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BackgroundProlonged sitting time is a risk factor for chronic disease, yet recent global surveillance is not well described. The aims were to clarify: (i) the countries that have collected country-level data on self-reported sitting time; (ii) the single-item tools used to collect these data; and (iii) the duration of sitting time reported across low- to high-income countries.MethodsCountry-level data collected within the last 10 years using single-item self-report were included. The six-stage methodology: (1) reviewing Global Observatory for Physical Activity! Country Cards; (2-4) country-specific searches of PubMed, the Demographic and Health Survey website and Google; (5) analysing the Eurobarometer 88.4; and (6) country-specific searches for World Health Organization STEPwise reports.ResultsA total of 7641 records were identified and screened for eligibility. Sixty-two countries (29%) reported sitting time representing 47% of the global adult population. The majority of data were from high-income (61%) and middle income (29%) countries. The tools used were the International Physical Activity Questionnaire (IPAQ; n = 34), a modified IPAQ (n = 1) or the Global Physical Activity Questionnaire (GPAQ; n = 27). The median of mean daily sitting times was 4.7 (IQR: 3.5-5.1) hours across all countries. Higher-income countries recorded a longer duration of sitting time than lower-income countries (4.9 vs 2.7 h).ConclusionsThis study provides an updated collation of countries collecting self-reported sitting time data. The daily sitting time findings should be interpreted cautiously. Current surveillance of sitting time is limited by a lack of coverage. Measures of population sitting time that are valid, feasible and sensitive to change should be embedded within global surveillance systems, to help guide future policy, research and practice.Trial registrationNot applicable.

Aims: Incidental physical activity as part of daily living may offer feasibility advantages over traditional exercise. We examined the joint associations of incidental physical activity and sedentary behaviour with major adverse cardiovascular events (MACE) risk., Methods and Results: Analyses included 22 368 non-exercising adults from the UK Biobank accelerometry sub-study (median age [IQR]: 62.9 [11.6] years; 41.8% male). Physical activity and sedentary behaviour exposures were derived using a machine learning-based intensity and posture classification schema. We assessed the tertile-based joint associations of sedentary behaviour and the following: a) incidental vigorous (VPA), b) incidental moderate to vigorous (MVPA), c) vigorous intermittent lifestyle physical activity (VILPA; bouts lasting up to 1 min), and d) moderate to vigorous intermittent lifestyle physical activity (MV-ILPA; bouts lasting up to 3 min) with MACE risk. Over an 8.0-year median follow-up, 819 MACE events occurred. Compared to the highest physical activity and lowest sedentary time, high sedentary behaviour (>11.4 h/day) with low incidental VPA (<2.1 min/day) had an HR of 1.34 (95% CI: 0.98, 1.84) and low incidental MVPA (<21.8 min/day) had a 1.89 HR (95% CI: 1.42, 2.52) for MACE. Sedentary behaviour was not associated with MACE at medium and high levels of VPA or VILPA. Completing 4.1 min/day of VPA or VILPA may offset the MACE risk associated with high sedentary behaviour. Conversely, 31-65 min of incidental MVPA or 26-52 min of MV-ILPA per day largely attenuated the associations with MACE., Conclusion: Brief intermittent bursts of vigorous incidental physical activity may offset cardiovascular risks from high sedentary behaviour., Lay Summary: Literature to date has examined the role of total or leisure time physical activity in mitigating the health risks associated with high sedentary behaviour. However, the vast majority of adults achieve their daily physical activity incidentally through day-to-day activities. In this study of 22 368 adults from the UK Biobank accelerometry sub-study, we provide the first investigation into whether a) incidental vigorous (VPA), b) incidental moderate to vigorous (MVPA), c) vigorous intermittent lifestyle physical activity (VILPA; bouts lasting up to 1 min), and d) moderate to vigorous intermittent lifestyle physical activity (MV-ILPA; bouts lasting up to 3 min) completed through normal daily living can offset the risk of major adverse cardiovascular events (MACE) associated with high sedentary behaviour (>11.4 h per day). We demonstrate that incidental VPA and MVPA may offset the MACE risk associated with high-sedentary behaviour even if accrued in brief bursts lasting <3 min. Completing 4.1 min/day of VPA or VILPA may offset the MACE risk associated with high sedentary behaviour.A daily duration of 31-65 min of incidental MVPA or 26-52 min of MV-ILPA per day largely attenuated the associations with MACE., Competing Interests: Conflict of interest: ES is a paid consultant and holds equity in Complement Theory Inc., a US-based company whose products and services relate to physical activity. All other authors disclose no conflict of interest for this work., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Background: Blood pressure (BP)-lowering effects of structured exercise are well-established. Effects of 24-hour movement behaviors captured in free-living settings have received less attention. This cross-sectional study investigated associations between a 24-hour behavior composition comprising 6 parts (sleeping, sedentary behavior, standing, slow walking, fast walking, and combined exercise-like activity [eg, running and cycling]) and systolic BP (SBP) and diastolic BP (DBP)., Methods: Data from thigh-worn accelerometers and BP measurements were collected from 6 cohorts in the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) (n=14 761; mean±SD, 54.2±9.6 years). Individual participant analysis using compositional data analysis was conducted with adjustments for relevant harmonized covariates. Based on the average sample composition, reallocation plots examined estimated BP reductions through behavioral replacement; the theoretical benefits of optimal (ie, clinically meaningful improvement in SBP [2 mm Hg] or DBP [1 mm Hg]) and minimal (ie, 5-minute reallocation) behavioral replacements were identified., Results: The average 24-hour composition consisted of sleeping (7.13±1.19 hours), sedentary behavior (10.7±1.9 hours), standing (3.2±1.1 hours), slow walking (1.6±0.6 hours), fast walking (1.1±0.5 hours), and exercise-like activity (16.0±16.3 minutes). More time spent exercising or sleeping, relative to other behaviors, was associated with lower BP. An additional 5 minutes of exercise-like activity was associated with estimated reductions of -0.68 mm Hg (95% CI, -0.15, -1.21) SBP and -0.54 mm Hg (95% CI, -0.19, 0.89) DBP. Clinically meaningful improvements in SBP and DBP were estimated after 20 to 27 minutes and 10 to 15 minutes of reallocation of time in other behaviors into additional exercise. Although more time spent being sedentary was adversely associated with SBP and DBP, there was minimal impact of standing or walking., Conclusions: Study findings reiterate the importance of exercise for BP control, suggesting that small additional amounts of exercise are associated with lower BP in a free-living setting.

