Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease

Associations of Accelerometer-Derived Moderate-to-Vigorous Physical Activity and Atrioventricular Block in a Healthy Elderly Population

BACKGROUND

There is limited information on protective factors related to atrioventricular (AV) block.

OBJECTIVE

This study examines the association between accelerometer-derived moderate-to-vigorous physical activity (MVPA) and AV block in healthy elderly individuals.

METHODS

A total of 23,590 UK Biobank participants ≥60 years involved in a wrist-worn accelerometer study with no history of hypertension, diabetes mellitus, dyslipidemia, and coronary heart disease were analyzed. The associations of MVPA with primary (second- or third-degree AV block) and secondary outcome (third-degree AV block, pacemaker implantation) were evaluated by Cox regression analysis. The associations of MVPA with electrocardiogram parameters were evaluated by linear regression analysis.

RESULTS

The mean age was 63.8 ± 2.8 years, and 57.4% were women. During the median follow-up period of 6.1 years, 115 primary outcome events occurred. As compared with quintile 1 (<89 min/week), those in quintile 4 (280-449 min/week) had a 63% lower incidence of primary outcome (HR: 0.37, 95% CI: 0.19 to 0.73, P=0.004); however, the result was attenuated in quintile 5. This pattern was consistently observed in the relationship between MVPA and third-degree AV block (quintile 4 vs quintile 1: HR: 0.29, 95% CI: 0.11 to 0.74, P=0.010) and pacemaker implantation. MVPA per 150 min/week increase was independently negatively associated with normalized PQ interval (msec) (β: ‒2.13, 95% CI: ‒3.03 to ‒1.24, P<0.001).

CONCLUSIONS

In the healthy elderly population, MVPA was associated with a lower risk of second- or third-degree AV block, which correlates with the reduction of normalized PQ interval. However, excessive MVPA attenuated the results.

Associations of adiposity and device-measured physical activity with cancer incidence: UK Biobank prospective cohort study

BACKGROUND

High adiposity and low physical activity are associated with cancer risk. Whether different amounts and intensities of physical activity can mitigate this association is unclear. We aimed to examine the independent and combined associations of adiposity and device-measured physical activity levels of different intensities with cancer incidence and mortality.

METHODS

This prospective cohort study included data from 70,747 UK Biobank participants (mean age 61.6 ± 7.9 years, 56.4% women) with wrist-worn accelerometer measurements of physical activity and without chronic diseases or mobility limitations. Physical activity exposures included min per week of light (LPA), moderate-to-vigorous (MVPA), and vigorous (VPA) intensity physical activity, along with total weekly volume. Body mass index (BMI) was calculated from anthropometric measurements. Participants were categorized into 9 groups based on joint tertiles of physical activity and BMI categories (normal weight, overweight, and obesity). Secondary analyses included adiposity using bio-impedance and waist circumference measurements. The outcome was incidence and death from cancer retrieved from national registries. Associations between adiposity, physical activity, and cancer hazard were calculated as subdistribution hazard ratios. A secondary analysis focused on cancer types strongly associated with physical activity.

RESULTS

We observed 2625 events (2572 non-fatal and 53 fatal) during a median follow-up of 6.1 years. Compared with the referent (normal weight and high physical activity), overweight and obesity were associated with a 6% to 36% higher cancer hazard across physical activity intensities. However, high MVPA and VPA (approximately 500 min and 32 min per week in the top tertiles, respectively) attenuated the hazard associated with overweight and obesity. Being normal weight was not associated with a higher cancer hazard regardless of physical activity level. The results were similar, although more pronounced, when modeling cancer types strongly associated with physical activity as the outcome.

CONCLUSION

High MVPA and VPA levels may attenuate the association of overweight and obesity with cancer hazard, but maintaining a normal weight seems comparatively more important than physical activity to reduce the hazard. Maintaining a healthy body weight and engaging in physical activity is needed to minimize risk of some cancer types.

Accelerometer-Measured Sedentary Behavior and Risk of Future Cardiovascular Disease

BACKGROUND

Beyond serving as a marker for insufficient physical activity, sedentary behavior may directly affect future cardiovascular (CV) disease risk.

OBJECTIVES

This study sought to examine associations between accelerometer-measured sedentary behavior with risk of specific CV outcomes, including potential relations with moderate to vigorous physical activity (MVPA).

METHODS

Among participants of the UK Biobank prospective cohort study, we fit Cox models adjusted for demographic and lifestyle factors to assess associations between accelerometer-measured daily sedentary time with incident atrial fibrillation (AF), myocardial infarction (MI), heart failure (HF), and CV mortality. We assessed the potential effect of MVPA on associations between sedentary time and CV disease by including MVPA as an adjustment variable, as well as performing subgroup analyses stratified at the guideline-recommended MVPA threshold (ie, ≥150 min/wk). We then performed compositional analyses to estimate the effects of reallocating sedentary time to other activities.

RESULTS

Among 89,530 individuals (age 62 ± 8 years, 56.4% women) undergoing 1 week of accelerometry, median sedentary time was 9.4 h/d (Q1-Q3: 8.2-10.6). In multivariable models, using the second quartile (8.2-9.4 h/d) as a referent, sedentary time in the top quartile (>10.6 h/d) was associated with greater risks of HF (HR: 1.45; 95% CI: 1.28-1.65) and CV mortality (HR: 1.62; 95% CI: 1.34-1.96), with an inflection of risk at 10.6 h/d. Higher sedentary time was also associated with greater risks of incident AF (HR: 1.11; 95% CI: 1.01-1.21) and MI (HR: 1.15; 95% CI: 1.00-1.32), with an approximately linear relation. Associations with HF and CV mortality persisted among individuals meeting guideline-recommended MVPA levels. Among individuals with >10.6 h/d of sedentary time, reallocating sedentary behavior to other activities substantially reduced the excess CV risk conferred by sedentary behavior (eg, 30-minute decrease in sedentary time for HF: HR: 0.93; 95% CI: 0.90-0.96), even among individuals meeting guideline-recommended MVPA (HR: 0.93; 95% CI: 0.87-0.99).

CONCLUSIONS

Sedentary behavior is broadly associated with future adverse CV outcomes, with particularly prominent effects on HF and CV mortality, where risk inflected at approximately 10.6 h/d. Although guideline-adherent MVPA partially mitigates excess risk, optimizing sedentary behavior appears to be important even among physically active individuals.

Optimal Instruments for Measurement of Dietary Intake, Physical Activity, and Sleep Among Adults in Population-Based Studies: Report of a National Heart, Lung, and Blood Institute Workshop

The National Heart, Lung, and Blood Institute convened a virtual workshop in September 2022 to discuss "Optimal Instruments for Measurement of Diet, Physical Activity, and Sleep." This report summarizes the proceedings, identifying current research gaps and future directions for measuring different lifestyle behaviors in adult population-based studies. Key discussions centered on integrating report-based methods, like questionnaires, with device-based assessments, including wearables and physiological measures such as biomarkers and omics to enhance self-reported metrics and better understand the underlying biologic mechanisms of chronic diseases. Emphasis was placed on the need for data harmonization, including the adoption of standard terminology, reproducible metrics, and accessible raw data, to enhance the analysis through artificial intelligence and machine learning techniques. The workshop highlighted the importance of standardizing procedures for integrated behavioral phenotypes using time-series data. These efforts aim to refine data accuracy and comparability across studies and populations, thereby advancing our understanding of lifestyle behaviors and their impact on chronic disease outcomes over the life course.

Association between Accelerometer-Measured Irregular Sleep Duration and Longitudinal Changes in Body Mass Index in Older Adults

Irregular sleep duration may disrupt circadian rhythms and contribute to metabolic, behavioral, and mood changes, potentially increasing the risk for obesity. However, quantitative data on the relationship between sleep duration irregularity and weight change are lacking. In this prospective study, we analyzed data from 10,572 participants (mean age: 63 years) in the UK Biobank who wore accelerometers for a week between 2013-2015 and had two body mass index (BMI; kg/m 2 ) measurements on average 2.5 years apart. Irregular sleep duration was assessed by the within-person standard deviation (SD) of 7-night accelerometer-measured sleep duration. Participants with sleep duration SD >60 minutes versus ≤30 minutes had 0.24 kg/m 2 (95% CI: 0.08, 0.40) higher BMI change (kg/m 2 ), standardized to three-year intervals, and 80% (95% CI: 1.28, 2.52) higher risk for incident obesity, after adjusting for sociodemographic factors, shift work, and baseline BMI or follow-up period (p-trend<0.02 for both). These associations remained consistent after adjusting for lifestyle, comorbidities, and other sleep factors, including sleep duration. Age, sex, baseline BMI, and genetic predisposition to higher BMI (measured with a polygenic risk score) did not appear to modify the association. Since irregular sleep duration is common, trials of interventions targeting sleep irregularity might lead to new public health strategies that tackle obesity.

