ experiences of physical activity [ 15 ] and acceptability of physical activity interventions [ 16 ]. Qualitative research can contribute to our understanding of factors that influence sedentary behaviour, assist with identification of modifiable determinants, and help identify barriers and facilitators to promoting sedentary behaviour change.

The aims of the current review were to produce a systematic, thematic synthesis of qualitative research investigating (i) adults’ experiences of sedentary behaviour, and (ii) participation in interventions designed to reduce sedentary behaviour in adults. We sought to understand peoples’ perceptions and experiences of sedentary behaviour in order to identify what barriers and facilitators influence sedentary behaviour in adults. As this review was undertaken as part of a research programme that will develop and test a community-based sedentary behaviour reduction intervention for stroke survivors, we excluded workplace-based studies.

We also aimed explore the views of carers, relatives and health and social care professionals in relation to sedentary behaviour in adults, however, we were not able to identify any data to directly address this aim.

Search strategy

This review has been undertaken in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. The protocol was published on the PROSPERO database ID: CRD42017083436.

A systematic search of seven databases (MEDLINE, AMED, Cochrane Database of Systematic Reviews, PsychINFO, SPORTDiscus, CINAHL and Web of Science) was performed in September 2017. Search terms were developed in collaboration with an information specialist (Additional file  1 ). Inclusion and exclusion criteria are listed in Table  1 .

Following the search, three reviewers (GHR, RW and DJC) jointly screened the first 150 titles and abstracts – this allowed for review and refinement of the inclusion criteria. Thereafter, GHR and RW independently screened the remaining titles and abstracts (50% each). Full text articles were independently reviewed by the same two reviewers; disagreements were resolved through discussion with a third reviewer (DJC). A backward search of references of eligible papers did not identify any additional studies.

Data extraction

The following data were extracted from each study: author(s), year of publication, study purpose, sample characteristics, country, methodological considerations, findings and discussion. One-third of the included studies were randomly selected and subject to double data extraction and quality assessment. The data extraction tables and quality assessment reports for papers subject to double data extraction and quality assessment were reviewed by a third reviewer (DJC), then discussed with the primary reviewers (GHR and RW). The level of agreement for data extraction was found to be good; there was also a satisfactory level of consistency in the quality assessment ratings for these papers.

Quality assessment

The National Institute for Health and Care Excellence qualitative appraisal guidance was used to assess methodological quality [ 17 ]. Studies were evaluated using a 14-item quality assessment checklist (Table  3 ). Reviewers endorsed the presence or absence of domain characteristics as clear, unclear or not reported. The checklist assessment of study quality can be marked: (++) the majority of the criteria have been fulfilled; (+) some of the criteria have been fulfilled; or (−) very few of the criteria have been met. Differences in quality assessment ratings between the reviewers were discussed until consensus was reached. Quality was assessed for descriptive purposes; papers were not excluded on the basis of the quality assessment; we drew upon relevant data from all included studies.

Data synthesis

A thematic synthesis approach was used [ 18 ]. Data from primary studies were used to initially develop descriptive themes that closely reflected study findings. Analytical themes were then formulated that go beyond the data and generate new interpretations of the results [ 18 ]; this involved three main stages:

Key findings, including the title of themes, from each article, specific to the review aims, were coded by GHR and RW using NVivo 10 [ 19 ].

Codes were organised to identify relationships, similarities and differences between the data. This stage identified key descriptive themes and sub-themes.

Analytical themes were developed. This was an iterative and cyclical process. Reviewers explored the descriptive themes to generate novel findings based on the amalgamated data with the view of helping to inform future intervention development, policy and practice towards sedentary behaviour.

In this review, ‘’ represent authors’ quotations whereas “” are used for participants’ own words.

Literature search

From 25,170 titles and abstracts identified (Fig.  1 ), 25,020 were excluded. Full texts of 150 papers were assessed for eligibility; 44 were found to be eligible; reasons for exclusions are stated in Fig. 1 . The 44 eligible studies fell into two categories; studies of the experiences of individuals outside of the workplace ( n  = 30), including, the experiences of those with a medical condition and those who had participated in programmes to reduce sedentary behaviour and, studies focused on sedentary behaviour in the workplace ( n  = 14). As previously stated, we made a post-hoc decision to remove studies that specifically examined workplace associated sedentary behaviour. Included studies are listed in Table  2 . See Fig. 1 for PRISMA diagram and Additional file  2 for the references for the 14 workplace studies.

PRISMA diagram

Study characteristics

Studies were published between 1995 [ 20 ] and 2017 [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]; 25 were published between 2008 and 2017 (Table 2 ). All but two studies [ 29 , 30 ] examined the perceptions, experiences and sedentary behaviours of individuals living in Western countries. Whilst contemporary definitions differentiate between sedentary behaviour and physical activity [ 9 ], in the papers included in the review, thirteen focused specifically on sedentary behaviour and seventeen considered sedentary behaviour experiences, perceptions or reduction of sedentary behaviour in the context of physical activity participation.

Eleven studies examined experiences and/or perceptions of sedentary behaviour in older adults (typically ≥60 years) [ 20 , 22 , 25 , 26 , 28 , 29 , 31 , 32 , 33 , 34 , 35 ]; ten studies focused on sedentary behaviour in people diagnosed with a medical condition [ 21 , 23 , 24 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], four explored the perceived impact of socio-economic status on sedentary behaviour [ 43 , 44 , 45 , 46 ], two examined university students’ experiences of sedentary behaviour [ 27 , 47 ], two focused on working-age adults [ 30 , 48 ], and one focused on cultural influences on sedentary behaviour [ 49 ]. The views of 918 individuals from ten countries are represented. Participants’ ages ranged from 18 to 92 years. Sample size ranged from 9 [ 31 ] to 90 [ 29 ]. In 20 studies, the sample was predominantly female or only recruited females, two studies investigated men only, and the remaining eight studies explored experiences of both men and women.

Overall, 22 studies examined adult’s experiences and perceptions of sedentary behaviour, and eight studies investigated participant’s experiences of engaging in interventions designed to reduce sedentary behaviour. The intervention studies included older adults [ 22 , 25 , 28 , 34 ], overweight women [ 37 , 42 ], women living in disadvantaged neighbourhoods [ 45 ] or adults at risk of type 2 diabetes [ 21 ].

Most studies were graded highly across the fourteen quality domains (Table 3 ). Twenty-one (70%) papers were graded ++ (good). Six papers were graded + (moderate); in these papers description of data generation and analysis was limited; in five [ 21 , 22 , 34 , 38 , 43 ] the role of researcher(s) was not described in sufficient detail, and ways in which the relationship between participants and researcher(s) may have influenced the study were not considered. Three papers were rated as - (low). These papers did not clearly report how data were generated, nor the stages of or who was involved in the analysis. These papers did not discuss research limitations and were evaluated as being narrow in their conclusions.

• Thematic synthesis

In total, 354 raw codes were recorded, from which ten descriptive themes emerged. After further analysis, three analytical themes were identified focusing on (i) the impact of different life stages on sedentary behaviour, (ii) lifestyle factors influencing sedentary behaviour, and (iii) barriers and facilitators to changing sedentary behaviour (Fig.  2 ).

Framework of emergent descriptive and analytical themes

Theme 1: the impact of different life stages on sedentary behaviour

Some participants perceived their attitudes and behaviour related to their current sedentary behaviours were established in childhood. Individuals explained how social and physical environments in which they grew up in influenced their levels of sedentariness [ 38 , 46 ]. Parenting style as a determinant of sedentary behaviour was also described. In one study published in 1995 interviewing older women, one ‘ inactive woman ’ [defined as someone who did not exercise ‘ regularly ’] reported that her mother would at times tell her she was “ overdoing it ” and she had “ better sit down and read a book or do a bit of sewing ”. While this reflects a single perspective on the influence of parental attitudes toward activity in a different time period, it highlights the perceived importance of parental influences on shaping later life attitudes toward sedentary behaviour [ 20 ].

Other factors impacting levels of sedentary behaviour at this stage were family norms, social pressures, and the interests and capabilities of the participant [ 20 ]. For example, a ‘ turning point’ in later childhood was described; individuals would compare, for example, their performance or competency in sport to that of their peers. This comparison led some to focus their efforts on less active pursuits: “ If you are not good at organised sport you are not going to continue it ”. Such turning points could shape later life decisions to engage in pursuits which gave pleasure, such as knitting, needlework and watching television, but which were nonetheless sedentary. [ 20 ]. However, attitudes toward sedentary behaviour formed at this stage were not immutable and could be subject to change as a result of later life experiences. In more recent studies, one participant explained that after leaving home her level of sedentary behaviour remained the same as that imposed in the family home [ 46 ] , while in contrast, another interviewee explained that he was now free to engage in as much sedentary behaviour as he wanted [ 47 ] .

Student and adulthood

Naturally, social and family roles, employment and economic circumstances changed over time in adulthood. Such factors were reported as directly influencing time spent sedentary, the consequences of which, could act as facilitators or barriers to reducing sedentary time. In the two studies focused on university student experiences, students reported engaging in high levels of sedentary behaviour. They identified that academic pressures and university culture required long periods of sitting. The sedentary tendency promoted in academic settings seemed to encroach on other areas of life as participants “ become used to living like that” [ 38 ]. Healthy life choices were described as being “ sacrificed ” over gaining an education. For some, this appeared to be a source of conflict as the behaviour was inconsistent with their knowledge of a healthy lifestyle [ 27 , 47 ].

Sedentary behaviour associated with employment and the influence of employment on daily life emerged as an important determinant of sedentariness. Employment (or lack of [ 41 ]) was described as “ directly ” influencing levels of sedentary behaviour [ 48 ]. This was evident across a range of different participant groups. Factors that increased sedentary behaviour included: commuting to work; inconsistent or long working hours meaning people found it difficult to be active; having to sit at a desk; attend meetings [ 37 ]; or due to the effects associated with work, including stress and fatigue [ 38 , 43 , 48 , 49 ]. In contrast, participants in a study of stroke survivors explained that, after resigning from employment due to their health, they used exercise to fill their empty daily schedules [ 41 ].

In adulthood, family roles or “ obligations ” [ 38 ] such as increased responsibilities around the home, relationships or being a parent appeared to be a common factor that affected levels of sedentariness: “you get tied up with the social engagement of your family” [ 30 ] . These pressures were also reflected in experiences of sedentary behaviour interventions as family and work commitments were a common barrier to compliance [ 37 , 42 ]. While physical and time demands associated with children generally limited parents’ opportunities to engage in physical activity, for some, responsibilities for children meant that they did not have the free time to be sedentary. Indeed, some described children as ‘ energetic resources’ [ 20 , 44 , 48 ].

Retirement and later life

Older participants, in the later stages of life, described a general slowing down and becoming more sedentary as a result of internal (i.e. interests, routines and ageing) and external (i.e. expectations, social norms) pressures. The hobbies and leisure activities that older adults took part in were predominately sedentary e.g. passive television (TV) viewing [ 25 , 28 ], reading, sewing [ 38 ], and knitting [ 26 ]. It seemed that while older adults acknowledged the negative consequences related to their sedentary activities, such concerns were displaced if the behaviours were enjoyable, and associated with cognitive or social benefits: ‘ Many of the participants described how their preferred sedentary behaviour provided them opportunities to meet new people ’ [ 22 ].

Stigmatising aspects of participants’ social identities also emerged and cohered around the view that older adults can be viewed by others as ‘ tired, sick, lonely, or depressed’ [ 26 ], and that they should ‘ sit all day’ . While it was not explicitly reported, this view appeared to be held by society, friends and family (as well as some older adults themselves). Older adults interviewed in one study reported feeling ‘ typecast ’ as “ not useful ” or “ unable ” and that sitting should be their ‘main mode of living’ [ 31 ]. Despite these perceived pressures, some participants endeavoured to stay physically active, and harboured what was described as an ‘ active ageing attitude’ [ 32 ] . Notwithstanding this however, older adults’ experiences and perceptions of limitations in relation to their ageing bodies appeared highly salient: “ I use to do a lot more things but now… you just can’t do it ” [ 26 , 29 ]. For some, an increase in sedentary behaviour was motivated by their concerns that ‘ standing up more would interfere with the strategies they had put in place’, in response to their declining health or mobility [ 31 ] (see 3.5.4).

There were mixed views about the health benefits of reducing sedentary behaviour and maintaining a physically active lifestyle. A widely held belief that older adults should ‘ rest’ [ 31 ] was reported, and whilst encouraging rest may be perceived as a ‘ caring gesture’ by family or friends, participants pointed out that this behaviour ‘ took opportunities for being active and independence away from them’ [ 31 ]. On the other hand, some studies highlighted how family members positively influenced and supported older adults to reduce time spent sedentary through the shared responsibility of looking after grandchildren [ 48 , 49 ].

It was commonly reported that the impact of poor health contributed to prolonged periods of sedentary behaviour. Participants explained that symptoms associated with health status, such as ‘fatigue’ and ‘pain’ increased sedentary behaviour [ 24 , 36 , 39 ]. Interestingly however, pain and stiffness were also reported as reasons for breaking up periods of sedentary behaviour and increasing activity levels [ 40 , 41 , 42 ]. This bi-directional relationship between sedentary behaviour and illness was further exemplified when participants described sedentariness and mental health [ 31 , 38 ]. Depression was commonly linked to use of sedentary behaviour [ 24 , 31 ] with some explaining that: ‘ overcoming depression is essential to reducing sedentary behaviour’ [ 24 ], that they became more active ‘ to fight depression’ [ 31 ], or sedentary behaviour was used to ‘ switch off’ and ‘ remove themselves from their depressive frame of mind’ [ 46 ].

Engaging in sedentary behaviour was a common strategy used by participants to prevent declining health or further injury, and transitioning back into illness. Sedentary behaviour was adopted by some as a means to recover from and manage chronic disease symptoms [ 24 , 31 ] and rest was viewed as an important element in the recovery process [ 24 ], suggesting that sedentary behaviour was used as a precautionary or protective behaviour. There was also evidence to suggest that caring for and looking after family or friends who lived with a health problem reduced levels of activity and increased sedentary time [ 31 , 36 , 38 , 48 ]: “My wife has a serious lung disease. We are very limited in doing things …Before, we always went out to concerts” [ 36 ] .