Background: Ubiquitous car ownership may affect children's activity and health. We assessed the cross-sectional and longitudinal associations between household car ownership and children's daily time spent sedentary (SED) and in moderate to vigorous physical activity (MVPA)., Methods: Pooled cohort data were from the International Children's Accelerometry Database. Outcome measures were average daily accelerometer-measured SED and MVPA (in minutes per day). Exposures were household car ownership (none, 1, and ≥2) and change in car ownership. Associations were examined using multivariable mixed-effects linear regression., Results: Mean age of participants (N = 4193) was 10.4 years (SD = 2.0), 53.4% were girls, and mean follow-up duration (N = 1333) was 3.3 years (SD = 1.1). Cross-sectionally, household car ownership was associated with higher SED (vs none: 1 car: β = 14.1 min/d, 95% CI, 6.7-21.5; ≥2 cars: 12.8, 95% CI, 5.3-20.4) and lower MVPA (vs none: 1 car: β = -8.8, 95% CI, -11.9 to -5.7; ≥2 cars: β = -8.8, 95% CI, -12.0 to -5.7). Associations were stronger in boys than girls and in children from lower socioeconomic backgrounds (SED only). Prospectively, there were no associations with increased car ownership. Reductions in household car ownership (of which 93.1% had ≥ 2 cars at baseline) were associated with a greater reduction in MVPA (vs no change: β = -8.4, 95% CI, -13.9 to -3.0) but not SED., Conclusions: Children in households with car access were more SED and less active than those without. Losing access to a second car was associated with greater decreases in MVPA, potentially related to losing access to activity-enabling environments. Reducing car access and use are important public health targets (eg, reducing air pollution), but their potential impact on children's activity opportunities should be mitigated.

