Combined Use of Digital and Analog Physical Therapy in Patients With Musculoskeletal Disorders and Indicators of Chronicity: German Claims Data Analysis
Bibliographic record
Abstract
Background: Musculoskeletal disorders are highly prevalent worldwide and contribute significantly to the overall burden of disease. Regular physical therapy with trained physiotherapists is recommended in the guidelines. Recently, digital physical therapy offered by digital health interventions was shown to be effective. However, the evidence on its real-world usage in health care systems is limited. Objective: Based on claims data, this study examined the current usage of digital health applications (DiGAs) for musculoskeletal disorders in the German health care system. Patients with standalone digital physical therapy were compared to those with a combination of analog and digital physical therapy. In addition, predictors for concomitant use were identified. Methods: This retrospective cohort study analyzed claims data from Germany's largest statutory health insurance. Patients who used DiGA for musculoskeletal disorders at least once were included. Sociodemographic and medical characteristics of patients receiving standalone and concomitant physical therapy were compared. Statistical analyses comprised univariate analyses and binomial logistic regression. Results: Of the 6090 individuals, 58.2% (3543/6090) were prescribed physical therapy within 6 months before or after DiGA prescription. In this population, 36.3% (2210/6090) used DiGA and analog physical therapy at the same time. Concomitant physical therapy was significantly more likely in patients with chronicity risk (odds ratio [OR] 1.49, 95% CI 1.31-1.69; P<.001) or established chronicity (OR 2.76, 95% CI 2.22-3.47; P<.001), female gender (OR 1.48, 95% CI 1.33-1.66; P<.001), and higher age (OR 1.02, 95% CI 1.02-1.02; P<.001). Conclusions: The findings highlight the diverse utilization patterns of DiGAs among patients with musculoskeletal disorders. Chronicity emerged as an important predictor for combined digital and analog physical therapy. These findings support considerations on integrating digital health interventions into current guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".