Tag: mortality

Physical activity may offset short sleep duration increase in CVD mortality

April 09, 2023

2 min read


Disclosures:
The authors report no relevant financial disclosures.


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Physical activity may offset the increased CVD mortality that is associated with unusual sleep duration, according to a study published in the European Journal of Preventive Cardiology.

“To our knowledge, the present study was the first to use accelerometry to document the joint association of physical activity and sleep duration with all-cause, CVD and cancer mortality,” Yannis Yan Liang, MD, PhD, of the Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital and Guangdong Academy of Medical Sciences in Guangdong, China, and colleagues wrote.



Man exercising

Physical activity may offset the increased CVD mortality that is associated with unusual sleep duration.
Image: Adobe Stock

In a population-based cohort study using data from the UK Biobank between February 2013 and December 2015, 92,221 participants were divided into groups based on sleep duration, volume of physical activity and moderate to vigorous physical activity (MVPA) based on WHO guidelines.

Most of the participants reported a normal sleep duration, whereas 19.7{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} reported a long sleep duration and 7.1{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} reported a short sleep duration. One-third of the participants had a low volume of physical activity, and 39.1{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} did not meet MVPA recommended targets.

After a median follow-up period of 7 years, 3,080 all-cause deaths occurred, with 1.16{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of those deaths from CVD and 2.03{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} from cancer.

The researchers found that short sleep duration was associated with all-cause (HR = 1.34; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 1.18-1.51) and CVD (HR = 1.56; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 1.29-1.89) mortality, but was not significantly associated with cancer mortality.

In addition, compared with those who had normal sleep duration and guideline-recommended MVPA, those who had long or short sleep duration and did not achieve guideline-recommended MVPA had elevated risk for all-cause mortality (HR for short sleep duration = 1.88; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 1.61-2.2; HR for long sleep duration = 1.69; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 1.49-1.9), the researchers wrote.

The researchers also found that MVPA exceeding the recommendations decreased all-cause (HR = 0.67; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 0.48-0.59), CVD (HR = 0.61; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 0.52-0.71) and cancer (HR = 0.73; 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI, 0.65-0.81) mortality.

When examining the joint association of physical activity and sleep duration, the researchers found that high volume of physical activity or WHO-recommended MVPA reduced the detrimental impact of abnormal sleep duration.

“Our study using accelerometry offered robust evidence supporting the large body of research relying on self-reported data, showing independent associations of physical activity and sleep duration with mortality,” Liang and colleagues wrote.


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Associations of timing of physical activity with all-cause and cause-specific mortality in a prospective cohort study

Participants and accelerometer assessment

This large cohort study was conducted based on the UK Biobank. The UK Biobank received ethical approval from the North West Multi-center Research Ethics Committee to collect and distribute samples and data from the participants (Reference numbers: 16/NW/0274& 21/NW/0157; https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/governance/ethics-advisory-committee), which covers the work in this study under approved Application 58082. All UK Biobank participants provided informed consent. In addition, we have obtained approvals from our institutions for analyzing the UK Biobank data in this study. UK Biobank is a large population-based cohort that recruited over 500,000 participants aged 40–73 years between 2006 and 201038. Participants visited one of 22 assessment centers across England, Scotland, and Wales, and underwent detailed baseline assessments, including various sociodemographic, lifestyle, health, and physical assessments. Details of the rationale, design, and measurements for the UK Biobank are available online (www.ukbiobank.ac.uk). Between February 2013 and December 2015 (on average, approximately 5.5 years after their initial baseline recruitment), 236,519 UK Biobank participants were invited to participate in an accelerometer study. Among them, 106,053 participants agreed to participate and were provided with a wrist-worn accelerometer (Axivity AX3)39. Participants who accepted accelerometry measurement showed similar baseline demographic and health-related characteristics as those who declined the measurement40. The accelerometer was set up to start at 10 a.m. two working days after postal dispatch (to ensure that the accelerometer would not start recording during delivery), and capture triaxial acceleration data over 7 days at 100 Hz with a dynamic range of ±8 gravity. Participants were instructed to wear the device on their dominant wrist continuously for seven days while continuing with their usual activities. Participants were asked to mail the device in a pre-paid envelope back to the coordinating centers, after the seven-day monitoring period.

