Development and Use of Mobile Messaging for Individuals With Musculoskeletal Pain Conditions: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Population studies show that musculoskeletal conditions are a leading contributor to the total burden of healthy life lost, second only to cancer and with a similar burden to cardiovascular disease. Prioritizing the delivery of effective treatments is necessary, and with the ubiquity of consumer smart devices, the use of digital health interventions is increasing. Messaging is popular and easy to use and has been studied for a range of health-related uses, including health promotion, encouragement of behavior change, and monitoring of disease progression. It may have a useful role to play in the management and self-management of musculoskeletal conditions. OBJECTIVE: Previous reviews on the use of messaging for people with musculoskeletal conditions have focused on synthesizing evidence of effectiveness from randomized controlled trials. In this review, our objective was to map the musculoskeletal messaging literature more broadly to identify information that may inform the design of future messaging interventions and summarize the current evidence of efficacy, effectiveness, and economics. METHODS: Following a prepublished protocol developed using the Joanna Briggs Institute Manual for Evidence Synthesis, we conducted a comprehensive scoping review of the literature (2010-2022; sources: PubMed, CINAHL, Embase, and PsycINFO) related to SMS text messaging and app-based messaging for people with musculoskeletal conditions. We described our findings using tables, plots, and a narrative summary. RESULTS: We identified a total of 8328 papers for screening, of which 50 (0.6%) were included in this review (3/50, 6% previous reviews and 47/50, 94% papers describing 40 primary studies). Rheumatic diseases accounted for the largest proportion of the included primary studies (19/40, 48%), followed by studies on multiple musculoskeletal conditions or pain sites (10/40, 25%), back pain (9/40, 23%), neck pain (1/40, 3%), and "other" (1/40, 3%). Most studies (33/40, 83%) described interventions intended to promote positive behavior change, typically by encouraging increased physical activity and exercise. The studies evaluated a range of outcomes, including pain, function, quality of life, and medication adherence. Overall, the results either favored messaging interventions or had equivocal outcomes. While the theoretical underpinnings of the interventions were generally well described, only 4% (2/47) of the papers provided comprehensive descriptions of the messaging intervention design and development process. We found no relevant economic evaluations. CONCLUSIONS: Messaging has been used for the care and self-management of a range of musculoskeletal conditions with generally favorable outcomes reported. However, with few exceptions, design considerations are poorly described in the literature. Further work is needed to understand and disseminate information about messaging content and message delivery characteristics, such as timing and frequency specifically for people with musculoskeletal conditions. Similarly, further work is needed to understand the economic effects of messaging and practical considerations related to implementation and sustainability. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-048964.
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 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,024 | 0,022 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».