Examining Technology Perspectives of Older Adults With Mild Cognitive Impairment: Scoping Review
Notice bibliographique
Résumé
Background: Mild cognitive impairment (MCI) affects up to 20% of people older than the age of 65 years. The global incidence of MCI is increasing, and technology is being explored for early intervention. Theories of technology adoption predict that useful and easy-to-use solutions will have higher rates of adoption; however, these models do not specifically consider older adults with cognitive impairments or the unique human-computer interaction challenges posed by MCI. There are gaps in understanding the combined impacts of aging and cognitive impairment on factors affecting technology adoption for older adults with MCI, and it is not clear how MCI impacts human-computer interaction and device and interaction modality preferences in this population. Objective: This study aimed to collate perspectives from older adults with MCI about technology solutions proposed for them, to understand whether solutions are perceived as useful, easy to use, and what changes are suggested. It also identifies which devices and interaction modalities are preferred, and other factors that may affect usage and adoption. Methods: This scoping review was completed according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A consistent search was performed across 9 electronic databases (ACM Digital Library, EBSCOhost CINAHL Plus with Full Text, EBSCOhost Computers and Applied Sciences Complete, Google Scholar, JMIR Publications, IEEE Xplore, EBSCOhost MEDLINE, Scopus, and Web of Science Core Collection) for studies published between January 1, 2014, and May 1, 2024. Extracted data were analyzed using inductive thematic analysis. Results: We identified 4271 studies, and after the removal of duplicates and screening, 83 studies were included for data extraction. Inductive thematic analysis of feedback from older adults with MCI about technology solutions proposed for them identified five themes: (1) purpose and need, (2) solution design and ease of use, (3) self-impression, (4) lifestyle, and (5) interaction modality. Solutions were perceived as useful, even though gaps in functional support exist; however, they were not perceived as entirely easy to use due to issues related to usability and user experience. Devices that are lightweight, portable, familiar, and have large screens are preferred, as is multimodal interaction-particularly speech, visual or text, and touch. Conclusions: Using technology can create feelings that positively or negatively affect a user's comfort, confidence, and overall well-being. Older adults with MCI value independence and autonomy, and solution designs should support these. Usefulness, ease of use, security, privacy, cost, physical comfort, and convenience are important considerations for technology use. Reliable technology creates trust, confidence, and feelings of empowerment. This review recommends future work to (1) improve usability and user experience, (2) enhance personalization, (3) better understand interaction preferences and effectiveness, (4) enable options for multimodal interaction, and (5) more seamlessly integrate solutions into users' lifestyles.
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 | 0,017 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,024 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 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 ».