Device-measured physical activity and cardiometabolic health: the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium.

BACKGROUND AND AIMS: Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers. METHODS: Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours. RESULTS: The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with -0.63 (95% confidence interval -0.48, -0.79), -0.43 (-0.25, -0.59), -0.40 (-0.25, -0.56), and -0.15 (0.05, -0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day. CONCLUSIONS: Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.

Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium.

AIMS/HYPOTHESIS: The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. METHODS: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. RESULTS: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. CONCLUSIONS/INTERPRETATION: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review.

BACKGROUND: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. METHODS: The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. RESULTS: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. CONCLUSION: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

The cross-sectional associations of chronic conditions and disability with self-reported physical activity among adults in England.

OBJECTIVE: Using cross-sectional data from the 2018 Health Survey for England, this study describes the types of impairment reported by people with chronic conditions and the association of chronic conditions and impairments with physical activity(PA). METHODS: Participants self-reported the presence of seven chronic health conditions (diabetes; stroke/ischemic heart disease; hypertension; chronic obstructive pulmonary disease (COPD); asthma; arthritis/rheumatism/fibrositis; back problems), 11 types of impairment (vision, hearing, mobility, dexterity; learning; memory; mental health; stamina; social or behavioural; other; none); and their PA using the International Physical Activity Questionnaire. Multivariable Poisson regression was used to estimate the association of a)impairment type, b)number of impairments, and c)impairment type and chronic condition (mutually adjusted) with PA. RESULTS: In total, 2243 adults (55% female, 44% age > 55 yrs) reported having a chronic condition. PA volume (MET minutes per week: median (IQR)) was highest in participants with asthma (2093 (693-4479)), and lowest in those with COPD (454 (0-2079)). There was a negative association between number of impairments and levels of PA. After adjustment for age, sex, ethnicity and education, and mutually adjusting for all other conditions and impairments, diabetes (Incident rate ratio (95% confidence interval): 0.83 (0.73-0.94)), COPD (0.76 (0.59-0.99)), a mobility impairment (0.63 (0.56-0.72)), a dexterity impairment (0.86 (0.75-0.98)), or a memory impairment (0.84 (0.72-0.99)) was negatively associated with PA. CONCLUSION: Future PA research requires consideration of the number and types of impairments that individuals experience, as well as assessing chronic conditions. This will improve understanding of the barriers to PA participation and inform interventions.

The Association of Contemporary Screen Behaviours with Physical Activity, Sedentary Behaviour and Sleep in Adolescents: a Cross-sectional Analysis of the Millennium Cohort Study.

BACKGROUND: Screen behaviours are highly prevalent in adolescents and may be adversely associated with physical and mental health. Understanding how screen behaviours inter-relate with physical activity and sleep may help to clarify pathways through which they impact health and potential routes to behaviour change. This cross-sectional study examines the association of contemporary screen behaviours with physical activity, sedentary behaviour and sleep in adolescents. METHOD: Data are from sweep 6 (2015/2016) of the Millennium Cohort Study, conducted when participants were aged 14 years. Outcome variables were accelerometer-assessed overall physical activity and moderate-to-vigorous physical activity (MVPA), self-reported sedentary behaviour and sleep duration. Screen behaviours were assessed using a 24-h time-use diary. Multivariable regression was used to examine the association between screen behaviours and each outcome variable separately for weekdays and weekend days. RESULTS: The use of social network sites was associated with (beta coefficient, 95% confidence interval (CI); minutes/day) less time in MVPA (weekdays: - 5.2 (- 10.3, - 0.04); weekend: - 10.0 (- 15.5, - 4.5)), and sedentary behaviours (weekdays: - 19.8 (- 31.0, - 8.6); weekend: - 17.5 (- 30.9, - 4.1)). All screen behaviours were associated with shorter sleep duration on weekdays, whereas only the use of email/texts and social network sites was associated with shorter sleep duration on weekend days. The association of using social network sites with overall physical activity was stronger in girls than in boys; the association of internet browsing with sedentary behaviour was stronger in boys than in girls. CONCLUSION: Intervention strategies to enhance MVPA and sleep duration by limiting screen-based activities may be warranted.