Using the raw accelerometer data from 103,682 participants, the UK Biobank accelerometer expert working group conducted data processing and generated physical activity intensity data (average vector magnitude in milligravity units) in 5-s epochs (field ID 90004) using the raw accelerometer data (field ID 90001). The raw acceleration signals were calibrated to gravity. Non-wear time was defined as consecutive stationary episodes lasting for at least one hour where all three axes had a standard deviation of less than 13.0 milligravity39. Epochs representing non-wear time were imputed based on all wear-time data at a similar time of the day on different days for each participant. More details about the data processing and analysis have been published39.

The exclusion criteria are as follows: (1) those who withdrew from UK Biobank; (2) those who had no PA data in any one hour of the 24-h cycle; (3) Similar to the previous study20, those who had high nocturnal activity (>10{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} PA accumulated between 01:00 and 04:00), as we focused on individuals with a diurnal lifestyle; (4) those with unreliable or invalid accelerometry data. The criteria for unreliable or invalid accelerometry data included (1) unexpectedly small or large size (Field ID: 90002); (2) less than 72 h or did not provide data for all 1-h periods within a 24-h cycle during the 7-day data collection (Field ID: 90015); (3) not well-calibrated (Field ID: 90016); (4) recalibrated using the previous accelerometer record from the same device worn by a different participant (Field ID: 90017); (5) data with a non-zero count of interrupted recording periods (Field ID: 90180); (6) data with more than 768 (Q3 + 1.5 × IQR) data recording errors (Field ID: 90182). In total 11,543 participants were excluded. Finally, 92,139 participants (88.87{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) with valid data were included in the current study for the main analysis with the imputation of missing data, while 89,141 participants with the complete set of data were included for sensitivity analysis (Supplementary Fig. 4).

Exposure

The processed activity intensity data were further used to yield MVPA. MVPA, often defined as requiring a moderate to a large amount of effort and with a notable to substantial acceleration in heart rate, is a well-validated surrogate for PA40. More importantly, as suggested by the previous research20, focusing on high-intensity levels of PA, such as MVPA, helps to determine a clear timing effect. Light-intensity PA was not included in this study because it occurs during walking and even sitting hours, thereby obscuring the temporal distribution of the more effective PA with higher intensity20. In this study, we tried to generate PA timing grouping using the averaged acceleration data, and only 0.74{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 680), 10.1{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 9316), and 0.70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 641) of the participants were assigned to the morning, midday-afternoon, and evening group, respectively. The remaining 88.5{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} (n = 81,502) of the participants were assigned to the mixed group. Therefore, similar to previous research20, we focused on PA at a relatively high-intensity level (i.e., MVPA) to determine a robust timing phenotype.

Moderate-intensity physical activity was collected in sessions (5-min periods where more than 80{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of 5-s epochs had a mean acceleration of 100 to 400 milligravity)40. Vigorous-intensity physical activity was defined as the 5-s epochs, where the mean acceleration was above 400 milligravity41. We included individuals with a diurnal lifestyle. Furthermore, high nocturnal activity always means sleep disturbances. Due to these reasons, we calculated the total minutes of MVPA by summing the minutes of moderate-intensity physical activity and vigorous-intensity physical activity between 05:00 and 24:00. Among 8354 (9.07{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) participants who provided <1 week of accelerometry data, we extrapolated MPVA data to seven days.

