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The study examined how clinically measured walking capacity contributes to real-world walking performance in persons with Parkinson's disease (PD).Cross-sectional baseline data (n = 82) from a PD clinical trial were analyzed. The 6-Minute Walk Test (6MWT) and 10-Meter Walk Test (10MWT) were used to generate capacity metrics of walking endurance and fast gait speed, respectively. An activity monitor worn for seven days was used to generate performance metrics of mean daily steps and weekly moderate intensity walking minutes. Univariate linear regression analyses were used to examine associations between each capacity and performance measure in the full sample and less and more active subgroups.Walking capacity significantly contributed to daily steps in the full sample (endurance: RWalking capacity contributed to, but explained a relatively small portion of the variance in, real-world walking performance. The contribution was somewhat greater in less active individuals. The study adds support to the idea that clinically measured walking capacity may have limited benefit for understanding real-world walking performance in PD. Factors beyond walking capacity may better account for actual walking behavior.

Digital therapeutics, treatments delivered remotely and enabled by modern technology, facilitate the provision of personalized, evidence-based, interdisciplinary interventions to manage the complexities associated with Parkinson’s disease. In the context of the COVID-19 pandemic, the need for digital therapeutics has arguably never been greater. However, despite new advances in technology and a heightened interest due to the pandemic, digital therapeutics remain underdeveloped and underutilized. In this paper, we briefly review practical applications and emerging advances in digital therapeutic platforms that target motor and non-motor signs and healthy lifestyle behaviors such as regular exercise, a healthful diet and optimal sleep hygiene habits. Future applications which could transform personalized self-management and patient care are presented. Opportunities, drawbacks and barriers to access are discussed.

BACKGROUND AND PURPOSE Walking activity in persons with Parkinson disease (PD) is important for preventing functional decline. The contribution of walking activity to home and community mobility in PD is poorly understood. METHODS Cross-sectional baseline data (N = 69) were analyzed from a randomized controlled PD trial. The Life-Space Assessment (LSA) quantified the extent, frequency, and independence across 5 expanding levels of home and community mobility, producing individual subscores and a total score. Two additional summed scores were used to represent mobility within (Levels 1-3) and beyond (Levels 4-5) neighborhood limits. An accelerometer measured walking activity for 7 days. Regression and correlation analyses evaluated relationships between daily steps and mobility scores. Mann-Whitney U tests secondarily compared differences in mobility scores between the active and sedentary groups. RESULTS Walking activity contributed significantly to the summed Level 1-3 score (β = 0.001, P = 0.004) but not to the summed Level 4-5 (β = 0.001, P = 0.33) or total (β = 0.002, P = 0.07) scores. Walking activity was significantly related to Level 1 (ρ = 0.336, P = 0.005), Level 2 (ρ = 0.307, P = 0.010), and Level 3 (ρ = 0.314, P = 0.009) subscores. Only the summed Level 1-3 score (P = 0.030) was significantly different between the active and sedentary groups. DISCUSSION AND CONCLUSIONS Persons with PD who demonstrated greater mobility beyond the neighborhood were not necessarily more active; walking activity contributed more so to home and neighborhood mobility. Compared with LSA total score, the Level 1-3 summed score may be a more useful participation-level measure for assessing the impact of changes in walking activity.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A349).

Background The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions. Objective We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms. Methods A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user’s ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated. Results A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P = .011) and fast (∆ 0.093 ± 0.032 m/s, P = .016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence ( R2 > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home. Conclusions In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.

In individuals with motor impairments such as those post-stroke or with cerebral palsy, the function of the knee extensors may be affected during walking, resulting in decreased mobility. We have designed a lightweight, hinge-free wearable robot combining soft textile exosuit components with integrated rigid components, which assists knee extension when needed but is otherwise highly transparent to the wearer. The exosuit can apply a wide range of assistance profiles using a flexible multi-point reference trajectory generator. Additionally, we implemented a controller safety limit to address the risk of hyperextension stemming from the hinge-free design. The exosuit was evaluated on six healthy participants walking uphill and downhill on a treadmill at a 10° slope with a set of joint power-inspired assistance profiles. A comparison of sagittal plane joint angles between no exosuit and exosuit unpowered conditions validated the device transparency. With positive power assistance, we observed reduction in average positive knee biological power during uphill walking (left: 17.5±3.21%, $p=0.005$ ; right: 23.2±3.54%, ${p} =0.008$ ). These initial findings show promise for the assistive capability of the device and its potential to improve the quality of gait and increase mobility in clinical populations.

