Wearable devices can capture unexplored movement patterns such as brief bursts of vigorous intermittent lifestyle physical activity (VILPA) that is embedded into everyday life, rather than being done as leisure time exercise. Here, we examined the association of VILPA with all-cause, cardiovascular disease (CVD) and cancer mortality in 25,241 nonexercisers (mean age 61.8 years, 14,178 women/11,063 men) in the UK Biobank. Over an average follow-up of 6.9 years, during which 852 deaths occurred, VILPA was inversely associated with all three of these outcomes in a near-linear fashion. Compared with participants who engaged in no VILPA, participants who engaged in VILPA at the sample median VILPA frequency of 3 length-standardized bouts per day (lasting 1 or 2 min each) showed a 38%–40% reduction in all-cause and cancer mortality risk and a 48%–49% reduction in CVD mortality risk. Moreover, the sample median VILPA duration of 4.4 min per day was associated with a 26%–30% reduction in all-cause and cancer mortality risk and a 32%–34% reduction in CVD mortality risk. We obtained similar results when repeating the above analyses for vigorous physical activity (VPA) in 62,344 UK Biobank participants who exercised (1,552 deaths, 35,290 women/27,054 men). These results indicate that small amounts of vigorous nonexercise physical activity are associated with substantially lower mortality. VILPA in nonexercisers appears to elicit similar effects to VPA in exercisers, suggesting that VILPA may be a suitable physical activity target, especially in people not able or willing to exercise.

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.

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. 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. 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. 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. Australian National Health, Medical Research Council, and Wellcome Trust.

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. 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. 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. 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. Hierarchy of favourable movement behaviours across the 24 h day suggests more time spent in moderate-vigorous physical activity and less time spent sedentary are most strongly associated with healthier cardiometabolic outcomes. BMI, body mass index; HbA1c, glycated haemoglobin.

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 (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.