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.

Joint associations of diet and physical activity with incident type 2 diabetes and hypertension: an analysis of 144,288 UK Biobank participants.

BACKGROUND: Studies examining the joint associations of lifestyle exposures can reveal novel synergistic and joint effects, but no study has examined the joint association of diet and physical activity (PA) with type 2 diabetes (T2D) and hypertension. The aim of this study is to examine the joint associations of PA and diet with incidence of type T2D and hypertension, as a combined outcome and separately in a large sample of UK adults. METHODS: This prospective cohort study included 144,288 UK Biobank participants aged 40-69. Moderate to vigorous PA (MVPA) was measured using the International Physical Activity Questionnaire and a wrist accelerometer. We categorised PA and diet indicators (diet quality score (DQS) and energy intake (EI)) based on tertiles and derived joint PA and diet variables. Outcome was major cardiometabolic disease incidence (combination of T2D and hypertension). RESULTS: A total of 14,003(7.1%) participants developed T2D, 28,075(19.2%) developed hypertension, and 30,529(21.2%) developed T2D or hypertension over a mean follow-up of 10.9(3.7) years. Participants with middle and high self-reported MVPA levels had lower risk of major cardiometabolic disease regardless of diet, e.g. among high DQS group, hazard ratios in middle and high MVPA group were 0.90 (95%CI:0.86-0.94), and 0.88(95%CI:0.84-0.92), respectively. Participants with jointly high device-measured MVPA and high DQS levels had lower major cardiometabolic disease risk (HR: 0.84, 95%CI:0.71-0.99). The equivalent joint device-measured MVPA and EI exposure analyses showed no clear pattern of associations with the outcomes. CONCLUSION: Higher PA is an important component in cardiometabolic disease prevention across all diet quality and total EI groups. The observed lack of association between diet health outcomes may stem from a lower DQS.

Contribution of Nutritional, Lifestyle, and Metabolic Risk Factors to Parkinson's Disease.

BACKGROUND: Modifiable risk factors for Parkinson's disease (PD) are poorly known. OBJECTIVES: The aim is to evaluate independent associations of different nutritional components, physical activity, and sedentary behavior and metabolic factors with the risk of PD. METHODS: In this population-based prospective cohort study using the data of the United Kingdom Biobank (from 2006-2010), 502,017 men and women who were free from PD (International Classification of Diseases 10th edition; "G20") at baseline were included. We implemented a Cox proportion hazard's model to evaluate the associations of different levels of physical activity, sitting time, sleep habits, diet quality, alcohol and coffee consumption, smoking, and body mass index with PD risk, adjusting for several confounding variables. RESULTS: During a median follow-up of 12.8 years, lifestyle factors including vigorous physical activity (hazard ration [HR] = 0.84; 95% confidence interval [CI], 0.75-0.94), low-to-moderate sitting time (HR = 0.89; 95% CI, 0.81-0.97), and high sleep quality (HR = 0.89; 95% CI, 0.80-0.99) were associated with a reduced risk of PD. Small amounts of coffee (HR = 0.88; 95% CI, 0.82-0.95), red meat (HR = 0.86; 95% CI, 0.76-0.97), and current smoking (HR = 0.65; 95% CI, 0.56-0.75) were also associated with a lower risk of PD, whereas alcohol intake (HR = 1.29; 95% CI, 1.06-1.56) with higher PD risk. Secondary analysis, including metabolic risk factors, confirmed these findings and highlighted the potential protective effect of plasma vitamin D and uric acid, but of low-density lipoprotein-cholesterol, triglycerides, and C-reactive protein as well. CONCLUSIONS: Vigorous physical activity, reduced sitting time, good sleep quality together with small coffee intake and vitamin D supplementation are potentially neuroprotective lifestyle interventions for the prevention of PD. © 2024 International Parkinson and Movement Disorder Society.

Device-measured sedentary time and intensity-specific physical activity in relation to all-cause and cardiovascular disease mortality: the UK Biobank cohort study.

