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 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.

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.

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.

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.

Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models.

Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented "one-size fits all" group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0-99.3%) exhibited a significantly higher accuracy than G (80.9-94.7%) and GP classifiers (78.7-94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.

Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.

OBJECTIVES: Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers. DESIGN: Validity study. METHODS: Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ±20 min/day, moderate-physical activity: ±15, vigorous-physical activity: ±2.5, light-physical activity: ±30). RESULTS: The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, -3.5, interquartile range [-15.8, 12.2]; moderate-physical activity, -6.0 [-17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: -0.9 [-3.1, 0.5]; light-physical activity, -1.3 [-28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %. CONCLUSIONS: One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation.

Timing of Moderate to Vigorous Physical Activity, Mortality, Cardiovascular Disease, and Microvascular Disease in Adults With Obesity.

OBJECTIVE: To assess the association between timing of aerobic moderate to vigorous physical activity (MVPA) and risk of cardiovascular disease (CVD), microvascular disease (MVD), and all-cause mortality in adults with obesity and a subset with obesity and type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: Participants included adults with obesity (BMI ≥30 kg/m2) and a subset of those with T2D from the UK Biobank accelerometry substudy. Aerobic MVPA was defined as bouts of MVPA lasting ≥3 continuous minutes. Participants were categorized into morning, afternoon, or evening MVPA based on when they undertook the majority of their aerobic MVPA. The reference group included participants with an average of less than one aerobic MVPA bout per day. Analyses were adjusted for established and potential confounders. RESULTS: The core sample included 29,836 adults with obesity, with a mean age of 62.2 (SD 7.7) years. Over a mean follow-up period of 7.9 (SD 0.8) years, 1,425 deaths, 3,980 CVD events, and 2,162 MVD events occurred. Compared with activity in the reference group, evening MVPA was associated with the lowest risk of mortality (hazard ratio [HR] 0.39; 95% CI 0.27, 0.55), whereas afternoon (HR 0.60; 95% CI 0.51, 0.71) and morning MVPA (HR 0.67; 95% CI 0.56, 0.79) demonstrated significant but weaker associations. Similar patterns were observed for CVD and MVD incidence, with evening MVPA associated with the lowest risk of CVD (HR 0.64; 95% CI 0.54, 0.75) and MVD (HR 0.76; 95% CI 0.63, 0.92). Findings were similar in the T2D subset (n = 2,995). CONCLUSIONS: Aerobic MVPA bouts undertaken in the evening were associated with the lowest risk of mortality, CVD, and MVD. Timing of physical activity may play a role in the future of obesity and T2D management.

Modification of incident cancer risk with changes in metabolic syndrome status: A prospective cohort study in Taiwan.

PURPOSE: We aimed to investigate the effect of altered metabolic syndrome (MetS) status on cancer risk. METHODS: From 2002 through 2008 of the Taiwan MJ cohort, there were 111,616 adults who had repeated MetS measurements performed 3.3 years apart and were followed up for cancer incidence over 11.8 years. Cancer was confirmed based on histopathological reports. RESULTS: Participants were categorized as MetS-free (n = 80,409; no MetS at the first or last health screening), MetS-developed (n = 9833; MetS absence at the first screening and presence at the last screening), MetS-recovered (n = 8958; MetS presence at the first screening and absence at the last screening), and MetS-persisted (n = 12,416; MetS presence at the first and last screenings). We used the Fine-Gray sub-distribution method, with death as competing risk, to determine the association between MetS changes and incident cancer risk. During 1320,796 person-years of follow-up, 5862 individuals developed cancer. The incidence rate of cancer per 1000 person-years was 3.89 in the MetS-free, 5.26 in MetS-developed, 4.61 in MetS-recovered, and 7.33 in MetS-persisted groups (P < .001). Compared with the MetS-free group, MetS-persisted individuals had a higher risk of incident cancer. CONCLUSIONS: Persistent MetS was found to be associated with a high risk of incident cancer.

Occupational physical activity, all-cause, cardiovascular disease, and cancer mortality in 349,248 adults: Prospective and longitudinal analyses of the MJ Cohort.

BACKGROUND: Evidence on the health benefits of occupational physical activity (OPA) is inconclusive. We examined the associations of baseline OPA and OPA changes with all-cause, cardiovascular disease (CVD), and cancer mortality and survival times. METHODS: This study included prospective and longitudinal data from the MJ Cohort, comprising adults over 18 years recruited in 1998-2016, 349,248 adults (177,314 women) with baseline OPA, of whom 105,715 (52,503 women) had 2 OPA measures at 6.3 ± 4.2 years (mean ± SD) apart. Exposures were baseline OPA, OPA changes, and baseline leisure-time physical activity. RESULTS: Over a mean mortality follow-up of 16.2 ± 5.5 years for men and 16.4 ± 5.4 years for women, 11,696 deaths (2033 of CVD and 4631 of cancer causes) in men and 8980 deaths (1475 of CVD and 3689 of cancer causes) in women occurred. Combined moderately heavy/heavy baseline OPA was beneficially associated with all-cause mortality in men (multivariable-adjusted hazard ratio (HR) = 0.93, 95% confidence interval (95%CI): 0.89-0.98 compared to light OPA) and women (HR = 0.86, 95%CI: 0.79-0.93). Over a mean mortality follow-up of 12.5 ± 4.6 years for men and 12.6 ± 4.6 years for women, OPA decreases in men were detrimentally associated (HR = 1.16, 95%CI: 1.01-1.33) with all-cause mortality, while OPA increases in women were beneficially (HR = 0.83, 95%CI: 0.70-0.97) associated with the same outcome. Baseline or changes in OPA showed no associations with CVD or cancer mortality. CONCLUSION: Higher baseline OPA was beneficially associated with all-cause mortality risk in both men and women. Our longitudinal OPA analyses partly confirmed the prospective findings, with some discordance between sex groups.

