Habitual physical activity patterns in a nationally representative sample of U.S. adults

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Better together? Comparing physical activity of parents walking outdoors with and without their child

This study compares the physical activity levels and enjoyment of parents (n ​= ​50; age ​= ​41.8 ​± ​4.0 years) during outdoor, self-paced walking sessions while walking alone and when walking with their 6-to-12-year-old child. Step counts, moderate-to-vigorous physical activity (MVPA), vigorous physical activity, and enjoyment were compared using paired t-tests. Regression modeling was used to examine the impact of children's age and adults' and children's body mass index on the disparity in step counts experienced by adults. Adults attained high percentages of time spent in MVPA while walking alone (99.8%) and when walking with their child (96.6%). However, more of this time was categorized as vigorous physical activity when walking alone (13.6% vs. 3.0%, p ​= ​0.006). Adults' step counts decreased by an average of 6.4% while walking with their child compared to walking alone (p ​< ​0.001). This reduction was greater for parents of younger children (p ​= ​0.004). In obese adults, the reduction in step counts experienced while walking with their child was not as great as the reduction experienced by normal-weight adults (p ​= ​0.042). Although adults obtained higher step counts and vigorous physical activity while walking alone, they enjoyed walking more with their child (p ​< ​0.001). Health practitioners should be aware of this tradeoff when making recommendations for parent-child co-participation in physical activity, and parents may wish to supplement their physical activity obtained while walking with their child(ren) with additional activities that result in vigorous physical activity.

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia

The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18−60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution.

Golf and Health, More than 18 Holes—A Bibliometric Analysis

Despite golf’s contribution to health, scientific production related to golf and health has been relatively scarce. This work aims to investigate the state of the art on golf and health and to identify existing gaps and the principal and most notable potential future research trends, contributing to connecting the reality of the facilities dedicated to the practice of this sport and its contribution to raising awareness of the importance of sport in maintaining health. A total of 179 articles were analyzed following the steps for systematic reviews and meta-analysis protocols based on the PRISMA 2020 methodology and QUORUM, and a bibliometric analysis was carried out. Research to date has mainly focused on the benefits of golf in improving health, preventing illness, slowing down aging, or as rehabilitation and on exploring the risks and injuries involved in playing golf. The different ways of promoting participation or changing the image of golf by showing its healthy side are outlined as research trends in the coming years. There is a lack of exploration of the use of technology, the effects of the sport on certain disorders related to psychosocial factors, and further knowledge of the relationships between playing intentions and health. This research provides essential information for researchers who plan to work with golf in the future.

Psychosocial Mediation of Light-Moderate Physical Activity and Cognitive Performance among Adults Aged 60+ in China

Physical activity is a key determinant of healthy ageing; yet, little is known about the varying degrees of benefits by intensity nor the mediating mechanisms that operate through psychosocial health. Leveraging structural mediation analysis using the 2018 China Health and Retirement Longitudinal Study (CHARLS) national survey data, we screened 4371 community-dwelling older adults, and investigated the mediation mechanism of psychosocial health on the link between light-moderate physical activity and cognitive performance. Physical activity intensity, psychosocial health, and cognitive performance were measured by the international physical activity questionnaire (IPAQ), the Center for Epidemiological Studies Depression Scale (CESD), and the mini-mental state examination (MMSE) instruments, respectively. Results show that, while light physical activity (LPA) and moderate physical activity (MPA) both significantly contribute to better cognitive performance, psychosocial health is a significant mediator only for LPA but not for MPA. For direct pathways, both LPA (std. β = 0.062, p < 0.001, 95% confidence interval = 0.032−0.091) and MPA (std. β = 0.049, p = 0.001, 95% CI = 0.019−0.078) have significant influence on cognitive performance. For mediation pathways, results show that there exists only one indirect channel through which psychosocial health mediates the influence of LPA (std. β = 0.024, p < 0.001, 95% CI = 0.016−0.033), which accounts for 27.9% of the total effect linking LPA and cognitive performance. Findings uncover an important indirect psychosocial mediation channel through which LPA affects cognitive performance among older adults.

Accelerometer-Measured Daily Step Counts and Adiposity Indicators among Latin American Adults: A Multi-Country Study

The aim of the present study was to examine the sex-related associations between accelerometer-measured daily step counts and adiposity indicators in adults from eight Latin American countries. We analyzed data from 2524 adults (aged 18–65 years) from the Latin American Study of Nutrition and Health. Device-measured daily step counts were measured by accelerometers (ActiGraph GT3X). The outcomes were body mass index (BMI; (kg/m2), waist and neck circumference (in cm). Overall, the mean of daily steps counts, BMI, waist and neck circumference were 10699.8, 27.3, 89.6, and 35.8. Weak and negative associations were observed between daily steps counts and BMI (r = −0.17; p < 0.05) and waist circumference (r = −0.16; p < 0.05); however, step counts was not associated with neck circumference. Daily steps counts were negatively associated with BMI (β: −0.054; 95%CI: −0.077; −0.012) and waist circumference (−0.098; −0.165; −0.030) independently of age and socioeconomic level. In men, there were significant negative associations between daily steps counts with BMI (−0.075; −0.119; −0.031) and waist circumference (−0.140; −0.233; −0.048), and in women, there was no significant association with either of the body composition indicators. The findings from this study need to be examined in prospective settings that use device-measured from Latin America.