2014 — 2017 |
Jayaraman, Arun |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Understanding Real-Life Falls in Amputees Using Mobile Phone Technology @ Rehabilitation Institute of Chicago
DESCRIPTION (provided by applicant): Falls are a significant cause of death and serious injury and result in significant health-care costs. Individuals with a lower extremity amputation due to vascular disease are overwhelmingly elderly (at least 65 years of age) and are at especially high risk of falling. Successful fall prevention strategies depend on understanding how, why, when, and where individuals fall, and what types of falls (e.g., trip, slip, or lateral fll) are likely in a given population. Most studies on falls in amputees to date have relied surveys or questionnaires that are often completed a significant time after the fall and thus rely both on the individual's ability to remember the details of their fall and their willingness to be objective abut how and why they fell. Such approaches are susceptible both to inaccurate memories of the fall and to recall bias-for example, due to embarrassment about falling- and are especially unreliable in the elderly amputees. Mobile phones provide a simple, cost-effective method for detection and characterization of falls. Most available smart phones today have a tri-axial accelerometer, which provides highly accurate fall detection in real-time. Other available applications (or apps) can provide data on activity (running, walking etc.) and environment-such as the weather conditions or population density-that may have contributed to the fall and can pin-point the location of the fall-using GPS technology and highly accurate maps. Mobile phones also have inbuilt data storage and transfer capability, allowing for real-time acquisition and transmission of data. Additionally, mobile phones provide a simple means to contact the individual immediately after a suspected fall to confirm details of the fall (and to ascertain the need for medical assistance). Because mobile phone use is so widespread, there is no stigma associated with carrying such a device, which is likely to lead to high compliance. This study aims to use a mobile phone-based fall detection system in dysvascular amputees to detect falls, characterize the type of fall, analyze environmental conditions that may have contributed to the fall, and determine the longer-term consequences of each type of fall. Data acquired may be used to improve rehabilitation protocols or design better prostheses in order to prevent falls. This technology is ultimately transferrable to many populations with a high risk of falling-for example, the elderly, stroke survivors, or those with other musculoskeletal disorders or disabilities-leading to the design of specific fall prevention strategies for those populations.
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1 |
2020 |
Jayaraman, Arun |
P2CActivity Code Description: To support multi-component research resource projects and centers that will enhance the capability of resources to serve biomedical research. |
Center For Smart Use of Technology to Assess Real-World Outcomes (C-Star) @ Rehabilitation Institute of Chicago D/B/a Shirley Ryan Abilitylab
Project Summary?Technology Development Based on the collective experiences of the clinicians, scientists, engineers, and patient collaborators who comprise the Center for Smart use of Technologies to Assess Real-world Outcomes (C-STAR), we propose three specific aims with the primary goals of: (1) addressing the need for laboratory, clinical, and community assessment, (2) providing a resource for the rehabilitation research community, (3) extending technologies for which we have significant preliminary data, and (4) providing resources for use C-STAR clients during Pilot Studies, sabbaticals, or other sponsored collaborative activities. We have previously developed and tested a new class of epidermal electronic sensor (EES)-based technologies that has tremendous potential to track real-world outcomes for rehabilitation researchers. EES- based technologies package conventional inorganic semiconductor technologies into thin, lightweight, mechanically `soft' (i.e., flexible, stretchable) devices that provide advanced, wireless biosensing capabilities. Epifluidic devices integrate electronic components with microfluidic sweat collection systems to enable non- invasive, continuous monitoring of sweat dynamics (loss, instantaneous rate, and average rate), biochemical composition, and physiology, skin health, and hydration. For Aim 1, we will add the capacity for real-time measurement of cortisol levels in sweat to this sensor. Many technologies, such as smart watches or mobile phones, generally have many capabilities and are easy to use. Although the raw data measured with such technologies (accelerations, angular velocities, barometric readings, etc.) are of high quality, the algorithms used to interpret these data do not translate well for individuals with disability. It is critical to calibrate mobility prediction algorithms using properly labelled, condition-specific data collected from individuals with disability. For Aim 2, we will convene expert panels of clinicians, scientists, and users to create standardized protocols for collecting labelled ?benchmark? sensor data specific to stroke survivors, persons with spinal cord injury, traumatic brain injury, or Parkinson's disease. We will then collect labelled activity data from mobile phones, smart watches, and inertial sensors from cohorts of individuals with these conditions to generate a publicly available, online database. The Rehabilitation Measures Database (RMD) is a leading resource for benchmarks and outcomes, featuring more than 400 measures supported by doctors, clinicians, therapists, and rehabilitation researchers and achieving an average of 11,000 hits per day. While the site works well for laptop and desktop computers, improvements would allow access to RMD in the field using smart phones and tablets. For Aim 3, we will develop a RMD application (app) with an intuitive user interface that can be used with Android and iOS operating systems. These aims build on our current technologies to generate resources that will be of immense value to the rehabilitation research community.
