Objective: Subthreshold depression has been found to be associated with considerable impairment and an increased risk of developing major depression. Although several randomized trials have examined the effects of psychological interventions for subthreshold depression in children and adolescents, no meta-analysis has integrated the results of these trials. Method: We searched 4 bibliographic databases and included randomized trials comparing psychological interventions with control conditions in children and adolescents scoring above a cutoff of a depression questionnaire but not meeting diagnostic criteria for major depression (or persistent depressive disorder) according to a diagnostic interview. Effect sizes and incidence rates of major depression were pooled with random effects metaanalyses. Results: A total of 12 trials with 1,576 children and adolescents met inclusion criteria. The overall effect size indicating the difference between treatment and control at post-test was g ¼ 0.38 (95% CI ¼ 0.14-0.63), which corresponds to a number-needed-to-treat (NNT) of 8.4. Heterogeneity was moderate to high (I 2 ¼ 61; 95% CI ¼ 28-79), and there was significant risk of publication bias (p < .04). The 2 studies in children less than 12 years of age showed nonsignificant effects (g ¼ 0.01; 95% CI ¼-1.16 to 1.18). We found no significant effect on the incidence of major depression at follow-up (relative risk ¼ 0.52; 95% CI ¼ 0.25-1.08), although this may be related to low statistical power. Conclusion: Interventions for subthreshold depression may have positive acute effects in adolescents. There is currently insufficient evidence, however, that these interventions are effective in children less than 12 years of age, or that they prevent the onset of major depression at follow-up.
Background Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots. Objective This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations. Methods Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules. Results Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules. Conclusions Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.
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