The association between physical activity, sedentary behavior and health-related quality of life in children and adolescents has been mostly investigated in those young people with chronic disease conditions. No systematic review to date has synthesized the relationship between physical activity, sedentary behavior and health-related quality of life in the general healthy population of children and adolescents. The purpose of this study was to review systematically the existing literature that evaluated the relations between physical activity, sedentary behavior and health-related quality of life in the general population of children and adolescents.
We conducted a computer search for English language literature from databases of MEDLINE, EMBASE, PSYCINFO and PubMed-related articles as well as the reference lists of existing literature between 1946 and the second week of January 2017 to retrieve eligible studies. We included the studies that assessed associations between physical activity and/or sedentary behavior and health-related quality of life among the general population of children and adolescents aged between 3–18 years. The study design included cross-sectional, longitudinal and health intervention studies. We excluded the studies that examined associations between physical activity, sedentary behavior and health-related quality of life among children and adolescents with specific chronic diseases, and other studies and reports including reviews, meta-analyses, study protocols, comments, letters, case reports and guidelines. We followed up the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement in the reporting of this review. The risk of bias of the primary studies was assessed by the Newcastle-Ottawa Scale. We synthesized the difference in health-related quality of life scores between different levels of physical activity and sedentary time.
In total, 31 studies met the inclusion criteria and were synthesized in the review. Most of the included studies used a cross-sectional design (n = 21). There were six longitudinal studies and three school-based physical activity intervention studies. One study used both cross-sectional and longitudinal designs. We found that higher levels of physical activity were associated with better health-related quality of life and increased time of sedentary behavior was linked to lower health-related quality of life among children and adolescents. A dose-response relation between physical activity, sedentary behavior and health-related quality of life was observed in several studies suggesting that the higher frequency of physical activity or the less time being sedentary, the better the health-related quality of life.
The findings in this study suggest that school health programs promoting active lifestyles among children and adolescents may contribute to the improvement of health-related quality of life. Future research is needed to extend studies on longitudinal relationships between physical activity, sedentary behavior and health-related quality of life, and on effects of physical activity interventions on health-related quality of life among children and youth.
Citation: Wu XY, Han LH, Zhang JH, Luo S, Hu JW, Sun K (2017) The influence of physical activity, sedentary behavior on health-related quality of life among the general population of children and adolescents: A systematic review. PLoS ONE 12(11): e0187668. https://doi.org/10.1371/journal.pone.0187668
Editor: Jacobus P. van Wouwe, TNO, NETHERLANDS
Received: June 7, 2017; Accepted: October 24, 2017; Published: November 9, 2017
Copyright: © 2017 Wu 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 paper and in the Supporting Information files.
Funding: The present study was funded by the Natural Science Foundation of Shandong Province, China (ZR2017LH060).
Competing interests: The authors have declared that no competing interests exist.
The associations between physical activity (PA), sedentary behavior (SB) and physical and mental health among children and adolescents have been well established. Systematic reviews and primary studies of PA and health have indicated that children and adolescents who engaged in increased levels of physical activities had better physical and mental health and psychosocial well-being than those in an inactive lifestyle [1–7]. Promoting PA among children and adolescents has been demonstrated to benefit a number of disease conditions, including obesity [8,9], coronary heart disease and other health problems [1,4]. Sedentary behavior characterized often as screen-based media use behaviors including watching television (TV), using computers/smartphones and playing video games  are associated with various negative health consequences [2,5–7,10]. The adverse consequences resulted from sedentary behaviors include an increased risk of obesity, cardiovascular disease and all-cause mortality, and a range of impaired psychological health [2,3,5–7,10,11]. Sedentary behavior also contributes to a delay of cognitive development and a decrease in academic achievement of children and youth .
Health-related quality of life (HRQOL) has been increasingly used as a health outcome among children and adolescents to assess their physical and social functioning, mental health and well-being, and to evaluate population-based intervention programs . HRQOL is a multidimensional construct that covers physical, psychological, and social health and hence represents overall health of an individual . Assessment of HRQOL among children and adolescents is important in identifying subgroups with poor health status and in guiding effective intervention strategies to improving health of the younger population. The association between PA and HRQOL in children and adolescents has been mainly investigated among those with chronic disease conditions such as obesity, asthma and cancer [15–18]. These studies have reported that children and adolescents who undertake an active lifestyle experience better HRQOL than their peers who engage in an inactive lifestyle. In the general population, the relationship between PA and HRQOL has been well investigated in adults  relative to children and youth (e.g.,school or population-based samples). Moreover, much less is known about the relationship between sedentary behavior and HRQOL . In the past decade, we have found accumulating studies that examined the effect of PA and sedentary behavior on HRQOL among populations of children and adolescents. To our best knowledge, no systematic review to date has been published to evaluate the relationship between PA, sedentary behavior and HRQOL among the general popluation of relatively healthy children and adolescents. Particularly, it is essential to explore how PA and sedentary behavior influence different aspects of physical, psychological and social functioning of HRQOL among children and adolescents, and whether a dose-response relation exists between PA levels, time spent on sedentary behaviors and HRQOL. Such information will help to provide an evidence-base for public health policy to invest in school-based health promotion programs in order to enhance health and quality of life among children and adolescents.
The purpose of the present study was to 1) review and synthesize the existing literature that investigated associations between PA, sedentary behavior and HRQOL in the general population of children and adolescents; 2) provide evidence-based recommendations for guiding school-based health behavior intervention programs to enhance HRQOL among children and adolescents.
A computer search was performed for English language literature using databases of MEDLINE (1946-Week 2, January, 2017), EMBASE (1974-Week 2, January, 2017), PSYCINFO (1987-Week 2, January, 2017). MeSH headings and keywords used included ‘physical activity’, ‘exercise’, ‘sedentary behavior’, ‘screen time’, ‘television’, ‘computers’, ‘video games’, ‘lifestyle’, ‘health-related quality of life’, ‘quality of life’, ‘health status’,‘children’, ‘adolescents’. From the relevant articles, we searched the PubMed related articles and manually examined the reference lists of the existing literature to retrieve other eligible studies.
The electronic search was conducted by a single researcher (XYW). The reviewer screened the citations and abstracts and selected the eligible articles based on the inclusion and exclusion criterion. In case there were studies that the primary reviewer was uncertain whether a paper was eligible for the review, the full text articles were obtained. The full-text articles of all potentially eligible studies were retrieved, and then reviewed by the two reviewers (XYW, LHH) separately for inclusion criteria. Disagreements regarding the eligibility of the studies for inclusion were resolved by discussion among all the researchers.
