Gait biomechanics and functional outcomes as predictors of response to a hip strengthening exercise program in knee osteoarthritis patients

(1) Background—The application of neuromuscular electrical stimulation (NMES) combined with low-intensity exercise to the elderly can be more efficient than low-intensity exercise only in terms of delaying the loss of muscle mass. We aimed to assess the adjunct of NMES to low-intensity lower limb strengthening exercise to prevent falls in frail elderly for a relatively short period of 4 weeks. (2) Methods—Thirty elderly women aged 65 or above were randomly categorized into three groups: control group (CON, n = 8), exercise group (EX, n = 10), and NMES with exercise group (EX + NMES, n = 9). The exercise group took part in a lower limb strengthening exercise program for one hour three times a week for four weeks. Furthermore, the NMES with exercise group had added NMES stimulation when exercising. The limbs’ muscle mass, body fat mass, calf circumference, grip force, five times sit-to-stand test, timed up-and-go test (TUG), one-leg stand test, and Y-balance test (YBT) were evaluated at baseline and 4 weeks after. (3) Results—Comparisons between the three groups showed that the TUG was significantly decreased and the YB was significantly increased in NMES with exercise group (p < 0.05). (4) Conclusions—These results suggested that a combination of NMES stimulation and exercises was more helpful in strengthening balance than exercises alone in the short term.

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3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

Applied Sciences ◽

10.3390/app11020834 ◽

2021 ◽

Vol 11 (2) ◽

pp. 834

Author(s):

Marwa Mezghani ◽

Nicola Hagemeister ◽

Youssef Ouakrim ◽

Alix Cagnin ◽

Alexandre Fuentes ◽

...

Keyword(s):

Physical Exercise ◽

Knee Osteoarthritis ◽

Decision Trees ◽

Exercise Program ◽

Patient Specific ◽

Pain Level ◽

Knee Kinematic ◽

Kinematic Data ◽

3D Kinematics ◽

The Impact

Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.

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