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Digital health technologies (DHTs) should expand access to clinical research to represent the social determinants of health (SDoH) across the population. The frequency of reporting participant SDoH data in clinical publications is low and is not known for studies that utilize DHTs. We evaluated representation of 11 SDoH domains in 126 DHT-enabled clinical research publications and proposed a framework under which these domains could be captured and subsequently reported in future studies. Sex, Race, and Education were most frequently reported (in 94.4%, 27.8%, and 20.6% of publications, respectively). The remaining 8 domains were reported in fewer than 10% of publications. Medical codes were identified that map to each of the proposed SDoH domains and the resulting resource is suggested to highlight that existing infrastructure could be used to capture SDoH data. An opportunity exists to increase reporting on the representation of SDoH among participants to encourage equitable and inclusive research progress through DHT-enabled clinical studies.

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BACKGROUND: Although ongoing exercise is known to reduce disability in people with multiple sclerosis (MS), participation in lower-extremity exercise programs can be limited by their existing mobility impairments. Lower-extremity exoskeletons could address this problem by facilitating home and community locomotion and enhancing exercise capability but little data is available on the potential of this technology for reducing disability of people with MS. METHODS: We evaluated the Keeogo™ exoskeleton for people with MS using an open-label randomised cross-over design. The trial design allowed us to quantify rehabilitation effects (tested without device) and training effects (tested with device) using functional outcomes: 6-minute walk test (6MWT), timed stair test (TST), and timed up-and-go (TUG). Baseline and post-study self-report instruments included Medical Outcomes Survey Short Form-36 (SF36), MS Walking Scale (MSWS), and others. Amount of home use was documented by daily activity log. Partial correlation analysis was used to explore the relationships between changes in functional outcomes and self-report disability, controlling for amount of home use of the device. RESULTS: Twenty-nine participants with MS completed the trial. Change scores for MSWS, SF36 physical function and SF36 emotional well-being correlated positively with changes in 6MWT which was explained by amount of home use. CONCLUSIONS: The benefits in physical functioning and emotional well-being from using the exoskeleton at home were linked to amount of device usage. Low-profile robotic exoskeletons could be used to deliver facilitated exercise while assisting with locomotor activities of daily living, such as walking and stair climbing in the home and community environment.IMPLICATIONS FOR REHABILITATIONExoskeletons for home use may have the potential to benefit people with MS in terms of physical functioning and emotional well-being.The benefits in physical functioning and emotional well-being appeared to be linked to amount of usage.Exoskeletons might be useful for delivering facilitated exercise while assisting with walking and stair climbing in the home.

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A multi-site study was conducted to evaluate the efficacy of the Keeogo™ exoskeleton as a mobility assist device for use in the clinic and at home in people with knee osteoarthritis (KOA). Twenty-four participants were randomized in a two-stage cross-over design that evaluated the immediate effects of using the exoskeleton in the clinic and the cumulative effects of training and home use. Immediate effects were quantified by comparing 1) physical performance with|without (W|WO) the device during a battery of mobility tests, and 2) physical activity levels at home (actigraphy) for one month, two weeks W|WO the device. Cumulative effects were quantified as change in physical performance W and WO over time. WOMAC and other self-report scales were measured and usability assessed. There were no immediate effects on physical performance or physical activity at home; however, there were cumulative effects as indicated by improved stair time (p = .001) as well as improved WOMAC pain (p = .004) and function (p = .003). There was a direct relationship between improved physical function and improved WOMAC pain (r = -.677, p < .001) and stiffness (r = .537, p = .007). Weight and battery life were identified as important to usability. A full-scale RCT with more participants, longer study period, and better usage monitoring is warranted.

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Voice analysis is an emerging technology which has the potential to provide low-cost, at-home monitoring of symptoms associated with a variety of health conditions. While voice has received significant attention for monitoring neurological disease, few studies have focused on voice changes related to flu-like symptoms. Herein, we investigate the relationship between changes in acoustic features of voice and self-reported symptoms during recovery from a flu-like illness in a cohort of 29 subjects. Acoustic features were automatically extracted from "sick" and "well" visit data collected in the laboratory setting, and feature down-selection was used to identify those that change significantly between visits. The selected acoustic features were extracted from at-home data and used to construct a combined distance metric that correlated with self-reported symptoms (0.63 rank correlation). Changes in self-reported symptoms corresponding to 10% of the ordinal scale used in the study were detected with an area under the curve of 0.72. The results show that acoustic features derived from voice recordings may provide an objective measure for diagnosing and monitoring symptoms of respiratory illnesses.

