Navigating the complexity of User Engagement: need for normative evaluations in digital interventions

Abstract Digital behavioral change interventions (DBCI), leveraging digital technology to promote cognitive behavioral therapy or positive psychology techniques, can potentially revolutionize health promotion. With applications across a spectrum of health behaviors-from managing common mental health disorders to enhancing physical activity, addressing smoking cessation, alcohol use, and weight management-these interventions offer promise for both healthy and clinical populations. However, their effectiveness hinges significantly on user engagement. Digital interventions are prone to attrition due to their self-help and unguided nature, which impedes their translation from research to real-life settings. This talk discusses the findings of a scoping review to shed light on these complexities. Firstly, we explore the various methodologies used to evaluate engagement across studies, hindering effective comparison and understanding. Secondly, we address the lack of systematic assessment of engagement strategies, advocating for experimental research to fill this gap. Lastly, we emphasize the need to establish an optimal level of engagement for desired outcomes, noting that continuous engagement may not always be conducive to behavioral change interventions, which aim to instill lasting changes in individuals. This underscores the importance of comprehensive assessment using both objective and subjective measures. In conclusion, engagement with digital interventions encompasses both quantitative usage metrics and qualitative subjective experiences, highlighting the need for comprehensive assessment beyond the mere evaluation of frequency or duration. Contrary to common belief, increased engagement does not always lead to better outcomes. Too much engagement can lead to exhaustion, while too little can result in inefficiency. Therefore, designing a DBCI that encourages optimal use is essential, fostering a balance that maximizes effectiveness while minimizing adverse effects.