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

Reallocating Time Between 24-h Movement Behaviors for Obesity Management Across the Lifespan: A Pooled Data Meta-Analysis of More Than 9800 Participants from Seven Countries

BACKGROUND

The distribution of time across physical activity, sedentary behaviors, and sleep appears to be essential for the management of obesity. However, the impact of reallocating time among these behaviors, collectively known as 24-h movement behaviors, remains underexplored.

OBJECTIVE

This study examines the theoretical effects of reallocating time between 24-h movement behaviors on obesity indicators across different age groups.

METHODS

We performed a pooled data meta-analysis of 9818 participants from 11 observational and experimental studies. To estimate the time spent in movement behaviors, we reprocessed and harmonized individual-level raw accelerometer-derived data. Isotemporal substitution models estimated theoretical changes in body mass index (BMI) and waist circumference (WC) associated with time reallocation between movement behaviors. We performed the analysis separately for children, adolescents, adults, and older adults.

RESULTS

Even minor reallocations of 10 min led to significant changes in obesity indicators, with pronounced effects observed when 30 min were reallocated. The most substantial adverse effects on BMI and WC occurred when moderate-to-vigorous physical activity (MVPA) was reallocated to other movement behaviors. For 30-min reallocations, the largest increase in BMI (or BMI z-score for children) occurred when MVPA was reallocated to light-intensity physical activity (LPA) in children (0.26 units, 95% confidence interval [CI] 0.15, 0.37) and to sedentary behavior (SB) in adults (0.72 kg/m2, 95% CI 0.47, 0.96) and older adults (0.73 kg/m2, 95% CI 0.59, 0.87). The largest increase in WC was observed when MVPA was substituted with LPA in adults (2.66 cm, 95% CI 1.42, 3.90) and with SB in older adults (2.43 cm, 95% CI 2.07, 2.79). Conversely, the highest magnitude of the decrease in obesity indicators was observed when SB was substituted with MVPA. Specifically, substituting 30 min of SB with MVPA was associated with a decrease in BMI z-score by - 0.15 units (95% CI - 0.21, - 0.10) in children and lower BMI by - 0.56 kg/m2 (95% CI - 0.74, - 0.39) in adults and by - 0.52 kg/m2 (95% CI - 0.61, - 0.43) in older adults. Reallocating time away from sleep and LPA showed several significant changes but lacked a consistent pattern. While the predicted changes in obesity indicators were generally consistent across age groups, inconsistent findings were observed in adolescents, particularly for reallocations between MVPA and other behaviors.

CONCLUSIONS

This investigation emphasizes the crucial role of MVPA in mitigating obesity risk across the lifespan, and the benefit of substituting SB with low-intensity movement behaviors. The distinct patterns observed in adolescents suggest a need for age-specific lifestyle interventions to effectively address obesity. Emphasizing manageable shifts, such as 10-min reallocations, could have significant public health implications, promoting sustainable lifestyle changes that accommodate individuals with diverse needs, including those with severe obesity.

Monitoring postures and motions of hospitalized patients using sensor technology: a scoping review

BACKGROUND

Sensor technology could provide solutions to monitor postures and motions and to help hospital patients reach their rehabilitation goals with minimal supervision. Synthesized information on device applications and methodology is lacking.

OBJECTIVES

The purpose of this scoping review was to provide an overview of device applications and methodological approaches to monitor postures and motions in hospitalized patients using sensor technology.

METHODS

A systematic search of Embase, Medline, Web of Science and Google Scholar was completed in February 2023 and updated in March 2024. Included studies described populations of hospitalized adults with short admission periods and interventions that use sensor technology to objectively monitor postures and motions. Study selection was performed by two authors independently of each other. Data extraction and narrative analysis focused on the applications and methodological approaches of included articles using a personalized standard form to extract information on device, measurement and analysis characteristics of included studies and analyse frequencies and usage.

RESULTS

A total of 15.032 articles were found and 49 articles met the inclusion criteria. Devices were most often applied in older adults (n = 14), patients awaiting or after surgery (n = 14), and stroke (n = 6). The main goals were gaining insight into patient physical behavioural patterns (n = 19) and investigating physical behaviour in relation to other parameters such as muscle strength or hospital length of stay (n = 18). The studies had heterogeneous study designs and lacked completeness in reporting on device settings, data analysis, and algorithms. Information on device settings, data analysis, and algorithms was poorly reported.

CONCLUSIONS

Studies on monitoring postures and motions are heterogeneous in their population, applications and methodological approaches. More uniformity and transparency in methodology and study reporting would improve reproducibility, interpretation and generalization of results. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3-14

BACKGROUND

ActiMotus, a thigh-accelerometer-based software used for the classification of postures and movements (PaMs), has shown high accuracy among adults and school-aged children; however, its accuracy among younger children and potential differences between sexes are unknown. This study aimed to evaluate the accuracy of ActiMotus to measure PaMs among children between 3 and 14 years and to assess if this was influenced by the sex or age of children.

