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

BACKGROUND

Mobile 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.

OBJECTIVE

This 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.

METHODS

A 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.

RESULTS

Participants 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.

CONCLUSIONS

To 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.

Reliability and validity of a Global Physical Activity Questionnaire adapted for use among pregnant women in Nepal

BACKGROUND

Physical activity (PA) plays an important role in optimizing health outcomes throughout pregnancy. In many low-income countries, including Nepal, data on the associations between PA and pregnancy outcomes are scarce, likely due to the lack of validated questionnaires for assessing PA in this population. Here we aimed to evaluate the reliability and validity of an adapted version of Global Physical Activity Questionnaire (GPAQ) among a sample of pregnant women in Nepal.

METHODS

A cohort of pregnant women (N=101; age 25.9±4.1 years) was recruited from a tertiary, peri-urban hospital in Nepal. An adapted Nepali version of GPAQ was administered to gather information about sedentary behavior (SB) as well as moderate and vigorous PA across work/domestic tasks, travel (walking/bicycling), and recreational activities, and was administered twice and a month apart in both the 2^nd and 3^rd trimesters. Responses on GPAQ were used to determine SB (min/day) and total moderate to vigorous PA (MVPA; min/week) across all domains. GPAQ was validated against PA data collected by a triaxial accelerometer (Axivity AX3; UK) worn by a subset of the subjects (n=21) for seven consecutive days in the 2^nd trimester. Intra-class correlation coefficients (ICC) and Spearman's rho were used to assess the reliability and validity of GPAQ.

RESULTS

Almost all of the PA in the sample was attributed to moderate activity during work/domestic tasks or travel. On average, total MVPA was higher by 50 minutes/week in the 2^nd trimester as compared to the 3^rd trimester. Based on the World Health Organization (WHO) guidelines, almost all of the participants were classified as having a low or moderate level of PA. PA scores for all domains showed moderate to good reliability across both the 2^nd and 3^rd trimesters, with ICCs ranging from 0.45 (95%CI: (0.17, 0.64)) for travel PA at 2^nd trimester to 0.69 (95%CI: (0.51, 0.80)) for travel PA at 3^rd trimester. Reliability for total MVPA was higher in the 3^rd trimester compared to 2^nd trimester [ICCs 0.62 (0.40, 0.75) vs. 0.55 (0.32, 0.70)], whereas the opposite was true for SB [ICCs 0.48 (0.19, 0.67) vs. 0.64 (0.46, 0.76)]. There was moderate agreement between the GPAQ and accelerometer for total MVPA (rho = 0.42; p value <0.05) while the agreement between the two was poor for SB (rho= 0.28; p value >0.05).

CONCLUSIONS

The modified GPAQ appears to be a reliable and valid tool for assessing moderate PA, but not SB, among pregnant women in Nepal.

A Kinematic Data-Driven Approach to Differentiate Involuntary Choreic Movements in Individuals With Neurological Conditions

OBJECTIVE

The ability to differentiate similar choreic involuntary movements could lay the groundwork for the development of a minimally-invasive screening tool for their etiology and provide in-depth understandings of pathophysiology. As a first step, we investigate kinematic differences between Huntington's disease (HD) chorea and Parkinson's disease (PD) choreic levodopa-induced dyskinesia (LID), which have distinct pathological causes yet share a great kinematic resemblance.

METHODS

Twenty subjects with HD and ten subjects with PD stood with both upper limbs in front of them for approximately 60 seconds. The three-dimensional velocity time-series of involuntary movements of both hands were segmented into one-dimensional sub-movements abutted by velocity zero-crossings. A combination of unsupervised and supervised machine learning algorithms was employed to automatically select data features extracted from sub-movements and distinguish the two types of involuntary choreic movements.

RESULTS

The trained model was able to accurately classify chorea vs. LID with an Area Under the Receiver Operating Characteristic Curve of 99.5%. A set of important features contributing to the construction of the classification model were identified and investigated.

CONCLUSION

The trained model may serve as a tool for the automatic identification of different types of involuntary choreic movements, enabling continuous monitoring and personalized treatment for patients in various clinical settings.

