Total and temporal patterning of physical activity in adolescents and associations with mental wellbeing.

BACKGROUND: There is limited understanding of the extent to which differences in physical activity across the day and week may be associated with mental wellbeing. Such an understanding is needed for better targeting of interventions. In this study, we describe total and temporal patterning of physical activity across the week in adolescents (age 13-14y) and assess their prospective associations with mental wellbeing. METHODS: 1,983 13-14-year-old adolescent participants based in Cambridgeshire and Essex, recruited between 2016 and 2017 into the Get Others Active Trial provided data at baseline and 4 months. Physical activity was measured at baseline using wrist-worn accelerometers across different time segments (whole week, weekday schooltime, weekday out of school, and weekend), and operationalized as average movement-related acceleration for each time segment. Mental Wellbeing at baseline and 4 months was measured using the Warwick Edinburgh MentalWellbeing Scale. Associations between physical activity across different time segments (whole week, weekday schooltime, weekday out of school, and weekend) and mental wellbeing at 4 months were investigated using sex-stratified multi-level regression models, adjusted for covariates, and both adjusted and unadjusted for baseline mental wellbeing. RESULTS: Our analyses found positive associations between physical activity and mental wellbeing at 4 months, unadjusted for baseline wellbeing. Among girls, positive associations were shown when considering physical activity across the whole week 0.07 (95% CI, 0.03-0.12), and across all separate time periods studied: weekday schooltime 0.07 (95% CI, 0.02-0.11), weekday out-of-school time 0.07 (95% CI, 0.03-0.12), and weekend 0.07 (95% CI, 0.02-0.11). For boys, similar associations were observed for activity across the week 0.07 (95% CI, 0.03-0.11), during weekday schooltime 0.08 (95% CI, 0.04-0.12), and weekday out-of-school time 0.07 (95% CI, 0.03-0.11), but not the weekend 0.01 (95% CI, -0.03-0.05). For both girls and boys, associations were attenuated below significance after adjusting for baseline wellbeing. CONCLUSIONS: This longitudinal analysis showed positive associations between physical activity and later mental wellbeing in both male and female adolescents across most time segments. Higher physical activity throughout the week may be associated with better mental wellbeing in the adolescent population. Further research is required to understand determinants of change in wellbeing over time. TRIAL REGISTRATION: Registration Number: ISRCTN31583496. Registered: 18/02/2014.

Recruitment and retention into longitudinal health research from an adolescent perspective: a qualitative study.

BACKGROUND: High quality longitudinal studies investigating changes in health behaviours over the transition into early adulthood are critical. However, recruiting and retaining adolescents is challenging. This study explored adolescents' perspectives of signing up to and continuing involvement in a hypothetical longitudinal health research study. METHODS: Forty-eight individuals (15-20y) participated in nine in-person focus groups about recruitment and retention in research. Participants were (a) school students in the last year of compulsory school (Year 11, 15-16y), (b) school/college students in Sixth Form (Year 13, 17-18y), (c) Further Education students studying after secondary education, but not higher education (16-18y) and (d) young adults not in education, employment, or training (18-20y) across England. Thematic analysis resulted in seven themes. RESULTS: Driving factors for sign-up included social connection e.g., joining with peer groups, personalised feedback, and incentives, primarily financial. Key barriers were lack of interest, the perception of commitment, and timing of recruitment. Young people preferred recruitment processes via social media with messages tailored to their motivations, monthly data collection of maximally 20-30 min, and hybrid data collection with some in-person contact with a consistent, non-judgemental researcher. The provision of autonomy, choice, and financial incentives were perceived to promote retention. CONCLUSIONS: Adolescent recruitment and retention strategies need to align with contemporary interests and motivations. Studies should involve adolescents early to develop a planned, systematic approach to participant sign-up and follow-up. Effective and ineffective recruitment and retention strategies should be reported as part of study findings. Future research should trial how perceived barriers to study engagement can be overcome.

Associations between mentally-passive and mentally-active sedentary behaviours during adolescence and psychological distress during adulthood.

