A scoping review of the use of intelligent assistive technologies in rehabilitation practice with older adults
Notice bibliographique
Résumé
Purpose There is growing interest in intelligent assistive technologies (IATs) in the rehabilitation and support of older adults, however, the factors contributing to or preventing their use in practice are not well understood. This study aimed to develop an overview of current knowledge on barriers and facilitators to the use of smart technologies in rehabilitative practice with older adults.Materials and Methods We undertook a scoping review following guidelines proposed by Arksey and O’Malley (2005) and Levac et al. (2010). A computerised literature search was conducted using the Scopus and Ovid databases, yielding 7995 citations. Of these, 94 studies met inclusion criteria. Analysis of extracted data identified themes which were explored in semi-structured interviews with a purposefully selected sample of seven clinical rehabilitation practitioners (three physical therapists, two occupational therapists, and two speech-language pathologists).Results Barriers and facilitators to using these technologies were associated with accessibility, reported effectiveness, usability, patient-centred considerations, and staff considerations.Conclusions Collaborative efforts of policy-makers, researchers, manufacturers, rehabilitation professionals, and older persons are needed to improve the design of technologies, develop appropriate funding and reimbursement strategies, and minimise barriers to their appropriate use to support independence and quality of life. Any strategies to improve upon barriers to prescribing smart technologies for older people should leverage the expertise of rehabilitation professionals operating at the interface between older people; their health/mobility; their families; and technology-based solutions.Implications for rehabilitationThere is growing interest in intelligent assistive technologies (IATs) in the rehabilitation of older adults, as well as barriers to their use in practice.Rehabilitation professionals can play a key role in enabling access to IATs by recommending or prescribing their use to their older clients. Strategies to address barriers to the use of IATs for older people should leverage the expertise of rehabilitation professionals operating at the interface between older people, their families, and technology-based solutions.Older people and their families require technical support to initiate and continue to use IATs for rehabilitation. While rehabilitation providers may be well-placed to offer this support, they may require time and organizational support to build and maintain expertise in the fast-advancing field of smart technologies for rehabilitation.Cost and usability are universal challenges across the types of smart technologies considered in this review. Participatory approaches to involving older people in the design and development of smart assistive technologies contribute to better usability of these technologies. Devices and interventions that leverage more readily available devices and lower-cost components may overcome cost barriers to accessibility.
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 | 0,021 | 0,090 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,026 | 0,027 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».