Inclusion and exclusion criteria
In this review, we aimed to collect studies that focused on examinations of the associations between physical activity, sedentary behavior and HRQOL among the population of healthy children and adolescents. The inclusion criteria were as follows: (1) The outcome of interest: Studies used one or more multi-dimensional HRQOL measures, and the outome has to be indicated as quality of life (QOL) or health-related quality of life. (2) Population of interest: The genereal population of children and adolescents aged between 3–18 years, including school-aged, or community-based children and adolescents. The general population of children and adolescents refers to all children and adolescents in communities or schools in a geographic region or a country who are relatively healthy in comparison with those children and adolescents with specific diseases (e.g., patients with diabetes, obesity or cerebral palsy, etc.). For longitudinal and intervention studies with a follow-up age of greater than 18 years, the age of children and youth had to be 3 to 18 years at baseline when at least one exposure of PA and sedentary behavior was measured. (3) Types of studies: Cross-sectional, longitudinal and health intervention studies. The intervention studies for quality of life were those studies that targeted to promoting PA and reducing sedentary behavior among children and adolescents. (4) Measure of the exposure: PA is defined as any bodily movement worked with muscles that requires energy expenditure . Sedentary behavior is any waking behavior while sitting, lying, reclining or standing with low energy expenditure [22,23]. Both subjective and objective measures of PA and sedentary behavior were included.
The exclusion criteria were: (1) Studies that examined associations between PA, sedentary behavior and HRQOL among children and adolescents with specific chronic disease conditions (e.g.,diabetes, asthma, obesity). (2) Studies that assessed associations between PA, sedentary behavior and HRQOL among adults. (3) Studies that used only a single item of self-rated/self-perceived health as a marker of HRQOL. (4) Studies that used combined indicators of PA, sedentary behavior and other variables (e.g.,diet factors,sleep) that were generated from cluster analysis. (5) Reviews, meta-analyses, study protocols, comments, letters, case reports and guidelines.
A single researcher (XYW) abstracted data from all eligible full text articles that included study publication year, primary author, country, study design, sample, PA and sedentary behavior assessments, HRQOL measures, statistical methods and main findings. Another reviewer (LHH) checked the abstracted information for completion and accuracy.
Risk of bias assessment
We used the Newcastle-Ottawa Scale to assess the study quality . This scale is a checklist with eight items that consists of three quality components: selection, comparability and outcome. Each item can be scored as one or two points and summed up to a total score, ranging from 0 to 9, with a higher score indicating low risk of bias or better quality . Previous research categorized the risk of bias score of individual studies into high, moderate and low risk of bias . We grouped the summed scores into high (0–4), moderate (5–6), and low (7–9) risk of bias.
The main findings were synthesized using descriptive tables for qualitative comparisons. The study characteristics, the assessment of the exposure and the outcome, the association between PA, sedentary behavior and HRQOL, and the risk of bias were presented for each included study in the evidence table. For studies that utilized similar measures of the exposure (e.g., PA and screen time) and the outomes (e.g., the PedsQL 4.0 total score), we performed meta-analyses for the overall association between PA, screen time and HRQOL. Pooled estimates were obtained for the differences in total HRQOL scores and their 95% confidence intervals between different levels of physical activity and sedentary time. A forest plot was generated for the overall effect across the studies. We used the random-effects model in meta-analyses to account for heterogeneity across the studies. The tau-square (Tau2) value and I2 (squared) index was used to test the degree of heterogeneity . Review Manager Software 5.2 (The Cochrane Collaboration, Copenhagen Denmark) was used for the meta-analysis. Reporting of this review was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement .
Characteristics of the included primary studies
A total of 6,071 relevant citations (MEDLINE: 3,108, EMBASE: 2,545, PSYCINFO: 418) were identified through electronic database searching and screened for eligibility. Additional 22 articles were identified through searching PubMed-related articles and reference lists from existing relevant studies. After the title and abstract review, and removing duplicate references, 49 full text articles were retrieved for detailed examinations. Among these studies, 18 were excluded due to inappropriate outcomes (e.g., anxiety,self-esteem), inappropriate exposure (e.g., cluster of PA,screen-based behavior and diets), adult participants or a review [28–45]. In total, 31 studies were included in the final synthesis [46–76]. Selection process of the studies was displayed in the PRISMA flow diagram (Fig 1).
Table 1 shows the characteristics of the included studies. In total, 79,046 participants were included in the statistical analysis in the primary studies with a sampe size ranging from 152 (Dalton et al., 2010) to 16,560 (Galán et al., 2013) students. The included studies came from 15 countries. Seven studies were conducted in Australia and four studies were from Spain. The remaining 20 studies were conducted in the following countries: United States (n = 3), Japan (n = 3), Germany (n = 2), Switzerland(n = 2), Canada (n = 2), UK (n = 1), Brazil (n = 1), Italy (n = 1), France (n = 1), Norway (n = 1), China (n = 1), Malaysia (n = 1), Iran (n = 1).
Most of these studies were cross-sectional studies (n = 21). Six were longitudinal studies [48,53,54,71,74,75], and three studies evaluated the effect of school-based PA programs on HRQOL in school children and adolescents using clustered-randomized controlled trial (RCT) design [51,67,68]. One additional study examined both cross-sectional and longitudinal associations between physical activity, sedentary behavior and HRQOL among adolescents .
Of the included studies, 16 studies assessed the association between both physical activity and sedentary behavior and HRQOL among children and youth. A total of 13 studies examined the association between physical activity only and HRQOL among children and youth [51,54,56,58–60,62,67–70,73,74]. One study analyzed the long-term effect of youth physical activity (measured at age of 7–18 years) on their HRQOL 22 years later in adulthood . One study investigated the association between electronic media use (television, video game, computer and telephone) and HRQOL among adolescents .
Assessment of physical activity and sedentary behavior
Most included studies used self-report of physical activity (Table 1). Two observational studies used parent-report of physical activity for children [54,74]. One Canadian study used a composite measure of PA that was based on the combination of child’s self-report and parent-report questions . One study collected PA data objectively using accelerometers . Physical activity by self- or parent-report was measured as hours/minutes a day, or number of times a week or number of days over a week or a month (Table 1). In two Japanese studies, frequency of PA was measured on a four-point scale: very often, often, seldom, almost never [75,76].
Sedentary behavior was measured in most studies by number of hours or minutes spent daily in watching television, playing video games, using computers and telephones. Average time spent daily on sedentary activities was calculated in most of the studies. Some studies also calculated number of hours spent daily for television viewing, playing video games or using computers, respectively. One study assessed sedentary time by accelerometer .
Measurement of health-related quality of life
With respect to the HRQOL instrument, the Paediatric Quality of Life Inventory 4.0 Generic Core Scales (PedsQL 4.0) was used in 12 studies (Table 2). Three studies applied the EQ-5D-Y, and three studies utilized the KIDSCREEN-10. Other HRQOL measures included the Japanese COOP charts (n = 3), the Child Health Questionnaire (CHQ-PF50) (n = 2), the Child Health Utility 9D (CHU9D) (n = 2), the Child Health and Illness Profile-Child Edition (CHIP-CE) (n = 2), German KINDL-R (n = 2), KIDSCREEN-27 (n = 1), the Duke Health Profile (DHP) (n = 1), the Medical Outcomes Study Short Form 36 (SF-36) (n = 1), and the WHOQOL-Bref (n = 1) (Table 2). Most of the HRQOL outcomes were reported as continuous variables (e.g., PedsQL total and subscale scores, KIDSCREEN-10 index, CHU9D). Some studies also used subscales of the HRQOL measures (e.g., the EQ-5D-Y dimensions) as categorical outcome variables in the analysis [62,74–76].