SIGNIFICANCE

The results provide insights into kinematic characteristics of HD chorea and PD LID, which is the first step towards an improved general understanding of involuntary choreic movements.

Remote Delivery of Yoga Interventions Through Technology: Scoping Review

BACKGROUND

The 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.

OBJECTIVE

This 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.

METHODS

This 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.

RESULTS

A 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.

CONCLUSIONS

Several 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.

Analysis of Gait Sub-Movements to Estimate Ataxia Severity using Ankle Inertial Data

Objective: Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia. Methods: A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features. Results: Estimated total BARS showed strong agreement with clinician-evaluated scores with r² = 0.72 and a root mean square error of 2.6 BARS points. Movement element-based models significantly outperformed conventional, spatiotemporal gait feature-based models. Conclusion: The proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task. Significance: Our work could support fine-grained monitoring of disease progression and patients' responses to medical/clinical interventions.

Tele-yoga for the management of Parkinson disease: A safety and feasibility trial

Objectives

Despite current standard treatments, persons with Parkinson disease (PD) still experience motor and non-motor symptoms that impact daily function and quality of life, warranting the investigation of additional interventions. Holistic complementary interventions such as yoga have been shown to be beneficial for persons with PD. However, there are multiple barriers to in-person interventions such as transportation difficulties and disease-related mobility impairments which may be mitigated by digital health applications. Therefore, this study’s purpose was to assess the safety and feasibility of a synchronous tele-yoga intervention for persons with PD.

Methods

Sixteen participants were enrolled in a single group safety and feasibility trial. The entire study was conducted remotely and consisted of a baseline assessment followed by a six-week waiting period, then a second assessment, a six-week tele-yoga intervention period, a post-intervention assessment, a six-week follow-up period, and lastly a follow-up assessment. During the tele-yoga period, participants completed two one-on-one 30-minute tele-yoga sessions weekly for a total of 12 sessions. Primary outcomes included adverse events, adherence, technological challenges, and usability. Secondary outcomes included enjoyment and clinically relevant outcome measures assessing both motor and non-motor symptoms.

Results

No severe adverse events were attributed to the intervention. Retention was 87.5%, assessment session adherence was 100%, and intervention session adherence was 97%. Technological challenges did not impact feasibility. The intervention was usable and enjoyable. While this study was not powered or designed to assess the efficacy of the intervention, preliminary improvements were shown for some of the clinically relevant outcome measures.

Conclusions

Overall, this study showed that the implementation of a synchronous one-on-one tele-yoga intervention was safe, feasible, usable, and enjoyable for persons with PD. Randomized control trials investigating its efficacy should be initiated. The study was registered with ClinicalTrials.gov (NCT04240899, https://clinicaltrials.gov/ct2/show/NCT04240899).

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).

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.

Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository.

A Simple Low-Cost Wearable Sensor for Long-Term Ambulatory Monitoring of Knee Joint Kinematics

OBJECTIVE

Accurate monitoring of joint kinematics in individuals with neuromuscular and musculoskeletal disorders within ambulatory settings could provide important information about changes in disease status and the effectiveness of rehabilitation programs and/or pharmacological treatments. This paper introduces a reliable, power efficient, and low-cost wearable system designed for the long-term monitoring of joint kinematics in ambulatory settings.

METHODS

Seventeen healthy subjects wore a retractable string sensor, fixed to two anchor points on the opposing segments of the knee joint, while walking at three different self-selected speeds. Joint angles were estimated from calibrated sensor values and their derivatives in a leave-one-subject-out cross validation manner using a random forest algorithm.

RESULTS

The proposed system estimated knee flexion/extension angles with a root mean square error (RMSE) of 5.0°±1.0° across the study subjects upon removal of a single outlier subject. The outlier was likely a result of sensor miscalibration.

CONCLUSION

The proposed wearable device can accurately estimate knee flexion/extension angles during locomotion at various walking speeds.

SIGNIFICANCE

We believe that our novel wearable technology has great potential to enable joint kinematic monitoring in ambulatory settings and thus provide clinicians with an opportunity to closely monitor joint recovery, develop optimal, personalized rehabilitation programs, and ultimately maximize therapeutic outcomes.

