Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium

AIMS/HYPOTHESIS

The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health.

METHODS

We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines.

RESULTS

We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers.

CONCLUSIONS/INTERPRETATION

In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

Physical Activity Levels and Sedentary Behavior of People Living With Mild Cognitive Impairment: A Cross-Sectional Study Using Thigh-Worn Accelerometers

Community-dwelling people with Mild Cognitive Impairment self-reporting not to be meeting recommended physical activity levels participated in this study to (a) determine compliance of wearing (thigh-worn) accelerometers, (b) describe physical activity levels and sedentary behavior, and (c) determine the validity of the Physical Activity Scale for the Elderly (PASE) compared with activPAL accelerometers. A total of 79 people had valid accelerometer data (median [interquartile range]: age, 71 [54-75] years). Compliance was 86.81%. Participants were sedentary for 10.6 hr per day and engaged in a median of 9 min per day of moderate-intensity physical activity. Fair correlations were found between the PASE and total stepping time per day (r = .35, p < .01), total number of steps per day (r = .36, p < .01), and number of steps in stepping activities completed for ≤1 min (r = .42, p < .01). The PASE and Standing time (r = .04, p = .724) and PASE and Sitting time (r = .04, p = .699) had little to no relationship. The use of thigh-worn accelerometers for this population is achievable. People with Mild Cognitive Impairment have high levels of sedentary behavior and minimal engagement in moderate-intensity physical activity. The PASE has fair, positive criterion validity with activity-based outcomes measured by activPAL accelerometers but not with sedentary behavior, which is high for this population.

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.

Cost and statistical efficiency of posture assessment by inclinometry and observation, exemplified by paper mill work

Postures at work are paramount in ergonomics. They can be determined using observation and inclinometry in a variety of measurement scenarios that may differ both in costs associated with collecting and processing data, and in efficiency, i.e. the precision of the eventual outcome. The trade-off between cost and efficiency has rarely been addressed in research despite the obvious interest of obtaining precise data at low costs. Median trunk and upper arm inclination were determined for full shifts in 28 paper mill workers using both observation and inclinometry. Costs were estimated using comprehensive cost equations; and efficiency, i.e. the inverted standard deviation of the group mean, was assessed on basis of exposure variance components. Cost and efficiency were estimated in simulations of six sampling scenarios: two for inclinometry (sampling from one or three shifts) and four for observation (one or three observers rating one or three shifts). Each of the six scenarios was evaluated for 1 through 50 workers. Cost-efficiency relationships between the scenarios were intricate. As an example, inclinometry was always more cost-efficient than observation for trunk inclination, except for observation strategies involving only few workers; while for arm inclination, observation by three observers of one shift per worker outperformed inclinometry on three shifts up to a budget of €20000, after which inclinometry prevailed. At a budget of €10000, the best sampling scenario for arm inclination was 2.5 times more efficient than the worst. Arm inclination could be determined with better cost-efficiency than trunk inclination. Our study illustrates that the cost-efficiency of different posture measurement strategies can be assessed and compared using easily accessible diagrams. While the numeric examples in our study are specific to the investigated occupation, exposure variables, and sampling logistics, we believe that inclinometry will, in general, outperform observation. In any specific case, we recommend a thorough analysis, using the comparison procedure proposed in the present study, of feasible strategies for obtaining data, in order to arrive at an informed decision support.

Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

BACKGROUND

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

OBJECTIVE

This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

METHODS

Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

RESULTS

A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

CONCLUSIONS

This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

Leave the screen: The influence of everyday behaviors on self-reported interoception

The influence of physical activity on interoception is apparent, however little is known about within-person variability following physical activity and sedentary behavior in daily life. To test this, 70 healthy adults (Mage 21.67 ± 2.50) wore thigh-mounted accelerometers for 7-days, with self-reported interoception recorded on movement-triggered smartphones. Participants additionally reported the predominant activity type performed across the last 15 min. Investigating this timeframe, multi-level analyses revealed that each one-unit increase in physical activity was associated with an increase in self-reported interoception (B = 0.0025, p = .013), whereas contrastingly, each one-minute increase in sedentary behavior was associated with a decrease (B = -0.06. p = .009). Investigating the influence of different activity types in comparison to screen time behavior, both partaking in exercise (B = 4.48, p < .001) and daily-life physical activity (B = 1.21, p < .001) were associated with an increase in self-reported interoception. Regarding other behavior categories, non-screen time behavior both with (B = 1.13, p < .001) and without (B = 0.67, p = .004) social interaction were also associated with an increase in self-reported interoception compared to screen-time behavior. Extending from previous laboratory-based studies, these findings indicate that physical activity influences interoceptive processes in real-life, further supplemented by the novel and contrasting findings regarding sedentary behavior. Furthermore, associations with activity type reveal important mechanistic information, highlighting the importance of reducing screen-time behavior to preserve and support interoceptive perceptions. Findings can be used to inform health recommendations for reducing screen-time behavior and guiding evidence-based physical activity interventions to promote interoceptive processes.