There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (R = 0.94, P = 3.6 × 10–25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Objective Academic physical therapy has no universal metrics by which educational programs can measure outcomes, limiting their ability to benchmark to their own historical performance, to peer institutions, or to other health care professions. The PT-Graduation Questionnaire (GQ) survey, adapted from the Association of American Medical Colleges’ GQ, addresses this gap by offering both inter-professional insight and fine-scale assessment of physical therapist education. This study reports the first wave of findings from an ongoing multi-site trial of the PT-GQ among diverse academic physical therapy programs, including (1) benchmarks for academic physical therapy, and (2) a comparison of the physical therapist student experience to medical education benchmarks. Methods Thirty-four doctor of physical therapy (DPT) programs (13.2% nationwide sample) administered the online survey to DPT graduates during the 2019 to 2020 academic year. PT-GQ and Association of American Medical Colleges data were contrasted via Welch’s unequal-variance t test and Hedges g (effect size). Results A total of 1025 respondents participated in the study (response rate: 63.9%). The average survey duration was 31.8 minutes. Overall educational satisfaction was comparable with medicine, and respondents identified areas of curricular strength (eg, anatomy) and weakness (eg, pharmacology). DPT respondents provided higher ratings of faculty professionalism than medicine, lower rates of student mistreatment, and a lesser impact of within-program diversity on their training. One-third of respondents were less than “satisfied” with student mental health services. DPT respondents reported significantly higher exhaustion but lower disengagement than medical students, along with lower tolerance for ambiguity. Of DPT respondents who reported educational debt, one-third reported debt exceeding $150,000, the threshold above which the DPT degree loses economic power. Conclusions These academic benchmarks, using the PT-GQ, provided insight into physical therapist education and identified differences between physical therapist and medical student perceptions. Impact This ongoing trial will establish a comprehensive set of benchmarks to better understand academic physical therapy outcomes.

A clinical practice guideline on Parkinson disease was developed by an American Physical Therapy Association volunteer guideline development group that consisted of physical therapists and a neurologist. The guideline was based on systematic reviews of current scientific and clinical information and accepted approaches for management of Parkinson disease. The Spanish version of this clinical practice guideline is available as a supplement (Suppl. Appendix 1).

Objective To evaluate the proof of concept of an innovative model of physical therapy Rehabilitation Enhancing Aging through Connected Health (REACH) and evaluated its feasibility and effect on physical function and health care utilization. Design Quasi-experimental 12-month clinical trial. Setting Two outpatient rehabilitation centers. Participants Community-dwelling older primary care patients with a treatment arm undergoing the intervention (n=75; mean age=77±5.9y; 54% women) and propensity matched controls derived from a longitudinal cohort study (n=430; mean age=71±7.0y; 68% women) using identical recruitment criteria (N=505). Intervention Combined outpatient and home PT augmented with a commercially available app and computer tablet. Measurements Primary outcomes included a feasibility questionnaire, exercise adherence, self-reported function, and the Short Physical Performance Battery (SPPB). Secondary outcomes included the rates of emergency department (ED) visits and hospitalizations. Results Among REACH participants, we observed a 9% dropout rate. After accounting for dropouts, with propensity matching, n=68 treatments and n=100 controls were analyzed. Over the 12-month study duration, 85% of participants adhered to the exercise program an average of 2 times a week and evaluated the treatment experience favorably. In comparison to controls, after 1 year of treatment and within multivariable regression models, REACH participants did not manifest a significant difference in patient reported function (group x time effect 1.67 units, P=.10) but did manifest significant differences in SPPB (group x time effect 0.69 units, P=.03) and gait speed (group x time effect .08m/s, P=.02). In comparison to controls, after 1 year, the rate of ED visits (group x time treatment rate=0.27, P Conclusion The REACH intervention is feasible and has proof of concept in preventing functional decline and favorably affecting health care utilization. Evaluation on a larger scale is warranted.

