The Quality of Health Apps and Their Potential to Promote Behavior Change in Patients With a Chronic Condition or Multimorbidity: Systematic Search in App Store and Google Play
Notice bibliographique
Résumé
BACKGROUND: Mobile apps offer an opportunity to improve the lifestyle of patients with chronic conditions or multimorbidity. However, for apps to be recommended in clinical practice, their quality and potential for promoting behavior change must be considered. OBJECTIVE: We aimed to investigate the quality of health apps for patients with a chronic condition or multimorbidity (defined as 2 or more chronic conditions) and their potential for promoting behavior change. METHODS: We followed the Cochrane Handbook guidelines to conduct and report this study. A systematic search of apps available in English or Danish on App Store (Apple Inc) and Google Play (Google LLC) for patients with 1 or more of the following common and disabling conditions was conducted: osteoarthritis, heart conditions (heart failure and ischemic heart disease), hypertension, type 2 diabetes mellitus, depression, and chronic obstructive pulmonary disease. For the search strategy, keywords related to these conditions were combined. One author screened the titles and content of the identified apps. Subsequently, 3 authors independently downloaded the apps onto a smartphone and assessed the quality of the apps and their potential for promoting behavior change by using the Mobile App Rating Scale (MARS; number of items: 23; score: range 0-5 [higher is better]) and the App Behavior Change Scale (ABACUS; number of items: 21; score: range 0-21 [higher is better]), respectively. We included the five highest-rated apps and the five most downloaded apps but only assessed free content for their quality and potential for promoting behavior change. RESULTS: We screened 453 apps and ultimately included 60. Of the 60 apps, 35 (58%) were available in both App Store and Google Play. The overall average quality score of the apps was 3.48 (SD 0.28) on the MARS, and their overall average score for their potential to promote behavior change was 8.07 (SD 2.30) on the ABACUS. Apps for depression and apps for patients with multimorbidity tended to have higher overall MARS and ABACUS scores, respectively. The most common app features for supporting behavior change were the self-monitoring of physiological parameters (eg, blood pressure monitoring; apps: 38/60, 63%), weight and diet (apps: 25/60, 42%), or physical activity (apps: 22/60, 37%) and stress management (apps: 22/60, 37%). Only 8 out of the 60 apps (13%) were completely free. CONCLUSIONS: Apps for patients with a chronic condition or multimorbidity appear to be of acceptable quality but have low to moderate potential for promoting behavior change. Our results provide a useful overview for patients and clinicians who would like to use apps for managing chronic conditions and indicate the need to improve health apps in terms of their quality and potential for promoting behavior change.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».