Theme 2: lifestyle factors influencing sedentary behaviour

The range of sedentary behaviours individuals reported engaging in were considerable and included: reading, watching TV, crosswords, meditation, knitting, bingo, eating, gaming, studying, religious functions, motorised transport and ‘ simply lying down’ [ 24 ]. Participants’ interests (or at least their levels of activity and sedentary behaviour [ 30 ]) seemed to be influenced by age, gender [ 48 ], physical mobility, culture [ 49 ] and socio-economic status [ 43 ]. One interviewee reported that the sedentary activities engaged in ‘ were an important part of their life and self-image’ [ 42 ] and to change this would not only be difficult, but it would change who they are as a person. Participants in two studies reported engaging in sedentary behaviour because it was “ comfortable ” and “ relaxing ” [ 24 , 38 ]. Indeed, this was described as a potential barrier as people were concerned that breaking up sedentary behaviour would ‘ ruin ’ their enjoyment. In line with the pleasurable attributes of sedentary behaviour, individuals described using it as a reward [ 42 ]. Given this level of enjoyment, certain sedentary activities appeared to be a ‘compulsion’ as some participants described needing ‘ self-discipline’ [ 47 ] or having to make a ‘ conscious effort’ [ 24 ] to be less sedentary.

People reported engaging in sedentary behaviour for specific activities, such as reading or using the computer. This was due to the associative benefits, for example, when engaged in sitting individuals reported that they could give greater attention to the task at hand [ 25 ]. Sedentary behaviour however was not always associated with interests, need or comfort as it was also attributed to people being ‘ lazy ’, using it to ‘ pass the time’ [ 46 ] or their disinterest in more active pursuits [ 31 , 36 ]. As such, sedentary behaviour appeared to be an “ easy ”, [ 32 , 42 ] cheap or habitual alternative [ 47 ] to more active behaviours. Some forms of sedentary behaviour were seen as being integral components to daily life [ 25 ], for instance, across studies it was common for participants to sit down to rest after work [ 31 , 38 , 48 , 49 ]. However, participants involved in focus groups investigating their experiences of a sedentary behaviour intervention explained that, reducing sitting time at home or in the evenings would be easier than limiting sitting at work [ 21 ].

There were a number of facilitators towards individuals being more active (and reducing sedentary behaviour). These included: being ‘ motivated and determined’ to be less sedentary [ 24 , 48 ]; adhering to physical activity guidelines; motivated to age well [ 32 ]; to keep their independence [ 33 ]; and to look good and be healthy.

Environmental

Individuals’ physical environment was an important factor when understanding the determinants of sedentary behaviour. People described being more likely to be sedentary during the winter months, when it was cold or wet, and short daylight hours [ 23 , 24 , 26 , 31 , 38 , 40 , 45 , 46 ]. In line with this, symptoms associated with illnesses or ageing were barriers, for example, people with impaired eyesight expressed concerns over obstacles i.e. shrubs, which posed as hazards [ 33 ].

Other practical constraints influencing sedentariness were financial costs [ 22 ], poor transport links making walking to certain places difficult; home location [ 48 ], work-life balance and neighbourhood crime. Problems associated with childcare [ 46 ], and lack of availability of gyms, parks or greenspace, and poor quality of services [ 21 , 44 , 46 ] were also reported. Similar restrictions were described as logistical barriers by participants in sedentary behaviour interventions [ 22 ]. There was evidence to suggest that some individuals externalised fault, blaming practical factors for being less sedentary; when students were asked how they could reduce their sedentary behaviour, they predominately reported changes that others could make as opposed to actions that they could perform themselves [ 27 , 47 ].

Socio-cultural

Family, friends and pets [ 27 , 32 , 38 , 41 ] were described as being able to prompt, remind and motivate participants to decrease their sitting time and engage in more physically active pursuits [ 42 , 46 , 48 ]: “ He [a friend] lost three stone in a year…And it suddenly clicked and I decided I wasn’t a lost cause ” [ 48 ]. However, they could also discourage participants: one interviewee explained that if she went out on a Saturday with her mother she would “ go on foot ”, whereas if she went out with her father they would go by car as he “ doesn’t want to walk ” [ 38 ]. The benefits of social support were also described by participants in sedentary behaviour interventions, such as meeting new people, or feeling that they must attend sessions as to not let others down [ 22 ]. Although the current review included only two studies examining sedentary behaviour outside of Western culture, different socio-cultural norms and family traditions were shown to influence sedentary behaviour [ 42 ]. For example, in one study examining sedentary behaviour in South Asian women living in the United Kingdom (UK), the culturally accepted norm when becoming a mother-in-law was being ‘ entitled to do a great deal of sitting after having raised a family ’ [ 49 ].

Notwithstanding a high proportion of the studies reviewed here examined a female dominant sample, a strong gendered dimension emerged [ 30 , 43 , 48 ]. Several participants made reference to the limited culturally appropriate options to be less sedentary available to women: “ The ladies who have no job, what [option] will they have except sitting at home? They cannot just go around roaming between the houses, socially it’s not acceptable ” [ 30 ]. Differences in socio-economic status appeared in the value afforded to certain leisure-time sedentary behaviours. Women of all socio-economic groups reported preference for TV viewing, but this appeared particularly popular as a pastime among women of low socio-economic status and, to a lesser extent, mid socio-economic status [ 43 , 46 ].

The media reportedly played an important role in influencing participants’ perceptions of sedentariness. While it helped some individuals to live a healthier lifestyle, for others, it desensitised them or caused feelings of hopelessness as they felt there is little they could do about being sedentary [ 26 , 48 ]. The importance of how key messages around sedentary behaviour are delivered was further demonstrated in intervention studies, as participants explained some of the information provided came across as being patronising [ 42 , 45 ].

Theme 3: barriers and facilitators to changing sedentary behaviour

Sedentary behaviour education.

Many participants were unfamiliar with the term sedentary behaviour and were not aware of the associated health consequences [ 23 , 24 , 25 , 28 , 47 , 49 ]. Further, misconceptions around sedentary behaviour were described: a stroke survivor showed ‘ surprise when told that lying down during non-sleeping hours was considered sedentary behaviour ’ [ 24 ]. Lack of knowledge contributed to cognitive distortions with some individuals demonstrating all-or-nothing thinking, perceiving that if they were not physically active they must be sedentary [ 21 , 46 , 47 ]. Other participants found it difficult to understand that their level of sedentary behaviour was problematic because they regularly engaged in physical activity [ 42 ]. Also, as discussed in Theme 1 (retirement and later life), there seemed to be cognitive dissonance around sedentary behaviour as while many viewed sedentariness negatively, they felt that the seated activities they engaged in were not negative because they perceived those behaviours had ‘ many social and cognitive benefits’ [ 26 ]. Despite participants’ limited knowledge of sedentary behaviour, it was apparent that, on some level, individuals did understand that living a sedentary lifestyle was unhealthy. For example, participants described the guilt they associated with being sedentary [ 38 , 46 ], negative connotations and the stigma of identifying as being sedentary [ 26 , 48 ]; and some actively reduced their sedentary behaviour to be a good role model [ 46 ]. In one intervention study, participants described the link between too much sitting and health as ‘ logical, maybe even obvious …’ [ 21 ].

Educating people about sedentary behaviour was a common suggestion made by participants and researchers to reduce sedentary time. Participants felt this could be achieved in schools, workplace settings, community centres, places of worship, and health and social care settings [ 23 , 38 ]. Although none of the studies included in this review explored the perceptions or experiences of healthcare professionals in relation to sedentary behaviour, healthcare providers reportedly played an important role in educating and influencing participants’ sedentary behaviour. One interviewee explained [ 48 , 49 ]: “ I actually do stand a lot when I’m watching TV…I’ve been given advice by my GP [General Practitioner] to do it ” [ 46 ].

Strategies to change sedentary behaviour

Different strategies were described to reduce the total amount and break up bouts of sedentary behaviour. In one study [ 21 ] participants were asked to list key strategies used to ‘ sit less or move more ’ during a sedentary behaviour intervention. Eighteen different methods were suggested; the most common being walking, standing during TV breaks, reducing or turning off the TV, going to the gym, and standing while talking on the phone [ 21 ]. However, participants tended to focus more on strategies that specifically increased physical activity providing ‘ little to no specific recommendations’ targeting sedentary behaviour [ 21 , 46 , 47 ] [ 46 ]. In intervention studies, there was a mixture of attitudes towards alternatives to sedentariness. Some participants were not in favour of modifying current sedentary behaviour, doubted the effectiveness of suggested strategies [ 21 ] or felt alternatives were too artificial or forced [ 42 ]. Others however, appeared to enjoy this version of ‘ multitasking ’ as it was a ‘ new way of exercising’ [ 22 ]. Nevertheless, it was clear that any changes needed to be incorporated into participants’ everyday lives and become habitual [ 34 ].

Participants in sedentary behaviour interventions described the use of different behaviour change techniques. These included: monitoring their own sedentary behaviour [ 42 ]; having the opportunity to problem solve and overcome barriers to being more active; reading leaflets or booklets that discuss the importance of physical activity and reducing sitting time [ 45 ]; and regular prompts and reminders, for instance, key messages such as ‘sit less’, ‘move more’ and ‘ stand more’ [ 21 ]). Financial incentives (e.g. reduced gym fees); opportunities for social comparisons and support [ 21 ]; being able to set their own sedentary reduction goals [ 25 , 42 ]; praise from others [ 42 ]; and rewards for reducing sedentary behaviours [ 40 ] were also reported. Technology-related behaviour change techniques were discussed including, wearable devices and computer or smart-phone/tablet applications (apps). Such strategies were described as helping to track progress, ‘ enable ’, ‘ prompt ’ or ‘ remind ’ participants to sit less [ 21 ], as well as being a key resource for information. While many participants had something positive to report about these methods, problems were experienced - this typically consisted of devices not being user friendly or practical [ 21 , 42 ].

For some people, their experience of the strategies designed to alter sedentary behaviour seemed to change as the intervention progressed. It was noted, for example, that some techniques could become rather agitating or frustrating and some individuals felt that failing to achieve intervention goals “ could be depressing ” [ 45 ]. Both substantial, long-term changes as well as more subtle, short-term nudges to reduce sedentary behaviour were suggested [ 21 , 45 ]. It was identified that strategies to reduce sedentary behaviour had to be suitable, straightforward, achievable, enjoyable [ 35 , 37 ], time efficient, and tailored to the individual’s particular circumstance, ability, and personal characteristics (such as age or gender) [ 45 ]. Indeed, ‘ the suitability of the activities could either motivate physical activity or sedentariness’ [ 48 ]; in one study, stroke survivors explained that if strategies were unsustainable or unrealistic, then they were ‘ needless ’ [ 24 ].

Benefits of changing sedentary behaviour

Through changing levels of sedentary time and activity, participants in sedentary behaviour intervention studies reported a range of benefits. This included: increased stamina; balance; weight loss [ 21 ]; general ‘ physical and psychological’ wellbeing [ 22 ]; a more active and ‘ fulfilling ’ life; pride at having made a change [ 42 ]; improved mood; enhanced sleep quality [ 34 ]; cognitive benefits; quality of life and ‘ mental health’ . Participants explained that they were motivated to change by the short-term achievements “… you’re immediately rewarded when you stand up and you’re not so stiff… ” [ 42 ]) as well as the anticipated long-term gains [ 25 , 42 ]: “ Weight loss always motivates women ” [ 45 ].

This review aimed to synthesise current knowledge in regards to the experience and perception of sedentary behaviour and participation in interventions designed to reduce sedentary behaviour in adults. We synthesised data from 918 participants from 30 studies and identified three analytical themes: (i) the impact of different life stages on sedentary behaviour, (ii) lifestyle factors influencing sedentary behaviour and (iii) barriers and facilitators to changing sedentary behaviour.

The first theme reflected the micro and macro pressures experienced at different life stages that are influential in shaping individuals’ beliefs, attitudes and behaviour related to sedentariness. The Capability, Opportunity, Motivation and Behaviour (COM-B) model [ 50 ] recognises that behaviour is part of an interacting system. The heterogeneous nature of the participant groups in the current review allowed us to trace how these different components may be shaped depending on life stage. In childhood, individuals described having the motivation and capability of being active; however, parental and academic influences could limit opportunities, sometimes promoting sitting time. In adulthood, all components were influenced by personal experiences, social and working commitments, and economic circumstances. Overall, in the studies reviewed, this meant that participation in exercise reduced and sedentariness typically increased between childhood and adulthood. In later life, declining health meant that individuals were not always capable of being active and cultural expectations reduced opportunities, promoting sedentariness, regardless of whether individuals were motivated to be less sedentary or not.

Participants in some studies described using sedentary behaviour to cope with changes in health status. Increased sedentary behaviour in illness has been reported elsewhere [ 51 ]. Notwithstanding that some sedentariness is necessary and inevitable in illness; our review highlights other important motivations behind this behaviour, suggesting it is also perpetuated by social and family norms, personal experiences and associated benefits, such as gratification. There is a risk however, that using sedentary time as a protective behaviour could become a self-fulfilling prophecy. For example, the belief that sedentary behaviour must be engaged in when ill, in addition to declining physical fitness caused by limited activity, may lead to further reduced mobility and impact negatively on health. Additionally, this behaviour may be generalised to cope with other demands associated with daily life.

Interventions designed to reduce sedentary behaviour should consider external and internal influences on individuals and groups at different life stages [ 52 ]. Individuals with (and without) medical conditions may need specific support to develop alternative coping techniques associated with less health risk.

The second theme demonstrated the multifaceted nature of sedentary behaviour. In our review, sedentary behaviour reportedly played a large role in participants’ daily lives. However, the motives behind the adoption of this behaviour differed. When looking to change behaviour it is important to first formulate and understand the behaviour and approach the situation in a balanced way, recognising that not all sedentary behaviours/activities are inherently negative. Identifying personalised goals for sedentary behaviour reduction [ 53 ] will help guide what and how intensive behaviour change strategies need to be. This can incorporate understanding core beliefs associated with sedentary behaviours and identify alternatives to and adapt existing sedentary activities.

Environmental factors, in particular the weather, were commonly discussed as variables influencing sedentariness. The environmental barriers were similar to those reported in the literature on physical activity [ 54 ]. To reduce many of the practical barriers, sedentary behaviour reduction interventions could target where and when to change behaviour; while exercise is likely to be managed externally (away from the home), reducing sedentary behaviour can be achieved in the workplace or at home.

