Understanding seasonal variation in physical activity is important for informing public health surveillance and intervention design. The aim of the current study was to describe seasonal variation in ...children's objectively measured physical activity and sedentary time.
Data are from the UK Millennium Cohort Study. Participants were invited to wear an accelerometer for 7 d on five occasions between November 2008 and January 2010. Outcome variables were sedentary time (<100 counts per minute, min·d(-1)) and moderate to vigorous physical activity (MVPA) (>2241 counts per minute, min·d(-1)). The season was characterized using a categorical variable (spring, summer, autumn, or winter) and a continuous function of day of the year. Cross-classified linear regression models were used to estimate the association of each of these constructs with the outcome variables. Modification of the seasonal variation by sex, weight status, urban/rural location, parental income, and day of the week (weekday/weekend) was examined using interaction terms in regression models.
At least one wave of valid accelerometer data was obtained from 704 participants (47% male; baseline age, 7.6 (0.3) yr). MVPA was lower in autumn and winter relative to spring, with the magnitude of this difference varying by weekday/weekend, sex, weight status, urban/rural location, and family income (P for interaction <0.05 in all cases). Total sedentary time was greater in autumn and winter compared with spring; the seasonal effect was stronger during the weekend than during the weekday (P for interaction <0.01).
Lower levels of MVPA and elevated sedentary time support the implementation of intervention programs during autumn and winter. Evidence of greater seasonal variation in weekend behavior and among certain sociodemographic subgroups highlights targets for tailored intervention programs.
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.
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).
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).
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.
Research examining sedentary behaviour as a potentially independent risk factor for chronic disease morbidity and mortality has expanded rapidly in recent years.
We present a narrative overview of ...the sedentary behaviour measurement literature. Subjective and objective methods of measuring sedentary behaviour suitable for use in population-based research with children and adults are examined. The validity and reliability of each method is considered, gaps in the literature specific to each method identified and potential future directions discussed.
To date, subjective approaches to sedentary behaviour measurement, e.g. questionnaires, have focused predominantly on TV viewing or other screen-based behaviours. Typically, such measures demonstrate moderate reliability but slight to moderate validity. Accelerometry is increasingly being used for sedentary behaviour assessments; this approach overcomes some of the limitations of subjective methods, but detection of specific postures and postural changes by this method is somewhat limited. Instruments developed specifically for the assessment of body posture have demonstrated good reliability and validity in the limited research conducted to date. Miniaturization of monitoring devices, interoperability between measurement and communication technologies and advanced analytical approaches are potential avenues for future developments in this field.
High-quality measurement is essential in all elements of sedentary behaviour epidemiology, from determining associations with health outcomes to the development and evaluation of behaviour change interventions. Sedentary behaviour measurement remains relatively under-developed, although new instruments, both objective and subjective, show considerable promise and warrant further testing.
Understanding the determinants of sedentary time during childhood contributes to the development of effective intervention programmes.
To examine family and home-environmental determinants of 1-year ...change in objectively measured sedentary time after-school and at the weekend.
Participants wore accelerometers at baseline and 1 year later. Longitudinal data for after-school and weekend analyses were available for 854 (41.5%male, mean ± SD age 10.2 ± 0.3 years) and 718 (41.8%male, age 10.2 ± 0.3 years) participants. Information on 26 candidate determinants, including socioeconomic status (SES), availability of electronic media and parental rules for sedentary behaviours was self-reported by children or their parents at baseline. Change in the proportion of registered time spent sedentary was used as the outcome variable in multi-level 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.
Children from higher socioeconomic status families exhibited greater increases in after-school (beta; 95% CI for change in % time spent sedentary 1.02; 0.37, 1.66) and weekend (1.42; 0.65, 2.18) sedentary time. Smaller increases in after-school sedentary time were observed in children with more siblings (-1.00; -1.69, -0.30), greater availability of electronic media (-0.81; -1.29, -0.33) and, for boys, more frequent family visits to the park (-1.89; -3.28, -0.51) and family participation in sport (-1.28; -2.54, -0.02). Greater maternal weekend screen-time (0.45; 0.08, 0.83) and, in girls, greater parental restriction on playing outside (0.91; 0.08, 1.74) were associated with larger increases in weekend sedentary time. The analytical sample was younger, more likely to be female, had lower BMI and was of higher SES than the original baseline sample.
Intervention strategies aimed at reducing parents' weekend screen-time, increasing family participation in sports or recreation (boys) and promoting freedom to play outside (girls) may contribute towards preventing the age-related increase in sedentary time.
Summary
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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)).
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.
Abstract 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 10–20 min 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.
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