Blended Care in Patients With Knee and Hip Osteoarthritis in Physical Therapy: Delphi Study on Needs and Preconditions
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is a major public health concern. Despite existing evidence-based treatment options, the health care situation remains unsatisfactory. Digital care options, especially when combined with in-person sessions, seem to be promising. OBJECTIVE: The aim of this study was to investigate the needs, preconditions, barriers, and facilitators of blended physical therapy for osteoarthritis. METHODS: This Delphi study consisted of interviews, an online questionnaire, and focus groups. Participants were physical therapists, patients with hip and/or knee osteoarthritis with or without experience in digital care, and stakeholders of the health care system. In the first phase, interviews were conducted with patients and physical therapists. The interview guide was based on the Consolidated Framework For Implementation Research. The interviews focused on experiences with digital and blended care. Furthermore, needs, facilitators, and barriers were discussed. In the second phase, an online questionnaire and focus groups served the process to confirm the needs and collect preconditions. The online questionnaire contained statements drawn by the results of the interviews. Patients and physical therapists were invited to complete the questionnaire and participate in one of the three focus groups including (1) patients; (2) physical therapists; and (3) a patient, a physical therapist, and stakeholders from the health care system. The focus groups were used to determine concordance with the results of the interviews and the online questionnaire. RESULTS: Nine physical therapists, seven patients, and six stakeholders confirmed that an increase of acceptance of the digital care part by physical therapists and patients is crucial. One of the most frequently mentioned facilitators was conducting regular in-person sessions. Physical therapists and patients concluded that blended physical therapy must be tailored to the patients' needs. Participants of the last focus group stated that the reimbursement of blended physical therapy needs to be clarified. CONCLUSIONS: Most importantly, it is necessary to strengthen the acceptance of patients and physical therapists toward digital care. Overall, for development and usage purposes, it is crucial to take the needs and preconditions into account. TRIAL REGISTRATION: German Clinical Trials Register DRKS00023386; https://drks.de/search/en/trial/DRKS00023386.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".