Cross-sectional and longitudinal associations of active travel, organised sport and physical education with accelerometer-assessed moderate-to-vigorous physical activity in young people: the International Children's Accelerometry Database.

BACKGROUND: Physical activity (PA) declines during childhood. Important sources of PA are active travel, organised sport and physical education (PE), but it is unclear how these domain-specific PA sources contribute to (changes in) daily moderate-to-vigorous PA (MVPA) in young people. This study aimed to examine (1) the cross-sectional association between domain-specific physical activity (i.e., active travel, organised sport and PE) and daily minutes in accelerometer-assessed MVPA; and (2) the longitudinal association between domain-specific physical activity at baseline and change in daily minutes in MVPA. METHODS: Participants (baseline age 11.3 ± .1.2 years) were drawn from three studies in the International Children's Accelerometry Database. The contribution of self-reported standardised active travel, organised sport and PE to accelerometer-measured daily minutes in MVPA was examined using linear regression. In cross-sectional analyses, MVPA was regressed on each PA domain in separate models, adjusted for study, age, sex, maternal education, season, and monitor wear time. In longitudinal analyses, change in MVPA was regressed on each of the baseline PA domains, additionally adjusting for changes in season and wear time, follow-up duration, and baseline MVPA. R-squared was used to compare variance explained by each PA domain. RESULTS: In the cross-sectional analyses (n = 3871), organised sport (standardised ß = 3.81, 95% confidence interval [95%CI] = 3.06, 4.56) and active travel (ß = 3.46, 95%CI = 2.73, 4.19) contributed more to daily MVPA than PE (ß = 0.82, 95%CI = -0.02, 1.66). Compared to the base model which included only covariates (R2 = 21.5%), organised sport (absolute change: + 1.9%) and active travel (+ 1.7%) models explained more of the variance than the PE model (± < 0.1%). Associations followed a similar pattern in the longitudinal analyses (n = 2302), but none of the PA domains predicted change in MVPA (organised sport: standardised ß = 0.85, 95%CI = -0.03, 1.72; active travel: ß = 0.68, 95%CI = -0.14, 1.50; PE: ß = 0.02, 95%CI = -0.87, 0.91). CONCLUSIONS: A multi-sectoral approach covering a wide range of PA domains should be promoted to minimise the age-related decline in MVPA during childhood.

International trends in screen-based behaviours from 2012 to 2019.

Many adults accumulate considerable time in screen-based behaviours, some of which have been associated with negative physical and psychological health outcomes. The aims of this study were to characterise contemporary patterns of screen-based behaviours and describe their temporal trends by global region, age, sex and education. Data covering the period 2012-2019 were obtained in aggregated form from GWI (previously known as Global Web Index), a global market research company. Temporal trends in the duration of adults' (16-64 years) self-reported personal computer, laptop and tablet use, mobile phone use, broadcast television viewing, online television viewing and games console use were described using data from over 2 million participants from 46 countries. For each activity, participants selected from response options ranging from less than 30 min to more than 10 h. Internationally, daily screen time increased from approximately 9 h in 2012 to 11 h in 2019, with notable increases in mobile phone use (approx. 2 h), online television viewing (approx. 37 min) and games console use (approx. 26 min). Differences were seen in the duration of time spent engaging in screen-based behaviours across regions and between socio-demographic groups, with Latin America, the Middle East and Africa and younger age groups seeing greater increases in overall screen time. The findings have important implications for health behaviour surveillance and for research exploring the links between screen-based behaviours and health.

The diurnal pattern and social context of screen behaviours in adolescents: a cross-sectional analysis of the Millennium Cohort Study.