Purpose: Disabled people are more likely than non-disabled people to be physically inactive, placing them at increased risk of ill-health. Many disabled people want to be more physically active yet feel there is inadequate support to do so. Evidence on the tangible actions that would support disabled people to be active is limited. The aim of this study was to explore the key actions that would best support disabled people's participation in physical activity., Materials and Methods: Twenty-one disabled people (62% >45 years, 48% male) were purposively sampled to take part in online focus groups (2-6 participants per group). Six focus groups were undertaken using a semi-structured guide, with transcripts thematically analysed., Results: Thirteen themes were identified across four levels of the ecological model - interpersonal, institutional, community, and policy. Priority actions include mandatory training on how to support disabled people, improvements to the accessibility of facilities and equipment, improved frequency of public transport to activity centres, and actions to improve societal attitudes towards disabled people., Conclusions: A systems-based approach is needed, combining actions across levels of the ecological model, to ensure equitable access to physical activity for disabled people.

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, HbA 1c 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., (© 2024. The Author(s).)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Evidence on the prevalence, determinants, and health outcomes of physical activity in disabled people is limited. It is possible that the limited availability of high-quality scientific evidence is due to the extent and nature of disability assessment in physical activity research. This scoping review explores how disability has been measured in epidemiological studies that included accelerometer-based measurement of physical activity., Methods: Data sources: MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus, and CINAHL., Eligibility Criteria: Prospective and cross-sectional studies that included an accelerometer measurement of physical activity. Survey instruments used in these studies were obtained, and questions relating to the International Classification of Functioning, Disability and Health domains of (1) health conditions, (2) body functions and structures, and (3) activities and participation, were extracted for analysis., Results: Eighty-four studies met the inclusion criteria, from which complete information on the 3 domains was obtained for 68. Seventy-five percent of studies (n = 51) captured whether a person had at least one health condition, 63% (n = 43) had questions related to body functions and structures, and 75% (n = 51) included questions related to activities and participation., Conclusion: While most studies asked something about one of the 3 domains, there was substantial diversity in the focus and style of questions. This diversity indicates a lack of consensus on how these concepts should be assessed, with implications for the comparability of evidence across studies and subsequent understanding of the relationships between disability, physical activity, and health.

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)., (© 2023. The Author(s).)

Background: Temporal changes in sedentary behavior patterns reflect the evolving nature of our built and social environments, particularly the expanding availability of electronic media. It is important to understand what types of sedentary behavior are assessed in national surveillance to determine whether, and to what extent, they reflect contemporary patterns. The aims of this review were to describe the characteristics of questionnaires used for national surveillance of sedentary behavior and to identify the types of sedentary behaviors being measured., Method: We reviewed questionnaires from national surveillance systems listed on the Global Observatory for Physical Activity (GoPA!) country cards to locate items on sedentary behavior. Questionnaire characteristics were categorized using the Taxonomy of Self-reported Sedentary Behavior Tools (TASST). The purpose and type of sedentary behaviors captured were classified using the Sedentary Behavior International Taxonomy (SIT)., Results: Overall, 346 surveillance systems were screened for eligibility, of which 93 were included in this review. Most questionnaires used a single-item direct measure of sitting time (n = 78, 84%). Work and domestic were the most frequently captured purposes of sedentary behavior, while television viewing and computer use were the most frequently captured types of behaviors., Conclusion: National surveillance systems should be periodically reviewed in response to evidence on contemporary behavior patterns in the population and the release of updated public health guidelines.

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., (© 2022. The Author(s).)

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated., Competing Interests: The authors have declared that no competing interests exist.