Risk of bias assessment
The average score of risk of bias among all included studies was 5.71 (SD = 1.47). The risk of bias score ranged between 3 and 8 for the individual studies. A total of 20 studies were scored as moderate risk of bias (n = 14) and high risk of bias (n = 6). Eleven studies were rated as low risk of bias (Table 2). The common reasons for the low quality included small sample size, lack of control for important confounders, inadequate statistical methods, and non-response bias. The overal evidence of the study quality was low since most of the studies were based on cross-sectional design with only a few longitudinal studies and RCTs.
Associations between physical activity and HRQOL among children and adolescents
Findings of the cross-sectional studies.
Table 2 presents the fingdings for the association between PA, sedentary behavior and HRQOL. Of the 21 cross-sectional studies that examined the relationship between PA and HRQOL among children and adolescents, 19 studies found that children and adolescents in a higher level of PA compared with those in the lower level of PA had significantly higher HRQOL. Finne et al.(2013) found in a representative sample of German children and adolescents aged 11–17 years that a higher frequency of PA was related to higher HRQOL with small to moderate effect sizes for daily PA relative to no regular PA in both boys and girls. Gopinath et al.(2012) reported that adolescents (n = 1,094) in the highest tertile of total PA had significantly 3.15 points higher in total PedsQL scores compared with those in the lowest tertile of total PA . Similarly, adolescents in the highest tertile relative to the lowest tertile of total PA had higher scores in the physical summary (5.8 points difference) and social (4.18 points difference) domains . Lacy et al.(2011) found among a large sample of Australian adolescents (n = 3,040) that higher levels of PA were associated with higher HRQOL for both boys and girls, and the relationship remained after adjusting for weight status .
A dose-response relation between PA and HRQOL was reported by three studies with large samples [57,58,76]. A significant linear trend between PA and HRQOL was observed in a dose-response manner for most subscales of the PedsQL 4.0 in the study by Finne et al. . Galán et al.(2013) reported a dose-response association between moderate to vigorous physical activity (MVPA) and HRQOL in a national sample of Spanish children and adolescents (n = 16,560, aged 11–18 years), where an increase in PA levels was related to additional improvements in HRQOL . One Japanese study (Chen et al.,2005b) showed a dose-response relation between PA and HRQOL, where children who engaged in lower frequency of PA had a higher odds of experiencing poor HRQOL .
Three studies used the EQ-5D-Y, a recent developed HRQOL measure for children and youth between 8–18 years that has been previously validated [77,78]. Petracci et al.(2013) used quantile regression in their study and found that children who exercised less than 2 hours a week had significantly lower scores of the EQ-5D-Y VAS relative to children who exercised more than 11 hours a week . Wu et al. (2012) observed in a population-based sample of Canadian grade five students (n = 3,421) that physically active children reported significantly less HRQOL problems (based on the adjusted odds ratio) relative to their peers who were physically inactive on four of the five EQ-5D-Y dimensions: ‘looking after myself’, ‘doing usual activities’, ‘having pain or discomfort’, and ‘feeling worried, sad or unhappy’. Children who were physically active had a significanlty higher VAS score (difference = 4.49 points) compared with those peers who were physically inactive . The observed relations between PA and HRQOL were independent of the potential confounding effect of the socio-demographic factors (gender, parental education level and family income), diet quality and weight status. Boyle et al. (2010) observed that children who achieved the recommendation of 60 minute PA per day had significantly better EQ-5D-Y VAS scores than those who did not achieve the recommendation .
One study did not observe a significant relation between PA and HRQOL, and the study was rated as having a high risk of bias due to small sample and reliance only on the analysis of Pearson correlation . Another study observed a significant correlation between PA and HRQOL based on Pearson correlation, but not in multiple regression analysis .
Findings of the longitudinal studies and the randomised controlled trials.
Of the seven studies that examined longitudinal associations between PA and HRQOL, six studies [48,53,54,61,74,75] observed a significant effect among children and adolescents. Omorou et al.(2016) found that the cumulative level of high PA was associated with high HRQOL at 2-year follow up among adolescents (n = 1,445) in all four dimensions of the Duke Health Profile: physical, mental, social and general health . Vella,et al.(2014) reported that children who maintained participation in sports throughout the 2-year follow up had better HRQOL at follow-up than children who did not participate in sports, dropped out of sports, and commenced participation after the baseline .The magnitude of differences in total PedsQL score between sport participants and nonparticipants was approximately 5 units, greater than the minimum clinically meaningful difference of 4.5 units on PedsQL . Gopinath et al.(2012) examined temporal relations between PA and HRQOL in adolescents . The result showed that adolescents who remained in the highest level of PA over the 5 years of follow up compared with those in the lowest level of total PA had higher scores in the PedsQL total score (P = 0.04), physical summary (P = 0.0001), and social (P = 0.02) domains. Two Japanese cohort studies evaluated longitudinal relations between PA and HRQOL among children [74,75]. Wang et al. (2008) found that children who were less active in early childhood (aged 3 years) had a greater odds (OR = 1.51, p = 0.016) of having lower QOL in their early adolescents (first year of junior high school study) compared with the peers who were active in early childhood . Chen et al. (2005a) reported in a cohort of 7,794 children aged 9–10 years that children with higher frequency of PA (‘very often’) relative to lower frequency of PA (‘seldom’ or ‘almost never’) at baseline survey (1999) experienced better QOL after 3 years of follow up (2002) . In comparison with children who maintained higher PA as ‘often’, children who changed from ‘often’ to ‘seldom’ and who remained ‘seldom’ were more likely to have poor QOL (OR = 2.10, 95% CI:1.84–2.39; OR = 2.21, 95% CI:1.88–2.59) . The longitudinal study in Canada did not find a sigificant effect of physical activity in youth on the HRQOL 22 years later in adulthood using a subgroup of participants aged 7–18 years at baseline from a Canadian population-based survey .
Of the three cluster-randomized controlled trials, one study assessed the effect of a school-community program on HRQOL in adolescent girls in Australia , and two cluster-RCTs evaluated the effect of a school-based PA program on HRQOL in school children in Switzerland [67,68]. Casey et al. (2014) found that the intervention program positively influenced quality of life of adolescent girls . The other two RCTs did not show improved physical QOL among the children, and only a little positive influence (p<0.05) of the program was observed on psychosocial QOL in first grade students [67,68].
Associations between sedentary behavior and HRQOL among children and adolescents
Findings of the cross-sectional studies.