BACKGROUND AND AIMS: Understanding the amounts of intensity-specific movement needed to attenuate the association between sedentary time and mortality may help to inform personalized prescription and behavioral counselling. Herein, we examined the joint associations of sedentary time and intensity-specific physical activity with all-cause and cardiovascular disease (CVD) mortality. METHODS: Prospective cohort study including 73,729 adults from the UK Biobank who wore an Axivity AX3 accelerometer on their dominant wrist for at least 3 days, being one a weekend day, between June 2013 and December 2015. We considered the median tertile values of sedentary time and physical activity in each intensity band to determine the amount of physical activity needed to attenuate the association between sedentary time and mortality. RESULTS: During a median of 6.9 years of follow-up (628,807 person-years), we documented 1521 deaths, including 388 from CVD. Physical activity of any intensity attenuated the detrimental association of sedentary time with mortality. Overall, at least a median of 6 min/day of vigorous physical activity, 30 min/day of MVPA, 64 min/day of moderate physical activity, or 163 min/day of light physical activity (mutually-adjusted for other intensities) attenuated the association between sedentary time and mortality. High sedentary time was associated with higher risk of CVD mortality only among participants with low MVPA (HR 1.96; 95% CI 1.23 to 3.14). CONCLUSIONS: Different amounts of each physical activity intensity may attenuate the association between high sedentary time and mortality.

The Effect of Using Anchored Wake Time to Derive 24-h Device Measured Circadian Physical Behavior Patterns.

INTRODUCTION: Tailoring physical activity interventions to individual chronotypes and preferences by time of day could promote more effective and sustainable behavior change; however, our understanding of circadian physical behavior patterns is very limited. OBJECTIVE: To characterize and compare 24-h physical behavior patterns expressed relative to clock time (the standard measurement of time-based on a 24-h day) versus wake-up time in a large British cohort age 46. METHODS: Data were analyzed from 4979 participants in the age 46 sweep of the 1970 British Cohort Study who had valid activPAL accelerometer data across ≥4 days. Average steps and upright time (time standing plus time stepping) per 30-min interval were determined for weekdays and weekends, both in clock time and synchronized to individual wake-up times. RESULTS: The mean weekday steps were 9588, and the mean weekend steps were 9354. The mean weekday upright time was 6.6 h, and the mean weekend upright time was 6.4 h. When synchronized to wake-up time, steps peaked 1 h after waking on weekdays and 2.5 h after waking on weekends. Upright time peaked immediately, in the first 30-min window, after waking on both weekdays and weekends. CONCLUSIONS: Aligning accelerometer data to wake-up times revealed distinct peaks in stepping and upright times shortly after waking. Activity built up more gradually across clock time in the mornings, especially on weekends. Synchronizing against wake-up times highlighted the importance of circadian rhythms and personal schedules in understanding population 24-h physical behavior patterns, and this may have important implications for promoting more effective and sustainable behavior change.

Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East.

BACKGROUND: Physical behaviors such physical activity, sedentary behavior, and sleep are associated with mortality, but there is a lack of epidemiological data and knowledge using device-measured physical behaviors. PURPOSE: To assess the feasibility of baseline data collection using the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) protocols in the specific context of Saudi Arabia. ProPASS is a recently developed global platform for collaborative research that aims to harmonize retrospective and prospective data on device-measured behaviors and health. Using ProPASS methods for collecting data to perform such studies in Saudi Arabia will provide standardized data from underrepresented countries. METHOD: This study explored the feasibility of baseline data collection in Saudi Arabia between November and December 2022 with a target recruitment of 50 participants aged ≥ 30 years. Established ProPASS methods were used to measure anthropometrics, measure blood pressure, collect blood samples, carry out physical function test, and measure health status and context of physical behaviors using questionnaires. The ActivPal™ device was used to assess physical behaviors and the participants were asked to attend two sessions at (LHRC). The feasibility of the current study was assessed by evaluating recruitment capability, acceptability, suitability of study procedures, and resources and abilities to manage and implement the study. Exit interviews were conducted with all participants. RESULT: A total of 75 participants expressed an interest in the study, out of whom 54 initially agreed to participate. Ultimately, 48 participants were recruited in the study (recruitment rate: 64%). The study completion rate was 87.5% of the recruited participants; 95% participants were satisfied with their participation in the study and 90% reported no negative feelings related to participating in the study. One participant reported experiencing moderate skin irritation related to placement of the accelerometer. Additionally, 96% of participants expressed their willingness to participate in the study again. CONCLUSION: Based on successful methodology, data collection results, and participants' acceptability, the ProPASS protocols are feasible to administer in Saudi Arabia. These findings are promising for establishing a prospective cohort in Saudi Arabia.

Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels? A device-based cohort study.

OBJECTIVES: This study aims to examine the associations of daily step count with all-cause mortality and incident cardiovascular disease (CVD) by sedentary time levels and to determine if the minimal and optimal number of daily steps is modified by high sedentary time. METHODS: Using data from the UK Biobank, this was a prospective dose-response analysis of total daily steps across low (<10.5 hours/day) and high (≥10.5 hours/day) sedentary time (as defined by the inflection point of the adjusted absolute risk of sedentary time with the two outcomes). Mortality and incident CVD was ascertained through 31 October 2021. RESULTS: Among 72 174 participants (age=61.1±7.8 years), 1633 deaths and 6190 CVD events occurred over 6.9 (±0.8) years of follow-up. Compared with the referent 2200 steps/day (5th percentile), the optimal dose (nadir of the curve) for all-cause mortality ranged between 9000 and 10 500 steps/day for high (HR (95% CI)=0.61 (0.51 to 0.73)) and low (0.69 (0.52 to 0.92)) sedentary time. For incident CVD, there was a subtle gradient of association by sedentary time level with the lowest risk observed at approximately 9700 steps/day for high (0.79 (0.72 to 0.86)) and low (0.71 (0.61 to 0.83)) sedentary time. The minimal dose (steps/day associated with 50% of the optimal dose) of daily steps was between 4000 and 4500 steps/day across sedentary time groups for all-cause mortality and incident CVD. CONCLUSIONS: Any amount of daily steps above the referent 2200 steps/day was associated with lower mortality and incident CVD risk, for low and high sedentary time. Accruing 9000-10 500 steps/day was associated with the lowest mortality risk independent of sedentary time. For a roughly equivalent number of steps/day, the risk of incident CVD was lower for low sedentary time compared with high sedentary time.

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).

Associations of changes in physical activity and diet with incident obesity and changes in adiposity: Longitudinal findings from the UK Biobank.

We examined the association of changes in physical activity and diet with obesity development and changes in body fat percentage, body mass index, and waist circumference. 31,344 adults without obesity at baseline (age = 56.0 ± 7.5 years; female = 49.1%) from the UK Biobank were included. Physical activity was categorised based on public health guidelines as: inactive; insufficient; and sufficient. Diet category was assigned based on an established composited score that included consumption of fruits, vegetables, fish, red meat (unprocessed), and processed meat. Diet was categorised as: poor; reasonable; and good. Physical activity and diet changes were categorised based on changes in category: worsened; stable; increased (physical activity)/improved (diet). During a mean follow up of 6.8 (SD = ±2.3) years, 1354 (4.3%) participants developed obesity. Compared to stable physical activity-diet, increasing physical activity was associated with the lowest obesity odds, across diet changes (e.g., OR [95%CI]: diet worsened (0.89 [0.69, 1.15]); diet improved (0.65 [0.48, 0.89])). Increasing physical activity with improved diet was associated with the largest difference in body fat percentage (ß:-0.62 [-0.82, -0.41]), body mass index (-0.37 [-0.47, -0.28]), and waist circumference (-1.21 [-1.63, -0.79]). Excluding adults with a history of smoking, or major illness, lowered obesity odds among participants with increased physical activity by an additional 11%-21%. In those who decreased physical activity obesity was attenuated when combined with diet improvement. Improvements in physical activity or diet mutually attenuated the deleterious associations of the other behaviour's deterioration. In most analyses, increases in physical activity conferred consistent positive associations against the development of obesity, across dietary change groups.

Association between device-measured stepping behaviors and cardiometabolic health markers in middle-aged women: The Australian Longitudinal Study on Women's Health.

