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.

IMPORTANCERecommendations for the number of steps per day may be easier to enact for some people than the current time-and intensity-based physical activity guidelines, but the evidence to support steps-based goals is limited.OBJECTIVE To describe the associations of step count and intensity with all-cause mortality and cancer and cardiovascular disease (CVD) incidence and mortality. DESIGN, SETTING, AND PARTICIPANTSThis population-based prospective cohort study used data from the UK Biobank for 2013 to 2015 (median follow-up, 7 years) and included adults 40 to 79 years old in England, Scotland, and Wales. Participants were invited by email to partake in an accelerometer study. Registry-based morbidity and mortality were ascertained through October 2021. Data analyses were performed during March 2022.EXPOSURES Baseline wrist accelerometer-measured daily step count and established cadence-based step intensity measures (steps/min): incidental steps, (<40 steps/min), purposeful steps (Ն40 steps/min); and peak-30 cadence (average steps/min for the 30 highest, but not necessarily consecutive, min/d).MAIN OUTCOMES AND MEASURES All-cause mortality and primary and secondary CVD or cancer mortality and incidence diagnosis. For cancer, analyses were restricted to a composite cancer outcome of 13 sites that have a known association with reduced physical activity. Cox restricted cubic spline regression models were used to assess the dose-response associations. The linear mean rate of change (MRC) in the log-relative hazard ratio for each outcome per 2000 daily step increments were also estimated. RESULTSThe study population of 78 500 individuals (mean [SD] age, 61 [8] years; 43 418 [55%] females; 75 874 [97%] White individuals) was followed for a median of 7 years during which 1325 participants died of cancer and 664 of CVD (total deaths 2179). There were 10 245 incident CVD events and 2813 cancer incident events during the observation period. More daily steps were associated with a lower risk of all-cause (MRC, −0.08; 95% CI, −0.11 to −0.06), CVD (MRC, −0.10; 95% CI, −0.15 to −0.06), and cancer mortality (MRC, 95% CI, −0.11; −0.15 to −0.06) for up to approximately 10 000 steps. Similarly, accruing more daily steps was associated with lower incident disease. Peak-30 cadence was consistently associated with lower risks across all outcomes, beyond the benefit of total daily steps. CONCLUSIONS AND RELEVANCEThe findings of this population-based prospective cohort study of 78 500 individuals suggest that up to 10 000 steps per day may be associated with a lower risk of mortality and cancer and CVD incidence. Steps performed at a higher cadence may be associated with additional risk reduction, particularly for incident disease.

ImportanceStep-based recommendations may be appropriate for dementia-prevention guidelines. However, the association of step count and intensity with dementia incidence is unknown.ObjectiveTo examine the dose-response association between daily step count and intensity and incidence of all-cause dementia among adults in the UK.Design, Setting, and ParticipantsUK Biobank prospective population-based cohort study (February 2013 to December 2015) with 6.9 years of follow-up (data analysis conducted May 2022). A total of 78 430 of 103 684 eligible adults aged 40 to 79 years with valid wrist accelerometer data were included. Registry-based dementia was ascertained through October 2021.ExposuresAccelerometer-derived daily step count, incidental steps (less than 40 steps per minute), purposeful steps (40 steps per minute or more), and peak 30-minute cadence (ie, mean steps per minute recorded for the 30 highest, not necessarily consecutive, minutes in a day).Main Outcomes and MeasuresIncident dementia (fatal and nonfatal), obtained through linkage with inpatient hospitalization or primary care records or recorded as the underlying or contributory cause of death in death registers. Spline Cox regressions were used to assess dose-response associations.ResultsThe study monitored 78 430 adults (mean [SD] age, 61.1 [7.9] years; 35 040 [44.7%] male and 43 390 [55.3%] female; 881 [1.1%] were Asian, 641 [0.8%] were Black, 427 [0.5%] were of mixed race, 75 852 [96.7%] were White, and 629 [0.8%] were of another, unspecified race) over a median (IQR) follow-up of 6.9 (6.4-7.5) years, 866 of whom developed dementia (mean [SD] age, 68.3 [5.6] years; 480 [55.4%] male and 386 [54.6%] female; 5 [0.6%] Asian, 6 [0.7%] Black, 4 [0.4%] mixed race, 821 [97.6%] White, and 6 [0.7%] other). Analyses revealed nonlinear associations between daily steps. The optimal dose (ie, exposure value at which the maximum risk reduction was observed) was 9826 steps (hazard ratio [HR], 0.49; 95% CI, 0.39-0.62) and the minimal dose (ie, exposure value at which the risk reduction was 50% of the observed maximum risk reduction) was 3826 steps (HR, 0.75; 95% CI, 0.67-0.83). The incidental cadence optimal dose was 3677 steps (HR, 0.58; 95% CI, 0.44-0.72); purposeful cadence optimal dose was 6315 steps (HR, 0.43; 95% CI, 0.32-0.58); and peak 30-minute cadence optimal dose was 112 steps per minute (HR, 0.38; 95% CI, 0.24-0.60).Conclusions and RelevanceIn this cohort study, a higher number of steps was associated with lower risk of all-cause dementia. The findings suggest that a dose of just under 10 000 steps per day may be optimally associated with a lower risk of dementia. Steps performed at higher intensity resulted in stronger associations.

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.

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 &gt;0 to &lt;10 min, 1.78% (1.53%, 2.03%) for 10 to &lt;30 min, 1.47% (1.21%, 1.73%) for 30 to &lt;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.

BackgroundCerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA intensity or energy expenditure using activity specific regression equations may be more accurate than standalone regression models. However, the feasibility and validity of ML methods has not been explored in youth with CP. Therefore, the purpose of this study was to develop and test ML models for the automatic identification of PA type in ambulant children with CP.MethodsTwenty two children and adolescents (mean age: 12.8 ± 2.9 y) with CP classified at GMFCS Levels I to III completed 7 activity trials while wearing an ActiGraph GT3X+ accelerometer on the hip and wrist. Trials were categorised as sedentary (SED), standing utilitarian movements (SUM), comfortable walking (CW), and brisk walking (BW). Random forest (RF), support vector machine (SVM), and binary decision tree (BDT) classifiers were trained with features extracted from the vector magnitude (VM) of the raw acceleration signal using 10 s non-overlapping windows. Performance was evaluated using leave-one-subject out cross validation.ResultsSVM (82.0–89.0%) and RF (82.6–88.8%) provided significantly better classification accuracy than BDT (76.1–86.2%). Hip (82.7–85.5%) and wrist (76.1–82.6%) classifiers exhibited comparable prediction accuracy, while the combined hip and wrist (86.2–89.0%) classifiers achieved the best overall performance. For all classifiers, recognition accuracy was excellent for SED (94.1–97.9%), good to excellent for SUM (74.0–96.6%) and brisk walking (71.5–86.0%), and modest for comfortable walking (47.6–70.4%). When comfortable and brisk walking were combined into a single walking class, recognition accuracy ranged from 90.3 to 96.5%.ConclusionsML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0456-x) contains supplementary material, which is available to authorized users.

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Machine learning algorithms such as RF and SVM are useful for predicting PA class from accelerometer data collected in preschool children. Although classifiers trained on hip or wrist data provided acceptable recognition accuracy, the combination of hip and wrist accelerometer delivered better performance.