Dataset Information

Estimated Physical Activity in Adolescents by Wrist-Worn GENEActiv Accelerometers.

ABSTRACT: BACKGROUND:Reports of physical activity (PA) measured via wrist-worn accelerometers in adolescents are limited. This study describes PA levels in adolescents at baseline of an obesity prevention and weight management trial. METHODS:Adolescents (n = 930) at 8 high schools wore an accelerometer for 7 days, with average acceleration values of <50 mg, >150 mg, and >500 mg categorized as sedentary, moderate, and vigorous PA, respectively. In a 3-level mixed-effects generalized linear model, PA was regressed on sex, weight status, and day of week. Daily PA was nested within students, and students within schools, with random effects included for both. RESULTS:Adolescents accumulated a median of 40 minutes daily of moderate to vigorous PA (MVPA). MVPA was significantly different for teens with obesity versus teens with normal weight (-5.4 min/d, P = .03); boys versus girls (16.3 min/d, P < .001); and Sundays versus midweek (-16.6 min/d, P < .001). Average sedentary time increased on weekends (Saturday: 19.1 min/d, P < .001; Sunday: 44.8 min, P < .001) relative to midweek but did not differ by sex or weight status. CONCLUSIONS:Interventions to increase PA in adolescents may benefit from focusing on increasing weekend PA and increasing MVPA in girls.

SUBMITTER: Sanders SG

PROVIDER: S-EPMC7234278 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Estimated Physical Activity in Adolescents by Wrist-Worn GENEActiv Accelerometers.

Sanders Sarah G SG Jimenez Elizabeth Yakes EY Cole Natalie H NH Kuhlemeier Alena A McCauley Grace L GL Van Horn M Lee ML Kong Alberta S AS

Journal of physical activity & health 20190717 9

<h4>Background</h4>Reports of physical activity (PA) measured via wrist-worn accelerometers in adolescents are limited. This study describes PA levels in adolescents at baseline of an obesity prevention and weight management trial.<h4>Methods</h4>Adolescents (n = 930) at 8 high schools wore an accelerometer for 7 days, with average acceleration values of <50 mg, >150 mg, and >500 mg categorized as sedentary, moderate, and vigorous PA, respectively. In a 3-level mixed-effects generalized linear m ...[more]

PMID: 31310994

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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.

Project description:IntroductionObjective methods like accelerometers are feasible for large studies and may quantify variability in day-to-day physical activity better than self-report. The variability between days suggests that day of the week cannot be ignored in the design and analysis of physical activity studies. The purpose of this paper is to investigate the optimal number of days needed to obtain reliable estimates of weekly habitual physical activity using the wrist-worn GENEActiv accelerometer.MethodsData are from a subsample of the Mitchelstown cohort; 475 (44.6% males; mean aged 59.6±5.5 years) middle-aged Irish adults. Participants wore the wrist GENEActiv accelerometer for 7-consecutive days. Data were collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised according to intensity based on validated cut-offs. Spearman pairwise correlations determined the association between days of the week. Repeated measures ANOVA examined differences in average minutes across days. Intraclass correlations examined the proportion of variability between days, and Spearman-Brown formula estimated intra-class reliability coefficient associated with combinations of 1-7 days.ResultsThree hundred and ninety-seven adults (59.7±5.5yrs) had valid accelerometer data. Overall, men were most sedentary on weekends while women spent more time in sedentary behaviour on Sunday through Tuesday. Post hoc analysis found sedentary behaviour and light activity levels on Sunday to differ to all other days in the week. Analysis revealed greater than 1 day monitoring is necessary to achieve acceptable reliability. Monitoring frame duration for reliable estimates varied across intensity categories, (sedentary (3 days), light (2 days), moderate (2 days) and vigorous activity (6 days) and MVPA (2 days)).ConclusionThese findings provide knowledge into the behavioural variability in weekly activity patterns of middle-aged adults. Since Sunday differed from all other days in the week this suggests that day of the week cannot be overlooked in the design and analysis of physical activity studies and thus should be included in the study monitoring frames. Collectively our data suggest that six days monitoring, inclusive of Saturday and Sunday, are needed to reliably capture weekly habitual activity in all activity intensities using the wrist-worn GENEActiv accelerometer.

Project description:Background:Agreement between questionnaires and accelerometers to measure physical activity (PA) differs between studies and might be related to demographic, lifestyle, and health characteristics, including disability and depressive symptoms. Methods:We included 1,410 individuals aged 51-94 years from the population-based Rotterdam Study. Participants completed the LASA Physical Activity Questionnaire and wore a wrist-worn accelerometer on the nondominant wrist for 1 week thereafter. We compared the Spearman correlation and disagreement (level and direction) for total PA across levels of demographic, lifestyle, and health variables. The level of disagreement was defined as the absolute difference between questionnaire- and accelerometer-derived PA, whereas the direction of disagreement was defined as questionnaire PA minus accelerometer PA. We used linear regression analyses with the level and direction of disagreement as outcome, including all demographic, lifestyle, and health variables in the model. Results:We observed a Spearman correlation of 0.30 between questionnaire- and accelerometer-derived PA in the total population. The level of disagreement (ie, absolute difference) was 941.9 (standard deviation [SD] 747.0) minutes/week, and the PA reported by questionnaire was on average 529.4 (SD 1,079.5) minutes/week lower than PA obtained by the accelerometer. The level of disagreement decreased with higher educational levels. Additionally, participants with obesity, higher disability scores, and more depressive symptoms underestimated their self-reported PA more than their healthier counterparts. Conclusion:We observed large differences in PA time derived from the LASA Physical Activity Questionnaire and the wrist-worn accelerometer. Differences between the methods were related to body-mass index, level of disability, and presence of depressive symptoms. Future studies using questionnaires and/or accelerometers should account for these differences.

Dataset Information

Estimated Physical Activity in Adolescents by Wrist-Worn GENEActiv Accelerometers.

Publications

Estimated Physical Activity in Adolescents by Wrist-Worn GENEActiv Accelerometers.

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