Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative

Abstract

Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.

Keywords: Communication; Intervention; Motivation; Obesity prevention; Process evaluation; Simulation; Sustainability of implementation; System dynamics.

Figure 1

Potential program impact trajectories

Figure 2. Process flow of our research method.

The five processes took place in 2, 2, 1, 3, and 6 months, respectively.

Figure 3. Effect of costs and benefits on motivation.

Successful implementation depends on the competition between a reinforcing loop (R1: Seeing the impact) and a balancing loop (B1: Costly program). If R1 dominates, successful implementation is possible.

Figure 4. Effect of motivation of stakeholders and communication among stakeholders on design quality.

Small problems in design can lead to loss of motivation, reduced communication, and more design problems as implementation progresses.

Figure 5. Mechanisms affecting the maintenance of interventions.

The erosion rate is influenced by motivation, design quality, and communication, creating three additional reinforcing loops that can drive a wedge between successful and unsuccessful maintenance of programs.

Figure 6. Implemented components (A), communication sufficiency (B), motivation of stakeholders to implement (C), and perceived net benefit ratio (D).

Baseline (blue line) is based on 24 hours effort of interventionists per month during the maintenance phase. More effort (red line) is based on 26 hours effort of interventionists per month. The big difference between the outputs of these two scenarios relates to the tipping threshold, e.g., a level of interventionists’ efforts that once exceeded causes a sustained intervention. Y-axis in (A) shows the number of implemented components (capped at 20 components). Y-axis in (B) shows the level of communication sufficiency (>=0), where values below one represent shortfalls in communication. Y-axis in (C) shows motivation of stakeholders (0<= Motivation <=1), where one presents high levels of motivation. Y-axis in (D) shows the ratio of perceived benefits to perceived implementation costs (>=0), where benefits exceed costs for values above one.

Figure 7. Implementation accomplishment in post-implementation.

The rate of resource allocation in the post-implementation stage is proportional to that of the implementation stage, such that a value of 50% means that half of resources used during the implementation are allocated in the post-implementation.

Figure 8. Trade-offs in resource allocations.

The coloured lines present the rates of sustained intervention components at the end of the simulations.