The Use of Gamification and Incentives in Mobile Health Apps to Improve Medication Adherence: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Emerging health care strategies addressing medication adherence include the use of direct-to-patient incentives or elements adapted from computer games. However, there is currently no published evidence synthesis on the use of gamification or financial incentives in mobile apps to improve medication adherence. OBJECTIVE: The aim of this scoping review is to synthesize and appraise the literature pertaining to the use of mobile apps containing gamification or financial incentives for medication adherence. There were two objectives: to explore the reported effectiveness of these features and to describe and appraise the design and development process, including patient involvement. METHODS: The following databases were searched for relevant articles published in English from database inception to September 24, 2020: Embase, MEDLINE, PsycINFO, CINAHL, and Web of Science. The framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist guided this scoping review. Using a systematic screening process, studies were included if incentives or game features were used within mobile apps to specifically address medication adherence. An appraisal using risk of bias tools was also applied to their respective study design. RESULTS: A total of 11 studies from the initial 691 retrieved articles were included in this review. Across the studies, gamification alone (9/11, 82%) was used more than financial incentives (1/11, 9%) alone or a combination of the two (1/11, 9%). The studies generally reported improved or sustained optimal medication adherence outcomes; however, there was significant heterogeneity in the patient population, methodology such as outcome measures, and reporting of these studies. There was considerable variability in the development process and evaluation of the apps, with authors opting for either the waterfall or agile methodology. App development was often guided by a theory, but across the reviewed studies, there were no common theories used. Patient involvement was not commonly evident in predevelopment phases but were generally reserved for evaluations of feasibility, acceptance, and effectiveness. Patient perspectives on gamified app features indicated a potential to motivate positive health behaviors such as medication adherence along with critical themes of repetitiveness and irrelevance of certain features. The appraisal indicated a low risk of bias in most studies, although concerns were identified in potential confounding. CONCLUSIONS: To effectively address medication adherence via gamified and incentivized mobile apps, an evidence-based co-design approach and agile methodology should be used. This review indicates some adoption of an agile approach in app development; however, patient involvement is lacking in earlier stages. Further research in a generalized cohort of patients living with chronic conditions would facilitate the identification of barriers, potential opportunities, and the justification for the use of gamification and financial incentives in mobile apps for medication adherence.
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 | 0,025 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».