BACKGROUND: Screen behaviours are highly prevalent in young people and excessive screen use may pose a risk to physical and mental health. Understanding the timing and social settings in which young people accumulate screen time may help to inform the design of interventions to limit screen use. This study aimed to describe diurnal patterns in adolescents' screen-based behaviours and examine the association of social context with these behaviours on weekdays and weekend days. METHODS: Time use diary data are from the sixth wave (2015/2016) of the Millennium Cohort Study, conducted when participants were aged 14 years. Outcome variables were electronic games/Apps, TV-viewing, phone calls and emails/texts, visiting social networking sites and internet browsing. Social context was categorised as alone only, parents only, friends only, siblings only, parents and siblings only. Multilevel multivariable logistic regression was used to examine the association between social contexts and screen activities. RESULTS: Time spent in TV-viewing was greatest in the evening with a peak of 20 min in every hour between 20:00 and 22:00 in both sexes on weekdays/weekend days. Time spent using electronic games/Apps for boys and social network sites for girls was greatest in the afternoon/evening on weekdays and early afternoon/late evening on weekend days. Screen activities were mainly undertaken alone, except for TV-viewing. Compared to being alone, being with family members was associated with (Odds Ratio (95% Confidence Interval)) more time in TV-viewing in both boys and girls throughout the week (Weekdays: Boys, 2.84 (2.59, 3.11); Girls, 2.25 (2.09, 2.43); Weekend days: Boys, 4.40 (4.16, 4.67); Girls, 5.02 (4.77, 5.27)). Being with friends was associated with more time using electronic games on weekend days in both sexes (Boys, 3.31 (3.12, 3.51); Girls, 3.13 (2.67, 3.67)). CONCLUSIONS: Reductions in screen behaviours may be targeted throughout the day but should be sensitive to differing context. Family members, friends, and adolescent themselves may be important target groups in behaviour change interventions. Future research to address the complex interplay between social context, content and quality of screen behaviours will aid the design of behaviour change interventions.

Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis.

We examined associations between youth 24-hour activity behaviour compositions and mental health. Data were collected from 359 participants (aged 9-13 years). Activity behaviours (sleep, sedentary time (ST), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA)) were assessed using wrist-worn accelerometers. Questionnaires and a computerized cognitive test battery assessed mental health outcomes. Linear mixed models examined associations between activity behaviour compositions and mental health. Post-hoc analyses modelled the influence of reallocating fixed durations of time between activity behaviours on mental health. ST was associated with worse internalizing problems (all participants; p< 0.05) and poorer prosocial behaviour (primary school participants only; p< 0.05), relative to the other activity behaviours. LPA was associated with worse cognitive test scores among primary school participants; p< 0.05). For all participants, reallocating time to ST from sleep and MVPA was associated with higher internalizing problems. Among primary school participants, reallocating time to ST from any other behaviour was associated with poorer prosocial behaviour, and reallocating time to LPA from any other behaviour was associated with lower executive function. Children's mental health may be promoted by schools integrating opportunities for MVPA throughout the day. Our results provide further evidence for the influence of daily activity behaviours on youth mental health.

Tracking of total sedentary time and sedentary patterns in youth: a pooled analysis using the International Children's Accelerometry Database (ICAD).

BACKGROUND: To gain more understanding of the potential health effects of sedentary time, knowledge is required about the accumulation and longitudinal development of young people's sedentary time. This study examined tracking of young peoples' total and prolonged sedentary time as well as their day-to-day variation using the International Children's Accelerometry Database. METHODS: Longitudinal accelerometer data of 5991 children (aged 4-17y) was used from eight studies in five countries. Children were included if they provided valid (≥8 h/day) accelerometer data on ≥4 days, including ≥1 weekend day, at both baseline and follow-up (average follow-up: 2.7y; range 0.7-8.2). Tracking of total and prolonged (i.e. ≥10-min bouts) sedentary time was examined using multilevel modelling to adjust for clustering of observations, with baseline levels of sedentary time as predictor and follow-up levels as outcome. Standardized regression coefficients were interpreted as tracking coefficients (low: < 0.3; moderate: 0.3-0.6; high: > 0.6). RESULTS: Average total sedentary time at study level ranged from 246 to 387 min/day at baseline and increased annually by 21.4 min/day (95% confidence interval [19.6-23.0]) on average. This increase consisted almost entirely of prolonged sedentary time (20.9 min/day [19.2-22.7]). Total (standardized regression coefficient (B) = 0.48 [0.45-0.50]) and prolonged sedentary time (B = 0.43 [0.41-0.45]) tracked moderately. Tracking of day-to-day variation in total (B = 0.04 [0.02-0.07]) and prolonged (B = 0.07 [0.04-0.09]) sedentary time was low. CONCLUSION: Young people with high levels of sedentary time are likely to remain among the people with highest sedentary time as they grow older. Day-to-day variation in total and prolonged sedentary time, however, was rather variable over time.