Then, we ran the exploratory analyses to determine the appropriate boundaries for the time windows used for the main analyses. Exposure-dependent methods, such as the equally spaced intervals (which do not appear to be clinically driven), are generally arbitrary and may not be helpful in assessing a variable’s actual predictive value27. In contrast, the outcome-based methods allow an “optimal” cutoff to be estimated42,43. Therefore, this study ran an exploratory analysis for the identification of time window boundaries using the ‘outcome-based’ method. In addition, to balance sample size and accuracy, we chose the 2-h time window intervals (3-h only for the 21:00–24:00 period) in the exploratory analyses. The 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} method of assigning timing groups was similar to that previously used20, and this method avoids the participants being assigned to multiple timing groups. If over 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of total daily MVPA occurred during the same 2-h period, participants would be assigned to the corresponding groups. For those who spent less than 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the total daily MVPA in any of the 2-h time windows, we assigned them to the mixed group. As shown in Supplementary Fig. 5, which mimics the nonlinear exposure-outcome curves, two change points at 11:00 and 17:00 were consistently observed for all mortality outcomes. Compared with the mixed group, the 2-h timing groups of the morning (05:00–11:00) and evening (17:00–24:00) periods seemed to have higher mortality risks. The 2-h timing groups of the midday to afternoon period (11:00–17:00) presented comparable mortality risks with the mixed group (Supplementary Fig. 5). Finally, morning (05:00–11:00), midday to afternoon (11:00–17:00), and evening (17:00–24:00) time windows were used in subsequent MVPA timing grouping and statistical analyses.

Similar to the previous study20, if ≥50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of total daily MVPA occurred during the same time window, participants would be assigned to the corresponding MVPA timing groups: morning (05:00–11:00), midday-afternoon (11:00–17:00), and evening (17:00-24:00) groups. For those who spent <50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the total daily MVPA in any of three time windows, we assigned them to the mixed group.

Outcomes

The outcomes were all-cause, CVD, and cancer mortality. Cause-specific mortality was ascertained using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (Supplementary Table 20). We measured specific mortality due to CVDs (codes I00–I99) and cancer (codes C00-C97) using the death registry. The date and cause of death were obtained from the death datasets of the National Health Service Information Center and the NHS Central Register. At the time of analysis, we censored the Cox regression analyses at the date of death or the date of available mortality data (12 November 2021), whichever came first.

Covariates

Data on these possible covariates were obtained using self-reported questionnaires, accelerometers, and registry records. Age (continuous) was calculated from the date of birth and the date of wearing the accelerometer. Self-reported questionnaires were used to determine sex (female/male), ethnicity (white/others), recruitment center (England/Wales/Scotland), and Townsend deprivation index (continuous) based on the postcode of residence using aggregated data on unemployment, car and home ownership, and household overcrowding. The data on the season of accelerometry wear (i.e., spring, March to May; summer, June to August; autumn, September to November; winter, for December to February; UK Meteorological Office definitions) was obtained from accelerometer data. Other covariate data including educational attainment (degree or above/any other qualification/no qualification), smoking status (never/previous/current), frequency of alcohol intake (not current/less than three times a week/three or more times a week), and diet-related factors were obtained from touchscreen questions. We calculated the healthy diet score by using the following factors: vegetable intake of at least four tablespoons each day (median), fruit intake of at least three pieces each day (median), fish intake of at least twice per week (median), unprocessed red meat intake of no more than twice per week (median), and processed meat intake of no more than two per week (median). Sleep duration (<7 h per day/7–8 h per day/> 8 h per day) and sleep midpoint (<02:30/02:30-03:30/> 03:30) were measured using the accelerometer. Obesity (body mass index ≥30 kg/m2) was obtained from touchscreen questions. Previous diagnoses of diabetes, longstanding illness, depression, CVDs, and cancer were obtained from the self-reported questionnaires, hospital records, and death registry. The values of some covariates, including education level, smoking status, alcohol consumption, healthy diet score, obesity, diabetes history, longstanding illness, and cancer history, were obtained from touchscreen questionnaires at the time-point closest to the accelerometry (Supplementary Fig. 6). Detailed information sources, assessment timeline, and missing percentages are shown in Supplementary Fig. 6 and Supplementary Tables 20–21.

