Tri-axial accelerometer-assessed physical activity and its association with weight status in a sample of elementary-school children

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

BACKGROUND

Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized.

OBJECTIVE

This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks?

METHODS

This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers.

RESULTS

After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%).

CONCLUSIONS

Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

Influence of Body Mass Index, Physical Fitness, and Physical Activity on Energy Expenditure during Recess

This study aims to investigate the potential relationship between energy expenditure, physical fitness level, physical activity, and body mass index among children taking part in a 30 min school recess. A total of 259 participants from three schools were included in this study. Data on energy expenditure during recess, age, gender, anthropometric measurements, global physical fitness, and physical activity index were recorded. The evaluation sessions occurred twice a week on alternate days over two weeks. A significant gender difference was observed in energy expenditure during recess, favoring boys (p < 0.01). The participants classified as very active exhibited significantly higher scores compared to those categorized as sedentary and moderately active, respectively (p < 0.01), with a poor but significant correlation (rho: 0.208; p < 0.001). There were significant differences between energy expenditure and cardiorespiratory fitness and energy expenditure and global physical fitness (p < 0.01) with fair (rho: 0.289; p < 0.001) and poor (rho: 0.196, p = 0.001) correlation, respectively. In contrast, there were no significant differences by body mass index categories (p = 0.571). These results suggest that gender, physical activity index, and global physical fitness were found to influence energy expenditure during a 30 min recess. However, no significant relationships were found with the body mass index.