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OBJECTIVE: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis. METHODS: Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two. RESULTS: The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores. CONCLUSION: This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data. SIGNIFICANCE: This is important in the context of developing precision rehabilitation interventions.

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The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.

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BACKGROUND: Despite numerous trials investigating robot-assisted therapy (RT) effects on upper-extremity (UE) function after stroke, few have explored the relationship between three-dimensional (3D) reach-to-target kinematics and clinical outcomes. The objectives of this study were to 1) investigate the correlation between kinematic parameters of 3D reach-to-target movements and UE clinical outcome measures, and 2) examine the degree to which differences in kinematic parameters across individuals can account for differences in clinical outcomes in response to RT. METHODS: Ten chronic stroke survivors participated in a pilot RT intervention (eighteen 1-h sessions) integrating cognitive skills training and a home-action program. Clinical outcome measures and kinematic parameters of 3D reach-to-target movements were collected pre- and post-intervention. The correlation between clinical outcomes and kinematic parameters was investigated both cross-sectionally and longitudinally (i.e., changes in response to the intervention). Changes in clinical outcomes and kinematic parameters were tested for significance in both group and subject-by-subject analyses. Potential associations between individual differences in kinematic parameters and differences in clinical outcomes were examined. RESULTS: Moderate-to-strong correlation was found between clinical measures and specific kinematic parameters when examined cross-sectionally. Weaker correlation coefficients were found longitudinally. Group analyses revealed significant changes in clinical outcome measures in response to the intervention; no significant group changes were observed in kinematic parameters. Subject-by-subject analyses revealed changes with moderate-to-large effect size in the kinematics of 3D reach-to-target movements pre- vs. post-intervention. Changes in clinical outcomes and kinematic parameters varied widely across participants. CONCLUSIONS: Large variability was observed across subjects in response to the intervention. The correlation between changes in kinematic parameters and clinical outcomes in response to the intervention was variable and not strong across parameters, suggesting no consistent change in UE motor strategies across participants. These results highlight the need to investigate the response to interventions at the individual level. This would enable the identification of clusters of individuals with common patterns of change in response to an intervention, providing an opportunity to use cluster-specific kinematic parameters as a proxy of clinical outcomes. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02747433 . Registered on April 21st, 2016.

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Humans respond to mechanical perturbations that affect their gait by changing their motor control strategy. Previous work indicates that adaptation during gait is context dependent, and perturbations altering long-term stability are compensated for even at the cost of higher energy expenditure. However, it is unclear if gait adaptation is driven by unilateral or bilateral mechanisms, and what the roles of feedback and feedforward control are in the generation of compensatory responses. Here, we used a robot-based adaptation paradigm to investigate if feedback/feedforward and unilateral/bilateral contributions to locomotor adaptation are also context dependent in healthy adults. A robot was used to induce two opposite unilateral mechanical perturbations affecting the step length over multiple gait cycles. Electromyographic signals were collected and analyzed to determine how muscle synergies change in response to perturbations. The results unraveled different unilateral modulation dynamics of the muscle-synergy activations during adaptation, characterized by the combination of a slow-progressive feedforward process and a fast-reactive feedback-driven process. The relative unilateral contributions of the two processes to motor-output adjustments, however, depended on which perturbation was delivered. Overall, these observations provide evidence that, in humans, both descending and afferent drives project onto the same spinal interneuronal networks that encode locomotor muscle synergies.

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Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