Statistical analyses

The event numbers of all outcomes were sufficient as per the rule-of-thumb estimation44, which requires at least ten events per variable. We conducted multiple imputations to assign any missing covariate values using the “mice” package (v3.13.0) in R45. The overall sample and complete case sample showed similar baseline characteristics (Supplementary Table 22). Before investigating the timing effect of MVPA, the linear and nonlinear associations of total MVPA volume and MVPA within the three time windows with mortality risk were assessed using penalized cubic splines fitted in the fully adjusted Cox models. In addition, we assessed the linear and nonlinear associations between the proportions of MVPA accumulated within the three time windows and mortality risk. Based on the fully adjusted model, cumulative risk curves were generated to show the standardized risks of mortality outcomes according to MVPA timing groups. Collinearity between all covariates was examined via correlation matrix analysis, which revealed no problem of multicollinearity. Cox proportional hazard regression (using the “survival” package v3.2-11 in R) was used to examine the associations of the timing of MVPA with mortality. HRs and their 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CIs were calculated. We conducted careful adjustments. Model 1 adjusted for age and sex. Model 2 additionally adjusted for ethnicity, Townsend deprivation index, recruitment center, education level, the season of accelerometer wear, smoking status, alcohol intake, and healthy diet score. To investigate the associations independent of sleep duration, sleep phase, and total MVPA volume, model 3 further adjusted for these factors.

We performed a series of sensitivity analyses. First, we used different MVPA fraction cutoffs (55, 60, 65, and 70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}) to assign timing groups. We did not conduct sensitivity analyses using cutoffs of >70{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} due to the small sample size for timing groups (other than mixed group). Second, Fine-Gray subdistribution hazards (using the “cmprsk” package v2.2-10 in R) were calculated, incorporating other-cause death as a competing risk for cause-specific mortality46. Third, we further adjusted for health-related variables potentially on causal pathways6, including obesity, diabetes history, longstanding illness, depression history, CVDs, and cancer. Fourth, we restricted the analyses to participants without shift work history and those without any missing covariate data, respectively. Fifth, we excluded participants who wore accelerometers during daylight-saving time transitions. Sixth, we ran the analyses by controlling for the month of accelerometer wear instead of the season of accelerometer wear. Seventh, we excluded events that occurred within one year of follow-up. In addition, we performed analyses by censoring up to 31 Dec 2019 (the start of the COVID-19 pandemic47). Finally, we repeated the analyses among those with ≥6 days of accelerometer wear.

Multiplicative and additive interaction (using the “interactionR” package v0.1.3.9000 in R) analyses and subgroup analyses were performed on age, sex, MVPA level (meeting the WHO recommendation1,3 or not), CVDs, and obesity (BMI ≥30 kg/m2). All statistical tests were two-sided, and a P value of <0.05 was regarded as statistically significant. To account for multiple testing, P values in fully adjusted models were corrected using the false-discovery rate (FDR)48. All statistical analyses were performed using R v4.0.4 and SPSS v26. R codes are available with the online version of this article (Supplementary Code) and at https://github.com/hlfeng99/Supplementary-Code.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality

Sample and design

Figure 1 describes the derivation of the analytic sample. The UK Biobank Study is a prospective cohort study of adults aged between 40 and 69 years whose baseline measurements took place between 2006 and 2010. Participants provided informed consent and ethical approval was provided by the UK’s National Health Service, National Research Ethics Service (Ethics Committee reference number: 11/NW/0382).

Between 2013 and 2015 (median 5.5 years after the baseline measurements), 103,684 UK Biobank participants wore a wrist-worn accelerometer for 7 days24,25. We excluded participants with missing covariates and insufficient valid wear days. Monitoring days were considered valid if wear time was greater than 16 h. To be included in analysis, participants were required to have at least three valid monitoring days, with at least one of those days being a weekend day45,46. We excluded participants who reported that they cannot walk.

