A Digital Video and Text Messaging Intervention to Support People With Chronic Pain During Opioid Tapering: Content Development Using Co-design
Bibliographic record
Abstract
BACKGROUND: People living with chronic pain report that tapering prescribed opioids is challenging and more support is needed. In our formative research, consumers indicated that mobile health (mHealth) technology could be an acceptable form of support for opioid tapering and may improve tapering self-efficacy. OBJECTIVE: We aimed to evaluate and improve the content of an mHealth intervention before pilot-testing, based on consumer and clinician feedback. METHODS: Participants were 12 consumers and 12 clinicians who evaluated an initial draft of a video script and 90 SMS text messages. Consumers and clinicians rated the appropriateness and likely usefulness (consumers) or likely effectiveness (clinicians) of a video script and a random selection of 15 SMS text messages using a 5-point Likert-type scale (1=totally disagree; 5=totally agree). Each draft SMS text message was reviewed by 2 consumers and 2 clinicians. Texts were deemed acceptable for inclusion in the pilot intervention only if the summed participant ratings of text appropriateness and usefulness or effectiveness were ≥8. Participants were also invited to provide open-text feedback on the draft script and SMS text messages. RESULTS: Consumers generally agreed that the draft video script and text content were likely to be appropriate (video: mean 4.4, SD 0.52; text: mean 4.3, SD 0.79) and useful (video: mean 4.3, SD 0.65; text: mean 4.2, SD 0.84). Similarly, clinicians generally agreed that the draft video script and text content were likely to be appropriate (video: mean 4.5, SD 0.67; text: mean 4.4, SD 0.81) and effective (video: mean 4.0, SD 0.43; text: mean 4.3, SD 0.76). Overall, 77% (69/90) of the draft texts met the threshold rating for acceptability for inclusion in the pilot test of mHealth intervention by consumers, and 82% (74/90) met the threshold for acceptability by clinicians. Consumers' and clinicians' ratings were used to rank order the texts. The top 56 draft texts (all meeting the threshold levels of acceptability) were selected for inclusion in the pilot intervention. When consumer or clinician feedback was provided, the texts meeting the criteria for inclusion in the pilot were further revised and improved. Feedback on the video script was also used to further improve the acceptability of the video script before pilot-testing the intervention. CONCLUSIONS: This study describes the process by which a 28-day mHealth intervention to support patients with chronic pain to taper opioid medications was evaluated and improved before pilot-testing. The mHealth intervention consisted of a 10-minute psychoeducational video about pain and opioid tapering and 56 unique SMS text messages providing information and reassurance (texts delivered twice per day for 28 days). Having established that the content of the mHealth intervention is acceptable to both consumer and clinician groups, the mHealth intervention will be piloted in future research.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".