The associations between different types and contexts of stepping behaviors and cardiometabolic (CM) health markers are unclear. This study aimed to examine the associations of daily total, walking, stair, incidental and purposeful steps with cardiometabolic risk. A total of 943 women (mean age ± SD = 44.1 ± 1.6 years) from the Australian Longitudinal Study on Women's Health (ALSWH) were included in this cross-sectional study. Daily total, walking, stair, incidental, and purposeful steps were measured using thigh-worn accelerometry. Outcomes comprised of CM markers of adiposity, blood pressure, resting heart rate, lipids, glycaemia, and the composite CM score. We used generalized linear modeling and multiple linear regression to assess the associations. We observed that all stepping behaviors were beneficial to CM health, for example, compared to the lowest quartile (Q1), the change of the composite CM score across low to high quartile of purposeful steps was -0.12 (Q2, 95% CI: -0.41, 0.17), -0.16 (Q3, -0.46, 0.14), and -0.36 (Q4, -0.66, -0.05). Stair steps showed linear associations with blood pressure and adiposity biomarkers, for example, the change of quartile of waist circumference was -1.45 cm (Q2, -4.35, 1.44), -3.56 cm (Q3, -6.52, -0.60), and -7.08 cm (Q4, -10.31, -3.86). Peak 30-min walking intensity showed independent association with adiposity biomarkers (p linear < 0.001 and p = 0.002 for waist circumference and BMI, respectively). Our study showed that all stepping forms were beneficial to CM health. Higher stair steps and peak 30-min walking cadence were associated with a steep decline of adiposity biomarkers. Purposeful steps showed more consistent associations with CM biomarkers than incidental steps.

Do associations of physical activity and sedentary behaviour with cardiovascular disease and mortality differ across socioeconomic groups? A prospective analysis of device-measured and self-reported UK Biobank data.

OBJECTIVE: To examine if individual-level and area-level socioeconomic status (SES) modifies the association of moderate-to-vigorous physical activity (MVPA), domain-specific physical activity and sedentary behaviour with all-cause mortality (ACM) and incident cardiovascular disease (CVD). METHODS: We used self-reported (International Physical Activity Questionnaire short form) and accelerometer-measured physical activity and sedentary behaviour data from the UK Biobank. We created an individual-level composite SES index using latent class analysis of household income, education and employment status. The Townsend Index was the measure of area-level SES. Cox proportional hazards regression models stratified across SES were used. RESULTS: In 328 228 participants (mean age 55.9 (SD 8.1) years, 45% men) with an average follow-up of 12.1 (1.4) years, 18 033 deaths and 98 922 incident CVD events occurred. We found an increased ACM risk of low physical activity and high sedentary behaviour and an increased incident CVD risk of low accelerometer-measured moderate-to-vigorous physical activity (ACCEL_MVPA) and high sitting time. We observed statistically significant interactions for all exposures in ACM analyses by individual-level SES (p<0.05) but only for screen time in area-level SES-ACM analysis (p<0.001). Compared with high self-reported moderate-to-vigorous physical activity (IPAQ_MVPA), adjusted ACM HRs for low IPAQ_MVPA were 1.14 (95% CI 1.05 to .25), 1.15 (95% CI 1.06 to 1.24) and 1.22 (95% CI 1.13 to 1.31) in high, medium and low individual-level SES, respectively. There were higher detrimental associations of low ACCEL_MVPA with decreasing area-level SES for both outcomes and of high screen time with ACM in low area-level SES. CONCLUSION: We found modest evidence suggesting that the detrimental associations of low MVPA and high screen time with ACM and incident CVD are accentuated in low SES groups.

Vigorous physical activity, incident heart disease, and cancer: how little is enough?