To enable examination of VILPA in our study (brief bouts of nonexercise VPA occurring during daily living), we included only participants who reported no leisure time exercise participation and no more than one recreational walk per week. Participation in exercise and recreational walking was measured through a close-ended touch-screen questionnaire that asked participants to report if, how often, and for how long they participate in such activities (Supplementary Table 2). Among the included 14,982 participants who were walking for recreation once a week or less, the average spacing of VILPA bouts was 165.7 (47.0) min within days and 16.7 (5.5) h between days (last session of a day versus first session the day after). The modal median length of the (at most) one and only weekly walking session these participants reported was 30–60 min (32.5{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the 14,982 participants), effectively eliminating the possibility that the device-recorded VILPA bouts occurred during recreational walking.

To provide a comparison between effects of VILPA and (context-agnostic) VPA we repeated the main analyses among ‘exercisers’, defined as those UK Biobank accelerometry substudy participants who did not meet the above criteria to be considered nonexercisers; that is, those who reported any leisure time exercise or more than one recreational walking session per week (Supplementary Table 1).

Definition of VILPA and choice of bout length

We based the choice of VILPA bout length entered in our analyses on an ongoing study of 58 adults (mean age 55.7 (s.d. 10.1) years) aimed at developing an empirical definition of VILPA (M.N.A., N. Johnson, C.T.-N., M.J.G. and E.S., unpublished data). Participants completed five activities of daily living while wearing an indirect calorimetry unit (Cosmed K5) and Polar heart-rate monitor. The activities included: (1) walking on a flat surface at a self-selected ‘very fast’ pace; (2) walking on a flat surface while carrying shopping-like bags equivalent to 5{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of body weight at a self-defined ‘fast’ pace; (3) walking on a flat surface while carrying shopping-like bags equivalent to 10{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of body weight at a self-defined ‘fast’ pace; (4) walking at a 2.5{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} gradient at a self-defined ‘very fast’ pace (treadmill); and (5) walking at a 7.0{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} gradient at a self-defined ‘very fast’ pace (treadmill). The sequence of activities was randomized for each participant and counterbalanced across participants to prevent biases due to residual fatigue accumulating during the protocol.

Participants performed each activity until vigorous intensity was reached for two of three criteria: (1) {af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}VO2max (percentage of maximal oxygen updake) (≥64{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}); (2) {af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}HRmax (percentage of maximal heart rate) (≥77{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}); and (3) rating of perceived exertion (Borg scale) ≥15. For {af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}VO2max and {af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}HRmax, the threshold had to be met for at least 30 consecutive seconds to minimize the effects of noise. VO2max was calculated using the Ebbeling treadmill test and HRmax was calculated using the Tanaka equation47. Between activities, participants had 5 min of seated recovery, or until heart rate and breathing returned to resting levels. Resting VO2 and heart rate were measured at the beginning of each session with the participant lying supine using 5 min of steady-state (coefficient of variation ≤ 10{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db}). The duration to reach vigorous intensity across all five activities is shown in Supplementary Table 7. As the mean time required to reach vigorous intensity in two of the above three physiological intensity indices was 73.5 s (s.d. 26.2 s) across all activities, we decided to test VILPA bouts lasting up to 1 and up to 2 min in the present analyses. As the length of raw bouts within these two VILPA frequency exposures was highly variable, we length-standardized analytic bouts to one minute (for raw bouts lasting up to 1 minute) or two minutes (for raw bouts lasting up to 2 minutes) using a rolling sum on the time-series data until 1 or 2 minutes, respectively, was reached or exceeded. For example, a participant with five consecutive raw bouts lasting up to 1 minute each (20, 30, 20, 40, and 10 seconds long), would be assigned 1.83 analytic bouts: the first three raw bouts would count as one and the rolling sum would be reset; then the last two raw counts would count as 0.83 length-standardised bouts (50 seconds divided by 60). This bout handling has analytic and interpretational advantages: a) it mitigates against the problem of multicollinearity between raw VILPA frequency and daily VILPA duration, and b) permits a more concrete behavioural interpretation of the VILPA frequency findings than raw bouts, as each length-standardised bout can be specifically interpreted as lasting 1 minute or 2 minutes.