Introduction: The United Kingdom and World Health Organization recently changed their youth physical activity (PA) guidelines from 60 min of moderate- to vigorous-intensity PA (MVPA) every day, to an average of 60 min of MVPA per day, over a week. The changes are based on expert opinion due to insufficient evidence comparing health outcomes associated with different guideline definitions. This study used the International Children's Accelerometry Database to compare approaches to calculating youth PA compliance and associations with health indicators., Methods: Cross-sectional accelerometer data (n = 21,612, 5-18 yr) were used to examine compliance with four guideline definitions: daily method (DM; ≥60 min MVPA every day), average method (AM; average of ≥60 min MVPA per day), AM5 (AM compliance and ≥5 min of vigorous PA [VPA] on ≥3 d), and AM15 (AM compliance and ≥15 min VPA on ≥3 d). Associations between compliance and health indicators were examined for all definitions., Results: Compliance varied from 5.3% (DM) to 29.9% (AM). Associations between compliance and health indicators were similar for AM, AM5, and AM15. For example, compliance with AM, AM5, and AM15 was associated with a lower BMI z-score (statistics are coefficient [95% CI]): AM (-0.28 [-0.33 to -0.23]), AM5 (-0.28 [-0.33 to -0.23], and AM15 (-0.30 [-0.35 to -0.25]). Associations between compliance and health indicators for DM were similar/weaker, possibly reflecting fewer DM-compliant participants with health data and lower variability in exposure/outcome data., Conclusions: Youth completing 60 min of MVPA every day do not experience superior health benefits to youth completing an average of 60 min of MVPA per day. Guidelines should encourage youth to achieve an average of 60 min of MVPA per day. Different guideline definitions affect inactivity prevalence estimates; this must be considered when analyzing data and comparing studies., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.)

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., (© 2022. The Author(s).)

Objective: To examine the evidence on the associations between socioeconomic position and young people's physical activity and sedentary behaviours in the UK., Design: Scoping review., Data Sources: PubMed, SCOPUS and Web of Science databases were searched for articles published up to and including January 2021., Eligibility Criteria for Selecting Studies: Observational studies in children and adolescents (aged 5-18 years) from the UK that had assessed associations between at least one indicator of socioeconomic position and at least one outcome of physical activity and/or sedentary behaviour., Data Extraction and Synthesis: Data were extracted by one reviewer and 20% were double checked. Indicators of socioeconomic position were tabulated with domains of physical activity and sedentary behaviour., Results: Fifty-seven publications were included in the review; 37 publications from 19 studies (k=23) of children and 21 publications from 15 studies (k=23) of adolescents. Most studies were cross-sectional. 63% of studies of children, and 40% of studies of adolescents assessed Index of Multiple Deprivation. Eighteen studies measured physical activity in children, 13 measured sedentary behaviour. Eleven studies of adolescents included a measure of physical activity, 10 included a measure of sedentary behaviour. Among children and adolescents, the association between socioeconomic position and measures of either physical activity or sedentary behaviour was highly variable depending on the measure of both socioeconomic position used and the behavioural outcome, with the exception of higher family affluence which was consistently associated with higher reported physical activity among adolescents., Conclusion: Physical activity and sedentary behaviours of children and adolescents in the UK are complex and influenced by multiple indicators of socioeconomic position that are, in most cases, different across age stages, outcomes examined and measurement tools. Greater consistency in the use and measures of socioeconomic position as well as outcomes of behaviour are required for robust country-specific meta-analyses., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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 β = 3.81, 95% confidence interval [95%CI] = 3.06, 4.56) and active travel (β = 3.46, 95%CI = 2.73, 4.19) contributed more to daily MVPA than PE (β = 0.82, 95%CI = -0.02, 1.66). Compared to the base model which included only covariates (R 2  = 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 β = 0.85, 95%CI = -0.03, 1.72; active travel: β = 0.68, 95%CI = -0.14, 1.50; PE: β = 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., (© 2022. The Author(s).)

