Accelerometer-Measured Daily Steps, Physical Function, and Subsequent Fall Risk in Older Women: The Objective Physical Activity and Cardiovascular Disease in Older Women Study

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.

Association of dog ownership with accelerometer-measured physical activity and daily steps in 70-year-old individuals: a population-based cross-sectional study

Background

Dog ownership (DO) has been associated with higher levels of self-reported walking and physical activity. However, compared to device-based measures, self-reported measures of physical activity may suffer from bias due to recall and social desirability. They are also incapable of quantifying light-intensity physical activity (LPA) and step volume, both of which may have important health benefits, especially for older adults. In this study, we investigated the association of DO with accelerometer-measured physical activity of different intensities and daily steps in 70-year-old individuals.

Methods

This was a population-based cross-sectional study including 1406 participants aged 70 years [54.1% female] who participated in a health survey in Umeå, Sweden between February 2017–November 2019. All participants self-reported DO [yes/no]. Daily averages of LPA, moderate-to-vigorous-intensity physical activity (MVPA), and steps per day [steps/d] were measured for 1 week using hip-mounted Actigraph GT3X+ accelerometers. Associations were investigated using linear- and logistic regression models, adjusted for sociodemographic and health-related factors, date of examination, and accelerometer wear time.

Results

The prevalence of DO was 14.1% [N = 199]. After adjustment for all covariates, DO was associated with 19.2 more minutes/d of LPA [95% CI, 8.8–29.6], 11.4 more minutes/d of MVPA [95% CI, 8.0–14.9] and 1738 more steps/d [95% CI, 1326–2149]. DO was also associated with twice the odds of meeting the physical activity recommendations [OR, 2.07, 95% CI, 1.48–2.90]. Exploratory interaction analyses showed that the association between DO and steps/d was stronger [P_interaction = 0.030] in female [β = 2165, 95% CI, 1585–2744] than in male [β =1255, 95% CI, 664–1845], with a similar trend for MVPA [P_interaction = 0.082].

Conclusions

In this study of community-dwelling 70-year-old individuals, DO was associated with higher levels of daily LPA, MVPA, and steps. With the limitation of the observational design of the study, these findings add knowledge regarding the beneficial role that DO may play for promoting physical activity in the older population. In turn, these findings could support the development and evaluation of targeted interventions seeking to promote dog-friendly environments and facilitate dog walking in the community.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12401-4.