Investigating the Acceptance and Implementation Conditions of Telerehabilitation in Germany Among Patients and Health Care Professionals: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Telerehabilitation has become increasingly important worldwide, as the COVID-19 pandemic forced many rehabilitation centers to change their daily care routine and find new ways to provide medical rehabilitation and aftercare. OBJECTIVE: This study aims to investigate the acceptance and implementation conditions of telerehabilitation in Germany, particularly following the COVID-19 pandemic. METHODS: We conducted qualitative semistructured interviews with patients (n=9) and health care professionals (n=8) between September 2023 and January 2024. To explore individual and structural barriers to and facilitators of telerehabilitation adoption, we used the extended unified theory of acceptance and use of technology and the Consolidated Framework for Implementation Research. RESULTS: Patients and health care professionals perceived telerehabilitation as positive, mainly due to its flexibility and accessibility. Patients expressed high acceptance levels, anticipating health benefits, although they found it challenging to familiarize themselves with the technology and establish routines. Health care professionals highlighted the need for adequate resources (financial, time, and personnel) and management support to implement telerehabilitation successfully. Both groups saw higher acceptance and cost coverage of telerehabilitation services as essential for successful implementation and use in Germany. CONCLUSIONS: This study identified institutional barriers, such as concerns about resource availability, team communication, and initial resistance among health care staff to the introduction of new technologies. At an individual level, we found that patients struggled with routine establishment and that digital and in-person support from institutions and peers could mitigate this challenge. Implementing a hybrid approach and improving funding and approval processes would enhance telerehabilitation integration in the German health care sector.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".