Development of a Virtual Home-Based Cycling Intervention for Individuals With Chronic Obstructive Pulmonary Disease: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality, and exercise has been shown to reduce both. Health conditions, environmental factors, and logistical challenges are often barriers for participation in pulmonary rehabilitation (PR). Given the barriers many individuals with COPD face when attending health care centers for PR, virtual home-based cycling exercise could be an option. OBJECTIVE: This study aimed to explore the development of a home-based cycling exercise intervention for individuals with COPD, focusing on aspects such as bicycle selection, app functionality, and pilot testing. Furthermore, it aimed to explore participants' and nonparticipants' attitudes toward the intervention. METHODS: Using a phenomenological-hermeneutic approach, data were gathered from 15 semistructured interviews, including test pilots, participants, and nonparticipants. A thematic analysis was used to analyze the data. RESULTS: Thematic analysis identified 8 key themes: bicycle selection, individual guidance needs, geographical and video quality, online connectivity, comfort and accessibility of home-based cycling, flexibility, energy levels, and practical limitations. Findings highlighted a preference for pedal bicycles with adjustable intensity, the importance of flexibility in scheduling, and the autonomy provided by a home-based setup. While participants appreciated the virtual journey on videos, barriers such as lack of energy, stress, and limited space were reported by nonparticipants. CONCLUSIONS: Recommendations include enhancing app features and addressing individual needs to improve adherence. The study underscores the potential of tailored home-based exercise interventions in overcoming traditional PR challenges. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjresp-2024-002573.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".