Comparing different methods of gait speed estimation using wearable sensors in individuals with varying levels of mobility impairments

Wearable sensors, such as inertial measurement units (IMU), provide the ability to quantify gait parameters outside of traditional gait laboratory settings. Walking speed has been shown to be associated with morbidity and mortality. Therefore, the ability of a clinician to easily and inexpensively measure gait speed within their clinic or patients' home setting can improve patient management and care. This study highlights multiple methods used to estimate patient walking speeds based only on IMU data and minimal anthropometric data, and identifies the algorithm appearing to be the most robust; one relying on identifying swing phases of gait first.Clinical relevance- Providing a clinician with a simple, inexpensive and reliable protocol for measuring patients' gait speed and other parameters could offer prevention and individualized care.

Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor

Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. We present a novel method to seamlessly and unobtrusively measure the quality of individual reaching movements by leveraging a motor control theory that describes how the central nervous system plans and executes movements. We trained and evaluated our system on 19 stroke survivors to estimate the Functional Ability Scale (FAS) of reaching movements. The analysis showed that we can estimate the FAS scores of reaching movements, with some confusion between adjacent scores. Furthermore, we estimated the average FAS scores of subjects with a normalized root mean square error (NRMSE) of 22.5%. Though our model's high error on two severe subjects influenced our overall estimation performance, we could accurately estimate scores in most of the mild-to-moderate subjects (NRMSE of 13.1% without the outliers). With further development and testing, we believe the proposed technique can be applied to monitor patient recovery in home and community settings.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease

Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.

Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task

Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% ( r²=0.70 ) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.

Yoga as an Intervention for the Reduction of Symptoms of Anxiety and Depression in Children and Adolescents: A Systematic Review

Purpose: The purpose of this review is to evaluate the implementation and effectiveness of yoga for the reduction of symptoms of anxiety and depression in youth. To our knowledge, there are no systematic reviews to date looking at the reduction of symptoms of both anxiety and depression. Methods: Numerous scientific databases were searched up to November 2018 for experimental studies assessing changes in symptoms of anxiety and/or depression in youths following yoga interventions. Quality and level of evidence were assessed, and information was synthesized across studies. Results: Twenty-seven studies involving youth with varying health statuses were reviewed. Intervention characteristics varied greatly across studies revealing multiple factors that may impact intervention efficacy, however 70% of the studies overall showed improvements. For studies assessing anxiety and depression, 58% showed reductions in both symptoms, while 25% showed reductions in anxiety only. Additionally, 70% of studies assessing anxiety alone showed improvements and 40% of studies only assessing depression showed improvements. Conclusion: The studies reviewed, while of weak to moderate methodological quality, showed that yoga, defined by the practice of postures, generally leads to some reductions in anxiety and depression in youth regardless of health status and intervention characteristics.

Remote Assessment of Cognitive Impairment Level Based on Serious Mobile Game Performance: An Initial Proof of Concept

Individuals with cognitive impairments are evaluated using clinically validated cognitive assessment tools, which need to be administered by trained therapists. This serves as a major barrier for frequent and longitudinal monitoring of patients' cognitive impairment level. We introduce Neuro-World, a set of six mobile games designed to challenge visuospatial short-term memory and selective attention, which allows one to self-administer the assessment of his/her cognitive impairment level. Game performance is analyzed to estimate a widely accepted clinical measure, the mini mental state examination (MMSE), which highlights the translational impact of the system in real-world settings. We collected game-specific performance data from 12 post-stroke patients at baseline and a three-month follow-up, which were used to train supervised machine learning models to estimate the corresponding MMSE scores. The results presented herein show that the proposed approach can estimate the MMSE scores with a normalized root mean square error of 5.75%. We also validate the system's responsiveness to longitudinal changes in cognitive impairment level and demonstrate the system's positive usability in cognitively impaired individuals and their willingness to adhere to the longitudinal use. This study demonstrates that Neuro-world has great potential to be used to evaluate the cognitive impairment level and monitor its long-term change. This study enables new clinical and research opportunities for accurate, longitudinal assessment of cognitive function via mobile games.

