Development and Acceptability of a Method to Investigate Prescription Drug Misuse in Daily Life: Ecological Momentary Assessment Study
Bibliographic record
Abstract
BACKGROUND: Prescription drug misuse and abuse is an established public health challenge, and young adults are particularly affected. There is a striking lack of real-time, naturalistic data collection assessing intentions to misuse and other precipitating factors at the time of actual misuse, leaving the conditions under which individuals are most likely to misuse prescription medications unknown. Ecological momentary assessment (EMA) apps and protocols designed to capture this information would accelerate and expand the knowledge base and could directly contribute to prevention and treatment efforts. OBJECTIVE: The objectives of this study are to describe the development and administration of a mobile app and the EMA protocol designed to collect real-time factors associated with college students' prescription drug misuse intentions and behaviors in daily life; present completion rates, compliance, acceptability, and reactivity associated with the EMA protocol for participants who endorsed recent prescription drug misuse at screening (ie, risk group; n=300) and those who did not (ie, nonrisk group; n=55); and establish initial construct validity by linking the reports of misuse behaviors in daily life collected via the EMA app to prescription drug misuse reported on a standard survey. METHODS: An EMA data collection app and protocol were designed specifically to capture hypothesized contextual factors along with prescription drug misuse intentions and behaviors in daily life. Using this protocol, young adult college students (N=352) completed signal- and event-contingent reports over a 28-day period. When the intention to misuse a prescription drug was endorsed, a brief follow-up prompt was sent 15 min later to collect participants' indications of whether or not misuse had occurred. RESULTS: Risk-group participants were significantly more likely than nonrisk counterparts to endorse any prescription drug misuse intentions in daily life (P<.001), to complete one or more follow-up reports (P<.001), and to endorse any prescription drug misuse behavior in daily life on the follow-ups (P<.001). Overall, participants demonstrated consistent engagement with the EMA procedures and returned an average of 74.5 (SD 23.82; range 10-122) reports. Participants in the risk and nonrisk groups did not differ in the number of reports they completed (P=.12), the number of their reporting days (P=.32), or their average completion rates (P=.14). The results indicated some evidence of reactivity to the momentary reporting procedure. Participants reported uniformly positive experiences and remained highly engaged throughout the reporting protocol and broader study. CONCLUSIONS: The novel EMA app and protocol provide an effective way to assess real-time factors associated with prescription drug misuse intentions and behaviors in daily life. The resulting investigations offer the potential to provide highly translatable information for research and prevention efforts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".