Of the included studies, 17 studies assessed the association between sedentary behavior and HRQOL among children and adolescents, including 13 cross-sectional studies and 4 longitudinal studies (Table 2). Most of the cross-sectional studies reported a significant association between sedentary behavior and poor HRQOL. The findings for the association are consistent across different types of sedentary behaviors, such as television viewing, using computers, playing video games, reading and doing homework and screen time measured by an accelerometer. Two cross-sectional studies did not observe a significant association between screen time and HRQOL, and these studies had relatively small samples (n = 156 and n = 371) that may compromise adequate statistical power [50,63]. Finne et al.(2013) reported a dose-response relation between time spent on screen-based media use and HRQOL .
Similarly to the effect of PA on the domains of HRQOL, sedentary behavior was linked to the multiple domains of HRQOL in childhood and adolescence. Children and adolescents who spent more time in sedentary activities reported lower HRQOL in physical, mental and psychosocial health, school functioning, and general health domains [47,57,65,66]. Chen G,et al. (2014) and Xu,et al. (2014) found that longer time spent on TV viewing, playing computers or video games, or doing homework was associated with a lower utility score measured by the Child Health Utility 9D [52,55].
Findings of the longitudinal studies.
All four studies with the longitudinal design found consistently that more time spent on sedentary activities (television viewing, use of computers or video games, telephones) correlated with reduced HRQOL [48,53,61,75]. Omorou et al.and Gopinath et al. observed that greater screen time during follow-up was related to lower scores in several domains of QOL including physical, psychosocial, mental, emotional and school domains [48,61]. In addition, Chen X, et al.(2005a) reported that there is a dose-response relation between screen time and HRQOL, where children who engaged in longer screen time were more likely to have poor HRQOL at follow up .
Findings from the meta-analysis
Meta-analysis was performed for five observational studies using the PedsQL measure [54,61,63,64,69]. There was a significant difference in total PedsQL scores (mean difference = 3.86, 95% CI: 2.44–5.27, P<0.01) between inactive and active children and adolescents (Fig 2). A higher level of PA was associated with an increased PedsQL total score.
Study by Lacy et al. 2011 has two groups: adolescents <15 years (Lacy et al. 2011); adolescents ≥15 years (Lacy2 et al. 2011).
Pooled analysis for the four subgroups of the two studies indicated a significant overall difference in total PedsQL scores (mean difference = 2.71, 95% CI: 1.59–3.83, P<0.01) between sedentary (>2 hours/day) and non-sedentary (≤2 hours/day) children and adolescents [61,64], suggesting that more time of being sedentary is related to worse HRQOL (Fig 3).
Study by Gopinath et al. has two groups by screen time: the first group (Gopinath, et al. 2012) indicates 2nd tertile of screen time 2.57–3.86 hours relative to the non-sedentary group (≤2.5 hours/day); the second group (Gopinath2, et al. 2012) indicates 3rd tertile of screen time ≥3.93 hours relative to the non-sedentary group. Study by Lacy et al. has two groups by age of the adolescents: <15 years in the first group (Lacy et al., 2011); and ≥15 years in the second group (Lacy2 et al., 2011); the non-sedentary (reference) group is defined as total daily screen time≤2.0 hours/day.
This review synthesized the relationship between physical activity, sedentary behavior and health-related quality of life among the general population of healthy children and adolescents. We found the evidence that elevated levels of physical activity are associated with higher HRQOL and sedentary behavior is inversely related to HRQOL among children and adolescents. Physical activity and sedentary behavior have significant effects on multiple physical, mental and psychosocial domains of HRQOL.
The results from cross-sectional studies revealed that children and adolescents who participated in higher levels of physical activities had better HRQOL. The association between PA and HRQOL is consistent irrespective of weight status, age, sex, and socio-economic characteristics. The findings are also in agreement with the previous observation in general adult populations showing that PA has a positive influence on HRQOL .
We found some evidence in this review for a dose-response effect between PA and HRQOL. For example, Finne et al. reported a linear trend between frequency of PA and PedsQL total and subscale scores, indicating a dose-response relation in both genders . Galán revealed an increasing dose-response relationship between moderate to vigorous physical activity and HRQOL in both genders of adolescents, with an observation of both linear and quadratic trends . The dose-response association between frequency of PA and HRQOL was also showed in a Japanese cross-sectional study in children . Yet more studies are needed to confirm the association and whether the relationship of HRQOL with PA is linear or nonlinear since the finding was primarily based on cross-sectional studies and previous studies have also reported nonlinear associations between PA and HRQOL among adults [19,79].
The relationship between physical activity and HRQOL was observed for both total QOL score and subscale scores including physical, psychological and social subscales [48,49,51,53,54,57,61–64,66,67]. These results lend a support to a number of previous studies demonstrating that children and adolescents who maintain an active lifestyle exhibit better physical and better psychosocial health [2,3,5,42].
Longitudinal observational studies support a positive asscoiation between PA and HRQOL. Six studies found a significant longitudinal or temporal association between PA and HRQOL, and most of these studies analysed large samples of children and adolesents [48,53,54,61,74,75]. A dose-response effect of PA on HRQOL was observed in a cohort of children with a three years follow up. However, the HRQOL data were not available at baseline in some cohort studies, and thus limiting the ability to examine changes in QOL outcomes in relation to changes in PA and sedentary behavior [61,74,75]. Further, it did not permit to account for the impact of baseline HRQOL on the HRQOL at follow up. Despite the longitudinal and some intervention studies demonstrated that PA predicts future HRQOL, we can not conclude a causal effect of PA on QOL given that the number of longitudinal and RCT studies is small and one study also found that HRQOL predicts PA later .
The studies that assessed the effect of sedentary behavior on HRQOL support the finding that longer sedentary time is connected with decreased HRQOL in children and adolescents. The association between sedentary behavior and HRQOL is independent of the PA, gender, age and body weight among children and adolescents. Watching TV, use of computers and playing video games for more than two hours a day are significantly associated with lower HRQOL [52,57,61,64,72,76]. The findings from the longitudinal studies showed that children and adolescents who spent greater time on sedentary activities during follow-up experienced worse HRQOL at follow up [48,53,61,75], suggesting a negative effect of sedentary behavior for future HRQOL. A dose-response relation between screen time and adverse QOL was reported in three observational studies [57,75,76]. Children and youth who spent excessive amount of time on sedentary behaviors reported lower HRQOL in physical, mental and psychosocial health domains [47,48,57,61,65,66]. For example, Finne et al., 2013 reported that there was a dose-response relation for most subscales of the KINDL-R except for the family domain among girls, and a dose-response relation for physical well-being and school domains among boys . The evidence of the adverse impact of sedentary behavior on mental/psychosocial health is consistent with a number of previous studies that focused on the outcomes of mental health in school-aged children and adolescents .
