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.

Understanding voluntary human movement variability through data-driven segmentation and clustering.

Recently, we proposed a novel approach where movements are decomposed into sub-segments, termed movement elements. This approach, to date, provides a robust construct of how the brain may generate simple as well as complex movements. Here, we address the issue of motor variability during voluntary movements by applying an unsupervised clustering algorithm to group movement elements according to their morphological characteristics. We observed that most movement elements closely match the theoretical bell-shaped velocity profile expected from goal-directed movements. However, for those movement elements that deviate from this theoretical shape, a small number of defined patterns in their shape can be identified. Furthermore, we observed that the axis of the body from which the movement elements are extracted (i.e., medio-lateral, antero-posterior, and vertical) affect the proportion of the movement elements matching the theoretical model. These results provide novel insight into how the nervous system controls voluntary movements and may use variability in movement element properties to explore the environment.

Relationships between Self-Efficacy and Post-Stroke Activity Limitations, Locomotor Ability, Physical Activity, and Community Reintegration in Sub-Saharan Africa: A Cross-Sectional Study.

Stroke self-efficacy is under-investigated in sub-Saharan Africa. In particular, studies focusing on the relationship between self-efficacy and post-stroke functional outcomes are scarce. This cross-sectional study aimed to explore the association between self-efficacy and post-stroke activity limitations, locomotor ability, physical activity, and community reintegration in Benin, a sub-Saharan African country. To achieve this purpose, a selection of stroke patients was made from the admission registers of the physiotherapy clinics (rehabilitation units) of three reference hospitals in Benin from January to April 2018. Stroke patients who were still continuing their rehabilitation sessions were informed by direct contact. Those who had already finished their sessions were informed by telephone. Sixty stroke patients of those contacted gave their consent and were recruited for this study. The sample consisted of 44 men and 16 women with a mean age of 56.7 ± 10.4 years. Activity limitations, locomotor ability, physical activity, community reintegration, and self-efficacy were self-reported using ACTIVLIM-Stroke, Abiloco-Benin, the Africa francophone version of the International Physical Activity Questionnaire (IPAQ-AF), the Reintegration to Normal Living Index (RNLI), and a French version of the Stroke Self-efficacy Questionnaire (SSEQ-F), respectively. Spearman's rank correlation coefficients (ρ) were calculated to characterize the relationship between self-efficacy and activity limitations, locomotor ability, physical activity, and community reintegration. According to the results, self-efficacy showed a moderate correlation with physical activity (ρ = 0.65; p < 0.001) and high correlations with activity limitations (ρ = 0.81; p < 0.001), locomotor ability (ρ = 0.72; p < 0.001), and community reintegration (ρ = -0.84; p < 0.001). Thus, self-efficacy emerges as an important factor associated with the functional recovery of stroke patients in sub-Saharan Africa.

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 2nd and 3rd 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 2nd 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 2nd trimester as compared to the 3rd 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 2nd and 3rd trimesters, with ICCs ranging from 0.45 (95%CI: (0.17, 0.64)) for travel PA at 2nd trimester to 0.69 (95%CI: (0.51, 0.80)) for travel PA at 3rd trimester. Reliability for total MVPA was higher in the 3rd trimester compared to 2nd 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.

Relationships between overall physical activity and step counts in able-bodied adults and stroke survivors in developing countries: a cross-sectional study.

PURPOSE: To investigate how step counts relate to overall physical activity (PA) in able-bodied adults and stroke survivors in developing countries. MATERIALS AND METHODS: Sixty able-bodied adults (mean age: 48.8 ± 11.7 years old) and 60 stroke survivors (mean age: 56.7 ± 10.4 years old) were recruited in Benin (West-Africa). Step counts were collected for 7 consecutive days using the Garmin Forerunner 15 activity tracker. Then, participants completed the adapted French version of the International Physical Activity Questionnaire (IPAQ-AF) to report their PA over the same period. Spearman's rank correlation coefficients (ρ) were calculated between IPAQ-AF scores and the mean steps per day. RESULTS: Very high correlations were observed between IPAQ-AF total scores and step counts, in able-bodied adults (ρ = 0.94; p < 0.001), and in stroke survivors (ρ = 0.91; p < 0.001). IPAQ-AF leisure-time presented negligible correlation with step counts in able-bodied adults. No activity was reported in occupation and intense domains in stroke survivors. CONCLUSION: There is a strong relationship between overall PA and step counts in able-bodied adults and stroke survivors in Benin. Step counts appear to be an indicator of PA levels in developing countries, suggesting walking as a potential exercise to improve PA levels in stroke survivors.Implications for RehabilitationStep counts could serve as an indicator of physical activity levels in stroke survivors in developing countries.Walking may be suggested as exercise to improve physical activity levels in stroke survivors in developing countries.In Africa French speaking countries, the IPAQ-AF could be used to assess physical activity in stroke survivors and findings might guide programs to promote an active lifestyle as needed.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.