Arabic translation and cultural adaptation of sedentary behavior, dietary habits, and preclinical mobility limitation questionnaires: A cognitive interview study

BACKGROUND

Developing global evidence on the influence of health-related behaviors (e.g., sedentary behavior, diet) and mobility limitations on health requires global consortia from diverse sets of countries. Thus, the purpose was to translate and culturally adapt (i) the Sedentary Behavior Questionnaire (SBQ); (ii) the Dietary Habits Questionnaire adapted from the Survey of Health, Aging and Retirement in Europe (SHARE) study; (iii) the Preclinical Mobility Limitation questionnaire for use in the Saudi Arabian context.

METHOD

50 adult Saudi participants (mean age 41.7±9.6, 48% female) participated in this study. We followed a systematic cross-cultural adaptation process that involved forward translation, synthesis, back-translation, expert panel, and pre-testing (cognitive interviewing). Four rounds of cognitive interviews were held with 40 participants for the SBQ, SHARE questionnaire, and the Preclinical Mobility Limitation questionnaire, an additional round was needed for the Preclinical Mobility Limitation questionnaire. Descriptive data (means ± standard deviations and frequencies with percentages) were reported for characteristics.

RESULT

With some minor changes to the questionnaires, the SBQ, Dietary Habits, and Preclinical Mobility Limitation questionnaires were translated and cross-culturally adapted into Arabic. 100% of the participants confirmed that the resulting Arabic versions of the SBQ, Dietary Habits questionnaire, and Preclinical Mobility Limitation questionnaires were appropriate and fully understandable for Arabic speakers in communicating the intended meanings of the items in each. For example, item SBQ1, 'Watching television (including videos on VCR/DVD)' was changed to 'Sitting and watching television or videos (including smartphones, tablets)'.

CONCLUSION

The SBQ, Dietary Habits questionnaire, and Preclinical Mobility Limitation questionnaire were successfully cross-culturally adapted into Arabic and are now ready for use in Saudi Arabian.

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions

BACKGROUND

Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing.

METHODS

We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions.

RESULTS

Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk".

CONCLUSION

Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

Comparison of physical activity metrics from two research-grade accelerometers worn on the non-dominant wrist and thigh in children

This study compared physical activity metrics from the activPAL (AP) worn on the thigh with the ActiGraph worn on the non-dominant wrist using open-source methods. Measures included average acceleration, intensity gradient (IG) and the minimum acceleration value of the most active X mins (MX). Fifty-two children (26 boys; age: 10.4 ± 0.6 years) provided≥1 day (24 h) of concurrent wear time from the activPAL and ActiGraph. Measures tended to be lower from the activPAL versus the ActiGraph. Poor agreement was evident for average acceleration but good for the IG. For the IG, the absolute and relative zones needed to reach equivalence was 4% and 0.4 SDs, respectively and for average acceleration were 10% and 1.2 SDs, respectively. Good agreement was evident for M60, M30, M20, M15 and M10 between devices. Regardless of the reference device used, equivalent estimates for the intensity gradient, M60, M30, M20, M15 and M10 were observed with relative and absolute equivalence zones being≤4% and≤0.5 SDs, respectively. The IG, M60, M30, M20, M15 and M10 appear good candidates for comparing activity data collected from the activPAL and ActiGraph. Future research can use the AP to report on sedentary behaviours as well as PA outcomes.

Development and performance of a sleep estimation algorithm using a single accelerometer placed on the thigh: an evaluation against polysomnography

Accelerometers placed on the thigh provide accurate measures of daily physical activity types, postures and sedentary behaviours, over 24 h and across consecutive days. However, the ability to estimate sleep duration or quality from thigh-worn accelerometers is uncertain and has not been evaluated in comparison with the 'gold-standard' measurement of sleep polysomnography. This study aimed to develop an algorithm for sleep estimation using the raw data from a thigh-worn accelerometer and to evaluate it in comparison with polysomnography. The algorithm was developed and optimised on a dataset consisting of 23 single-night polysomnography recordings, collected in a laboratory, from 15 asymptomatic adults. This optimised algorithm was then applied to a separate evaluation dataset, in which, 71 adult males (mean [SD] age 57 [11] years, height 181 [6] cm, weight 82 [13] kg) wore ambulatory polysomnography equipment and a thigh-worn accelerometer, simultaneously, whilst sleeping at home. Compared with polysomnography, the algorithm had a sensitivity of 0.84 and a specificity of 0.55 when estimating sleep periods. Sleep intervals were underestimated by 21 min (130 min, Limits of Agreement Range [LoAR]). Total sleep time was underestimated by 32 min (233 min LoAR). Our results evaluate the performance of a new algorithm for estimating sleep and outline the limitations. Based on these results, we conclude that a single device can provide estimates of the sleep interval and total sleep time with sufficient accuracy for the measurement of daily physical activity, sedentary behaviour, and sleep, on a group level in free-living settings.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

BACKGROUND

Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours.

METHODS

The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss.

RESULTS

123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent.

CONCLUSION

Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Device-Measured Physical Activity and Sedentary Behavior in Relation to Cardiovascular Diseases and All-Cause Mortality: Systematic Review and Meta-Analysis of Prospective Cohort Studies

Introduction

This review synthesized evidence from prospective cohort studies on the association of device-measured physical activity and sedentary behavior with cardiovascular disease and all-cause mortality among adults.