It is unclear if different types of sedentary behaviour during the adolescence are differentially associated with psychological distress during adolescence and adulthood. It is also unknown what may mediate this potential proposed association. The current study aimed to analyse the association of mentally-active and mentally-passive sedentary behaviours during adolescence (16y) with subsequent psychological distress during adulthood (42y), and to examine the role of potential mediators (42y). Data from the 1970 British Cohort Study was used (N = 1787). At age 16y participants reported time and frequency in mentally-passive (TV-viewing and watching movies) and mentally-active (reading books, doing homework and playing computer games) sedentary behaviours, psychological distress and organized sports participation. At 42y, participants reported cognition (vocabulary test), TV-viewing, psychological distress, self-rated health, body mass index and employment status. Education was collected throughout the follow-up years. Logistic regression and mediation models assessed associations. Multiple imputation using chained equations was used to assess the impact of missing data. Mentally-passive sedentary behaviour in adolescence was a risk factor for psychological distress during adulthood in complete-cases analysis [OR:1.44(95%CI:1.09-1.90)], which was confirmed by the model with multiple imputation. Mentally-active sedentary behaviour at 16y was not associated with psychological distress at 42y. Adult TV-viewing during weekends (24.7%), and self-rated health (19.0%) mediated the association between mentally-passive sedentary behaviour during adolescence and psychological distress during adulthood. However, the mediation was not clear in the models with multiple imputation. Mentally-passive sedentary behaviour during adolescence was associated with elevated psychological distress during adulthood and this association was mediated TV-viewing and self-rated health in adulthood.

Effectiveness and cost-effectiveness of the GoActive intervention to increase physical activity among UK adolescents: A cluster randomised controlled trial.

BACKGROUND: Less than 20% of adolescents globally meet recommended levels of physical activity, and not meeting these recommended levels is associated with social disadvantage and rising disease risk. The determinants of physical activity in adolescents are multilevel and poorly understood, but the school's social environment likely plays an important role. We conducted a cluster randomised controlled trial to assess the effectiveness of a school-based programme (GoActive) to increase moderate-to-vigorous physical activity (MVPA) among adolescents. METHODS AND FINDINGS: Non-fee-paying, co-educational schools including Year 9 students in the UK counties of Cambridgeshire and Essex were eligible for inclusion. Within participating schools (n = 16), all Year 9 students were eligible and invited to participate. Participants were 2,862 13- to 14-year-olds (84% of eligible students). After baseline assessment, schools were computer-randomised, stratified by school-level pupil premium funding (below/above county-specific median) and county (control: 8 schools, 1,319 participants, mean [SD] participants per school n = 165 [62]; intervention: 8 schools, 1,543 participants, n = 193 [43]). Measurement staff were blinded to allocation. The iteratively developed, feasibility-tested 12-week intervention, aligned with self-determination theory, trained older adolescent mentors and in-class peer-leaders to encourage classes to conduct 2 new weekly activities. Students and classes gained points and rewards for engaging in any activity in or out of school. The primary outcome was average daily minutes of accelerometer-assessed MVPA at 10-month follow-up; a mixed-methods process evaluation evaluated implementation. Of 2,862 recruited participants (52.1% male), 2,167 (76%) attended 10-month follow-up measurements; we analysed the primary outcome for 1,874 participants (65.5%). At 10 months, there was a mean (SD) decrease in MVPA of 8.3 (19.3) minutes in the control group and 10.4 (22.7) minutes in the intervention group (baseline-adjusted difference [95% confidence interval] -1.91 minutes [-5.53 to 1.70], p = 0.316). The programme cost £13 per student compared with control; it was not cost-effective. Overall, 62.9% of students and 87.3% of mentors reported that GoActive was fun. Teachers and mentors commented that their roles in programme delivery were unclear. Implementation fidelity was low. The main methodological limitation of this study was the relatively affluent and ethnically homogeneous sample. CONCLUSIONS: In this study, we observed that a rigorously developed school-based intervention was no more effective than standard school practice at preventing declines in adolescent physical activity. Interdisciplinary research is required to understand educational-setting-specific implementation challenges. School leaders and authorities should be realistic about expectations of the effect of school-based physical activity promotion strategies implemented at scale. TRIAL REGISTRATION: ISRCTN Registry ISRCTN31583496.

