The Effects of Physical Activity in Parkinson’s Disease: A Review

Background: Physical activity (PA) is increasingly advocated as an adjunct intervention for individuals with Parkinson’s disease (PD). However, the specific benefits of PA on the wide variety of impairments observed in patients with PD has yet to be clearly identified. Objective: Highlight health parameters that are most likely to improve as a result of PA interventions in patients with PD. Methods: We compiled results obtained from studies examining a PA intervention in patients with PD and who provided statistical analyses of their results. 868 outcome measures were extracted from 106 papers published from 1981 to 2015. The results were classified as having a statistically significant positive effect or no effect. Then, outcome measures were grouped into four main categories and further divided into sub-categories. Results: Our review shows that PA seems most effective in improving Physical capacities and Physical and cognitive functional capacities. On the other hand, PA seems less efficient at improving Clinical symptoms of PD and Psychosocial aspects of life, with only 50% or less of results reporting positive effects. The impact of PA on Cognitive functions and Depression also appears weaker, but few studies have examined these outcomes. Discussion: Our results indicate that PA interventions have a positive impact on physical capacities and functional capacities. However, the effect of PA on symptoms of the disease and psychosocial aspects of life are moderate and show more variability. This review also highlights the need for more research on the effects of PA on cognitive functions, depression as well as specific symptoms of PD

Effectiveness of a Serious Game for Cognitive Training in Chronic Stroke Survivors with Mild-to-Moderate Cognitive Impairment: A Pilot Randomized Controlled Trial

Previous cognitive training games for stroke survivors required the close supervision of therapists. We aim to demonstrate the preliminary therapeutic effectiveness of Neuro-World, serious mobile games for cognitive training, in chronic stroke survivors with mild-to-moderate cognitive impairment without therapist supervision. For that, we conducted a randomized, controlled clinical trial at a single long-term care rehabilitation center with 50 stroke survivors in the chronic stage with mild-to-moderate cognitive impairment. Participants were randomized to standard medical care (n = 25) or standard medical care plus administration of Neuro-World (n = 25) over 12 weeks. A two-way mixed model ANOVA and Tukey’s post hoc tests identified significant differences in outcomes between the experimental and the control groups at post-intervention but not at baseline. Within the experimental group, there were statistically significant improvements in all the outcomes except for the language category of the Mini-Mental State Examination and Digit Forward Span. The improvements were clinically significant for the total Mini-Mental State Examination, Digit Forward Span, and Digit Backward Span. Within the control group, there were no improvements in any of the outcomes. The practice of Neuro-World led to significant improvements in cognitive function and marginal mitigation of depressive symptoms in chronic stroke survivors with mild-to-moderate cognitive impairment

Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants’ decomposed wrist velocity time-series. A machine learning regression model was trained to estimate overall ataxia severity, as measured by the Brief Ataxia Rating Scale (BARS). Classification models were trained to distinguish between ataxia participants and controls and between ataxia and parkinsonism phenotypes. Movement decomposition revealed expected and novel characteristics of the ataxia phenotype. The distance, speed, duration, morphology, and temporal relationships of decomposed movements exhibited strong relationships with disease severity. The regression model estimated BARS with a root mean square error of 3.6 points, r2 = 0.69, and moderate-to-excellent reliability. Classification models distinguished between ataxia participants and controls and ataxia and parkinsonism phenotypes with areas under the receiver-operating curve of 0.96 and 0.89, respectively. Movement decomposition captures core features of ataxia and may be useful for objective, precise, and frequent assessment of ataxia in home and clinic environments

Technology in Parkinson's disease:challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Could Wearable and Mobile Technology Improve the Management of Essential Tremor?

Essential tremor (ET) is the most common movement disorder. Individuals exhibit postural and kinetic tremor that worsens over time and patients may also exhibit other motor and non-motor symptoms. While millions of people are affected by this disorder worldwide, several barriers impede an optimal clinical management of symptoms. In this paper, we discuss the impact of ET on patients and review major issues to the optimal management of ET; from the side-effects and limited efficacy of current medical treatments to the limited number of people who seek treatment for their tremor. Then, we propose seven different areas within which mobile and wearable technology may improve the clinical management of ET and review the current state of research in these areas

