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Over a six months period, WBVE in combination with QRE was superior to QRE in most outcomes.

There was no evidence for effects of whole body vibration on balance in people with stroke. Effects of whole body vibration on mobility and gait performance remain inconclusive. More large and high-quality trials are required.

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Objective Pulsed electromagnetic field (PEMF) therapy is a potentially useful treatment for osteoarthritis (OA), but its effectiveness is still controversial. This study aimed to examine the effects of PEMF therapy and PEMF parameters on symptoms and quality of life (QOL) in patients with OA. Methods Cochrane Central Register of Controlled Trials, PubMed, CINAHL, EMBASE, PEDro, clinical trial registers, and reference lists were searched until April 2019. This study examined randomized, placebo-controlled trials, patients with OA, symptom and/or QOL related outcomes, and articles published in English. Two authors extracted data and completed quality assessment. Results Sixteen studies were included in our systematic review, while 15 studies with complete data were included in the meta-analysis. Our primary outcome was the standardized mean difference, which was equal to the treatment effect in the PEMF group minus the treatment effect in the placebo group divided by the pooled standard deviation. For pain, the standardized mean difference was 1.06 (95% CI = 0.61 to 1.51), for stiffness 0.37 (95% CI = 0.07 to 0.67), for function 0.46 (95% CI = 0.14 to 0.78), and for QOL 1.49 (95% CI = −0.06 to 3.04). PEMF parameters did not influence symptoms. Conclusions Compared with placebo, there was a beneficial effect of PEMF therapy on pain, stiffness, and physical function in patients with OA. Duration of treatment may not be a critical factor in pain management. Further studies are required to confirm the effects of PEMF therapy on QOL. Impact Our study suggests that PEMF therapy has clinically significant effects on pain in patients with OA. The current evidence was limited to the short-term effects of PEMF therapy.

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The aim of this study was to investigate the effect of low-level laser therapy (LLLT) on short-term and long-term joint pain, synovitis, anabolic, and catabolic factors in the cartilage of a rabbit model with progressive osteoarthritis (OA) induced by anterior cruciate ligament transection (ACLT). A total of 160 New Zealand white rabbits were randomly assigned into two groups (ACLT group and LLLT group). All rabbits received ACLT surgery, and 2-, 4-, 6-, and 8-week treatment after the surgery, with 20 rabbits being tested biweekly over every study period. The LLLT group received LLLT with a helium-neon (He-Ne) laser (830 nm) of 1.5 J/cm(2) three times per week, and the ACLT group received placebo LLLT with the equipment switched off. Long-term and short-term pain was tested via weight-bearing asymmetry; synovitis was assessed histologically; and knee joint cartilage was evaluated by gross morphology, histology, and gene expression analysis of anabolic and catabolic factors. The histological assessment of pain and synovitis showed that at least 6-week intermittent irradiation of LLLT could relief knee pain and control synovium inflammation. Gross morphologic inspection and histological evaluation showed that 6 weeks of LLLT could decrease cartilage damage of medical femoral condyle and 8 weeks of LLLT could decrease cartilage damage of medical and lateral femoral condyles and medical tibial plateau. Gene expression analysis revealed two results: At least 6 weeks of LLLT could decrease production of catabolic factors, for example, interleukin 1β (IL-1β), inducible nitric oxide synthase (iNOS), and MMP-3, and slow down the loss of anabolic factors, mainly TIMP-1. Eight weeks of LLLT treatment could slow down the loss of collagen II, aggrecan, and anabolic factors, mainly transforming growth factor beta (TGF-β). The study suggests that LLLT plays a protective role against cartilage degradation and synovitis in rabbits with progressive OA by virtue of the regulation of catabolic and anabolic factors in the cartilage.

Delivery stop identification is a crucial but challenging step in the measurement of urban freight performance. This paper presents the application of a robust learning method, support vector machine (SVM), in identifying delivery stops with GPS data. The duration of a stop, the distance from a stop to the center of the city, and the distance to a stop's closest major bottleneck were extracted as the three features used in the SVM model. A linear SVM with nested K-fold cross validation proved to be highly reliable and robust in identifying delivery stops with relatively long stop duration, such as those made for grocery stores. Second-by-second freight GPS data collected in New York City were used to conduct a case study. The identification accuracy for the case study was higher than 99% for the training and testing data sets.