Replacement of sedentary behavior with various physical activities and the risk of incident depression: a prospective analysis of accelerator-measured and self-reported UK Biobank data

PURPOSE

To examine the dose‒response relationships of sedentary behavior (SB) and physical activities (PAs) with depression, and to explore the effects of replacing SB with PAs on depression risk.

METHODS

The study used data from UK Biobank aged 37 to 73 years. Light physical activity (LPA), moderate-to-vigorous activity (MVPA), sleep duration, and total sedentary behavior (TSB) were measured by accelerometers. Self-reported SB was also adopted when daily screen-sedentary behavior time (SSB) and leisure-sedentary behavior time (LSB) were the focus. Incident depression was obtained from the part of mental and behavioral disorders in the "first occurrence fields" of UK Biobank. A Cox proportional hazard model and isotemporal substitution model were performed to explore the associations of LPA, MVPA, TSB, LSB, SSB, and sleep on depression and the effects of replacing SB time with equal PA time.

RESULTS

Highest levels of MVPA (HR = 0.58, 95%CI: 0.50-0.68) were associated with decreased depression risk compared with the lowest level (Q1). Longer SSB time (HR = 1.18, 95%CI: 1.06-1.32), LSB time (HR = 1.19, 95%CI: 1.07-1.32), and TSB time (HR = 1.17, 95%CI: 1.00-1.38) could increase depression risk significantly. Replacing 1h/day TSB, SSB, and LSB with MVPA brought the greatest risk reductions [31% (HR = 0.69, 95%CI: 0.62-0.77), 30% (HR = 0.70, 95%CI: 0.65-0.77), and 29% (HR = 0.71, 95%CI: 0.65-0.77)]. Under the same conditions, the effects of LPA replacement were also significant, but weaker than those of MVPA. Subgroup analyses showed that replacing 1h/d TSB with LPA could significantly decrease the depression risk for the females, but not for the males.

CONCLUSION

Large benefits for reducing the risk of incident depression could be attained by replacing a period of TSB, SSB, or LSB with equal PA time, especially for MVPA. Regular PA and less SB were recommended for improving mental health.

Accelerometer-Derived Physical Activity, Sedentary Behavior, and the Risk of Depression and Anxiety in Middle-aged and Older Adults: A Prospective Cohort Study of 71,556 UK Biobank Participants

OBJECTIVES

To investigate the associations between accelerometer-measured physical activity and sedentary behavior with depression and anxiety.

METHODS

We used accelerometer data from the UK biobank. Time spent in moderate-to-vigorous physical activity (MVPA) was classified into four categories: very-low (0-74.9 min/week), low (75-149.9 min/week), moderate (150-299.9 min/week), and high (≥300 min/week). Associations were examined using Cox proportional hazard regression models. Restricted cubic splines were used to evaluate dose-response associations.

RESULTS

A total of 71556 adults (mean [SD] age, 62.11 [7.83] years; 54.5% were female) were included. When stratified by MVPA, 10562 participants were in the very-low group (14.8%), 11578 were in the low group (16.2%), 20700 were in the moderate group (28.9%), and 28716 were in the high group (40.1%). Both MVPA and total physical activity showed nonlinear associations with the risk of depression and anxiety. Compared with very-low level MVPA, moderate MVPA might reduce the risk of depression (HR, 0.71; 95% CI, 0.63-0.79) and anxiety (HR, 0.80; 95% CI, 0.71-0.90). High MVPA was associated with a 30% lower risk of depression (HR, 0.70; 95% CI, 0.62-0.78) and anxiety (HR, 0.70; 95% CI, 0.62-0.79). For sedentary behavior, quartile 4 (≥10.60 h/d) was associated with a 19% higher risk of depression (HR, 1.19; 95% CI, 1.05-1.35) compared to quartile 1 (<8.21 h/d).

CONCLUSION

The WHO guideline of 150-300 min/week of MVPA may reduce the risk of depression by 29% and anxiety by 20% compared to less than 75 min/week. Prolonged sedentary behavior was associated with a higher risk of depression.

CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition

Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision. This results in small datasets with a limited number of activities and heterogeneity, lacking the mixed and nuanced movements normally found in free-living scenarios. As such, models trained on laboratory-style datasets may not generalise out of sample. To address this problem, we introduce a new dataset involving wrist-worn accelerometers, wearable cameras, and sleep diaries, enabling data collection for over 24 hours in a free-living setting. The result is CAPTURE-24, a large activity tracker dataset collected in the wild from 151 participants, amounting to 3883 hours of accelerometer data, of which 2562 hours are annotated. CAPTURE-24 is two to three orders of magnitude larger than existing publicly available datasets, which is critical to developing accurate human activity recognition models.

Associations of "Weekend Warrior" Physical Activity With Incident Disease and Cardiometabolic Health

BACKGROUND

Achievement of guideline-recommended levels of physical activity (≥150 minutes of moderate-to-vigorous physical activity per week) is associated with lower risk of adverse cardiovascular events and represents an important public health priority. Although physical activity commonly follows a "weekend warrior" pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 or 2 days rather than spread more evenly across the week (regular), the effects of physical activity pattern across a range of incident diseases, including cardiometabolic conditions, are unknown.

METHODS

We tested associations between physical activity pattern and incidence of 678 conditions in 89 573 participants (62±8 years of age; 56% women) of the UK Biobank prospective cohort study who wore an accelerometer for 1 week between June 2013 and December 2015. Models were adjusted for multiple baseline clinical factors, and P value thresholds were corrected for multiplicity.

RESULTS

When compared to inactive (<150 minutes moderate-to-vigorous physical activity/week), both weekend warrior (267 total associations; 264 [99%] with lower disease risk; hazard ratio [HR] range, 0.35-0.89) and regular activity (209 associations; 205 [98%] with lower disease risk; HR range, 0.41-0.88) were broadly associated with lower risk of incident disease. The strongest associations were observed for cardiometabolic conditions such as incident hypertension (weekend warrior: HR, 0.77 [95% CI, 0.73-0.80]; P=1.2×10-27; regular: HR, 0.72 [95% CI, 0.68-0.77]; P=4.5×10-28), diabetes (weekend warrior: HR, 0.57 [95% CI, 0.51-0.62]; P=3.9×10-32; regular: HR, 0.54 [95% CI, 0.48-0.60]; P=8.7×10-26), obesity (weekend warrior: HR, 0.55 [95% CI, 0.50-0.60]; P=2.4×10-43, regular: HR, 0.44 [95% CI, 0.40-0.50]; P=9.6×10-47), and sleep apnea (weekend warrior: HR, 0.57 [95% CI, 0.48-0.69]; P=1.6×10-9; regular: HR, 0.49 [95% CI, 0.39-0.62]; P=7.4×10-10). When weekend warrior and regular activity were compared directly, there were no conditions for which effects differed significantly. Observations were similar when activity was thresholded at the sample median (≥230.4 minutes of moderate-to-vigorous physical activity/week).

CONCLUSIONS

Achievement of measured physical activity volumes consistent with guideline recommendations is associated with lower risk for >200 diseases, with prominent effects on cardiometabolic conditions. Associations appear similar whether physical activity follows a weekend warrior pattern or is spread more evenly throughout the week.

Reallocation of time to moderate-to-vigorous physical activity and estimated changes in physical fitness among preschoolers: a compositional data analysis

BACKGROUND

Previous research has examined the associations of preschoolers' 24-h movement behaviours, including light and moderate-to-vigorous physical activity (LPA and MVPA), sedentary behaviour (SB), sleep, with physical fitness in isolation, ignoring intrinsically compositional nature of movement data while increasing the risk of collinearity. Thus, this study investigated the associations of preschoolers' 24-h Movement behaviours composition with physical fitness, estimated changes in physical fitness when time was reallocated between movement behaviours composition, and determined whether associations differ between different genders, using compositional data analysis.

METHODS

In the cross-sectional study, a total of 275 preschoolers (3 ~ 6 y) from China were included. SB, LPA and MVPA times were objectively monitored with an ActiGraph GT9X accelerometer for 7 consecutive days. Sleep duration was obtained using parental reports. Physical fitness parameters, including upper and lower limb strength, static balance, speed-agility, and cardiorespiratory fitness (CRF), were determined with the PREFIT battery. The associations of 24-h movement behaviours composition with each physical fitness parameter were examined employing compositional multivariable linear regression models. The changes following time reallocation among behaviours were estimated employing compositional isotemporal substitution analyses.

RESULTS

Greater MVPA, but not LPA, was significantly related to better upper and lower limb strength, speed-agility, and CRF. Reallocating time from LPA or SB to MVPA was related to better physical fitness. The associations were non-symmetrical: the estimated detriments to physical fitness from replacing MVPA with LPA or SB were larger than the estimated benefits associated with adding MVPA of the same magnitude. The aforementioned associations with lower limb strength, CRF, and speed-agility were observed in boys, while associations with upper and lower limb strength were noted in girls.

CONCLUSION

Our findings reinforce the importance of physical activity (PA) intensity for the development of physical fitness in preschoolers. Replacing LPA or SB time with MVPA may be an appropriate strategy for enhancing preschoolers' physical fitness.