Summary Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.

Stroke is a leading medical issue that can impact the person’s ability to walk effectively. There is limited research into the design and biomechanical response for exosuits that assist more proximal joints such as the hip. For this case series, three subjects with chronic stroke participated in a single-session study to evaluate hip flexion exosuit assistance in overground walking. We iteratively tuned the unilateral hip flexion assistance profiles in overground gait. Compared to the initial unpowered baseline, walking speed at the end of tuning and with the device powered increased for 2 of 3 individuals and on average by 0.16 m s\(^{-1}\). We then compared powered versus unpowered overground walking during a 5 min evaluation period. Circumduction was reduced by 9 \(\pm 1\) mm and cost of transport was reduced by 8.6 \(\pm 1.7\)% for the 3 participants.

Background: Soft robotic exosuits can facilitate immediate increases in short- and long-distance walking speeds in people with post-stroke hemiparesis. We sought to assess the feasibility and rehabilitative potential of applying propulsion-augmenting exosuits as part of an individualized and progressive training program to retrain faster walking and the underlying propulsive strategy.Methods: A 54-yr old male with chronic hemiparesis completed five daily sessions of Robotic Exosuit Augmented Locomotion (REAL) gait training. REAL training consists of high-intensity, task-specific, and progressively challenging walking practice augmented by a soft robotic exosuit and is designed to facilitate faster walking by way of increased paretic propulsion. Repeated baseline assessments of comfortable walking speed over a 2-year period provided a stable baseline from which the effects of REAL training could be elucidated. Additional outcomes included paretic propulsion, maximum walking speed, and 6-minute walk test distance.Results: Comfortable walking speed was stable at 0.96 m/s prior to training and increased by 0.30 m/s after training. Clinically meaningful increases in maximum walking speed (Δ: 0.30 m/s) and 6-minute walk test distance (Δ: 59 m) were similarly observed. Improvements in paretic peak propulsion (Δ: 2.80 %BW), propulsive power (Δ: 0.41 W/kg), and trailing limb angle (Δ: 6.2 degrees) were observed at comfortable walking speed (p's < 0.05). Likewise, improvements in paretic peak propulsion (Δ: 4.63 %BW) and trailing limb angle (Δ: 4.30 degrees) were observed at maximum walking speed (p's < 0.05).Conclusions: The REAL training program is feasible to implement after stroke and capable of facilitating rapid and meaningful improvements in paretic propulsion, walking speed, and walking distance.

Human locomotor control ranges on a spectrum of automaticity, from highly automatic strategies that require minimal cognitive input, to attention-demanding executive-control strategies. The neural circuitry that facilitates automaticity is impaired by stroke, resulting in a compensatory shift toward executive-control, as well as reduced paretic propulsion and increased step-to-step variability. We have developed a soft robotic exosuit to augment paretic propulsion by providing paretic plantarflexor assistance during the propulsive phase of walking. For this preliminary study, we hypothesized that changes in walking automaticity would accompany changes in paretic propulsion. When plantarflexor assistance timings were tuned to reduce propulsion variability-a biomechanical measure of automaticity-a -14.7±2.5% variability reduction was accompanied by increased paretic propulsion (%Δ+6.4±6.3%) and prefrontal cortex activity (A oxygenated hemoglobin:+ 1.08E-04±1.05E-04 M mm). When plantarflexor assistance timings were instead tuned to reduce prefrontal cortex activity-a neurophysiological measure of automaticity-a -1.3E-05±1.1E-05 M mm decrease in oxygenated hemoglobin was accompanied by both increased paretic propulsion (%Δ:+4.4±8.1 %) and reduced propulsion variability (%Δ:-3.7±19.3%). Biomechanical and neurophysiological measures of automaticity are sensitive to exosuit assistance timing changes, but are differentially affected, highlighting the need for individualized tuning.

