Global trends and hotspots of the digital therapeutics in rehabilitation: A bibliometric analysis. (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Background: Digital therapeutics (DTx) are emerging as a dynamic field at the intersection of healthcare and technology, utilizing software-driven interventions to prevent, manage, or treat medical conditions. Within rehabilitation, DTx has gained prominence for its potential to improve patient outcomes and refine therapeutic strategies. This paper conducts a bibliometric analysis of existing literature to uncover current research trends and offer insights for future investigations. Methods: The Science Citation Index Expanded database from the Web of Science Core Collection was used to retrieve articles and reviews related to digital therapeutics in rehabilitation. The data were subjected to systematic analysis using the VOSviewer and Citespace software tools, which facilitated the examination of publication data at the country, institutional, author, journal, citation, and keyword levels. Results: A total of 6,593 papers published between 2000 and 2024 were reviewed. The volume of publications has steadily increased over the past two decades, reaching a peak in 2022 (n = 832). Leading contributors to research output and citation impact include the USA, Italy, China, Canada, and England. Current research hotspots include the efficacy of DTx in rehabilitation, with a focus on innovative applications in psychiatric disorders, telerehabilitation, and cognitive rehabilitation. The top 10 researchers were all from Italy, and half of them were affiliated to the Centro Neurolesi Bonino Pulejo Messina, and all had higher citations than those from other Italian organisations. Conclusions: This study represents the first comprehensive bibliometric analysis of DTx within the rehabilitation field. It identifies current research frontiers and emerging directions, serving as a valuable resource for scholars and researchers investigating DTx's impact on rehabilitation practices.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.132 | 0.219 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".