Patterns of health behaviour associated with active travel: a compositional data analysis.

BACKGROUND: Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. METHODS: We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. RESULTS: 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. CONCLUSIONS: Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel.

Harmonising data on the correlates of physical activity and sedentary behaviour in young people: Methods and lessons learnt from the international Children's Accelerometry database (ICAD).

BACKGROUND: Large, heterogeneous datasets are required to enhance understanding of the multi-level influences on children's physical activity and sedentary behaviour. One route to achieving this is through the pooling and co-analysis of data from multiple studies. Where this approach is used, transparency of the methodology for data collation and harmonisation is essential to enable appropriate analysis and interpretation of the derived data. In this paper, we describe the acquisition, management and harmonisation of non-accelerometer data in a project to expand the International Children's Accelerometry Database (ICAD). METHOD: Following a consultation process, ICAD partners were requested to share accelerometer data and information on selected behavioural, social, environmental and health-related constructs. All data were collated into a single repository for cataloguing and harmonisation. Harmonised variables were derived iteratively, with input from the ICAD investigators and a panel of invited experts. Extensive documentation, describing the source data and harmonisation procedure, was prepared and made available through the ICAD website. RESULTS: Work to expand ICAD has increased the number of studies with longitudinal accelerometer data, and expanded the breadth of behavioural, social and environmental characteristics that can be used as exposure variables. A set of core harmonised variables, including parent education, ethnicity, school travel mode/duration and car ownership, were derived for use by the research community. Guidance documents and facilities to enable the creation of new harmonised variables were also devised and made available to ICAD users. An expanded ICAD database was made available in May 2017. CONCLUSION: The project to expand ICAD further demonstrates the feasibility of pooling data on physical activity, sedentary behaviour and potential determinants from multiple studies. Key to this process is the rigorous conduct and reporting of retrospective data harmonisation, which is essential to the appropriate analysis and interpretation of derived data. These documents, made available through the ICAD website, may also serve as a guide to others undertaking similar projects.

Sedentary behaviour across the primary-secondary school transition: A systematic review.

The transition from primary/middle school to secondary/high school is likely to be a key period in children's development, characterised by significant changes in their social and physical environment. However, little is known about the changes in sedentary behaviour that accompany this transition. This review aimed to identify, critically appraise and summarise the evidence on changes in sedentary behaviour across the primary - secondary school transition. Published English language studies were located from computerised and manual searches in 2015. Inclusion criteria specified a longitudinal design, baseline assessment when children were in primary/middle school with at least one follow-up during secondary/high school and a measure of sedentary behaviour at both (or all) points of assessment. Based on data from 11 articles (19 independent samples), tracking coefficients were typically in the range of 0.3 to 0.5 and relatively consistent across the different sedentary behaviours examined and durations of follow-up. Both screen-based sedentary behaviour and overall sedentary time increased during the school transition. Overall there was an increase of approximately 10-20min per day per year in accelerometer-assessed sedentary time. Consistent with the broader age-related changes in behaviour observed during this period, sedentary behaviour increases during the transition from primary/middle to secondary/high school. Investigating features of the social and physical environment that might exacerbate or attenuate this trend would be a valuable next step.

School polices, programmes and facilities, and objectively measured sedentary time, LPA and MVPA: associations in secondary school and over the transition from primary to secondary school.