Advances in medical management of Parkinson's disease (PD) have resulted in living longer with disability. Although disability worsens over the course of the disease, there are signs of disability even in the early stages. Several studies reveal an early decline in gait and balance and a high prevalence of nonmotor signs in the prodromal period that contribute to early disability. There is a growing body of evidence revealing the benefits of physical therapy and exercise to mitigate motor and nonmotor signs while improving physical function and reducing disability. The presence of early disability coupled with the benefits of exercise suggests that physical therapy should be initiated earlier in the disease. In this review, we present the evidence revealing early disability in PD and the effectiveness of physical therapy and exercise, followed by a discussion of a secondary prevention model of rehabilitation to reduce early disability and optimize long-term outcomes.

Background: People with Parkinson’s disease (PwPD) are less active than their age-matched peers. Non-motor symptoms, specifically, deficient motivation, may influence decision-making for exercise due to the impaired mesolimbic dopaminergic pathway. Objective: The purpose of this study was to determine if effort-based decision-making for physical effort was different in PwPD compared to healthy controls. We sought to determine the relationship between effort-based decision making for exercise and a discrete motor task as well as the impact of components of motivation on decision-making for physical effort in PwPD. Methods: An effort-based decision-making paradigm using a discrete motor task (button pressing) and a continuous exercise task (cycling) was implemented in 32 PwPD and 23 healthy controls. Components of motivation were measured using the Apathy Scale and the Temporal Experience of Pleasure Scale- Anticipatory Pleasure scale. Results: The presence of Parkinson’s disease (PD) did not moderate decisions for either physical effort task. There was a moderate correlation between decisions for both tasks, within each group. The anticipation of pleasure and apathy were predictors of decisions for both physical effort tasks in PwPD, but not in healthy controls. Conclusion: PwPD responded similarly to effort and reward valuations compared to those without PD. Individuals were consistent in their decisions, regardless of the physical effort task. The anticipation of pleasure and apathy were significant predictors of decisions for exercise in PwPD only. Increased anticipation of pleasure, reduction of apathy, and the use of rewards may enhance engagement in high effort exercise among PwPD.

Background Many people with Parkinson disease do not have access to exercise programs that are specifically tailored to their needs and capabilities. This mobile app allows people with Parkinson disease to access Parkinson disease–specific exercises that are individually tailored using in-app demographic questions and performance tests which are fed into an algorithm which in turn produces a video-guided exercise program. Objective To test the feasibility, safety, and signal of efficacy of a mobile app that facilitates exercise for people with Parkinson disease. Methods A prospective, single-cohort design of people with Parkinson disease who had downloaded the 9zest app for exercise was used for this 12-week pilot study. Participants, who were recruited online, were encouraged to exercise with the full automated app for ≥150 minutes each week. The primary endpoints were feasibility (app usage and usability questions) and safety (adverse events and falls). The primary endpoints for signal of efficacy were a comparison of the in-app baseline and 8-week outcomes on the 30-second Sit-To-Stand (STS) test, Timed Up and Go (TUG) test, and the Parkinson’s Disease Questionnaire 8 (PDQ8). Results For feasibility, of the 28 participants that completed the study, 12 participants averaged >150 minutes of app usage per week (3 averaged 120-150, 4 averaged 90-120, and 9 averaged less than 90 minutes). A majority of participants (>74%) felt the exercise was of value (16/19; 9 nonrespondents), provided adequate instruction (14/19; 9 nonrespondents), and was appropriate for level of function (16/19; 9 nonrespondents). For safety, there were no serious adverse events that occurred during the app-guided exercise. There were 4 reports of strain/sprain injuries while using the app among 3 participants, none of which necessitated medical attention. For signal of efficacy, there was improvement for each of the primary endpoints: STS (P=.01), TUG (P Conclusions Independent, video-guided exercise using a mobile app designed for exercise in Parkinson disease was safe and feasible though there was variability in app usage. Despite this, the results provide evidence for a signal of efficacy as there were improvements in 3 of the 3 outcomes. Trial Registration ClinicalTrials.gov NCT03459586; https://clinicaltrials.gov/ct2/show/NCT03459586