The third theme identified that while physical activity appears to be a widely understood term, the concept of sedentary behaviour and associated negative health consequences were less well known. Moreover, some participants dichotomised sedentariness and physical activity, believing that, if they are not physically active in line with guidelines they are sedentary, thus failing to recognise the value of light intensity physical activity as means to reduce sedentary behaviour. There is a need to educate people about the health risks of sedentary behaviour, as well as about methods and benefits of reducing sedentariness. However, Leask et al. pointed out that people are unlikely to be motivated to reduce their time spent sedentary if they are unaware or do not understand the impact of sedentary behaviour. Moreover, due to the importance and enjoyment of sedentary-based activities, ‘ demonising ’ all forms of sedentary behaviour is unlikely to be effective [ 25 ]. A sedentary behaviour reduction programme co-produced by older adult’s highlighted the value of adequately and sensitively framing this kind of information. Group members suggested educational approaches should focus on the ‘ drawbacks ’ of sedentary behaviour as well as the positives of reducing sedentary behaviour and emphasise that some sedentary behaviours are ‘ beneficial ’, such as cognitively stimulating seated activities [ 25 ]. Making a distinction between active, purposeful and passive sedentary activities is likely to be beneficial; this categorisation is consistent with how some individuals conceptualise and justify their sedentary behaviour [ 55 ]. In addition, given that some participants recognised the negative effects of sedentary behaviour and yet were still sedentary, it is clear that knowledge alone is insufficient to bridge the gap between cognitions and behaviour or to bring about sustained change. Additional strategies are required that look to serve different functions. Education may be effective in managing beliefs about sedentary behaviour. However, other methods such as individually tailored goal setting and action planning are needed to change established behaviours. Strategies aimed at initiating change will not necessarily be sustainable and methods to maintain change are unlikely to be acceptable if initial strategies fail to motivate individuals. Although we have identified some of the motivators for reducing sedentary behaviour, we are unable to draw firm conclusions concerning which sedentary behaviour specific strategies could be implemented and for what populations. Our findings do however support the use of multiple techniques and intervention functions [ 50 ], and confirm that one single approach is unlikely to be suitable for all.

In highlighting the multifaceted nature of sedentary behaviour, our review findings are consistent with the elements of the social-ecological model [ 56 ] and also with the findings of a consensus study that developed a system-based framework consisting of six clusters of determinants of sedentary behaviour [ 57 ]. Sedentary behaviour in adults is influenced by a range of interrelated factors; public health interventions must take account of these factors. Strategies to reduce sedentary behaviours must be easily incorporated into participants’ daily lives and be purposeful.

Limitations

We only included studies published in English and the majority of studies reviewed examined experiences of sedentary behaviour in Western countries. Therefore, the review findings cannot easily be generalised to other parts of the world. Exploring experiences of sedentary behaviour in a range of different cultures and populations would provide further insight into how socio-cultural, socio-economic and environmental factors shape peoples’ attitudes and behaviours towards sedentariness.

There is no single, best approach to conduct a qualitative synthesis. Instead, the method used should be guided by the aims and purpose of the synthesis [ 58 , 59 ]. We used a thematic synthesis approach in this review; but we recognise that there is debate about whether it is appropriate to synthesis data generated in research using different qualitative methods. One limitation of qualitative synthesis such is that the meta-themes developed are often broad and overarching; the specific contexts in and about which participants speak are difficult to retain in this kind of synthesis.

An additional aim of the review and thematic synthesis was to explore the views of carers, relatives and health and social care professionals in relation to sedentary behaviour in adults. We did not find any articles investigating views of these groups in relation to sedentary behaviour. It is possible that relevant articles were missed because our search terms were not specific to carers, relatives and health and social care professionals. Research is needed to the explore role that carers, relatives and health and social care professionals play in influencing sedentariness and whether and how their roles can be optimised.

The initial search for this review was conducted at the end of September 2017. We acknowledge that this area of public health research is experiencing considerable growth in numbers of publications. Studies since the end of September 2017 were not included in the current synthesis. Recognising this limitation, we repeated the search using the same parameters in April 2019. Overall, 7273 unique articles were identified. Two reviewers completed title and abstract screening and identified 33 titles for full text screening; nine of these studies met our criteria. Five papers investigated sedentary behaviour in those with a medical condition [ 60 , 61 , 62 , 63 , 64 ], three explored factors affecting older adults’ sedentary behaviours and the acceptability of potential strategies to reduce sedentary time [ 65 , 66 , 67 ] and one focused on factors influencing time spent in sedentary behaviour and explored strategies to reduce this sedentariness in African American women in home, work, and social environments [ 68 ]. This demonstrates the growing interest in understanding people’s experiences of sedentary behaviour. Whilst the reported findings of these studies appear to be largely consistent with those we report following our thematic synthesis, the iterative nature of a thematic synthesis means that it would not have been appropriate to analyse and interpret these data in a post-hoc addition to our synthesis. What is more, qualitative research is less concerned with generalisability of findings, as it is with seeking situational, as opposed to demographic representativeness [ 69 ].

Sedentary behaviour is influenced by a complex interaction between individual, environmental, socio-economic and socio-cultural factors. Micro and macro pressures are experienced at different life stages, including childhood, adulthood, and later-life and in the context of long-term illness that shape individuals’ beliefs and behaviour related to sedentariness. Our findings suggest that knowledge of sedentary behaviour and the associated health consequences is limited in adult populations. At a population level there is a need for a clear and understandable definition of sedentary behaviour. This should be associated with provision of accessible information about ways in which sedentary behaviour reduction might be integrated in peoples’ daily lives. Interventions targeting a reduction in sedentary behaviour will need to consider the multiple influences on sedentariness when designing and implementing interventions.

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Abbreviations

Applications

Body Mass Index

Capability Opportunity, Motivation and Behaviour

General Practitioner

National Institute for Health Research

Physical Activity

Programme Grants for Applied Health Research

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Sedentary Behaviour

Socio-Economic Status

Sedentary Time

United Kingdom, USA United States of America

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Acknowledgements

The authors wish to thank Deirdre Andre for her advice regarding database searching and for her work in tracking down papers for including in the review. We thank Dr. Mark Perry for his contribution to development of the search strategy and review protocol development. We also thank Dr. Rekesh Corepal for his assistance in screening titles, abstracts and full texts identified in the updated April 2019 search.

National Institute for Health Research (NIHR) Programme Grant for Applied Research (PGfAR): RP-PG-0615-20019.

This paper presents independent research funded by the National Institute for Health Research under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0615-20019). The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health.

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GHR, RW, DJC, AF, CF, RL, GM, CE, AP and IH were responsible for the conception and design of this review. GHR, RW and DJC designed and implemented the initial search strategy. GHR, RW and DJC completed full text screening and GHR and RW assessed the quality of the studies with oversight from DJC. GHR and RW extracted study data which was then reviewed by DJC. GHR drafted the paper with subsequent editing provided by DJC and RW. AF, CF, RL, GM, CE, AP and IH critically reviewed and revised the different versions of the manuscript. All authors read and approved the final manuscript.

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Rawlings, G.H., Williams, R.K., Clarke, D.J. et al. Exploring adults’ experiences of sedentary behaviour and participation in non-workplace interventions designed to reduce sedentary behaviour: a thematic synthesis of qualitative studies. BMC Public Health 19 , 1099 (2019). https://doi.org/10.1186/s12889-019-7365-1

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Sedentary behaviors and risk of depression: a meta-analysis of prospective studies

• Yuchai Huang 1   na1 ,
• Liqing Li 2   na1 ,
• Yong Gan 1 ,
• Chao Wang 1 ,
• Heng Jiang 3 , 4 ,
• Shiyi Cao 1 &
• Zuxun Lu 1  

Translational Psychiatry volume  10 , Article number:  26 ( 2020 ) Cite this article

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Epidemiological evidence on the association between sedentary behaviors and the risk of depression is inconsistent. We conducted a meta-analysis of prospective studies to identify the impact of sedentary behaviors on the risk of depression. We systematically searched in the PubMed and Embase databases to June 2019 for prospective cohort studies investigating sedentary behaviors in relation to the risk of depression. The pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated with random-effect meta-analysis. In addition, meta-regression analyses, subgroup analyses, and sensitivity analyses were performed to explore the potential sources of heterogeneity. Twelve prospective studies involving 128,553 participants were identified. A significantly positive association between sedentary behavior and the risk of depression was observed (RR = 1.10, 95% CI 1.03–1.19, I 2  = 60.6%, P  < 0.01). Subgroup analyses revealed that watching television was positively associated with the risk of depression (RR = 1.18, 95% CI 1.07–1.30), whereas using a computer was not (RR = 0.99, 95% CI 0.79–1.23). Mentally passive sedentary behaviors could increase the risk of depression (RR = 1.17, 95% CI 1.08–1.27), whereas the effect of mentally active sedentary behaviors were non-significant (RR = 0.98, 95% CI 0.83–1.15). Sedentary behaviors were positively related to depression defined by clinical diagnosis (RR = 1.08, 95% CI 1.03, 1.14), whereas the associations were statistically non-significant when depression was evaluated by the CES-D and the Prime-MD screening. The present study suggests that mentally passive sedentary behaviors, such as watching television, could increase the risk of depression. Interventions that reduce mentally passive sedentary behaviors may prevent depression.

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Introduction.

Sitting is a typical way to relax and get efficient storage of energy in a short time 1 . As a common type of sitting behavior, sedentariness refers to any sitting behavior with an energy expenditure ≤1.5 metabolic equivalents 2 . In fact, sedentary behaviors are very common in daily life, especially for many occupations, such as researchers, clerks, drivers, and programmers. There is currently no clear definition of mentally passive and mentally active sedentary behaviors. In general, behaviors including watching television, sitting around, listening, and talking while sitting are considered as “mentally passive”, whereas using a computer, reading books or newspapers, car driving, attending a meeting, knitting, or sewing are interpreted as “mentally active” 3 , 4 . Studies have found that, sedentary behaviors could have adverse impacts on physical health, increasing the risk of chronic diseases including cancer, cardiovascular diseases, diabetes mellitus, and so on 5 , 6 , 7 , 8 , 9 , 10 . Mental health damage such as stress, dementia, and sleeping problem caused by sedentary behaviors have also been reported in a number of studies 11 , 12 , 13 , 14 . In recent years, emerging studies have found different effects between mentally active and mentally passive sedentary behaviors on an individual’s well-being. A cross-sectional study found that mentally passive sedentary behaviors might be deleterious, and mentally active sedentary behaviors could be beneficial to health 3 . Another 13-year prospective study found that substituting mentally passive with mentally active sedentary behaviors may reduce the risk of depression 4 . As sedentary behavior is so common in our daily life, more attention should be paid to its effects on health, and those of specific mentally passive and mentally active sedentary behaviors if necessary.

Depression is a common mental disorder and major human blight that affects up to 25% of women and 12% of men 15 , 16 . According to the World Health Organization, ~ 350 million people had suffered from depression in 2010 16 . Specific clinical and therapeutic features for depression have been described in childhood and the elderly respectively 17 , 18 . Research has found depression to be more common in women than men, and sex differences have been found to have age specific effects on the risk of depression 19 , 20 , 21 , 22 . A previous meta-analysis, that pooled the results of cohort and case–control studies, suggested that there was a positive relation between sedentary behaviors and the risk of depression for the entire population 23 . Another meta-analysis published in 2016 concluded that sedentary behaviors were associated with depression in a non-linear relation for adolescents 24 . However, it remains controversial whether this effect was consistent in adults, as a great deal of research have contradicted their findings or drawn out statistically non-significant conclusions. A prospective study with a follow-up of 9.3 years indicated that sedentary behaviors increased the risk of depression 25 . Although another prospective cohort study with 13 years of follow-up found different effects on depression for mentally passive and mentally active sedentary behaviors 26 , with a significant beneficial influence of mentally active sedentary behaviors on incident depression and non-significant effect of mentally passive sedentary behaviors or total sedentary behaviors on the risk of depression.

Epidemiological evidence on the association between sedentary behaviors and risk of depression are inconsistent 23 , 25 , 26 , 27 , 28 . It remains unclear whether there is a relation between sedentary behaviors and the risk of depression in adults or not? Pooling existing evidence, we conducted a meta-analysis of prospective studies to examine the association of sedentary behaviors with the risk of depression.

We designed and conducted this review under the guidelines of the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) 29 .

Search strategy

We systematically searched the electronic databases of PubMed and Embase through June 2019 to identify studies examining the relationship between sedentary behaviors and the risk of depression. The following terms and their combinations were used in the search: “watching television”, “screen time”, “sitting”, “computer use”, “media use”, “car driving”, “sedentary time”, “sedentary behaviors”, and “depression”. We also reviewed the references of included articles to identify any eligible studies omitted by previous database searches.

Inclusion criteria

Studies meeting the following criteria were included in this meta-analysis: (1) the study design was prospective; (2) the exposure was sedentary behaviors, including watching television, using a computer, reading, driving, and so on; (3) the outcome was depression; (4) the study population was average adults; (5) the study reported relative risk (RR) or hazard ratio (HR), with their 95% confidence intervals (CIs) available or accountable.

Data extraction

For all included studies, we extracted information on the year of publication, name of the first author, country, follow-up year, follow-up rate, number of participants, average age of participants at baseline, depression diagnosis measure, cohort name, specified sedentary behaviors, RR with their 95% CIs, and adjusted variables. All the extracted RRs were adjusted for most covariates.

Quality assessment

The quality of included studies were assessed by the nine-point Newcastle-Ottawa scale (NOS) 30 , which is a common tool in meta-analysis for observational studies. The scale awards prospective research a maximum of nine points (four for selection quality, two for comparability, and three for outcome of interests). A total score of 0–3 was considered as low quality, 4–6 was considered as moderate quality, and 7–9 was assigned to high quality.

Statistical analyses

We primarily used the full-adjusted RRs to pool results, except for one study with crude RR 31 . For studies where sedentary time was divided into several levels, RRs were recalculated by pooling values of upper groups, whereas the lowest level of sedentary time was taken as reference. Only one study took the highest classification as reference group, so we used a reciprocal function to process original data 32 .

We conducted subgroup analyses and meta-regression analyses to explore potential heterogeneity across studies. Stratified analyses were performed, respectively, by covariates, specific sedentary behavior, depression diagnosis criteria, and baseline depression status (excluded or adjusted). As there was currently no clear definition for mentally passive and mentally active sedentary behaviors, we referred to one previous study that it took sedentary behaviors including watching television, chatting while sitting and sitting around as “mentally passive”, whereas interpreted using a computer, reading, and car driving as “mentally active” 3 . According to the NOS degree, we specifically excluded low quality studies to examine whether the primarily pooled result was stable. Simultaneously, sensitivity analyses were performed by leaving-one-out procedure to evaluate the underlying between-study heterogeneity.