Due to the methodological heterogeneities in the measurement of PA, sedentary behavior and HRQOl, we highlighted the results in this review from qualitative synthesis. We performed meta-analyses for five studies that used the same QOL measure of PedsQL, and similar assessment methods for PA and sedentary behavior. The findings from the pooled analysis support a beneficial effect of PA and a detrimental effect of sedentary behaviors for HRQOL among children and youth. Future research would warrant to examine the magnitude of the difference in QOL from meta-analysis by inclusion of more studies that utilize similar measures of PA, sedentary behavior and HRQOL. Specifically, consistent measures for physical activity and sedentary behavior across studies are required to assess intensity and frequency of the health-related behaviors to facilitate the inter-group comparisons.
The strength of the associations between HRQOL and PA or sedentary behavior in this study was largely based on judgement from statistical significance of the difference in QOL outcomes. When comparing the difference in HRQOL values across groups by PA and sedentary behavior, it is also important to examine the extent of the score difference (e.g.,effect size) in order to determine the minimally important difference (MID) of QOL scores  as the MID signifies a meaningful difference that has clinical and practical importance for public health and health interventions. Based on Cohen’s criteria, effect size for HRQOL is defined as small (0.2), moderate (0.5), and large (0.8) in the magnitude for differences between groups . In our review, the MID or effect size for group comparisons in QOL was discussed or reported in eleven studies [48,51,52,54,55,57,58,62,67–69]. The MID criterion varies with HRQOL instruments, and is more frequently used in clinical settings to compare HRQOL among patients with chronic disease conditions than in healthy populations . For example, the study by Omorou et al. indicated that the difference in HRQOL scores between active and inactive living adolescents during two years of follow-up was close to or greater than 5 points (considered as clinically meaningful difference) for all the four dimensions of the Duke Health Profile: physical, mental, social and general health . Vella,et al.(2014) reported that children who maintained participation in sports throughout a 2-year follow up period had an approximately 5 units higher total PedsQL score at follow-up than children who did not participate in sports . A 4.5-unit difference in HRQOL between group comparisons was considered as the MID value for the PedsQL .
There are several strengths of this review. The present study is the first to review the associations between physical activity, sedentary behavior and HRQOL among studies using population-based samples of children and adolescents. We conducted a comprehensive literature search and did rigorous selection and assessments for the eligible studies using predefined inclusion and exclusion criteria. We included both PA and sedentary behavior as the predictor variables for HRQOL which allowed us to examine the independent effect of the two health-related behaviors on HRQOL. The majority of the included studies analysed data from large samples of children and adolescents. This offered good opportunities for the analysis to adjust for the effect of important confounders such as body weight, age, gender, parental education and household income. Thus the systematic review contributes to the existing literature for the associations between PA, sedentary behavior and HRQOL which are independent of body weight and socio-demographic characteristics of children and youth. Consistent associations between PA, sedentary behavior and HRQOL were reported across studies in different countries, suggesting that these results are robust. The outcome was measured by a variety of generic multi-dimensional HRQOL measures that have been validated for pediatric and youth populations, allowing for investigation of the effect of PA, sedentary behaviors on a single dimension of QOL as well as the overall QOL. Most primary studies applied multivariable regression analyses and some used multilevel regressions to account for hierarchical feature of school data, and thus providing appropriate and rigorous statistical methods for parameter estimates.
Limitations of this review also deserve to be clarified. Most of the included studies are cross-sectional, and very few longitudinal and intervention studies are identified. Informative conclusion can not be made about causal relationship between PA, sedentary behavior and HRQOL. PA and sedentary behavior assessments in the included studies were largely based on child and youth self-report or parent-report, and thus may have affected the measurement error. The use of objective measures of PA (e.g., pedometer) and sedentary behavior (e.g., accelerometer, inclinometer or screen use monitor) is needed to make more accurate assessments of these behaviors . In addition, the citation and abstract screening for selection of the eligible studies was done by a single reviewer (XYW) rather than use of two reviewers. However, we made alternative efforts to ensure the study selection is intensive, rigorous, complete and accurate to meet the study goal. These work included the use of predefined inclusion and exclusion criteria, the evaluation of the full-text articles of potetial eligible studies by the two reviewers (XYW, LHH), and comprehensive screening for PubMed-related articles and reference lists of the relevant studies to identify other eligible studies.
Children and adolescents spend more time engaging in sedentary activities than a decade ago due to the increasing use of screen-based electronic devices (e.g., smart phones, laptops) and widespread accessibility to the internet . Many countries have so far developed physical activity and/or sedentary behavior guidelines for school-age children and adolescents in order to improve their health [85–87]. WHO suggests that children and youth aged 5–17 years old should accumulate at least 60 minutes of moderate to vigorous-intensity of physical activity everyday . Yet majority of the young people in many countries do not meet the recommendations of PA levels [89–93]. HRQOL comprises multifaceted aspects of health. The importance of PA and sedentary behavior for QOL is that promoting PA and decreasing sedentary behaviors among young people may benefit not only to a specific health condition (e.g., obesity) but also to their mental health and overal health status. Therefore, it is worth to further investigate to determine the appropriate amount of PA, time spent in sedentary behaviors for HRQOL improvement. For exampple, whether a threshold exists for having no additional benefits on total or a dimension of HRQOL when PA or sedentary time exceeds a certain amount. As PA and sedentary behavior can be measured and expressed in different types (e.g., objective or subjective), different aspects (e.g., leisure time PA, TV or computer use) and different units (e.g., frequency, intensity and duration), future research is needed to investigate which types or aspects of physical activity and sedentary behavior would be the most significant contributors for QOL. In the meantime, to facilitate comparisons using quantitative synthesis of different studies, standardized measures as well as the objective measures of these health-related behavioral indicators for QOL are needed. Future studies are also needed to examine prospectively how changes in these behaviors affect changes in HRQOL among children and adolescents. This will provide useful information for evalations of the effectiveness health promotion programs targeting to modify unfavorable inactive and sedentary behaviors.
The present review has found the evidence that a higher level of physical activity and less time spent on sedentary behavior are associated with increased health-related quality of life among the general population of children and adolescents. Future research is needed to identify potential causal mechanisms for these relationships. More longitudinal and cluster-randomized controlled trials are required to assess the dose-response effect of physical activity and sedentary behavior on health-related quality of life among children and adolescents. This will help justify school health intervention efforts promoting active lifestyle, reducing sedentary behaviors to enhance quality of life of the young population. The findings in this review may be used as evidence to inform primary prevention and public health policy for promoting the health of children and youth.
S1 Table. Literature search strategies for the databases of MEDLINE, EMBASE and PSYCINFO (Table A-Table C).
- 1. Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7:40. pmid:20459784
- 2. Iannotti RJ, Janssen I, Haug E, Kololo H, Annaheim B, Borraccino A, et al. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int J Public Health. 2009;54 Suppl 2:191–198.
