Associations Between Obesity, Physical Inactivity, Healthcare Capacity, and the Built Environment: Geographic Information System Analysis

Effect of health belief model-based training and social support on the physical activity of overweight middle-aged women: a randomized controlled trial

Introduction

The highest incidence of overweight among adults is found among women, predominantly middle-aged women. While it has been demonstrated that being overweight increases mortality by compromising physical and mental health, it also imposes substantial costs on the healthcare system. Lack of physical activity is a primary contributing factor to becoming overweight. The majority of inactive adults are women, particularly middle-aged women. Consequently, this study investigated the training program for overweight women based on the health belief model (HBM) and social support approach.

Methods

A randomized, controlled trial involving 73 overweight middle-aged women (control group: 37, intervention group: 36) was conducted using simple random sampling. The intervention group participated in six 120-min sessions per week for 6 weeks of a training program based on HBM and social support through physical activity, group discussion, role play, and media. Data were collected using the Physical Activity Questionnaire (IPAQ), Bandura's Exercise Self-Efficacy Scale (Bandura's ESE), and a researcher-made questionnaire before and 4 weeks after the training. The collected data were analyzed using descriptive and inferential statistics via SPSS 27 software. p-values <0.05 were considered statistically significant.

Results

A training program based on HBM and social support led to improved perceived benefits (p < 0.001), cues to action (p = 0.03), and self-efficacy (p < 0.001) of physical activity; decreased perceived barriers (p = 0.001); increased social support (p = 0.001); and increased physical activity (p < 0.001). In addition, the BMI of the intervention group decreased after the training program (p = 0.01).

Conclusion

The findings of the study demonstrate the efficacy of the training program based on HBM and the social support approach in improving social support and physical activity of women. In addition, the study evaluates the long-term outcome in populations with varying social, economic, and cultural standings.

Clinical Trials Registration

https://clinicaltrials.gov/, (IRCT201706236261N17).

Contextualizing physical activity in rural adults: Do relationships between income inequality, neighborhood environments, and physical activity exist?

OBJECTIVE

To examine if income inequality, social cohesion, and neighborhood walkability are associated with physical activity among rural adults.

DATA SOURCE

Cross-sectional data came from a telephone survey (August 2020-March 2021) that examined food access, physical activity, and neighborhood environments across rural counties in a southeastern state.

STUDY DESIGN

Multinomial logistic regression models assessed the likelihood of being active versus inactive and insufficiently active versus inactive in this rural population. Coefficients are presented as relative risk ratios (RRRs). Statistical significance was determined using 95% confidence intervals (CIs). All analyses were performed in STATA 16.1.

DATA COLLECTION/EXTRACTION METHODS

Trained university students administered the survey. Students verbally obtained consent, read survey items, and recorded responses into Qualtrics software. Upon survey completion, respondents were mailed a $10 incentive card and printed informed consent form. Eligible participants were ≥18 years old and current residents of included counties.

PRINCIPAL FINDINGS

Respondents in neighborhoods with relatively high social cohesion versus low social cohesion were more likely to be active than inactive (RRR = 2.50, 95% CI: 1.27-4.90, p < 0.01), after accounting for all other variables in the model. Income inequality and neighborhood walkability were not associated with different levels of physical activity in the rural sample.

CONCLUSIONS

Study findings contribute to limited knowledge on the relationship between neighborhood environmental contexts and physical activity among rural populations. The health effects of neighborhood social cohesion warrant more attention in health equity research and consideration when developing multilevel interventions to improve the health of rural populations.

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10109-023-00415-y.