BackgroundIndividual social support is positively related to physical activity participation. However, less is known about how neighborhood-level social structures relate to physical activity participation.MethodsWe analyzed 2017 National Health Interview Survey data for adult participants who completed all questions on physical activity and neighborhood cohesion (N = 23,006). Each cohesion question was binary coded (cohesion or not) and used as a predictor individually and for a composite score measuring total social cohesion. We used linear regression to estimate minutes of moderate aerobic activity, and we used logistic regression to estimate the odds of meeting aerobic guidelines (≥150 min/wk), strength guidelines (≥2 d/wk of muscle strengthening exercises), or both guidelines, predicted by the 5 definitions of cohesion (composite cohesion and the 4 questions separately). Models were adjusted for sex, age, race/ethnicity, family-income-to-poverty ratio, education, nativity, language, and neighborhood tenure.ResultsRespondents who reported having more social cohesion had 45.0 more minutes of aerobic activity and increased odds of meeting aerobic, strength, and combined guidelines (odds ratio [OR] = 1.22, OR = 1.13, and OR = 1.14, respectively; P < .01 for all). Reporting having availability of help when needed, neighbors to count on, trustworthy neighbors, and close-knit neighbors all resulted in increased odds of meeting aerobic guidelines but not increased odds for meeting strength guidelines in the latter 3 components or combined guidelines for the latter 2 components.ConclusionsHaving neighborhood social cohesion or select individual components of neighborhood cohesion are positively related to meeting aerobic, strength, and combined guidelines.

Active transportation differed across demographic and workplace factors. These relationships could inform infrastructure policy decisions and workplace wellness programming targeting increased active transportation.

Heat stress and PPE training, as well as the implementation of a work-to-rest ratio that avoids dehydration and possible heat stress issues, are recommended. (Disaster Med Public Health Preparedness. 2017;11:580-586).

Heat stress is a growing concern in the occupational setting as it endangers worker health, safety, and productivity. Heat-related reductions in physical work capacity and missed workdays directly and indirectly cause productivity losses and may substantially affect the economic wellbeing of the organization. This review highlights the physiological, physical, psychological, and financial harms of heat stress on worker productivity and proposes strategies to quantify heat-related productivity losses. Heat stress produces a vicious-cycle feedback loop that result in adverse outcomes on worker health, safety, and productivity. We propose a theoretical model for implementing an occupational heat safety plan that disrupts this loop, preventing heat-related productivity losses while improving worker health and safety.

Purpose: To examine differences in activity patterns across employment and occupational classifications. Design: Cross-sectional. Setting: A 2005-2006 Coronary Artery Risk Development in Young Adults (CARDIA) study. Sample: Participants with valid accelerometry data (n = 2068). Measures: Uniaxial accelerometry data (ActiGraph 7164), accumulated during waking hours, were summarized as mean activity counts (counts/min) and time spent (min/d) in long-bout sedentary (≥30 minutes, SED≥30), short-bout sedentary (<30 minutes, SED<30), light physical activity (LPA), short-bout moderate-to-vigorous physical activity (<10 minutes, MVPA<10), and long-bout MVPA (≥10 minutes, MVPA≥10) using Freedson cut-points. Employment status was self-reported as full time, part time, unemployed, keeping house, or raising children. Self-reported job duties were categorized into 23 major groups using the 2010 Standard Occupational Classification. Analysis: Omnibus differences were analyzed using adjusted analysis of covariance and repeated after stratification by race (black/white) and sex (female/male). Results: SED≥30, SED<30, LPA, and MVPA<10 differed significantly by employment and occupational categories ( P ≤ .05), while MVPA≥10 did not ( P ≥ .50). SED≥30, SED<30, and LPA differed by occupational classification in men, women, blacks, and whites ( P < .05). Mean activity counts, MVPA<10, and MVPA≥10 were significantly different across occupational classifications in whites ( P ≤ .05), but not in blacks ( P > .05). Significant differences in mean activity counts and MVPA<10 across occupational classifications were found in males ( P ≤ .001), but not in females ( P > .05). Conclusion: Time within activity intensity categories differs across employment and occupational classifications and by race and sex.