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Using accelerometers in youth physical activity studies: a review of methods.

ABSTRACT: BACKGROUND:In 2005, investigators convened by the National Cancer Institute recommended development of standardized protocols for accelerometer use and reporting decision rules in articles. A literature review was conducted to document accelerometer methods and decision rule reporting in youth physical activity articles from 2005-2010. METHODS:Nine electronic databases identified 273 articles that measured physical activity and/or sedentary behavior using the most-used brand of accelerometer (ActiGraph). Six key methods were summarized by age group (preschool, children, and adolescents) and trends over time were examined. RESULTS:Studies using accelerometers more than doubled from 2005-2010. Methods included 2 ActiGraph models, 6 epoch lengths, 6 nonwear definitions, 13 valid day definitions, 8 minimum wearing day thresholds, 12 moderate-intensity physical activity cut points, and 11 sedentary cut points. Child studies showed the most variation in methods and a trend toward more variability in cut points over time. Decision rule reporting improved, but only 54% of papers reported on all methods. CONCLUSION:The increasing diversity of methods used to process and score accelerometer data for youth precludes comparison of results across studies. Decision rule reporting is inconsistent, and trends indicate declining standardization of methods. A methodological research agenda and consensus process are proposed.

SUBMITTER: Cain KL

PROVIDER: S-EPMC6331211 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Using accelerometers in youth physical activity studies: a review of methods.

Cain Kelli L KL Sallis James F JF Conway Terry L TL Van Dyck Delfien D Calhoon Lynn L

Journal of physical activity & health 20130301 3

<h4>Background</h4>In 2005, investigators convened by the National Cancer Institute recommended development of standardized protocols for accelerometer use and reporting decision rules in articles. A literature review was conducted to document accelerometer methods and decision rule reporting in youth physical activity articles from 2005-2010.<h4>Methods</h4>Nine electronic databases identified 273 articles that measured physical activity and/or sedentary behavior using the most-used brand of ac ...[more]

PMID: 23620392

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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:BackgroundPhysical activity reduces the risk of noncommunicable diseases and is therefore an essential component of a healthy lifestyle. Regular engagement in physical activity can produce immediate and long term health benefits. However, physical activity levels are not as high as might be expected. For example, according to the global World Health Organization (WHO) 2017 statistics, more than 80% of the world's adolescents are insufficiently physically active. In response to this problem, physical activity programs have become popular, with step counts commonly used to measure program performance. Analysing step count data and the statistical modeling of this data is therefore important for evaluating individual and program performance. This study reviews the statistical methods that are used to model and evaluate physical activity programs, using step counts.MethodsAdhering to PRISMA guidelines, this review systematically searched for relevant journal articles which were published between January 2000 and August 2017 in any of three databases (PubMed, PsycINFO and Web of Science). Only the journal articles which used a statistical model in analysing step counts for a healthy sample of participants, enrolled in an intervention involving physical exercise or a physical activity program, were included in this study. In these programs the activities considered were natural elements of everyday life rather than special activity interventions.ResultsThis systematic review was able to identify 78 unique articles describing statistical models for analysing step counts obtained through physical activity programs. General linear models and generalized linear models were the most popular methods used followed by multilevel models, while structural equation modeling was only used for measuring the personal and psychological factors related to step counts. Surprisingly no use was made of time series analysis for analysing step count data. The review also suggested several strategies for the personalisation of physical activity programs.ConclusionsOverall, it appears that the physical activity levels of people involved in such programs vary across individuals depending on psychosocial, demographic, weather and climatic factors. Statistical models can provide a better understanding of the impact of these factors, allowing for the provision of more personalised physical activity programs, which are expected to produce better immediate and long-term outcomes for participants. It is hoped that this review will identify the statistical methods which are most suitable for this purpose.

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Using accelerometers in youth physical activity studies: a review of methods.

Publications

Using accelerometers in youth physical activity studies: a review of methods.

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