JMIR Publications

ABSTRACT

Background:

This research studies preventive digital health interventions (P-DHIs) that utilize mobile apps to improve diet and physical activity (PA) behaviors. RCT studies indicate that P-DHIs can be effective in preventing health problems for some persons in some situations but results from these studies are mixed. Adoption studies have identified multiple factors related to individual persons and the context in which they live that complicate the transfer of positive results from RCT studies to practical use, as individuals may respond differently to the same P-DHI. Implementation studies have revealed barriers to the large-scale implementation of mHealth solutions in general. Consequently, there is no clear path to delivering predictable outcomes from P-DHIs and achieving effectiveness when scaling up interventions to reduce health problems in society at large.

Objective:

This research attempts to expand our understanding of how to increase outcome predictability of P-DHIs focusing on PA and diet behaviors and amplify our understanding of how to improve effectiveness in large-scale implementations.

Methods:

The research objective is pursued through a multidisciplinary scoping review. The literature review was conducted using Web of Science and Google Scholar, reflecting the multidisciplinary nature of P-DHIs. Theoretical models from Information Systems and Public Health and quantitative and qualitative mHealth studies were included in the literature review. The first step was to use Information Systems theory to identify key constructs influencing outcomes from IT in general. The second step was to identify factors from the Public Health and mHealth literature influencing the outcome, specifically from P-DHIs and influencing the possibilities for successful large-scale mHealth implementation. In the third step, the factors from the Public Health and mHealth literature were categorized using the constructs from Information Systems theory. Finally, the P-DHI investment model was developed based on the Information Systems constructs and factors from the Public Health and mHealth literature.

Results:

196 articles met the inclusion criteria. The included articles use a variety of methodologies, including literature reviews, interviews, surveys, and RCT studies. The P-DHI investment model suggests which constructs and related factors should be emphasized to increase the predictability of P-DHI outcomes and improve effectiveness from large-scale implementations. The constructs in the P-DHI investment model are: 1. The P-DHI itself, including IT and non-IT investments. IT investments are investments in apps and wearables integrated with healthcare systems. Non-IT investments are additional investments in changes in healthcare organizations and the services they provide and in society in general necessary for delivering prevention using the P-DHIs, as well as additional investments made by individual persons to change behavior. 2. The context in which P-DHIs are implemented and used, e.g., the community in which individual persons using the P-DHI live. 3. The performance of processes changed by the P-DHI, i.e., processes in healthcare organizations delivering preventive healthcare services and processes in terms of individual persons’ health-related behaviors. 4. Lag effects, for example, learning how to use P-DHIs, which can delay positive outcomes. 5. Outcome. There are different kinds of outcomes that healthcare organizations and individual persons might achieve. The model describes the interdependence between the constructs and the difficulties in understanding and controlling the factors related to them.

Conclusions:

The research suggests that outcome predictability could be improved by including descriptions of the constructs and factors in the P-DHI investment model when reporting from empirical studies. Doing so would increase our understanding of when and why P-DHIs succeed or fail. Effectiveness from large-scale implementations might be improved by using the P-DHI investment model to evaluate potential difficulties and possibilities in implementing P-DHIs to create better environments for the use of P-DHIs before investing in them and when designing and implementing the P-DHIs. The cost-effectiveness of large-scale implementations is unknown, implementations are far more complicated than just downloading and using apps, and there is uncertainty accompanying implementations given the lack of coordinated control over the constructs and factors that influence the outcome.