Abstract P185: Investigation of Community and Home Factors in Child Physical Activity Levels

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
MIchael Graham ◽  
Vanessa Shannon ◽  
Christa Ice ◽  
Lesley Cottrell

It is known that physical activity (PA) behavior is influenced by many factors within the social ecological model. Using results from parent surveys distributed after their children’s completion of a cardiovascular risk screening program, we explored the relationship between home and community environments on the amount of PA in which children engaged. Our hypothesis was that more immediate factors such as parent activity would have a greater impact on child activity than factors in their community environments. A large sample (n=450) of children (ages 5-10 years) were examined. Children’s physical activity was assessed by adding the total minutes of active time weekly. Parent physical activity was measured with two self report items regarding the number of days per week they were active . Two scales were constructed to evaluate to effects of the home (9 items; α = .829) and community (16 items; α = .868). The home environment scale measured elements related to activity opportunities and home schedules; the community scale assessed presence of playgrounds, or safe sidewalks, for example. To assess associations between factors and children’s PA, we conducted a linear stepwise regression with child age, parent PA, Home scale, and Community scale as predictors and the log transformed total weekly activity time as the dependent variable. Sixteen percent of children’s PA was explained by the tested model. Figure 1 provides specific information about each variable. The home scale had the greatest weight (β=0.360, p<.001), and proved to have a larger predictive effect than parent PA. These findings are significant for identifying which aspects of a child’s surrounding to intervene for maximum impact on physical activity. Figure 1. R² Change between model levels

Author(s):  
Li-Ting Chen ◽  
Ya-Wen Hsu

Using bike share could increase physical activity and improve health. This study used the social-ecological model to identify predictors of frequent bike share trips for different purposes. Participants residing in the U.S. were recruited via Amazon Mechanical Turk (MTurk). Self-report trip purposes were used to group participants into using bike share for commuting only (n = 260), social/entertainment only (n = 313), exercise only (n = 358), dual or triple-purpose (n = 501), and purposes other than commuting, social/entertainment, and exercise (n = 279). Results showed that at the intrapersonal level, perceived use of bike share to be helpful for increasing physical activity was a significant predictor for all groups, except for the other purpose group. Adjusting outdoor activity based on air quality was a significant predictor for the dual or triple-purpose group. At the interpersonal level, having four or more friends/family using bike share was a significant predictor for the other purpose group. At the community level, distance to the nearest bike share within acceptable range was a significant predictor for social/entertainment and dual or triple-purpose groups. The findings suggest that it is important to consider factors at multiple levels for predicting bike share usage. Moreover, health educators and policy makers should adopt different strategies for promoting bike share usage based on trip purposes.


2016 ◽  
Vol 28 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Patricia Davern Soderlund

Hispanic women are less physically active and have higher rates of type 2 diabetes (DM2) when compared with other population groups. This review uses the social ecological model as a framework to identify the individual and social environmental factors associated with successful physical activity (PA) interventions for Hispanic women with DM2. Research questions include (a) Which social ecological levels have been applied to PA interventions? (b) Which individual and social environmental intervention strategies are associated with successful PA outcomes? Database searches using CINAHL, PubMed, and Scopus for the years 2000 to 2015 identified 10 studies; with 6 using quasi-experimental study designs and 4 using randomized controlled designs. Inclusion criteria were Hispanic/Latina women with DM2, ≥70% women, PA interventions, measures of PA, and quantitative designs. Future research should focus on a combination of intervention levels, and DM2 programs should place a greater emphasis on PA intervention strategies.


2005 ◽  
Vol 7 (4) ◽  
pp. 137-142 ◽  
Author(s):  
Erin M. Snook ◽  
Mina C. Mojtahedi ◽  
Ellen M. Evans ◽  
Edward McAuley ◽  
Robert W. Motl

Individuals with multiple sclerosis (MS) engage in less physical activity than the general population. This level of inactivity may increase a person's risk of being overweight and obese. The relationship between physical activity and body composition is examined among 34 ambulatory adults with a definite diagnosis of MS. Participants wore pedometers and accelerometers, objective measures of physical activity, for 7 days; completed a self-report measure of physical activity; and underwent various measurements of body composition, including body mass index (BMI), waist circumference, and relative body fat by dual energy X-ray absorptiometry (DXA). Statistically significant negative correlations were found between physical activity levels and measures of body fatness, and the correlations were strong between the objective measures of physical activity and DXA measures of body composition. The correlations were moderate between the self-report measure of physical activity and less precise measures of body composition. Our findings suggest that inactivity plays an important role in body fatness among people with MS, and subjective measures of physical activity and less precise measures of body fatness, such as BMI, may underestimate the strength of the relationship between physical activity and risk for obesity in the MS population.