Modification of the all-cause and cardiovascular disease related mortality risk with changes in the metabolic syndrome status: a population-based prospective cohort study in Taiwan.

AIM: To examine whether changes in metabolic syndrome (MetS) status over time are associated with risk of all-cause and cardiovascular disease related (CVD) mortality. METHODS: This prospective cohort study consisted of 544,749 individuals who participated in a self-funded comprehensive health surveillance program offered by Taiwan MJ Health Management Institution between 1998 and 2016. We included 236,216 adults who had at least two repeated MetS measures 5.9 (4.6) years apart and were followed up for mortality over 18.8 (5.2) years. Participants were classified according to the change in their MetS status as follows: MetS-free at both time points (n = 173,116), MetS-developed (n = 22,607), MetS-recovered (n = 13,616), and MetS-persistent (n = 26,877). Multivariable Cox proportional hazards model was used to determine the association between change in MetS status and risk of all-cause and CVD mortality. RESULTS: Over the 4,436,842 person-years follow-up period, 14,226 participants died, including 2671 (19%) of CVD-related causes. The crude CVD mortality rate per 1000 person-years in the study groups were MetS-free, 0.32; MetS-developed, 0.75; MetS-recovered, 1.22; and MetS-persistent, 2.00 (P < 0.001). Compared to the persistent MetS group, participants in the MetS-recovered group had a lower risk of all-cause (adjusted hazard ratio [aHR], 0.87; 95%CI, 0.82-0.92) and CVD mortality (aHR, 0.81; 95% confidence interval [CI], 0.71-0.93). Development of MetS increased the risk for all-cause (aHR, 1.11; 95%CI, 1.05-1.17) and CVD mortality (aHR, 1.22; 95%CI, 1.07-1.39), compared to the MetS-free group. CONCLUSION: Recovery from MetS was significantly associated with a lower risk of all-cause and CVD mortality, whereas development of MetS was associated with increased risk.

Brief bouts of device-measured intermittent lifestyle physical activity and its association with major adverse cardiovascular events and mortality in people who do not exercise: a prospective cohort study.

BACKGROUND: Guidelines emphasise the health benefits of bouts of physical activity of any duration. However, the associations of intermittent lifestyle physical activity accumulated through non-exercise with mortality and major adverse cardiovascular events (MACE) remain unclear. We aimed to examine the associations of bouts of moderate-to-vigorous intermittent lifestyle physical activity (MV-ILPA) and the proportion of vigorous activity contributing within these bouts with mortality and MACE. METHODS: In this prospective cohort study, we used data from the UK Biobank on adults who do not exercise (ie, those who did not report leisure-time exercise) who had wrist-worn accelerometry data available. Participants were followed up until Nov 30, 2022, with the outcome of interest of all-cause mortality obtained through linkage with NHS Digital of England and Wales, and the NHS Central Register and National Records of Scotland, and MACE obtained from inpatient hospitalisation data provided by the Hospital Episode Statistics for England, the Patient Episode Database for Wales, and the Scottish Morbidity Record for Scotland. MV-ILPA bouts were derived using a two-level Random Forest classifier and grouped as short (<1 min), medium (1 to <3 min; 3 to <5 min), and long (5 to <10 min). We further examined the dose-response relationship of the proportion of vigorous physical activity contributing to the MV-ILPA bout. FINDINGS: Between June 1, 2013, and Dec 23, 2015, 103 684 Biobank participants wore an accelerometer on their wrist. 25 241 adults (mean age 61·8 years [SD 7·6]), of whom 14 178 (56·2%) were women, were included in our analysis of all-cause mortality. During a mean follow-up duration of 7·9 years (SD 0·9), 824 MACE and 1111 deaths occurred. Compared with bouts of less than 1 min, mortality risk was lower for bouts of 1 min to less than 3 min (hazard ratio [HR] 0·66 [0·53-0·81]), 3 min to less than 5 min (HR 0·56 [0·46-0·69]), and 5 to less than 10 min (HR 0·48 [0·39-0·59]). Similarly, compared with bouts of less than 1 min, risk of MACE was lower for bouts of 1 min to less than 3 min (HR 0·71 [0·54-0·93]), 3 min to less than 5 min (0·62 [0·48-0·81]), and 5 min to less than 10 min (0·59 [0·46-0·76]). Short bouts (<1 min) were associated with lower MACE risk only when bouts were comprised of at least 15% vigorous activity. INTERPRETATION: Intermittent non-exercise physical activity was associated with lower mortality and MACE. Our results support the promotion of short intermittent bouts of non-exercise physical activity of moderate-to-vigorous intensity to improve longevity and cardiovascular health among adults who do not habitually exercise in their leisure time. FUNDING: Australian National Health, Medical Research Council, and Wellcome Trust.