AIMS: Vigorous physical activity (VPA) is a time-efficient way to achieve recommended physical activity levels. There is a very limited understanding of the minimal and optimal amounts of vigorous physical activity in relation to mortality and disease incidence. METHODS AND RESULTS: A prospective study in 71 893 adults [median age (IQR): 62.5 years (55.3, 67.7); 55.9% female] from the UK Biobank cohort with wrist-worn accelerometry. VPA volume (min/week) and frequency of short VPA bouts (≤2 min) were measured. The dose-response associations of VPA volume and frequency with mortality [all-cause, cardiovascular disease (CVD) and cancer], and CVD and cancer incidence were examined after excluding events occurring in the first year. During a mean post-landmark point follow-up of 5.9 years (SD ± 0.8), the adjusted 5-year absolute mortality risk was 4.17% (95% confidence interval: 3.19%, 5.13%) for no VPA, 2.12% (1.81%, 2.44%) for >0 to <10 min, 1.78% (1.53%, 2.03%) for 10 to <30 min, 1.47% (1.21%, 1.73%) for 30 to <60 min, and 1.10% (0.84%, 1.36%) for ≥60 min. The 'optimal dose' (nadir of the curve) was 53.6 (50.5, 56.7) min/week [hazard ratio (HR): 0.64 (0.54, 0.77)] relative to the 5th percentile reference (2.2 min/week). There was an inverse linear dose-response association of VPA with CVD mortality. The 'minimal' volume dose (50% of the optimal dose) was ∼15 (14.3, 16.3) min/week for all-cause [HR: 0.82 (0.75, 0.89)] and cancer [HR: 0.84 (0.74, 0.95)] mortality, and 19.2 (16.5, 21.9) min/week [HR: 0.60 (0.50, 0.72)] for CVD mortality. These associations were consistent for CVD and cancer incidence. There was an inverse linear association between VPA frequency and CVD mortality. 27 (24, 30) bouts/week was associated with the lowest all-cause mortality [HR: 0.73 (0.62, 0.87)]. CONCLUSION: VPA of 15-20 min/week were associated with a 16-40% lower mortality HR, with further decreases up to 50-57 min/week. These findings suggest reduced health risks may be attainable through relatively modest amounts of VPA accrued in short bouts across the week.

The effects of active classroom breaks on moderate to vigorous physical activity, behaviour and performance in a Northern NSW primary school: A quasi-experimental study.

ISSUE ADDRESSED: Approximately 77% of NSW children aged 5 to 15 years do not meet physical activity guidelines and many spend a considerable amount of time sitting. Active breaks at primary school are feasible, may increase daily moderate to vigorous physical activity (MVPA) and decrease off-task behaviour without adversely affecting cognitive function and learning. METHODS: In this quasi-experimental study, 101 primary school children in six intervention classrooms participated in three 10-minute active breaks per day for six-weeks during class time, while five control classrooms were run as usual (n = 89). Physical activity levels were measured using wrist-worn Actigraph wGT3X-BT accelerometers and analysed using a random forest model. Students' off-task behaviour, wellbeing, cognitive function and maths performance were also measured. School staff completed a brief feedback survey. RESULTS: Children in the intervention group engaged in 15.4 and 10.9 minutes more MVPA per day at 3 and 6 weeks respectively (P < .001). Participation significantly increased the proportion of children who met the Australian 24-Hour Movement Guidelines (P < .001). At pre, middle and end of intervention, 44.4%, 60.8% and 55.1% of intervention children and 46.5%, 45.9% and 45.8% of controls met the guidelines. Significantly fewer students engaged in off-task behaviour in the intervention classes at mid and final weeks of intervention (-1.4 students, P = .003). No significant intervention effects were found for wellbeing, cognitive and maths performance. CONCLUSIONS: Active classroom breaks are an effective way to increase physical activity among primary school children while reducing off-task classroom behaviour. SO WHAT?: Primary school students' health would benefit from active breaks with no detrimental effects on wellbeing, maths and cognitive performance.

Associations of changes in physical activity and discretionary screen time with incident obesity and adiposity changes: longitudinal findings from the UK Biobank.