Impact of Replacing Sedentary Behavior with Physical Activity and Sleep on Stroke Risk: A Prospective Cohort Study

Objective

Our research explores how leisure-time sedentary behavior (SB) correlates with stroke risk. Additionally, we utilize the isotemporal substitution model (ISM) to examine how replacing brief durations of leisure-time SB with light physical activity (LPA), moderate physical activity (MPA), vigorous physical activity (VPA), and sleep might influence the risk of stroke.

Methods

This investigation tracked 478,198 participants from the UK Biobank. Data regarding individual leisure-time SB and PA were collected through a standardized questionnaire. A Cox proportional hazards model, alongside an isotemporal substitution model (ISM), was utilized.

Results

We identified 10,003 cases of incident stroke over 12.7 years. When compared to participants who engaged in leisure-time SB for less than 4 hours per day, the hazard ratios (HRs) for stroke incidence increased with more prolonged leisure-time SB: HRs were 1.06 (95% CI: 1.01 to 1.11) for 4-6 h/d, 1.16 (95% CI: 1.10 to 1.23) for 6-8 h/d, and 1.24 (95% CI: 1.15 to 1.33) for over 8 h/d. According to the ISM analysis, substituting leisure-time SB with various forms of PA could markedly reduce stroke risk. For individuals sleeping ≤8h/d, replacing one hour of leisure-time SB with an equivalent duration of LPA, VPA, or sleep corresponded to a 3.0%, 7.0%, and 22.0% decrease in stroke risk, respectively. Meanwhile, for those already sleeping more than 8h/d, substituting one hour of leisure-time SB with an equivalent duration of LPA or VPA resulted in a notable decrease in the risk of stroke by 6.0% and 18.0%, respectively.

Conclusion

The findings demonstrate that leisure-time SB and unhealthy sleep durations are confirmed risk factors for stroke. For individuals sleeping 8 hours or less per day, and for those who sleep more than 8 hours, substituting SB with an adequate amount of sleep or engaging in VPA, respectively, emerges as an effective strategy for reducing stroke risk.

A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis

Background

Compositional data analysis (CoDA) techniques are well suited for examining associations between 24-h movement behaviors (i.e., sleep, sedentary behavior, physical activity) and indicators of health given they recognize these behaviors are co-dependent, representing relative parts that make up a whole day. Accordingly, CoDA techniques have seen increased adoption in the past decade, however, heterogeneity in research reporting practices may hinder efforts to synthesize and quantify these relationships via meta-analysis. This systematic review described reporting practices in studies that used CoDA techniques to investigate associations between 24-h movement behaviors and indicators of health.

Methods

A systematic search of eight databases was conducted, in addition to supplementary searches (e.g., forward/backward citations, expert consultation). Observational studies that used CoDA techniques involving log-ratio transformation of behavioral data to examine associations between time-based estimates of 24-h movement behaviors and indicators of health were included. Reporting practices were extracted and classified into seven areas: (1) methodological justification, (2) behavioral measurement and data handling strategies, (3) composition construction, (4) analytic plan, (5) composition-specific descriptive statistics, (6) model results, and (7) auxiliary information. Study quality and risk of bias were assessed by the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

Results

102 studies met our inclusion criteria. Reporting practices varied considerably across areas, with most achieving high standards in methodological justification, but inconsistent reporting across all other domains. Some items were reported in all studies (e.g., how many parts the daily composition was partitioned into), whereas others seldom reported (e.g., definition of a day: midnight-to-midnight versus wake-to-wake). Study quality and risk of bias was fair in most studies (85%).

Conclusions

Current studies generally demonstrate inconsistent reporting practices. Consistent, clear and detailed reporting practices are evidently needed moving forward as the field of time-use epidemiology aims to accurately capture and analyze movement behavior data in relation to health outcomes, facilitate comparisons across studies, and inform public health interventions and policy decisions. Achieving consensus regarding reporting recommendations is a key next step.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44167-024-00062-8.

Accelerometer-derived 'weekend warrior' physical activity pattern and brain health

Extensive evidence shows the beneficial effect of adhering to a regular physical activity (PA) pattern on brain health. However, whether the 'weekend warrior' pattern, characterized by concentrated moderate-to-vigorous PA (MVPA) over 1-2 days, is associated with brain health is unclear. Here, we perform a prospective cohort study including 75,629 participants from the UK Biobank with validated accelerometry data. Individuals were classified into three PA patterns using current guideline thresholds: inactive (<150 min week-1 of MVPA), weekend warrior (≥150 min week-1 with ≥50% of total MVPA occurring within 1-2 days) and regularly active (≥150 min week-1 but not meeting weekend warrior criteria). We find that the weekend warrior pattern is associated with similarly lower risks of dementia, stroke, Parkinson's disease, depressive disorders and anxiety compared to a regularly active pattern. Our findings highlight the weekend warrior pattern as a potential alternative in preventive intervention strategies, particularly for those unable to maintain daily activity routines.

Accelerometer-derived moderate-to-vigorous physical activity and incident nonalcoholic fatty liver disease

BACKGROUND

The liver effects of concentrated vs. more evenly distributed moderate-to-vigorous physical activity (MVPA) patterns remain unclear. We aimed to examine the association of accelerometer-measured MVPA and different MVPA patterns with liver outcomes.

METHODS

Eighty-eight thousand six hundred fifty-six participants without prior liver diseases from UK Biobank were included. MVPA was measured by a wrist-worn accelerometer. Based on the guideline-based threshold (≥ 150 min/week), MVPA patterns were defined as inactive (< 150 min/week), active weekend warrior (WW; ≥ 150 min/week with ≥ 50% of total MVPA achieved within 1-2 days), and regularly active (≥ 150 min/week but not active WW) patterns. The primary outcome was incident nonalcoholic fatty liver disease (NAFLD).

RESULTS

During a median follow-up of 6.8 years, 562 participants developed NAFLD. Overall, there was a nonlinear inverse association of total MVPA with incident NAFLD (P for nonlinearity = 0.009): the risk of NAFLD rapidly decreased with the increment of MVPA (per 100 min/week increment: HR = 0.68; 95%CI, 0.57-0.81) when MVPA < 208 min/week, while moderately declined (HR = 0.91; 95%CI, 0.84-0.99) when MVPA ≥ 208 min/week. For MVPA patterns, compared with inactive group, both active WW (HR = 0.55, 95%CI, 0.44-0.67) and active regular (HR = 0.49, 95%CI, 0.38-0.63) group were associated with a similar lower risk of NAFLD. Similar results were observed for each secondary outcome, including incident severe liver diseases, incident liver cirrhosis, and liver magnetic resonance imaging-based liver steatosis and fibrosis.

CONCLUSIONS

Regardless of whether MVPA was concentrated within 1 to 2 days or spread over most days of the week, more MVPA was associated with a lower risk of incident liver outcomes, including NAFLD, liver cirrhosis, liver steatosis, and fibrosis, to MVPA more evenly distributed.

Dose/Exposure Relationship of Exercise and Distant Recurrence in Primary Breast Cancer

PURPOSE

Postdiagnosis exercise is associated with lower breast cancer (BC) mortality but its link with risk of recurrence is less clear. We investigated the impact and dose-response relationship of exercise and recurrence in patients with primary BC.

METHODS

Multicenter prospective cohort analysis among 10,359 patients with primary BC from 26 centers in France between 2012 and 2018 enrolled in the CANcer TOxicities study, with follow-up through October 2021. Exercise exposure was assessed using the Global Physical Activity Questionnaire-16, quantified in standardized metabolic equivalent of task-hours per week (MET-h/wk). We examined the dose/exposure response of pretreatment exercise on distant recurrence-free interval (DRFI) for all patients and stratified by clinical subtype and menopausal status using inverse probability treatment weighted multivariable Cox models to estimate hazard ratios (HRs).

RESULTS

For the overall cohort, the relationship between exercise and DRFI was nonlinear: increasing exercise ≥ 5 MET-h/wk was associated with an inverse linear reduction in DRFI events up to approximately 25 MET-h/wk; increasing exercise over this threshold did not provide any additional DRFI benefit. Compared with <5 MET-h/wk, the adjusted HR for DRFI was 0.82 (95% CI, 0.61 to 1.00) for ≥ 5 MET-h/wk. Stratification by subtype revealed the hormone receptor-/human epidermal growth factor receptor 2- (HR-/HER2-; HR, 0.59 [95% CI, 0.38 to 0.92]) and HR-/HER2+ (HR, 0.37 [95% CI, 0.14 to 0.96]) subtypes were preferentially responsive to exercise. The benefit of exercise was observed especially in the premenopausal population.

CONCLUSION

Postdiagnosis/pretreatment exercise is associated with lower risk of DRFI events in a nonlinear fashion in primary BC; exercise has different impact on DRFI as a function of subtype and menopausal status.