Statistical heterogeneity across studies was estimated by I 2 of Higgins and Thompson 33 , with values of 25%, 50%, and 75%, representing heterogeneity levels of low, moderate, and high, respectively. The DerSimonian and Laird random-effects model was used to synthesize the RRs when a relatively high heterogeneity was detected, otherwise, the fixed-effects model was used.

Potential publication bias was visually inspected through funnel plot by referencing the Begg correlation test and Egger linear regression test 34 , 35 . All the statistical analyses were performed with STATA V.12.0 (StataCorp, College Station, Texas, USA), with a two-sided significance level of 0.05.

Literature selection

A total of 491 relevant articles were found by searching the databases, of which 469 articles were precluded after reviewing titles and/or abstracts. Subsequently, 12 articles were excluded after full-text review for not complying with the inclusion criteria, including one that focused on the association in post-pregnant women. Another two studies were further identified from the references of qualified articles. A total of 12 literatures 25 , 26 , 27 , 28 , 31 , 32 , 36 , 37 , 38 , 39 , 40 , 41 were finally included in this meta-analysis. The study selection procedure is presented in the flow chart in Fig. 1 .

Flow chart of study identification.

Study characteristics

The characteristics of included studies are presented in Table 1 . Among them, two studies were conducted in the United States 25 , 37 , three were in Australia 26 , 38 , 40 , and the other seven studies were performed in Europe, including Finland, Sweden, Denmark, Spain, and the United Kingdom 27 , 28 , 31 , 32 , 36 , 39 , 41 . The follow-up time ranged from 1 to 13 years, with an average of 5.97 years. All included studies had high follow-up rates of above 90%. The total number of participants was 128,553. Eight studies excluded the baseline cases of depression in their cohort design 25 , 28 , 31 , 36 , 37 , 38 , 39 , 41 , three studies adjusted for the baseline depression status in their analyses 26 , 27 , 40 , and one study neither excluded nor adjusted for the baseline depression status 32 . With regard to the criteria of depression diagnosis, one study did not report any criteria 38 , four studies used the CES-D-10 (versions of the Centre for Epidemiological Studies Depression Scale) criteria 25 , 27 , 32 , 40 , and the other seven studies used different criteria including the Finnish modified version of Beck’s 3-item depression scale 36 , the Primary Care Evaluation of Mental Disorders screening form 31 , 41 , the Major Depression Inventory 28 , physician diagnosis 37 , 39 , and medical registers 26 . All of the studies adjusted for multiple variables, including basic demographic characteristics, smoking status, body mass index (BMI), alcohol consumption, physical activity, and so on, except for one with unadjusted results 31 . Classification of sedentary behaviors differed among studies, four of which reported total screen time 36 , 37 , 38 , 39 , whereas the others classified sedentary behaviors as computer use 25 , 27 , 28 , 38 , 40 , watching television 25 , 27 , 28 , 32 , 37 , 40 , being a passenger or driver in a car 25 , and so on. Two studies explored the effects in women only 38 , 40 , three studies reported the RRs in men and women, respectively 27 , 31 , 41 , and the other included studies did not classify by gender 25 , 26 , 28 , 32 , 36 , 37 , 39 .

The average NOS score was 7.75. All the included studies, except for one with the score equal to 5 31 , were defined as high quality (Table 2 ).

Association between sedentary behavior and the risk of depression

Figure 2 shows the multivariable-adjusted RRs of included studies, as well as the pooled result of the random-effect model. The pooled RR was 1.10 (95% CI: 1.03,1.19), which showed that sedentary behaviors were positively associated with risk of depression, with a moderate heterogeneity ( P  = 0.003; I 2  = 60.6%).

Forest plot of sedentary behavior associated with depression.

Subgroup analyses

When stratified by specific sedentary behavior, we found that watching television was positively associated with the risk of depression (RR = 1.18, 95% CI 1.07–1.30), whereas using a computer was statistically non-significant (RR = 0.99, 95% CI 0.79–1.23). Mentally passive sedentary behaviors could increase the risk of depression (RR = 1.17, 95% CI 1.08–1.27), whereas the effect of mentally active sedentary behaviors were non-significant (RR = 0.98, 95% CI 0.83–1.15). We observed non-significant pooled effects after stratification by gender, which was consistent with original studies 27 , 31 , 38 , 40 , 41 . Sedentary behaviors were positively related to depression defined by clinical diagnosis (RR = 1.08, 95% CI 1.03, 1.14), whereas the association was statistically non-significant when depression was evaluated by the CES-D (RR = 1.12, 95% CI 0.94,1.34), Prime-MD screening (RR = 1.08, 95% CI 1.01–1.17) and other criteria such as the Major Depression Inventory and medical registers. The pooled result of studies that excluded baseline depression in their cohort or analyses suggested a significantly positive association between sedentary behavior and risk of depression, whereas the summarized effect of studies that adjusted for baseline depression in their analyses showed a non-significant negative relation with the risk of depression. Results having adjusted for smoking, as well as, not adjusting for BMI and physical activity showed a positive association on this issue, as shown in Table 3 . All the meta-regression analyses showed non-significant interaction effects, which weakened our explanation for the possible source of assumed heterogeneity. Whether adjusted for covariates or not, values of I 2 fluctuated substantially. Compared with the pooled result of unadjusted studies’, I 2 changed from 23.1% to 73% when we pooled the results of studies which adjusted for BMI. (Table 3 ).

Sensitivity analyses

Sensitivity analysis was performed by omitting one study in turn, with the pooled RRs fluctuating between 1.08 (95% CI 1.01, 1.16) and 1.12 (95% CI 1.04, 1.21), which supported the stability of our results. In addition, the RR was 1.10 (95% CI 1.02, 1.19) after removing the low-quality study 31 , which further supported that sedentary behavior was related to depression.

Publication bias

Evidence of publication bias was visually inspected through the funnel plot (Fig. 3 ) and neither the Egger’s test nor the Begg’s test indicated statistically significant potential for publication bias in either case (Egger, P  = 0.939; Begg, P  = 1.000).

The horizontal line represents summary effect estimates, and the dotted lines are pseudo 95% CIs.

In this meta-analysis based on 12 prospective cohort studies, we found that sedentary behaviors, especially mentally passive behaviors, were positively associated with the risk of depression. Each additional increment of time spent on passively sedentarily watching television could increase the risk of depression, but the estimated effects differed by covariates adjustment. The pooled results stratified by gender were non-insignificant. Subgroup analyses suggested that sedentary behavior was positively related to depression defined by physician diagnosis, whereas the pooled effects were statistically non-significant when depression was diagnosed by other criteria.

There are mechanisms explaining the association between sedentary behaviors and risk of depression. First, sedentary behaviors like using a computer can hinder direct communication between individuals, causing a reduction in social interaction and increasing the potential for depression 42 . Second, sedentary behaviors also cut back on time spent in physical exercise, which is an effective prevention and treatment for depression 43 , 44 , 45 . Many observational studies have focused on the association between sedentary behaviors and the risk of depression, whereas evidence from experimental studies is still limited. Experimental studies investigating the biological mechanisms of this association are still needed to provide more powerful explanations.

Interestingly, we found that mentally active sedentary behaviors, as well as using a computer, were unrelated to the risk of depression ( P  > 0.05). As mentioned above, using a computer is one kind of mentally positive sedentary behaviors, which could be beneficial to mental health 3 . A previous prospective cohort study showed that there were protective effect of mentally active sedentary behaviors on the incidence of depression 26 . However, original studies that took computer use as the exposure were more likely to find null or negative relationship between sedentary behaviors and the risk of depression 46 . What’s more, a previous meta-analysis suggested that computer use could increase the risk of depression 23 . As the findings are contradictory, future studies focusing on the relationship between using a computer or mentally active sedentary behaviors and risk of depression with its underlying mechanisms should be encouraged. Also, as we found that mentally passive sedentary behaviors, such as watching television, could be detrimental to mental health and may increase the risk of depression, the distinct association between mentally passive and mentally active sedentary behaviors with their effects on depression should also be brought into attention.

Subgroup analysis also showed that sedentary behavior was detrimental for the risk of depression when unadjusted for physical activity. Studies focusing on the combined effect of physical activity and sedentary behaviors on depression have reported inconsistent conclusions. Two observational studies found that people with more time spent in physical activity and less time spent in sedentary behaviors were more likely to develop depression 47 , 48 . Also, the substitution of sedentary behaviors with physical activity was found to be protective for depression 4 . However, a randomized controlled trial did not observe the joint effect of sedentary behaviors and exercise on depression 49 . As sedentary behavior and physical activity are both common human activities and generally exist in our daily life, future studies are needed to determine whether or which combinations of the two behaviors could protect against depression.

Most of the subgroup effects were statistically non-significant, which might be attributed to methodological discrepancies between studies. For all included studies, the shortest follow-up time was 1 year, which might not be long enough for the occurrence of depression. The difference of sample sizes might influence the statistical power of original studies. In addition, the elderly and new adults differed in the prevalence of depression, which contributed to the heterogeneity between studies, and thus influenced the results in our subgroup analyses. The number of original studies classified by gender was limited, which might be the cause of non-significant results in stratification analyses by sex. As mentioned before, diagnoses criteria of depression seemed to influence the results, which suggested that different criteria might affect the sensitivity and specificity of depression diagnoses. Prospective cohort studies starting from exposures and observing the occurrence of outcome provided stronger evidence for a cause–effect association than other observational studies 50 . Among included studies in this meta-analysis, those with depression cases pre-excluded have stronger statistical power than those with baseline depression adjusted in their statistical models. Considering the weakening effects caused by adjustment of baseline depression in statistical analyses of original studies and the limitation in the number of studies, it is not surprising that the pooled effect in this subgroup was non-significant. Statistical heterogeneity might be related to the differences in adjustment factors. Non-adjustment for drinking and physical activity contributed to the positive association between sedentary behaviors and depression, as alcohol and lacking physical activity are both independent risk factors for depression 45 , 51 .

The observed heterogeneity was mostly significant in subgroup analyses. I 2 fluctuated especially in groups of stratification by gender, ways of diagnosing depression, whether baseline depression cases were excluded in the analyses and whether BMI was adjusted for. But all the meta-regression analyses showed non-significant interaction effects, which weakened the possibility of heterogeneity caused by these three factors. After excluding one low-quality study 31 , the I 2 of pooled value did not change materially, thus eliminating the possibility that study quality influences between-study heterogeneity. Future studies considering the disturbing factors above are needed to determine the underlying heterogeneity.

Not only is sedentary behavior a risk factor for depression, studies have also suggested that depression may have an effect on sedentary status. Studies focused on sedentary status among depression patients have presented that adults with depression engaged in low levels of physical activity and high levels of sedentary behaviors 52 , 53 . In addition, for adolescents, evidence also suggested that sedentary behaviors increase the risk of depression 24 , 54 . As sedentary behaviors and depression have interaction in different age groups, slightly restricting sedentary behaviors may produce great benefits for human health.

Strengths of this meta-analysis should be mentioned. First, the studies included in our analyses were all prospective, which can provide strong reliability of results. Second, except for one low-quality study that did not report adjusted RR, all the original studies were of high quality and reported multivariate adjusted RRs. Third, our study was based on adults and had a large number of participants, with which we can provide powerful statistical evidence for the estimated effects. Fourth, this is the first meta-analysis that classifies sedentary behaviors into mentally active and mentally passive, and discusses their effects on depression respectively.

Limitations should also be acknowledged. First, owing to limited information on original studies, we did not conduct dose–response analyses and were unable to explore the relationship between other forms of sedentary behaviors (like online chatting and car driving) and risk of depression. Second, we did not find out credible evidence for sources of heterogeneity, which were needed to be supported by further research. Third, this meta-analysis did not deal with possible existing bias caused by differences in methodology between studies. Fourth, we simply took computer use as mentally active sedentary behavior, whereas it could also be mentally passive when a computer was used for watching television, film, etc. As there was no clear definition for the two kinds of sedentary behaviors, future studies could pay attention to developing questionnaires that accurately measure these diverse behaviors and building up a classification system.

In conclusion, our analyses suggest there is a positive association between mentally passive sedentary behavior and risk of depression, and the increment of time spent on sedentarily watching television could increase the risk of depression. Given the increasing prevalence of depression and widespread sedentary behaviors in modern society, the results of our study are of importance for clinical and public health. Restrictions on mentally passive sedentary behaviors should be recommended to prevent depression. As depression is just one kind of mental disorder, future researches focusing on the impact of interruptions in sedentary behaviors on other aspects of mental health also should be brought into attention.

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Acknowledgements

This study was supported by Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Yuchai Huang, Yong Gan, Chao Wang, Shiyi Cao & Zuxun Lu

Department of Management Science and Engineering, School of Economics and Management, Jiangxi Science and Technology Normal University, Nanchang, Jiangxi, China

Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia

Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

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Y.C.H. conceived and designed the study. Y.C.H. and L.Q.L. extracted and analyzed the data. Y.G. and S.Y.C. gave advice on methodology. Y.C.H. and L.Q.L. drafted the manuscript, and C.W, S.Y.C, Y.G, and H.J revised the manuscript. L.Q.L. and H.J. improved the grammar and spelling and edited the manuscript. All authors read and approved the final manuscript. Z.X.L, Y.C.H, and L.Q.L. are the guarantors of this work and had full access to all the data in the study and take responsibility for its integrity and the accuracy of the data analyses.

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Huang, Y., Li, L., Gan, Y. et al. Sedentary behaviors and risk of depression: a meta-analysis of prospective studies. Transl Psychiatry 10 , 26 (2020). https://doi.org/10.1038/s41398-020-0715-z

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EDITORIAL article

Editorial: the impact of sedentary behavior and virtual lifestyle on physical and mental wellbeing: social distancing from healthy living.

• 1 Public Health Program, Torrens University Australia, Melbourne, VIC, Australia
• 2 Higher Education College, Chisholm Institute, Dandenong, VIC, Australia
• 3 Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
• 4 Department of Bariatric Surgery, Hamad Medical Centre, Doha, Qatar

Editorial on the Research Topic The impact of sedentary behavior and virtual lifestyle on physical and mental wellbeing: social distancing from healthy living

Introduction

In recent decades, technological advancements have transformed the way we live and work, offering convenience and comfort. However, this comfort comes at a cost to our health. These advancements result in increasingly prevalent sedentary lifestyle and inactivity which negatively impacts our wellbeing. The adverse effects of prolonged sitting and physical inactivity are not only limited to the present generation; they also pose a serious threat to our future, as they interfer in human natural growth and development ( 1 ). Therefore, it is vital to assess the profound impact of sedentary behavior on human health to better inform policies, and health interventions or programs to combat this growing crisis.