Wearable device-based physical activity classification

The methods we describe here were used to classify physical activity intensity in both the nonexercisers (main analyses) and exercisers (additional analyses) strata. Supplementary Fig. 4 summarizes how activity intensity was classified using a previously validated random forest (RF) activity classifier33. RF is an ensemble of multiple decision trees. Each tree is learned on a bootstrap sample of training data and each node in the tree is split using the best among a randomly selected set of acceleration features. The decisions from each tree are aggregated and a final model prediction is based on majority vote. The RF model requires very little preprocessing of the data because the features do not need to be normalized. In addition, the model is resistant to over-fitting the training data because each tree within the forest is independently grown to maximum depth using a randomly selected subset of features.

This two-stage classifier first categorized physical activity in 10-s windows into one of four activity classes: sedentary, standing utilitarian movements (for example, ironing a shirt, washing dishes), walking activities (for example, gardening, active commuting, mopping floors), running/high energetic activities (for example, active playing with children). These activity classes were then assigned to one of four activity intensities: sedentary, light, moderate and vigorous. Walking activities were classified as light (an acceleration value of <100 mg), moderate (≥100 mg) and vigorous (≥400 mg) intensity48. For example, for a VILPA bout lasting up to 2 min, 12 consecutive 10-s windows needed to be classified as vigorous. When there were more than 12 consecutive vigorous activity windows, these bouts counted as long VPA sessions in the corresponding analyses (2.3{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of all VPA bouts). Differentiation between sleep36 and nonwear35 was identified using the change in tilt angle and acceleration standard deviation. Monitors were calibrated49 and corrected for orientation50 using previously published methods, although residual signal and alignment uncertainties may persist.

Activities in an independent sample of 98 participants (age 56.4 ± 15.7 years ; 53.1{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} female) from the US51 (University of California Irvine Center for Machine Learning and Intelligent Systems Physical Activity Monitoring for Aging People study (published data), accessible at https://archive.ics.uci.edu/ml/datasets) and Australia52 (University of Queensland Where and When at Work study (published data) and University of Sydney Intermittent Lifestyle Physical Activity Study (unpublished data)) providing 103,607 activity samples from structured and free-living activities (17,267 min) were used to assess robustness and generalizability of the classifier (Supplementary Tables 8 and 9). For free-living activities participant-worn or researcher-held Go-Pro video-recordings were used to attain ground-truth physical activity. Video files were imported into the Noldus Observer XT software v16.0 for continuous direct observation coding. A two-stage direct observation scheme was implemented in which the participant’s movement behavior was coded for activity type and then activity intensity based on the Compendium of Physical Activities53. The direct observation system generated a vector of date–time stamps corresponding to the start and finish of each movement event, which were used to assign the activity codes to the corresponding time segments of the accelerometer data. Interobserver reliability was assessed by dual coding. The intraclass correlation coefficient for coding activities was 0.91 (0.87–0.94).

Performance was further evaluated in a separate sample of 151 adults (age range 18–91 years, 65.6{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} female; Supplementary Fig. 5) recruited from the UK34 (University of Oxford Capture 24 study (published data), accessible at https://ora.ox.ac.uk/objects/uuid:99d7c092-d865-4a19-b096-cc16440cd001). Participants in this data set wore body cameras that provided pictures every 20 s to annotate ground-truth free-living activity labels. The picture-based activity coding scheme has been previously described34. A total of 172,360 activity samples (28,727 min) were provided by participants.

