This paper examines the mechanisms by which PHLAME (Promoting Healthy Lifestyles: Alternative Models' Effects), a health promotion intervention, improved healthy eating and exercise behavior among firefighters, a population at high risk for health problems due to occupational hazards. In a randomized trial, 397 firefighters participated in either the PHLAME team intervention with their work shift or a control condition. Intervention sessions taught benefits of a healthy diet and regular exercise, and sought to improve social norms and social support from coworkers for healthy behavior. At posttest, team intervention participants had increased their fruit and vegetable consumption as compared to control participants. An increase in knowledge of fruit and vegetable benefits and improved dietary coworker norms partially mediated these effects. Exercise habits and VO2 max were related to targeted mediators but were not significantly changed by the team intervention. Partial support was found for both the action and conceptual theories underlying the intervention. Our findings illustrate how an effective program's process can be deconstructed to understand the underpinnings of behavior change and refine interventions. Further, fire stations may improve the health of firefighters by emphasizing the benefits of healthy diet and exercise behaviors while also encouraging behavior change by coworkers as a whole.
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http://dx.doi.org/10.1037/a0023002 | DOI Listing |
Eur J Dent Educ
January 2025
QU Health College of Dental Medicine, Qatar University, Doha, Qatar.
Aims: This study aimed to evaluate the impact of community-based dental education (CBDE) on the learning experiences of undergraduate dental students and recent dental graduates from two diverse geographical regions.
Methods: The study followed a cross-sectional design, conducted online using Google Forms, with ethical approval from Qatar University. A non-probability purposive sampling method was used to recruit dental students and recent graduates from three institutions in India and one in Qatar.
Behav Res Methods
January 2025
CAP Team, Centre de Recherche en Neurosciences de Lyon - INSERM U1028 - CNRS UMR 5292 - UCBL - UJM, 95 Boulevard Pinel, 69675, Bron, France.
J Gen Intern Med
January 2025
Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Active surveillance (AS) is the guideline-recommended treatment for low-risk prostate cancer and involves routine provider visits, lab tests, imaging, and prostate biopsies. Despite good uptake, adherence to AS, in terms of receiving recommended follow-up testing and remaining on AS in the absence of evidence of cancer progression, remains challenging.
Objective: We sought to better understand urologist, primary care providers (PCPs), and patient experiences with AS care delivery to identify opportunities to improve adherence.
Sci Rep
January 2025
Faculty of Sports and Exercise Science, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Through a literature review, expert interviews, questionnaires, and statistical methods, this study constructs an evaluation index system and calculates the score for the integrated development of sports, culture, and tourism at sports event venues, specifically the Hemei Rural Football Super League ("Village Super League") in Rongjiang, Guizhou. First, we reviewed and analyzed the relevant literature, which led to the formation of an initial index consisting of 18 items. The index was optimized after the analysis to include three main dimensions and 13 specific measurement items.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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