Remote Delivery of Yoga Interventions Through Technology: Scoping Review

BackgroundThe popularity of yoga and the understanding of its potential health benefits have recently increased. Unfortunately, not everyone can easily engage in in-person yoga classes. Over the past decade, the use of remotely delivered yoga has increased in real-world applications. However, the state of the related scientific literature is unclear. ObjectiveThis scoping review aimed to identify gaps in the literature related to the remote delivery of yoga interventions, including gaps related to the populations studied, the yoga intervention characteristics (delivery methods and intervention components implemented), the safety and feasibility of the interventions, and the preliminary efficacy of the interventions. MethodsThis scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Item for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Scientific databases were searched throughout April 2021 for experimental studies involving yoga delivered through technology. Eligibility was assessed through abstract and title screening and a subsequent full-article review. The included articles were appraised for quality, and data were extracted from each article. ResultsA total of 12 studies of weak to moderate quality were included. Populations varied in physical and mental health status. Of the 12 studies, 10 (83%) implemented asynchronous delivery methods (via prerecorded material), 1 (8%) implemented synchronous delivery methods (through videoconferencing), and 1 (8%) did not clearly describe the delivery method. Yoga interventions were heterogeneous in style and prescribed dose but primarily included yoga intervention components of postures, breathing, and relaxation and meditation. Owing to the heterogeneous nature of the included studies, conclusive findings regarding the preliminary efficacy of the interventions could not be ascertained. ConclusionsSeveral gaps in the literature were identified. Overall, this review showed that more attention needs to be paid to yoga intervention delivery methods while designing studies and developing interventions. Decisions regarding delivery methods should be justified and not made arbitrarily. Studies of high methodological rigor and robust reporting are needed

Designing and Developing a Mobile App for Management and Treatment of Gestational Diabetes in Nepal: User-Centered Design Study

BackgroundMobile apps can aid with the management of gestational diabetes mellitus (GDM) by providing patient education, reinforcing regular blood glucose monitoring and diet/lifestyle modification, and facilitating clinical and social support. ObjectiveThis study aimed to describe our process of designing and developing a culturally tailored app, Garbhakalin Diabetes athawa Madhumeha—Dhulikhel Hospital (GDM-DH), to support GDM management among Nepalese patients by applying a user-centered design approach. MethodsA multidisciplinary team of experts, as well as health care providers and patients in Dhulikhel Hospital (Dhulikhel, Nepal), contributed to the development of the GDM-DH app. After finalizing the app’s content and features, we created the app’s wireframe, which illustrated the app’s proposed interface, navigation sequences, and features and function. Feedback was solicited on the wireframe via key informant interviews with health care providers (n=5) and a focus group and in-depth interviews with patients with GDM (n=12). Incorporating their input, we built a minimum viable product, which was then user-tested with 18 patients with GDM and further refined to obtain the final version of the GDM-DH app. ResultsParticipants in the focus group and interviews unanimously concurred on the utility and relevance of the proposed mobile app for patients with GDM, offering additional insight into essential modifications and additions to the app’s features and content (eg, inclusion of example meal plans and exercise videos).The mean age of patients in the usability testing (n=18) was 28.8 (SD 3.3) years, with a mean gestational age of 27.2 (SD 3.0) weeks. The mean usability score across the 10 tasks was 3.50 (SD 0.55; maximum score=5 for “very easy”); task completion rates ranged from 55.6% (n=10) to 94.4% (n=17). Findings from the usability testing were reviewed to further optimize the GDM-DH app (eg, improving data visualization). Consistent with social cognitive theory, the final version of the GDM-DH app supports GDM self-management by providing health education and allowing patients to record and self-monitor blood glucose, blood pressure, carbohydrate intake, physical activity, and gestational weight gain. The app uses innovative features to minimize the self-monitoring burden, as well as automatic feedback and data visualization. The app also includes a social network “follow” feature to add friends and family and give them permission to view logged data and a progress summary. Health care providers can use the web-based admin portal of the GDM-DH app to enter/review glucose levels and other clinical measures, track patient progress, and guide treatment and counseling accordingly. ConclusionsTo the best of our knowledge, this is the first mobile health platform for GDM developed for a low-income country and the first one containing a social support feature. A pilot clinical trial is currently underway to explore the clinical utility of the GDM-DH app

Combining dopaminergic facilitation with robot-assisted upper limb therapy in stroke survivors

Despite aggressive conventional therapy, lasting hemiplegia persists in a large percentage of stroke survivors. The aim of this article is to critically review the rationale behind targeting multiple sites along the motor learning network by combining robotic therapy with pharmacotherapy and virtual reality-based reward learning to alleviate upper extremity impairment in stroke survivors. Methods for personalizing pharmacologic facilitation to each individual's unique biology are also reviewed. At the molecular level, treatment with levodopa was shown to induce long-term potentiation-like and practice-dependent plasticity. Clinically, trials combining conventional therapy with levodopa in stroke survivors yielded statistically significant but clinically unconvincing outcomes because of limited personalization, standardization, and reproducibility. Robotic therapy can induce neuroplasticity by delivering intensive, reproducible, and functionally meaningful interventions that are objective enough for the rigors of research. Robotic therapy also provides an apt platform for virtual reality, which boosts learning by engaging reward circuits. The future of stroke rehabilitation should target distinct molecular, synaptic, and cortical sites through personalized multimodal treatments to maximize motor recovery.Supported, in part, by grant Engineering for Neurologic Rehabilitation, NIH-NICHD, grant R24HD050821 and by grant entitled Improving Outcome Measurement for Medical Rehabilitation Clinical Trials, NIH-NICHD, grant R24HD065688