A Novel Method for Setting Meaningful Thresholds for RMS-Energy Oscillation Detectors

Abstract

Many mHealth interventions for health behavior change are considered effective for improving health outcomes. However, there is a limited understanding of the role of the components in an intervention on its effectiveness. Insights into intervention components such as content and software features are needed to design efficient and effective interventions. In this study, we conducted an exploratory analysis of objective data from the usage of a weight management app to understand the role of intervention components in weight loss. We identified a positive correlation between weight loss and the use of the intervention. We also found differences in the app feature use among those who lost weight. To lose weight, users needed to comply with the intervention by completing a combination of tasks. They needed to complete 70% of some tasks and up to a maximum of 30% of other tasks. In the future, we hope to use other types of collected data (logged and survey data) to gain more nuanced insights into how interventions are used. With the help of data analytics, we may find optimal paths of use and determine a satisfactory level of compliance to achieve desired goals. This can deepen our understanding of what works in an intervention.

Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials

“…Additionally, knowing the individual contribution of persuasive components and synergistic effects in this regard is desired [ 56 ]. Further research shows that the patterns of use of the intervention influence its effectiveness in achieving the desired outcome [ 57 ].…”