BACKGROUND: Physical activity (PA) and discretionary screen time (DST; television and computer use during leisure) are both associated with obesity risk, but little longitudinal evidence exists on their combined influence. This study examined the independent and joint associations of changes in PA and DST with incident obesity, body mass index (BMI) and waist circumference (WC). METHODS: We analysed the data of individuals aged 40-69 years from the UK Biobank, a large-scale, population-based prospective cohort study. PA was measured using the International Physical Activity Questionnaire and DST was defined as the total of daily TV viewing and non-occupational computer use. Changes in PA and DST over time were defined using departure from sex-specific baseline tertiles and categorised as worsened (PA decreased/DST increased), maintained, and improved (PA increased/DST decreased). We then used each exposure change to define a joint PA-DST change variable with nine mutually exclusive groups. We used multivariable adjusted mixed-effects linear and Poisson models to examine the independent and joint associations between PA and DST changes with BMI and WC and incident obesity, respectively. Development of a BMI ≥ 30 kg/m2 was defined as incident obesity. RESULTS: Among 30,735 participants, 1,628 (5.3%) developed incident obesity over a mean follow-up of 6.9 (2.2) years. In the independent association analyses, improving PA (Incident Rate Ratio (IRR) 0.46 (0.38-0.56)) was associated with a lower risk of incident obesity than maintaining PA, maintaining DST, or improving DST. Compared to the referent group (both PA and DST worsened), all other combinations of PA and DST changes were associated with lower incident obesity risk in the joint association analyses. We observed substantial beneficial associations in the improved PA groups, regardless of DST change [e.g., DST worsened (IRR 0.31 (0.21-0.44)), maintained (IRR 0.34 (0.25-0.46)), or improved (IRR 0.35 (0.22-0.56)]. The most pronounced decline in BMI and WC was observed when PA was maintained or improved and DST was maintained. CONCLUSION: We found that improved PA had the most pronounced beneficial associations with incident obesity, irrespective of DST changes. Improvements in PA or DST mutually attenuated the deleterious effects of the other behaviour's deterioration.

Changes in physical activity and adiposity with all-cause, cardiovascular disease, and cancer mortality.

BACKGROUND: The relationship between joint changes in physical activity and adiposity with mortality is not well understood. We examined the association of changes in these two established risk factors with all-cause (ACM), cardiovascular disease (CVD), and cancer mortality. METHODS: We used longitudinal data from Taiwan's MJ Cohort, comprising 116,228 general population adults recruited from 1998-2013 with repeated measures 4.6 y (2.5) apart and followed up for mortality for 11.9 y (3.5). Physical activity, body mass index (BMI), waist circumference (WC), and body fat percentage (BF%) groups and changes were based on public health and clinical guidelines. RESULTS: Compared to stable-insufficient physical activity, increasing physical activity from any baseline level was associated with lower ACM (HR [95%CI]): 0.85 [0.74, 0.96]) and CVD mortality (0.72 [0.55, 0.93]) risk. This was approximately equal to meeting physical activity guidelines at both timepoints (eg: 0.71 [0.58, 0.88] for CVD mortality). Compared to stable-overweight/moderate adiposity, decreasing adiposity level attenuated but did not offset mortality risk for all three outcomes (eg: BMI = 0.95 [0.76, 1.16] for CVD mortality). Only maintaining a healthy adiposity level at both timepoints offset mortality risk (BMI = 0.75 [0.61, 0.89]) for CVD mortality). In the joint changes analyses, lower mortality risk was a consequence of increases in physical activity across adiposity change groups (eg: WC decrease = 0.57 [0.48, 0.67]; WC stability = 0.73 [0.66, 0.80], WC increase = 0.83 [0.72, 0.97] for ACM). Decreasing adiposity attenuated the negative associations of decreased physical activity (BF% = 1.13 [0.95, 1.35] for ACM). CONCLUSIONS: We found a lower risk for ACM, CVD, and cancer mortality from increasing physical activity and an attenuation from decreasing adiposity regardless of baseline levels. The beneficial associations of joint changes were primarily driven by physical activity, suggesting lower mortality risk may be more immediate through physical activity improvements compared to adiposity improvements alone.

Lifestyle risk factors and infectious disease mortality, including COVID-19, among middle aged and older adults: Evidence from a community-based cohort study in the United Kingdom.

