Clinical Management of Medication-Assisted Treatment for Opioid Use Disorder Using a Mobile Health App Within a Primary Care Clinic: Quasi-Experimental Study
Bibliographic record
Abstract
Background: Medication-assisted treatment (MAT) is an effective strategy for treating opioid use disorder and reducing opioid-related overdose deaths, yet retention in treatment remains low. Mobile health (mHealth) platforms may be a useful tool for increasing long-term engagement in MAT programs, but evaluation studies of such platforms are limited. Objective: This study aimed to determine whether the use of the Opioid Addiction Recovery Support (OARS) software platform increased MAT engagement for patients with opioid use disorder. Methods: The Technology Improving Success of Medication-Assisted Treatment in Primary Care Study was a quasi-experimental study conducted at a primary care clinic in the United States between January 2021 and February 2022. OARS is a software platform and mobile app (Q2i, LLC) that includes a dashboard of real-time appointment attendance, urine toxicology (UTOX) results, and educational content as well as messaging and journaling features. All patients who were invited to use OARS and had available data across the study were included in the analysis. The primary outcomes were engagement in treatment, defined as no more than a 35-day gap in appointment attendance, and UTOX. Changes in treatment engagement between the treatment as usual (TAU) period and OARS intervention period were assessed using the effect size (Cohen g) and McNemar chi-square test of discordant pairs. Results: Among 205 patients invited to use OARS, 123 had available data and were thus included in the analysis. The median age was 37 (IQR 31-42.5 ) years, 61% (75/123) identified as men, and 95.1% (117/123) identified as non-Hispanic White. There were no statistically significant differences in demographic characteristics for patients who used OARS on more than 1 day compared to patients who used OARS on 0 or 1 day, or patients who did versus did not have available data. Among all patients, 20% (25/123) were engaged in appointment attendance during TAU only compared to 27% (33/123) during OARS only (g=0.07; P=.36), and 13% (16/123) were engaged in UTOX during TAU only and 33% (41/123) during OARS only (g=0.21; P≤.01). Among a subsample of 52 patients who used OARS on more than 1 day, 17% (9/52) were engaged in appointment attendance during TAU only compared to 23% (12/52) during OARS only (g=0.07, P=.67), and 13% (7/52) were engaged in UTOX during TAU only and 35% (18/52) during OARS only (g=0.22, P=.05). Conclusions: Introduction of OARS in a primary care setting may be associated with a moderate change in MAT engagement as measured by UTOX, but not appointment attendance. While barriers to implementation and adoption, including difficulty fully integrating OARS with the clinic's electronic health record, may have attenuated the potential effect of the intervention, this study provides evidence that mHealth interventions, such as OARS, are a promising addition to the MAT treatment landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".