Background and purpose Improvement of walking performance is a primary goal for individuals poststroke or with Parkinson disease (PD) who receive physical therapy. More data about day-to-day variability of walking performance are critical for determining if changes in performance have occurred. Methods Baseline assessments were utilized from an ongoing, observational, prospective cohort study including 84 individuals poststroke (n = 37) or with PD (n = 47) receiving outpatient physical therapy services to improve mobility. Participants wore step activity monitors for up to 7 days to measure walking performance (steps per day, walking duration, maximum 30-minute output, and peak activity index) in daily life. Correlation analyses evaluated relationships between both capacity and performance measures as well as the relationships between mean performance variables and day-to-day variability. Regression analyses explored factors that contribute to variability in day-to-day performance variables. Results Mean steps per day for participants poststroke (5376 ± 2804) and with PD (8149 ± 4490) were consistent with previously reported cohorts. Greater amounts of walking were related to more day-to-day variability, with moderate correlations found between the mean and day-to-day variability of each performance measure, regardless of medical diagnosis or walking speed. Day-to-day variability is large (upwards of 50% of the mean), with the amount of walking performance serving as the primary predictor of day-to-day variability in walking performance. Discussion and conclusions The results of this study elucidate the factors that are related to and predict day-to-day variability of performance. Walking performance metrics should be evaluated over multiple days and greater variability should be anticipated with greater amounts of performance.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A319).

Background Declining physical activity commonly occurs in people with Parkinson disease (PD) and contributes to reduced functional capacity and quality of life. Objective The purpose of this study was to explore the preliminary effectiveness, safety, and acceptability of a mobile health (mHealth)–mediated exercise program designed to promote sustained physical activity in people with PD. Design This was a 12-month single-blind (assessor), pilot, comparative-effectiveness, randomized controlled study. Methods An mHealth-mediated exercise program (walking with a pedometer plus engagement in planned exercise supported by a mobile health application) was compared over 1 year with an active control condition (walking with a pedometer and exercise only). There were 51 participants in a community setting with mild-to-moderately severe (Hoehn and Yahr stages 1–3) idiopathic PD. Daily steps and moderate-intensity minutes were measured using a step activity monitor for 1 week at baseline and again at 12 months. Secondary outcomes included the 6-Minute Walk Test, Parkinson Disease Questionnaire 39 mobility domain, safety, acceptability, and adherence. Results Both groups increased daily steps, moderate-intensity minutes, and 6-Minute Walk Test, with no statistically significant between-group differences observed. In the less active subgroup, changes in daily steps and moderate-intensity minutes were clinically meaningful. An improvement in the Parkinson Disease Questionnaire 39 mobility score favored mHealth in the overall comparison and was statistically and clinically meaningful in the less active subgroup. Limitations The limitation of the current study was the small sample size. Conclusions Both groups improved physical activity compared with expected activity decline over 1 year. The addition of the mHealth app to the exercise intervention appeared to differentially benefit the more sedentary participants. Further study in a larger group of people with low activity at baseline is needed.

Exercise is increasingly recognized as an important element in the treatment of Parkinson’s disease but what is exercise targeting? What accounts for the benefits observed in Parkinson’s disease? Is exercise disease modifying? Several modes of exercise have been studied in various doses across a heterogeneous Parkinson’s population. Yet more clarity is needed as to who benefits most and when, from what type of exercise and at which intensity. In this paper, we briefly review the state of the art in key areas and speculate on the likely state of research in each area in the next 20 years. Key areas relate to: (1) the physiological benefits of exercise with respect to disease modification; (2) the best type of exercise; (3) the optimal intensity of exercise; and (4) implementation strategies to increase exercise uptake. A better understanding of these concepts would allow for a more effective, personalized approach, rather than the current “one size fits all” and could most likely confer greater benefits.