Methods

Five databases were searched from 2000 through April 29, 2020. Study quality was appraised using the NIH Quality Assessment Tool. Pooled hazard ratio and 95% CI were obtained from random-effects meta-analyses. Subgroup analyses by age and sex were conducted for studies on all-cause mortality.

Results

Of 29 articles included in the systematic review, 5 studies on cardiovascular disease mortality and 15 studies on all-cause mortality were included in meta-analyses. Comparing the highest with the lowest exposure categories, the pooled hazard ratios (95% CIs) for cardiovascular disease mortality were 0.29 (CI=0.18, 0.47) for total physical activity, 0.37 (CI=0.25, 0.55) for moderate-to-vigorous physical activity, 0.62 (0.41-0.93) for light physical activity, and 1.89 (CI=1.09, 3.29) for sedentary behavior. The pooled hazard ratios (95% CIs) for all-cause mortality were 0.42 (CI=0.34, 0.53) for total physical activity, 0.43 (CI=0.35, 0.53) for moderate-to-vigorous physical activity, 0.58 (CI=0.43, 0.80) for light physical activity, and 1.58 (CI=1.19, 2.09) for sedentary behavior. The pooled hazard ratio (95% CI) for all-cause mortality was 0.35 (CI=0.29, 0.42) for steps per day, but the studies available for analysis were conducted in older adults. The results of subgroup analyses were consistent with the main results.

Discussion

Rapidly accumulating evidence suggests that more physical activity and less sedentary behavior are associated with a lower risk of cardiovascular disease and all-cause mortality. Similar beneficial relationships were found for step counts and all-cause mortality among older adults. Future studies employing standardized research methodologies and up-to-date data processing approaches are warranted to recommend specific amounts of physical activity and limits to sedentary behavior.

A Call for Action on Chronic Respiratory Diseases within Physical Activity Policies, Guidelines and Action Plans: Let's Move!

Global policy documents for the promotion of physical activity (PA) play an important role in the measurement, evaluation, and monitoring of population PA levels. The World Health Organisation (WHO) guidelines include, for the first time, recommendations for specific populations, including individuals living with a range of non-communicable diseases. Of note, is the absence of any chronic respiratory diseases (CRDs) within the recommendations. Globally, CRDs are highly prevalent, are attributable to significant individual and societal burdens, and are characterised by low PA. As a community, there is a need to come together to understand how to increase CRD representation within global PA policy documents, including where the evidence gaps are and how we can align with PA research in other contexts. In this commentary, the potential for synergy between evidence into the relationships between PA in CRDs globally and the relevance to current policies, guidelines and action plans on population levels of PA are discussed. Furthermore, actions and considerations for future research, including the need to harmonize and promote PA assessment (particularly in low- and middle-income countries) and encompass the synergistic influences of PA, sedentary behaviour and sleep on health outcomes in CRD populations are presented.

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

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

CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35

Background

Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults.

Methods

Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training.

Results

Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2.

Conclusions

Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children

BACKGROUND

Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data.

METHODS

Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration).

RESULTS

Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL.

CONCLUSIONS

There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review

Use of raw acceleration data and/or "novel" analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.

OBJECTIVE

This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.

APPROACH

Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.

MAIN RESULTS

Studies (N=168) included adults (n=143), and/or children (n=38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n=107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n=48) and/or by providing access to code (n=13).

SIGNIFICANCE

The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

Data-Driven User-Type Clustering of a Physical Activity Promotion App: Usage Data Analysis Study

Background

Physical inactivity remains a leading risk factor for mortality worldwide. Owing to increasing sedentary behavior (activities in a reclining, seated, or lying position with low-energy expenditures), vehicle-based transport, and insufficient physical workload, the prevalence of physical activity decreases significantly with age. To promote sufficient levels of participation in physical activities, the research prototype Fit-mit-ILSE was developed with the goal of making adults aged ≥55 years physically fit and fit for the use of assistive technologies. The system combines active and assisted living technologies and smart services in the ILSE app.

Objective

The clustering of health and fitness app user types, especially in the context of active and assisted living projects, has been mainly defined by experts through 1D cluster thresholds based on app usage frequency. We aimed to investigate and present data-driven methods for clustering app user types and to identify usage patterns based on the ILSE app function Fit at home.

Methods

During the 2 phases of the field trials, ILSE app log data were collected from 165 participants. Using this data set, 2 data-driven approaches were applied for clustering to group app users who were similar to each other. First, the common approach of user-type clustering based on expert-defined thresholds was replaced by a data-driven derivation of the cluster thresholds using the Jenks natural breaks algorithm. Second, a multidimensional clustering approach using the Partitioning Around Medoids algorithm was explored to consider the detailed app usage pattern data.

Results

Applying the Jenks clustering algorithm to the mean usage per day and clustering the users into 4 groups showed that most of the users (63/165, 38.2%) used the Fit at home function between once a week and every second day. More men were in the low usage group than women. In addition, the younger users were more often identified as moderate or high users than the older users, who were mainly classified as low users; moreover, the regional differences between Vienna and Salzburg were identified. In addition, the multidimensional approach identified 4 different user groups that differed mainly in terms of time of use, gender, and region. Overall, the younger women living in Salzburg were the users with highest average app usage.

