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)
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,018 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».