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Introduction The objective of this study was to assess the efficacy of telerehabilitation for patients after total knee arthroplasty (TKA) compared with face-to-face rehabilitation. Methods Medline, SCOPUS, Google Scholar, EMBASE, Springer, Science Direct, and Cochrane databases were searched electronically. Relevant journals and references of studies included were hand-searched for randomized controlled trials (RCTs) regarding the efficacy of telerehabilitation on functional recovery in patients after TKA. Two reviewers independently performed data extraction and quality assessment. Data were analysed using RevMan 5.3 software and Stata 12.0 software. Results Four RCTs involving 442 patients were included in the meta-analysis. Overall, compared with face-to-face rehabilitation, telerehabilitation could achieve comparable pain relief (mean difference = 0.52; 95% confidence interval (CI) = -0.20 to 1.24; p = 0.16) and better Western Ontario and McMaster Universities Osteoarthritis Index improvement (mean difference = 1.13; 95% CI = 0.23 to 2.02; p = 0.014). In addition, telerehabilitation treatment resulted in a significantly higher extension range ( p < 0.00001) and quadriceps strength ( p = 0.0002) than face-to-face rehabilitation. Discussion Telerehabilitation should be recommended for patients after TKA because of its comparable pain control and better improvement of functional recovery as compared to face-to-face rehabilitation.

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Monte Carlo simulation (MCS) provides a conceptually simple and robust method to evaluate the system reliability of slope stability, particularly in spatially variable soils. However, it suffers from a lack of efficiency at small probability levels, which are of great interest in geotechnical design practice. To address this problem, this paper develops a MCS-based approach for efficient evaluation of the system failure probability P f ,s of slope stability in spatially variable soils. The proposed approach allows explicit modeling of the inherent spatial variability of soil properties in a system reliability analysis of slope stability. It facilitates the slope system reliability analysis using representative slip surfaces (i.e., dominating slope failure modes) and multiple stochastic response surfaces. Based on the stochastic response surfaces, the values of P f ,s are efficiently calculated using MCS with negligible computational effort. For illustration, the proposed MCS-based system reliability analysis is applied to two slope examples. Results show that the proposed approach estimates P f ,s properly considering the spatial variability of soils and improves the computational efficiency significantly at small probability levels. With the aid of the improved computational efficiency offered by the approach, a series of sensitivity studies are carried out to explore the effects of spatial variability in both the horizontal and vertical directions and the cross-correlation between uncertain soil parameters. It is found that both the spatial variability and cross-correlation affect P f ,s significantly. The proposed approach allows more insights into such effects from a system analysis point of view.