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: To examine the association of 24-hour time-use compositions with mental health in a large, geographically diverse sample of UK adolescents., Design: Cross-sectional, secondary data analysis., Setting: Millennium Cohort Study (sixth survey), a UK-based prospective birth cohort., Participants: Data were available from 4642 adolescents aged 14 years. Analytical samples for weekday and weekend analyses were n=3485 and n=3468, respectively (45% boys, 85% white ethnicity)., Primary and Secondary Outcome Measures: Primary outcome measures were the Strengths and Difficulties Questionnaire (SDQ, socioemotional behaviour), Mood and Feelings Questionnaire (MFQ, depressive symptoms) and Rosenberg Self-Esteem Scale (RSE, self-esteem). Behavioural exposure data were derived from 24-hour time-use diaries., Results: On weekdays, participants spent approximately 54% of their time in sleep, 3% in physical activity, 9% in school-related activities, 6% in hobbies, 11% using electronic media and 16% in domestic activities. Predicted differences in SDQ, MFQ and RSE were statistically significant for all models (weekday and weekend) that simulated the addition or removal of 15 min physical activity, with an increase in activity being associated with improved mental health and vice versa. Predicted differences in RSE were also significant for simulated changes in electronic media use; an increase in electronic media use was associated with reduced self-esteem., Conclusion: Small but consistent associations were observed between physical activity, electronic media use and selected markers of mental health. Findings support the delivery of physical activity interventions to promote mental health during adolescence, without the need to specifically target or protect time spent in other activities., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Sedentary behaviors are highly prevalent in youth and may be associated with markers of physical and mental health. This systematic review and meta-analysis aimed to quantify the age-related change in sedentary behavior during childhood and adolescence. Ten electronic databases were searched. Inclusion criteria specified longitudinal observational studies or control group from an intervention; participants aged ≥5 and ≤18 years; a quantitative estimate of the duration of SB; and English language, peer-reviewed publication. Meta-analyses summarized weighted mean differences (WMD) in device-assessed sedentary time and questionnaire-assessed screen-behaviors over 1-, 2-, 3-, or more than 4-year follow-up. Effect modification was explored using meta-regression. Eighty-five studies met inclusion criteria. Device-assessed sedentary time increased by (WMD 95% confidence interval [CI]) 27.9 (23.2, 32.7), 61.0 (50.7, 71.4), 63.7 (53.3, 74.0), and 140.7 (105.1, 176.4) min/day over 1-, 2-, 3-, and more than 4-year follow-up. We observed no effect modification by gender, baseline age, study location, attrition, or quality. Questionnaire-assessed time spent playing video games, computer use, and a composite measure of sedentary behavior increased over follow-up duration. Evidence is consistent in showing an age-related increase in various forms of sedentary behavior; evidence pertaining to variability across socio-demographic subgroups and contemporary sedentary behaviors are avenues for future research., (© 2021 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.)

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.

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.

Objective: To develop a model to assess the long-term costs and health outcomes of physical activity interventions targeting adolescents., Design: A Markov cohort simulation model was constructed with the intention of being capable of estimating long-term costs and health impacts of changes in activity levels during adolescence. The model parameters were informed by published literature and the analysis took a National Health Service perspective over a lifetime horizon. Univariate and probabilistic sensitivity analyses were undertaken., Setting: School and community., Participants: A hypothetical cohort of adolescents aged 16 years at baseline., Interventions: Two exemplar school-based: a comparatively simple, after-school intervention and a more complex multicomponent intervention compared with usual care., Primary and Secondary Outcome Measures: Incremental cost-effectiveness ratio as measured by cost per quality-adjusted life year gained., Results: The model gave plausible estimates of the long-term effect of changes in physical activity. The use of two exemplar interventions suggests that the model could potentially be used to evaluate a number of different physical activity interventions in adolescents. The key model driver was the degree to which intervention effects were maintained over time., Conclusions: The model developed here has the potential to assess long-term value for money of physical activity interventions in adolescents. The two applications of the model indicate that complex interventions may not necessarily be the ones considered the most cost-effective when longer-term costs and consequences are taken into account., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.)

Background: Active living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time., Methods and Findings: We analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006-2010) and follow up (2009-2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible., Conclusions: Active commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30-60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

Accelerometer-based studies of children's physical activity have reported seasonal patterns in activity levels. However, the inability of many accelerometers to detect activity while the wearer is swimming or cycling may introduce a bias to the estimation of seasonality if participation in these activities are themselves seasonally patterned. We explore seasonal patterns in children's swimming and cycling among a sample of 7-8 year olds ( N  = 591) participating in the Millennium Cohort Study, UK. Participating children wore an accelerometer for one week on up to five occasions over the year and their parents completed a diary recording daily minutes spent swimming and cycling. Both swimming and cycling participation showed seasonal patterns, with 2.7 (SE 0.8) more minutes swimming and 5.7 (0.7) more minutes cycling performed in summer compared to winter. Adding swimming and cycling time to accelerometer-determined MVPA increased the summer-winter difference in MVPA from 16.6 (1.6) to 24.9 min. The seasonal trend in swimming and cycling appears to follow the same pattern as accelerometer-measured MVPA. Studies relying solely on accelerometers may therefore underestimate seasonal differences in children's activity.

