Self-Management Support Apps for Spinal Cord Injury: Results of a Systematic Search in App Stores and Mobile App Rating Scale Evaluation
Notice bibliographique
Résumé
BACKGROUND: The use of mobile technology to meet health needs, widely referred to as mobile health (mHealth), has played a critical role in providing self-management support for chronic health conditions. However, despite its potential benefits, mHealth technologies such as self-management support apps for spinal cord injury (SCI) have received little research attention, and an understanding of their public availability is lacking. Therefore, an overview of these apps is needed to complement findings from the literature for a complete understanding of mHealth self-management support tools for SCI to support the selection and improvement of existing apps and the development of new ones. OBJECTIVE: This study aimed to identify and describe quantity, quality, focus, strengths, and weaknesses of self-management support apps for SCI available on major mobile app digital distribution platforms. METHODS: A systematic search of the Google Play Store and Apple App Store was conducted to identify and summarize apps for SCI that have been updated since 2017. A supplementary systematic literature review was conducted across 11 bibliographic databases to identify publications that provided more detailed descriptions of the identified apps than what is typically available in app stores. The data synthesis was guided by self-management tasks and skills taxonomies. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines informed the reporting. RESULTS: The 13 apps included in the final synthesis were launched between 2013 and 2021, mostly originating in the United States, with availability in 72 countries and support for 14 languages. Most apps used the Android operating system (10/13, 77%), while 31% (4/13) used iOS. The identified apps mainly focused on activities of daily living, physical activity promotion, health literacy, and therapeutic exercise. All 3 self-management tasks (medical, role, and emotional management) and most self-management skills and support activities were supported by the apps. The mean Mobile App Rating Scale score was 3.86 (SD 0.54), indicating good overall quality. No publications were found describing these apps. CONCLUSIONS: Despite their good overall quality, as measured by the Mobile App Rating Scale assessment, the 13 identified apps, alone or combined, do not appear to offer a comprehensive self-management approach that incorporates theory-based strategies. Besides working to improve comprehensiveness, future research and practice should consider adopting new technologies, such as artificial intelligence, to enhance future self-management support apps for SCI. Furthermore, adopting new app development methods, such as low-code development platforms, could help reduce barriers to development, such as time, cost, and securing scarce expertise.
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 | 0,041 | 0,140 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,013 |
| Bibliométrie | 0,015 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».