BACKGROUND: There is increasing policy interest in ensuring that the school environment supports healthy behaviours. We examined the cross-sectional and longitudinal associations between schools' policies, programmes and facilities for physical activity (PA) and adolescents' objectively-measured activity intensity during the school day and lunchtime. METHODS: Accelerometer-derived PA (proportion of time spent in sedentary (SED), light PA (LPA) and moderate-to-vigorous PA (MVPA)) during school hours and lunchtime from 325 participants in the SPEEDY study were obtained from baseline measurements (primary school, age 9/10 years) and +4y follow-up (secondary school). School environment characteristics were assessed by teacher questionnaire. Multivariable multi-level linear regression analyses accounting for school and adjusted for sex, age, BMI and family socio-economic status assessed cross-sectional associations with lunchtime and school-day SED, LPA and MVPA; effect modification by sex was investigated. The association of changes in school environment with changes in outcomes was examined using multivariable cross-classified linear regression models. RESULTS: There were significant differences between primary and secondary schools for 6/10 school environment characteristics investigated (including secondary schools reporting shorter breaks, more lunchtime PA opportunities, and higher number of sports facilities). Cross-sectional analyses showed that boys attending secondary schools with longer breaks spent significantly less time in SED and more time in MVPA during the school day. Longitudinally, an increase in break-time duration between primary and secondary school was associated with smaller reductions in MVPA during the school day. Moreover, participants who moved from a primary school that did not provide opportunities for PA at lunchtime to a secondary school that did provide such opportunities exhibited smaller increases in SED and smaller reductions in MVPA at lunchtime. CONCLUSIONS: Schools should consider the potential negative impact of reducing break time duration on students' MVPA and SED during the school day. School-based interventions that combine longer breaks and more PA opportunities during lunchtime may be a fruitful direction for future research. Further research should also explore other factors in the school environment to explain the school-level clustering observed, and study sex differences in the way that the school environment influences activity intensity for adolescent populations.

Identifying correlates and determinants of physical activity in youth: How can we advance the field?

This commentary provides a critical discussion of current research investigating the correlates and determinants of physical activity in young people, with specific focus on conceptual, theoretical and methodological issues. We draw on current child and adolescent literature and our own collective expertise to illustrate our discussion. We conclude with recommendations that will strengthen future research and help to advance the field.

Revising on the run or studying on the sofa: prospective associations between physical activity, sedentary behaviour, and exam results in British adolescents.

BACKGROUND: We investigated prospective associations between physical activity/sedentary behaviour (PA/SED) and General Certificate of Secondary Education (GCSE) results in British adolescents. METHODS: Exposures were objective PA/SED and self-reported sedentary behaviours (screen (TV, Internet, Computer Games)/non-screen (homework, reading)) measured in 845 adolescents (14·5y ± 0·5y; 43·6 % male). GCSE results at 16y were obtained from national records. Associations between exposures and academic performance (total exam points) were assessed using multilevel mixed-effects linear regression adjusted for mood, BMI z-score, deprivation, sex, season and school; potential interactions were investigated. RESULTS: PA was not associated with academic performance. One-hour more accelerometer-assessed SED was associated with (ß(95 % CI)) 6·9(1·5,12·4) more GCSE points. An extra hour of screen time was associated with 9.3(-14·3,-4·3) fewer points whereas an extra hour of non-screen time (reading/homework) was associated with 23·1(14·6,31·6) more points. Screen time was still associated with poorer scores after adjusting for objective PA/SED and reading/homework. CONCLUSIONS: An extra hour/day of screen time at 14·5y is approximately equivalent to two fewer GCSE grades (e.g., from B to D) at 16y. Strategies to achieve the right balance between screen and non-screen time may be important for improving academic performance. Concerns that encouraging more physical activity may result in decreased academic performance seem unfounded.

Perceived family functioning and friendship quality: cross-sectional associations with physical activity and sedentary behaviours.