Background: The minimum intensity of physical activity (PA) that is associated with favourable body composition and cardiorespiratory fitness (CRF) remains unknown., Objective: To investigate cross-sectional associations of PA and sedentary time (ST) with body composition and CRF in mid-childhood., Methods: PA, ST, body composition and CRF were measured in a population-based sample of 410 children (aged 7.6 ± 0.4 years). Combined heart-rate and movement sensing provided estimates of PA energy expenditure (PAEE, kJ/kg/day) and time (min/day) at multiple fine-grained metabolic equivalent (MET) levels, which were also collapsed to ST and light PA (LPA), moderate PA (MPA) and vigorous PA (VPA). Fat mass index (FMI, kg/m 2 ), trunk fat mass index (TFMI, kg/m 2 ) and fat-free mass index (FFMI, kg/m 2.5 ) were derived from dual-energy X-ray absorptiometry. Maximal workload from a cycle ergometer test provided a measure of CRF (W/kg FFM). Linear regression and isotemporal substitution models were used to investigate associations., Results: The cumulative time above 2 METs (221 J/min/kg) was inversely associated with FMI and TFMI in both sexes (p < 0.001) whereas time spent above 3 METs was positively associated with CRF (p ≤ 0.002); CRF increased and adiposity decreased dose-dependently with increasing MET levels. ST was positively associated with FMI and TFMI (p < 0.001) but there were inverse associations between all PA categories (including LPA) and adiposity (p ≤ 0.002); the magnitude of these associations depended on the activity being displaced in isotemporal substitution models but were consistently stronger for VPA. PAEE, MPA and to a greater extent VPA, were all positively related to CRF (p ≤ 0.001)., Conclusions: PA exceeding 2 METs is associated with lower adiposity in mid-childhood, whereas PA of 3 METs is required to benefit CRF. VPA was most beneficial for fitness and fatness, from a time-for-time perspective, but displacing any lower-for-higher intensity may be an important first-order public health strategy. Clinical trial registry number (website): NCT01803776 ( https://clinicaltrials.gov/ct2/show/NCT01803776 ).

Little is known about the longitudinal association of familial socio-demographic factors, behaviours, attitudes, or home environment with meeting physical activity guidelines. Our objective was to a) describe 4-year change in the prevalence of meeting guidelines, and characteristics of participants across categories of physical activity maintenance, and b) identify familial factors in childhood that are longitudinally associated with meeting guidelines in adolescence. Data on 17 parent- and child-reported family variables and objectively measured physical activity (ActiGraph GT1M) were available from 406 children (10.3 ± 0.3 years, 53.5% female) participating in the SPEEDY study. Average duration of week- and weekend day moderate-to-vigorous physical activity (MVPA, ≥ 2000 cpm) at baseline and follow-up (14.3 ± 0.3 years) were calculated to determine whether participants met 60 min MVPA/day guidelines at each assessment. Descriptives were calculated across four MVPA change categories. Multi-level logistic regression examined the association of baseline familial factors with meeting guidelines at follow-up, adjusting for sex, baseline physical activity, family socio-economic position, and school clustering. At follow-up, 51.5% and 36.1% of adolescents met guidelines on weekdays and weekend days, respectively (baseline: 68.0%, 67.2%). Girls were less likely than boys to remain sufficiently active, particularly on weekdays. Family social support was positively associated with adolescents meeting guidelines at weekends (OR 1.2; 95% CI 1.0-1.4). The presence of play equipment at home was negatively associated with meeting guidelines on weekdays (OR 0.5; 95% CI 0.3-0.8). Interventions that foster parent's facilitation of physical activity may help to encourage the upkeep of healthy behaviours during the transition from childhood to adolescence.