- 3. Ussher MH, Owen CG, Cook DG, Whincup PH. The relationship between physical activity, sedentary behaviour and psychological well being among adolescents. Soc Psychiatry Psychiatr Epidemiol. 2007;42(10):851–856. pmid:17639309
- 4. Hallal PC, Victora CG, Azevedo MR, Wells JC. Adolescent physical activity and health: a systematic review. Sports Med. 2006;36(12):1019–1030. pmid:17123326
- 5. Iannotti RJ, Kogan MD, Janssen I, Boyce WF. Patterns of adolescent physical activity, screen-based media use, and positive and negative health indicators in the U.S. and Canada. J Adolesc Health. 2009;44(5):493–499. pmid:19380098
- 6. Prentice-Dunn H, Prentice-Dunn S. Physical activity, sedentary behavior, and childhood obesity: A review of cross-sectional studies. Psychol Health Med. 2012;17(3):255–273. pmid:21995842
- 7. Leung MM, Agaronov A, Grytsenko K, Yeh MC. Intervening to reduce sedentary behaviors and childhood obesity among school-age youth: A systematic review of randomized trials. J Obes. 2012;2012:685430. pmid:22132321
- 8. Veugelers PJ, Fitzgerald AL. Effectiveness of school programs in preventing childhood obesity: a multilevel comparison. Am J Public Health. 2005;95(3):432–435. pmid:15727972
- 9. Brown T, Summerbell C. Systematic review of school-based interventions that focus on changing dietary intake and physical activity levels to prevent childhood obesity: an update to the obesity guidance produced by the National Institute for Health and Clinical Excellence. Obes Rev. 2009;10(1):110–141. pmid:18673306
- 10. Page AS, Cooper AR, Griew P, Jago R. Children's screen viewing is related to psychological difficulties irrespective of physical activity. Pediatrics. 2010;126(5):e1011–1017. pmid:20937661
- 11. Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. J Adolesc Health. 2013;52(4):382–392. pmid:23299000
- 12. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98. pmid:21936895
- 13. Solans M, Pane S, Estrada MD, Serra-Sutton V, Berra S, Herdman M, et al. Health-related quality of life measurement in children and adolescents: a systematic review of generic and disease-specific instruments. Value Health. 2008;11(4):742–764. pmid:18179668
- 14. World Health Organization. Constitution of the World Health Organization. Geneva: World Health Organization, 1948.
- 15. Yackobovitch-Gavan M, Nagelberg N, Phillip M, Ashkenazi-Hoffnung L, Hershkovitz E, Shalitin S. The influence of diet and/or exercise and parental compliance on health-related quality of life in obese children. Nutr Res. 2009;29(6):397–404. pmid:19628106
- 16. Shoup JA, Gattshall M, Dandamudi P, Estabrooks P. Physical activity, quality of life, and weight status in overweight children. Qual Life Res. 2008;17(3):407–412. pmid:18293100
- 17. Crosbie A. The effect of physical training in children with asthma on pulmonary function, aerobic capacity and health-related quality of life: a systematic review of randomized control trials. Pediatr Exerc Sci. 2012;24(3):472–489. pmid:22971562
- 18. Paxton RJ, Jones LW, Rosoff PM, Bonner M, Ater JL, Demark-Wahnefried W. Associations between leisure-time physical activity and health-related quality of life among adolescent and adult survivors of childhood cancers. Psychooncology. 2010;19(9):997–1003. pmid:19918964
- 19. Bize R, Johnson JA, Plotnikoff RC. Physical activity level and health-related quality of life in the general adult population: a systematic review. Prev Med. 2007;45(6):401–415. pmid:17707498
- 20. Davies CA, Vandelanotte C, Duncan MJ, van Uffelen JG. Associations of physical activity and screen-time on health related quality of life in adults. Prev Med. 2012;55(1):46–49. pmid:22588226
- 21. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126–131. pmid:3920711
- 22. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al.; SBRN Terminology Consensus Project Participants. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. pmid:28599680
- 23. Network SBR. Letter to the Editor: Standardized use of the terms "sedentary" and "sedentary behaviours". Appl Physiol Nutr Metab. 2012;37(3):540–542. pmid:22540258
- 24. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed on May 21, 2017.
- 25. Suchert V, Hanewinkel R, Isensee B. Sedentary behavior and indicators of mental health in school-aged children and adolescents: A systematic review. Prev Med. 2015;76:48–57. pmid:25895839
- 26. Higgins JPT, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558. pmid:12111919
- 27. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. J Clin Epidemiol. 2009;62(10):1006–1012. pmid:19631508
- 28. Dumuid D, Olds T, Lewis LK, Martin-Fernández JA, Katzmarzyk PT, Barreira T, et al. International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) research group. Health-related quality of life and lifestyle behavior clusters in school-aged children from 12 countries. J Pediatr. 2017;183:178–183.e2. pmid:28081885
- 29. Liu J, Sekine M, Tatsuse T, Fujimura Y, Hamanishi S, Lu F, Zheng X. Outdoor physical activity and its relation with self-reported health in Japanese children: results from the Toyama birth cohort study. Child Care Health Dev. 2015;41(6):920–927. pmid:26073535
- 30. Padilla-Moledo C, Castro-Piñero J, Ortega FB, Pulido-Martos M, Sjöström M, Ruiz JR. Television viewing, psychological positive health, health complaints and health risk behaviors in Spanish children and adolescents. J Sports Med Phys Fitness. 2015;55(6):675–683. pmid:25895471
- 31. Smart JE, Cumming SP, Sherar LB, Standage M, Neville H, Malina RM. Maturity associated variance in physical activity and health-related quality of life in adolescent females: a mediated effects model. J Phys Act Health. 2012;9(1):86–95. pmid:22232510
- 32. Kalak N, Gerber M, Kirov R, Mikoteit T, Yordanova J, Pühse U, et al. Daily morning running for 3 weeks improved sleep and psychological functioning in healthy adolescents compared with controls. J Adolesc Health. 2012;51(6):615–622. pmid:23174473
- 33. Gunnell KE, Brunet J, Sabiston C, Bélanger M. Linking psychological need satisfaction and physical activity to dimensions of health-related quality of life during adolescence: A test of direct, reciprocal, and mediating effects. J Sport Exerc Psychol. 2016;38(4):367–380. pmid:27736288
- 34. Pindus DM, Cumming SP, Sherar LB, Gammon C, Coelho e Silva M, Malina RM. Maturity-associated variation in physical activity and health-related quality of life in British adolescent girls: moderating effects of peer acceptance. Int J Behav Med. 2014;21(5):757–766. pmid:25356455
