The number of hip arthroplasty procedures has steadily increased in the United States over the last decade [Wolford, et. al, 2015]. This trend will continue as this treatment is the most effective approach in relieving pain, improving mobility, reducing fall risk and improving the quality of life in patients with end-stage osteoarthritis. The effectiveness of recovery can be impacted by factors such as access to postoperative physical therapy regimens. During the recovery period, it is important for therapists to be guided in their therapeutic decision making by accurate data concerning the patient's performance on a variety of measures. This project is designed to map the gait recovery curves of individuals who have undergone unilateral hip arthroplasty. To date, eight individuals (4 females, mean age 64.9, SD 11.1) have participated in the study. Five of the patients were treated by traditional press-fit Accolade II implants (Stryker, Mahwah, NJ USA) through a direct anterior approach THA and the other three has been treated by using DAA THA and using neck preserving MinihipTM short stem implant (Corin Ltd., Cirencester, UK). Each participant walked on an instrumented treadmill as a self-selected speed for three minutes pre-surgery. Post-surgery data collections occurred at three and six weeks, and three and six months and employed the same treadmill speed as used prior to surgery. Bilateral lower limb kinematic data was collected with a 12 camera motion capture system Vicon® (Oxford Metrics, Oxford, UK) using reflective markers attached to the hip, knee, ankle, heel and toe. Force plates embedded in the treadmill provided kinetic data that aided in the detection of heel strike and toe off. The temporal features associated with gait, including stride, stance time and double support time were obtained for both the surgical and non-surgical limbs and were used to assess changes in performance during the recovery period. The stance and double support data were also converted to a percentage of stance values which provide additional insights into gait control strategies. Repeated measure MANOVAs were used to evaluate any potential differences in the variables either between limbs or over time. Results from the statistical testing revealed that there were no significant differences between the two limbs for either stride or stance time. This was expected since global asymmetrical gait would have led to the participants veer off of the treadmill. There was a main effect of ‘Time’ for both stride and stance times. Post hoc testing indicated that the 6-month post-surgery measures were significantly reduced when compared to the Pre-surgery and 3-week post-surgery measures. Similarly, there were no significant differences in double support times between the two legs but there was a main effect for time. Post hoc testing revealed that the 6-month post-surgery double support time was significantly less than the Pre-surgery and 3-week post-surgery measures. These significant changes clearly indicate that surgery is effective in improving gait parameters. Moreover, clinicians may want to consider assessing double support time as this measure is a particularly robust indicator of the effectiveness of unilateral hip arthroplasty.
Hip arthroplasty is commonly used as the final treatment approach for patients experiencing end-stage osteoarthritis. The number of these patients needing this treatment is expected to grow significantly by year 2030 to more than 572000 patients [Kurtz et al., 2007]. One of the important outcomes of hip arthroplasty is to improve patients' functions postoperatively. The evaluation of walking can provide a wealth of information regarding the efficiency of this treatment in improving a patient's mobility. Assessing the kinematic features of gait collected with a motion capture system combined with the aid of a motor-driven treadmill provides the advantage of enabling the evaluator to collect precise information about a large number of strides in a short period of time. Body segment kinematics (i.e. joint motion) are most often represented in the form of time series data with the abscissa (X axis) representing time and the ordinate (y axis) representing the motion of a particular joint. Although a great deal of information can be gained from the analyses of time series data, non-linear analyses tools can provide an additional and important dimension to a clinician's assessment of gait recovery. In this study eight patients (4 females, mean age 64.9, SD 11.1) have currently been assessed after unilateral hip arthroplasty. All surgeries were conducted by direct anterior approach by using two different approaches; three of the patients were treated by bone preservation technique and received Minihip short stem implant (Corin Ltd., Cirencester, UK) and five were treated by using a press fit stem implant Accolade II (Stryker, Mahwah, NJ USA). Patients performed a single three-minute trial of walking on a motor-driven treadmill at a self-selected pace. Using a 12 camera system, bilateral lower limb joint motion was collected prior to the surgery, at three and six weeks and at three and 6 months after the surgery. Depending upon the patient's preferred walking pace; between 40 and 45 strides were collected during each trial. Kinematic data obtained from force plates embedded in the treadmill were used to identify the heel strike and toe off events for each stride. After time normalizing the each of the joint angles (i.e. hip, knee, ankle) for each stride to 100 data points the data were then amplitude normalized to the initial point of the pre-surgery data. The non-linear tools of angle-angle and phase plane were used to explore relationships that are not readily apparent with linear wave form analyses. Angle-angle diagrams between a variety of joints angles both within a single limb or bilaterally enabled us to explore segmental coordination patterns and how they changed over the six months after surgery. Phase plane analyses included comparing joint motion relative to the velocity of that motion. This technique provided insights into the nature of the control of the joint. The additional information that results from the use of non-linear analyses provides an additional dimension of that can aide the clinician in understanding the recovery curve. This additional insight can be used to guide therapeutic decision making.