Conclusions

The application of different clustering approaches showed that data-driven calculations of user groups can complement expert-based definitions, provide objective thresholds for the analysis of app usage data, and identify groups that can be targeted individually based on their specific group characteristics.

Patterns of Accelerometer-Measured Physical Activity and Health Outcomes in Adults: A Systematic Review

PURPOSE

The aim of this study was to systematically review the literature on accelerometer-measured physical activity and health outcomes in adults.

METHODS

Eight electronic databases were searched for relevant articles published up to March 2021. Only population-based studies of adults (age ≥18 yr) that directly compared two or more categories of physical activity (i.e., bout duration, intensity, and daily/weekly frequency) with a health outcome (e.g., mortality, cardiometabolic, healthy aging, depression, sleep, and brain structure) were included.

RESULTS

Of the 15,923 publications retrieved, 52 articles were included. Twenty-eight studies directly compared the associations between physical activity accumulated in different bout durations, 31 studies directly compared the associations between physical activity accumulated in different intensities, and 9 studies directly compared the associations between the effects of varying daily and weekly frequencies of physical activity, with health outcomes. Most showed no differences in relationships with health outcomes when physical activity was accumulated in short (<10-min) or long (≥10-min) bouts. Overall, there were no differences in the relationships with most health outcomes when different intensities and daily/weekly frequencies were compared. However, in most studies, researchers did not adjust their analyses for total volume of physical activity. Moreover, variations in researcher-driven decisions about data collection and processing methods made it difficult to compare study findings.

CONCLUSIONS

These findings suggest that physical activity accumulated in many patterns of bout duration, intensity, or daily/weekly frequency is associated with a range of beneficial health outcomes in adults. Lack of adjustment for total volume of physical activity in most studies and inconsistent methods for defining components of physical activity prevent firm conclusions about which specific patterns of bout duration, intensity, and daily/weekly frequency are most important for health benefits.

The Surveillance of Physical Activity, Sedentary Behavior, and Sleep: Protocol for the Development and Feasibility Evaluation of a Novel Measurement System

BACKGROUND

There is increasing recognition of the need for more comprehensive surveillance data, including information on physical activity of all intensities, sedentary behavior, and sleep. However, meeting this need poses significant challenges for current surveillance systems, which are mainly reliant on self-report.

OBJECTIVE

The primary objective of this project is to develop and evaluate the feasibility of a sensor-based system for use in the surveillance of physical activity, sedentary behavior, and sleep (SurPASS) at a national level in Denmark.

METHODS

The SurPASS project involves an international, multidisciplinary team of researchers collaborating with an industrial partner. The SurPASS system consists of (1) a thigh-worn accelerometer with Bluetooth connectivity, (2) a smartphone app, (3) an integrated back end, facilitating the automated upload, analysis, storage, and provision of individualized feedback in a manner compliant with European Union regulations on data privacy, and (4) an administrator web interface (web application) to monitor progress. The system development and evaluation will be performed in 3 phases. These phases will include gathering user input and specifications (phase 1), the iterative development, evaluation, and refinement of the system (phase 2), and the feasibility evaluation (phase 3).

RESULTS

The project started in September 2020 and completed phase 2 in February 2022. Phase 3 began in March 2022 and results will be made available in 2023.

CONCLUSIONS

If feasible, the SurPASS system could be a catalyst toward large-scale, sensor-based surveillance of physical activity, sedentary behavior, and sleep. It could also be adapted for cohort and interventional research, thus contributing to the generation of evidence for both interventions and public health policies and recommendations.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/35697.

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies

Objectives

Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing.

Design

Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time).

Data sources

Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970–July 2021).

Eligibility criteria for selecting studies

Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals.

Results

Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as ‘low risk’ or ‘some concerns’.

Summary

Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed.

PROSPERO registration number

CRD42021230894.

Cross-sectional survey on researchers' experience in using accelerometers in health-related studies

Objectives

Accelerometers are widely applied in health studies, but lack of standardisation regarding device placement, sampling and data processing hampers comparability between studies. The objectives of this study were to assess how accelerometers are applied in health-related research and problems with accelerometer hardware and software encountered by researchers.

Methods

Researchers applying accelerometry in a health context were invited to a cross-sectional web-based survey (August 2020–September 2020). The questionnaire included quantitative questions regarding the application of accelerometers and qualitative questions on encountered hardware and software problems. Descriptive statistics were calculated for quantitative data and content analysis was applied to qualitative data.

Results

In total, 116 health researchers were included in the study (response: 13.7%). The most used brand was ActiGraph (67.2%). Independently of brand, the main reason for choosing a device was that it was the standard in the field (57.1%–83.3%). In children and adolescent populations, sampling frequency was higher (mean: 73.3 Hz ±29.9 Hz vs 47.6 Hz ±29.4 Hz) and epoch length (15.0s±15.6s vs 30.1s±25.9s) and non-wear time (42.9 min ±23.7 min vs 65.3 min ±35.4 min) were shorter compared with adult populations. Content analysis revealed eight categories of hardware problems (battery problems, compliance issues, data loss, mechanical problems, electronic problems, sensor problems, lacking waterproofness, other problems) and five categories of software problems (lack of user-friendliness, limited possibilities, bugs, high computational burden, black box character).