Outcome ascertainment

Because of the nature of rolling updates for the data linkage, participants were followed up to 31 October 2021, with deaths obtained through linkage with the National Health Service (NHS) Digital of England and Wales or the NHS Central Register and National Records of Scotland. CVD mortality was defined as death attributed to diseases of the circulatory system, excluding hypertension, diseases of arteries and lymph (ICD-10 codes: I0, I11, I13, I20–I51, I60–I69). Cancer mortality was defined as death attributed to any cancer excluded in situ, benign, uncertain, nonmelanoma skin cancer or non-well-defined cancers (ICD-10 codes beginning ‘C0’, ‘C1’, ‘C2’, ‘C3’, ‘C4’ (excluding C49.9), ‘C5’, ‘C6’, ‘C70’, ‘C71’, ‘C72’, ‘C73’, ‘C74’, ‘C75’, ‘C7A’, ‘C8’ or ‘C9’).

Statistical analyses

In our study, the range of VILPA values (and context-agnostic VPA values in exercisers) was capped at the 97.5 percentile to minimize the influence of sparse data. To reduce the possibility of reverse causation through prodromal/undiagnosed disease, all analyses excluded those with an event within the first 2 years of follow-up. We also excluded those with prevalent CVD and prevalent cancer at baseline (CVD and cancer mortality analyses, respectively).

We examined the dose–response of average daily duration and frequency of VILPA bouts lasting up to 1 min and up to 2 min using Cox proportional hazards (all-cause mortality) and Fine–Gray subdistribution hazards to account for competing mortality risks (CVD and cancer mortality)54. In all analyses, we set knots at the 10th, 50th and 90th percentiles. Departure from linearity was assessed by a Wald test. Proportional hazards assumptions were tested using Schoenfeld residuals in the models with all three outcomes and no violations were observed (all P > 0.05). Analyses were adjusted for age, sex, daily duration of light- and of moderate-intensity physical activity, mutual adjustment for daily duration and frequency of vigorous-intensity physical activity bouts lasting more than 1 to 2 min as appropriate, smoking, alcohol, accelerometry-estimated sleep duration35,36, fruit and vegetable consumption, education, parental history of CVD and cancer, medication use (insulin, blood pressure, cholesterol). All-cause mortality analyses were also adjusted for prevalent CVD and cancer, CVD analyses were adjusted for prevalent cancer, and cancer analyses were adjusted for prevalent CVD (Supplementary Table 3 provides full covariate definitions).

In the exercisers stratum of the UK Biobank accelerometry sample, we repeated the above multivariable-adjusted analyses for daily duration and frequency of (context-agnostic) VPA for bouts lasting up to 2 min, and we compared findings with the equivalent VILPA findings using overlay dose–response plots.

To assert the degree to which VILPA and VPA may contribute to mortality beyond the associations of overall movement volume, we also carried out a volume analysis based on energy expenditure using methods analogous to the study by Strain et al.18 We calculated physical activity energy expenditure for all VILPA and VPA bouts lasting up to 2 min.

To provide conservative point estimates we calculated the ‘minimal dose’, defined as VILPA volume/frequency associated with 50{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} of the optimal risk reduction37,38. We also present point estimates (HRs and 95{af0afab2a7197b4b77fcd3bf971aba285b2cb7aa14e17a071e3a1bf5ccadd6db} CI) associated with the median and maximum volume/frequency VILPA values. We calculated E-values to estimate the plausibility of bias from unmeasured confounding30,55.

We conducted sensitivity analyses of VILPA with additional adjustment for body mass index. To investigate potential reverse causation bias we also excluded participants who had poor self-rated health. In another sensitivity analysis, we tested the influence of applying a conservative definition of ‘nonexercisers’ by restricting analyses to the 10,230 participants who reported no recreational walking and no leisure time exercise.

We performed all analysis using R statistical software v.4.2.1 with RMS v.6.3.0 and survival package v.3.3.1.

We reported this study as per the Strengthening the Reporting of Observational Studies in Epidemiology guidelines (Supplementary Table 10).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.