METHOD

Forty-eight children attended a structured ~1-hour data collection session at a laboratory. Thigh acceleration was measured using a SENS accelerometer, which was classified into nine PaMs using the ActiMotus software. Human-coded video recordings of the session provided the ground truth.

RESULTS

Based on both F1 scores and balanced accuracy, the highest levels of accuracy were found for lying, sitting, and standing (63.2-88.2%). For walking and running, accuracy measures ranged from 48.0 to 85.8%. The lowest accuracy was observed for classifying stair climbing. We found a higher accuracy for stair climbing among girls compared to boys and for older compared to younger age groups for walking, running, and stair climbing.

CONCLUSIONS

ActiMotus could accurately detect lying, sitting, and standing among children. The software could be improved for classifying walking, running, and stair climbing, particularly among younger children.

Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children

BACKGROUND

The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds.

METHODS

Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds.

RESULTS

Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56-0.67) for lying to 0.97 (0.94-0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48-0.75) for lying to 0.96 (0.92-0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95-0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions.

CONCLUSIONS

The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry

BACKGROUND

Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.

METHODS

We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure.

RESULTS

The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively.

CONCLUSIONS

The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Validity and reliability of self-reported methods for assessment of 24-h movement behaviours: a systematic review

BACKGROUND

Time spent in sleep, sedentary behaviour (SB), and physical activity are exhaustive and mutually exclusive parts of a 24-h day that need to be considered in a combination. The aim of this study was to identify validated self-reported tools for assessment of movement behaviours across the whole 24-h day, and to review their attributes and measurement properties.

METHODS

The databases PubMed, Scopus, and SPORTDiscus were searched until September 2023. Inclusion criteria were: (i) published in English language, (ii) per-reviewed paper, (iii) assessment of self-reported time spent in sleep, SB, and physical activity, (iv) evaluation of measurement properties of all estimates across the full 24-h day, and (v) inclusion of adolescents, adults, or older adults. The methodological quality of included studies was assessed using the Consensus-based Standards for the selection of health Measurement Instruments checklist.

RESULTS

Our search returned 2064 records. After studies selection, we included 16 articles that reported construct validity and/or test-retest reliability of 12 unique self-reported tools - eight questionnaires, three time-use recalls, and one time-use diary. Most tools enable assessment of time spent in sleep, and domain-specific SB and physical activity, and account that sum of behaviours should be 24 h. Validity (and reliability) correlation coefficients for sleep ranged between 0.22 and 0.69 (0.41 and 0.92), for SB between 0.06 and 0.57 (0.33 and 0.91), for light-intensity physical activity between 0.18 and 0.46 (0.55 and 0.94), and for moderate- to vigorous-intensity physical activity between 0.38 and 0.56 (0.59 and 0.94). The quality of included studies being mostly fair-to-good.

CONCLUSIONS

This review found that only a limited number of validated self-reported tools for assessment of 24-h movement behaviours are currently available. Validity and reliability of most tools are generally adequate to be used in epidemiological studies and population surveillance, while little is known about adequacy for individual level assessments and responsiveness to behavioural change. To further support research, policy, and practice, there is a need to develop new tools that resonate with the emerging 24-h movement paradigm and to evaluate measurement properties by using compositional data analysis.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022330868.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

BACKGROUND

The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS.

METHODS

A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels.

RESULTS

A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen's kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types.

CONCLUSIONS

The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.

Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis

BACKGROUND

As the number of cancer survivors increases, maintaining health-related quality of life in cancer survivorship is a priority. This necessitates accurate and reliable methods to assess how cancer survivors are feeling and functioning. Real-world digital measures derived from wearable sensors offer potential for monitoring well-being and physical function in cancer survivorship, but questions surrounding the clinical utility of these measures remain to be answered.

OBJECTIVE

In this secondary analysis, we used 2 existing data sets to examine how measures of real-world physical behavior, captured with a wearable accelerometer, were related to aerobic fitness and self-reported well-being and physical function in a sample of individuals who had completed cancer treatment.

METHODS

Overall, 86 disease-free cancer survivors aged 21-85 years completed self-report assessments of well-being and physical function, as well as a submaximal exercise test that was used to estimate their aerobic fitness, quantified as predicted submaximal oxygen uptake (VO2). A thigh-worn accelerometer was used to monitor participants' real-world physical behavior for 7 days. Accelerometry data were used to calculate average values of the following measures of physical behavior: sedentary time, step counts, time in light and moderate to vigorous physical activity, time and weighted median cadence in stepping bouts over 1 minute, and peak 30-second cadence.

