The role of pain duration and pain intensity on the effectiveness of app-delivered self-management for low back pain: Secondary analysis of the SELFBACK randomized controlled trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Clinical guidelines for non-specific low back pain (LBP) recommend self-management tailored to individual needs and capabilities as a first-line treatment. mHealth solutions are a promising method for delivering tailored self-management interventions to patients with non-specific LBP. However, it is not clear if the effectiveness of such self-management interventions depends on patients’ initial pain characteristics. High pain intensity and long-term symptoms of LBP have been associated with an unfavorable prognosis, and current best evidence indicates that long-term LBP (lasting more than 3 months) requires a more extensive treatment approach compared to more acute LBP. The artificial intelligence-based SELFBACK app supports tailored and evidence-based self-management of non-specific LBP. In a recent randomized controlled trial, we showed that individuals who received the SELFBACK app in addition to usual care had lower LBP-related disability at 3 months follow-up compared to those who received usual care only. This effect was sustained at 6 and 9 months. OBJECTIVE To explore if baseline duration and intensity of LBP influence the effectiveness of the SELFBACK intervention in a secondary analysis of the SELFBACK randomized controlled trial. METHODS In the SELFBACK trial, 461 adults (≥18 years) who sought care for non-specific LBP in primary care or at an outpatient spine clinic were randomized to receive the SELFBACK intervention adjunct to usual care (n=232) or usual care alone (n=229). In this secondary analysis, the participants were stratified according to duration of the current LBP episode at baseline (≤12 weeks vs >12 weeks) or baseline LBP intensity (≤5 points vs >5 points) measured by a 0-10 numeric rating scale (NRS). Outcomes were LBP-related disability measured by the Roland Morris Disability Questionnaire (0-24 point scale), average LBP intensity, pain self-efficacy, and global perceived effect. To assess whether duration and intensity of LBP influenced the effect of SELFBACK, we estimated the difference in treatment effect between the strata at 3 and 9 months follow-up with a 95% confidence interval. RESULTS Overall, there was no difference in effect for patients with different duration or intensity of LBP at either 3 or 9 months follow-up. However, there was suggestive evidence that the effect of the SELFBACK intervention on LBP-related disability at 3 months follow-up was largely confined to people with the highest vs the lowest LBP intensity (mean difference between the intervention and control group; -1.8 (95% CI -3.0 to -0.7) vs 0.2 (95% CI -1.1 to 0.7), respectively), but this was not sustained at 9 months follow-up. CONCLUSIONS The results suggest that the intensity and duration of LBP have negligible influence on the effectiveness of the SELFBACK intervention on LBP-related disability, average LBP intensity, pain self-efficacy and global perceived effect. CLINICALTRIAL The multicenter SELFBACK RCT is registered in Clinicaltrials.gov: (NTC03798288). Registered 7 January 2019. INTERNATIONAL REGISTERED REPORT RR2-10.2196/14720
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".