- 35. Quaresma AM, Palmeira AL, Martins SS, Minderico CS, Sardinha LB. Effect of a school-based intervention on physical activity and quality of life through serial mediation of social support and exercise motivation: the PESSOA program. Health Educ Res. 2014;29(6):906–917. pmid:25274722
- 36. Conry MC, Morgan K, Curry P, McGee H, Harrington J, Ward M, et al. The clustering of health behaviours in Ireland and their relationship with mental health, self-rated health and quality of life. BMC Public Health. 2011;11:692. pmid:21896196
- 37. Guallar-Castillón P, Bayán-Bravo A, León-Muñoz LM, Balboa-Castillo T, López-García E, Gutierrez-Fisac JL, et al. The association of major patterns of physical activity, sedentary behavior and sleep with health-related quality of life: a cohort study. Prev Med. 2014;67:248–254. pmid:25138382
- 38. Saunders TJ, Gray CE, Poitras VJ, Chaput JP, Janssen I, Katzmarzyk PT, et al. Combinations of physical activity, sedentary behaviour and sleep: relationships with health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S283–293. pmid:27306434
- 39. Fazah A, Jacob C, Moussa E, El-Hage R, Youssef H, Delamarche P. Activity, inactivity and quality of life among Lebanese adolescents. Pediatr Int. 2010;52(4):573–578. pmid:20030747
- 40. Takano M, Matsukura M, Harada K, Wei CN, Ohmori S, Miyakita T, et al. Behavior and lifestyle factors related to quality of life in junior high school students. Environ Health Prev Med. 2005;10(2):94–102. pmid:21432147
- 41. Herman KM, Hopman WM, Sabiston CM. Physical activity, screen time and self-rated health and mental health in Canadian adolescents. Prev Med. 2015;73:112–116. pmid:25660484
- 42. Strauss RS, Rodzilsky D, Burack G, Colin M. Psychosocial correlates of physical activity in healthy children. Arch Pediatr Adolesc Med. 2001;155(8):897–902. pmid:11483116
- 43. Sharma B, Nam EW, Kim D, Yoon YM, Kim Y, Kim HY. Role of gender, family, lifestyle and psychological factors in self-rated health among urban adolescents in Peru: a school-based cross-sectional survey. BMJ Open. 2016;6(2):e010149. pmid:26842274
- 44. Tymms PB, Curtis SE, Routen AC, Thomson KH, Bolden DS, Bock S, et al. Clustered randomised controlled trial of two education interventions designed to increase physical activity and well-being of secondary school students: the MOVE Project. BMJ Open. 2016;6(1):e009318. pmid:26739729
- 45. Breslin G, Gossrau-Breen D, McCay N, Gilmore G, McDonald L, Hanna D. Physical activity, gender, weight status, and wellbeing in 9- to 11-year-old children: a cross sectional survey. J Phys Act Health. 2012;9(3):394–401. pmid:21934158
- 46. Muros JJ, Salvador Pérez F, Zurita Ortega F, Gámez Sánchez VM, Knox E. The association between healthy lifestyle behaviors and health-related quality of life among adolescents. J Pediatr (Rio J). 2017;93(4):406–412.
- 47. Jalali-Farahani S, Amiri P, Chin YS. Are physical activity, sedentary behaviors and sleep duration associated with body mass index-for-age and health-related quality of life among high school boys and girls? Health Qual Life Outcomes. 2016;14:30. pmid:26921272
- 48. Omorou AY, Langlois J, Lecomte E, Briançon S, Vuillemin A. Cumulative and bidirectional association of physical activity and sedentary behaviour with health-related quality of life in adolescents. Qual Life Res. 2016;25(5):1169–1178. pmid:26542533
- 49. Wafa SW, Shahril MR, Ahmad AB, Zainuddin LR, Ismail KF, Aung MM, et al. Association between physical activity and health-related quality of life in children: a cross-sectional study. Health Qual Life Outcomes. 2016;14:71. pmid:27146199
- 50. Sigvartsen J, Gabrielsen LE, Abildsnes E, Stea TH, Omfjord CS, Rohde G. Exploring the relationship between physical activity, life goals and health-related quality of life among high school students: a cross-sectional study. BMC Public Health. 2016;15:709. pmid:27488255
- 51. Casey MM, Harvey JT, Telford A, Eime RM, Mooney A, Payne WR. Effectiveness of a school-community linked program on physical activity levels and health-related quality of life for adolescent girls. BMC Public Health. 2014;14:649. pmid:24966134
- 52. Chen G, Ratcliffe J, Olds T, Magarey A, Jones M, Leslie E. BMI, health behaviors, and quality of life in children and adolescents: a school-based study. Pediatrics. 2014;133(4):e868–874. pmid:24590749
- 53. Gopinath B, Louie JC, Flood VM, Burlutsky G, Hardy LL, Baur LA, et al. Influence of obesogenic behaviors on health-related quality of life in adolescents. Asia Pac J Clin Nutr. 2014;23(1):121–127. pmid:24561980
- 54. Vella SA, Cliff DP, Magee CA, Okely AD. Sports participation and parent-reported health-related quality of life in children: longitudinal associations. J Pediatr. 2014;164(6):1469–1474. pmid:24657117
- 55. Xu F, Chen G, Stevens K, Zhou H, Qi S, Wang Z, et al. Measuring and valuing health-related quality of life among children and adolescents in mainland China—a pilot study. PLoS One. 2014; 9(2):e89222. pmid:24586607
- 56. Gu X, Solmon MA, Zhang T. Understanding middle school students’ physical activity and health-related quality of life: An Expectancy-Value Perspective. Applied Research in Quality of Life. 2014;9(4):1041–1054.
- 57. Finne E, Bucksch J, Lampert T, Kolip P. Physical activity and screen-based media use: cross-sectional associations with health-related quality of life and the role of body satisfaction in a representative sample of German adolescents. Health Psychol Behav Med. 2013;1(1):15–30. pmid:25264498
- 58. Galán I, Boix R, Medrano MJ, Ramos P, Rivera F, Pastor-Barriuso R, et al. Physical activity and self-reported health status among adolescents: a cross-sectional population-based study. BMJ Open. 2013;3:e002644. pmid:23676798
- 59. Petracci E, Cavrini G. The effect of weight status, lifestyle, and body image perception on health-related quality of life in children: a quantile approach. Qual Life Res. 2013;22(9):2607–2615. pmid:23423756
- 60. Spengler S, Woll A. The more physically active, the healthier? The relationship between physical activity and health-related quality of life in adolescents: the MoMo study. J Phys Act Health. 2013;10(5):708–715. pmid:23006665
- 61. Gopinath B, Hardy LL, Baur LA, Burlutsky G, Mitchell P. Physical activity and sedentary behaviors and health-related quality of life in adolescents. Pediatrics. 2012;130(1):e167–174. pmid:22689863
- 62. Wu XY, Ohinmaa A, Veugelers PJ. Diet quality, physical activity, body weight and health-related quality of life among grade 5 students in Canada. Public Health Nutr. 2012;15(1):75–81. pmid:22014537
- 63. Perry TT., Moore PC., Redwine KM., Robbins JM., Weber JL. Physical activity, screen time and pediatric health related quality of life in the Mississippi Delta. J of Prev Med. 2012;2(1):1–7.