While the human body has basic need for movement and physical activity ( 2 ), our modern lifestyle has significantly reduced our daily activity levels, resulting in a multitude of health problems ( 3 ). Presta et al. in this Research Topic described how “engaging in regular physical activity (by playing sports and being as active as possible during the daily routine)” is evidently protective against “NCDs and NCDs-related risk factors, namely overweight and obesity, and hypertension.” The combination of obesity and sedentary behavior has also been negatively associated with longevity and life expectancy ( 4 ). Being active in general has also been linked to quality of life. A Chinese study published in this Research Topic found that the combination of good level of physical activity and reduced sedentary behavior has a positive impact on the quality of life among children and adolescents ( Shi et al. ).

Consequences of prolonged sedentary behavior relevant to occupational settings have also been assessed extensively. Musculoskeletal disorders, including back pain, neck pain, and joint problems, are widespread among individuals with sedentary jobs. Prolonged occupational sitting leads to excessive strain on the spine and muscles, ultimately causing postural imbalances and chronic pain ( 5 ). The above study added that the lack of physical activity and increased sedentary time may increase the risk of developing mental health issues. A study in this Research Topic found that screen-based sedentary behavior was associated with anxiety symptoms among college students ( Huang et al. ). Other studies showed clear links between sedentary behavior and increased levels of anxiety and depression, as well as decreased cognitive function ( 6 ). The plausible mechanism is that lack of physical activity limits the release of endorphins, serotonin, and other mood-enhancing chemicals, resulting in a higher risk of mental health disorders.

When the COVID-19 pandemic hit, governments around the world moved fast to declare emergencies and implemented policies that restricted movement across all ages, occupations and educational institutes. We started to work and study remotely and the “social distancing” that was designed to protect us from the virus ultimately led to “social distancing” from healthy living. Two studies in this Research Topic addressed the impact of COVID-19 pandemic measures and fear of transmission and spread, on the reduced level of physical activity and increased level of sedentary behavior and subsequent health outcomes. Al-Hindawi et al. found that 2 in 3 medical and nursing students reported a decrease in physical activity and increase in sedentary behavior during the pandemic. Another study conducted by Shpakou et al. compared countries with “different approaches of anti-pandemic measures” and reported decline in levels of physical activity among university students and students athletes. The above study added that lower levels of physical activity in one of the countries were associated with lower life satisfaction and lower levels of ability to cope stress ( Shpakou et al. ).

Pandemic or no pandemic, the countless health threats associated with unhealthy lifestyle and behavior have undoubtedly gotten the attention of researchers in recent times. A community-based study in this Research Topic conducted by Zhang et al. addressed the impact of “unhealthy lifestyle” prior to the pandemic. This study reported that participants with unhealthy lifestyle scores, including physical inactivity and sedentary behavior, were more likely to report depressive symptoms ( Zhang et al. ). The above unhealthy scores got worse with additional unhealthy behaviors such as smoking, sleep deprivation and excessive alcohol consumption, indicating accumulated risk, and subsequently, further decline of health and wellbeing.

There is no doubt that sedentary lifestyle poses a serious threat to human health and wellbeing, which is aggravated by the pandemic. Considering the pandemic and other forms of large-scale disasters that warrant lockdowns and long-term restriction on movement, public health planners and policy-makers should develop and implement appropriate strategies to reduce sedentary behaviors in the community, particularly those at risk. However, addressing the reality of sedentary lifestyle entails a multifaceted approach and partnership with individuals, communities, and policymakers. Workplace interventions and programs should aim at reducing sedentary time and promoting a culture of healthy lifestyle and wellness. Moreover, public health campaigns and interventions should focus on raising awareness of prolonged sitting and inactivity and encourage undertaking physical activity compromising by adhering to public health orders of not spreading the infectious disease during pandemics.

Author contributions

FH: Conceptualization, Investigation, Resources, Supervision, Visualization, Writing—original draft, Writing—review and editing. EY: Supervision, Validation, Writing—review and editing. ME-S: Conceptualization, Project administration, Visualization, Writing—review and editing.

Acknowledgments

The authors would like to acknowledge all of the authors who contributed to this Research Topic. We would also like to acknowledge our affiliated institutions for the support provided to us during this assignment.

Conflict of interest

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

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Keywords: sedentary lifestyle, physical activity, COVID-19, physical health, mental health

Citation: Hanna F, You E and El-Sherif M (2023) Editorial: The impact of sedentary behavior and virtual lifestyle on physical and mental wellbeing: social distancing from healthy living. Front. Public Health 11:1265814. doi: 10.3389/fpubh.2023.1265814

Received: 23 July 2023; Accepted: 28 July 2023; Published: 03 August 2023.

Edited and reviewed by: Christiane Stock , Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany

Copyright © 2023 Hanna, You and El-Sherif. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fahad Hanna, fahad.hanna@torrens.edu.au

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Balance declines with age, but exercise can help stave off some of the risk of falling

Associate Professor of Physical Therapy and Rehabilitation Science, Tufts University

Disclosure statement

Evan Papa receives funding from the National Institutes of Health / National Institute of General Medical Sciences under the Mountain West Center for Translational Research Infrastructure grant #U54GM104944, and the Idaho Elks Rehab Society.

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My wife and I were in the grocery store recently when we noticed an older woman reaching above her head for some produce. As she stretched out her hand, she lost her balance and began falling forward. Fortunately, she leaned into her grocery cart, which prevented her from falling to the ground.

Each year, about 1 in every 4 older adults experience a fall . In fact, falls are the leading cause of injuries in adults ages 65 and older. Falls are the most common cause of hip fractures and traumatic brain injuries .

Injuries like those are also risk factors for placement in a nursing home , where the fall risk is nearly three times higher than for people living in the community .

A number of physical changes with aging often go unseen preceding falls, including muscle weakness, decreased balance and changes in vision.

I am a physical therapist and clinical scientist focused on fall prevention in older adults, commonly ages 65 and older. I’ve spent most of my career investigating why older adults fall and working with patients and their families to prevent falls.

Why aging leads to increased risk of falls

Aging is a process that affects the systems and tissues of every person. The rate and magnitude of aging may be different for each person, but overall physical decline is an inevitable part of life. Most people think aging starts in their 60s, but in fact we spend most of our life span undergoing the process of decline , typically beginning in our 30s.

Older adults are more prone to falling for various reasons, including age-related changes in their bodies and vision changes that leave them vulnerable to environmental factors such as curbs, stairs and carpet folds.

Based on my experience, here are some common reasons older adults may experience falls:

First, aging leads to a natural loss of muscle strength and flexibility, making it more challenging to maintain balance and stability. The loss of strength and poor balance are two of the most common causes of falls.

Second, older adults often have chronic conditions such as arthritis, Parkinson’s disease or diabetes that can affect their mobility, coordination and overall stability.

In addition, certain medications commonly taken by older adults, such as sedatives or blood pressure drugs , can cause dizziness, drowsiness or a drop in blood pressure, leading to an increased risk of falls.

Age-related vision changes, such as reduced depth perception and peripheral vision and difficulty in differentiating colors or contrasts, can make it harder to navigate and identify potential hazards. Hazards in the environment, such as uneven surfaces, slippery floors, inadequate lighting, loose rugs or carpets or cluttered pathways, can significantly contribute to falls among older adults .

Older adults who lead a sedentary lifestyle or have limited physical activity may also experience reduced strength, flexibility and balance.

And finally, such conditions as dementia or Alzheimer’s disease can affect judgment, attention and spatial awareness, leading to increased fall risk.

Theories of aging

There are numerous theories about why we age but there is no one unifying notion that explains all the changes in our bodies. A large portion of aging-related decline is caused by our genes , which determine the structure and function of bones, muscle growth and repair and visual depth perception, among other things. But there are also numerous lifestyle-related factors that influence our rate of aging including diet, exercise, stress and exposure to environmental toxins.

A recent advance in scientific understanding of aging is that there is a difference between your chronological age and your biological age . Chronological age is simply the number of years you’ve been on the Earth. Biological age, however, refers to how old your cells and tissues are. It is based on physiological evidence from a blood test and is related to your physical and functional ability. Thus, if you’re healthy and fit, your biological age may be lower than your chronological age. However, the reverse can also be true.

I encourage patients to focus on their biological age because it empowers them to take control over the aging process. We obviously have no control over when we are born. By focusing on the age of our cells, we can avoid long-held beliefs that our bodies are destined to develop cancer, diabetes or other conditions that have historically been tied to how long we live .

And by taking control of diet, exercise, sleep and other lifestyle factors you can actually decrease your biological age and improve your quality of life. As one example, our team’s research has shown that moderate amounts of aerobic exercise can slow down motor decline even when a person begins exercise in the latter half of the life span.

Fall prevention

Adopting lifestyle changes such as regular, long-term exercise can reduce the consequences of aging , including falls and injuries. Following a healthy diet, managing chronic conditions, reviewing medications with health care professionals, maintaining a safe home environment and getting regular vision checkups can also help reduce the risk of falls in older adults.

There are several exercises that physical therapists use to improve balance for patients. It is important to note however, that before starting any exercise program, everyone should consult with a health care professional or a qualified physical therapist to determine the most appropriate exercises for their specific needs. Here are five forms of exercise I commonly recommend to my patients to improve balance:

Balance training can help improve coordination and proprioception , which is the body’s ability to sense where it is in space. By practicing movements that challenge the body’s balance, such as standing on one leg or walking heel-to-toe, the nervous system becomes better at coordinating movement and maintaining balance. A large research study analyzing nearly 8,000 older adults found that balance and functional exercises reduce the rate of falls by 24% .

Strength training exercises involve lifting weights or using resistance bands to increase muscle strength and power. By strengthening the muscles in the legs, hips and core, older adults can improve their ability to maintain balance and stability. Our research has shown that strength training can also lead to improvements in walking speed and a reduction in fall risk .

Tai chi is a gentle martial art that focuses on slow, controlled movements and shifting body weight. Research shows that it can improve balance, strength and flexibility in older adults. Several combined studies in tai chi have demonstrated a 20% reduction in the number of people who experience falls .

Certain yoga poses can enhance balance and stability. Tree pose, warrior pose and mountain pose are examples of poses that can help improve balance. It’s best to practice yoga under the guidance of a qualified instructor who can adapt the poses to individual abilities.

Flexibility training involves stretching the muscles and joints, which can improve range of motion and reduce stiffness. By improving range of motion, older adults can improve their ability to move safely and avoid falls caused by limitations in mobility.

Use of assistive devices can be helpful when strength or balance impairments are present. Research studies involving the evaluation of canes and walkers used by older adults confirm that these devices can improve balance and mobility . Training from a physical or occupational therapist in the proper use of assistive devices is an important part of improving safety.

When I think back about the woman who nearly fell in the grocery store, I wish I could share everything we have learned about healthy aging with her. There’s no way to know if she was already putting these tips into practice, but I’m comforted by the thought that she may have avoided the fall by being in the right place at the right time. After all, she was standing in the produce aisle.

• Sedentary lifestyles
• Patient falls
• Older people
• Muscle weakness
• Gerontology
• Lifestyle changes
• Healthy aging
• Muscle loss
• Chronic health conditions

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Open Access

Peer-reviewed

Research Article

Map ping s edentary b ehaviour (MAPS-B) in winter and spring using wearable sensors, indoor positioning systems, and diaries in older adults who are pre-frail and frail: A feasibility longitudinal study

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Current address: Department of Medicine, University of McMaster, Hamilton, Ontario, Canada

Affiliations Faculty of Health Sciences, Department of Medicine, McMaster University, Hamilton, ON, Canada, Department of Community Health Sciences, University of Manitoba, Max Rady College of Medicine, Winnipeg, Canada

Roles Data curation

Affiliation Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Roles Data curation, Writing – review & editing

Affiliation Faculty of Science, Department of Life Sciences, McMaster University, Hamilton, ON, Canada

Roles Methodology, Resources, Writing – review & editing

Affiliation Faculty of Health Sciences, Department of Medicine, McMaster University, Hamilton, ON, Canada

Roles Investigation, Writing – review & editing

Affiliation Faculty of Science, Department of Kinesiology, McMaster University, Hamilton, ON, Canada

Roles Funding acquisition, Investigation, Methodology, Resources, Software, Writing – review & editing

Affiliation Faculty of Engineering, Department of Engineering Physics, McMaster University, Hamilton, ON, Canada

Roles Investigation, Methodology, Writing – review & editing

Roles Investigation, Resources, Writing – review & editing

Affiliation Department of Surgery, Division of Orthopaedic Surgery, McMaster University, Hamilton, ON, Canada

Affiliations Faculty of Health Sciences, Department of Medicine, McMaster University, Hamilton, ON, Canada, Department of Health Research Methods, McMaster University, Evidence and Impact, Hamilton, ON, Canada

Affiliation Faculty of Engineering, Department of Computing and Software, McMaster University, Hamilton, ON, Canada

• Isabel B. Rodrigues, 
• Suleman Tariq, 
• Alexa Kouroukis, 
• Rachel Swance, 
• Jonathan Adachi, 
• Steven Bray, 
• Qiyin Fang, 
• George Ioannidis, 
• Dylan Kobsar, 

• Published: May 16, 2024
• https://doi.org/10.1371/journal.pone.0290197
• Peer Review
• Reader Comments

Older adults who are frail are likely to be sedentary. Prior interventions to reduce sedentary time in older adults have not been effective as there is little research about the context of sedentary behaviour (posture, location, purpose, social environment). Moreover, there is limited evidence on feasible measures to assess context of sedentary behaviour in older adults. The aim of our study was to determine the feasibility of measuring context of sedentary behaviour in older adults with pre-frailty or frailty using a combination of objective and self-report measures. We defined “ feasibility process” using recruitment (20 participants within two-months), retention (85%), and refusal (20%) rates and “ feasibility resource ” if the measures capture context and can be linked (e.g., sitting-kitchen-eating-alone) and are all participants willing to use the measures. Context was assessed using a wearable sensor to assess posture, a smart home monitoring system for location, and an electronic or hard-copy diary for purpose and social context over three days in winter and spring. We approached 80 potential individuals, and 58 expressed interest; of the 58 individuals, 37 did not enroll due to lack of interest or medical mistrust (64% refusal). We recruited 21 older adults (72±7.3 years, 13 females, 13 frail) within two months and experienced two dropouts due to medical mistrust or worsening health (90% retention). The wearable sensor, indoor positioning system, and electronic diary accurately captured one domain of context, but the hard copy was often not completed with enough detail, so it was challenging to link it to the other devices. Although not all participants were willing to use the wearable sensor, indoor positioning system, or electronic diary, we were able to triage the measures of those who did. The use of wearable sensors and electronic diaries may be a feasible method to assess context of sedentary behaviour, but more research is needed with device-based measures in diverse groups.