Cohabiting and becoming a parent: associations with changes in physical activity in the 1970 British cohort study.

BACKGROUND: We examined the association between family-related life events (cohabitation/marriage and becoming a parent) and change in physical activity. METHODS: Longitudinal data (n = 8045) from the 1970 British Cohort Study (30 and 34 years) were included. Life events (beginning cohabitation/marriage and becoming a parent) were reported and coded: 0 no, 1 yes, for each event occurring between 30 and 34 years. Participants reported frequency of participation in leisure-time physical activity at 30 and 34 years (Likert scale: mean change calculated ranging between - 4 and 4). Linear regression models were used to examine the association between life events and physical activity change (comparing individuals experiencing events between 30 and 34 years versus never experiencing the event - excluding participants that experienced previous events - with a final analysis sample of n = 3833 in parenthood analysis; n = 1137 in cohabitation analysis). Interaction terms were used to analyse combined parenthood and cohabitation status. Analyses were adjusted for level of education achieved, ethnicity, country of origin and other life events. ANCOVA was used to examine associations between change in physical activity and child age. RESULTS: Compared to remaining without children, becoming a parent was associated with a greater reduction in physical activity among men [ß:-0.234(95%CI:-0.396 to - 0.072)] but not women [0.126(- 0.048;0.301)]. No associations were found between cohabitation and physical activity. Men who became fathers both while cohabitating [- 0.201(- 0.383;-0.020)] and without cohabiting [- 0.937(- 1.623;-0.250)] experienced greater physical activity declines than those remaining single and without children; the decline was greatest among non-cohabiting fathers. These associations did not differ by child age. CONCLUSIONS: Parenthood appears to differentially impact physical activity for men and women; this association also differs by cohabitation status. Parenthood appears to be most detrimental to physical activity levels among men. Interventions for physical activity could target new or soon-to-be parents, especially fathers. Further analyses with device-measured physical activity data would be valuable to advance understanding of these associations.

Change in physical activity from adolescence to early adulthood: a systematic review and meta-analysis of longitudinal cohort studies.

OBJECTIVE: To systematically review and meta-analyse how physical activity (PA) changes from adolescence to early adulthood (13-30 years). DATA SOURCES: Seven electronic databases were searched: Medline, Embase, PsycInfo, SCOPUS, ASSIA, SPORTdiscus and Web of Science. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: English-language, longitudinal studies (from 01/1980 to 01/2017) assessing PA ≥twice, with the mean age of ≥1 measurement in adolescence (13-19 years) and ≥1 in young adulthood (16-30 years) were included. Where possible, data were converted to moderate-to-vigorous physical activity (MVPA) min/day, and meta-analyses were conducted between weighted mean differences (WMDs) in adolescence and adulthood. Heterogeneity was explored using meta-regression. RESULTS: Of 67 included studies, 49 were eligible for meta-analysis. PA was lower during adulthood than adolescence WMD (95% CI) -5.2 (-7.3 to -3.1) min/day MVPA over mean (SD) 3.4 (2.6) years; heterogeneity was high (I2 >99.0%), and no predictors explained this variation (all p>0.05). When we restricted analysis to studies with data for males (n=29) and females (n=30) separately, there were slightly larger declines in WMD (-6.5 (-10.6 to -2.3) and -5.5 (-8.4 to -2.6) min/day MVPA) (both I2 >99.0%). For studies with accelerometer data (n=9), the decline was -7.4 (-11.6 to -3.1) and longer follow-up indicated more of a decline in WMD (95% CI) (-1.9 (-3.6 to -0.2) min/day MVPA), explaining 27.0% of between-study variation. Of 18 studies not eligible for meta-analysis, nine statistically tested change over time: seven showed a decline and two showed no change. CONCLUSION: PA declines modestly between adolescence and young adulthood. More objective longitudinal PA data (eg, accelerometry) over this transition would be valuable, as would investigating how PA change is associated with contemporaneous social transitions to better inform PA promotion interventions. REGISTRATION: PROSPERO ref:CRD42015030114.

