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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. [ABSTRACT FROM AUTHOR]

This study developed and evaluated machine learning algorithms to predict children's physical activity category from raw accelerometer data collected at the hip. Fifty participants (mean age = 13.9 ± 3.0 y) completed 12 activity trials that were categorized into 5 categories: sedentary (SED), light household activities and games (LHHAG), moderate-vigorous games and sports (MVGS), walking (WALK), and running (RUN). Random Forest (RF) and Logistic Regression (LR) classifiers were trained with features extracted from the vector magnitude using 10 s non-overlapping windows. Classification accuracy was evaluated using leave-one-subject-out cross validation. Overall accuracy for the RF and LR classifiers was 95.7% and 94.3%, respectively. Classification accuracy was excellent for SED (96.3%-98.1%), LHHAG (92.3%-95.2%), WALK (94.5%-97.1%), RUN (99.5%-99.6%); and MVGS (87.5%-92.7%). The results indicate that classifiers trained on features in the raw acceleration from the hip can be used for activity recognition in young people.

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. [ABSTRACT FROM AUTHOR]

Background: For a health behavior intervention to be sustainable within preschool centers, the intervention should be implemented by classroom teachers. Unfortunately, teachers are constrained by demands such as meeting early childhood education standards. Therefore, the purpose of this pilot study was to examine the feasibility and preliminary efficacy of integrating a health behavior intervention into early education learning standards on physical activity (PA), diet, and sleep (PADS) behavior of preschoolers. Methods: Two preschool centers were randomized to either the PADS (children, n = 60) or the control (CON; children, n = 54) group. The PADS intervention consisted of PADS lesson plans and activities embedded into Massachusetts early learning standards and were implemented for 4 days per week for 12 weeks. The CON preschool participated in their usual curriculum. PA was assessed using accelerometers for 7 consecutive days at baseline, 6 weeks, and 12 weeks. Other outcome variables were assessed with parental surveys at baseline and 12 weeks. Results: Significant group by time interactions were observed for moderate to vigorous PA (percentage of time) during the preschool day (PADS: baseline = 10.6% (4.2%), 12 wk = 13.2% (2.3%); CON: baseline = 12.4% (3.9%), 12 wk = 11.2% (3.6%); P =.02). Conclusion: This pilot study provides preliminary evidence that integrating health behaviors into learning standards is feasible and potentially an effective way for increasing preschoolers' PA levels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Vigorous intermittent lifestyle physical activity (VILPA) are bursts of incidental vigorous activity that occur during day-to-day activities outside of the exercise-domain. VILPA has shown promise in lowering risk of mortality and chronic disease. However, there is an absence of an empirically derived definition. Using physiological and effort-based metrics commonly used to define vigorous intensity, we investigated the minimum time needed to elicit physiological and perceived exertion responses to standardised activities of daily living., Methods: Seventy adults (Age = 58.0 ± 9.6y; 35 female) completed 9 VILPA activities of daily living in a randomised order, that included: fast walking, fast incline walking, stair climbing, stationary cycling, and carrying external weight equal to 5% and 10% of body weight. Metabolic rate (by continuous indirect calorimetry), heart rate (telemetry) and perceived effort (Borg Scale) were measured during exercise. Time to reach VILPA was assessed using %VO 2 max, %HRmax, and rating of perceived exertion thresholds., Results: The mean time to elicit VILPA ranged from 65-95 seconds (mean ± sd = 76.7 ± 3.8 seconds) for %VO 2 max, 68 to 105 seconds (mean ± sd = 82.8 ± 6.8 seconds) for %HRmax, and 20 to 60 seconds (mean ± sd = 44.6 ± 6.7 seconds) for rating of perceived exertion. For each of the three indices, there was no difference in the time to elicit VILPA responses by sex or age (p > 0.08), and times were also consistent between activities of daily living tasks. For example, for females and males, the average time to elicit vigorous responses while walking on a flat surface was 85.8 (±16.9) and 80 (±13.9) seconds, respectively, and for stair climbing while carrying 10% of body weight the duration was 78.4 (±17.6) and 76.9 (±17.7) seconds., Conclusions: When participants undertook activities of daily living, VILPA elicited a physiological response at an average of 77-83 seconds for %VO 2 max and %HRmax, and 45 seconds for perceived exertion. The absence of a difference in the time to reach VILPA between sex and age suggests that a consistent behavioural VILPA translation can be used in interventions and population-based studies designed to assess the health effects of incidental physical activity., Competing Interests: Conflict of Interest and Funding Source: The authors have no conflicts to disclose. Funding for this study came from the National Health and Medical Research Council Ideas Grant (APP 1180812)., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.)