Design Requirements for Cardiac Telerehabilitation Technologies Supporting Athlete Values: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Cardiac telerehabilitation (CTR) interventions can provide accessible and affordable remote rehabilitation services. However, as cardiac rehabilitation (CR) primarily targets inactive patients, little is known about the experiences with CR of highly active patients (ie, recreational athletes or, simply, athletes) with established coronary artery disease. Consequently, existing CTR interventions do not address the specific needs of the athletic subpopulation. Understanding the needs and values of athletes is crucial for designing meaningful CTR interventions that enhance user acceptance and engagement, thereby facilitating effective rehabilitation for this patient subgroup. OBJECTIVE: This study aimed to inform the design of technologies that facilitate CTR for athletes. We intended to identify athletes' values related to CR, including health and sports tracking, as well as high-level requirements for technologies that can facilitate the CTR of athletes according to the identified values. METHODS: We used value-sensitive design with a human-centric design approach to elicit design requirements for CTR that can serve athletes with established coronary artery disease. To identify athletes' values, we conducted 25 value-oriented semistructured interviews with 15 athletic patients and 10 health care professionals involved in CR programs. In a second phase, we conducted 6 card-sorting focus group sessions with 13 patients and 7 health care professionals to identify desired CTR features. Finally, we derived high-level CTR technology requirements connected to the athletes' needs and values. RESULTS: We defined 12 athlete values divided into 3 categories: body centric, care centric, and data and technology centric. We clustered findings from the card-sorting activity into CTR technology requirements, such as remotely monitored sport-specific training and training data representations next to clinical limitations, and paired them with corresponding values. CONCLUSIONS: Athletes have distinct values and health goals in CR compared to general populations targeted by CTR interventions. Designing patient-centric CTR interventions that address these needs is crucial to support optimal recovery, safe return to sports, and adherence to CTR technologies in the home environment.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".