Change in diet in the period from adolescence to early adulthood: a systematic scoping review of longitudinal studies.

BACKGROUND: Late adolescence to early adulthood is a period of lifestyle change and personal development which may influence dietary behaviour. Understanding dietary trajectories across this age range may help in targeting interventions appropriately. This scoping review aimed to assess how longitudinal change in diet is conceptualised and measured between the ages of 13 to 30. METHODS: We searched Medline, SCOPUS, Embase, PsycInfo (EBSCO), ASSIA, Sportdiscus, and Web of Science Core Collection (January 2016) using search terms combining diet outcomes, longitudinal methods and indicators of adolescent or young adult age. Titles and abstracts were screened and data extracted following published guidelines for scoping reviews. Data were analysed to summarize key data on each study and map availability of longitudinal data on macronutrients and food groups by age of study participants. RESULTS: We identified 98 papers reporting on 40 studies. Longitudinal dietary data were available on intake of energy, key macronutrients and several food groups, but this data had significant gaps and limitations. Most studies provided only two or three waves of data within the age range of interest and few studies reported data collected beyond the early twenties. A range of dietary assessment methods were used, with greater use of less comprehensive dietary assessment methods among studies reporting food group intakes. CONCLUSION: Despite limited availability of longitudinal data to aid understanding of dietary trajectories across this age range, this scoping review identified areas with scope for further evidence synthesis. We identified a paucity of longitudinal data continuing into the mid and late twenties, variability in (quality of) dietary assessment methods, and a large variety of macronutrients and food groups studied. Advances in dietary assessment methodologies as well as increased use of social media may facilitate new data collection to further understanding of changing diet across this life stage.

Changes in diet from age 10 to 14 years and prospective associations with school lunch choice.

BACKGROUND: There is limited evidence on how diet changes over the transition from primary to secondary school. In this study we investigated changes in diet from age 10 (2007) to age 14 years (2011) and the contribution of school-time consumption and school lunch choice to such changes. METHODS: The 351 participants with dietary data (4 day food record) available at baseline (age 10 years) and follow-up (age 14 years) were included. Multi-level regression models were fitted for absolute or change in food and nutrient intake, cross-classified by primary and secondary school attended as appropriate, with adjustment for covariates and mis-reporting. RESULTS: From age 10 to age 14 years, children decreased energy intake from sugars (-2.6% energy (%E)) (standard error (SE) 0.44) and from saturated fats (-0.54%E (SE 0.18)), decreased fruit (-3.13 g/MJ (SE 1.04)) and vegetables (-1.55 g/MJ (SE 0.46)) consumption and increased sugar sweetened beverage (SSB) (4.66  g/MJ (SE 1.87)) and fries (1.31  g/MJ (SE 0.39)) consumption. Intake of snack foods, SSBs, and fries, but also fruits and vegetables was higher outside school hours. Prospective change from non-school lunch to school lunch, compared to maintaining non-school lunch consumption, was associated with decreased consumption of savoury snacks (-8.32 g/day (SE 2.03)), increased consumption of fries (12.8 g/day (SE 4.01)) and decreased consumption of fruit (-25.16 g/day (SE 11.02)) during school hours. CONCLUSIONS: Changes in diet from age 10 to age 14 years differed within and outside of school hours. Consumption of a school lunch, compared to lunch obtained elsewhere, was associated with negative as well as positive changes in diet, suggesting that any efforts to encourage school lunch take-up need to be accompanied by further efforts to improve school lunch provision to meet nutritional guidelines.

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.

The changing relationship between rainfall and children's physical activity in spring and summer: a longitudinal study.

