Accelerometer-Measured Physical Activity and Sedentary Time among Children in Japan before and during COVID-19: A Cross-Sectional and Longitudinal Analysis

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.

Physical activity and sleep changes among children during the COVID-19 pandemic

Daily routines, including in-person school and extracurricular activities, are important for maintaining healthy physical activity and sleep habits in children. The COVID-19 pandemic significantly disrupted daily routines as in-person school and activities closed to prevent spread of SARS-CoV-2. We aimed to examine and assess differences in objectively measured physical activity levels and sleep patterns from wearable sensors in children with obesity before, during, and after a period of school and extracurricular activity closures associated with the COVID-19 pandemic. We compared average step count and sleep patterns (using the Mann-Whitney U Test) before and during the pandemic-associated school closures by using data from activity tracker wristbands (Garmin VivoFit 3). Data were collected from 94 children (aged 5-17) with obesity, who were enrolled in a randomized controlled trial testing a community-based lifestyle intervention for a duration of 12-months. During the period that in-person school and extracurricular activities were closed due to the COVID-19 pandemic, children with obesity experienced objectively-measured decreases in physical activity, and sleep duration. From March 15, 2020 to March 31, 2021, corresponding with local school closures, average daily step count decreased by 1655 steps. Sleep onset and wake time were delayed by about an hour and 45 min, respectively, while sleep duration decreased by over 12 min as compared with the pre-closure period. Step counts increased with the resumption of in-person activities. These findings provide objective evidence for parents, clinicians, and public health professionals on the importance of in-person daily activities and routines on health behaviors, particularly for children with pre-existing obesity. Trial Registration: Clinical trial registration: NCT03339440.

Moderate-to-vigorous intensity physical activity among U.S. adolescents before and during the COVID-19 pandemic: Findings from the Adolescent Brain Cognitive Development Study

Only 16.1% percent of U.S. adolescents meet the recommendation of at least 60 minutes of moderate-to-vigorous intensity physical activity (MVPA) per day. Studies report declined levels of adolescent MVPA in early stages of the pandemic, but gaps remain in understanding changes beyond the initial three months of the pandemic. This study aims to describe and compare self-reported adolescent MVPA levels at multiple timepoints before and during the COVID-19 pandemic among 11,865 9-11-year-old U.S. adolescents from the Adolescent Brain Cognitive Development (ABCD) Study, including pre-pandemic (September 2016-October 2018), early (May, June, and August 2020), and later (October and December 2020, March 2021) stages of the pandemic. Poisson regression models with robust error variance were used to estimate crude and adjusted prevalence ratios (APRs) of the proportion of adolescents meeting national MVPA guidelines during early and later stages of the pandemic compared to pre-pandemic. The proportion of adolescents meeting MVPA guidelines decreased from pre (16.4%), early (11.0%), and later (4.7%) COVID-19 pandemic timepoints. Adolescent MVPA guideline adherence at early- and later-pandemic stages was 24% lower (APR 0.76, 95% CI 0.62, 0.93) and 68% lower (APR 0.32, 95% CI 0.24, 0.43) than pre-pandemic adherence, respectively. Weekly MVPA duration decreased throughout May 2020 to March 2021 (χ2 = 488.9, p < 0.0001). Study findings build upon existing evidence that the low achievement of national MVPA guidelines before the pandemic became even lower during the pandemic, demonstrating the need to support and improve access to adolescent MVPA opportunities during COVID-19 pandemic recovery efforts and in future pandemics.

Changes in insulin resistance, body mass index and degree of obesity among junior high school students: A comparison before and after the outbreak of the COVID-19 pandemic

This study aimed to investigate how the COVID-19 pandemic since 2020 has affected the homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI) and degree of obesity among Japanese children. HOMA-IR, BMI and degree of obesity were calculated for 378 children 14-15 years old (boys/girls, 208/170) who underwent checkups during 2015-2021. Changes in these parameters over time and correlations between parameters were assessed, and the proportions of participants with IR (HOMA-IR ≥2.5) were compared. HOMA-IR values increased significantly over the study period (p < 0.001), with a significantly large proportion of participants with IR in 2020-2021 (p < 0.001). Conversely, BMI and degree of obesity did not change significantly. HOMA-IR did not correlate with BMI or degree of obesity during 2020-2021. In conclusion, the COVID-19 pandemic may have had an impact on the increase in the proportion of children with IR, regardless of BMI or degree of obesity.

Sedentary behavior is associated with poor sleep quality during the COVID-19 pandemic, and physical activity mitigates its adverse effects

BACKGROUND

We aimed to evaluate the association of sedentary behavior (SB) and moderate to vigorous leisure-time physical activity (MVPA) with sleep quality during the COVID-19 pandemic.

METHODS

Cross-sectional, population-based study in adults, conducted from October to December 2020 in the Iron Quadrangle region, Brazil. The outcome was sleep quality, evaluated with the Pittsburgh Sleep Quality Index. SB was assessed by self-report of total sitting time, before and during the pandemic. Individuals with ≥ 9 h of total sitting time were classified as SB. In addition, the ratio of time spent in MVPA to time in SB was analyzed. A contrasted directed acyclic graph (DAG) model was constructed to adjust logistic regression models.

RESULTS

A total of 1629 individuals were evaluated, the prevalence of SB before the pandemic was 11.3% (95%CI: 8.6-14.8), and during the pandemic 15.2% (95%CI: 12.1-18.9). In multivariate analysis, the chance of poor sleep quality was 77% higher in subjects with SB ≥ 9 h per day (OR: 1.77; 95% CI: 1.02-2.97). Furthermore, a one-hour increase in SB during the pandemic, increased the chance of poor sleep quality by 8% (OR: 1.08; 95%CI 1.01-1.15). In the analysis of the ratio of MVPA per SB in individuals with SB ≥ 9 h, practicing 1 min of MVPA per hour of SB reduces the chance of poor sleep quality by 19% (OR: 0.84; 95%CI: 0.73-0.98).

CONCLUSION

SB during the pandemic was a factor associated with poor sleep quality, and the practice of MVPA can reduce the effects of SB.