Objectives: Stakeholder engagement and public involvement are considered as integral to developing effective public health interventions and is encouraged across all phases of the research cycle. However, limited guidelines and appropriate tools exist to facilitate stakeholder engagement-especially during the intervention prioritisation phase. We present the findings of an online 'Delphi' study that engaged stakeholders (including young people) in the process of prioritising secondary school environment-focused interventions that aim to increase physical activity., Setting: Web-based data collection using an online Delphi tool enabling participation of geographically diverse stakeholders., Participants: 37 stakeholders participated, including young people (age 13-16 years), parents, teachers, public health practitioners, academics and commissioners; 33 participants completed both rounds., Primary and Secondary Outcome Measures: Participants were asked to prioritise a (short-listed) selection of school environment-focused interventions (eg, standing desks, outdoor design changes) based on the criteria of 'reach', 'equality', 'acceptability', 'feasibility', 'effectiveness' and 'cost'. Participants were also asked to rank the criteria and the effectiveness outcomes (eg, physical activity, academic achievement, school enjoyment) from most to least important. Following feedback along with any new information provided, participants completed round 2 4 weeks later., Results: The intervention prioritisation process was feasible to conduct and comments from participants indicated satisfaction with the process. Consensus regarding intervention strategies was achieved among the varied groups of stakeholders, with 'active lessons' being the favoured approach. Participants ranked 'mental health and well-being' as the most important outcome followed by 'enjoyment of school'. The most important criteria was 'effectiveness', followed by 'feasibility'., Conclusions: This novel approach to engaging a wide variety of stakeholders in the research process was feasible to conduct and acceptable to participants. It also provided insightful information relating to how stakeholders prioritise interventions. The approach could be extended beyond the specific project to be a useful tool for researchers and practitioners., Competing Interests: Conflicts of Interest: None declared., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

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., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Sedentary behaviours (SB) are highly prevalent in young people and may be adversely associated with physical and mental health. Understanding of the modifiable determinants of SB is necessary to inform the design of behaviour change interventions but much of the existing research is cross-sectional and focussed upon screen-based behaviours., Purpose: To examine the social, psychological and environmental determinants of change in children's objectively measured sedentary time from age 11 to 14 years., Methods: Data are from the second (2008) and third (2011) waves of assessment in the Sport, Physical Activity, and Eating Behaviour: Environmental Determinants in Young People (SPEEDY) study, conducted in the county of Norfolk, United Kingdom. Longitudinal data on accelerometer assessed sedentary time were available for 316 (53.5% female, 11.2±0.3 years at baseline) and 264 children after-school and at the weekend respectively. Information on 14 candidate determinants, including school travel mode and electronic media ownership, was self-reported. Change in the proportion of registered time spent sedentary was used as the outcome variable in cross-classified linear regression models, adjusted for age, sex, body mass index and baseline sedentary time. Simple and multiple models were run and interactions with sex explored., Results: Daily sedentary time increased by 30-40 minutes after-school and at the weekend from baseline to follow-up. Participants who travelled to school by cycle exhibited smaller increases in after-school sedentary time (beta; 95%CI for change in % time spent sedentary: -3.3;-6.7,-0.07). No significant determinants of change in weekend sedentary time were identified., Conclusions: Time spent sedentary increased during the three-year duration of follow-up but few of the variables examined were significantly associated with changes in sedentary time. Children's mode of school travel may influence changes in their sedentary time over this period and should be examined further, alongside broader efforts to identify modifiable determinants of SB during childhood., Competing Interests: The authors have declared that no competing interests exist.

Understanding how physical activity (PA) patterns vary within and between days may guide PA promotion in young people. We aimed to 1) describe and compare the frequency (bouts/day) and duration (min/bout) of bouts of moderate-to-vigorous intensity PA (MVPA) on weekdays vs. weekends and in-school vs. out-of-school, and 2) assess associations of bout frequency and duration in these time-segments with overall PA. We used cross-sectional accelerometer data from 2737 children (aged 6-19 years) in the United States National Health and Nutrition Examination Survey (NHANES) 2003-2006. A bout was defined as MVPA (≥ 2000 counts per minute [cpm]) lasting ≥ 3 min. Adjusted Wald tests were used to assess differences in bout characteristics between time-segments. Linear regression was used to examine the association of time-segment specific bout characteristics with daily minutes of MVPA and PA volume (average cpm). Bout frequency was higher on weekdays than weekends (median [IQR] 4.3 [2.2-7.2] vs. 3.0 [1.0-6.5] bouts/day, p < 0.001); however, bout duration did not differ (4.7 [4.0-5.7] vs. 4.5 [3.7-5.8] min/bout, p = 0.33). More bouts were accumulated out-of-school compared with in-school (2.2 [1.0-4.0] vs. 1.8 [0.8-3.2] bouts/day, p < 0.001), but bout duration was similar (4.7 [3.8-5.8] vs. 4.5 [3.8-5.7] min/bout, p = 0.158). For all time-segments, the frequency and duration of bouts of MVPA were independently and positively associated with overall MVPA and PA volume. In conclusion, the characteristics of children's PA vary within and between days; accounting for this in intervention design may improve future interventions. However, increasing bout frequency or duration in any time-segment may be beneficial for overall PA.