In this community-based cohort study, we investigated the relationship between combinations of modifiable lifestyle risk factors and infectious disease mortality. Participants were 468,569 men and women (56.5 ± 8.1, 54.6% women) residing in the United Kingdom. Lifestyle indexes included traditional and emerging lifestyle risk factors based on health guidelines and best practice recommendations for: physical activity, sedentary behaviour, sleep quality, diet quality, alcohol consumption, and smoking status. The main outcome was mortality from infectious diseases, including pneumonia, and coronavirus disease 2019 (COVID-19). Meeting public health guidelines or best practice recommendations among combinations of lifestyle risk factors was inversely associated with mortality. Hazard ratios ranged between 0.26 (0.23-0.30) to 0.69 (0.60-0.79) for infectious disease and pneumonia. Among participants with pre-existing cardiovascular disease or cancer, hazard ratios ranged between 0.30 (0.25-0.34) to 0.73 (0.60-0.89). COVID-19 mortality risk ranged between 0.42 (0.28-0.63) to 0.75 (0.49-1.13). We found a beneficial dose-response association with a higher lifestyle index against mortality that was consistent across sex, age, BMI, and socioeconomic status. There was limited evidence of synergistic interactions between most lifestyle behaviour pairs, suggesting that the dose-response relationship among different lifestyle behaviours is not greater than the sum of the risk induced by each behaviour. Improvements in lifestyle risk factors and meeting public health guidelines or best practice recommendations could be used as an ancillary measure to ameliorate infectious disease mortality.

Machine learning to quantify habitual physical activity in children with cerebral palsy.

AIM: To investigate whether activity-monitors and machine learning models could provide accurate information about physical activity performed by children and adolescents with cerebral palsy (CP) who use mobility aids for ambulation. METHOD: Eleven participants (mean age 11y [SD 3y]; six females, five males) classified in Gross Motor Function Classification System (GMFCS) levels III and IV, completed six physical activity trials wearing a tri-axial accelerometer on the wrist, hip, and thigh. Trials included supine rest, upper-limb task, walking, wheelchair propulsion, and cycling. Three supervised learning algorithms (decision tree, support vector machine [SVM], random forest) were trained on features in the raw-acceleration signal. Model-performance was evaluated using leave-one-subject-out cross-validation accuracy. RESULTS: Cross-validation accuracy for the single-placement models ranged from 59% to 79%, with the best performance achieved by the random forest wrist model (79%). Combining features from two or more accelerometer placements significantly improved classification accuracy. The random forest wrist and hip model achieved an overall accuracy of 92%, while the SVM wrist, hip, and thigh model achieved an overall accuracy of 90%. INTERPRETATION: Models trained on features in the raw-acceleration signal may provide accurate recognition of clinically relevant physical activity behaviours in children and adolescents with CP who use mobility aids for ambulation in a controlled setting. WHAT THIS PAPER ADDS: Machine learning may assist clinicians in evaluating the efficacy of surgical and therapy-based interventions. Machine learning may help researchers better understand the short- and long-term benefits of physical activity for children with more severe motor impairments.

Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data.

Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms are implemented in parallel. Non-wear algorithms were based on the standard deviation (SD), the high-pass filtered acceleration, or tilt angle. Rules for differentiating sleep from non-wear consisted of an override rule in which any overlap between non-wear and sleep was deemed non-wear; and a 75% rule in which non-wear periods were deemed sleep if the duration was < 75% of the sleep period. Non-wear algorithms were evaluated in 47 children who wore an ActiGraph GT3X+ accelerometer during school hours for 5 days. Rules for differentiating sleep from non-wear were evaluated in 15 adults who wore a GeneActiv Original accelerometer continuously for 24 hours. Classification accuracy for the non-wear algorithms ranged between 0.86-0.95, with the SD of the vector magnitude providing the best performance. The override rule misclassified 37.1 minutes of sleep as non-wear, while the 75% rule resulted in no misclassification. Non-wear algorithms based on the SD of the acceleration signal can effectively detect non-wear periods, while application of the 75% rule can effectively differentiate sleep from non-wear when examined concurrently.

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children's movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%-86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children's movement behaviors under real-world conditions.