Conclusions

The study confirms heterogeneity regarding accelerometer use in health-related research. Moreover, several hardware and software problems were documented. Both aspects must be tackled to increase validity, practicability and comparability of research.

The impact of different step rate threshold methods on physical activity intensity in older adults

BACKGROUND

Older adults benefit most from engaging in higher-intensity physical activity, which is often determined using step rate thresholds. Fixed step rate thresholds that correspond to moderate (MPA) and vigorous-intensity physical activity (VPA) have been developed for heuristic activity promotion. The activPAL monitor uses step rate thresholds to determine activity intensity. Stepping thresholds may also vary based on body mass index (BMI) or aerobic fitness level in older adults. Despite the various thresholds used in the literature, it is unclear whether they produce similar outcomes.

RESEARCH QUESTION

How does time spent in physical activity intensities compare between different step rate thresholds in older adults?

METHODS

Thirty-eight participants (24♀; 67 ± 4 years; BMI: 26.6 ± 4.4 kg/m²) wore an activPAL monitor 24-hr/day for up to 7-d (total: 205-d). Aerobic fitness (V̇O₂max: 23 ± 8 ml/kg/min) was determined via indirect calorimetry during a maximal, graded cycling test. Time spent in each intensity category (light-physical-activity [LPA], MPA, VPA) was determined using the fixed (MPA/VPA) 100/130, 110/130, and activPAL step rate thresholds (74/212), as well as BMI-adjusted absolute (108.5 ± 2.5/134.0 ± 4.8) and BMI-adjusted relative (40%/60% V̇O₂max; 111.4 ± 14.7/132.0 ± 19.0) cut-offs. Times spent in each intensity category were compared between methods.

RESULTS

The activPAL and 100/130 thresholds yielded less LPA and more MPA than all other methods. The activPAL had no time spent in VPA at all. The BMI-adjusted absolute and relative thresholds produced statistically equivalent time in LPA and MPA (via equivalence testing), but not VPA. No two methods yielded similar time spent in LPA, MPA, or VPA.

SIGNIFICANCE

The choice of step rate threshold has a major impact on physical activity intensity outcomes in older adults. Inherently, strategies that adjust for older adults' body size and/or aerobic fitness level provide a more individualized data processing strategy than fixed thresholds that assume the same threshold for all older adults.

Use of activPAL to Measure Physical Activity in Community Dwelling Older Adults, A Systematic Review

Objective

To perform a systematic review of the literature to describe how the activPAL accelerometer has been used to measure physical activity (PA) in community-dwelling older adults to standardize collection of PA data in this population using this thigh-worn accelerometer.

Data Sources

A comprehensive search of the following databases was completed: Cumulative Index to Nursing and Allied Health Complete, Embase, OVID Medicine, PubMed/Web of Science, and Scopus.

Study Selection

Studies were included if published before August 1, 2020, were written in English, and used activPAL to measure PA in community-dwelling, noninstitutionalized adults 65 years or older. Titles and abstracts were independently reviewed, and the decision to include or exclude was made by 100% consensus.

Data Extraction

Three research team members independently extracted the data from included studies. Extracted data were compared and discussed with relevant information included. Study quality was assessed using the Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

Data Synthesis

A total of 7 articles met the inclusion criteria. Three of the 7 studies used activPAL to report steps/d, ranging from 864-15847 steps/d. Time spent stepping or walking was reported by 4 studies using various units. Sit-to-stand transitions were reported by 4 studies, averaging 10-63 transitions/d. Sedentary time was assessed in 6 studies, whereas moderate to vigorous physical activity was not measured using activPAL in any study.

Conclusions

The activPAL is most often used to collect data on step count and walking, sit-to-stand transitions, and sedentary time in community-dwelling older adults.

Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis

OBJECTIVE

To examine the joint associations of daily time spent in different intensities of physical activity, sedentary behaviour and sleep with all-cause mortality.

METHODS

Federated pooled analysis of six prospective cohorts with device-measured time spent in different intensities of physical activity, sedentary behaviour and sleep following a standardised compositional Cox regression analysis.

PARTICIPANTS

130 239 people from general population samples of adults (average age 54 years) from the UK, USA and Sweden.

MAIN OUTCOME

All-cause mortality (follow-up 4.3-14.5 years).

RESULTS

Studies using wrist and hip accelerometer provided statistically different results (I2=92.2%, Q-test p<0.001). There was no association between duration of sleep and all-cause mortality, HR=0.96 (95% CI 0.67 to 1.12). The proportion of time spent in moderate to vigorous physical activity was significantly associated with lower risk of all-cause mortality (HR=0.63 (95% CI 0.55 to 0.71) wrist; HR=0.93 (95% CI 0.87 to 0.98) hip). A significant association for the ratio of time spent in light physical activity and sedentary time was only found in hip accelerometer-based studies (HR=0.5, 95% CI 0.42 to 0.62). In studies based on hip accelerometer, the association between moderate to vigorous physical activity and mortality was modified by the balance of time spent in light physical activity and sedentary time.