Background Wearable, consumer-grade activity trackers have become widely available as a means of monitoring physical activity in the form of step counts. However, step counts may not be accurate in persons with Parkinson disease (PD) due to atypical gait characteristics. Objective This study aimed to investigate the accuracy of 4 consumer-grade activity trackers in individuals with PD while ambulating during continuous and discontinuous walking tasks. Design This study used a cross-sectional design. Methods Thirty-three persons with PD (Hoehn & Yahr stages 1–3) donned 4 models of activity trackers on the less affected side of their bodies. Participants performed 2 continuous walking tasks (2-minute walk tests at comfortable and fast speeds) and 2 discontinuous walking tasks (a simulated household course and an obstacle negotiation course) in an outpatient setting. Bland-Altman plots and intraclass correlation coefficients [ICC(2,1)] were computed as a measure of agreement between actual steps taken (reference standard: video recording) and steps recorded by each tracker. Results The accuracy of the activity trackers varied widely, with ICCs ranging from −0.03 to 0.98. Overall, the most accurate device across all tasks was the Fitbit Zip, and the least accurate was the Jawbone Up Move during the simulated household course. All activity trackers were more accurate for continuous walking tasks compared with discontinuous walking tasks. Waist-mounted devices were more accurate than wrist-mounted devices with continuous tasks. Bland-Altman plots revealed that all activity trackers underestimated step counts. Limitations All walking tasks were measured over relatively short distances. Conclusions In persons with mild-to-moderate PD, waist-worn activity trackers may be prescribed to monitor bouts of continuous walking with reasonable accuracy; however, activity trackers have little utility in monitoring discontinuous walking common in household settings.

OBJECTIVE: To evaluate the effects on common poststroke gait compensations of a soft wearable robot (exosuit) designed to assist the paretic limb during hemiparetic walking. DESIGN: A single-session cohort study of eight individuals in the chronic phase of stroke recovery was conducted. Two testing conditions were compared: walking with the exosuit powered versus walking with the exosuit unpowered. Each condition was eight minutes in duration. RESULTS: Compared to walking with the exosuit unpowered, walking with the exosuit powered resulted in reductions in hip hiking (27±6%, P= 0.004) and circumduction (20±5%, P= 0.004). A relationship between changes in knee flexion and changes in hip hiking was observed (Pearson r = −0.913, P< 0.001). Similarly, multivariate regression revealed that changes in knee flexion (β= −0.912, P= 0.007), but not ankle dorsiflexion (β= −0.194, P= 0.341), independently predicted changes in hip hiking (R(2)= 0.87, F(2, 4)= 13.48, P= 0.017). CONCLUSIONS: Exosuit assistance of the paretic limb during walking is able to produce immediate changes in the kinematic strategy used to advance the paretic limb. Future work is necessary to determine how exosuit-induced reductions in paretic hip hiking and circumduction during gait training could be leveraged to facilitate more normal walking behavior during unassisted walking.

We investigated the relationships between average gait speed collected with the 10 Meter Walk Test (Comfortable and Fast) and 6 Minute Walk Test (6MWT) in 346 people with Parkinson disease (PD) and how the relationships change with increasing disease severity. Pearson correlation and linear regression analyses determined relationships between 10 Meter Walk Test and 6MWT gait speed values for the entire sample and for sub-samples stratified by Hoehn & Yahr (H&Y) stage I (n=53), II (n=141), III (n=135) and IV (n=17). We hypothesized that redundant tests would be highly and significantly correlated (i.e. r > 0.70, p < 0.05) and would have a linear regression model slope of 1 and intercept of 0. For the entire sample, 6MWT gait speed was significantly (p

Exercise is increasingly being recognized as a key element in the overall management of persons living with Parkinson's disease (PD) but various (disease-specific) barriers may impede even motivated patients to participate in regular exercise. We aimed to provide a comprehensive review of the various barriers and motivators for exercise in persons with PD. We scrutinized data on compliance-related factors published in cross-sectional studies, randomized controlled trials and reviews. We classified the barriers and motivators to exercise from a patient perspective according to the International Classification of Functioning, Disability and Health. We present an overview of the large range of potential motivators and barriers for exercise in persons with PD. Healthcare professionals should consider a wide and comprehensive range of factors, in order to identify which specific determinants matter most for each individual. Only when persons with PD are adequately motivated in a way that appeals to them and after all person-specific barriers have been tackled, we can begin to expect their long-term adherence to exercise. Such long-term compliance will be essential if exercise is to live up to its expectations, including the hope that prolonged engagement in regular exercise might help to modify the otherwise relentlessly progressive course of PD. ispartof: JOURNAL OF PARKINSONS DISEASE vol:10 issue:4 pages:1293-1299 ispartof: location:Netherlands status: published

