ArticleNpj mental health research2024
Evaluating a mobile app's effects on depression and anxiety in medication-treated opioid use disorder.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.Journal of substance use and addiction treatment · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Depression and anxiety frequently co-occur with opioid use disorder (OUD) yet are often overlooked in standard OUD treatments. This study evaluated the feasibility, acceptability, and preliminary effectiveness of a mobile application designed to address these symptoms in individuals receiving medications for OUD (MOUD). A randomized controlled trial recruited N = 63 adults with OUD who received MOUD and screened positive for moderate depression or generalized anxiety. Participants were randomized to an app-based digital intervention or treatment-as-usual for 4 weeks, and completed follow-ups at 4 and 8 weeks. Primary outcomes were self-reported severity measures for depression and generalized anxiety, and urine drug screens (UDS). Secondary outcomes included self-reported OUD severity, craving intensity, and digital biomarkers derived from passive smartphone sensors. The application was well-received (median app rating = 4/5 stars). The intervention group showed significant reductions in depressive and generalized anxiety symptoms post-intervention and at 8 weeks follow-up (d > 0.70), with large (d = 0.78) and moderate (d = 0.38) effect sizes, respectively, compared to controls. Both groups exhibited substantial decreases in self-reported severity of opioid use symptoms (d > 2.50). UDS suggested similar between-group adherence to MOUD, with a marginal decrease in opioid (MOP) use in the intervention group and increase in controls, yielding medium between group effect sizes (d = 0.44). Passive sensor data suggested significant increases in social connectedness in the intervention group, evidenced by a significant rise in incoming and outgoing calls and text connections. Initial evidence supports the feasibility and acceptability of a digital intervention for treating anxiety and depressive symptoms in persons receiving MOUD. While underpowered to confidently determine statistical significance beyond directionality, the intervention showed promise in reducing depressive and anxiety symptoms, suggesting its potential as a cost-effective and scalable adjunctive therapy alongside standard OUD treatment. Due to the preliminary nature of this pilot study, further research with sample sizes permitting greater statistical power is needed to confirm findings and explore long-term effects.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.