CONCLUSION

This federated analysis shows a joint dose-response association between the daily balance of time spent in physical activity of different intensities and sedentary behaviour with all-cause mortality, while sleep duration does not appear to be significant. The strongest association is with time spent in moderate to vigorous physical activity, but it is modified by the balance of time spent in light physical activity relative to sedentary behaviour.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Descriptive Epidemiology of Interruptions to Free-Living Sitting Time in Middle-Age and Older Adults

National guidelines recommend physically active interruptions to sitting time, however, the characteristics of these interruptions are broadly stated and ill-defined. A robust methodology for population surveillance for such interruptions is needed.

PURPOSE

To describe the frequency and characteristics (i.e., duration, stepping time, and estimated intensity) of all interruptions and physically active interruptions to adults' free-living sitting time (i.e., transitions from sitting to upright posture) across segments of the population.

METHODS

Australian Diabetes, Obesity and Lifestyle (AusDiab) study participants (321 men; 406 women; mean ± SD 58.0 ± 10.3 years) wore the activPAL3TM for ≥1 valid day. The characteristics of interruptions from laboratory studies demonstrating health benefits were selected to define active interruptions (≥5 min upright and/or ≥ 2 min stepping) and ambulatory interruptions (≥2 min stepping). The frequency and characteristics of all, active, and ambulatory interruptions were described and compared by age, gender, diabetes status, and body mass index.

RESULTS

Adults averaged 55.0 ± 21.8 interruptions per day, but only 20.3 ± 6.7 were active and 14.0 ± 5.4 were ambulatory. Median (25th, 75th percentile) duration was 2.6 (0.9, 7.8) minutes, stepping time was 0.8 (0.3, 2.0) minutes, and estimated energy expenditure was 4.3 (1.4, 12.5) MET-min. Those who were older, had obesity, or had diabetes had significantly (p < 0.05) fewer interruptions of all types and less stepping time during active interruptions than their counterparts (Cohen's d < 0.2).

CONCLUSION

Free-living interruptions were often less active than interruptions performed in effective acute laboratory studies and their content varied widely between population groups. Monitoring all interruptions as well as those that are more active is advisable to provide a comprehensive understanding of free-living sedentary behavior.

Striking the Right Balance: Evidence to Inform Combined Physical Activity and Sedentary Behavior Recommendations

BACKGROUND

Crucial evidence gaps regarding: (1) the joint association of physical activity and sedentary time with health outcomes and (2) the benefits of light-intensity physical activity were identified during the development of recommendations for the World Health Organization Guidelines on physical activity and sedentary behavior (SB). The authors present alternative ways to evidence the relationship between health outcomes and time spent in physical activity and SB and examine how this could be translated into a combined recommendation in future guidelines.

METHODS

We used compositional data analysis to quantify the dose-response associations between the balance of time spent in physical activity and SB with all-cause mortality. The authors applied this approach using 2005-2006 National Health and Nutrition Examination Survey accelerometer data.

RESULTS

Different combinations of time spent in moderate- to vigorous-intensity physical activity, light-intensity physical activity, and SB are associated with similar all-cause mortality risk level. A balance of more than 2.5 minutes of moderate- to vigorous-intensity physical activity per hour of daily sedentary time is associated with the same magnitude of risk reduction for all-cause mortality as obtained by being physically active according to the current recommendations.

CONCLUSION

This method could be applied to provide evidence for more flexible recommendations in the future with options to act on different behaviors depending on individuals' circumstances and capacity.

Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial Accelerometers

Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (−135,141) min in the development dataset and +6.5 (−145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands.

Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement

Introduction

The Prospective Physical Activity Sitting and Sleep consortium (ProPASS) is an international collaboration platform committed to harmonise thigh-worn accelerometry data. The aim of this paper is to (1) outline observational thigh-worn accelerometry studies and (2) summarise key strategic directions arising from the inaugural ProPASS meeting.

Methods

(1) We performed a systematic scoping review for observational studies of thigh-worn triaxial accelerometers in free-living adults (n≥100, 24 hours monitoring protocols). (2)Attendees of the inaugural ProPASS meeting were sent a survey focused on areas related to developing ProPASS: important terminology (Q1); accelerometry constructs (Q2); advantages and distinct contribution of the consortium (Q3); data pooling and harmonisation (Q4); data access and sharing (Q5 and Q6).

Results

(1) Eighty eligible articles were identified (22 primary studies; n~17 685). The accelerometers used most often were the ActivPAL3 and ActiGraph GT3X. The most commonly collected health outcomes were cardiometabolic and musculoskeletal. (2) None of the survey questions elicited the predefined 60% agreement. Survey responses recommended that ProPASS: use the term physical behaviour or movement behaviour rather than 'physical activity' for the data we are collecting (Q1); make only minor changes to ProPASS's accelerometry construct (Q2); prioritise developing standardised protocols/tools (Q4); facilitate flexible methods of data sharing and access (Q5 and Q6).

Conclusions

Thigh-worn accelerometry is an emerging method of capturing movement and posture across the 24 hours cycle. In 2020, the literature is limited to 22 primary studies from high-income western countries. This work identified ProPASS's strategic directions-indicating areas where ProPASS can most benefit the field of research: use of clear terminology, refinement of the measured construct, standardised protocols/tools and flexible data sharing.

