Robotic Walking Device to Improve Mobility in Parkinson's Disease

Use of a Robotic Walking Device to Improve Home and Community Mobility in People With Parkinson's Disease

This Phase II randomized controlled trial proposes to examine the impact of long-term use of a novel light-weight and wearable assistive robotic device, called the Honda Walking Assist (HWA) device, to improve mobility in the home and community in individuals with mild to moderate Parkinson's disease (PD). Specific aims of the project are to: 1) determine the short-term impact of mechanical gait assistance on efficiency and ease of walking in individuals with PD, and 2) determine the effect of long-term HWA device usage on the ease and ability to walk unassisted in the home and community in individuals with PD.

Difficulties with walking in people with Parkinson's disease contribute to injurious falls and decreased quality of life. The Honda Walking Assist (HWA) robotic device is designed to assist individuals with gait impairments to take longer strides and walk faster. This study will investigate the impact of HWA usage on mobility in the home and community in individuals with PD. It will also examine feasibility and safety of HWA usage in the PD population. Specific Aim 1: Determine the short-term impact of mechanical gait assistance on efficiency and ease of walking in individuals with PD. With disease progression, individuals with PD develop gait impairments (e.g., slower gait velocity, shorter step lengths, increased step-to-step variability, and freezing of gait), that interfere with their abilities to perform daily living tasks and participate in work, home, and social activities and predispose them to falls. The investigators hypothesize that wearing the HWA device will improve gait efficiency, gait parameters, and perceived ease of walking in individuals with PD compared to unassisted walking over a one session period. Specific Aim 2: Determine the effect of long-term HWA device usage on the ease and ability to walk unassisted in the home and community in individuals with PD. Angular sensors embedded in the HWA monitor the cadence, angular velocity, and degree of hip extension and flexion of the device user. When the user initiates walking, the HWA automatically adjusts leg movements to reach target walk ratios (step length/cadence) by increasing the amount of hip flexion and/or extension using power supplied by the device. Thus, the HWA applies continuous, step-by-step cueing to individuals with PD to take bigger and more symmetrical steps, thereby producing a faster and more efficient walking pattern. By wearing the HWA device over an extended period of time, individuals with PD will repetitively practice walking with a more "normal" gait pattern, possibly driving neuroplastic changes that will translate to improve unassisted walking. The investigators hypothesize that an 8-week intervention of HWA device usage will improve gait efficiency, gait parameters, perceived ease of walking, self-confidence, and daily physical activity in the home and community in individuals with PD with and without the use of the device.

Parkinson, Exoskeleton, Honda, Walking Assist, Walking, Gait, Mobility, Rehabilitation, Physical Therapy

Gait assessments will be conducted on participants randomized to the Trained group first during unassisted walking followed by HWA-assisted walking. Participants randomized to the Trained group will receive physical therapist supervised home and community-based walking training wearing the HWA device 2 times per week for 45-60 minutes for 8 weeks. Training will consist of walking in and outside of the home while encouraging larger and more symmetrical steps with practice of activities that challenge the person's balance and motor control. Rest breaks will be allowed as needed. If the therapist is unable to adjust the HWA to provide a safe gait pattern, the session will be ended and the device removed.

The Untrained group will continue their usual daily activities including any exercise regimen that they typically perform. However, they will be asked not to start any new exercise program during the study period.

The double support time coefficient of variation was calculated by first calculating the mean and SD of the double support time for each of the participants. The calculation of the coefficient of variation were done by taking the standard deviations and dividing them by the mean. Thus calculation of the double support time coefficient of variation required multiple measures for each participant. After calculating the double support time coefficients of variation for each participant, we then calculated the mean and SD of the double support time coefficients of variation. This explains the data value with the measure of central tendency reported in the data table.

The swing time coefficient of variation of right leg was calculated by first calculating the mean and SD of the swing time of the right leg for each of the participants. The calculation of the coefficient of variations were done by taking the standard deviations and dividing them by the mean. Thus calculation of the swing time of right leg coefficient of variation required multiple measures for each participant. After calculating the swing time of right leg coefficients of variation for each participant, we then calculated the mean and SD of the swing time of the right leg coefficients of variation. This explains the data value with the measure of central tendency reported in the data table.

The swing time coefficient of variation of left leg was calculated by first calculating the mean and SD of the swing time of the left leg for each of the participants. The calculation of the coefficient of variations were done by taking the standard deviations and dividing them by the mean. Thus calculation of the swing time of left leg coefficient of variation required multiple measures for each participant. After calculating the swing time of left leg coefficients of variation for each participant, we then calculated the mean and SD of the swing time of the left leg coefficients of variation. This explains the data value with the measure of central tendency reported in the data table.

The stride length coefficient of variation was calculated by first calculating the mean and SD of the stride length for each of the participants. The calculation of the coefficient of variations were done by taking the standard deviations and dividing them by the mean. Thus calculation of the stride length coefficient of variation required multiple measures for each participant. After calculating the stride length coefficients of variation for each participant, we then calculated the mean and SD of the stride length coefficients of variation. This explains the data value with the measure of central tendency reported in the data table.

Indicated on a visual analog scale (line with demarcations of 0-10 spaced evenly and with anchors "Not at all easy" at 0 on the left end, moderately easy at 5, and "Extremely easy" at 10 on the right end). Participants are asked to rate where on line it indicates how easy it felt for them to walk during the six minute walk test.

Questionnaire to measure participants' confidence (I.e., self-efficacy) in performing daily activities. The score for the scale is the mean of the 6 items with a range of possible scores from 1 to 10. Higher number (10) indicates higher self-efficacy.

The Freezing of Gait Questionnaire assesses Freezing of Gait (FOG) severity unrelated to falls in patients with Parkinson's disease (PD), FOG frequency, disturbances in gait, and relationship to clinical features conceptually associated with gait and motor aspects. Six items are scored on 0-4 scale for a total score range of 0-24, with higher scores indicating worse outcomes.

Inclusion Criteria: Diagnosis of idiopathic Parkinson's disease Age 50-80 years Able to ambulate without assistance (Hoehn & Yahr stages 1-3) On stable doses of Parkinson's medications for at least 4 weeks prior to the study. Exclusion Criteria: Presence of other significant cardiac, neurological or orthopedic problems that affect gait Weight more than 220 pounds and height greater than 6'8" Electronic medical devices embedded in the body Participating in any physical therapy Inability to understand instructions required by the study.