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BackgroundThis study hopes to establish the timeframe for a safe return to driving under different speed conditions for patients after minimally invasive total knee arthroplasty and further explores how well various kinds of functional tests on knee performance can predict the patients’ braking ability.Methods14 patients with right knee osteoarthritis were included in the present study and instructed to perform three simulated driving tasks at preoperative, 2 weeks postoperative and 4 weeks postoperative.ResultsThe results showed that the total braking time at 4 week postoperative has attained the preoperative level at the driving speed 50 and 70 km/hr but not at the driving speed 90 km/hr. It had significantly improving in knee reaction time and maximum isometric force at 4 weeks postoperative. Besides, there was a moderate to high correlation between the scores of the step counts and the total braking time.ConclusionsSummary, it is recommended that driving may be resumed 4 weeks after a right knee replacement but had to drive at low or moderate speed and the best predictor of safety driving is step counts.

The results of this study suggest that women with PMS could attend short-term yoga exercise in the luteal phase to make themselves feel better and maintain a better attention level.

[Purpose] Patients with severe bilateral knee osteoarthritis (KOA) often suffer from low back pain (LBP). However, few studies have examined the relationship between LBP and KOA in downward reach and pick-up movements. [Subjects] Eight KOA patients with LBP (LBP group), 8 KOA patients without LBP (NLBP group), and 7 healthy participants (Control group), without osteoarthritis or low back pain, were recruited for this study. [Methods] All subjects were asked to pick up a bottle with one hand, placed at the diagonal on the opposite side of the body. A 3D motion analysis system was used to record trunk and lower limb movements. [Results] The knee flexion angle on the side ipsilateral to the bottle was significantly smaller in both KOA groups than in the controls in the downward reach and pick-up movements. KOA patients showed a significantly lower trunk flexion angle and greater pelvis anterior tilt angle than the controls. In addition, no significant differences were found between the LBP and NLBP group. [Conclusion] We suspect that severe knee pain due to OA determines the priority of movement in strategic planning for the execution of pick-up movements. The knee strategy was abandoned by our severe knee OA patients, even when they had mild LBP.

To protect the Achilles tendon, AT-Achilles taping is recommended since it tends to decrease ATF. Conversely, to enhance athlete performance, we recommend KT-Achilles taping to speed up kendo striking motion. However, the Achilles tendon must withstand greatest forces concurrently. This finding implies that AT-Achilles taping can protect the injured Achilles tendon and KT-Achilles taping can enhance performance on the kendo striking motion.

Background: In this study, an automatic scoring system for the functional movement screen (FMS) was developed. Methods: Thirty healthy adults fitted with full-body inertial measurement unit sensors completed six FMS exercises. The system recorded kinematics data, and a professional athletic trainer graded each participant. To reduce the number of input variables for the predictive model, ordinal logistic regression was used for subset feature selection. The ensemble learning algorithm AdaBoost.M1 was used to construct classifiers. Accuracy and F score were used for classification model evaluation. The consistency between automatic and manual scoring was assessed using a weighted kappa statistic. Results: When all the features were used, the predict model presented moderate to high accuracy, with kappa values between fair to very good agreement. After feature selection, model accuracy decreased about 10%, with kappa values between poor to moderate agreement. Conclusions: The results indicate that higher prediction accuracy was achieved using the full feature set compared with using the reduced feature set.

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