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Introduction: Robot-assisted therapy for upper extremity (UE) impairments post-stroke has yielded modest gains in motor capacity and little evidence of improved UE performance during activities of daily living. A paradigm shift that embodies principles of motor learning and exercise dependent neuroplasticity may improve robot therapy outcomes by incorporating active problem solving, salience of trained tasks, and strategies to facilitate the transfer of acquired motor skills to use of the paretic arm and hand during everyday activities. Objective: To pilot and test the feasibility of a novel therapy protocol, the Active Learning Program for Stroke (ALPS), designed to complement repetitive, robot-assisted therapy for the paretic UE. Key ALPS ingredients included training in the use of cognitive strategies (e.g., STOP, THINK, DO, CHECK) and a goal-directed home action plan (HAP) to facilitate UE self-management and skill transfer. Methods: Ten participants with moderate impairments in UE function >6 months after stroke received eighteen 1-h treatment sessions 2-3/x week over 6-8 weeks. In addition to ALPS training, individuals were randomly assigned to either robot-assisted therapy (RT) or robot therapy and task-oriented training (RT-TOT) to trial whether the inclusion of TOT reinforced participants' understanding and implementation of ALPS strategies. Results: Statistically significant group differences were found for the upper limb subtest of the Fugl-Meyer Assessment (FMA-UE) at discharge and one-month follow-up favoring the RT group. Analyses to examine overall effects of the ALPS protocol in addition to RT and RT-TOT showed significant and moderate to large effects on the FMA-UE, Motor Activity Log, Wolf Motor Function Test, and hand portion of the Stroke Impact Scale. Conclusion: The ALPS protocol was the first to extend cognitive strategy training to robot-assisted therapy. The intervention in this development of concept pilot trial was feasible and well-tolerated, with good potential to optimize paretic UE performance following robot-assisted therapy.

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BACKGROUND: Although physical activity and exercise is known to benefit people with multiple sclerosis (MS), the ability of these individuals to participate in such interventions is difficult due to the mobility impairments caused by the disease. Keeogo is a lower-extremity powered exoskeleton that may be a potential solution for enabling people with MS to benefit from physical activity and exercise. METHODS: An open-label, randomized, cross-over trial was used to examine the immediate performance effects when using the device, and the potential benefits of using the device in a home setting for 2 weeks. Clinical performance tests with and without the device included the 6 min walk test, timed up and go test and the 10-step stair test (up and down). An activity monitor was also used to measure physical activity at home, and a patient-reported questionnaire was used to determine the amount and extent of home use. Generalized linear models were used to test for trial effects, and correlation analysis used to examine relationships between trial effects and usage. RESULTS: Twenty-nine patients with MS participated. All measures showed small decrements in performance while wearing the device compared to not wearing the device. However, significant improvements in unassisted (Rehab effect) performance were found after using the device at home for 2 weeks, compared to 2 weeks at home without the device, and participants improved their ability to use the device over the trial period (Training effect). Rehab and Training effects were related to the self-reported extent that participants used Keeogo at home. CONCLUSIONS: Keeogo appears to deliver an exercise-mediated benefit to individuals with MS that improved their unassisted gait endurance and stair climbing ability. Keeogo might be a useful tool for delivering physical activity interventions to individuals with mobility impairment due to MS. TRIAL REGISTRATION: ClinicalTrials.gov : NCT02904382 . Registered 19 September 2016 - Retrospectively registered.

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OBJECTIVE: To evaluate a new portable toolkit for quantifying upper and lower extremity muscle tone in patients with upper motor neuron syndrome (UMNS). APPROACH: Cross-sectional, multi-site, observational trial to test and validate a new technology. SETTING: Neurorehabilitation clinics at tertiary care hospitals. PARTICIPANTS: Four cohorts UMNS patient, >6 mo post acquired brain injury, spinal cord injury, multiple sclerosis and cerebral palsy, and a sample of healthy age-matched adult controls. MEASURES: Strength: grip, elbow flexor and extensor, and knee extensor; range of motion (ROM): passive ROM (contracture) and passive-active ROM (paresis); objective spasticity: stretch-reflex test for elbow, and pendulum test for knee; subjective spasticity: modified Ashworth scale scores for elbow and knee flexors and extensors. RESULTS: Measures were acquired for 103 patients from three rehabilitation clinics. Results for patient cohorts were consistent with the literature. Grip strength correlated significantly with elbow muscle strength and all patient populations were significantly weaker in upper- and lower-extremity compared to controls. Strength and paresis were correlated for elbow and knee but neither correlated with contracture. Elbow spasticity correlated with strength and paresis but not contracture. Knee spasticity correlated with strength, and subjective spasticity correlated with contracture. SIGNIFICANCE: The BioTone™ toolkit provided comprehensive objective measures for assessing muscle tone in patients with UMNS. The toolkit could be useful for standardizing outcomes measures in clinical trials and for routine practice.

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High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a [Formula: see text]-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an [Formula: see text]-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.