BACKGROUND: Weather conditions, along with day length, are proposed as the main drivers of the seasonal patterns in children's physical activity (PA), but little is known about how they affect children at different ages. This study examines the relationship between rainfall and PA in a longitudinal cohort of initially 9-10 year-old children in Norfolk, UK. METHODS: Participants were 283 children from the SPEEDY study who wore accelerometers ≤7 days on three occasions in the summer of 2007, 2008 and 2011 at ages 9-10, 10-11, and 13-14y. Daily weather data were obtained for two local weather stations. Relationships between rainfall and PA (moderate-to-vigorous-PA (MVPA; ≥2000) vigorous PA (VPA; ≥4000), counts per minute (cpm)) and sedentary time were assessed in multiple-membership multilevel models. PA was assessed over the whole day, and over parts of the school day; commute time (8 am-9 am and 3 pm-4 pm), lunchtime (12noon-2 pm), and after school (4 pm-9 pm). RESULTS: At ages 9-10 and 10-11y, PA declined with increasing rainfall, with an average of 14.0 (SE 2.9) and 11.4 (3.0) minutes less MVPA on the wettest days (≥1.7 mm rain) compared to dry days respectively. There was no significant trend in MVPA across rainfall categories at age 13-14 years. Between ages 9-10 and 13-14, MVPA decline was largest on dry days (-15.2 (2.7) minutes). These patterns were also apparent during school lunchtime and after school, however they were not seen during school commute times. Similar patterns were seen for other PA intensities. CONCLUSIONS: Increased rainfall is associated with significant decreases in PA among primary school children, but not secondary school children. PA declines most steeply between the ages of 9-10 and 13-14 on dry days. Interventions to increase activity on wet days may be most relevant at primary schools. Our results also highlight the importance of habitualising behavior to make children more resilient both to bad weather, and potentially age-related decline in activity.

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.

Magnitude and determinants of change in objectively-measured physical activity, sedentary time and sleep duration from ages 15 to 17.5y in UK adolescents: the ROOTS study.

BACKGROUND: Self-reported physical activity (PA) and sleep duration (SLP) change markedly throughout adolescence. We sought to quantify changes in objectively-measured PA, sedentary time (ST) and SLP through adolescence, and to investigate baseline body composition and baseline activity levels as determinants of change. METHODS: Individually calibrated combined heart rate and movement sensing was used to estimate PA energy expenditure (PAEE), SLP, daily ST and time in light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous physical activity (MVPA) in 144 adolescents (50% boys) of mean age 15.1(±0.3)y at baseline and 17.5(±0.3)y at follow-up. Changes in PA (ΔPA), ST (ΔST) and SLP (ΔSLP) were calculated as follow-up minus baseline values. Waist circumference (WC) was measured at baseline and follow-up, as was fat mass index (FMI) and fat-free mass index (FFMI) by a pooled estimation method including bio-impedance. Comparison of baseline and follow-up activity was made by mixed-model ANOVA. Linear regression adjusted for baseline demographics, total and weekend hours of monitor wear time and the seasons of activity measurements, was used to investigate baseline body composition as determinants of ΔPA, ΔST and ΔSLP. A further model adjusted for baseline of the outcome assessed baseline activity as a predictor of behaviour change, and investigated associations for baseline body composition independent of the baseline level of the outcome. RESULTS: From baseline to follow-up levels of MPA and VPA declined (p ≤ 0.039). The annual decline in MVPA was equivalent to -4.5 and -3.0 min/d in boys and girls, respectively. Baseline FMI, FFMI and WC were positively associated with ΔLPA and negatively associated with ΔST in boys when adjusted for baseline of the outcome (p ≤ 0.037 for all). SLP increased from baseline to follow-up (p = 0.004) but ΔSLP was not associated with baseline body composition (p ≥ 0.13). For all variables, higher baseline levels were associated with greater declines over time (p ≤ 0.003). CONCLUSIONS: Levels of higher-intensity PA decline from mid-to-late adolescence, whereas the duration of sleep increases. Changes in LPA and ST may be associated with baseline body composition, but the baseline level of the outcome is consistently the strongest predictor of changes in adolescent activity.