Effectiveness of the RAPAEL Smart Board for Upper Limb Therapy in Stroke Survivors: A Pilot Controlled Trial

We aim to assess the effectiveness of using the RAPAEL Smart Board as an assistive tool for therapists in clinical rehabilitation therapy settings and to investigate if it can be used to improve the motor recovery rate of stroke survivors. The RAPAEL Smart Board is a therapy tool where therapists actively engage patients, giving necessary verbal and physical interventions as in traditional treatment sessions. We conducted a randomized controlled study with 17 stroke survivors. An experimental group received therapy using the RAPAEL Smart Board for 30 minutes a day, 5 days per week, for 4 weeks in addition to their traditional treatments (i.e., 30 minutes of functional arm movement therapy). A control group received two 30-minute sessions of traditional treatment 5 days per week, for 4 weeks. The upper-extremity function was measured using the Wolf Motor Function Test before and after the 4-week interventions. Our results demonstrate that using the RAPAEL Smart Board, in combination with traditional treatment, significantly improves motor recovery when compared to traditional treatments alone.

Towards the Ambulatory Assessment of Movement Quality in Stroke Survivors using a Wrist-worn Inertial Sensor

Stroke is a leading cause of long-term disability that may lead to significant functional motor impairments in the upper limb (UL). Wrist-worn inertial sensors have emerged as an objective, minimally-obtrusive tool to monitor UL motor function in the real-world setting, such that rehabilitation interventions can be individually tailored to maximize functional performance. However, current wearable solutions focus on capturing the quantity of movement without considering the quality of movement. This paper introduces a novel approach to unobtrusively estimate the quality of UL movements in stroke survivors using a single wrist-worn inertial sensor during any type of voluntary UL movements. The proposed method exploits kinematic characteristics of voluntary limb movements that are optimized by the central nervous system during motor control. This work demonstrates that the proposed method could extract clinically important information during random UL movements in 16 stroke survivors, showing a statistically significant correlation to the Functional Ability Scale - a clinically validated score for movement quality.

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.

A novel method for assessing the severity of levodopa-induced dyskinesia using wearable sensors

Patients with Parkinson's disease often experience significant changes in the severity of dyskinesia when they undergo titration of their medications. Dyskinesia is marked by involuntary jerking movements that occur randomly in a burst-like fashion. The burst-like nature of such movements makes it difficult to estimate the clinical scores of severity of dyskinesia using wearable sensors. Clinical observations are generally made over intervals of 15-30 s. On the other hand, techniques designed to estimate the severity of dyskinesia based on the analysis of wearable sensor data typically use data segments of approximately 5 s. Consequently, some data segments might include dyskinetic movements, whereas others might not. Herein, we propose a novel method suitable to automatically select data segments from the training dataset that are marked by dyskinetic movements. The proposed method also aggregates results derived from the testing dataset using a machine learning algorithm to estimate the severity of dyskinesia from wearable sensor data. Results obtained from the analysis of sensor data collected from seven subjects with Parkinson's disease showed a marked improvement in the accuracy of the estimation of clinical scores of dyskinesia.

Clinical Management of Drug-Induced Dyskinesia in Parkinson’s Disease: Why Current Approaches May Need to Be Changed to Optimise Quality of Life

Parkinson’s disease is a complex, progressive neurodegenerative disorder associated with both motor and non-motor symptoms. Current treatment strategies mainly target the alleviation of motor symptoms through dopaminergic replacement therapy. Many patients with Parkinson’s disease will eventually experience motor complications associated with their anti-parkinsonian medication. One of those complications is drug-induced dyskinesia. This paper firstly reviews current approaches to the management of drug-induced dyskinesia, from modifications to the titration of medication, to more invasive approaches like deep brain stimulation. Following this we describe a recent proposal suggesting that the treatment of dyskinesia should be based on the impact on daily activities of patients rather than on the mere presence of the condition. Next, we discuss how this approach could improve the quality of life of patients and their caregivers and finally, we suggest possible ways of implementing this approach in practice.

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.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

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.