Feasibility of Using Short Message Service to Collect Pain Outcomes in a Low Back Pain Clinical Trial
Bibliographic record
Abstract
In Brief Study Design. Observational study nested within a randomized controlled trial. Objective. To evaluate the feasibility of using short message service (SMS) within a clinical trial of low back pain. Summary of Background Data. Technological advances have transformed communication technologies and opened the way for their use in clinical studies. SMSs have been tested for use in data collection for different patient groups, but little is known about use of this technology in low back pain research. Methods. Trial participants who owned a mobile phone and knew how to send SMS messages were included in the study. The participants were sent an SMS message asking them to rate their average pain, once a month for 12 consecutive months. Response rates during the year were calculated, and regression analyses were used to explore factors associated with phone ownership and response rates. Results. Of the 133 participants in the trial, 105 (61.1%) had mobile phones and 97 (56.4%) knew how to use SMS. The regression analyses showed that older patients were less likely to own a mobile phone (P < 0.000). Response rates to SMS alone during the 12 months ranged from 54.8% to 74.2%, and for SMS supplemented with phone interviews ranged from 91.5% to 99%. The median (interquartile range) number of the scheduled 12 assessments completed by SMS per patient was 9 (interquartile range, 5–11). The Poisson regression revealed no significant effect for any of the predictors studied: age, sex, education level, and pain level at baseline and after treatment (P ≥ 0.16). Conclusion. SMS supplemented with phone interviews, but not SMS alone, is a feasible option to collect simple data within a back pain clinical trial setting. We evaluated the feasibility of collecting monthly pain outcomes via short message service (SMS) within a back pain trial. The median response rate was 9 (interquartile range, 5–11) of the scheduled 12 assessments, a result supporting SMS supplemented with phone interviews, but not SMS alone, for simple data collection in randomized controlled trials.
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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 | 0.134 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".