RESULTS

Spearman correlation analyses indicated that 6 (86%) of the 7 accelerometry-derived measures of real-world physical behavior were not significantly correlated with Functional Assessment of Cancer Therapy-General total well-being or linked Patient-Reported Outcomes Measurement Information System-Physical Function scores (Ps≥.08). In contrast, all but one of the physical behavior measures were significantly correlated with submaximal VO2 (Ps≤.03). Comparing these associations using likelihood ratio tests, we found that step counts, time in stepping bouts over 1 minute, and time in moderate to vigorous activity were more strongly associated with submaximal VO2 than with self-reported well-being or physical function (Ps≤.03). In contrast, cadence in stepping bouts over 1 minute and peak 30-second cadence were not more associated with submaximal VO2 than with the self-reported measures (Ps≥.08).

CONCLUSIONS

In a sample of disease-free cancer survivors, we found that several measures of real-world physical behavior were more associated with aerobic fitness than with self-reported well-being and physical function. These results highlight the possibility that in individuals who have completed cancer treatment, measures of real-world physical behavior may provide additional information compared with self-reported and performance measures. To advance the appropriate use of digital measures in oncology clinical research, further research evaluating the clinical utility of real-world physical behavior over time in large, representative samples of cancer survivors is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03781154; https://clinicaltrials.gov/ct2/show/NCT03781154.

Socioeconomic gradients in 24-hour movement patterns across weekends and weekdays in a working-age sample: evidence from the 1970 British Cohort Study

BACKGROUND

Socioeconomic differences in movement behaviours may contribute to health inequalities. The aim of this descriptive study was to investigate socioeconomic patterns in device-measured 24-hour movement and assess whether patterns differ between weekdays and weekends.

METHODS

4894 individuals aged 46 years from the 1970 British Cohort Study were included. Participants wore thigh-worn accelerometers for 7 days. Movement behaviours were classified in two 24-hour compositions based on intensity and posture, respectively: (1) sleep, sedentary behaviour, light-intensity activity and moderate-vigorous activity; and (2) sleep, lying, sitting, standing, light movement, walking and combined exercise-like activity. Four socioeconomic measures were explored: education, occupation, income and deprivation index. Movement behaviours were considered compositional means on a 24-hour scale; isometric log ratios expressed per cent differences in daily time in each activity compared with the sample mean.

RESULTS

Associations were consistent across all socioeconomic measures. For example, those with a degree spent more time in exercise-like activities across weekdays (10.8%, 95% CI 7.3 to 14.7; ref: sample mean) and weekends (21.9%, 95% CI 17.2 to 26.9). Other patterns differed markedly by the day of the week. Those with no formal qualifications spent more time standing (5.1%, 95% CI 2.3 to 7.1), moving (10.8%, 95% CI 8.6 to 13.1) and walking(4.0%, 95% CI 2.2 to 6.1) during weekdays, with no differences on weekends. Conversely, those with no formal qualifications spent less time sitting during weekdays (-6.6%, 95% CI -7.8 to -4.8), yet more time lying on both weekends (8.8%, 95% CI 4.9 to 12.2) and weekdays (7.5%, 95% CI 4.0 to 11.5).

CONCLUSIONS

There were strong socioeconomic gradients in 24-hour movement behaviours, with notable differences between weekdays/weekends and behaviour type/posture. These findings emphasise the need to consider socioeconomic position, behaviour type/posture and the day of the week when researching or designing interventions targeting working-age adults.

Comparing national device-based physical activity surveillance systems: a systematic review

BACKGROUND

Physical activity surveillance systems are important for public health monitoring but rely mostly on self-report measurement of physical activity. Integration of device-based measurements in such systems can improve population estimates, however this is still relatively uncommon in existing surveillance systems. This systematic review aims to create an overview of the methodology used in existing device-based national PA surveillance systems.

METHODS

Four literature databases (PubMed, Embase.com, SPORTDiscus and Web of Science) were searched, supplemented with backward tracking. Articles were included if they reported on population-based (inter)national surveillance systems measuring PA, sedentary time and/or adherence to PA guidelines. When available and in English, the methodological reports of the identified surveillance studies were also included for data extraction.

RESULTS

This systematic literature search followed the PRISMA guidelines and yielded 34 articles and an additional 18 methodological reports, reporting on 28 studies, which in turn reported on one or multiple waves of 15 different national and 1 international surveillance system. The included studies showed substantial variation between (waves of) systems in number of participants, response rates, population representativeness and recruitment. In contrast, the methods were similar on data reduction definitions (e.g. minimal number of valid days, non-wear time and necessary wear time for a valid day).

CONCLUSIONS

The results of this review indicate that few countries use device-based PA measurement in their surveillance system. The employed methodology is diverse, which hampers comparability between countries and calls for more standardized methods as well as standardized reporting on these methods. The results from this review can help inform the integration of device-based PA measurement in (inter)national surveillance systems.

