Preventive medicine reports
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Several U. S. jurisdictions have adopted policies requiring healthy beverage defaults on children's menus, but it is unknown whether such policies or restrictions leads to fewer calories ordered. We recruited 479 caregivers of children for an online choice experiment and instructed participants to order dinner for their youngest child (2-6 years) from two restaurant menus. ⋯ There were no differences in the percent of orders or calories ordered from unhealthy beverages. Though Restriction participants ordered fewer calories from full-calorie soda [(3.0 (SD = 21.6)] relative to Control participants [13.4 (SD = 52.1); p = 0.04)] at Chili's, we observed no such difference between Default and Control participants, or across McDonald's conditions. Overall, there was no effect of healthy default beverages or restrictions in reducing total calories ordered from unhealthy beverages for children in our experiment.
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Regular physical activity is a key modifiable non-pharmacological treatment to enhance sleep quality, a key predictor of optimal health and wellbeing. Most of the evidence on physical activity and sleep quality is based on studies assessing the effects of aerobic moderate-to-vigorous physical activity (e.g. brisk walking, cycling, jogging). Emerging clinical evidence suggests that muscle-strengthening exercise (e.g. push-ups, using weight machines) may also be beneficial for sleep quality. ⋯ Poisson regression with robust error variance was used to calculate prevalence ratios of (PR) across weekly muscle-strengthening exercise frequency (None [reference]; 1, 2, 3-4 and ≥ 5 times/week), adjusting for potential confounders (e.g. age, sex, socioeconomic status, self-rated health, smoking, alcohol, aerobic physical activity). Compared with those reporting none, any muscle-strengthening exercise was associated with a reduced prevalence of 'poor' (PR range: 0.77-0.83) and 'very poor' (PR range: 0.57-0.70) quality sleep. Future health behavior modification strategies to enhance sleep quality at the population-level should consider promoting muscle-strengthening exercise.
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Non-response in prevention programs for cardiometabolic diseases (CMD) in primary care is often overlooked. The aim for this study was to define factors that influence the primary response to a selective CMD prevention program and to determine response-enhancing strategies that influence the willingness to participate. We conducted a non-response analysis within a randomized controlled trial evaluating a selective CMD prevention program, the study was conducted from 2013 to 2018 in Netherlands. ⋯ Although a relatively high proportion did not respond to the invitation for the risk score, the majority of them indicated to be willing to participate if a different invitation strategy would be used. With more time and energy, response rates for CMD prevention programs could possibly increase substantially. A next logical step in this process is to test potential response enhancing strategies in research setting.
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Appalachian youth tobacco use rates exceed the national average. Additional inquiry is needed to better understand youth product perceptions and use patterns. This study examined tobacco harm perceptions and their relationship with tobacco use among Appalachian youth. ⋯ Compared to never users, e-cigarette only users were more likely to disagree that smoking (AOR: 2.99, 95% CI: 1.30-6.90) and e-cigarettes cause health problems (AOR: 2.79, 95% CI: 1.64-4.75) and that e-cigarettes cause addiction (AOR: 2.48, 95% CI: 1.48-4.16). Most youth were aware of health dangers associated with smoking, but perceptions were split on whether e-cigarettes were associated with health problems or addiction. The findings indicate the need for additional youth tobacco use prevention efforts.
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We examine whether county-level tobacco retailer density and adult smoking prevalence are positively associated in the United States and determine whether associations differ in metropolitan vs. nonmetropolitan counties. We merged a list of likely tobacco retailers from the 2012 National Establishment Time-Series with smoking prevalence data from the Behavioral Risk Factor Surveillance System for 2828 US counties, as well as state tobacco policy information and county-level demographic data for the same year. We modeled adult smoking prevalence as a function of tobacco retailer density, accounting for clustering of counties within states. ⋯ This association, however, was only significant for metropolitan counties. Metropolitan counties in the highest tobacco retailer density quartile had smoking prevalence levels that were 1.9 percentage points higher than metropolitan counties in the lowest density quartile. Research should examine whether policies limiting the quantity, type and location of tobacco retailers could reduce smoking prevalence.