Locomotor impairments afflict more than 80% of people poststroke. Our group has previously developed a unilateral ankle exosuit aimed at assisting the paretic ankle joint of stroke survivors during walking. While studies to date have shown promising biomechanical and physiological changes, there remains opportunity to better understand how changes in plantarflexion (PF) assistance profiles impact wearer response. In healthy populations, studies explicitly varying augmentation power have been informative about how exosuit users are sensitive to changes in PF assistance; however there are challenges in applying existing methods to a medical population where significantly higher gait variability and limited walking capacity exist. This letter details an offline assistance optimization scheme that uses pre-recorded biomechanics data to generate torque profiles designed to deliver either positive or negative augmentation power in PF while being less sensitive to stride-by-stride variability. Additionally, we describe an admittance-control strategy that can effectively deliver PF force with RMS error less than 10 N. A preliminary study on six people poststroke demonstrates that offline assistance optimization can successfully isolate positive and negative augmentation power. Moreover, we show that in people poststroke, positive augmentation power effected changes in total positive ankle power while delivering negative augmentation power had no effect on total negative ankle power.

In this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson's disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to en-sure correct execution of the required exercises. Our re-sults also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its "active range of motion."

Home-based exercising is a vital part of any physical therapy program. With correct execution of the exercises, faster recovery from physical impairments can be achieved. With the conventional approaches, however, a home-based physical therapy program may not be as effective due to the lack of supervision by the therapist at home. ExerciseCheck is designed as a remote monitoring and evaluation platform for individuals involved in home-based physical therapy. The goal of ExerciseCheck is to provide patients and physical therapists with real-time visual feedback and quantitative analysis. In this paper, we discuss the progress we have made toward (1) a more comprehensive analysis of the performance of the patient and (2) informative feedback to the patient and the physical therapist. Furthermore, in order to validate the feasibility and effectiveness of our system, we have performed an experiment in a clinical setting involving five patients with Parkinson disease. We describe how ExerciseCheck can quantitatively evaluate the user's performance and identify their problems. We assessed how ExerciseCheck can impact the performance and the overall experience of the patient. Our results show that ExerciseCheck is a user-friendly system that patients and physical therapists can easily interact with. Patients benefit from visual and quantitative feedback and apply this to subsequent repetitions of the exercises.

ExerciseCheck is a scalable, accessible platform designed and developed for the remote monitoring and evaluation of physical therapy. Physical rehabilitation is an important aspect of a patient's recovery from injury and often requires the patient to perform a prescribed set of exercises over medium- to long-term periods. In the absence of a physical therapist, using a low-cost, non-intrusive solution can serve as a complement to in-clinic sessions and provide patients with valuable insights into their exercises. We present the design, implementation, and deployment of ExerciseCheck, a modular system that incorporates machine learning techniques with contemporary web technologies to enable a user-friendly experience for patients and physical therapists. Initially a proof-of-concept, the latest version of ExerciseCheck is now deployed as a hybrid desktop and web application at a Boston University rehabilitation clinic and has been employed by physical therapists in their sessions with individuals with Parkinson's disease. We provide insights into the usability requirements, architecture design, and implementation challenges of the development and deployment of a production-quality platform for remote physical therapy in a clinical setting.

Functional Near-Infrared Spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional Magnetic Resonance Imaging (fMRI), expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and Electroencephalography (EEG) for routine, increasingly unconstrained, and mobile brain imaging is leading towards a new domain that we term “Neuroscience of the Everyday World” (NEW). In this light, we review recent advances in hardware, study design and signal processing, and discuss challenges and future directions towards achieving NEW.