Use of Virtual Reality and Augmented Reality Technologies to Support Resilience and Skill-Building in Caregivers of Persons With Dementia: A Scoping Review
Notice bibliographique
Résumé
Dementia presents a growing public health challenge with most affected individuals living at home, placing significant responsibility on their caregivers. Various interventions, from traditional support groups and education programs to emerging technologies, and more specifically virtual reality (VR) and augmented reality (AR), aim to enhance caregiver skills. While VR/AR shows promise in educating and fostering empathy among caregivers and healthcare professionals, its overall effectiveness and practicality in older adults and dementia care warrant further exploration. This review aimed to summarize currently available VR and AR interventions tailored for family caregivers of persons living with dementia (PLWD) in home or clinical settings, including their level of effectiveness, and to compile a summary of features that contributed to technology acceptance in family caregivers of PLWD. We conducted a systematic search in OVID PsychInfo, CINAHL, Google Scholar, and ERIC, as well as CADTH's Grey Matters, OpenGrey, National Technical Information Service, OAIster, and Health Quality Ontario, to comprehensively summarize the existing evidence underscoring the role of VR and AR in supporting education, resilience-building, and skills training for family caregivers of PLWD. The search terms were built with the assistance of a research librarian and involved synonyms for VR, AR, and dementia. Two screeners conducted a rigorous screening and data extraction to analyze and summarize findings. Studies were included if they focused on family caregivers engaging in interventions utilizing a three-dimensional VR environment and/or Metaverse for group learning in psychotherapeutic modalities such as psychoeducation, therapy, communication, and skill-building. The primary outcome of the studies was assessing measures of well-being (e.g., quality of life, communication, interaction, personhood) and learning outcomes for caregivers, while the secondary outcomes focused on identifying barriers and facilitators influencing the acceptability of VR/AR among dementia caregivers. Content analysis and descriptive statistics were used to summarize key trends in technology and evidence effectiveness and acceptability. Of the 1,641 articles found, 112 were included, with six articles meeting inclusion for analysis. Studies differed in duration and frequency of data collection, with interventions varying from single events to months-long programs, often employing home-based approaches using VR or online platforms. No study used AR. Usability issues and unclear benefits of use were identified as factors that hinder technology acceptance for dementia caregivers. However, technologies demonstrated engaging user experiences, fostering skill-building, confidence, and competence among caregivers. Positive psychological effects were also observed, facilitated by immersive VR and AR interventions, resulting in improved caregiver empathy and reduced stress, depression, and loneliness. VR and AR interventions for family caregivers of PLWD show the potential to enhance empathy and skills and reduce stress. Challenges such as technological limitations and user inexperience issues persist. Home-based VR training aligns with caregiver comfort but lacks focus on financial aspects and cultural competencies. Co-design approaches offer solutions by addressing user concerns and promoting end-user engagement or empowerment.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| 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 | 0,000 |
| 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 ».