Movement behaviour typologies and their associations with adiposity indicators in children and adolescents: a latent profile analysis of 24-h compositional data

OBJECTIVES

Growing evidence supports the important role of 24-hour movement behaviours (MB) in preventing childhood obesity. However, research to understand the heterogeneity and variability of MB among individuals and what kind of typologies of individuals are at risk of developing obesity is lacking. To bridge this gap, this study identified typologies of 24-hour MB in children and adolescents and investigated their associations with adiposity indicators.

METHODS

In this cross-sectional study, 374 children and 317 adolescents from the Czech Republic wore wrist-worn accelerometers for seven consecutive days. Time spent in moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour (SB), and sleep was quantified using raw accelerometery data. Adiposity indicators included body mass index (BMI) z-score, fat mass percentage (FM%), fat mass index (FMI), and visceral adipose tissue (VAT). Bias-adjusted latent profile analysis was used on the 24-hour MB data to identify MB typologies and their associations with adiposity indicators. The models were adjusted for potential confounders. The identified typologies were labelled to reflect the behavioural profiles of bees to aid interpretability for the general public.

RESULTS

Two typologies were identified in children: highly active Workers characterised by high levels of MVPA and LPA, and inactive Queens characterised by low levels of MVPA and LPA, high levels of SB and longer sleep duration compared to Workers. In adolescents, an additional typology labelled as Drones was characterised by median levels of MVPA, LPA, SB and longest sleep duration. After controlling for covariates, we found that children labelled as Queens were associated with 1.38 times higher FM%, 1.43 times higher FMI, and 1.67 times higher VAT than Workers. In adolescents, Drones had 1.14 times higher FM% and Queens had 1.36 higher VAT in comparison with Workers, respectively.

CONCLUSION

Our study highlights the importance of promoting active lifestyles in children and adolescents to potentially reduce adiposity. These findings can provide insights for interventions aimed at promoting healthy MB and preventing childhood obesity.

The Within-Subject Association of Physical Behavior and Affective Well-Being in Everyday Life: A Systematic Literature Review

BACKGROUND

The interplay of physical activity (PA) with affective well-being (AWB) is highly critical to both health behaviors and health outcomes. Current prominent theories presume AWB to be crucial for PA maintenance, and PA is evidenced to foster mental health. However, thus far, PA-AWB associations have mainly been researched in laboratory settings and with interventional designs, but the everyday life perspective had not been focused on, mostly due to technological limitations. In the course of digitization, the number of studies using device-based methods to research the within-subject association of physical activity and affective well-being (PA-AWB) under ecological valid conditions increased rapidly, but a recent comprehensive systematic review of evidence across populations, age groups, and distinct AWB components remained inconclusive.

OBJECTIVES

Therefore, we aimed to firstly review daily-life studies that assessed intensive longitudinal device-based (e.g., electronic smartphone diaries and accelerometry) and real-time PA-AWB data, secondly to develop and apply a quality assessment tool applicable to those studies, and thirdly to discuss findings and draw implications for research and practice.

METHODS

To this end, the literature was searched in three databases (Web of Science, PubMed, Scopus) up to November 2022. The systematic review followed the PRISMA guidelines and had been pre-registered (PROSPERO id: CRD42021277327). A modified quality assessment tool was developed to illustrate the risk of bias of included studies.

RESULTS

The review of findings showed that, in general, already short PA bouts in everyday life, which clearly differ from structured exercise sessions, are positively associated with AWB. In particular, feelings of energy relate to incidental (non-exercise and unstructured) activity, and PA-AWB associations depend on population characteristics. The quality assessment revealed overall moderate study quality; however, the methods applied were largely heterogeneous between investigations. Overall, the reviewed evidence on PA-AWB associations in everyday life is ambiguous; for example, no clear patterns of directions and strengths of PA-AWB relationships depending on PA and AWB components (such as intensity, emotions, affect, mood) emerged.

CONCLUSIONS

The reviewed evidence can fuel discussions on whether the World Health Organization's notion "every move counts" may be extended to everyday life AWB. Concurrently, the PA-AWB relationship findings endorse prominent theories highlighting the critical role of AWB in everyday PA engagement and maintenance. However, the review also clearly highlights the need to advance and harmonize methodological approaches for more fine-grained investigations on which specific PA/AWB characteristics, contextual factors, and biological determinants underly PA-AWB associations in everyday life. This will enable the field to tackle pressing challenges such as the issue of causality of PA-AWB associations, which will help to shape and refine existing theories to ultimately predict and improve health behavior, thereby feeding into precision medicine approaches.

Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data

BACKGROUND

Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms.

METHODS

54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated.

RESULTS

The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%.

CONCLUSIONS

Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Relationship between rest-activity rhythms and cardiorespiratory fitness in middle-aged workers: a cross-sectional study with non-parametric analysis using accelerometers worn on the thigh

BACKGROUND

Rest-activity rhythms are directly related to health risks, but there are limited objective methods to assess them. This study aimed to investigate the relationship between rest-activity rhythms and cardiorespiratory fitness (CRF) in middle-aged workers.

