Short Video Viewing, and Not Sedentary Time, Is Associated with Overweightness/Obesity among Chinese Women

Association between physical activity, sedentary behavior patterns with bone health among Chinese older women

INTRODUCTION

We examined the association between physical activity (PA) and sedentary behavior (SEB) time with bone health and whether it changes depending on different patterns.

MATERIALS AND METHODS

Cross-sectional data were derived from the baseline of the Physical Activity and Health in Older Women Study. PA and SEB were measured using Actigraph wGT3X-BT accelerometers. Bone mineral density (BMD) was derived from the SONOST-2000 ultrasound bone densitometer, with unhealthy bone defined as a BMD T-score of <2.5 standard deviation a young adult reference population's mean. A 10 min-bouted SEB was defined as an SEB duration of >10 min (allow 2 min 0 counts), similar to 30 min-bouted and 60 min-bouted SEBs. Sporadic and bouted PAs were defined by PA duration of <10 min or ≥ 10 min. Multivariate logistic regression analysis investigated the associations between PA and SEB patterns with bone health.

RESULTS

Among 1111 female participants, 42.12 % had unhealthy bones. In a fully-adjusted model, increasing 30 min/day of SEB was associated with a higher odds ratio (OR) for an unhealthy bone (OR, 1.08; P = 0.005), similar to the 10 (OR, 1.06; P = 0.012), 30 (OR, 1.06; P = 0.043), and 60 min-bouted (OR 1.08, P = 0.032) SEBs. Total light PA (LPA) time (OR, 0.97; P = 0.005) had a lower OR for unhealthy bone. After adjusting for sporadic LPA time, bouted LPA (OR, 0.97; P = 0.005) retained this association. No association was observed between total moderate-to-vigorous PA (MVPA) and bone health, sporadic MVPA, and bouted MVPA.

CONCLUSIONS

Performing bouted LPA and reducing 10 min-bouted SEB may maintain bone health.

Short-Form Video Exposure and Its Two-Sided Effect on the Physical Activity of Older Community Women in China: Secondary Data Analysis

Background

There is a tendency for older adults to become more physically inactive, especially older women. Physical inactivity has been exacerbated since the COVID-19 pandemic. Lockdowns and information-based preventive measures for COVID-19 increased the number of short-form video app users and short-form video exposure, including content exposure and the duration of exposure, which has demonstrated important effects on youths' health and health-related behaviors. Despite more older adults viewing short-form videos, less is known about the status of their short-form video exposure or the impacts of the exposure on their physical activity.

Objective

This study aims to describe physical activity-related content exposure among older adults and to quantify its impacts along with the duration of short-form video exposure on step counts, low-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA).

Methods

We analyzed a subsample (N=476) of older women who used smartphones and installed short-form video apps, using the baseline data collected from an ongoing cohort study named the Physical Activity and Health in Older Women Study (PAHIOWS) launched from March to June 2021 in Yantai, Shandong Province, China. The information on short-form video exposure was collected by unstructured questions; physical activity-related content exposure was finalized by professionals using the Q-methodology, and the duration of exposure was transformed into hours per day. Step counts, LPA, and MVPA were assessed with ActiGraph wGT3X-BT accelerometers. Multiple subjective and objective covariates were assessed. Linear regression models were used to test the effects of short-form video exposure on step counts, LPA, and MVPA. MVPA was dichotomized into less than 150 minutes per week and 150 minutes or more per week, and the binary logistic regression model was run to test the effects of short-form video exposure on the achievement of spending 150 minutes or more on MVPA.

Results

Of 476 older women (mean age 64.63, SD 2.90 years), 23.7% (113/476) were exposed to physical activity-related short-form videos, and their daily exposure to short-form videos was 1.5 hours. Physical activity-related content exposure increased the minutes spent on MVPA by older women (B=4.14, 95% CI 0.13-8.15); the longer duration of short-form video exposure was associated with a reduced step count (B=-322.58, 95% CI -500.24 to -144.92) and minutes engaged in LPA (B=-6.95, 95% CI -12.19 to -1.71) and MVPA (B=-1.56, 95% CI -2.82 to -0.29). Neither content exposure nor the duration of exposure significantly increased or decreased the odds of older women engaging in MVPA for 150 minutes or more per week.

Conclusions

Short-form video exposure has both positive and negative impacts on the physical activity of older adults. Efforts are needed to develop strategies to leverage the benefits while avoiding the harms of short-form videos.

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

BACKGROUND

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

OBJECTIVE

This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

METHODS

Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

RESULTS

A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

CONCLUSIONS

This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

Overweight and Obesity: The Interplay of Eating Habits and Physical Activity

The prevalence of overweight and obesity has been steadily increasing over the last 50 years, with worldwide obesity rates tripling since 1975, thus reaching pandemic proportions [...].

Estimation of Appendicular Skeletal Muscle Mass for Women Aged 60-70 Years Using a Machine Learning Approach

OBJECTIVES

This article aimed to develop and validate an anthropometric equation based on the least absolute shrinkage and selection operator (LASSO) regression, a machine learning approach, to predict appendicular skeletal muscle mass (ASM) in 60-70-year-old women.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

Community-dwelling women aged 60-70 years.

METHODS

A total of 1296 community-dwelling women aged 60-70 years were randomly divided into the development or the validation group (1:1 ratio). ASM was evaluated by bioelectrical impedance analysis (BIA) as the reference. Variables including weight, height, body mass index (BMI), sitting height, waist-to-hip ratio (WHR), calf circumference (CC), and 5 summary measures of limb length were incorporated as candidate predictors. LASSO regression was used to select predictors with 10-fold cross-validation, and multiple linear regression was applied to develop the BIA-measured ASM prediction equation. Paired t test and Bland-Altman analysis were used to validate agreement.

RESULTS

Weight, WHR, CC, and sitting height were selected by LASSO regression as independent variables and the equation is ASM = 0.2308 × weight (kg) - 27.5652 × WHR + 8.0179 × CC (m) + 2.3772 × Sitting height (m) + 22.2405 (adjusted R² = 0.848, standard error of the estimate = 0.661 kg, P < .001). Bland-Altman analysis showed a high agreement between BIA-measured ASM and predicted ASM that the mean difference between the 2 methods was -0.041 kg, with the 95% limits of agreement of -1.441 to 1.359 kg.

CONCLUSIONS AND IMPLICATIONS

The equation for 60-70-year-old women could provide an available measurement of ASM for communities that cannot equip with BIA, which promotes the early screening of sarcopenia at the community level. Additionally, sitting height could predict ASM effectively, suggesting that maybe it can be used in further studies of muscle mass.