Patient Feedback on a Mobile Medication Adherence App for Buprenorphine and Naloxone: Closed and Open-Ended Survey on Feasibility and Acceptability
Bibliographic record
Abstract
BACKGROUND: Opioid use disorders impact the health and well-being of millions of Americans. Buprenorphine and naloxone (BUP and NAL) can reduce opioid overdose deaths, decrease misuse, and improve quality of life. Unfortunately, poor medication adherence is a primary barrier to the long-term efficacy of BUP and NAL. OBJECTIVE: We aimed to examine patient feedback on current and potential features of a Bluetooth-enabled pill bottle cap and associated mobile app for patients prescribed BUP and NAL for an opioid use disorder, and to solicit recommendations for improvement to effectively and appropriately tailor the technology for people in treatment for opioid use disorder. METHODS: A convenience sample of patients at an opioid use disorder outpatient clinic were asked about medication adherence, opioid cravings, experience with technology, motivation for treatment, and their existent support system through a brief e-survey. Patients also provided detailed feedback on current features and features being considered for inclusion in a technology designed to increase medication adherence (eg, inclusion of a personal motivational factor, craving and stress tracking, incentives, and web-based coaching). Participants were asked to provide suggestions for improvement and considerations specifically applicable to people in treatment for opioid use disorder with BUP and NAL. RESULTS: Twenty people with an opioid use disorder who were prescribed BUP and NAL participated (mean age 34, SD 8.67 years; 65% female; 80% White). Participants selected the most useful, second-most useful, and least useful features presented; 42.1% of them indicated that motivational reminders would be most useful, followed by craving and stress tracking (26.3%) and web-based support forums (21.1%). Every participant indicated that they had at least 1 strong motivating factor for staying in treatment, and half (n=10) indicated children as that factor. All participants indicated that they had, at some point in their lives, the most extreme craving a person could have; however, 42.1% indicated that they had no cravings in the last month. Most respondents (73.7%) stated that tracking cravings would be helpful. Most respondents (84.2%) also indicated that they believed reinforcers or prizes would help them achieve their treatment goals. Additionally, 94.7% of respondents approved of adherence tracking to accommodate this feature using smart packaging, and 78.9% of them approved of selfie videos of them taking their medication. CONCLUSIONS: Engaging patients taking treatment for opioid use disorder with BUP and NAL allowed us to identify preferences and considerations that are unique to this treatment area. As the technology developer of the pill cap and associated mobile app is able to take into consideration or integrate these preferences and suggestions, the smart cap and associated mobile app will become tailored to this population and more useful for them, which may encourage patient use of the smart cap and associated mobile app.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".