METHODS

Peak oxygen uptake was measured on a treadmill to assess CRF in 254 middle-aged workers who were divided into low, medium, and high-CRF groups based on tertiles. Participants were asked to wear an accelerometer (activPAL) on their thighs for 1 week, and the logarithmically transformed acceleration data were used for the analysis of a 24-hour rest-activity rhythm. Sex, age, body mass index, occupation, smoking status, and alcohol consumption were used as covariates in Model 1, with Model 2 also including walking count on non-workdays. Repeated measures analysis of variance was used to compare time course of rest-activity rhythms changes on workdays between groups, and post-hoc tests were conducted using Bonferroni's correlation.

RESULTS

Higher CRF correlated with increased physical activity. In model 1, higher CRF showed improved interdaily stability, but the significant difference disappeared in model 2 after adjusting for non-workday walking counts. A time-course group comparison showed that the high group had significantly higher activity levels than those of the low group from 6:00 to 8:59 and 17:00 to 17:59 and the medium group from 6:00 to 7:59 and 19:00 to 19:59.

CONCLUSIONS

Workers who have better rest-activity rhythms and engage in higher levels of physical activity on workdays tend to have higher CRF levels. Regular daily routines, influenced by physical activity during holidays, can positively impact cardiopulmonary endurance.

A Novel System for the Device-Based Measurement of Physical Activity, Sedentary Behavior, and Sleep (Motus): Usability Evaluation

BACKGROUND

Device-based measurements of physical behavior, using the current methods, place a large burden on participants. The Motus system could reduce this burden by removing the necessity for in-person meetings, replacing diaries written on paper with digital diaries, and increasing the automation of feedback generation.

OBJECTIVE

This study aims to describe the development of the Motus system and evaluate its potential to reduce participant burden in a two-phase usability evaluation.

METHODS

Motus was developed around (1) a thigh-worn accelerometer with Bluetooth data transfer; (2) a smartphone app containing an attachment guide, a digital diary, and facilitating automated data transfer; (3) a cloud infrastructure for data storage; (4) an analysis software to generate feedback for participants; and (5) a web-based app for administrators. We recruited 19 adults with a mean age of 45 (SD 11; range 27-63) years, of which 11 were female, to assist in the two-phase evaluation of Motus. A total of 7 participants evaluated the usability of mockups for a smartphone app in phase 1. Participants interacted with the app while thinking aloud, and any issues raised were classified as critical, serious, or minor by observers. This information was used to create an improved and functional smartphone app for evaluation in phase 2. A total of 12 participants completed a 7-day free-living measurement with Motus in phase 2. On day 1, participants attempted 20 system-related tasks under observation, including registration on the study web page, reading the information letter, downloading and navigating the smartphone app, attaching an accelerometer on the thigh, and completing a diary entry for both work and sleep hours. Task completion success and any issues encountered were noted by the observer. On completion of the 7-day measurement, participants provided a rating from 0 to 100 on the System Usability Scale and participated in a semistructured interview aimed at understanding their experience in more detail.

RESULTS

The task completion rate for the 20 tasks was 100% for 13 tasks, >80% for 4 tasks, and <50% for 3 tasks. The average rating of system usability was 86 on a 0-100 scale. Thematic analysis indicated that participants perceived the system as easy to use and remember, and subjectively pleasing overall. Participants with shift work reported difficulty with entering sleep hours, and 66% (8/12) of the participants experienced slow data transfer between the app and the cloud infrastructure. Finally, a few participants desired a greater degree of detail in the generated feedback.

CONCLUSIONS

Our two-phase usability evaluation indicated that the overall usability of the Motus system is high in free-living. Issues around the system's slow data transfer, participants with atypical work shifts, and the degree of automation and detail of generated feedback should be addressed in future iterations of the Motus system.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/35697.

Measuring objective physical activity in people with chronic low back pain using accelerometers: a scoping review

Purpose

Accelerometers can be used to objectively measure physical activity. They could be offered to people with chronic low back pain (CLBP) who are encouraged to maintain an active lifestyle. The aim of this study was to examine the use of accelerometers in studies of people with CLBP and to synthesize the main results regarding the measurement of objective physical activity.

Methods

A scoping review was conducted following Arksey and O'Malley's framework. Relevant studies were collected from 4 electronic databases (PubMed, Embase, CINHAL, Web of Science) between January 2000 and July 2023. Two reviewers independently screened all studies and extracted data.

Results

40 publications out of 810 citations were included for analysis. The use of accelerometers in people with CLBP differed across studies; the duration of measurement, physical activity outcomes and models varied, and several limitations of accelerometry were reported. The main results of objective physical activity measures varied and were sometimes contradictory. Thus, they question the validity of measurement methods and provide the opportunity to discuss the objective physical activity of people with CLBP.