- 64. Lacy KE, Allender SE, Kremer PJ, de Silva-Sanigorski AM, Millar LM, Moodie ML, et al. Screen time and physical activity behaviours are associated with health-related quality of life in Australian adolescents. Qual Life Res. 2012;21(6):1085–1099. pmid:21932139
- 65. Borras PA, Vidal J, Ponseti X, Cantallops J, Palou P. Predictors of quality of life in children. J Hum Sport Exerc. 2011;6(4):649–656.
- 66. Dalton WT 3rd, Schetzina KE, Pfortmiller DT, Slawson DL, Frye WS. Health behaviors and health-related quality of life among middle school children in Southern Appalachia: data from the winning with wellness project. J Pediatr Psychol. 2011;36(6):677–686. pmid:21131337
- 67. Hartmann T, Zahner L, Pühse U, Puder JJ, Kriemler S. Effects of a school-based physical activity program on physical and psychosocial quality of life in elementary school children: a cluster-randomized trial. Pediatr Exerc Sci. 2010;22(4):511–522. pmid:21242601
- 68. Kriemler S, Zahner L, Schindler C, Meyer U, Hartmann T, Hebestreit H, et al. Effect of school based physical activity programme (KISS) on fitness and adiposity in primary schoolchildren: cluster randomised controlled trial. BMJ. 2010;340:c785. pmid:20179126
- 69. Boyle SE, Jones GL, Walters SJ. Physical activity, quality of life, weight status and diet in adolescents. Qual Life Res. 2010;19:943–954. pmid:20454863
- 70. Gordia AP, Silva RCR, Quadros TMB, Campos Wagner de. Behavioral and sociodemographic variables are associated with the psychological domain of adolescents’ quality of life. Rev Paul Pediatr. 2010;28(1):29–35.
- 71. Herman KM, Hopman WM, Craig CL. Are youth BMI and physical activity associated with better or worse than expected health-related quality of life in adulthood? The Physical Activity Longitudinal Study. Qual Life Res. 2010;19(3):339–349. pmid:20077141
- 72. Mathers M., Canterford L, Olds T, Hesketh K, Ridley K, Wake M. Electronic media use and adolescent health and well-being: Cross-sectional community study. Academic Pediatrics. 2009; 9(5):307–314. pmid:19592322
- 73. Sánchez-López M, Salcedo-Aguilar F, Solera-Martínez M, Moya-Martínez P, Notario-Pacheco B, Martínez-Vizcaíno V. Physical activity and quality of life in schoolchildren aged 11–13 years of Cuenca, Spain. Scand J Med Sci Sports. 2009;19:879–884. pmid:18980609
- 74. Wang H, Sekine M, Chen X, Yamagami T, Kagamimori S. Lifestyle at 3 years of age and quality of life (QOL) in first-year junior high school students in Japan: results of the Toyama Birth Cohort Study. Qual Life Res. 2008;17(2):257–265. pmid:18157615
- 75. Chen X, Sekine M, Hamanishi S, Yamagami T, Kagamimori S. Associations of lifestyle factors with quality of life (QOL) in Japanese children: a 3-year follow-up of the Toyama Birth Cohort Study. Child Care Health Dev. 2005a;31(4):433–439.
- 76. Chen X, Sekine M, Hamanishi S, Wang H, Gaina A, Yamagami T, et al. Lifestyles and health-related quality of life in Japanese school children: across-sectional study. Prev Med. 2005b;40(6):668–678.
- 77. Wille N, Badia X, Bonsel G, Burström K, Cavrini G, Devlin N, et al. Development of the EQ-5D-Y: A child-friendly version of the EQ-5D. Qual Life Res. 2010;19(6):875–886. pmid:20405245
- 78. Ravens-Sieberer U, Wille N, Badia X, Bonsel G, Burström K, Cavrini G, et al. Feasibility, reliability, and validity of the EQ-5D-Y: results from a multinational study. Qual Life Res. 2010;19(6):887–897. pmid:20401552
- 79. Brown DW, Brown DR, Heath GW, Balluz L, Giles WH, Ford ES, et al. Associations between physical activity dose and health-related quality of life. Med Sci Sports Exerc. 2004;36(5):890–896. pmid:15126726
- 80. Jaeschke R, Singer J & Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10:407–415. pmid:2691207
- 81. Cohen J. Statistical power analysis for the behavioural sciences. 2nd ed. Hillsdale: NJ: Laurence Erlbaum; 1988.
- 82. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health meaure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3:329–341. pmid:14616041
- 83. Hardy LL, Hills AP, Timperio A, Cliff D, Lubans D, Morgan PJ, et al. A hitchhiker's guide to assessing sedentary behaviour among young people: deciding what method to use. J Sci Med Sport. 2013;16(1):28–35. pmid:22749939
- 84. Chinapaw MJ, Proper KI, Brug J, van Mechelen W, Singh AS. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev. 2011;12(7):e621–e632. pmid:21438990
- 85. School Health Guidelines to Promote Healthy Eating and Physical Activity. Centers for Disease Control and Prevention. US Department of Health and Human Services. 2011. https://www.cdc.gov/healthyschools/npao/pdf/mmwr-school-health-guidelines.pdf. Accessed on May 21,2017.
- 86. Tremblay MS, Leblanc AG, Janssen I, Kho ME, Hicks A, Murumets K, et al. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab. 2011;36(1):59–64; 65–71. pmid:21326378
- 87. Tremblay MS, Warburton DE, Janssen I, Paterson DH, Latimer AE, Rhodes RE, et al. New Canadian physical activity guidelines. Appl Physiol Nutr Metab. 2011;36(1):36–46; 47–58. pmid:21326376
- 88. Global recommendations on physical activity for health. WHO,2010. http://apps.who.int/iris/bitstream/10665/44399/1/9789241599979_eng.pdf. Accessed on May 21,2017.
- 89. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Medicine and Science in Sports and Exercise. 2008;40(1):181–188. pmid:18091006
- 90. McLure SA, Summerbell CD, Reilly JJ. Objectively measured habitual physical activity in a highly obesogenic environment. Child Care Health Development. 2009; 35(3):369–375.
- 91. Wafa SW, Hamzaid H, Talib RA, Reilly JJ. Objectively measured habitual physical activity and sedentary behaviour in obese and non-obese Malaysian children. J Trop Pediatr. 2014;60(2):161–163. pmid:24213306
- 92. Lee S, Wong J, Shanita S, Ismail M, Deurenberg P, Poh B. Daily physical activity and screen time, but not other sedentary activities, are associated with measures of obesity during childhood. Int J Environ Res Public Health. 2014;12(1):146–161. pmid:25546277
- 93. Colley RC, Garriguet D, Janssen I, Craig C, Clarke J, Tremblay MS. Physical activity of Canadian children and youth: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep. 2011;22(1):15–23. pmid:21510586