Use of the Digital Assistant Vigo in the Home Environment for Stroke Recovery: Focus Group Discussion With Specialists Working in Neurorehabilitation
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".