Feasibility and Acceptance of a Remotely Supervised Home-Based Group Mobility Exercise for Older Adults Using a Mobile Robotic Telepresence: A Pilot Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Mobile robotic telepresence could be used to remotely supervise physical activity programs. Our study aims to explore the feasibility, acceptance, and usability of a physical activity program offered synchronously via a mobile robotic telepresence platform. METHODS: For this interventional study, five Cutii robots were made available in two residences for older adults in Quebec. Participants received the exercise program, two sessions per week for 4-6 weeks, delivered remotely via the robot. The following feasibility indicators were collected during the study, as recommended by Thabane et al.: process, resources, management, and intervention. Acceptance and usability of the technology were measured using the extended version of the Unified Theory of Acceptance and Use of Technology-based questionnaire and the System Usability Scale, respectively. RESULTS: Eighty percent of participants completed the protocol, and 58% of participants attended 80% of the sessions. Various technical problems (internet connection and technology issues) limited the usability and acceptance of the technology. CONCLUSION: The results revealed that the program was feasible according to the process, management, and intervention indicators but unsatisfactory in terms of resources. For some participants, minor modifications were needed to improve technology support and facilitate connection to the mobile robotic telepresence. Further studies will need to focus on the evaluation of the effectiveness of this type of intervention. IMPLICATIONS: These results emphasize the importance of continuing research evaluating the opportunities provided by new technologies to enhance older adults' engagement in physical activity and inform clinicians and policymakers to improve future services in this domain.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".