BACKGROUND: This study examined the association of adolescent-reported family functioning and friendship quality with objectively-measured moderate to vigorous physical activity (MVPA), sedentary time, and self-reported sedentary behaviours. METHODS: Data are from the ROOTS study. MVPA and sedentary time were assessed using combined movement and heart rate sensing. Time spent TV viewing, using the internet, playing video games, doing homework and reading for pleasure was self-reported. Data on objectively-measured and self-reported outcomes for weekdays was available for 738 (age 14.5y, 55.7% female) and 800 (56.3% female) participants, respectively. Adolescents perceived family functioning and friendship quality (Two subscales: 'Good friendship qualities', 'Friendship difficulties') was assessed by questionnaire. Analyses were conducted using multi-level linear or logistic regression. RESULTS: Adolescents reporting better family functioning accumulated more MVPA on weekdays (beta; 95% confidence interval: 0.57; 0.17,0.98). Higher scores on the good friendship qualities subscale was associated with greater MVPA throughout the week (weekdays: 1.13; 0.62,1.65, weekend: 0.56; 0.09,1.02) and lower sedentary time on weekdays (-10.34; -17.03,-3.66). Boys from better functioning families were less likely to report playing video games at the weekend (OR; 95% confidence interval: 0.73; 0.57,0.93) or reading for pleasure (weekday: 0.73; 0.56,0.96 weekend: 0.75; 0.58,0.96). Boys who attained higher scores on the good friendship qualities scale were less likely to play video games at the weekend (0.61; 0.44,0.86) or report high homework on weekdays (0.54; 0.31,0.94). A higher score for good friendship qualities was associated with lower odds of girls playing video games during the week (0.76; 0.58,1.00) or reading for pleasure at the weekend (0.61; 0.42,0.88). Girls that reported fewer friendship difficulties had lower odds of high TV viewing (0.76; 0.62,0.93) or playing video games (0.71; 0.52,0.97) at the weekend, and lower odds of reading for pleasure (0.63; 0.49,0.81) or reporting high homework on weekdays (0.70; 0.52,0.95). DISCUSSION: Family functioning and friendship quality exhibit a complex pattern of association with physical activity and sedentary behaviour that varies by sex and day of the week. Findings highlight the potential value of targeting interpersonal aspects of the family and friendships as an adjunct to behaviour change interventions.

Change in objectively measured physical activity during the transition to adolescence.

BACKGROUND: Physical activity (PA) declines during adolescence but change in different PA intensities across population subgroups is rarely explored. We describe change in sedentary (SED) time, light (LPA), moderate (MPA) and vigorous PA (VPA) assessed at three time points over 4 years. METHODS: Accelerometer-assessed PA (min) was obtained at baseline (N=2064), 1 and 4 years later among British children (baseline mean±SD 10.2±0.3-year-old; 42.5% male). Change in SED (<100 counts/min (cpm)), LPA (101-1999 cpm), MPA (2000-3999 cpm) and VPA (≥4000 cpm) was studied using three-level (age, individual and school) mixed-effects linear regression including participants with data at ≥2 time points (N=990). Differences in change by sex, home location and weight status were explored with interactions for SED, LPA and moderate and vigorous PA (MVPA). RESULTS: SED increased by 10.6 (95% CI 9.1 to 12.2) min/day/year. MPA and VPA decreased by 1.4 (1.0 to 1.8) and 1.5 (1.1 to 1.8) min/day/year, respectively. VPA decreased more than MPA as a percentage of the baseline value. MVPA declined more steeply among boys (3.9 (3.0 to 4.8)) versus girls (2.0 (1.2 to 2.7) min/day/year) despite lower MVPA among girls at all ages; rural (4.4 (3.5 to 5.2)) versus urban individuals (1.3 (0.4 to 2.3) min/day/year) and on weekends (6.7 (5.2 to 8.1)) versus weekdays (2.8 (1.9 to 3.7) min/day/year). MVPA was consistently lower among overweight/obese individuals (-17.5 (-3.9 to -2.5) min/day/year). CONCLUSIONS: PA decreases and is replaced by SED during early adolescence in British youth. Results indicate the urgency of PA promotion among all adolescents but especially girls and in rural areas. Increasing VPA and targeting PA promotion during weekends appear important.