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.

Levels and patterns of objectively-measured physical activity volume and intensity distribution in UK adolescents: the ROOTS study.

BACKGROUND: Few studies have quantified levels of habitual physical activity across the entire intensity range. We aimed to describe variability in total and intensity-specific physical activity levels in UK adolescents across gender, socio-demographic, temporal and body composition strata. METHODS: Physical activity energy expenditure and minutes per day (min/d) spent sedentary and in light, moderate, and vigorous intensity physical activity were assessed in 825 adolescents from the ROOTS study (43.5% boys; mean age 15.0 ± 0.30 years), by 4 days of individually calibrated combined heart rate and movement sensing. Measurement days were classified as weekday or weekend and according to the three school terms: summer (April-July), autumn (September-December), and spring (January-March). Gender and age were self-reported and area-level SES determined by postcode data. Body composition was measured by anthropometry and bio-electrical impedance. Variability in physical activity and sedentary time was analysed by linear multilevel modelling, and logistic multilevel regression was used to determine factors associated with physical inactivity (<60 min moderate-to-vigorous intensity physical activity/d). RESULTS: During awake hours (15.8 ± 0.9 hrs/d), adolescents primarily engaged in light intensity physical activity (517 min/d) and sedentary time (364 min/d). Boys were consistently more physically active and less sedentary than girls, but gender differences were smaller at weekends, as activity levels in boys dropped more markedly when transitioning from weekday to weekend. Boys were more sedentary on both weekend days compared to during the week, whereas girls were more sedentary on Sunday but less sedentary on Saturday. In both genders light intensity physical activity was lower in spring, while moderate physical activity was lower in autumn and spring terms, compared to the summer term; sedentary time was also higher in spring than summer term. Adolescents with higher fatness engaged in less vigorous intensity physical activity. Factors associated with increased odds of physical inactivity were female gender, both weekend days in boys, and specifically Sunday in girls. CONCLUSIONS: Physical activity components vary by gender, temporal factors and body composition in UK adolescents. The available data indicate that in adolescence, girls should be the primary targets of interventions designed to increase physical activity levels.

How well do modelled routes to school record the environments children are exposed to? A cross-sectional comparison of GIS-modelled and GPS-measured routes to school.

BACKGROUND: The school journey may make an important contribution to children's physical activity and provide exposure to food and physical activity environments. Typically, Geographic Information Systems (GIS) have been used to model assumed routes to school in studies, but these may differ from those actually chosen. We aimed to identify the characteristics of children and their environments that make the modelled route more or less representative of that actually taken. We compared modelled GIS routes and actual Global Positioning Systems (GPS) measured routes in a free-living sample of children using varying travel modes. METHODS: Participants were 175 13-14 yr old children taking part in the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study who wore GPS units for up to 7 days. Actual routes to/from school were extracted from GPS data, and shortest routes between home and school along a road network were modelled in a GIS. Differences between them were assessed according to length, percentage overlap, and food outlet exposure using multilevel regression models. RESULTS: GIS routes underestimated route length by 21.0% overall, ranging from 6.1% among walkers to 23.2% for bus users. Among pedestrians food outlet exposure was overestimated by GIS routes by 25.4%. Certain characteristics of children and their neighbourhoods that improved the concordance between GIS and GPS route length and overlap were identified. Living in a village raised the odds of increased differences in length (odds ratio (OR) 3.36 (1.32-8.58)), while attending a more urban school raised the odds of increased percentage overlap (OR 3.98 (1.49-10.63)). However none were found for food outlet exposure. Journeys home from school increased the difference between GIS and GPS routes in terms of food outlet exposure, and this measure showed considerable within-person variation. CONCLUSIONS: GIS modelled routes between home and school were not truly representative of accurate GPS measured exposure to obesogenic environments, particularly for pedestrians. While route length may be fairly well described, especially for urban populations, those living close to school, and those travelling by foot, the additional expense of acquiring GPS data seems important when assessing exposure to route environments.