Citation: Rodrigues IB, Tariq S, Kouroukis A, Swance R, Adachi J, Bray S, et al. (2024) Map ping s edentary b ehaviour (MAPS-B) in winter and spring using wearable sensors, indoor positioning systems, and diaries in older adults who are pre-frail and frail: A feasibility longitudinal study. PLoS ONE 19(5): e0290197. https://doi.org/10.1371/journal.pone.0290197

Editor: Dimitrios Sokratis Komaris, Aston University, UNITED KINGDOM

Received: August 8, 2023; Accepted: February 12, 2024; Published: May 16, 2024

Copyright: © 2024 Rodrigues et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: IBR received the New Investigator Fund from the Hamilton Health Science to support the MAPS-B project [NIF-22541], as well as the 2022 MIRA-AGE-WELL Award and the 2022 CIHR Fellowship Award. This study was also funded by the Natural Sciences and Engineering Research Council of Canada's (NSERC) Collaborative Research and Training Experience (CREATE) program in the form of support for the Smart Mobility of the Aging Populations project which was received by RZ.

Competing interests: NO authors have competing interests.

Introduction

Older adults who are frail are more likely to be sedentary [ 1 , 2 ]. Frailty is a multidimensional syndrome characterized by a decline in function across multiple physiological systems including the cardiovascular, musculoskeletal, neurological, and immunological systems [ 3 , 4 ]. Sedentary behaviour is defined as any activity during awake hours in a seating, reclining, or laying posture that uses low energy expenditure (i.e., ≤ 1.5 metabolic equivalent of task [MET]) [ 5 ]. Sedentary behaviour is not merely the absence of moderate or vigorous physical activity but also a reduction in sit-to-stand transitions, stand time, and light physical activity [ 6 ]. Most older adults who are frail spend 60% of their awake time in a seated or laid position [ 6 ]. Prolonged periods of sedentary time can lead to muscle and bone unloading and are associated with declines in mobility and quality of life, and increased risk of falls, fractures, and death [ 1 , 6 – 9 ]. In addition, prolonged screen-based sedentary activities are associated with both depressive and metabolic syndromes [ 2 ]. The deleterious health effects of sedentary behaviour are different to those of physical inactivity and are partially independent of an individual’s physical activity levels [ 6 ]. Even older adults who meet the recommended aerobic exercise guidelines of moderate to vigorous physical activity might experience adverse effects of sedentary behaviour [ 6 ]. Thus, interventions to reduce periods of prolonged sedentary behaviour are necessary, especially among older adults who are living with frailty.

Prolonged sedentary behaviours are a recognised risk factor for many medical disorders, which makes it an urgent objective for preventative health interventions. To evaluate the effectiveness of such interventions, measures that are responsive to change are required [ 10 ]. Although accelerometer-derived assessments indicate that older adults have the highest levels of sedentary time [ 11 ], these objective measures do not provide contextual information to identify interventions or public health messages to reduce sedentary time [ 10 , 12 ]. Inclinometers are the most sensitive and valid measure of total sedentary time, but the limitation of such devices is its inability to accurately assess specific modalities of sedentary behaviour [ 13 ] Moreover, device-based measures have a high cost-to-utility ratio, which often limits their use in research [ 14 ]. A recent meta-analysis reported that current tools for assessing context of sedentary behaviour or total sedentary time either over-report or under-report the amount of time adults and older adults spend sitting [ 12 ]. For example, single item self-report questionnaires typically underestimate sedentary time when compared to device-based measures (accelerometers and inclinometers) [ 12 ]. On the other hand, multi-item questionnaires, ecological momentary assessments, and diaries with a short recall period are more accurate at measuring sedentary time; however, there is also a high degree of variability between and within those tools [ 12 ]. Currently, there is no gold standard to assess the context of sedentary behaviour, especially in older adults.

Almost all studies in older adults have assessed total sedentary time, which does not provide enough information to understand the context of sedentary behaviours [ 2 , 8 ]. The main reason to understand context is because not all sedentary behaviours need to be modified as some cognitively engaging sedentary behaviours (e.g., reading, socializing) appear to benefit health, while time spent in more passive activities may be detrimental. A sedentary behaviour research priorities international consensus statement suggests researchers should explore objective and self-report methods to assess context of sedentary behaviour among older adults [ 8 ]. We used the Sedentary Behaviours International Taxonomy to guide our definition of context of sedentary behaviour [ 15 ]. Context was defined as the purpose of the sedentary behaviours, the location where the behaviours occur, posture of the behaviours (e.g., lying, sitting), social context (e.g., alone or with others), and time of day the behaviours occur. To map the context of sedentary behaviour we used objective (i.e., accelerometer and home monitoring system with an indoor positioning system), and self-report (i.e., diary) measures; we chose three measures as one measure alone does not provide enough information about context. Our study is unique because it uses a combination of measures to assess context of sedentary behaviour; however, the feasibility of these combined measures in older adults is unknown. The primary purpose of our study was to determine the feasibility of using three measures to assess the context of sedentary behaviour in older adults who are pre-frail and frail. Our secondary objectives to quantify the context of sedentary behaviours [ 16 ] and to understand perspectives of sedentary behaviour [ 17 ] are reported elsewhere.

Materials and methods

Study design.

We conducted a mixed-methods longitudinal study with older adults who are pre-frail and frail. We followed the STROBE 2007 guidelines for reporting of observational studies ( S1 Table ) [ 18 ]. Ethics approval was obtained from the Hamilton Integrated Research Ethics Board. We registered our study with clinicaltrials.gov (NCT05661058) on December 22 nd , 2022. We assessed feasibility over three days (one weekend and two weekdays) in the winter and spring as sedentary behaviour may differ by the season.

We recruited participants from physicians’ offices, the local newspaper, and a local radio station. We also posted advertisements on social media using Facebook and Twitter. To ensure diversity in our recruitment process we partnered with CityHousing Hamilton Corporation, an organization that provides subsidized housing to low-income older adults, many of whom are of visible minorities, immigrants, and have visible disabilities (i.e., use a walker or cane). The results of our recruitment and retention strategy of diverse (members of racial and ethnic minorities, diverse genders, low socioeconomic status) are described elsewhere [ 19 ]. We recruited participants between January to February 2023. We obtained written informed consent from each participant prior to enrolling them in the study. Participants attended two study visits (once in the winter and another in the spring) in a private room at St. Peter’s Hospital, which is part of the Hamilton Health Sciences. We provided free transportation for participants with limited mobility or free parking at the hospital. Participants were grouped into four cohorts of five participants. During the first week, we met with five participants at St. Peter’s where they completed a series of questionnaires and physical performance measures. We provided each participant with a wearable sensor, explained how to set up and calibrate the indoor positioning system, and complete the electronic or hard copy diary. During the second week, we collected the devices and diaries and transferred the data to a McMaster University cloud, and cleaned and charged the devices. We repeated the process with each cohort and the entire process was repeated in the spring. Participants with limited mobility or transportation were provided with pre-paid boxes to return study items. At the end of the study, participants received remuneration as a gift card to an easily accessible location on the bus route with versatile buying options (e.g., groceries, clothing, furniture, cleaning supplies).

Participants

We included participants if they: 1) spoke English or attended with a translator or caregiver; 2) were ≥60 years and older; and 3) had a Morley Frail Scale score ≥3 (i.e., a score of 0 is robust, a score 1 or 2, pre-frail, and a score of 3 to 5, frail) [ 20 ]. We excluded individuals who: 1) used a wheelchair for at least 55% of the awake day due to medical conditions; 2) were not independently mobile (i.e., require assistance from another individual to ambulate); and 3) had travel plans or other commitments that required missing >30% of the rollout period. We sought to enroll both males and females as we anticipated that gender may influence sedentary behaviour through socially constructed norms and roles and can be affected by differential access to resources, opportunities, and power.

Measures and data sources

To map the context of sedentary behaviour we used objective (wearable sensor and indoor positioning system), and self-report (daily diary) measures. Participants were equipped with the wearable sensor and indoor positioning system, and completed a diary of daily activities over three days (one weekend and two weekdays) in the winter (February 1, 2023 to March 21 st , 2023) and spring (April 10 th , 2023 to May 27 th , 2023). The three measures were linked using date and time (e.g., sitting-living room-watching TV-alone weekend, Winter 3:30 pm to 5:15 pm).

Wearable sensor.

We used the activPAL4 TM to collect data on posture. The activPAL4 TM is a valid tool to use among older adults that generates totals for the time spent lying, sitting, standing, and stepping every second of the day [ 21 ]. The wearable sensor was secured to participant’s right upper thigh, midway between the iliac crest and the upper line of the patella, using a waterproof 3M Tegaderm Transparent bandage. Participants were asked to continue their normal daily activities as the wearable sensor would not interfere with their daily lives. Data was collected on the device’s hard drive and exported manually to a secure McMaster University cloud. We considered a valid wear day if the participant wore the monitor for the full 24-hour of inclinometer wear time for at least three days that included two weekdays and one weekend.

Indoor positioning system.

We used a custom-designed and developed indoor positioning system to obtain room level positioning information. The system was designed and validated to be used by older adults in their own homes without the need for a floor plan and only minimal initial setup and calibration; the system can also be used in homes with multiple stories with multiple residents [ 22 ]. The indoor positioning system consists of a smartwatch, a few beacons, and a data hub. The participants wore a commercially available, off-the-shelf smartwatch with customized software. The smartwatches were waterproof and could be used in the shower and pool. The location of the smartwatches is tracked by ambient (nonwearable) beacons plugged in regular wall outlets of different functional rooms of the participant’s homes; we defined functional rooms as areas that participants used at least 25% of the day (e.g., kitchen, bedroom, living room). The system detected location and tracked the room-to-room movements of the participants at seconds intervals [ 22 ]. The data was collected wirelessly by a data hub and stored on a secured McMaster cloud data server.

Each participant was asked to complete a diary of 24-hour daily activities using an electronic diary (Activities Collected over Time over 24-hours (ACT24)) or a hard copy version that asked participants to describe their activity, who they did the activity with, and the date and time. ACT24 was developed by the National Cancer Institute for research purposes [ 23 , 24 ]. ACT24 is an internet-based previous-day recall designed to estimate total time (hours/day) spent sleeping in bed, in sedentary behaviours during awake hours, and in physical activity [ 23 , 24 ]. ACT24 also provides estimates of energy expenditure associated with each behaviour (MET-hours/day) [ 23 , 24 ]. We provided all participants with several sheets of the hard copy diary and participants who used the electronic diary inputted their activities the next day into ACT24. We sent daily email reminders to participants to complete their electronic diary.

Health outcomes.

We collected baseline data on falls in the last 6-weeks, cognition score, frailty status, activities of daily living, health-related quality of life, depression, and anxiety in the winter and spring. Fall history was assessed by asking the following question: “ we would like to know about any falls you have had in the last 6-weeks . Have you had any fall including a slip or trip in which you lost your balance and part or all of your body landed on the floor or ground or lower level ?” [ 25 ]. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA); we administered version 8.2 English in winter and MoCA Basic in spring [ 26 ]. MoCA scores were adjusted for age and education level. Frailty scores were measured using the Fit-Frailty Assessment & Management Application (pre-frail scores 0.18 to 0.24 and frail >0.24) [ 27 ], activities of daily living with the Nottingham Extended Activities of Daily Living Scale [ 28 ], and health-related quality of life using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) questionnaire [ 29 ]. We assessed depression scores using the Geriatric Depression Scale [ 30 ] and anxiety using the Geriatric Anxiety Scale (GAS-10) [ 31 ]. Demographic characteristics were collected using PROGRESS (Place of residence, Race/ethnicity, Occupation, Gender and sex, Religion, Education, Socioeconomic status, and Social capital) [ 32 ].

Sample size

As the primary outcome is feasibility, we selected a sample size of 20, which was considered large enough to understand the practicability of using this novel approach to mapping sedentary behaviour. Sample sizes between 12 to 24 are considered reasonable for feasibility and pilot studies [ 33 , 34 ].

Our primary outcome was feasibility, which was defined using “feasibility process” and “feasibility resource” [ 35 ]. Feasibility process included recruitment, retention, and refusal rates, while feasibility resource was determined using the following questions: 1) can each measure capture its intended domain of context (e.g., does the diary capture purpose and social context); 2) can data be triaged by date and time; and 3) are all participants willing to use or complete the measures. Our criteria for success for feasibility process were to recruit 20 participants within two-months with 85% retention and 20% refusal rates. Our recruitment criterion is based on previous frailty research in which 1-in-5 individuals who are approached in clinic are successfully recruited [ 25 , 36 ]. We anticipated that the physicians could approach 10 potential participants per week for 8 weeks (80 total participants). Our criteria for retention and refusal rates were based on a frailty systematic review where retention rates range from 70% to 90% and refusal rates from 10% to 20% [ 37 ]. Our criteria for success for feasibility resources were determined if each measure could capture a domain of context, where if “yes” than feasibility is achieved, while if “no” or “sometimes”, feasibility is not achieved [ 35 ]. The same dichotomous methods were applied if the measures could be triaged using date and time (yes or no/sometimes), and if participants were willing to use activPAL4 TM , the indoor positioning system, and complete the ACT24 (yes or no/sometimes for each measure). We also conducted exit interviews with each participant to ask about experiences using each measure.

Statistical analysis

If demographic data, feasibility process, and feasibility resources were normally distributed, we reported the results using means and standard deviations or as a count and percentage; if data was not normally distributed, we reported it as a median and interquartile range (IQR). The Shapiro-Wilk Test was used to determine normality. Descriptive analyses were performed using Microsoft Excel (version 16.71). Each exit interview was audio-recorded, transcribed verbatim, and analysed using content analysis [ 38 ]. Missing values were reported as missing. Individuals who were loss to follow-up were included in the analysis if their data were available. Adverse events were reported using narrative description.

