Abstract
Background
The COVID-19 pandemic has accelerated the adoption of digital tools to support individuals struggling with their mental health. The use of a digital intervention plus human coaching (“dual” intervention) is gaining momentum in increasing overall engagement in digital cognitive behavioral interventions (dCBIs). However, there is limited insight into the methodologies and coaching models used by those deploying dual interventions. To achieve a deeper understanding, we need to identify and promote effective engagement that leads to clinical outcomes versus simply monitoring engagement metrics. Motivational interviewing (MI) is a collaborative, goal-oriented communication approach that pays particular attention to the language of change and is an effective engagement approach to help people manage mental health issues. However, this approach has been traditionally used for in-person or telephonic interventions, and less is known about the application of MI to digital interventions.
Objective
We sought to provide a dual intervention approach and address multiple factors across two levels of engagement to operationalize a dCBI that combined cognitive behavioral therapy–based techniques and MI-based interactions between the digital health coach (DHC) and user.
Methods
We reviewed hundreds of digital exchanges between DHCs and users to identify and improve training and quality assurance activities for digital interventions.
Results
We tested five hypotheses and found that: (1) users of a dual digital behavioral health intervention had greater engagement levels than users of a noncoached intervention (P<.001); (2) DHCs with a demonstrated competency in applying MI to digital messages had more engaged users, as measured by the DHC-to-user message exchange ratio (P<.001); (3) the DHC-to-user message exchange ratio was correlated with more engagement in app activities (r=0.28, 95% CI 0.23-0.33); (4) DHCs with demonstrated MI proficiency elicited a greater amount of “change talk” from users than did DHCs without MI proficiency (H=25.12, P<.001); and (5) users who were engaged by DHCs with MI proficiency had better clinical outcomes compared to users engaged by DHCs without MI proficiency (P=.02).
Conclusions
To our knowledge, this pilot was the first of its kind to test the application of MI to digital coaching protocols, and it demonstrated the value of MI proficiency in digital health coaching for enhanced engagement and health improvement. Further research is needed to establish coaching models in dCBIs that incorporate MI to promote effective engagement and optimize positive behavioral outcomes.
Subject
Computer Science Applications,Health Informatics,Medicine (miscellaneous)
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