Awareness of physical activity in healthy middle-aged adults: a cross-sectional study of associations with sociodemographic, biological, behavioural, and psychological factors.

BACKGROUND: Interventions to promote physical activity have had limited success. One reason may be that inactive adults are unaware that their level of physical activity is inadequate and do not perceive a need to change their behaviour. We aimed to assess awareness of physical activity, defined as the agreement between self-rated and objective physical activity, and to investigate associations with sociodemographic, biological, behavioural, and psychological factors. METHODS: We conducted an exploratory, cross-sectional analysis of awareness of physical activity using baseline data collected from 453 participants of the Feedback, Awareness and Behaviour study (Cambridgeshire, UK). Self-rated physical activity was measured dichotomously by asking participants if they believed they were achieving the recommended level of physical activity. Responses were compared to objective physical activity, measured using a combined accelerometer and heart rate monitor (Actiheart®). Four awareness groups were created: overestimators, realistic inactives, underestimators, and realistic actives. Logistic regression was used to assess associations between awareness group and potential correlates. RESULTS: The mean (standard deviation) age of participants was 47.0 (6.9) years, 44.4% were male, and 65.1% were overweight (body mass index ≥ 25). Of the 258 (57.0%) who were objectively classified as inactive, 130 (50.4%) misperceived their physical activity by incorrectly stating that they were meeting the guidelines (overestimators). In a multivariable logistic regression model adjusted for age and sex, those with a lower body mass index (Odds Ratio (OR) = 0.95, 95% Confidence Interval (CI) = 0.90 to 1.00), higher physical activity energy expenditure (OR = 1.03, 95% CI = 1.00 to 1.06) and self-reported physical activity (OR = 1.13, 95% CI = 1.07 to 1.19), and lower intention to increase physical activity (OR = 0.69, 95% CI = 0.48 to 0.99) and response efficacy (OR = 0.53, 95% CI = 0.31 to 0.91) were more likely to overestimate their physical activity. CONCLUSIONS: Overestimators have more favourable health characteristics than those who are realistic about their inactivity, and their psychological characteristics suggest that they are less likely to change their behaviour. Personalised feedback about physical activity may be an important first step to behaviour change.

Bedroom media, sedentary time and screen-time in children: a longitudinal analysis.

BACKGROUND: Having electronic media in the bedroom is cross-sectionally associated with greater screen-time in children, but few longitudinal studies exist. The aim of this study was to describe longitudinal patterns of ownership and examine cross-sectional and longitudinal associations of bedroom media with children's sedentary behaviour. METHODS: Data are from the Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) study, collected at 3 time-points: baseline (2007, T0; age 10.3 ± 0.3 years), 1-year (T1y) and 4-year (T4y) follow-up. For each assessment, 1512 (44.9% male), 715 (41.0% male), and 319 (48.3% male) participants provided valid accelerometer data. Outcome variables were accelerometer-assessed sedentary time and self-reported screen-time. The presence of a television or computer in the bedroom was self-reported by participants and a combined bedroom media score calculated as the sum of such items. Cross-sectional and longitudinal associations between bedroom media and each outcome were examined using multi-level linear regression. RESULTS: Bedroom TV ownership fell from 70.9% at T0 to 42.5% at T4y. Having a TV in the bedroom (beta; 95% CI*100, T0: -1.17; -1.88, -0.46. T1y: -1.68; -2.67, -0.70) and combined bedroom media (T0: -0.76; -1.26, -0.27. T1y: -0.79; -1.51, -0.07) were negatively associated with objectively measured weekly sedentary time at T0 and T1y. Having a computer in the bedroom (beta; 95% CI, T0: 0.15; 0.02, 0.29. T4y: 0.35; 0.10, 0.60) and combined bedroom media (T0: 0.09: 0.01, 0.18. T4y: 0.20; 0.05, 0.34) were positively associated with screen-time at T0 and T4y. Relative to participants without a computer throughout the study, children that had a computer in their bedroom at T0 but not at T4y (beta; 95% CI for change in screen-time: -8.02; -12.75, -3.29) reported smaller increases in screen-time. CONCLUSIONS: The bedroom media environment changes with age and exhibits a complex relationship with children's sedentary behaviour. Modifying children's bedroom media environment may impact upon screen-time but appears unlikely to influence overall sedentary time.

