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This study is complete and is no longer recruiting subjects.

Stroke is a leading cause of long-term disability, affecting . More than half of stroke survivors who are over the age of 65 will experience reduced movement and communication skills, resulting from weakness or paralysis on one side of the body. Early clinical treatments, including physical rehabilitation and medication, may help build upon the natural recovery that occurs in the brain following injury and help stroke patients return to a certain level of function before hospital discharge. However, determining whether a patient has recovered sufficiently for discharge often depends on measures that are subjective and imperfect. These measures may include infrequent clinical assessments, performance-based rehabilitation measures and patient self-reports ¡ª all of which are limited by a lack of sensitivity, reliability and validity in identifying changes or clinical efficacy of treatment or recovery progression.

Body-worn sensor technologies have the potential to enable quantitative measures of body function and activity, tracking biometric data and communication or mobility-related activities.

This project will evaluate use of non-invasive, portable and real-time body-worn sensor for continuous monitoring, quantification and interpretation of recovery during inpatient treatment of stroke. Clinicians and therapists will have customized access to a patient history of sensor-based biometric and activity data, allowing them to more fully measure recovery and assign treatment after stroke. We argue that more objective, reliable and continuous monitoring of stroke patients with wearable sensors will assist clinicians and therapists make informed decisions about stroke treatment and recovery.

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In Aim 1, we will assess the feasibility of continuous long-term monitoring of inpatients with stroke using wearable sensors. We will obtain quantitative health data from research-grade, wireless, wearable sensors (including MC10 BioStampRC) on individuals with subacute and chronic stroke in the inpatient setting, as well as healthy controls.

We will specifically check for variability in device data, as well as consistency and periodicity of sensor readings across the clinical study period. We will analyze test-retest reliability and inter-rater reliability of using the wearable sensor technology for clinical and monitoring applications. Furthermore, we will determine whether the sensors can distinguish biometric and activity characteristics between healthy controls and individuals with stroke.

In Aim 2, we will quantify upper- and lower-extremity movement impairments, mobility-related activities, speech and swallowing activities and clinical parameters during stroke recovery.

We will obtain continuous biometric and movement-based sensor data for clinical symptoms (e.g., muscle activation, heart rate variability, talk time, and gait quality) during the performance of validated clinical tests and during general inpatient activities (e.g., therapy, eating, and sleeping).

We will compare device data with clinically validated measures of movement and language function, such as the Modified Ashworth Scale or Western Aphasia Battery. We will describe variation of device data in subgroups of subjects defined by clinician assessed clinically validated measures (e.g., 10-Meter Walk Test, Mini-Mental Status Exam, etc.). We will also assess the ability of the sensors to capture response to treatment, such as movement therapy, speech therapy, medication and Botox by comparing sensor data before and after treatment. We will provide evidence about the degree to which the measured variables are intercorrelated. Lastly, we will evaluate and compare the state of recovery between patients at time of discharge using sensor-based outcomes.

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Arun Jayaraman, PT, PhD, Principal Investigator

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ÄÛBÑо¿Ôº ÄÛBÑо¿Ôº Foundation