Accuracy of Sedentary Behavior-Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study

Background

Sedentary behavior has received much attention in the scientific community over the past decade. There is growing evidence that sedentary behavior is negatively associated with physical and mental health. However, an in-depth understanding of the social and environmental context of sedentary behavior is missing. Information about sedentary behavior, such as how everyday sedentary behavior occurs throughout the day (eg, number and length of sedentary bouts), where, when, and with whom it takes place, and what people are doing while being sedentary, is useful to inform the development of interventions aimed at reducing sedentary time. However, examining everyday sedentary behavior requires specific methods.

Objective

The purpose of this paper is (1) to introduce sedentary behavior–triggered Ecological Momentary Assessment (EMA) as a methodological advancement in the field of sedentary behavior research and (2) to examine the accuracy of sedentary behavior–triggered EMA in 3 different studies in healthy adults. Moreover, we compare the accuracy of sedentary behavior–triggered EMA to simulations of random-trigger designs.

Methods

Sedentary behavior–triggered EMA comprises a continuous assessment of sedentary behavior via accelerometers and repeated contextual assessments via electronic diaries (ie, an application on a smartphone). More specifically, the accelerometer analyzes and transfers data regarding body position (a sitting or lying position, or an upright position) via Bluetooth Low Energy (BLE) to a smartphone in real time and triggers the deployment of questionnaires. Each time a participant spends a specified time (eg, 20 minutes) in a sedentary position, the e-diary triggers contextual assessments. To test the accuracy of this method, we calculated a percentage score for all triggered prompts in relation to the total number of bouts that could trigger a prompt.

Results

Based on the accelerometer recordings, 29.3% (5062/17278) of all sedentary bouts were classified as moderate-to-long (20-40 minutes) and long bouts (≥ 41 minutes). On average, the accuracy by participant was 82.77% (3339/4034; SD 21.01%, range 71.00-88.22%) on the study level. Compared to simulations of random prompts (every 120 minutes), the number of triggered prompts was up to 47.9% (n=704) higher through the sedentary behavior–triggered EMA approach. Nearly 40% (799/2001) of all prolonged sedentary bouts (≥ 20 minutes) occurred during work, and in 57% (1140/2001) of all bouts, the participants were not alone.

Conclusions

Sedentary behavior–triggered EMA is an accurate method for collecting contextual information on sedentary behavior in daily life. Given the growing interest in sedentary behavior research, this sophisticated approach offers a real advancement as it can be used to collect social and environmental contextual information or to unravel dynamic associations. Furthermore, it can be modified to develop sedentary behavior–triggered mHealth interventions.

Feasibility of Measuring Sedentary Time Using Data From a Thigh-Worn Accelerometer

In large-scale cohort studies, sedentary behavior has been routinely measured using self-reports or devices that apply a count-based threshold. We employed a gold standard postural allocation technique using thigh inclination and acceleration to capture free-living sedentary behavior. Participants aged 46.8 (standard deviation (SD), 0.7) years (n = 5,346) from the 1970 British Cohort Study (United Kingdom) were fitted with a waterproofed thigh-mounted accelerometer device (activPAL3 micro; PAL Technologies Ltd., Glasgow, United Kingdom) worn continuously over 7 days; data were collected in 2016-2018. Usable data were retrieved from 83.0% of the devices fitted, with 79.6% of the sample recording at least 6 full days of wear (at least 10 waking hours). Total daily sitting time (average times were 9.5 (SD, 2.0) hours/day for men and 9.0 (SD, 2.0) hours/day for women) accounted for 59.4% and 57.3% of waking hours in men and women, respectively; 73.8% of sample participants recorded ≥8 hours/day of sitting. Sitting in prolonged bouts of 60 continuous minutes or more accounted for 25.3% and 24.4% of total daily sitting in men and women, respectively. In mutually adjusted models, male sex, underweight, obesity, education, poor self-rated health, television-viewing time, and having a sedentary occupation were associated with higher device-measured sitting times. Thigh-worn accelerometry was feasibly deployed and should be considered for larger-scale national surveys.

Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

There is growing evidence that sedentary behavior is a risk factor for somatic and mental health. However, there is still a lack of objective field methods, which can assess both components of sedentary behavior: the postural (sitting/lying) and the movement intensity part. The purpose of the study was to compare the validity of different accelerometers (ActivPAL [thigh], ActiGraph [hip], move [hip], and move [thigh]). 20 adults (10 females; age 25.68 ± 4.55 years) participated in a structured protocol with a series of full- and semistandardized sessions under laboratory conditions. Direct observation via video recording was used as a criterion measure of body positions (sitting/lying vs. nonsitting/lying). By combining direct observation with metabolic equivalent tables, protocol activities were also categorized as sedentary or nonsedentary. Cohen’s kappa was calculated as an overall validity measure to compare accelerometer and video recordings. Across all conditions, for the measurement of sitting/lying body positions, the ActivPAL ([thigh], ĸ = .85) and Move 4 ([thigh], ĸ = .97) showed almost perfect agreement, whereas the Move 4 ([hip], ĸ = .78) and ActiGraph ([hip], ĸ = .67) showed substantial agreement. For the sedentary behavior part, across all conditions, the ActivPAL ([thigh], ĸ = .90), Move 4 ([thigh], ĸ = .95) and Move 4 ([hip], ĸ = .84) revealed almost perfect agreement, whereas the ActiGraph ([hip], ĸ = .69) showed substantial agreement. In particular, thigh-worn devices, namely the Move and the ActivPAL, achieved up to excellent validity in measuring sitting/lying body positions and sedentary behavior and are recommended for future studies.

