Interactive effects of work psychosocial factors on participation in workplace wellness programs.

This study explored concurrent effects of six work psychosocial factors on current participation and the self-reported likelihood of future participation in workplace wellness programs using a cross-sectional survey, an ad hoc focus group, and structured interviews. Classification and regression tree analysis was used to analyze survey responses from 343 employees (194 nonparticipants, 95 participants, and 54 engaged participants). A thematic analysis of focus group (n = 7) and interview (n = 5) narratives was also undertaken. In combination with high work control, high superior support was associated with an engaged participant profile. Job demand was the third important variable with low and very high levels associated with participation. With regard to high likelihood of future participation, among respondents with age older than 50, high predictability of occupational activities and control were identified as a significant factor, and among others, high superior support and control. The analysis of narratives revealed peer relations and flexible working hours to be positively linked to participation and general job stress was identified as having a bidirectional relationship. Employees stated that stress led them to take advantage of these programs as a source of relief and that their availability/participation has contributed to lowering their stress. These findings inform practitioners about the importance of addressing poor psychosocial factors as a participation barrier and having a holistic approach to employee well-being.

Modeling the spread of an obesity intervention through a social network.

With the growing prevalence of obesity and the public health implications, it is critical to develop and evaluate potential interventions. One approach is to investigate the spread of positive health outcomes through a social network. We employ the ground concepts of spread maximization problem and adapt it to best reflect the dynamics of a weight loss intervention. A diffusion model is then employed for the propagation of weight loss effect throughout the network of obese and overweight individuals. This diffusion model integrates both personal attributes and network-related attributes of an individual while engaged in a weight loss program. Simulation findings suggest that choosing initial agents based on individual attributes, including ability to lose weight, body mass index, and threshold, produced the highest total weight loss in the network. Greedy algorithm was also applied to choose the most effective subset of initial seeds.