Availability, Quality, and Evidence-Based Content of mHealth Apps for the Treatment of Nonspecific Low Back Pain in the German Language: Systematic Assessment
Notice bibliographique
Résumé
BACKGROUND: Nonspecific low back pain (NSLBP) carries significant socioeconomic relevance and leads to substantial difficulties for those who are affected by it. The effectiveness of app-based treatments has been confirmed, and clinicians are recommended to use such interventions. As 88.8% of the German population uses smartphones, apps could support therapy. The available apps in mobile app stores are poorly regulated, and their quality can vary. Overviews of the availability and quality of mobile apps for Australia, Great Britain, and Spain have been compiled, but this has not yet been done for Germany. OBJECTIVE: We aimed to provide an overview of the availability and content-related quality of apps for the treatment of NSLBP in the German language. METHODS: A systematic search for apps on iOS and Android was conducted on July 6, 2022, in the Apple App Store and Google Play Store. The inclusion and exclusion criteria were defined before the search. Apps in the German language that were available in both stores were eligible. To check for evidence, the apps found were assessed using checklists based on the German national guideline for NSLBP and the British equivalent of the National Institute for Health and Care Excellence. The quality of the apps was measured using the Mobile Application Rating Scale. To control potential inaccuracies, a second reviewer resurveyed the outcomes for 30% (3/8) of the apps and checked the inclusion and exclusion criteria for these apps. The outcomes, measured using the assessment tools, are presented in tables with descriptive statistics. Furthermore, the characteristics of the included apps were summarized. RESULTS: In total, 8 apps were included for assessment. Features provided with different frequencies were exercise tracking of prefabricated or adaptable workout programs, educational aspects, artificial intelligence-based therapy or workout programs, and motion detection. All apps met some recommendations by the German national guideline and used forms of exercises as recommended by the National Institute for Health and Care Excellence guideline. The mean value of items rated as "Yes" was 5.75 (SD 2.71) out of 16. The best-rated app received an answer of "Yes" for 11 items. The mean Mobile Application Rating Scale quality score was 3.61 (SD 0.55). The highest mean score was obtained in "Section B-Functionality" (mean 3.81, SD 0.54). CONCLUSIONS: Available apps in the German language meet guideline recommendations and are mostly of acceptable or good quality. Their use as a therapy supplement could help promote the implementation of home-based exercise protocols. A new assessment tool to obtain ratings on apps for the treatment of NSLBP, combining aspects of quality and evidence-based best practices, could be useful. TRIAL REGISTRATION: Open Science Framework Registries sq435; https://osf.io/sq435.
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 | 0,034 | 0,157 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,022 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».