This project aims at understanding how effectiveness of organizational level chronic disease interventions depends on dynamics of adoption, implementation and maintenance. To this end we will develop and test system dynamics simulation models of four obesity prevention interventions and parameterize them in eight empirical cases (two programs for each intervention). Building on these models a generic model of intervention implementation will be developed that can be applied to a wide array of public health interventions. Finally, an educational simulation-based game and a few written resources will be developed and pilot tested in collaboration with the Center for Training and Research Translation to assist in training stakeholders in implementing one of the studied interventions. The efficacy of lifestyle interventions aimed at obesity and related chronic diseases such as diabetes are well established. Overall effectiveness of these interventions, however, relies not only on the average efficacy of a generic intervention, but also on the successful adoption, implementation, and maintenance of each instance of that intervention (i.e. each program) within the responsible organizational and community context. Typically a lot of research goes into measuring and establishing the average efficacy of idealized interventions in controlled settings. However, in practice much variability in overall effectiveness of interventions arises from variations in Adoption, Implementation, and Maintenance (AIM) in various settings. Understanding the sources of variation in AIM is therefore central to enhancing the effectiveness of existing interventions and designing more effective new interventions. To this end the current study focuses on developing a detailed understanding, as well as grounded simulation models which will be validated using multiple sources of data and tests. Development of a generic model will be pursued to extend the findings from the studied cases to a broader range of interventions. Policy analysis using the models will inform policy conclusions from the study. Moreover a simulation based learning environment and other training material will also be developed to maximize the broader impact of the research.
This study will develop and test system dynamics simulation models of four obesity prevention interventions and parameterize them in eight empirical cases (two programs for each intervention). Building on these models a generic model of intervention implementation will be developed that can be applied to a wide array of public health interventions and policy analysis using the models will inform policy conclusions from the study. Finally, an educational simulation-based game and a few written resources will be developed and pilot tested in collaboration with the Center for Training and Research Translation to assist in training stakeholders in implementing one of the studied interventions.
| Jalali, Mohammad S; Rahmandad, Hazhir; Bullock, Sally Lawrence et al. (2017) Dynamics of Implementation and Maintenance of Organizational Health Interventions. Int J Environ Res Public Health 14: |
| Jalali, M S; Sharafi-Avarzaman, Z; Rahmandad, H et al. (2016) Social influence in childhood obesity interventions: a systematic review. Obes Rev 17:820-32 |
| Shoham, David A; Hammond, Ross; Rahmandad, Hazhir et al. (2015) Modeling social norms and social influence in obesity. Curr Epidemiol Rep 2:71-79 |
| Rahmandad, Hazhir (2014) Human growth and body weight dynamics: an integrative systems model. PLoS One 9:e114609 |
| Fallah-Fini, Saeideh; Rahmandad, Hazhir; Huang, Terry T-K et al. (2014) Modeling US adult obesity trends: a system dynamics model for estimating energy imbalance gap. Am J Public Health 104:1230-9 |
| Sabounchi, N S; Rahmandad, H; Ammerman, A (2013) Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations. Int J Obes (Lond) 37:1364-70 |
| Fallah-Fini, Saeideh; Rahmandad, Hazhir; Chen, Hsin-Jen et al. (2013) Connecting micro dynamics and population distributions in system dynamics models. Syst Dyn Rev 29:197-215 |