Conclusions

Accelerometers have the potential to monitor physical performance in people with CLBP; however, important technical limitations must be overcome.

Association between device-measured stepping behaviors and cardiometabolic health markers in middle-aged women: The Australian Longitudinal Study on Women's Health

The associations between different types and contexts of stepping behaviors and cardiometabolic (CM) health markers are unclear. This study aimed to examine the associations of daily total, walking, stair, incidental and purposeful steps with cardiometabolic risk. A total of 943 women (mean age ± SD = 44.1 ± 1.6 years) from the Australian Longitudinal Study on Women's Health (ALSWH) were included in this cross-sectional study. Daily total, walking, stair, incidental, and purposeful steps were measured using thigh-worn accelerometry. Outcomes comprised of CM markers of adiposity, blood pressure, resting heart rate, lipids, glycaemia, and the composite CM score. We used generalized linear modeling and multiple linear regression to assess the associations. We observed that all stepping behaviors were beneficial to CM health, for example, compared to the lowest quartile (Q1), the change of the composite CM score across low to high quartile of purposeful steps was -0.12 (Q2, 95% CI: -0.41, 0.17), -0.16 (Q3, -0.46, 0.14), and -0.36 (Q4, -0.66, -0.05). Stair steps showed linear associations with blood pressure and adiposity biomarkers, for example, the change of quartile of waist circumference was -1.45 cm (Q2, -4.35, 1.44), -3.56 cm (Q3, -6.52, -0.60), and -7.08 cm (Q4, -10.31, -3.86). Peak 30-min walking intensity showed independent association with adiposity biomarkers (p linear < 0.001 and p = 0.002 for waist circumference and BMI, respectively). Our study showed that all stepping forms were beneficial to CM health. Higher stair steps and peak 30-min walking cadence were associated with a steep decline of adiposity biomarkers. Purposeful steps showed more consistent associations with CM biomarkers than incidental steps.

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.

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.

Natural environments, psychosocial health, and health behaviors in a crisis - A scoping review of the literature in the COVID-19 context

The COVID-19 outbreak led to major restrictions globally, affecting people's psychosocial health and their health behaviors. Thus, the purpose of this scoping review was to summarize the available research regarding nature and health in the COVID-19 context. Keywords relating to natural environments and COVID-19 were combined to conduct a systematic online search in six major databases. Eligibility criteria were a) published since 2020 with data collected in the COVID-19 context b) peer-reviewed, c) original empirical data collected on human participants, d) investigated the association between natural environments and psychosocial health or health behaviors, and e) English, German, or Scandinavian languages. Out of 9126 articles being screened, we identified 188 relevant articles, representing 187 distinct studies. Most research focused on adults in the general population and was predominantly conducted in the USA, Europe, and China. Overall, the findings indicate that nature may mitigate the impact of COVID-19 on psychological health and physical activity. Through a systematic thematic analysis of the extracted data, three primary themes were identified: 1) type of nature assessed, 2) psychosocial health and health behaviors investigated, and 3) heterogeneity in the nature-health relationship. Research gaps in the COVID-19 context were identified regarding I) nature characteristics that promote psychosocial health and health behaviors, II) investigations of digital and virtual nature, III) psychological constructs relating to mental health promotion, IV) health-promoting behaviors other than physical activity, V) underlying mechanisms regarding heterogeneity in the nature-health relationship based on human, nature, and geographic characteristics, and VI) research focusing on vulnerable groups. Overall, natural environments demonstrate considerable potential in buffering the impact of stressful events on a population level on mental health. However, future research is warranted to fill the mentioned research gaps and to examine the long-term effects of nature exposure during COVID-19.

Sensitivity and specificity of measuring children's free-living cycling with a thigh-worn Fibion® accelerometer

Objective

Cycling is an important part of children's active travel, but its measurement using accelerometry is a challenge. The aim of the present study was to evaluate physical activity duration and intensity, and sensitivity and specificity of free-living cycling measured with a thigh-worn accelerometer.

Methods

Participants were 160 children (44 boys) aged 11.5 ± 0.9 years who wore a triaxial Fibion® accelerometer on right thigh for 8 days, 24 h per day, and reported start time and duration of all cycling, walking and car trips to a travel log. Linear mixed effects models were used to predict and compare Fibion-measured activity and moderate-to-vigorous activity duration, cycling duration and metabolic equivalents (METs) between the travel types. Sensitivity and specificity of cycling bouts during cycling trips as compared to walking and car trips was also evaluated.

Results

Children reported a total of 1,049 cycling trips (mean 7.08 ± 4.58 trips per child), 379 walking trips (3.08 ± 2.81) and 716 car trips (4.79 ± 3.96). There was no difference in activity and moderate-to-vigorous activity duration (p > .105), a lower cycling duration (-1.83 min, p < .001), and a higher MET-level (0.95, p < .001) during walking trips as compared to cycling trips. Both activity (-4.54 min, p < .001), moderate-to-vigorous activity (-3.60 min, p < .001), cycling duration (-1.74 min, p < .001) and MET-level (-0.99, p < .001) were lower during car trips as compared to cycling trips. Fibion showed the sensitivity of 72.2% and specificity of 81.9% for measuring cycling activity type during the reported cycling trips as compared to walking and car trips when the minimum required duration for cycling was less than 29 s.

