Smartphone-Based Contingency Management for Patients Who Use Methamphetamine: Qualitative Analysis of Patient and Clinician Perspectives
Bibliographic record
Abstract
BACKGROUND: Methamphetamine use disorder is a growing public health crisis with limited access to effective treatment. Contingency management (CM) has demonstrated efficacy for stimulant use disorders, but is typically delivered in person. Smartphone-based CM may overcome barriers such as limited access, but its effectiveness and real-world application remain understudied. This study explores patient and clinician experiences with a fully remote, smartphone-based CM intervention for methamphetamine use. OBJECTIVE: This exploratory, descriptive qualitative study analyzes interviews with patients and clinicians involved in a previously published single-arm trial in which smartphone-based CM was offered to individuals using methamphetamine through primary care or specialty addiction treatment clinics within a large health system. The study aims to identify and describe key facilitators, barriers, and perspectives related to engagement of both groups with the intervention, providing actionable insights to inform optimization and implementation of digital CM in health care settings. METHODS: We conducted a qualitative analysis of semistructured interviews with 14 patients and 14 clinicians from a prior pilot study of a fully remote, smartphone-based CM intervention for methamphetamine use. Interviews were analyzed using grounded theory in a 5-step process: transcript review, codebook development, coding, thematic reduction, and generation of overarching themes. The analysis focused on a priori themes related to facilitators, barriers, and suggestions for improvement. RESULTS: Patients and clinicians identified many benefits, viewing the program as valuable for individuals using methamphetamine. Patients appreciated the flexibility, accessibility, and motivational incentives. Clinicians saw CM as a low-risk, evidence-based strategy that could enhance engagement, especially among patients less responsive to traditional approaches. Common challenges included technological issues such as problems with video-based testing, app navigation, and internet access. Patients had mixed views about educational modules and described difficulty with correct substance test procedures and a lack of human connection. Clinicians expressed concerns for patients with significant psychosocial instability. Differences emerged in the types of concerns raised: patients focused on day-to-day engagement, while clinicians emphasized broader themes of equity, sustainability, and a preference for models rewarding improvement even without full abstinence. CONCLUSIONS: Smartphone-based CM shows promise for addressing methamphetamine use disorder, especially in settings lacking traditional treatment access. However, optimizing implementation requires addressing challenges related to technology, accessibility, and equity. Recommendations include integrating CM with clinical infrastructure, expanding rewardable behaviors beyond abstinence, enhancing user experience, and improving technological access. Future research should explore flexible models that incorporate broader recovery goals and strengthen both technical and human support.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".