A Mobile App for Postoperative Pain Management Among Older Veterans Undergoing Total Knee Arthroplasty: Mixed Methods Feasibility and Acceptability Pilot Study
Bibliographic record
Abstract
BACKGROUND: Prescription opioid misuse risk is disproportionate among veterans; military veterans wounded in combat misuse prescription opioids at an even higher rate (46.2%). Opioid misuse is costly in terms of morbidity, mortality, and humanitarian and economic burden and costs the Civilian Health and Medical Program of the Department of Veterans Affairs more than US $1.13 billion annually. Preventing opioid misuse at the time of prescription is a critical component in the response to the opioid crisis. The CPMRx mobile app has been shown to decrease the odds of opioid misuse during the postoperative period. OBJECTIVE: The overarching purpose of this feasibility pilot study was to explore whether deploying a mobile app (CPMRx) to track postoperative pain and medication use is feasible in a Department of Veterans Affairs medical center. In support of this goal, we had four complementary specific aims: (1) determine the technological and logistical feasibility of the mobile app, (2) assess the acceptability of the mobile app to participants, (3) measure demand for and engagement with the mobile app, and (4) explore the potential use of the mobile app to patients and providers. METHODS: Participants (N=10) were veterans undergoing total knee arthroplasty within the Veterans Health Administration provided with the CPMRx app to self-manage their pain during their 7-day at-home recovery following surgery. CPMRx uses scientifically validated tools to help clinicians understand how a patient can use the least amount of medication while getting the most benefit. The suite of software includes a mobile app for patients that includes a behavioral health intervention and a clinical decision support tool for health care providers that provides feedback about pain and medication use trends. Patients filled out paper questionnaires regarding acceptability at their postoperative follow-up appointment. RESULTS: Overall, quantitative measures of acceptability were high. The average rating for the amount of time required to use the app was 4.9 of 5 (5="very little"), and the average rating for ease of use was 4.4 of 5 (5="very easy"). Open-ended questions also revealed that most participants found ease of use to be high. Demand and engagement were high as well with a mean number of mobile app entries of 34.1 (SD 20.1) during the postoperative period. There were no reported technological or logistical issues with the mobile app. Participants took an average of 25.13 (SD 14.37) opioid tablets to manage their postoperative pain. CONCLUSIONS: Results of this study revealed that the use of a mobile app for pain and medication management during postoperative recovery was both feasible and acceptable in older veterans undergoing total knee arthroplasty within the Veterans Health Administration. The wide variation in opioid consumption across participants revealed the potential use of the mobile app to provide actionable insights to clinicians if adopted more widely.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".