Association between "weekend warrior" physical activity and the incidence of neurodegenerative diseases

While guidelines recommend 150 ​min of moderate to vigorous physical activity (MVPA) weekly to enhance health, it remains unclear whether concentrating these activities into 1-2 days of the week, "weekend warrior" (WW) pattern, has the same benefit for neurodegenerative diseases (NDDs). This study aimed to evaluate the associations of WW pattern and the risk of NDDs. This prospective study was conducted using accelerometer-based physical activity data for a full week from June 2013 to December 2015 in the UK Biobank. These individuals were categorized into distinct physical activity patterns, including the WW pattern (i.e., over 50% or 75% of recommended MVPA achieved over 1-2 days), regular pattern, and inactive pattern. Cox proportional hazards model was used to evaluate the association between physical activity patterns and outcomes. Compared to inactive group, WW pattern and regular pattern was similarly linked to a reduced risk of all-cause dementia (WW: Hazard Ratio [HR]: 0.68, 95% Confidence Interval [CI]: 0.56-0.84; regular: HR: 0.86, 95% CI: 0.67-1.1) and all-cause Parkinsonism (WW: HR: 0.47, 95% CI: 0.35-0.63; regular: HR: 0.69, 95% CI: 0.5-0.95). When the exercise threshold was increased to 75% of MVPA, both patterns still were associated with decreased risk of incident all-cause dementia (WW: HR: 0.61, 95% CI: 0.41-0.91; regular: HR: 0.76, 95% CI: 0.63-0.92) and all-cause Parkinsonism (WW: HR: 0.22, 95% CI: 0.10-0.47; regular: HR: 0.59, 95% CI: 0.46-0.75). Concentrating recommended physical activities into 1-2 days per week is associated with a lower incidence of NDDs.

Associations between Aircraft Noise, Sleep, and Sleep-Wake Cycle: Actimetric Data from the UK Biobank Cohort near Four Major Airports

BACKGROUND

Nighttime aircraft noise may affect people's sleep, yet large-scale evidence using objective and subjective measures remains limited.

OBJECTIVE

Our aim was to investigate associations between nighttime aircraft noise exposure and objectively measured sleep disturbance using a large UK cohort.

METHODS

We used data from 105,770 UK Biobank cohort participants exposed and unexposed to aircraft noise who lived in 44 local authority districts near 4 international airports in England. We used a generalized linear regression model to examine cross-sectional associations between aircraft noise L night (23:00 hours-07:00 hours) and 7-d actimetric measures collected 2013-2015 (n = 22,102 ). We also used Logit and generalized estimating equations models to examine associations between L night and self-reported sleep measures at enrollment (2006-2010) and follow-up (2012-2013). This approach allowed us to compare and contrast the results and support potential future meta-analyses on noise-related sleep disturbance.

RESULTS

Cross-sectional analyses of actimetric data suggested sleep disturbance associated with L night , showing higher level of movements during the least active continuous 8-h time period [β : 0.12 milligravitational units; 95% confidence interval (CI): 0.013, 0.23]. We also saw disrupted sleep-wake cycles as indicated by index scores of lower relative amplitude (β : - 0.006 ; 95% CI: - 0.007 , - 0.005 ), poorer interdaily stability (β : - 0.010 ; 95% CI: - 0.014 , - 0.006 ), and greater intradaily variability (β : 0.021; 95% CI: 0.019, 0.023), comparing L night ≥ 55 dB with < 45 dB. Repeated cross-sectional analyses found a 52% higher odds of more frequent daytime dozing [odds ratio (OR) = 1.52 ; 95% CI: 1.32, 1.75] for L night ≥ 55 dB in comparison with < 45 dB, whereas the likelihood for more frequent sleeplessness was more uncertain (OR = 1.13 ; 95% CI: 0.92, 1.39). Higher effect sizes were seen in preidentified vulnerable groups, including individuals > 65 y of age and those with diabetes or dementia.

CONCLUSION

Individuals exposed to higher levels of aircraft noise experienced objectively higher levels of sleep disturbance and changes in sleep-wake cycle. https://doi.org/10.1289/EHP14156.

Isotemporal Substitution Effects of Daily Time Use on Cardiorespiratory Fitness of Children in the OptiChild Study: A Mediation Analysis with Diet Quality

(1) Background: Although daily time-use is associated with diet quality and cardiorespiratory fitness (CRF) in children, their interdependence remains unexplored. This study first examined the associations between reallocating daily movement time and diet quality and CRF, and second the mediating role of diet quality in the relationship between daily time-use and CRF. (2) Methods: This study included 1131 Chinese children (aged 8 to 10 years; median [interquartile range]: 8.5 [8.3, 8.8]) at baseline (September 2022) and 1268 children at the 9-month follow-up (June 2023) from the OptiChild study. Daily durations of moderate-to-vigorous physical activity (MVPA), sleep, and sedentary behavior (e.g., screen time) were self-reported or proxy-reported by parents. Diet quality was assessed via the Diet Quality Questionnaire (DQQ), which uses a 24 h dietary recall and is categorized according to the Global Dietary Recommendations (GDR) score and Food Group Diversity Score (FGDS). The CRF was measured using VO2max after the 20 m shuttle run test. Longitudinal associations between daily time-use, diet quality, and CRF were calculated using isotemporal substitution models. Mediation analyses were used to determine whether diet quality mediated the associations between daily time-use and CRF. (3) Results: Reallocation of 30 min from screen time to MVPA resulted in significant improvements in the GDR score (β baseline = 0.11, p = 0.024; β follow-up = 0.26, p < 0.001), FGDS (β baseline = 0.11, p = 0.006; β follow-up = 0.19, p < 0.001), and CRF (β baseline = 0.40, p < 0.001; β follow-up = 0.26, p = 0.001). Diet quality partially mediated the associations between MVPA, screen time, and CRF. Substituting 30 min of screen time for MVPA led to diet quality mediating a proportion of the association with CRF (GDR score: 11.4%, FGDS: 6.6%). (4) Conclusions: These findings underscore the importance of optimizing daily time-use of MVPA and screen time and improving diet quality to promote physical fitness in school-aged children.

Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank

Importance

Higher physical activity levels have been suggested as a potential modifiable risk factor for lowering the risk of incident Parkinson's disease (PD). This study uses objective measures of physical activity to investigate the role of reverse causation in the observed association.

Objective

To investigate the association between accelerometer-derived daily step count and incident PD, and to assess the impact of reverse causation on this association.

Design

This prospective cohort study involved a follow-up period with a median duration of 7.9 years, with participants who wore wrist-worn accelerometers for up to 7 days.

Setting

The study was conducted within the UK Biobank, a large, population-based cohort.

Participants

The analysis included 94,696 participants aged 43-78 years (56% female) from the UK Biobank who provided valid accelerometer data and did not have prevalent PD.

Exposure

Daily step counts were derived using machine learning models to determine the median daily step count over the monitoring period.

Main Outcomes and Measures

The primary outcome was incident PD, identified through hospital admission and death records. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between daily step count and incident PD, with adjustments for various covariates and evaluation of reverse causation by splitting follow-up periods.

Results

During a median follow-up of 7.9 years (IQR: 7.4-8.4), 407 incident PD cases were identified. An inverse linear association was observed between daily step count and incident PD. Participants in the highest quintile of daily steps (>12,369 steps) had an HR of 0.41 (95% CI 0.31-0.54) compared to the lowest quintile (<6,276 steps; HR 1.00; 95% CI 0.84-1.19). A per 1,000 step increase was associated with an HR of 0.92 (95% CI 0.89-0.94). However, after excluding the first six years of follow-up, the association was not significant (HR 0.96, 95% CI 0.92-1.01).

Conclusions and Relevance

The observed association between higher daily step count and lower incident PD is likely influenced by reverse causation, suggesting changes in physical activity levels occur years before PD diagnosis. While step counts may serve as a predictor for PD, they may not represent a modifiable risk factor. Further research with extended follow-up periods is warranted to better understand this relationship and account for reverse causation.

Genetics of Cardiac Aging Implicate Organ-Specific Variation

Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging. In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics. We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near TTN, TNS1, LSM3, PALLD, DSP, PLEC, ANKRD1 and MYO18B) and an additional 16 loci (near MECOM, NPR3, KLHL3, HDGFL1, CDKN1A, ELN, SLC25A37, PI15, AP3M1, HMGA2, ADPRHL1, PGAP3, WNT9B, UHRF1 and DOK5). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality. These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.

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.

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.

Wearable device-measured moderate to vigorous physical activity and risk of degenerative aortic valve stenosis

BACKGROUND AND AIMS

Physical activity has proven effective in preventing atherosclerotic cardiovascular disease, but its role in preventing degenerative valvular heart disease (VHD) remains uncertain. This study aimed to explore the dose-response association between moderate to vigorous physical activity (MVPA) volume and the risk of degenerative VHD among middle-aged adults.

