Efficacy of Lower-Limb Wearables to Assess Recovery Following Total Hip or Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The purpose of this review was to assess the use of lower-limb wearable sensors in monitoring total hip arthroplasty (THA) or total knee arthroplasty (TKA) recovery. Outpatient postoperative assessment routinely focuses on patient-reported outcome measures, which can be limited by ceiling effects and subjective reporting. Wearable sensors provide objective, real-time, remote data, enabling recovery tracking, rehabilitation protocol adjustments, and patient exercise adherence. Lower-limb sensors are particularly useful, as close proximity allows monitoring of clinical outcomes specific to the affected joint. METHODS: Embase, PubMed, Scopus, and Web of Science were searched to identify studies on monitoring following arthroplasty. Included articles employed lower-limb wearable devices for continuous, at-home, postoperative monitoring within 18 months of surgery in patients who underwent THA or TKA. Outcomes of interest included patient-reported outcome measures and objective physical activity. Of 532 screened articles, 10 were included; five investigated TKA patients, while five focused on THA. Objective metrics included step counts (n = eight) and range of motion (n = four). RESULTS: Postoperative outcomes showed significant improvements in the Oxford Hip Score (P < 0.001), Oxford Knee Score (P = 0.020), and University of California, Los Angeles Activity Index (P < 0.001) at three, six, and 12 months postoperatively, respectively. While no significant change in daily steps was noted within six months postoperatively, it significantly increased at the final follow-up, within one year of surgery, with TKA and THA patients demonstrating comparable improvements. CONCLUSIONS: The integration of lower-limb wearables into postoperative care for TKA and THA presents an innovative approach to monitoring recovery. Findings from this analysis show differences between TKA and THA, suggesting a unique trajectory for each surgery. Continuous, objective data may help assess patient progress, identify atypical recovery patterns at earlier time points, and individualize rehabilitation strategies. This data tracking may allow for earlier clinical interventions, particularly in underserved regions.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".