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., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Physical activity declines during youth but most evidence reports on combined moderate and vigorous-intensity physical activity. We investigated how vigorous-intensity activity varies with age. Cross-sectional data from 24,025 participants (5.0-18.0 y; from 20 studies in 10 countries obtained 2008-2010) providing ≥ 1 day accelerometer data (International Children's Accelerometry Database (ICAD)). Linear regression was used to investigate age-related patterns in vigorous-intensity activity; models included age (exposure), adjustments for monitor wear-time and study. Moderate-intensity activity was examined for comparison. Interactions were used to investigate whether the age/vigorous-activity association differed by sex, weight status, ethnicity, maternal education and region. A 6.9% (95% CI 6.2, 7.5) relative reduction in mean vigorous-intensity activity with every year of age was observed; for moderate activity the relative reduction was 6.0% (5.6%, 6.4%). The age-related decrease in vigorous-intensity activity remained after adjustment for moderate activity. A larger age-related decrease in vigorous activity was observed for girls (- 10.7%) versus boys (- 2.9%), non-white (- 12.9% to - 9.4%) versus white individuals (- 6.1%), lowest maternal education (high school (- 2.0%)) versus college/university (ns) and for overweight/obese (- 6.1%) versus healthy-weight participants (- 8.1%). In addition to larger annual decreases in vigorous-intensity activity, overweight/obese individuals, girls and North Americans had comparatively lower average vigorous-intensity activity at 5.0-5.9 y. Age-related declines in vigorous-intensity activity during youth appear relatively greater than those of moderate activity. However, due to a higher baseline, absolute moderate-intensity activity decreases more than vigorous. Overweight/obese individuals, girls, and North Americans appear especially in need of vigorous-intensity activity promotion due to low levels at 5.0-5.9 y and larger negative annual differences.

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.

Background: The data regarding prospective associations between physical activity (PA) and adiposity in youth are inconsistent., Objective: The objective of this study was to investigate associations between baseline levels of objectively measured PA and changes in adiposity over 2.5 years from mid-to-late adolescence., Methods: This was an observational cohort study in 728 school students (43% boys) from Cambridgeshire, United Kingdom. Fat mass index (FMI, kg m(-2) ) was estimated at baseline (mean ± standard deviation age: 15 ± 0.3 years) and follow-up (17.5 ± 0.3 years) by anthropometry and bioelectrical impedance. Habitual PA was assessed at baseline by ≥3 d combined heart rate and movement sensing. Average daily PA energy expenditure (PAEE) and the time (min d(-1) ) spent in light, moderate and vigorous intensity PA (LPA, MPA and VPA, respectively) was estimated. Multilevel models were used to investigate associations between baseline PA and change in FMI (ΔFMI). Adjustment for baseline age, sex, follow-up duration, area-level socioeconomic status, season of PA assessment, sedentary time, energy intake and sleep duration was made; baseline FMI was also added in a second model., Results: FMI increased significantly over follow-up (0.6 ± 1.2 kg m(-2) , P < 0.001). Baseline PAEE and LPA positively predicted ΔFMI in overfat participants (P ≤ 0.030), as did VPA in initially normal fat participants (P ≤ 0.044). There were further positive associations between PAEE and ΔFMI in normal fat participants, and between MPA and ΔFMI in both fat groups, when adjusted for baseline FMI (P ≤ 0.024)., Conclusions: Baseline PAEE and its subcomponents were positively associated with small and unlikely clinically relevant increases in ΔFMI. These counter-intuitive findings may be explained by behavioural changes during the course of study follow-up., (© 2015 The Authors. Pediatric Obesity published by John Wiley & Sons Ltd on behalf of World Obesity.)