Conclusions

The thigh-worn Fibion® accelerometer measured a greater duration of cycling, a lower MET-level, and a similar duration of total activity and moderate-to-vigorous activity during free-living cycling trips as compared to walking trips, suggesting it can be used to measure free-living cycling activity and moderate-to-vigorous activity duration in 10-12-year-old children.

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).

Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model

Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70-95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.

A 24-h activity profile and adiposity among children and adolescents: Does the difference between school and weekend days matter?

BACKGROUND

Twenty-four-hour movement behaviours are gaining attention in the research community. However, no study has addressed how 24-h activity profiles vary between structured and less structured days and whether an unfavourable activity profile is associated with childhood obesity. We aimed to analyse differences between school day and weekend day 24-h activity profiles and their associations with adiposity indicators among children and adolescents.

METHODS

Participants were 382 children and 338 adolescents who wore wrist accelerometers for 24 hours a day for seven consecutive days. The 24-h activity profile expressed by the average acceleration (AvAcc) and intensity gradient (IG) were estimated from multi-day raw accelerometer data. Adiposity indicators included body mass index (BMI) z-score, fat mass percentage (FM%), fat mass index (FMI), and visceral adipose tissue (VAT). Multiple linear regression of activity profile metrics and adiposity indicators was performed separately for school and weekend days.

RESULTS

Weekend days AvAcc and IG were lower compared to school days in both age groups (p <0.001 for all). Specifically, AvAcc was lower by 9.4% and 11.3% in children and adolescents, respectively. IG on weekend days was lower (more negative) by 3.4% in children and 3.1% in adolescents. Among children, on school days AvAcc and IG were negatively associated with FM%, FMI, and VAT, whilst on weekend days AvAcc was positively associated with BMI z-score, FMI, and VAT (p < 0.05 for all). Among adolescents, negative associations were found between weekend day AvAcc and IG and FM% and FMI (p < 0.05 for all), respectively.

CONCLUSIONS

This study confirms the importance of 24-h activity profile as a potentially protective factor against excess adiposity. The variability of movement behaviours during structured and less structured days should be considered when optimizing the 24-h movement behaviours to prevent childhood obesity.

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.

Is Complexity of Daily Activity Associated with Physical Function and Life-Space Mobility among Older Adults?

PURPOSE

Information about mobility and physical function may be encoded in the complexity of daily activity pattern. Therefore, daily activity pattern complexity metrics could provide novel insight into the relationship between daily activity behavior and health. The purpose of the present study was to examine the association between the complexity of daily activity behavior and the mobility and physical function among community-dwelling older adults 75, 80, and 85 yr of age.

METHODS

A total of 309 participants wore accelerometers concurrently on the thigh and the trunk for at least three consecutive days. Five activity states (lying, sitting, standing, walking, or activity other than walking) were defined in three different temporal grains (5 s, 1 min, and 5 min), and Lempel-Ziv complexity was evaluated. We assessed complexity of daily activity behavior using the life-space mobility and physical function with distance in preferred pace 6-min walk and the Short Physical Performance Battery.

RESULTS

Weak positive associations were observed between the complexity of daily activity and the mobility and physical function at the finest temporal grains in both sexes (Spearman rho = 0.19 to 0.27, P < 0.05). No significant associations were observed in the coarsest temporal grain in either sex.

CONCLUSIONS

Lempel-Ziv estimates of daily activity complexity with a fine temporal grain seem to be associated with community-dwelling older adults' physical function. The coarsest 5-min temporal grain may have smoothed out physiologically meaningful short activity bouts. Because complexity encodes information related to timing, intensity, and patterning of behavior, complexity of activity could be an informative indicator of future physical function and mobility.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

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.

Associations of neighborhood walkability with moderate to vigorous physical activity: an application of compositional data analysis comparing compositional and non-compositional approaches

BACKGROUND

We compared the relation between neighborhood features and moderate to vigorous physical activity (MVPA) using linear regression analysis and the more novel compositional data analysis (CoDA). Compositional data analysis allows us to take the time children allocate to different movement behaviours during a 24-hour time period into account.

METHODOLOGY

Data from youth participants (n = 409) in the QUALITY (QUebec Adipose and Lifestyle InvesTigation in Youth) cohort were included. Time spent in MVPA, light physical activity, sedentary behavior, and sleep ("24-hour movement behaviours") was measured using accelerometers. Neighborhood data were collected using a geographic information system and through direct observation. In CoDA models, we used orthogonal logratio coordinates, which allows for the association of neighbourhood walkability with MVPA to be estimated with respect to the average composition of all other behaviours within a 24-hour time frame. In baseline linear regression models, MVPA was regressed cross-sectionally on neighborhood walkability. All models were stratified by sex, and controlled for BMI z-scores, pubertal development, seasonal variation, parental education, and neighbourhood safety.