METHODS

A full week of accelerometer-derived MVPA data from 87 248 UK Biobank participants (median age 63.3, female: 56.9%) between 2013 and 2015 were used for primary analysis. Questionnaire-derived MVPA data from 361 681 UK Biobank participants (median age 57.7, female: 52.7%) between 2006 and 2010 were used for secondary analysis. The primary outcome was the diagnosis of incident degenerative VHD, including aortic valve stenosis (AS), aortic valve regurgitation (AR), and mitral valve regurgitation (MR). The secondary outcome was VHD-related intervention or mortality.

RESULTS

In the accelerometer-derived MVPA cohort, 555 incident AS, 201 incident AR, and 655 incident MR occurred during a median follow-up of 8.11 years. Increased MVPA volume showed a steady decline in AS risk and subsequent AS-related intervention or mortality risk, levelling off beyond approximately 300 min/week. In contrast, its association with AR or MR incidence was less apparent. The adjusted rates of AS incidence (95% confidence interval) across MVPA quartiles (Q1-Q4) were 11.60 (10.20, 13.20), 7.82 (6.63, 9.23), 5.74 (4.67, 7.08), and 5.91 (4.73, 7.39) per 10 000 person-years. The corresponding adjusted rates of AS-related intervention or mortality were 4.37 (3.52, 5.43), 2.81 (2.13, 3.71), 1.93 (1.36, 2.75), and 2.14 (1.50, 3.06) per 10 000 person-years, respectively. Aortic valve stenosis risk reduction was also observed with questionnaire-based MVPA data [adjusted absolute difference Q4 vs. Q1: AS incidence, -1.41 (-.67, -2.14) per 10 000 person-years; AS-related intervention or mortality, -.38 (-.04, -.88) per 10 000 person-years]. The beneficial association remained consistent in high-risk populations for AS, including patients with hypertension, obesity, dyslipidaemia, and chronic kidney disease.

CONCLUSIONS

Higher MVPA volume was associated with a lower risk of developing AS and subsequent AS-related intervention or mortality. Future research needs to validate these findings in diverse populations with longer durations and repeated periods of activity monitoring.

Diffusion tensor imaging analysis along the perivascular space in the UK biobank

BACKGROUND

The recently discovered glymphatic system may support the removal of neurotoxic proteins, mainly during sleep, that are associated with neurodegenerative diseases such as Alzheimer's and Parkinson's Disease. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) has been suggested as a method to index the health of glymphatic system (with higher values indicating a more intact glymphatic system). Indeed, in small-scale studies the DTI-ALPS index has been shown to correlate with age, cognitive health, and sleep, and is higher in females than males.

OBJECTIVE

To determine whether these relationships are stable we replicated previous findings associating the DTI-ALPS index with demographic, sleep-related, and cognitive markers in a large sample of participants from the UK Biobank.

METHODS

We calculated the DTI-ALPS index in UK Biobank participants (n = 17723). Using Bayesian and Frequentist analysis approaches, we replicate previously reported relationships between the DTI-ALPS index.

RESULTS

We found the predicted associations between the DTI-ALPS index and age, longest uninterrupted sleep window (LUSWT) on a typical night, cognitive performance, and sex. However, these effects were substantially smaller than those found in previous studies. Parameter estimates from this study may be used as priors in subsequent studies using a Bayesian approach. These results suggest that the DTI-ALPS index is consistently, and therefore predictably, associated with demographics, LUWST, and cognition.

CONCLUSION

We propose that the metric, calculated for the first time in a large-scale, population-based cohort, is a stable measure, but one for which stronger links to glymphatic system function are needed before it can be used to understand the relationships between glymphatic system function and health outcomes reported in the UK Biobank.

Associations between accelerometer-derived sedentary behavior and physical activity with white matter hyperintensities in middle-aged to older adults

INTRODUCTION

We examined the relationship between sedentary behavior (SB), moderate-to-vigorous physical activity (MVPA), and white matter hyperintensity (WMH) volumes, a common magnetic resonance imaging (MRI) marker associated with risk of neurodegenerative disease in middle-aged to older adults.

METHODS

We used data from the UK Biobank (n = 14,415; 45 to 81 years) that included accelerometer-derived measures of SB and MVPA, and WMH volumes from MRI.

RESULTS

Both MVPA and SB were associated with WMH volumes (βMVPA = -0.03 [-0.04, -0.01], p < 0.001; βSB = 0.02 [0.01, 0.03], p = 0.007). There was a significant interaction between SB and MVPA on WMH volumes (βSB×MVPA = -0.015 [-0.028, -0.001], p SB×MVPA = 0.03) where SB was positively associated with WMHs at low MVPA, and MVPA was negatively associated with WMHs at high SB.

DISCUSSION

While this study cannot establish causality, the results highlight the potential importance of considering both MVPA and SB in strategies aimed at reducing the accumulation of WMH volumes in middle-aged to older adults.

Highlights

SB is associated with greater WMH volumes and MVPA is associated with lower WMH volumes.Relationships between SB and WMH are strongest at low levels of MVPA.Associations between MVPA and WMH are strongest at high levels of SB.Considering both SB and MVPA may be effective strategies for reducing WMHs.

Joint association of genetic risk and accelerometer-measured physical activity with incident coronary artery disease in the UK biobank cohort

Previous research demonstrates the joint association of self-reported physical activity and genotype with coronary artery disease. However, an existing research gap is whether accelerometer-measured overall physical activity or physical activity intensity can offset genetic predisposition to coronary artery disease. This study explores the independent and joint associations of accelerometer-measured physical activity and genetic predisposition with incident coronary artery disease. Incident coronary artery disease based on hospital inpatient records and death register data serves as the outcome of this study. Polygenic risk score and overall physical activity, measured as Euclidean Norm Minus One, and intensity, measured as minutes per day of moderate-to-vigorous intensity physical activity (MVPA), are examined both linearly and by decile. The UK Biobank population-based cohort recruited over 500,000 individuals aged 40 to 69 between 2006 and 2010, with 103,712 volunteers participating in a weeklong wrist-worn accelerometer study from 2013 to 2015. Individuals of White British ancestry (n = 65,079) meeting the genotyping and accelerometer-based inclusion criteria and with no missing covariates were included in the analytic sample. In the sample of 65,079 individuals, the mean (SD) age was 62.51 (7.76) and 61% were female. During a median follow-up of 6.8 years, 1,382 cases of coronary artery disease developed. At the same genetic risk, physical activity intensity had a hazard ratio (HR) of 0.41 (95% CI: 0.29-0.60) at the 90th compared to 10th percentile, equivalent to 31.68 and 120.96 minutes of moderate-to-vigorous physical activity per day, respectively, versus an HR of 0.61 (95% CI: 0.52-0.72) for overall physical activity. The combination of high genetic risk and low physical activity intensity showed the greatest risk, with an individual at the 10th percentile of genetic risk and 90th percentile of intensity facing an HR of 0.14 (95% CI: 0.09-0.21) compared to an individual at the 90th percentile of genetic risk and 10th percentile of intensity. Physical activity, especially physical activity intensity, is associated with an attenuation of some of the risk of coronary artery disease but this pattern does not vary by genetic risk. This accelerometer-based study provides the clearest evidence to date regarding the joint influence of genetics, overall physical activity, and physical activity intensity on coronary artery disease.

Accelerometer-Measured Behavior Patterns in Incident Cerebrovascular Disease: Insights for Preventative Monitoring From the UK Biobank

BACKGROUND

The goal was to compare patterns of physical activity (PA) behaviors (sedentary behavior [SB], light PA, moderate-to-vigorous PA [MVPA], and sleep) measured via accelerometers for 7 days between patients with incident cerebrovascular disease (CeVD) (n=2141) and controls (n=73 938).

METHODS AND RESULTS

In multivariate models, cases spent 3.7% less time in MVPA (incidence rate ratio [IRR], 0.963 [95% CI, 0.929-0.998]) and 1.0% more time in SB (IRR, 1.010 [95% CI, 1.001-1.018]). Between 12 and 24 months before diagnosis, cases spent more time in SB (IRR, 1.028 [95% CI, 1.001-1.057]). Within the year before diagnosis, cases spent less time in MVPA (IRR, 0.861 [95% CI, 0.771-0.964]). Although SB time was not associated with CeVD risk, MVPA time, both total min/d (hazard ratio [HR], 0.998 [95% CI, 0.997-0.999]) and guideline threshold adherence (≥150 min/wk) (HR, 0.909 [95% CI, 0.827-0.998]), was associated with decreased CeVD risk. Comorbid burden had a significant partial mediation effect on the relationship between MVPA and CeVD. Cases slept more during 12:00 to 17:59 hours (IRR, 1.091 [95% CI, 1.002-1.191]) but less during 0:00 to 5:59 hours (IRR, 0.984 [95% CI, 0.977-0.992]). No between-group differences were significant at subgroup analysis.

CONCLUSIONS

Daily behavior patterns were significantly different in patients before CeVD. Although SB was not associated with CeVD risk, the association between MVPA and CeVD risk is partially mediated by comorbid burden. This study has implications for understanding observable behavior patterns in cerebrovascular dysfunction and may help in developing remote monitoring strategies to prevent or reduce cerebrovascular decline.

Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan

Recognizing the pivotal role of circadian rhythm in the human aging process and its scalability through wearables, we introduce CosinorAge, a digital biomarker of aging developed from wearable-derived circadian rhythmicity from 80,000 midlife and older adults in the UK and US. A one-year increase in CosinorAge corresponded to 8-12% higher all-cause and cause-specific mortality risks and 3-14% increased prospective incidences of age-related diseases. CosinorAge also captured a non-linear decline in resilience and physical functioning, evidenced by an 8-33% reduction in self-rated health and a 3-23% decline in health-related quality of life score, adjusting for covariates and multiple testing. The associations were robust in sensitivity analyses and external validation using an independent cohort from a disparate geographical region using a different wearable device. Moreover, we illustrated a heterogeneous impact of circadian parameters associated with biological aging, with young (<45 years) and fast agers experiencing a substantially delayed acrophase with a 25-minute difference in peak timing compared to slow agers, diminishing to a 7-minute difference in older adults (>65 years). We demonstrated a significant enhancement in the predictive performance when integrating circadian rhythmicity in the estimation of biological aging over physical activity. Our findings underscore CosinorAge's potential as a scalable, economic, and digital solution for promoting healthy longevity, elucidating the critical and multifaceted circadian rhythmicity in aging processes. Consequently, our research contributes to advancing preventive measures in digital medicine.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Longitudinal associations of screen time, physical activity, and sleep duration with body mass index in U.S. youth

BACKGROUND

Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI.

METHODS

Data were baseline (2017-2018) and one-year follow-up (2018-2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth (N = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z-score, controlling for demographic characteristics, internalizing symptoms, and BMI z-score at baseline.

RESULTS

In females, isotemporal substitution models estimated that replacing 30 min of socializing (β [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z-score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z-score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z-score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA.

CONCLUSIONS

One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.

Joint associations of device-measured physical activity and abdominal obesity with incident cardiovascular disease: a prospective cohort study

OBJECTIVE

To examine the joint associations between physical activity and abdominal obesity with the risk of cardiovascular disease (CVD) events.

METHODS

We included 70 830 UK Biobank participants (mean age±SD=61.6 ± 7.9 years; 56.4% women) with physical activity measured by wrist-worn accelerometers and without major chronic diseases. Participants were jointly categorised into six groups based on their physical activity level (tertiles of total volume and specific intensity levels) and presence or absence of abdominal obesity based on measured waist circumference. Associations with incident CVD (fatal and non-fatal events) were determined using proportional subdistribution hazard models with multivariable adjustment.

RESULTS

After excluding events during the first 2 years of follow-up, participants were followed for a median of 6.8 years, during which 2795 CVD events were recorded. Compared with the low abdominal adiposity and highest tertile of physical activity, abdominal obesity was associated with higher risk of incident CVD, especially in those with low levels of vigorous-intensity physical activity (HR 1.42, 95% CI 1.22 to 1.64). Approximately 500 min per week of moderate-to-vigorous intensity and approximately 30-35 min of vigorous-intensity physical activity offset the association of abdominal obesity and the risk of having a CVD event.

CONCLUSION

Physical activity equivalent to approximately 30-35 min of vigorous intensity per week appears to offset the association between abdominal obesity and incident CVD. About 15 times more physical activity of at least moderate intensity is needed to achieve similar results.

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.

Replacing device-measured sedentary time with physical activity is associated with lower risk of coronary heart disease regardless of genetic risk

BACKGROUND

Excess sedentary time (ST) is recognized as an important modifiable risk factor for coronary heart disease (CHD). However, whether the associations of genetic susceptibility with CHD incidence can be modified by replacing wearable-device-measured ST with physical activity (PA) is unknown.

OBJECTIVES

To examine the associations of wearable-device-measured ST replaced by PA with incident CHD across strata of genetic susceptibility.

METHODS

This study included 77,500 White British (57% female) with valid wrist-worn accelerometry and without prevalent CHD/stroke from UK Biobank. Genetic susceptibility to CHD was quantified through weighted polygenic risk scores for CHD based on 300 single-nucleotide polymorphisms. Wrist-worn accelerometer data were used to derive ST, light PA, and moderate-to-vigorous PA (MVPA).

RESULTS

Reallocation of 60 min/day of ST into the same amount of MVPA was associated with approximately 9% lower relative risk of CHD for all participants and across strata of genetic risk: replacement of 1 min/day of ST associated with <1% lower relative risk of CHD. No evidence of interaction (p: 0.784) was found between genetic risk and ST for CHD risk. Reallocating 60 min/day of ST into the same MVPA time was associated with greater absolute CHD risk reductions at high genetic risk (0.27%) versus low genetic risk (0.15%).

CONCLUSIONS

Replacing any amount of ST with an equal amount of MVPA time is associated with a lower relative risk of CHD, irrespective of genetic susceptibility to CHD. Reductions in CHD absolute risk for replacing ST with MVPA are greater at high genetic risk versus low genetic risk.

Amount and intensity of physical activity and risk of incident cancer in the UK Biobank

Importance

The influence of total daily and light intensity activity on cancer risk remains unclear, as most existing knowledge is drawn from studies relying on self-reported leisure-time activities of moderate-vigorous intensity.

Objective

To investigate associations between total daily activity, including step counts, and activity intensity on incident cancer risk.

Design Setting and Participants

Prospective analysis of cancer-free UK Biobank participants who wore accelerometers for 7-days (between 2013-2015), followed for cancer incidence through national registries (mean follow-up 5.8 years (SD=1.3)).

Exposures

Time-series machine learning models derived daily total activity (average acceleration), behaviour time, step counts, and peak 30-minute cadence from wrist-based accelerometer data.

Main Outcomes and Measures

A composite cancer outcome of 13 cancers previously associated with low physical activity (bladder, breast, colon, endometrial, oesophageal adenocarcinoma, gastric cardia, head and neck, kidney, liver, lung, myeloid leukaemia, myeloma, and rectum) based on previous studies of self-reported activity. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% confidence intervals (CI), adjusted for age, sex, ethnicity, smoking, alcohol, education, Townsend Deprivation Index, and reproductive factors. Associations of reducing sedentary time in favour of increased light and moderate-vigorous activity were examined using compositional data analyses.

Results

Among 86 556 participants (mean age 62.0 years (SD=7.9) at accelerometer assessment), 2 669 cancers occurred. Higher total physical activity was associated with a lower overall cancer risk (HR1SD=0.85, [95%CI 0.81-0.89]). On average, reallocating one hour/day from sedentary behaviour to moderate-vigorous physical activity was associated with a lower risk (HR=0.92, [0.89-0.95]), as was reallocating one hour/day to light-intensity physical activity (HR=0.94, [0.92-0.96]). Compared to individuals taking 5 000 daily steps, those who took 9 000 steps had an 18% lower risk of physical-activity-related cancer (HR=0.82, [0.74-0.90]). We found no significant association with peak 30-minute cadence after adjusting for total steps.

Conclusion and Relevance

Higher total daily physical activity and less sedentary time, in favour of both light and moderate-vigorous intensity activity, were associated with a lower risk of certain cancers. For less active adults, increasing step counts by 4 000 daily steps may be a practical public health intervention for lowering the risk of some cancers.

Machine Learning Analysis of Physical Activity Data to Classify Postural Dysfunction

BACKGROUND

Machine learning (ML) analysis of biometric data in non-controlled environments is underexplored.

OBJECTIVE

To evaluate whether ML analysis of physical activity data can be employed to classify whether individuals have postural dysfunction in middle-aged and older individuals.

METHODS

A 1 week period of physical activity was measured by a waist-worn uni-axial accelerometer during the 2003-2004 National Health and Nutrition Examination Survey sampling period. Features of physical activity along with basic demographic information (42 variables) were paired with ML models to predict the success or failure of a standard 30 s modified Romberg test during which participants had their eyes closed and stood upon a 3-inch compliant surface. Model performance was evaluated by area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy, and F1-score.

RESULTS

The cohort was comprised of 1625 participants ≥40 years (median age 61, IQR 51-71). Approximately half (47%) were diagnosed with postural dysfunction having failed the binarized (pass/fail) scoring mechanism of the modified Romberg exam. Five ML models were trained on the classification task, achieving AUC values ranging from 0.67 to 0.73. The support vector machine (SVM) and a gradient-boosted model, XGBoost, achieved the highest AUC of 0.73 (SD 0.71-0.75). Age was the most important variable for SVM classification, followed by four features that evaluated accelerometer counts at various thresholds, including those delineating total, moderate, and moderate-vigorous activity.

CONCLUSIONS

ML analysis of accelerometer-derived physical activity data to classify postural dysfunction in middle-aged and older individuals is feasible in real-world environments such as the home.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:3529-3533, 2023.

