A Qualitative Study of the Perspectives of Healthcare Professionals on Features of Digital Health Interventions to Support Physical Activity in Solid Organ Transplant Recipients
Bibliographic record
Abstract
Introduction: Digital health interventions may support physical activity among solid organ transplant recipients. These interventions should be designed with users in mind, including healthcare professionals who counsel transplant recipients on physical activity to ensure acceptance and to promote an optimal user experience. The purpose of this study was to explore the perspectives of health care providers on the features of digital health interventions that would be useful in the promotion, implementation, and maintenance of physical activity among solid organ transplant recipients. Methods: This qualitative, cross-sectional study used semistructured interviews that were conducted remotely, via videoconferencing software, with providers who worked with transplant recipients. Interviews were transcribed, and an iterative-inductive, thematic analysis was used to identify common themes. Data were coded using NVivo software. Findings: Thirteen providers participated in this study. Four main themes were identified: (a) physical activity and exercise features (eg, physical activity guidelines, and exercise instructions); (b) credibility; (c) self-management; and (d) user engagement. Potential barriers to using digital health interventions included staffing requirements, professional regulatory issues, cost, perceived low patient motivation to use, and lack of technological literacy or access. Discussion: Digital health interventions were perceived to be a potential adjunct to current physical activity counseling practices, and part of an innovative strategy to address identified barriers to physical activity participation in solid organ transplant recipients.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".