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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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