Association of accelerometer-measured physical activity intensity, sedentary time, and exercise time with incident Parkinson's disease

Evidence regarding the association between physical activity and Parkinson's disease (PD) risk is generally limited due to the use of self-report questionnaires. We aimed to quantify the separate and combined effects of accelerometer-measured light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), sedentary time and exercise timing with incident PD. 96,422 participants without prior PD and with usable accelerometer data were included from UK Biobank. Time spent in sedentary activity, LPA, MVPA, and exercise timing were estimated using machine learning models. The study outcome was incident PD. Over a median follow-up duration of 6.8 years, 313 participants developed PD. There was a L-shaped association for LPA and MVPA, and a reversed L-shaped association for sedentary time, with the risk of incident PD (all P for nonlinearity < 0.001). Similar trends were found across three time-windows (morning, midday-afternoon, and evening). Compared with those with both low LPA (<3.89 h/day) and low MVPA (<0.27 h/day), the adjusted HR (95% CI) of PD risk was 0.49 (0.36-0.66), 0.19 (0.36-0.66) and 0.13 (0.09-0.18), respectively, for participants with high MVPA only, high LPA only, and both high LPA and high MVPA. Moreover, participants with both low LPA and high sedentary time (≥9.41 h/day) (adjusted HR, 5.59; 95% CI: 4.10-7.61), and those with both low MVPA and high sedentary time (adjusted HR, 3.93; 95% CI: 2.82-5.49) had the highest risk of incident PD. In conclusion, regardless of exercise timing (morning, midday-afternoon, and evening), there was an inverse association for accelerometer-measured MVPA and LPA, and a positive association for sedentary time, with incident PD.

Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants

BACKGROUND

Movement behaviours, including physical activity, sedentary behaviour, and sleep have been shown to be associated with several chronic diseases. However, they have not been objectively measured in large-scale prospective cohort studies in low-and middle-income countries. We aim to describe the patterns of device-measured movement behaviours collected in the China Kadoorie Biobank (CKB) study.

METHODS

During 2020 and 2021, a random subset of 25,087 surviving CKB individuals participated in the 3rd resurvey of the CKB. Among them, 22,511 (89.7%) agreed to wear an Axivity AX3 wrist-worn triaxial accelerometer for seven consecutive days to assess their habitual movement behaviours. We developed a machine-learning model to infer time spent in four movement behaviours [i.e. sleep, sedentary behaviour, light intensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)]. Descriptive analyses were performed for wear-time compliance and patterns of movement behaviours by different participant characteristics.

RESULTS

Data from 21,897 participants (aged 65.4 ± 9.1 years; 35.4% men) were received for demographic and wear-time analysis, with a median wear-time of 6.9 days (IQR: 6.1-7.0). Among them, 20,370 eligible participants were included in movement behavior analyses. On average, they had 31.1 mg/day (total acceleration) overall activity level, accumulated 7.7 h/day (32.3%) of sleep time, 8.8 h/day (36.6%) sedentary, 5.7 h/day (23.9%) in light physical activity, and 104.4 min/day (7.2%) in moderate-to-vigorous physical activity. There was an inverse relationship between age and overall acceleration with an observed decline of 5.4 mg/day (17.4%) per additional decade. Women showed a higher activity level than men (32.3 vs 28.8 mg/day) and there was a marked geographical disparity in the overall activity level and time allocation.

CONCLUSIONS

This is the first large-scale accelerometer data collected among Chinese adults, which provides rich and comprehensive information about device-measured movement behaviour patterns. This resource will enhance our knowledge about the potential relevance of different movement behaviours for chronic disease in Chinese adults.

Accelerometer derived physical activity and subclinical coronary and carotid atherosclerosis: cross-sectional analyses in 22 703 middle-aged men and women in the SCAPIS study

OBJECTIVES

The aim included investigation of the associations between sedentary (SED), low-intensity physical activity (LIPA), moderate-to-vigorous intensity PA (MVPA) and the prevalence of subclinical atherosclerosis in both coronaries and carotids and the estimated difference in prevalence by theoretical reallocation of time in different PA behaviours.

DESIGN

Cross-sectional.

SETTING

Multisite study at university hospitals.

PARTICIPANTS

A total of 22 670 participants without cardiovascular disease (51% women, 57.4 years, SD 4.3) from the population-based Swedish CArdioPulmonary bioImage study were included. SED, LIPA and MVPA were assessed by hip-worn accelerometer.

PRIMARY AND SECONDARY OUTCOMES

Any and significant subclinical coronary atherosclerosis (CA), Coronary Artery Calcium Score (CACS) and carotid atherosclerosis (CarA) were derived from imaging data from coronary CT angiography and carotid ultrasound.

RESULTS

High daily SED (>70% ≈10.5 hours/day) associated with a higher OR 1.44 (95% CI 1.09 to 1.91), for significant CA, and with lower OR 0.77 (95% CI 0.63 to 0.95), for significant CarA. High LIPA (>55% ≈8 hours/day) associated with lower OR for significant CA 0.70 (95% CI 0.51 to 0.96), and CACS, 0.71 (95% CI 0.51 to 0.97), but with higher OR for CarA 1.41 (95% CI 1.12 to 1.76). MVPA above reference level, >2% ≈20 min/day, associated with lower OR for significant CA (OR range 0.61-0.67), CACS (OR range 0.71-0.75) and CarA (OR range 0.72-0.79). Theoretical replacement of 30 min of SED into an equal amount of MVPA associated with lower OR for significant CA, especially in participants with high SED 0.84 (95% CI 0.76 to 0.96) or low MVPA 0.51 (0.36 to 0.73).

CONCLUSIONS

MVPA was associated with a lower risk for significant atherosclerosis in both coronaries and carotids, while the association varied in strength and direction for SED and LIPA, respectively. If causal, clinical implications include avoiding high levels of daily SED and low levels of MVPA to reduce the risk of developing significant subclinical atherosclerosis.

Accelerometry Measured Movement Behaviors in Middle-Aged and Older Adults: Cross-Sectional Analysis of the ELSA-Brasil Study

BACKGROUND

Little investigation of accelerometry assessed movement behaviors and physical inactivity was carried out in middle-aged and older adults in low-middle-income countries.

OBJECTIVE

Describe accelerometry-measured movement behaviors and prevalence of physical inactivity in middle-aged and older adults.

METHODS

We collected raw accelerometry data during the third visit (2017-2019) of ELSA-Brasil, a large-scale multicenter Brazilian cohort. Participants wore an ActiGraph wGT3X-BT on the waist for 24 hours for 7 days and documented sleep in a diary.

RESULTS

Nine thousand two hundred and seventy-nine participants had valid data (73.4% of the eligible cohort). Overall activity was higher for men (11.82mg; 95% confidence interval [CI], 11.7 to 11.93) than women (10.69mg; 95% CI, 10.6 to 10.77) and lower in older groups-women (-0.12mg/y; 95% CI, -0.13 to -0.11), men (-0.16mg/y; 95% CI, -0.17 to -0.14). Participants were more active from noon to midnight. Distribution of movement behaviors varied with sex and age, and sleep duration was longer in older individuals. Overall, 14.4% (95% CI, 13.7 to 15.1) were inactive, with inactivity being more frequent in women (16.4%; 95% CI, 15.4 to 17.4) than men (12.2%; 95% CI, 11 to 13). Higher rates were observed in the oldest. Retirement was associated with a higher prevalence of physical inactivity in both sexes.

CONCLUSION

Women were less active than men. Older individuals showed a high prevalence of physical inactivity, probably related to transition into retirement. These findings strengthen evidence for public policies promoting physical activity by emphasizing the need to target women, older individuals, and those transitioning to retirement to improve and/or maintain physical activity levels throughout the course of their lives.

Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects

Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.

Sedentary Behavior and Incident Dementia Among Older Adults

Importance

Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear.

Objective

To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia.

Design, Setting, and Participants

A retrospective study of prospectively collected data from the UK Biobank including 49 841 adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer and living in England, Scotland, or Wales. Follow-up began at the time of wearing the accelerometer (February 2013 to December 2015) and continued until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales.

Exposures

Mean daily sedentary behavior time (included in the primary analysis) and mean daily sedentary bout length, maximum daily sedentary bout length, and mean number of daily sedentary bouts (included in the secondary analyses) were derived from a machine learning-based analysis of 1 week of wrist-worn accelerometer data.

Main Outcome and Measures

Incident all-cause dementia diagnosis from inpatient hospital records and death registry data. Cox proportional hazard models with linear and cubic spline terms were used to assess associations.

