Exergaming Platform for Older Adults Residing in Long-Term Care Homes: User-Centered Design, Development, and Usability Study
Notice bibliographique
Résumé
BACKGROUND: Older adults (OAs) residing in long-term care (LTC) homes are often unable to engage in adequate amounts of physical activity because of multiple comorbidities, including frailty and severe cognitive impairments. This level of physical inactivity is associated with declines in cognitive and functional abilities and can be further compounded by social isolation. Exergaming, defined as a combination of exercise and gaming, has the potential to engage OAs in exercise and encourage social interaction. However, previously used systems such as the Nintendo Wii are no longer commercially available, and the physical design of other exergames is not suitable for OAs (ie, fall risks, accessibility issues, and games geared toward a younger population) with diverse physical and cognitive impairments. OBJECTIVE: This study aims to design and develop a novel, user-centered, evidence-based exergaming system for use among OAs in LTC homes. In addition, we aim to identify facilitators and barriers to the implementation of our exergaming intervention, the MouvMat, into LTC homes according to staff input. METHODS: This study used a user-centered design (UCD) process that consisted of 4 rounds of usability testing. The exergame was developed and finalized based on existing evidence, end user and stakeholder input, and user testing. Semistructured interviews and standardized and validated scales were used iteratively to evaluate the acceptability, usability, and physical activity enjoyment of the MouvMat. RESULTS: A total of 28 participants, 13 LTC residents, and 15 staff and family members participated in the UCD process for over 18 months to design and develop the novel exergaming intervention, the MouvMat. The iterative use of validated scales (System Usability Scale, 8-item Physical Activity Enjoyment Scale, and modified Treatment Evaluation Inventory) indicated an upward trend in the acceptability, usability, and enjoyment scores of MouvMat over 4 rounds of usability testing, suggesting that identified areas for refinement and improvement were appropriately addressed by the team. A qualitative analysis of semistructured interview data found that residents enjoyed engaging with the prototype and appreciated the opportunity to increase their PA. In addition, staff and stakeholders were drawn to MouvMat's ability to increase residents' autonomous PA. The intended and perceived benefits of MouvMat use, that is, improved physical and cognitive health, were the most common facilitators of its use identified by study participants. CONCLUSIONS: This study was successful in applying UCD to collaborate with LTC residents, despite the high number of physical and sensory impairments that this population experiences. By following a UCD process, an exergaming intervention that meets diverse requirements (ie, hardware design features and motivation) and considers environmental barriers and residents' physical and cognitive needs was developed. The effectiveness of MouvMat in improving physical and cognitive abilities should be explored in future multisite randomized controlled trials.
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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 | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».