Effectiveness of mHealth-Based Gamified Interventions on Physical Activity in Older Adults: Systematic Review
Notice bibliographique
Résumé
Background: Global aging presents significant socioeconomic and health challenges, particularly for older adults who face an increased risk of chronic diseases and reduced physical activity levels. Although physical activity is crucial for maintaining health, most older adults do not meet the recommended guidelines. Gamification and mobile health (mHealth) technologies offer innovative solutions to motivate physical activity; however, research focusing on older adults is limited, especially regarding the effectiveness and sustainability of such interventions. Objective: This study aims to synthesize evidence on the effectiveness of mHealth-based gamified interventions for improving physical activity in older adults. Methods: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Meta-Analysis of Observational Studies in Epidemiology guidelines and analyzed studies from PubMed, Embase, Web of Science, CINAHL, Scopus, and Wiley Online Library, covering relevant literature from their inception up to May 2025. The inclusion criteria focused on gamified mHealth interventions for adults aged 60+ years, excluding serious games. Quality assessment was conducted according to the Joanna Briggs Institute standards, with data extracted on study design, gamification elements, and outcomes such as step counts and moderate-to-vigorous physical activity. Results: Of 2944 studies identified from the database search, 1454 individuals from 8 trials were included. Gamified interventions significantly increased daily step counts and time spent in moderate-to-vigorous physical activity among older adults. Goal setting and rewards were the most frequently used components, and the combined use of mobile and wearable devices offered greater flexibility and accessibility. A classification framework indicated that interventions integrating multiple gamification elements with hybrid technology systems were most effective, particularly when guided by a theoretical basis. However, the heterogeneity in study designs, small sample sizes, and lack of long-term follow-up studies limited the generalizability of the findings. Conclusions: mHealth-based gamification interventions demonstrate potential for increasing physical activity in older adults. Future interventions should consider employing multifaceted designs combining advanced gamification with hybrid technology systems, while also prioritizing theoretical integration, long-term sustainability, and caregiver involvement to improve sustainability and inclusivity. This review highlights the need for theory-driven, technology-mediated strategies that address the unique health needs of older adults.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».