Food deserts exposure, density of fast-food restaurants, and park access: Exploring the association of food and recreation environments with obesity and diabetes using global and local regression models.

To prevent obesity and diabetes environmental interventions such as eliminating food deserts, restricting proliferation of food swamps, and improving park access are essential. In the United States, however, studies that examine the food and park access relationship with obesity and diabetes using both global and local regression are lacking. To guide county, state, and federal policy in combating obesity and diabetes, there is a need for cross-scale analyses to identify that relationship at national and local levels. This study applied spatial regression and geographically weighted regression to the 3,108 counties in the contiguous United States. Global regression show food deserts exposure and density of fast-food restaurants have non-significant association with obesity and diabetes while park access has a significant inverse association with both diseases. Geographically weighted regression that takes into account spatial heterogeneity shows that, among southern states that show high prevalence of obesity and diabetes, Alabama and Mississippi stand out as having opportunity to improve park access. Results suggest food deserts exposure are positively associated with obesity and diabetes in counties close to Alabama, Georgia, and Tennessee while density of fast-food restaurants show positive association with two diseases in counties of western New York and northwestern Pennsylvania. These findings will help policymakers and public health agencies in determining which geographic areas need to be prioritized when implementing public interventions such as promoting healthy food access, limiting unhealthy food options, and increasing park access.

Association of composite park quality with park use in four diverse cities.

Park use has numerous health benefits. However, little research has investigated how the combination of park facilities, amenities, and conditions are related to park visitation. This study examined the association between a novel composite park quality metric and the use of specific parks, including variations by demographics. Data were collected in 128 census block groups across four diverse cities in the USA. Adults (n = 262) used an online, map-based survey to indicate which parks within one half-mile they had used within the past 30 days. All parks (n = 263) were audited using the Community Park Audit Tool, and a composite quality metric was calculated by standardizing and averaging six key components: access, facilities, amenities, aesthetic features, quality concerns, and neighborhood concerns. A total of 2429 participant-park pairs were analyzed. The average park quality score was 40.4/100 (s.d. = 30.2). For the full sample, a greater park quality score was significantly related to park use (OR = 1.02, 95% CI = 1.01-1.03) such that for each one unit increase in a park's quality score, there was a 2% increase in the likelihood of the park being used. There was also a significant interaction by gender, with park quality associated with park use among females (OR = 1.03, 95% CI = 1.02-1.05) but not males (OR = 1.00, 95% CI = 0.99-1.02). Enhancing overall park quality may increase the likelihood of a park being used. Future research can explore the utility of this comprehensive quality metric for predicting other health behaviors and outcomes and how interventions to enhance park quality augment park use and health over time.

Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women.

OBJECTIVES: Independently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership. METHODS: A convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles. RESULTS: Rest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles. CONCLUSIONS: In this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions.

Can Building Design Impact Physical Activity? A Natural Experiment.

BACKGROUND: Workplace design can impact workday physical activity (PA) and sedentary time. The purpose of this study was to evaluate PA behavior among university employees before and after moving into a new building. METHODS: A pre-post, experimental versus control group study design was used. PA data were collected using surveys and accelerometers from university faculty and staff. Accelerometry was used to compare those moving into the new building (MOVERS) and those remaining in existing buildings (NONMOVERS) and from a control group (CONTROLS). RESULTS: Survey results showed increased self-reported PA for MOVERS and NONMOVERS. All 3 groups significantly increased in objectively collected daily energy expenditure and steps per day. The greatest steps per day increase was in CONTROLS (29.8%) compared with MOVERS (27.5%) and NONMOVERS (15.9%), but there were no significant differences between groups at pretest or posttest. CONCLUSIONS: Self-reported and objectively measured PA increased from pretest to posttest in all groups; thus, the increase cannot be attributed to the new building. Confounding factors may include contamination bias due to proximity of control site to experimental site and introduction of a university PA tracking contest during postdata collection. Methodology and results can inform future studies on best design practices for increasing PA.

Comparison of Accelerometry Methods for Estimating Physical Activity.

PURPOSE: This study aimed to compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques. METHODS: A convenience sample of middle-age to older women wore a GT3X+ accelerometer at the wrist and hip for 7 d. Physical activity estimates were calculated using three data processing techniques: single-axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes. Daily estimates were compared for the 321 women using generalized estimating equations. RESULTS: A total of 1420 d were analyzed. Compliance rates for the hip versus wrist location only varied by 2.7%. All differences between techniques, wear locations, and wear time protocols were statistically different (P < 0.05). Mean minutes per day in physical activity varied from 22 to 67 depending on location and method. On the hip, the 1952-count cut point found at least 150 min·wk of physical activity in 22% of participants, raw vector magnitude found 32%, and the machine-learned algorithm found 74% of participants with 150 min of walking/running per week. The wrist algorithms found 59% and 60% of participants with 150 min of physical activity per week using the raw vector magnitude and machine-learned techniques, respectively. When the wrist device was worn overnight, up to 4% more participants met guidelines. CONCLUSION: Estimates varied by 52% across techniques and by as much as 41% across wear locations. Findings suggest that researchers should be cautious when comparing physical activity estimates from different studies. Efforts to standardize accelerometry-based estimates of physical activity are needed. A first step might be to report on multiple procedures until a consensus is achieved.

Home and workplace built environment supports for physical activity.

BACKGROUND: Physical inactivity has been associated with obesity and related chronic diseases. Understanding built environment (BE) influences on specific domains of physical activity (PA) around homes and workplaces is important for public health interventions to increase population PA. PURPOSE: To examine the association of home and workplace BE features with PA occurring across specific life domains (work, leisure, and travel). METHODS: Between 2012 and 2013, telephone interviews were conducted with participants in four Missouri metropolitan areas. Questions included sociodemographic characteristics, home and workplace supports for PA, and dietary behaviors. Data analysis was conducted in 2013; logistic regression was used to examine associations between BE features and domain-specific PA. RESULTS: In home neighborhoods, seven of 12 BE features (availability of fruits and vegetables, presence of shops and stores, bike facilities, recreation facilities, crime rate, seeing others active, and interesting things) were associated with leisure PA. The global average score of home neighborhood BE features was associated with greater odds of travel PA (AOR=1.99, 95% CI=1.46, 2.72); leisure PA (AOR=1.84, 95% CI=1.44, 2.34); and total PA (AOR=1.41, 95% CI=1.04, 1.92). Associations between workplace neighborhoods' BE features and workplace PA were small but in the expected direction. CONCLUSIONS: This study offers empirical evidence on BE supports for domain-specific PA. Findings suggest that diverse, attractive, and walkable neighborhoods around workplaces support walking, bicycling, and use of public transit. Public health practitioners, researchers, and worksite leaders could benefit by utilizing worksite domains and measures from this study for future BE assessments.