Predictors of change differ for moderate and vigorous intensity physical activity and for weekdays and weekends: a longitudinal analysis.

BACKGROUND: Predictors of physical activity (PA) change are rarely investigated separately for different PA intensities and for weekdays/weekends. We investigated whether individual-level predictors of one-year change in objectively-measured physical activity differ for moderate PA (MPA) and vigorous PA (VPA) and for weekends and weekdays. METHODS: Accelerometer-assessed PA (mins) was obtained at baseline and +1 year (n = 875, 41.5% male, Mean ± SD baseline age: 9.8 ± 0.4 years-old). Potential predictors (n = 38) were assessed at baseline from psychological (e.g., self-efficacy), socio-cultural (e.g., parent support) and environmental domains (e.g., land use). Associations between predictors and change in MPA (2000-3999 counts/minute (cpm)) and VPA (≥4000 cpm) separately for weekdays and weekends were studied using multi-level linear regression. Analyses were adjusted for school clustering, sex and baseline PA. RESULTS: Weekend PA declined (MPA decline 4.6 ± 21.8 mins/day; VPA decline: 2.1 ± 20.1 mins/day; both p < 0.001) whereas weekday PA did not significantly change. Higher baseline PA and being a girl were associated with greater PA declines in all four outcomes; remaining predictors differed for MPA and VPA and/or weekdays and weekends. Family logistic support was associated with less of a decline in weekend MPA (CI 95%) 0.15 (0.05, 0.25) and VPA 0.19 (0.09, 0.29), and peer support with less of a decline in weekday MPA 0.18 (0.02, 0.34) and VPA 0.22 (0.06, 0.38). CONCLUSIONS: Results highlight the relevance of investigating predictors of PA change separately for different PA intensities and for weekdays/weekends. In addition to continued focus on school PA promotion, more effort to target interventions during weekends, such as in the family and community appears important. Encouraging peer support to increase weekday PA and targeting parent support for weekend PA may be health promotion priorities.

Individual, socio-cultural and environmental predictors of uptake and maintenance of active commuting in children: longitudinal results from the SPEEDY study.

BACKGROUND: Active commuting is prospectively associated with physical activity in children. Few longitudinal studies have assessed predictors of change in commuting mode. PURPOSE: To investigate the individual, socio-cultural and environmental predictors of uptake and maintenance of active commuting in 10-year-old children. METHODS: Children were recruited in 2007 and followed-up 12 months later. Children self-reported usual travel mode to school. 31 child, parent, socio-cultural and physical environment characteristics were assessed via self-reported and objective methods. Associations with uptake and maintenance of active travel were studied using multi-level multiple logistic regression models in 2012. RESULTS: Of the 912 children (59.1% girls, mean ± SD baseline age 10.2 ± 0.3 yrs) with complete data, 15% changed their travel mode. Those children who lived less than 1 km from school were more likely to take up (OR: 4.73, 95% CI: 1.97, 11.32, p = 0.001) and maintain active commuting (OR: 2.80 95% CI: 0.98, 7.96, p = 0.02). Children whose parents reported it was inconvenient to use the car for school travel were also more likely to take up (OR: 2.04, 95% CI: 1.08, 3.85, p = 0.027) and maintain their active commuting (OR: 5.43 95% CI: 1.95, 15.13, p = 0.001). Lower socio-economic status and higher road safety were also associated with uptake. CONCLUSIONS: Findings from this longitudinal study suggest that reducing the convenience of the car and improving the convenience of active modes as well as improving the safety of routes to school may promote uptake and maintenance of active commuting and the effectiveness of these interventions should be evaluated.