Home-Based, Low-Intensity, Gamification-Based, Interactive Physical-Cognitive Training for Older Adults Using the ADDIE Model: Design, Development, and Evaluation of User Experience
Notice bibliographique
Résumé
Background: Declines in physical and cognitive function are natural biological processes, leading to an increased risk of falls. Promising evidence suggests that combined physical-cognitive exercise has beneficial effects in improving both physical and cognitive health. Although moderate-to-high exercise intensity is commonly recommended, it might be impractical for older adults facing physical limitations or contraindications. Thus, low-intensity exercise is a viable option. The main barriers to engaging in exercise in older adults include transportation, time, motivation, and enjoyment. To overcome these challenges, a home-based, gamification-based training system may provide an effective approach to enhance exercise adherence. Objective: This study aimed to develop and evaluate the usability of a low-intensity, gamification-based, interactive physical-cognitive exercise for older adults in a home-based setting. Methods: The prototype of a game-based physical-cognitive exercise was created following the ADDIE model (analysis, design, development, implementation, and evaluation) and assessed for user experience in older adults. A total of 15 older adults engaged in the game-based physical-cognitive exercise at home for 60 minutes per day, 3 days per week, for 4 weeks. The usability of the game-based training system was evaluated using the system usability scale (SUS) after completion of a 4-week training program. As for satisfaction, the 8-item Physical Activity Enjoyment Scale (PACES) questionnaire was used to assess participants' enjoyment level after 1 week and 4 weeks of training. Descriptive statistics were used to illustrate the SUS score. A Wilcoxon signed-rank test was used to compare the PACES scores between the first week and the end of the 4-week period, with significance set at P<.05. Results: As for experts' consensus, the game-based training consisted of 3 games: Ocean Diver, Road Runner, and Moving and Memorizing. The games had 3 levels of difficulty: beginner, intermediate, and advanced. A computer vision-based system was selected as the delivery platform for a home setting. The total SUS score for all participants was mean 87.22 (SD 5.76), indicating the user's perception of the usability of a system ranging from good to excellent. At the end of the 4-week training, the total PACES score was significantly greater than the first week, suggesting an improvement in enjoyment (first week: mean 44.93, SD 3.99 vs fourth week: mean 50.53, SD 4.70; P=.001). Conclusions: The prototype of low-intensity, gamification-based, interactive physical-cognitive training was designed and developed using the ADDIE model, which included both experts and end users in the process. The findings showed that the exergame prototype was a usable and practical approach for a home-based setting, enhancing older adults' enjoyment and motivation. Further research is warranted to determine the effectiveness of such gamification-based training in promoting physical and cognitive functions.
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».