Research Progress and Hot Topics in Telerehabilitation for Hip or Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Many publications on telerehabilitation for hip or knee arthroplasty have been published in recent years. However, no specific studies have attempted to characterize research hotspots, global research collaborations, or trends related to telerehabilitation after hip or knee arthroplasty. Therefore, the aim of this bibliometric analysis was to provide an overview of the current status of research and map the research landscape on telerehabilitation for joint replacement to understand current trends, identify research gaps, and guide future research directions. METHODS: The Web of Science Core Collection and PubMed were comprehensively searched to identify all relevant English-language documents published from 2003 to June 7, 2024. Data from these published studies were then cleaned and structured. CiteSpace and VOSviewer were used to conduct the bibliometric visualization and comparative analysis of countries, institutions, journals, authors, references, and keywords. Then, the map illustrating the research hotspots and knowledge structure was plotted based on the analysis results. RESULTS: A total of 229 records were obtained, and the number of articles published has increased steadily over the investigated period. The largest increase was observed in 2022. With the highest number of publications and centrality, the United States was the most influential country. The University of Sherbrooke was the most productive institution. Author Boissy P. ranked first in terms of the number of publications, while Tousignant M. ranked highest in cited authors, with 7 publications and 65 citations. The Journal of Arthroplasty published the greatest number of articles, with 29 publications. The most popular keywords from 2018 to 2023 were "home telerehabilitation," "older adults," and "physical therapy". In terms of the strongest citation burst, the top five keywords were associated with "total knee arthroplasty," "in home tele rehabilitation," "physical activity," "motion," and "range." The frontier keywords were "patient satisfaction," "mobile application," "self-efficacy," "fear avoidance model," "home assessment tool," and "cost benefit analysis." CONCLUSIONS: The current status and trends in telerehabilitation for hip or knee arthroplasty are presented. A major concern at present is physical therapy for home telerehabilitation in the elderly. In the future, mobile app-based telerehabilitation programs for arthroplasty will continue to be encouraged, and some outcomes, such as "patient satisfaction," "self-efficacy," and "cost benefit analysis," are expected to receive more attention. Our work will serve as a valuable resource, providing fundamental references and a directional guide for future research.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.057 | 0.102 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".