Capacity to Invest Effort as a Predictor of Preference for Digital Mental Health Interventions Over Psychotherapy: Cross-Sectional Study Using an Ecological Digital Screening Tool

Background: Research typically shows a higher preference for professionally led face-to-face mental health interventions over digital ones. It remains unclear in which circumstances digital self-help tools are preferred. To address this gap, it is important to examine user characteristics that may help predict when digital interventions are more desirable, ultimately guiding their design to enhance engagement and appeal. Objective: This study aims to examine how distress severity and capacity to invest effort relate to intervention preferences, using an ecological assessment of individuals seeking to receive feedback on their mental health. Methods: A comprehensive digital mental health screening tool with automated feedback was developed and advertised on social media. The sample comprised 684 adult participants aged 18 to 82 years who opted to complete the screening to receive feedback on their mental health state. Participants completed questionnaires measuring general psychological distress, depression, generalized anxiety, and demographics. The Kessler Psychological Distress Scale-6 was used as the primary measure for distress. Participants were also presented with questions measuring capacity to invest effort and preferences for a professional therapist versus digital self-help tools and for psychotherapy versus a mobile app. The effectiveness of distress, capacity to invest effort, and background characteristics in predicting preferences (a professional vs digital self-help tools; psychotherapy vs a mobile app) was examined using hierarchical linear regressions. The distributions of dichotomized preferences were plotted against distress and capacity to invest effort for transparent visualization. Results: A hierarchical linear regression found that distress, capacity to invest, and currently being in psychotherapy significantly predicted preference for a professional versus digital self-help tools. Distress (β=.25, 95% CI .18-.32; P<.001) and capacity to invest effort (β=.23, 95% CI .16-.30; P<.001) were the strongest predictors, with similar effect size. The model explained 20% of the variance in preference, with the capacity to invest effort uniquely contributing 5%. Most participants experiencing distress with low capacity (158/239, 66.1%) preferred digital self-help tools, whereas most participants experiencing distress with high capacity (147/243, 60.5%) favored a professional. Similar results were obtained when using the Patient Health Questionnaire-4 as an alternative distress measure. Capacity to invest effort remained significant (β=.18, 95% CI .10-.26; P<.001) when predicting a preference for psychotherapy versus a mobile app, while distress was not significant (β=-.03, 95% CI -.10 to .05; P=.51). Conclusions: This study highlights that the preference for digital interventions is driven by a reduced capacity to invest effort in an intervention. Attempts to reduce the mental health treatment gap through digital interventions should focus on optimizing the effort elicited by users to improve desirability and engagement.