Sleep disturbance as a moderator of the association between physical activity and later pain onset among American adults aged 50 and over: evidence from the Health and Retirement Study

OBJECTIVE

To examine whether sleep disturbance modifies the association between physical activity and incident pain.

DESIGN

Prospective population-based study.

SETTING

Health and Retirement Study.

PARTICIPANTS

American adults aged ≥50 years who reported no troublesome pain in 2014 were re-assessed for pain in 2016. Of 9828 eligible baseline respondents, 8036 (82%) had complete follow-up data for adjusted analyses (weighted analysis population N=42 407 222).

EXPOSURES

Physical activity was assessed via interview with questions about time spent in moderate and vigorous physical activity. Sleep disturbance, assessed using a modified form of the Jenkins Sleep Scale, was examined as a potential moderator.

MAIN OUTCOME MEASURE

Troublesome pain.

RESULTS

In weighted analyses, 37.9% of the 2014 baseline pain-free sample participated in moderate or vigorous physical activity once a week or less, with an overall mean Physical Activity Index Score of 9.0 (SE=0.12). 18.6% went on to report troublesome pain in 2016. Each one-point higher on the Physical Activity Index Score was associated with a reduced odds ratio (OR) of incident pain for those who endorsed sleep disturbance never/rarely (OR=0.97, 95% CI 0.94 to 0.99), but not for those who endorsed sleep disturbance sometimes (OR=0.99, 95% CI 0.97 to 1.01) or most of the time (OR=1.01, 95% CI 0.99 to 1.03). The analysis of possible interaction demonstrated that frequency of sleep disturbance moderated the physical activity and incident pain association (Wald test: p=0.02).

CONCLUSIONS

The beneficial association of physical activity on reduced likelihood of later pain was only observed in persons who endorsed low levels of sleep disturbance.

Research Combining Physical Activity and Sleep: A Bibliometric Analysis

This study used a bibliometric analysis through the Scopus database to examine papers that combined physical activity and sleep, published between 1979 and 2018. Bibliometric indicators of productivity included publication volume and citation distribution, top 10 authors, average authors per paper, single- and multicountry collaboration, collaborative index, top 10 countries, leading journals, highly cited papers and network visualization for coauthorship, international collaboration, and co-occurrence of author keywords. The initial search identified 1,509 papers, of which 607 passed through comprehensive screening and were included in the final analysis. Most of the papers were research articles (90.8%) and published in English (90.8%). Most papers (81.4%) were published within the past decade, 2009–2018. The mean number of papers published per year was 15.2, the mean number of citations per paper was 257.3, and the mean number of authors per paper was 5.5. International collaboration was evident for 21.6% of the papers, and 95.6% of papers were multiauthored. The most prolific publishing institutions and authors were from the United States, Canada, Australia, Switzerland, and Brazil. Keyword analysis suggested that almost all age groups and study designs were covered, but most papers focus on noncommunicable diseases. Although there has been a rise in scientific production on combined physical activity and sleep research in recent years, future work in this area should include researchers from developing countries.

Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: a proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Background

Pooling data from thigh-worn accelerometers across multiple studies has great potential to advance evidence on the health benefits of physical activity. This requires harmonization of information on body postures, physical activity types, volumes and time patterns across different brands of devices. The aim of this study is to compare the physical behavior estimates provided by three different brands of thigh-worn accelerometers.

Methods

Twenty participants volunteered for a 7-day free-living measurement. Three accelerometers - ActiGraph GT3X+, Axivity AX3 and ActivPAL Micro4 - were randomly placed in a vertical line on the midsection of the right thigh. Raw data from each accelerometer was processed and classified into 8 physical activities and postures using the Acti4 software. Absolute differences between estimates and the respective coefficient of variation (CV) were calculated.

Results

We observed very minor differences between physical behavior estimates from three different accelerometer brands. When averaged over 24 h (1,440 min), the absolute difference (CV) between accelerometers were: 1.2 mins (0.001) for lying/sitting, 3.4 mins (0.02) for standing, 3.5 mins (0.06) for moving, 1.9 mins (0.03) for walking, 0.1 mins (0.19) for running, 1.2 mins (0.19) for stair climbing, 1.9 mins (0.07) for cycling. Moreover, there was an average absolute difference of 282 steps (0.03) per 24 h.

Conclusions

Physical behaviors were classified with negligible difference between the accelerometer brands. These results support harmonization of data from different thigh-worn accelerometers across multiple cohorts when analyzed in an identical manner.

Electronic supplementary material

The online version of this article (10.1186/s12966-019-0835-0) contains supplementary material, which is available to authorized users.