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BackgroundInteractive voice response (IVR) calls enhance health systems’ ability to identify health risk factors, thereby enabling targeted clinical follow-up. However, redundant assessments may increase patient dropout and represent a lost opportunity to collect more clinically useful data. ObjectiveWe determined the extent to which previous IVR assessments predicted subsequent responses among patients with depression diagnoses, potentially obviating the need to repeatedly collect the same information. We also evaluated whether frequent (ie, weekly) IVR assessment attempts were significantly more predictive of patients’ subsequent reports than information collected biweekly or monthly. MethodsUsing data from 1050 IVR assessments for 208 patients with depression diagnoses, we examined the predictability of four IVR-reported outcomes: moderate/severe depressive symptoms (score ≥10 on the PHQ-9), fair/poor general health, poor antidepressant adherence, and days in bed due to poor mental health. We used logistic models with training and test samples to predict patients’ IVR responses based on their five most recent weekly, biweekly, and monthly assessment attempts. The marginal benefit of more frequent assessments was evaluated based on Receiver Operator Characteristic (ROC) curves and statistical comparisons of the area under the curves (AUC). ResultsPatients’ reports about their depressive symptoms and perceived health status were highly predictable based on prior assessment responses. For models predicting moderate/severe depression, the AUC was 0.91 (95% CI 0.89-0.93) when assuming weekly assessment attempts and only slightly less when assuming biweekly assessments (AUC: 0.89; CI 0.87-0.91) or monthly attempts (AUC: 0.89; CI 0.86-0.91). The AUC for models predicting reports of fair/poor health status was similar when weekly assessments were compared with those occurring biweekly (P value for the difference=.11) or monthly (P=.81). Reports of medication adherence problems and days in bed were somewhat less predictable but also showed small differences between assessments attempted weekly, biweekly, and monthly. ConclusionsThe technical feasibility of gathering high frequency health data via IVR may in some instances exceed the clinical benefit of doing so. Predictive analytics could make data gathering more efficient with negligible loss in effectiveness. In particular, weekly or biweekly depressive symptom reports may provide little marginal information regarding how the person is doing relative to collecting that information monthly. The next generation of automated health assessment services should use data mining techniques to avoid redundant assessments and should gather data at the frequency that maximizes the value of the information collected.

Objective: To assess mental health symptoms, suicidal ideation/behaviors, and treatment among a nationally representative probability sample of student veterans. Participants: Student veterans enrolled in post-secondary educational institutions and matched comparison students. Methods: Sampled participants completed an online survey (n = 1,838). Analyses accounted for the complex sample design and non-response. Results: Substantial percentages of student veterans screened positive for: depression (36.9%, 95% CI: 31.1-42.7), PTSD (35.7%, 95% CI 29.9-41.5), anxiety (29.5%, 95% CI 26.8-32.2), and suicidal ideation (14.6%, 95% CI 12.1-17.1), with student veterans having odds ratios between 1.7 to 2.4 for positive screens compared to non-veteran students. Only 41.5% (95% CI 33.0-50.0) of student veterans with positive screens received treatment, although they had 50% higher odds of receiving treatment than non-veteran students. Conclusions: Student veterans have high rates of mental health symptoms and low rates of treatment. However, they are more likely to receive treatment than comparison students.

ObjectiveVarious models of peer support may be implemented in mental health settings. This randomized trial assessed the effectiveness of a telephone-delivered mutual peer support intervention.MethodsA total of 443 patients receiving ongoing depression treatment from the U.S. Department of Veterans Affairs were enrolled in either enhanced usual care (N=243) or the peer support intervention (N=200). Intent-to-treat analyses assessed outcomes at six months postenrollment, excluding 56 patients who experienced an unplanned telephone platform shutdown.ResultsAt baseline, patients had substantial depressive symptoms, functional limitations, and low quality of life. Both groups showed significant clinical improvements at six months, with no significant differences by group.ConclusionsTelephone-delivered mutual peer support for patients with depression did not improve outcomes beyond those observed with enhanced usual care. Other peer support models, with more "professionalized" peers delivering a structured curriculum, may be more effective.

Background: Depressive symptoms are common in veterans, and the presence of these symptoms increases disability as well as suicidal thoughts and behaviors. However, there is evidence that these symptoms often go untreated. Intervening before symptoms become severe and entrenched is related to better long-term outcomes, including improved functioning and less disease chronicity. Computer-delivered interventions may be especially appropriate for those veterans with mild to moderate depressive symptoms, because these interventions can require fewer resources and have lower barriers to access and thus have potential for wider reach. Despite this potential, there is a dearth of research examining computerized interventions for depressive symptoms in veteran samples. Objective: The aim of this study is to evaluate the efficacy of Deprexis (GAIA AG), a computerized intervention for depressive symptoms and related functional impairment. Methods: Veterans will be recruited through the US Department of Veterans Affairs electronic medical record and through primary care and specialty clinics. First, qualitative interviews will be completed with a small subset of veterans (n=16-20) to assess the acceptability of treatment procedures. Next, veterans (n=132) with mild to moderate depressive symptoms will be randomly assigned to the fully automated Deprexis intervention or a treatment-as-usual control group. The primary outcomes will be self-reported depressive symptoms and various dimensions of psychosocial functioning. Results: This project was funded in May 2024, and data collection will be conducted between October 2024 and April 2029. Overall, 4 participants have been recruited as of the submission of the manuscript, and data analysis is expected in June 2029, with initial results expected in November 2029. Conclusions: This study will provide initial evidence for the efficacy of self-guided, computerized interventions for depressive symptoms and functional impairment in veterans. If effective, these types of interventions could improve veteran access to low-resource psychosocial treatments. Trial Registration: ClinicalTrials.gov NCT06217198; https://www.clinicaltrials.gov/study/NCT06217198 International Registered Report Identifier (IRRID): PRR1-10.2196/59119 [ABSTRACT FROM AUTHOR]