Professionals’ Perspectives of Smart Stationary Bikes in Rehabilitation: Qualitative Study
Bibliographic record
Abstract
Background: Stationary bikes are used in numerous rehabilitation settings, with most offering limited functionalities and types of training. Smart technologies, such as artificial intelligence and robotics, bring new possibilities to achieve rehabilitation goals. However, it is important that these technologies meet the needs of users in order to improve their adoption in current practice. Objective: This study aimed to collect professionals' perspectives on the use of smart stationary bikes in rehabilitation. Methods: Twelve health professionals (age: mean 43.4, SD 10.1 years) completed an online questionnaire and participated in a semistructured interview regarding their needs and expectations before and after a 30-minute session with a smart bike prototype. Results: A content analysis was performed with inductive coding. Seven main themes emerged: (1) bike functionalities (cycling assistance, asymmetric resistance, and forward and backward cycling), (2) interface between bike and users (simple, user-friendly, personalized, with written reminders during training), (3) feedback to users (user and performance data), (4) training programs (preprogrammed and personalized, and algorithmic programs), (5) user engagement (telerehabilitation, group sessions, music, and automatic suggestion of training), (6) the bike as a physical device (dimensions, comfort, setup, screen, etc), and (7) business model (various pricing strategies, training for professionals, and after-sales service). Conclusions: This study provides an interpretive understanding of professionals' perspectives regarding smart stationary bikes and is the first to identify the expectations of health professionals regarding the development of future bikes in rehabilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".