Telerehabilitation for People With Physical Disabilities and Movement Impairment: A Survey of United Kingdom Practitioners
Notice bibliographique
Résumé
BACKGROUND: Telerehabilitation is a feasible and potentially effective alternative to face-to-face rehabilitation. However, specific guidance, training, and support for practitioners who undertake remote assessments in people with physical disabilities and movement impairment are limited. OBJECTIVE: The aims of this survey of United Kingdom-based health and social care practitioners were to explore experiences, assess training needs, and collate ideas on best practices in telerehabilitation for physical disabilities and movement impairment. The aim will be to use the findings to inform a practical tool kit and training package for telerehabilitation use. METHODS: UK rehabilitation practitioners were invited to complete an online questionnaire from November to December 2020. Opportunity and snowball sampling were used to recruit participants from professional and educational networks, special interest groups, and via social media. Closed questionnaire items were analyzed using descriptive statistics. Qualitative inductive analysis using NVivo was used for open responses. RESULTS: There were 247 respondents, of which 177 (72%) were physiotherapists and occupational therapists. Most (n=207, 84%) had used video-based consultations (typically supported by telephone and email), and the use of this method had increased in frequency since the COVID-19 pandemic. Practitioners perceived telerehabilitation positively overall and recognized benefits for patients including a reduced infection risk, convenience and flexibility, and reduced travel and fatigue. Common obstacles were technology related (eg, internet connection), practical (eg, difficulty positioning the camera), patient related (eg, health status), practitioner related (eg, lack of technical skills), and organizational (eg, lack of access to technology). Support from family members or carers was a major facilitator for successful remote consultations. Of the 207 respondents who had used video-based consultations, 103 (50%) had assessed physical impairments using this method, 107 (52%) had assessed physical function, and 121 (59%) had used patient-reported outcome measures. Although practitioners generally felt confident in delivering video-based consultations, they felt less proficient in undertaking remote physical assessments, expressing concerns about validity, reliability, and safety. Only 46 of the 247 (19%) respondents had received any training in telerehabilitation or video consultations, and some felt they were "feeling their way in the dark." Practitioners desired training and guidance on physical assessment tools suitable for remote use, when to use video-based consultations or alternative methods, governance issues, digital platforms, and signposting to digital skills training for themselves and their patients. CONCLUSIONS: In response to the COVID-19 pandemic, practitioners rapidly adopted telerehabilitation for people with physical disabilities and movement impairment. However, there are technical, practical, and organizational obstacles to overcome, and a clear need for improved guidance and training in remote physical assessments. The findings of this survey will inform the development of a tool kit of resources and a training package for the current and future workforce in telerehabilitation.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».