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Background User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. Objective The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. Methods Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. Results Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. Conclusions The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

This study introduces a new measure of the perceived utility of emotion, which is the degree to which emotions are perceived to be useful in achieving goals. In this study, we administered this new measure, the Perceived Affect Utility Scale (PAUSe), to a sample of 142 European American and 156 East Asian American college students. Confirmatory factor analyses provided support for a new, culturally informed parsing of emotion and for perceived utility of emotion to be distinguishable from ideal affect, a related but separate construct. Next, we explored the potential importance of perceived utility of emotion in cultural research. Through path analyses, we found that: (a) culturally relevant variables (e.g., independence) played a mediating role in the link between ethnic group and perceived utility of emotion; and (b) perceived utility of emotion played a mediating role in the link between culturally relevant variables and ideal affect. In particular, perceived utility of self-centered emotions (e.g., pride) was found to be associated with independence and ideal affect of those same emotions. In contrast, perceived utility of other-centered emotions (e.g., appreciation) was found to be associated with interdependence, dutifulness/self-discipline, and ideal affect of those same emotions. Implications for perceived utility of emotion in understanding cultural factors are discussed.

We explored certified peer specialists and older adults with serious mental illness perspectives of the impact of a peer-delivered medical and psychiatric self-management intervention, “PeerTECH”. Transcripts from interviews with consumers with serious mental illness and a focus group with certified peer specialists who were engaged in PeerTECH were analyzed. Consumer participants (N=8) had a mean age of 68.8 years (SD=4.9) and included individuals diagnosed with major depressive disorder (5 people), schizophrenia spectrum disorders (2 people), and bipolar disorder (1 person). Certified peer specialists (N=3) were aged 55 years or older. Themes included: internal and external forces of accountability, confidence, internal and external locus of hope, human bonding, and peer support. This exploratory qualitative study found that human support from peers can potentially influence health behavioral change in a combined peer and technology-based medical and psychiatric illness self-management intervention.

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