2014 ◽  
Vol 18 (11) ◽  
pp. 2055-2066 ◽  
Author(s):  
Punam Ohri-Vachaspati ◽  
Derek DeLia ◽  
Robin S DeWeese ◽  
Noe C Crespo ◽  
Michael Todd ◽  
...  

AbstractObjectiveThe Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children’s weight status.DesignCross-sectional survey of randomly selected households with children living in low-income diverse communities.SettingA telephone survey conducted in 2009–2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child’s residence to food and physical activity outlets.SubjectsAnalysis based on 560 children whose parents participated in the survey and provided measured heights and weights.ResultsMultiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children’s weight status.ConclusionsThe approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.


2016 ◽  
Vol 13 (6) ◽  
pp. 640-648 ◽  
Author(s):  
Mikihiro Sato ◽  
James Du ◽  
Yuhei Inoue

Background:Although previous studies supported the health benefits of physical activity, these studies were limited to individual-level research designs. Building upon a social-ecological model, we examined the relationship between physical activity and community health—the health status of a defined group of people—while accounting for the potential endogeneity of physical activity to health.Methods:We obtained U.S. county-level data from the 2012 Behavioral Risk Factor Surveillance System survey and the 2014 County Health Ranking Database. We first conducted an ordinary least squares (OLS) regression analysis to examine the relationship between the rate of physical activity and community health measured by the average perceived health score for each county. We then conducted a 2-stage least squares (2SLS) regression analysis to investigate this relationship after accounting for potential endogeneity.Results:Results from the OLS analysis indicated that the rate of physical activity was positively associated with community health. Results from the 2SLS analysis confirmed that the physical activity rate remained positively associated with community health.Conclusions:In line with the social-ecological model, our findings provide the first evidence for the health benefits of county-level physical activity. Our results support extant research that has shown relationships between physical activity and individual-level, health-related outcomes.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maria Apostolopoulos ◽  
Jill A. Hnatiuk ◽  
Jaimie-Lee Maple ◽  
Ellinor K. Olander ◽  
Leah Brennan ◽  
...  

Abstract Background Postpartum women are at higher risk of depression compared to the general population. Despite the mental health benefits an active lifestyle can provide, postpartum women engage in low physical activity and high screen time. Very little research has investigated the social ecological (i.e. individual, social and physical environmental) influences on physical activity and screen time amongst postpartum women, particularly amongst those with depressive symptoms. Therefore, this study sought to examine the influences on physical activity and screen time amongst postpartum women with heightened depressive symptoms. Methods 20 mothers (3–9 months postpartum) participating in the Mums on the Move pilot randomised controlled trial who reported being insufficiently active and experiencing heightened depressive symptoms participated in semi-structured telephone interviews exploring their perceptions of the key influences on their physical activity and screen time across various levels of the social ecological model. Strategies for promoting physical activity and reducing screen time were explored with participants. Thematic analyses were undertaken to construct key themes from the qualitative data. Results Findings showed that postpartum women with depressive symptoms reported individual (i.e. sleep quality, being housebound, single income), social (i.e. childcare, social support from partner and friends) and physical environmental (i.e. weather, safety in the local neighbourhood) influences on physical activity. Postpartum women reported individual (i.e. screen use out of habit and addiction, enjoyment) and social (i.e. positive role modelling, social isolation) influences on screen-time, but no key themes targeting the physical environmental influences were identified for screen time. Strategies suggested by women to increase physical activity included mother’s physical activity groups, home-based physical activity programs and awareness-raising. Strategies to reduce screen time included the use of screen time tracker apps, increasing social connections and awareness-raising. Conclusions Amongst postpartum women with heightened depressive symptoms, influences on physical activity encompassed all constructs of the social ecological model. However, screen time was only perceived to be influenced by individual and social factors. Intervention strategies targeting predominantly individual and social factors may be particularly important for this high-risk group. These findings could assist in developing targeted physical activity and screen time interventions for this cohort.


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