ArticleMilitary medicine2025
Artificial Intelligence-Enabled, Real-time Risk Monitoring for Substance Use Recovery in Veterans: An Initial Evaluation of the Behaivior Recovery Platform.
Article in Military medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
introductionSubstance use disorders (SUDs) are a growing concern in U.S. Military populations, including Veterans, reflecting both national trends and unique service-related risk factors. Behaivior's cloud-based predictive analytics system integrates artificial intelligence (AI) with smartwatches to predict and intervene in SUD-related health crises. Early success in civilian populations underscores its potential to support SUD recovery in Veterans. By integrating continuous monitoring with timely, context-specific support, the platform can foster sustainable behavior change, mitigate risks, and improve treatment adherence. Accessible via the World Wide Web and smartphone applications, the platform uses AI-driven algorithms to analyze biometric data, informing the delivery of tailored interventions and provider alerts. MATERIALS AND
methodsA user experience study including 10 Veterans with cocaine use disorder and a mobile health intervention study involving 27 Veterans with opioid use disorder were conducted. Both were IRB-approved through the VA Pittsburgh Healthcare System. The user experience study assessed the acceptability and desirability of mobile health interventions, leveraging methods including surveys and interviews. The intervention study evaluated preliminary engagement and efficacy signals over a 4-week trial of the Behaivior platform, capturing self-report data and wearable-monitored outcomes.
resultsIn the user experience study, all participants favored mobile health interventions for SUD treatment, with an average of 82.9% of proposed wearable device-enabled functions receiving positive endorsements. In the intervention study, engagement and adherence with wearable data collection were sufficient, with participants completing over 8 hours of remote physiological monitoring per day on average and completing most surveys. Substance use outcomes revealed a 29.6% return-to-opioid-use rate, with only 3.3% of specimens testing positive for opioids.
conclusionBehaivior's technology demonstrates strong potential to support SUD recovery by synthesizing real-time metrics, proactively identifying risk states, and informing provider decision-making. This platform holds promise for enhancing the health of Veterans and may also have potential to benefit active duty service members.
Indexed as
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.