Use of the Digital Assistant Vigo in the Home Environment for Stroke Recovery: Focus Group Discussion With Specialists Working in Neurorehabilitation
Notice bibliographique
Résumé
BACKGROUND: There is a lack of resources for the provision of adequate rehabilitation after a stroke, thus creating a challenge to provide the necessary high-quality, patient-centered, and cost-efficient rehabilitation services at a time when they are needed the most. Tablet-based therapeutic programs present an alternative way to access rehabilitation services and show a new paradigm for providing therapeutic interventions following a stroke anytime and anywhere. The digital assistant Vigo is an artificial intelligence-based app that provides an opportunity for a new, more integrative way of carrying out a home-based rehabilitation program. Considering the complexity of the stroke recovery process, factors such as a suitable population, appropriate timing, setting, and the necessary patient-specialist support structure need to be thoroughly researched. There is a lack of qualitative research exploring the perspectives of professionals working in neurorehabilitation of the content and usability of the digital tool for the recovery of patients after a stroke. OBJECTIVE: The aim of this study is to identify the requirements for a tablet-based home rehabilitation program for stroke recovery from the perspective of a specialist working in stroke rehabilitation. METHODS: The focus group study method was chosen to explore specialists' attitudes, experience, and expectations related to the use of the digital assistant Vigo as a home-based rehabilitation program for stroke recovery in domains of the app's functionality, compliance, usability, and content. RESULTS: In total, 3 focus groups were conducted with a participant count of 5-6 per group and the duration of the discussion ranging from 70 to 80 minutes. In total, 17 health care professionals participated in the focus group discussions. The participants represented physiotherapists (n=7, 41.2%), occupational therapists (n=7, 41.2%), speech and language therapists (n=2, 11.8%), and physical medicine and rehabilitation physicians (n=1, 5.9%). Audio and video recordings of each discussion were created for further transcription and analysis. In total, 4 themes were identified: (1) the clinician's views on using Vigo as a home-based rehabilitation system, (2) patient-related circumstances facilitating and limiting the use of Vigo; (3) Vigo's functionality and use process (program creation, individual use, remote support); and (4) complementary and alternative Vigo use perspectives. The last 3 themes were divided further into 10 subthemes, and 2 subthemes had 2 sub-subthemes each. CONCLUSIONS: Health care professionals expressed a positive attitude toward the usability of the Vigo app. It is important that the content and use of the app be coherent with the aim to avoid (1) misunderstanding its practical use and the need for integration in practice and (2) misusing the app. In all focus groups, the importance of close involvement of rehabilitation specialists in the process of app development and research was highlighted.
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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 | 0,014 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».