Results

A total of 49 841 older adults (mean age, 67.19 [SD, 4.29] years; 54.7% were female) were followed up for a mean of 6.72 years (SD, 0.95 years). During this time, 414 individuals were diagnosed with incident all-cause dementia. In the fully adjusted models, there was a significant nonlinear association between time spent in sedentary behavior and incident dementia. Relative to a median of 9.27 hours/d for sedentary behavior, the hazard ratios (HRs) for dementia were 1.08 (95% CI, 1.04-1.12, P < .001) for 10 hours/d, 1.63 (95% CI, 1.35-1.97, P < .001) for 12 hours/d, and 3.21 (95% CI, 2.05-5.04, P < .001) for 15 hours/d. The adjusted incidence rate of dementia per 1000 person-years was 7.49 (95% CI, 7.48-7.49) for 9.27 hours/d of sedentary behavior, 8.06 (95% CI, 7.76-8.36) for 10 hours/d, 12.00 (95% CI, 10.00-14.36) for 12 hours/d, and 22.74 (95% CI, 14.92-34.11) for 15 hours/d. Mean daily sedentary bout length (HR, 1.53 [95% CI, 1.03-2.27], P = .04 and 0.65 [95% CI, 0.04-1.57] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 0.48 hours) and maximum daily sedentary bout length (HR, 1.15 [95% CI, 1.02-1.31], P = .02 and 0.19 [95% CI, 0.02-0.38] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 1.95 hours) were significantly associated with higher risk of incident dementia. The number of sedentary bouts per day was not associated with higher risk of incident dementia (HR, 1.00 [95% CI, 0.99-1.01], P = .89). In the sensitivity analyses, after adjustment for time spent in sedentary behavior, the mean daily sedentary bout length and the maximum daily sedentary bout length were no longer significantly associated with incident dementia.

Conclusions and Relevance

Among older adults, more time spent in sedentary behaviors was significantly associated with higher incidence of all-cause dementia. Future research is needed to determine whether the association between sedentary behavior and risk of dementia is causal.

Diurnal rhythms of wrist temperature are associated with future disease risk in the UK Biobank

Many chronic disease symptomatologies involve desynchronized sleep-wake cycles, indicative of disrupted biorhythms. This can be interrogated using body temperature rhythms, which have circadian as well as sleep-wake behavior/environmental evoked components. Here, we investigated the association of wrist temperature amplitudes with a future onset of disease in the UK Biobank one year after actigraphy. Among 425 disease conditions (range n = 200-6728) compared to controls (range n = 62,107-91,134), a total of 73 (17%) disease phenotypes were significantly associated with decreased amplitudes of wrist temperature (Benjamini-Hochberg FDR q < 0.05) and 26 (6.1%) PheCODEs passed a more stringent significance level (Bonferroni-correction α < 0.05). A two-standard deviation (1.8° Celsius) lower wrist temperature amplitude corresponded to hazard ratios of 1.91 (1.58-2.31 95% CI) for NAFLD, 1.69 (1.53-1.88) for type 2 diabetes, 1.25 (1.14-1.37) for renal failure, 1.23 (1.17-1.3) for hypertension, and 1.22 (1.11-1.33) for pneumonia (phenome-wide atlas available at http://bioinf.itmat.upenn.edu/biorhythm_atlas/ ). This work suggests peripheral thermoregulation as a digital biomarker.

Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis

Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.

Accelerometer-Derived "Weekend Warrior" Physical Activity and Incident Cardiovascular Disease

Importance

Guidelines recommend 150 minutes or more of moderate to vigorous physical activity (MVPA) per week for overall health benefit, but the relative effects of concentrated vs more evenly distributed activity are unclear.

Objective

To examine associations between an accelerometer-derived "weekend warrior" pattern (ie, most MVPA achieved over 1-2 days) vs MVPA spread more evenly with risk of incident cardiovascular events.

Design, Setting, and Participants

Retrospective analysis of UK Biobank cohort study participants providing a full week of accelerometer-based physical activity data between June 8, 2013, and December 30, 2015.

Exposures

Three MVPA patterns were compared: active weekend warrior (active WW, ≥150 minutes with ≥50% of total MVPA achieved in 1-2 days), active regular (≥150 minutes and not meeting active WW status), and inactive (<150 minutes). The same patterns were assessed using the sample median threshold of 230.4 minutes or more of MVPA per week.

Main Outcomes and Measures

Associations between activity pattern and incident atrial fibrillation, myocardial infarction, heart failure, and stroke were assessed using Cox proportional hazards regression, adjusted for age, sex, racial and ethnic background, tobacco use, alcohol intake, Townsend Deprivation Index, employment status, self-reported health, and diet quality.

Results

A total of 89 573 individuals (mean [SD] age, 62 [7.8] years; 56% women) who underwent accelerometry were included. When stratified at the threshold of 150 minutes or more of MVPA per week, a total of 37 872 were in the active WW group (42.2%), 21 473 were in the active regular group (24.0%), and 30 228 were in the inactive group (33.7%). In multivariable-adjusted models, both activity patterns were associated with similarly lower risks of incident atrial fibrillation (active WW: hazard ratio [HR], 0.78 [95% CI, 0.74-0.83]; active regular: 0.81 [95% CI, 0.74-0.88; inactive: HR, 1.00 [95% CI, 0.94-1.07]), myocardial infarction (active WW: 0.73 [95% CI, 0.67-0.80]; active regular: 0.65 [95% CI, 0.57-0.74]; and inactive: 1.00 [95% CI, 0.91-1.10]), heart failure (active WW: 0.62 [95% CI, 0.56-0.68]; active regular: 0.64 [95% CI, 0.56-0.73]; and inactive: 1.00 [95% CI, 0.92-1.09]), and stroke (active WW: 0.79 [95% CI, 0.71-0.88]; active regular: 0.83 [95% CI, 0.72-0.97]; and inactive: 1.00 [95% CI, 0.90-1.11]). Findings were consistent at the median threshold of 230.4 minutes or more of MVPA per week, although associations with stroke were no longer significant (active WW: 0.89 [95% CI, 0.79-1.02]; active regular: 0.87 [95% CI, 0.74-1.02]; and inactive: 1.00 [95% CI, 0.90-1.11]).

Conclusions and Relevance

Physical activity concentrated within 1 to 2 days was associated with similarly lower risk of cardiovascular outcomes to more evenly distributed activity.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Background

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes.

Methods

We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality.

Findings

After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77).

Interpretation

Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

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.

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is caused by motor disinhibition during REM sleep and is a strong early predictor of Parkinson's disease. However, screening questionnaires for iRBD lack specificity due to other sleep disorders that mimic the symptoms. Nocturnal wrist actigraphy has shown promise in detecting iRBD by measuring sleep-related motor activity, but it relies on sleep diary-defined sleep periods, which are not always available. Our aim was to precisely detect iRBD using actigraphy alone by combining two actigraphy-based markers of iRBD - abnormal nighttime activity and 24-hour rhythm disruption. In a sample of 42 iRBD patients and 42 controls (21 clinical controls with other sleep disorders and 21 community controls) from the Stanford Sleep Clinic, the nighttime actigraphy model was optimized using automated detection of sleep periods. Using a subset of 38 iRBD patients with daytime data and 110 age-, sex-, and body-mass-index-matched controls from the UK Biobank, the 24-hour rhythm actigraphy model was optimized. Both nighttime and 24-hour rhythm features were found to distinguish iRBD from controls. To improve the accuracy of iRBD detection, we fused the nighttime and 24-hour rhythm disruption classifiers using logistic regression, which achieved a sensitivity of 78.9%, a specificity of 96.4%, and an AUC of 0.954. This study preliminarily validates a fully automated method for detecting iRBD using actigraphy in a general population.Clinical relevance- Actigraphy-based iRBD detection has potential for large-scale screening of iRBD in the general population.

Substituting device-measured sedentary time with alternative 24-hour movement behaviours: compositional associations with adiposity and cardiometabolic risk in the ORISCAV-LUX 2 study

BACKGROUND

There is a considerable burden of sedentary time in European adults. We aimed to quantify the differences in adiposity and cardiometabolic health associated with theoretically exchanging sedentary time for alternative 24 h movement behaviours.

METHODS

This observational cross-sectional study included Luxembourg residents aged 18-79 years who each provided  ≥ 4 valid days of triaxial accelerometry (n = 1046). Covariable adjusted compositional isotemporal substitution models were used to examine if statistically replacing device-measured sedentary time with more time in the sleep period, light physical activity (PA), or moderate-to-vigorous PA (MVPA) was associated with adiposity and cardiometabolic health markers. We further investigated the cardiometabolic properties of replacing sedentary time which was accumulated in prolonged (≥ 30 min) with non-prolonged (< 30 min) bouts.

RESULTS

Replacing sedentary time with MVPA was favourably associated with adiposity, high-density lipoprotein cholesterol, fasting glucose, insulin, and clustered cardiometabolic risk. Substituting sedentary time with light PA was associated with lower total body fat, fasting insulin, and was the only time-exchange to predict lower triglycerides and a lower apolipoprotein B/A1 ratio. Exchanging sedentary time with more time in the sleep period was associated with lower fasting insulin, and with lower adiposity in short sleepers. There was no significant evidence that replacing prolonged with non-prolonged sedentary time was related to outcomes.

CONCLUSIONS

Artificial time-use substitutions indicate that replacing sedentary time with MVPA is beneficially associated with the widest range of cardiometabolic risk factors. Light PA confers some additional and unique metabolic benefit. Extending sleep, by substituting sedentary time with more time in the sleep period, may lower obesity risk in short sleepers.

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

OBJECTIVE

Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers.

METHODS

Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories.

RESULTS

Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data.

CONCLUSION

AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.