RESULTS

Based on CoDA, girls who lived in more walkable neighborhoods had 10% higher daily MVPA (95% CI: 2%, 19%), taking into account all other movement behaviours. Based on linear regression, girls who resided in more walkable neighborhoods engaged in 4.2 (95% confidence interval [CI]: 1.2, 6.6) more minutes of MVPA per day on average than girls residing in less walkable neighborhoods.

CONCLUSIONS

Unlike with traditional linear models, all movement behaviours were included in a single model using CoDA, allowing for a more complete picture of the strength and direction of the association between neighbourhood Walkability and MVPA. Application of CoDA to investigate determinants of physical activity provides additional insight into potential mechanisms and the ways in which people allocate their time.

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.

How reliable is measurement of posture during sleep: real-world measurement of body posture and movement during sleep using accelerometers

Objective Understanding sleeping behaviours could improve prevention and treatment of sleep problems and associated health conditions. This study aimed to evaluate a method to assess body posture and movement during sleep using trunk-worn accelerometers for 28 days. Approach Participants (50 adults with low back pain (66% female); aged 32(±9) years) wore two activPAL-micro sensors (thigh, trunk) during their normal daily life for 28 consecutive days. Parameters related to body posture (e.g., time spent lying supine or prone) and movement (e.g., number of turns) during sleep were calculated for each night. Average values for each parameter were identified for different periods, the Spearman-Brown Prophecy Formula was used to estimate the minimum number of nights required to obtain a reliable estimate of each parameter, and repeatability of measures between different weeks was calculated. Main Results Participants spent 8.1(±0.8) hours asleep and most time (44%) was spent in a supine posture. The minimum number of nights required for reliable estimates varied between sleep parameters, range 4-21 nights. The most stable parameters (i.e., requiring less than seven nights) were "average activity", "no. of turns", "time spent prone", and "posture changes in the first hour". Some measures differed substantially between weeks. Significance Most sleep parameters related to body posture and movement require a week or more of monitoring to provide reliable estimates of behaviour over one month. Notably, one week may not reflect behaviour in another week, and the time varying nature of sleep needs to be considered.

Effect of physical activity and exercise on telomere length: Systematic review with meta-analysis

PURPOSE

To compare a physically active lifestyle or structured exercise program to physically inactive lifestyle or control groups on telomere length (TL).

METHOD

We searched PubMed, EMBASE, Cochrane Library, and Open Gray databases up to March 31, 2020. We calculated standardized mean differences (SMD) with 95% confidence intervals (CI) of TL comparing physically active to physically inactive individuals and exercise intervention to control groups. Risk of bias was judged using the Risk of Bias Assessment tool for Non-randomized Studies (RoBANS) for physical activity (PA) studies and the Cochrane risk-of-bias (RoB2) for exercise intervention studies. Certainty of evidence was judged using Grading of Recommendations Assessment, Development and Evaluation (GRADE).

RESULTS

We included 30 studies (24 assessing the effects of PA and 6 assessing the effects of exercise interventions) comprising 7418 individuals. Physically active individuals had longer telomeres (SMD = 0.70, 95% CI 0.12-1.28, very-low certainty), especially in middle-aged individuals (SMD = 0.90, 95% CI 0.08-1.72, very-low certainty) and when considering only athletes (SMD = 0.54, 95% CI 0.18-0.90, very-low certainty). Trim-and-fill analyses revealed that most of the pooled effects were overestimated. Exercise interventions did not yield any significant effect on TL.

CONCLUSION

There is very-low certainty that physically active individuals have longer telomeres with a moderate effect, but this effect is probably overestimated.

A Comparison of Associations Between Self-Reported and Device-Based Sedentary Behavior and Obesity Markers in Adults: A Multi-National Cross-Sectional Study

The purpose of this study was to examine the associations between self-reported and device-based measures of sedentary behavior (SB) with obesity markers in adults from Latin American countries. Sitting time and total time spent in different SBs were self-reported using two different questionnaires. Accelerometers were used to assess total sedentary time. Body mass index, waist, and neck circumferences were assessed. The highest self-reported sitting time was in Argentina, the highest total time spent in different SBs was in Brazil and Costa Rica, and the highest device-based sedentary time was observed in Peru. Neither self-reported sitting time, total time spent in different SBs or device-based sedentary time were associated with body mass index. Device-based sedentary time was positively associated with waist circumference and self-reported sitting time was positively associated with neck circumference. Caution is warranted when comparing the associations of self-reported and device-based assessments of SB with anthropometric variables.

Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.

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.