Engagement in Digital Mental Health Interventions: Can Monetary Incentives Help?

Eliane M Boucher,Haley E Ward,Amelia C Mounts,Acacia C Parks

doi:10.3389/fpsyg.2021.746324

Abstract

Digital mental health interventions (DMHI) are scalable and cost-effective strategies for increasing access to mental health care; however, dropout rates associated with digital interventions are high, particularly for open-access digital interventions. While some studies have focused on predictors of dropout from digital mental health programs, few studies have focused on engagement features that might improve engagement. In this perspective article, we discuss whether monetary incentives (MI) are one avenue to increasing user engagement in DMHI. We begin by reviewing the literature on the effects of MI for behavior change in health domains (e.g., dietary behaviors, substance use, and medication adherence). Then, drawing on a pilot study we conducted to test the effects of different levels of MI on usage and improvement in subjective well-being among users of a DMHI (Happify), we discuss the potential applications of MI for DMHI, the potential drawbacks of financial incentives in this context, and open questions for future research.

Highlights

In 2021, 85% of U.S adults reported having smartphones and 77% reported having broadband Internet (Perrin, 2021)
Engagement is a crucial aspect of any Digital mental health interventions (DMHI) (Eysenbach, 2005), making monetary incentives (MI) a hot topic in digital therapeutics
The first digital therapeutic to receive FDA clearance, reSET, uses Contingency management (CM) as part of its intervention (Maricich et al, 2021), there are still many questions we have yet to answer about the impact of MI in digital interventions

Summary

INTRODUCTION

In 2021, 85% of U.S adults reported having smartphones and 77% reported having broadband Internet (Perrin, 2021). There is very little research that examines patient engagement in this context, or how MI impacts users’ emotional and cognitive states regarding the intervention, the health behavior, or their underlying condition This is an important distinction because usage metrics, or technology engagement, may be indicative of passive adherence that is ineffective at promoting health management and quality of life long-term (Graffigna et al, 2014; Barello and Graffigna, 2015), which may explain why improvements in engagement or behavioral outcomes associated with MI are not always sustained once MI are removed. There is no research exploring the effects of MI on motivation, how MI affects people beyond their observable behavior and symptom change, or on reducing or removing MI within digital interventions, and few studies of in-person interventions include follow-up periods beyond 6 months (Giles et al, 2014). It will be important to understand the factors that predict a patient’s response to MI in digital interventions, including personality and cultural background

CONCLUSION

Findings

ETHICS STATEMENT