Dataset Information

Gender, age and socioeconomic variation in 24-hour physical activity by wrist-worn accelerometers: the FinHealth 2017 Survey.

ABSTRACT: Assessing movement over 24 hours increases our understanding of the total physical activity level and its patterns. In the FinHealth 2017 Survey, a population-based health examination study, 940 participants between 25 and 93 years were instructed to wear an accelerometer (Actigraph GT9X Link) on their non-dominant wrist for 24 hours on 7 consecutive days. Physical activity information was extracted from 100-Hz triaxial 60-second epoch data as average vector magnitude counts per minute (VM cpm). Results were analyzed by gender, 10-year age-groups, employment status, and education. Hourly means were plotted and compared. Analyses included 915 participants (44% men) who wore the device at least 10 hours on 4 or more days, with mean wear time being 149.5 hours (standard deviation of 615.2 minutes).Women had higher average VM cpm than men (p < 0.001), with significant gender differences in all age-groups until 65 years and older. Total physical activity was lower with age, unemployment, and retirement, where the hourly patterns mirrored the findings. Our findings agree with previous large-scale wrist-accelerometry data, but extend current knowledge by providing data on gender and socioeconomic variation in physical activity across 24 hours in a population-based adult sample representing a broad age range.

SUBMITTER: Wennman H

PROVIDER: S-EPMC6483989 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Gender, age and socioeconomic variation in 24-hour physical activity by wrist-worn accelerometers: the FinHealth 2017 Survey.

Wennman Heini H Pietilä Arto A Rissanen Harri H Valkeinen Heli H Partonen Timo T Mäki-Opas Tomi T Borodulin Katja K

Scientific reports 20190425 1

Assessing movement over 24 hours increases our understanding of the total physical activity level and its patterns. In the FinHealth 2017 Survey, a population-based health examination study, 940 participants between 25 and 93 years were instructed to wear an accelerometer (Actigraph GT9X Link) on their non-dominant wrist for 24 hours on 7 consecutive days. Physical activity information was extracted from 100-Hz triaxial 60-second epoch data as average vector magnitude counts per minute (VM cpm). ...[more]

PMID: 31024038

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Project description:BACKGROUND:Physical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem. OBJECTIVE:This study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers. METHODS:Wrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion. RESULTS:In total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ?2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ?14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA. CONCLUSIONS:This study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies.

Project description:BackgroundPhysical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season.MethodsParticipants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season.Results103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.ConclusionsIt is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

Dataset Information

Gender, age and socioeconomic variation in 24-hour physical activity by wrist-worn accelerometers: the FinHealth 2017 Survey.

Publications

Gender, age and socioeconomic variation in 24-hour physical activity by wrist-worn accelerometers: the FinHealth 2017 Survey.

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