Feasibility process

We approached 80 individuals, and 58 expressed interest in the study ( Fig 1 ). Of the 58 individuals, 37 declined (64% refusal rate) to enroll citing lack of interest because they initially thought the study was an exercise trial or they changed their mind (57%), medical mistrust (27%), or worsening medical health (16%). Twenty-eight of the 37 individuals who declined to participate identified as female, 1 as transgender male, and all 37 individuals had a Morley frail score ≥ 3. We enrolled 21 participants within two months. About 71% of participants were recruited from a physician’s office, 19% from advertisements posted in CityHousing Hamilton, and the other 10% from community advertisements posted on social media or the radio. A day after the initial study visit, one participant withdrew citing medical mistrust in the study and another participant withdrew after completing the winter period citing worsening medical health (90% retention rate). Both individuals who were lost to follow up were over the age of 75 years and categorized as frail. Five participants required transportation and three utilized the pre-paid box option.

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https://doi.org/10.1371/journal.pone.0290197.g001

Participant characteristics

Baseline characteristics are presented in Table 1 . Participants who were frail had poorer scores on the Geriatric Depression Scale, Geriatric Anxiety Scale, gait speed, and 5x sit-to-stand compared to individuals who were pre-frail. There were no differences between the frail and pre-frail group on the MoCA, grip strength, EQ-5D-5L, and the Nottingham Activities of Daily Living. We also found no differences between physical performance measures and health outcomes between the winter and spring. One participant did not complete the MoCA in winter because they forgot their reading glasses, and another did not complete the Geriatric Depression Scale in spring for personal reasons.

https://doi.org/10.1371/journal.pone.0290197.t001

Feasibility resource

We found that each measure captured its domain of context except for the hard copy diary. The hard copy diary was not completed with sufficient information about the activity or time of day. We were able to link data from activPAL4 TM , the indoor positioning system, and ACT24, but not with the hard copy diary. All participants were willing to complete the hard copy diary while almost all participants were willing to use the wearable sensor. Only some participants opted to use the indoor positioning system and complete ACT24.

Twenty of the 21 participants felt comfortable using the activPAL4 TM device to assess posture. One participant initially agreed to wear the device, and then removed it immediately after the study visit citing medical mistrust. All 20 participants found the device “comfortable to wear” and that they “did not really notice it”. Participants wore the devices continuously for three or four days to capture two weekdays and one weekend. Some participants were initially concerned the 3M Tegaderm would cause skin irritation, but we experienced no adverse events. From a research perspective, the devices were easy to set up, extract data, and charge. The median wear days was 4 days (Q 1 = 3, Q 3 = 4, n = 19) in the winter and spring. During the winter session, the wearable sensor was not set up properly for one participant, and so data on posture was not collected and considered invalid.

Six of the 21 participants were willing to use the indoor positioning system. The other 15 participants were not prepared to try the system as they anticipated challenges in the set up, which directly requires connection to their home WiFi router. These participants expressed concerns including not familiar with a home WiFi router, or difficulty accessing the router because it was in a hard-to-reach area. We also learned that four of the 15 participants used a cellular network (Long-Term Evolution system), which was not compatible with our version of the hub design. Overall, the six participants found the indoor positioning system easy to use but provided suggestions to improve the functional design. All six participants reported that the watch was “bulky” and “uncomfortable”. The watch required daily charging and so we ask participants to charge the watch overnight in the room where they slept. Most participants reported few challenges with setting up the beacons or the hub but found the black box design could be improved to be a brighter colour to make the devices less intimidating. Participants were unsure if the beacons and hub were working as there was no indicator light. From a research perspective, linking the data was a little challenging as several participants forgot to calibrate the devices; however, we were able to link it to the other measures. In addition, we know of one participant who switched one beacon to another room mid-way through data collection.

We collected 115 days of diary data of the 120 days (6 days x 20 participants). Eighteen participants submitted six days of diary data (three days in winter and three days in spring). One participant only submitted three days in the winter and then dropped out due to medical reasons, while the second participant only submitted four days total as they forgot to complete two days in the spring. Four participants had more than 5 hours of missing data per day; these four participants submitted a hard copy diary. Nine participants reported their daily activities using the ACT24 while the other 12 used a hard copy diary. Initially four participants agreed to use ACT24, but due to challenges in using the software, they decided to complete the hard copy instead. Challenges in using ACT24 included it being “difficult” and “complicated” at first because of the “five-min interval reporting”. Some participants found it challenging to navigate because there were so many options for activities; however, after some practice the majority of participants found ACT24 “fairly easy”. Participants who used the hard copy diary found it easy to complete; four participants had a caregiver complete their hard copy diary. From a research perspective, the hard copy diaries were not a feasible method to collect data as they were not completed with enough detail to extract time, purpose, and social context. The advantage of ACT24 is participants cannot submit an incomplete entry, which encouraged participants to provide enough details about their daily activities. Adherence to the diaries was good with all 20 participants completing either the electronic or hard copy diary probably because they received daily email reminders.

Adverse events

We experienced three adverse events among two participants, which were not related to the study. One participant fell twice due to improper footwear or stepping out of the shower onto a damp floor. Another participant with type II diabetes skipped breakfast and felt unwell during the study visit; after consuming orange juice, the person returned to baseline.

We conducted a study in older adults who were pre-frail and frail to understand the feasibility to measure the context of sedentary behaviour in the winter and spring. Context was assessed using a wearable sensor (activPAL4 TM , posture), a McMaster engineering home monitoring system (indoor positioning system, functional location within the home), and a diary (ACT24 or hard copy diary, purpose and social environment). We met our criteria for recruitment and retention but experienced high refusal rates mainly due to lack of interest or medical mistrust. We found that each measure captured context of sedentary behaviour except for the hard copy diary. Since the hard copy diary was not completed with sufficient detail, we found it challenging to link it to the other two measures. We were able to link data from activPAL4 TM , the indoor positioning system, and ACT24. All participants were willing to complete the hard copy diary while almost all participants were willing to use the wearable sensor. Only some participants opted to use the indoor positioning system and complete ACT24. The use of wearable sensors and electronic diaries may be a feasible method to assess context of sedentary behaviour, but more research is needed with device-based measures in diverse groups.

It is unclear what are the best measures to assess context of sedentary behaviour, especially in older adults. Objective measures, such as inclinometers or thigh-worn accelerometers, offer the highest validity for measuring sedentary time, although these measures are not able to identify the specific types of sedentary behaviours [ 39 ]. On the other hand, subjective measures, particularly diaries, are useful for recording the type and amount of time spent engaging in different sedentary behaviours, but their validity in gauging total sedentary time is low [ 39 ]. Using both objective and subjective approaches together can yield a more comprehensive measure of sedentary behaviour than one measure alone as they capture several domains of sedentary behaviour [ 39 ]. Although there is ample amount of data on devices to capture sedentary behaviour or time, there is little information about the feasibility of using a combination of measures in older adults [ 40 ]. Our results present interesting findings that suggest inclinometers and electronic diaries may be a feasible method to assess context of sedentary behaviour; however, the results are not generalizable to diverse older populations (e.g., lower socioeconomic status, visible minorities, lower education levels). There is evidence from qualitative research that suggest older adults living with chronic conditions perceive wearable activity trackers to be “useful” and “acceptable” [ 41 – 43 ]. But our study found that diverse older adults did not feel comfortable using any wearable device including the indoor positioning system or the wearable sensor. In fact, the most common reason for not joining the study was the fear of being tracked. The homogenous demographic characteristics of the participants in our study should be considered when interpreting the results. The majority of participants that partook in the study felt comfortable using the wearable sensor, but several participants were not willing to configure the smart home system because they were intimidated with the system and set-up process. Black boxes were used to set up the indoor positioning Bluetooth® system and several individuals found these boxes to be unsettling as they believed the boxes contained cameras. Another challenge in setting up the system was connecting the modem to the internet box. We found participants preferred to use the hard copy diary over the electronic diary, but the hard copy diary was not completed with enough detail making it challenging to link the time to the inclinometer. Those that chose to complete the electronic diary found it time consuming as they could not submit their diary if there were missing times in the day. Despite the challenge, participants found the electronic diary easy to use after enough practice. Our results suggest the combination of wearable sensors and electronic diaries may be a feasible method to capture context of sedentary behaviour; however, more research is needed to understand other methods to assess context of sedentary behaviour in diverse populations.

Smart home monitoring systems may be a potential device to assess context of sedentary behaviour. Artificial intelligence, machine learning, and fuzzy logic can be automatically rendered within smart home monitoring systems and be used to identify activities that older adults engage in (e.g., watching TV in the living room). One study developed a robot-integrated smart home (RiSH) for older adults, which used a sensor network to monitor body activities. The RiSH was able to recognize 37 distinct individual activities through sound actions with 88% accuracy and identify falling sounds with 80% accuracy [ 44 ]. Moreover, smart home systems could also be used to target and decrease certain sedentary behaviours. Rudzicz and colleagues developed a mobile robot to assist older adults with Alzheimer’s disease with their activities of daily living [ 45 ]; such systems could be used to promote safe mobility among older adults who are frail. There may be an advantage to using smart home monitoring systems that utilize artificial intelligence, machine learning, and fuzzy logic as it decreases burden on participants to constantly monitor their day-to-day activities in minute-by-minute intervals. However, introducing such technologies also requires educating certain groups that may be mistrustful of the devices. Educational outreach programs and involving diverse groups as patient partners during the co-design process should be conducted in parallel with pilot studies of smart home monitoring systems.

To date there are no set standards for the use of wearable devices with respect to wear time (minimal or maximum) or position of the device [ 46 ]. Some studies suggest that hip-worn wearable devices assess 24-hour movement more precisely than wrist-worn devices [ 12 ], whereas other investigators report reasonable precision with wrist-worn devices [ 12 , 14 ]. The methods researchers use to assess sedentary behaviour with wearable devices are dependent on the study aim, the design of the wearable device, the activity that is aimed to be captured, as well as the acceptability of the study population [ 46 ]. To date, most studies have used a single, objective measure to assess total sedentary time in older adults with wear time ranging from two to seven days [ 40 , 47 – 49 ]. There are few papers that used a combination of inclinometers and other measures to assess context of sedentary behaviour [ 50 , 51 ], which makes it challenging when selecting a wear time that accurately captured sedentary behaviour. A 2015 cohort study by Leask and colleagues claims to be the first study to explore the context of sedentary behaviour in older adults (46). The study employed a combination of a timelapse camera (Vicon Revue TM , formerly known as SenseCam) and an inclinometer (activPAL TM ) [ 50 ]. The average wear time for the devices was 1.5 days, with a median wear time of one day [ 50 ]. After discussions with the research team and patient partners, we decided to collect six days total with three days in the winter and three days in the spring. It was recommended by our patient partners that data collection for each season be limited to three days as to decrease the burden on participants when completing the daily diaries. It was discussed that as most individuals who are frail also have diminished cognitive impairments, the burden to accurately complete the diaries would be high. In addition, evidence from Marshall et al [ 52 ] has previously reported there are no significant differences between weekday or weekday and weekend sedentary behaviour in older adults, so we expected six days of activity would be sufficient.

Strengths and limitations

Our study had several strengths. We recruited a diverse group of older adults who were mainly frail and had cognitive impairments with diverse demographic characteristics including individuals who only completed grade school or high school. We also used a unique combination of objective and subjective measures to assess context of sedentary behaviour. While our study conformed to the highest standards, our study is not without its limitations. The disadvantage of using only one wearable sensor can result in device failure or corrupt data; we experienced one instance where the data was not captured during the winter period. Although we attempted to recruit diverse individuals (e.g., ethnic minorities, individuals of different genders), we experienced barriers including medical mistrust. Thus, the generalizability of the results may not be feasible in other groups. As this was a feasibility study, we only collected data over three days (two weekdays and one weekend) in the winter and spring, which may not be representative of the season or other time periods (i.e., summer and fall). Moreover, it is possible that three days per season is not enough to capture the diversity of day-to-day activities of older adults who are frail and so we need more data on wear time methods and how seasonality may influence day-to-day activities.

We met our criteria for recruitment and retention but experienced high refusal rates. We recruited 21 older adults who were pre-frail or frail within two months and experienced two dropouts due to medical mistrust or worsening health. We experienced high refusal rates as several participants who initially agreed to participate decided not to enroll. The wearable sensor, indoor positioning system, and ACT24 accurately captured one domain of context but participants experienced challenges completing the hard copy diary. The hard copy was not completed with enough details making it difficult to link it to the other devices. We also found some participants were not willing to utilize the wearable sensor, indoor positioning system, and electronic diary. However, we were able to triage the measures of participants who utilized the wearable sensor, indoor positioning system, and ACT24. Nevertheless, there is some merit to using a combination of assessment methods (e.g., wearable sensor and electronic diary) to capture the context of sedentary behaviour. Future studies will need to determine the most feasible and valid methods to assess the context of sedentary behaviour, especially in diverse older adults.

Supporting information

S1 table. 2007 strobe checklist for cohort studies..

https://doi.org/10.1371/journal.pone.0290197.s001

S1 File. TREND checklist.

https://doi.org/10.1371/journal.pone.0290197.s002

S2 File. Protocol MAPS-B.

https://doi.org/10.1371/journal.pone.0290197.s003

S3 File. De-identified feasibility data.

https://doi.org/10.1371/journal.pone.0290197.s004

Acknowledgments

The authors would like to thank our patient partners, Margaret Denton and Anne Pizzacalla from the Hamilton Council on Aging, and Priscilla Ching from Osteoporosis Canada for their input during the study. We would also like to thank our participants for helping collect this data.

Other information

This review was registered on clinicaltrials.gov (NCT05661058).

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World-first regulations to combat sedentary behavior among children in China show global promise

by University of Bristol

Pioneering measures to tackle sedentary behavior among children in China have proved effective, according to new research.

The study, led by the University of Bristol, reveals regulations recently introduced by the Chinese government to reduce school children's sedentary behavior by restricting online gaming companies catering for this age group, limiting the amount of homework schoolteachers can assign, and curtailing when private tuition businesses can provide lessons, significantly reduced total sedentary time as well as how long they spent on different sedentary activities.

The measures were associated with a 13.8% daily sedentary drop overall, equating to more than three-quarters of an hour less spent daily in this physically inactive mode.

The findings, published today in the International Journal of Behavioral Nutrition and Physical Activity , have important implications for future policies and measures aimed at improving children's physical and mental well-being globally.

Lead author Dr. Bai Li, from the Center for Exercise, Nutrition and Health Sciences at the University's School for Policy Studies, said, "The results are exciting as this type of regulatory intervention across multiple settings has never been tried before. Traditionally, children and their parents or caregivers have been guided with education and encouraged to make behavioral changes themselves, which hasn't really worked.

"With these regulatory measures, the onus has shifted to online gaming companies, schools and, private tutoring companies to comply. This very different approach appears to be more effective, because it is aimed at improving the environment in which children and adolescents live, supporting a healthier lifestyle."

