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Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy.Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

Suicide is the second leading cause of death among college students in the United States, and the percentage of students reporting suicidal thoughts is increasing. Nevertheless, many students at risk do not seek mental health (MH) services. This randomized controlled trial (RCT) examined the efficacy of Electronic Bridge to Mental Health for College Students (eBridge) for increasing at-risk students' linkage to MH services.Students from four universities were recruited via email; 40,347 (22.6%) completed the online suicide risk screen; and 3,363 (8.3%) met criteria for randomization based on suicide risk factors and lack of current treatment (62.2% female, 35.0% male, 2.8% transgender/nonbinary; 73.2% White, 7.0% Black, 19.9% Asian, 11.7% other; 12.4% Hispanic, 76.2% undergraduate). These students were randomized to eBridge [personalized feedback (PF) with option of online counseling] or Control (PF). The primary outcome was linkage to MH services within 6 months.Among students assigned to eBridge, 355 students (21.0%) posted ≥1 message, and 168 (10.0%) posted ≥2 messages to the counselor. In intent-to-treat analyses, there was no eBridge effect on obtaining MH services. However, within the eBridge group, students who posted ≥1 message were significantly more likely to link to MH services.eBridge shows promise for reaching a relatively small subset of college students at risk for suicide; however, engagement in eBridge was low. This study underscores the urgent need for more effective strategies to engage young adults in online mental health interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).