The team of researchers in the U.K. and China analyzed individually matched surveillance data gathered from more than 7,000 primary and secondary school students in 2020 and 2021, before and after the regulations were introduced. Participants were recruited from 31 urban or rural areas across 14 cities in the Guangxi region of Southern China.

Over this period, statistics showed the average amount of time students spent on sedentary pursuits each day reduced by 46 minutes. This was particularly pronounced among students in urban areas, compared to those from rural areas. Average daily screen-viewing time—including using mobile phones, handheld game consoles, tablets, televisions, games consoles connected to televisions, or computers—reduced by 6.4% (10 minutes).

Students were also shown to be 20% more likely to meet the screen time recommendation of less than two hours daily, applicable in the U.K. and U.S., after the regulations were implemented.

The findings revealed students overall were nearly three (2.8) times more likely to meet the Chinese government's recommendations for the maximum amount of time spent on homework. This likelihood lessened with age, dropping from 3.6 times among primary school children to 2.1 times among secondary school children.

Dr. Li, who directs a Master of Science (MSc) Program in Nutrition, Physical Activity and Public Health said, "Our findings certainly suggest the government regulations may have helped lower sedentary behavior among children and young people in this region of China. Further research is needed to assess whether such interventions have a similar impact in other regions of China and internationally."

Prof Boyd Swinburn, Professor of Global Health at the University of Auckland and Co-Director of the World Health Organization (WHO) Collaborating Center for Obesity Prevention at Deakin University in Melbourne, also a former Co-Chair of World Obesity Federation (WOF) Policy & Prevention section, said, "This is a fascinating study because most interventions to reduce sedentary behaviors have relied on educational approaches rather than the regulatory measures used here.

"While achieving similar regulations in countries outside China may be a challenge, the impact of the regulations does show how sensitive sedentary behaviors are to the prevailing environmental conditions and rules."

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• Sedentary Behavior

The Impact of Sedentary Behavior on Teen Heart Function

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As adolescents navigate their formative years, their habits around physical activity and sedentary behavior are shaping their future cardiovascular health. A distinct study, conducted by Dr. Eero A. Haapala and colleagues, published in the Journal of the American Heart Association, diligently examines the relationship between the accumulation of sedentary time from childhood to adolescence and its subsequent impacts on adolescent cardiac function.

The study engaged with 153 adolescents, who were first evaluated between the ages of 6 and 8 years and followed up again after 8 years. Sedentary time and physical activity levels were continuously monitored using accelerometers and heart rate monitors, and various measures of cardiac function were assessed using impedance cardiography at the 8-year mark.

The findings were striking; data suggested that excessive sedentary time during childhood correlated with increased cardiac work during the adolescent years. On the flip side, moderate to vigorous physical activity (MVPA) appeared to have a protective effect, inversely associated with cardiac work. This highlights the negative cardiovascular effects that could arise from a sedentary lifestyle begun in childhood, underscoring the need to instill and encourage active lifestyles from a young age.

To delve deeper, additional analyses factored in body fat percentage and other cardiometabolic risk markers. These showed that the associations between both sedentary behavior and physical activity with cardiac work were muddied by adiposity, suggesting that weight control and body composition are also pivotal factors in cardiac health during adolescence.

Understanding the complexities of these relationships is paramount, as interventions from childhood that focus on promoting physical activity, managing weight, and reducing sedentary time could be essential in preventing future heart function abnormalities. The implications for clinical practice are evident: from pediatricians to public health officials, educating families about the long-term benefits of physical activity for heart health could be transformative for generations to come.

For those interested in learning more about the in-depth methodologies and results, the complete study is available through the Journal of the American Heart Association. The insights from this research, born of almost a decade-long observation, offer a clarion call for proactive measures in the name of cardiovascular well-being for our youth.

The extensive knowledge and insights garnered from this study were made possible through the assistance of Buoy Health's online health tool, which provided critical support for the research. To explore further information and methods, please visit https://doi.org/10.1161/JAHA.123.031837

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Haapala, E. A., Leppänen, M. H., Lee, E., Savonen, K., Laukkanen, J. A., Kähönen, M., Brage, S., & Lakka, T. A. (2024). Accumulating Sedentary Time and Physical Activity From Childhood to Adolescence and Cardiac Function in Adolescence. Journal of the American Heart Association, 13, e031837. https://doi.org/10.1161/JAHA.123.031837

Scientists Identify Link Between Sitting and Dementia

N ew insights into the medical mysteries behind dementia have been revealed this week, with two studies identifying drivers of the brain-degenerating condition.

One study, released on September 11 in the journal General Psychiatry , shows that the shortening of little caps on the end of chromosomes may be linked to increased dementia risk. Another, published in the journal JAMA on September 12, reveals that spending more time sedentary, such as sitting down, may also increase the risk.

These studies may help scientists to further understand the mechanisms behind what causes dementia to develop, and therefore how to stop it.

How Aging Is Linked to Dementia

Dementia is a term for the symptoms of a decline in brain function, as a result of damage to or changes in the brain. Alzheimer's disease is a form of dementia, caused by an abnormal build-up of two types of protein: amyloid and tau. One of the major drivers of dementia development is age, as it is much more common in the elderly.

This may be somewhat explained by the findings of the General Psychiatry paper, which discovered that dementia risk increases with the shortening of the telomeres. These telomeres are little caps on the ends of chromosomes that prevent the functional genes from being lost during replication. As a person ages, their telomeres become shorter and shorter as a result of many years of cell division and chromosome replication.

According to the paper, data from UK Biobank—a large biomedical database—reveals that of patients between the ages of 37 and 73, those with shorter telomeres in their leucocytes (a type of white blood cell) were 14 percent more likely to be diagnosed with dementia than those with the longest telomeres, and 28 percent more likely to be diagnosed with Alzheimer's disease.

"We found that LTL [leucocyte telomere length] acts as an aging biomarker associated with the risk of dementia," the authors wrote. "Furthermore, we also observed linear associations of LTL with total and regional brain structure. These findings highlight telomere length as a potential biomarker of brain health."

"Since LTL is largely inherited, individuals who inherit shorter LTL may be predisposed to dementia, making LTL an appealing predictive biomarker for dementia. In addition, shorter LTL is widely regarded as an indicator of poorer neuropsychological condition, so measurement of LTL might be considered as an option offered to the public to motivate healthy lifestyle choices in the general population."

How Sedentary Behavior is Linked to Dementia

The JAMA paper reveals that another driver of dementia may be a sedentary lifestyle.

Also using UK Biobank data, as well as accelerometer data from study participants, the researchers found that people aged over 60 who spend more than 10 hours a day engaging in sedentary behaviors, such as sitting, are at a higher risk of dementia compared with those who are sedentary for less time.

"The link between sedentary behavior was nonlinear, so that at lower amounts of sitting time, there was no significant increase in risk," study author David Raichlen, professor of biological sciences and anthropology at the USC Dornsife College of Letters, Arts and Sciences, told Newsweek . "After about 10 hours per day of sedentary behavior, risks increased significantly. Ten hours per day of sedentary behavior was associated with an 8 percent increase in risk of dementia and 12 hours per day was associated with a 63 percent increase in risk of dementia."

Sedentary behavior was defined by the authors as any waking behavior characterized by "a low energy expenditure while in a sitting or reclining posture," Raichlen said.

"We were surprised to find that the risk of dementia begins to rapidly increase after 10 hours spent sedentary each day, regardless of how the sedentary time was accumulated," study author Gene Alexander, professor of psychology and psychiatry at the Evelyn F. McKnight Brain Institute at the University of Arizona and Arizona Alzheimer's Disease Research Center, said in a statement.

"This suggests that it is the total time spent sedentary that drove the relationship between sedentary behavior and dementia risk, but importantly lower levels of sedentary behavior, up to around 10 hours, were not associated with increased risk."

They also found that even if this sedentary time is broken up by periods of activity, it's only the total time spent sitting that appears to have an impact on dementia risk.

"Many of us are familiar with the common advice to break up long periods of sitting by getting up every 30 minutes or so to stand or walk around," Raichlen said in the statement. "We wanted to see if those types of patterns are associated with dementia risk. We found that once you take into account the total time spent sedentary, the length of individual sedentary periods didn't really matter."

The exact reason why sedentary lifestyles may be linked to dementia risk is unclear, the authors said, with more research being required to fully understand the mechanisms behind this association.

"Our study was not focused on mechanisms but it is possible that reductions in cerebral blood flow or links between sedentary behavior and cardiometabolic disease factors may play a role in increased risk for dementia. Future work will focus on identifying these mechanisms," Raichlen said.

The link between telomere length and dementia also needs more research, the General Psychiatry authors explain, including looking at telomere length in other cell types, and how dementia risk changes with changes in telomere length.

"Several limitations must be taken into account," the authors wrote. "LTL was measured only once at baseline in nearly 470,000 participants. Based on the results of the current study, we were unable to identify whether changes in LTL impact the chances of dementia development.

"Dementia diagnoses were obtained from electronic health records only, so some dementia cases may not have been fully covered; likewise, we inevitably omitted some undiagnosed dementia and less severe dementia cases as they might not have been mentioned in the electronic health records," they said. "Finally, given the nature of an observational study design, conclusions of causality should be made with caution."

Do you have a tip on a science story that Newsweek should be covering? Do you have a question about dementia? Let us know via [email protected].

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The Loneliness Curve

New research suggests people tend to be lonelier in young adulthood and late life. But experts say it doesn’t have to be that way.

By Christina Caron

When Surgeon General Vivek Murthy went on a nationwide college tour last fall, he started to hear the same kind of question time and again: How are we supposed to connect with one another when nobody talks anymore?

In an age when participation in community organizations , clubs and religious groups has declined, and more social interaction is happening online instead of in person, some young people are reporting levels of loneliness that, in past decades, were typically associated with older adults.

It’s one of the many reasons loneliness has become a problem at both the beginning and end of our life span. In a study published last Tuesday in the journal Psychological Science, researchers found that loneliness follows a U-shaped curve: Starting from young adulthood, self-reported loneliness tends to decline as people approach midlife only to rise again after the age of 60, becoming especially pronounced by around age 80.

While anyone can experience loneliness, including middle-aged adults , people in midlife may feel more socially connected than other age groups because they are often interacting with co-workers, a spouse, children and others in their community — and these relationships may feel stable and satisfying, said Eileen K. Graham, an associate professor of medical social sciences at the Northwestern University Feinberg School of Medicine and the lead author of the study.

As people get older, those opportunities can “start to fall away,” she said. In the study, which looked at data waves spanning several decades, starting as early as the 1980s and ending as late as 2018, participants at either end of the age spectrum were more likely to agree with statements such as: “I miss having people around me” or “My social relationships are superficial.”

“We have social muscles just like we have physical muscles,” Dr. Murthy said. “And those social muscles weaken when we don’t use them.”

When loneliness goes unchecked, it can be dangerous to our physical and mental health, and has been linked to problems like heart disease, dementia and suicidal ideation.

Dr. Graham and other experts on social connection said there were small steps we could take at any age to cultivate a sense of belonging and social connection.

Do a relationship audit.

“Don’t wait until old age to discover that you lack a good-quality social network,” said Louise Hawkley, a research scientist who studies loneliness at NORC, a social research organization at the University of Chicago . “The longer you wait, the harder it gets to form new connections.”

Studies suggest that most people benefit from having a minimum of four to six close relationships, said Julianne Holt-Lunstad, a professor of psychology and neuroscience and the director of the Social Connection and Health Lab at Brigham Young University.

But it’s not just the quantity that matters, she added, it’s also the variety and the quality.

“Different relationships can fulfill different kinds of needs,” Dr. Holt-Lunstad said. “Just like you need a variety of foods to get a variety of nutrients, you need a variety of types of people in your life.”

Ask yourself: Are you able to rely on and support the people in your life? And are your relationships mostly positive rather than negative?

If so, it’s a sign that those relationships are beneficial to your mental and physical well-being, she said.

Join a group.

Research has shown that poor health, living alone and having fewer close family and friends account for the increase in loneliness after about age 75.

But isolation isn’t the only thing that contributes to loneliness — in people both young and old, loneliness stems from a disconnect between what you want or expect from your relationships and what those relationships are providing.

If your network is shrinking — or if you feel unsatisfied with your relationships — seek new connections by joining a community group, participating in a social sports league or volunteering , which can provide a sense of meaning and purpose, Dr. Hawkley said.

And if one type of volunteering is not satisfying, do not give up, she added. Instead try another type.

Participating in organizations that interest you can offer a sense of belonging and is one way to accelerate the process of connecting in person with like-minded people.

Cut back on social media.

Jean Twenge, a social psychologist and the author of “Generations,” found in her research that heavy social media use is linked to poor mental health — especially among girls — and that smartphone access and internet use “ increased in lock step with teenage loneliness .”

Instead of defaulting to an online conversation or merely a reaction to someone’s post, you can suggest bonding over a meal — no phones allowed.

And if a text or social media interaction is getting long or involved, move to real-time conversation by texting, “Can I give you a quick call?” Dr. Twenge said.

Finally, Dr. Holt-Lunstad suggested asking a friend or family member to go on a walk instead of corresponding online. Not only is taking a stroll free, it also has the added benefit of providing fresh air and exercise.

Take the initiative.

“Oftentimes when people feel lonely, they may be waiting for someone else to reach out to them,” Dr. Holt-Lunstad said. “It can feel really hard to ask for help or even just to initiate a social interaction. You feel very vulnerable. What if they say no?”

Some people might feel more comfortable contacting others with an offer to help, she added, because it helps you focus “outward instead of inward.”

Small acts of kindness will not only maintain but also solidify your relationships, the experts said.

For example, if you like to cook, offer to drop off food for a friend or family member, Dr. Twenge said.

“You’ll not only strengthen a social connection but get the mood boost that comes from helping,” she added.

Christina Caron is a Times reporter covering mental health. More about Christina Caron

Managing Anxiety and Stress

Stay balanced in the face of stress and anxiety with our collection of tools and advice..

How are you, really? This self-guided check-in will help you take stock of your emotional well-being — and learn how to make changes .

These simple and proven strategies will help you manage stress , support your mental health and find meaning in the new year.

First, bring calm and clarity into your life with these 10 tips . Next, identify what you are dealing with: Is it worry, anxiety or stress ?

Persistent depressive disorder is underdiagnosed, and many who suffer from it have never heard of it. Here is what to know .

New research suggests people tend to be lonelier in young adulthood and late life. But experts say it doesn’t have to be that way .

How much anxiety is too much? Here is how to establish whether you should see a professional about it .