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1 |
2020 — 2021 |
Arora, Vineet Jayaraman, Arun |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Siesta (Sleep of Inpatients: Empower Staff to Act) For Acute Stroke Rehabilitation - Resubmission 01
Project Summary Stroke is a leading cause of long-term disability, reducing everyday mobility and communication skills in more than half of survivors. Acute rehabilitation through early interventions is a mainstay for recovery and improving physical function in stroke patients. Unfortunately, patients recovering from acute stroke are at risk for poor, non-restorative sleep, when sleep is critical for recovery and participation in intense physical rehabilitation. During acute rehabilitation, stroke patients face two sleep-related challenges: (1) uncoordinated and often unnecessary nocturnal interruptions (vitals, medications, etc.) due to medical or nursing care; and (2) a high risk of undiagnosed sleep disordered breathing. Given the critical role of sleep in enhancing neural recovery, motor learning, neuroprotection, and neuroplasticity, interventions to enhance sleep that target these two areas could improve recovery and rehabilitation outcomes for stroke patients. In this proposal, a multidisciplinary group of researchers with expertise in rehabilitation medicine, sleep medicine, nursing, physical therapy, wearable technologies, and implementation science will adapt, implement and evaluate a state-of-the-art intervention to promote sleep for stroke patients undergoing acute rehabilitation. SIESTA-Rehab, adapted from our previous unit-based intervention, bundles two sleep-promoting interventions to address the unique sleep challenges stroke patients face during acute rehabilitation: (1) nursing education and empowerment to reduce unnecessary disruptions; (2) a systematic protocol to screen, diagnose, and treat sleep-disordered breathing if present during acute stroke rehabilitation. This work will take place at the Shirley Ryan Ability Lab (SRALab), the first-ever translational research hospital that brings clinicians and researchers together to make breakthrough discoveries in rehabilitation. Researchers at their Center for Bionic Medicine have pioneered the development and validation of a high-resolution wearable sensor platform to monitor stroke recovery via continuous biometric and movement-based sensor data for clinical symptoms (e.g., movement, sleep, heart rate variability, speech & swallowing, and gait quality). These sensors have been tested during performance of validated clinical tests and inpatient activities (e.g., therapy, down-time, sleeping). We aim to study the effectiveness of SIESTA-Rehab on improving sleep and rehabilitation outcomes during acute rehabilitation for stroke and after discharge home. Because there are two stroke floors at SRALab and patients are admitted in a quasi-random allocation, we can implement SIESTA-Rehab in one unit while the other unit receives Usual Care (routine night nursing care and clinician judgment to order sleep study). This natural experiment enables a difference-in-differences approach, controlling for relevant covariates, to compare short and long-term sleep and rehabilitation outcomes between patients in the SIESTA-Rehab and Usual Care units.
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0.948 |
2021 |
Jayaraman, Arun |
P2CActivity Code Description: To support multi-component research resource projects and centers that will enhance the capability of resources to serve biomedical research. |
Collaboration With Other Institutions Component @ Rehabilitation Institute of Chicago D/B/a Shirley Ryan Abilitylab
Project Summary?Technology Development Based on the collective experiences of the clinicians, scientists, engineers, and patient collaborators who comprise the Center for Smart use of Technologies to Assess Real-world Outcomes (C-STAR), we propose three specific aims with the primary goals of: (1) addressing the need for laboratory, clinical, and community assessment, (2) providing a resource for the rehabilitation research community, (3) extending technologies for which we have significant preliminary data, and (4) providing resources for use C-STAR clients during Pilot Studies, sabbaticals, or other sponsored collaborative activities. We have previously developed and tested a new class of epidermal electronic sensor (EES)-based technologies that has tremendous potential to track real-world outcomes for rehabilitation researchers. EES- based technologies package conventional inorganic semiconductor technologies into thin, lightweight, mechanically `soft' (i.e., flexible, stretchable) devices that provide advanced, wireless biosensing capabilities. Epifluidic devices integrate electronic components with microfluidic sweat collection systems to enable non- invasive, continuous monitoring of sweat dynamics (loss, instantaneous rate, and average rate), biochemical composition, and physiology, skin health, and hydration. For Aim 1, we will add the capacity for real-time measurement of cortisol levels in sweat to this sensor. Many technologies, such as smart watches or mobile phones, generally have many capabilities and are easy to use. Although the raw data measured with such technologies (accelerations, angular velocities, barometric readings, etc.) are of high quality, the algorithms used to interpret these data do not translate well for individuals with disability. It is critical to calibrate mobility prediction algorithms using properly labelled, condition-specific data collected from individuals with disability. For Aim 2, we will convene expert panels of clinicians, scientists, and users to create standardized protocols for collecting labelled ?benchmark? sensor data specific to stroke survivors, persons with spinal cord injury, traumatic brain injury, or Parkinson's disease. We will then collect labelled activity data from mobile phones, smart watches, and inertial sensors from cohorts of individuals with these conditions to generate a publicly available, online database. The Rehabilitation Measures Database (RMD) is a leading resource for benchmarks and outcomes, featuring more than 400 measures supported by doctors, clinicians, therapists, and rehabilitation researchers and achieving an average of 11,000 hits per day. While the site works well for laptop and desktop computers, improvements would allow access to RMD in the field using smart phones and tablets. For Aim 3, we will develop a RMD application (app) with an intuitive user interface that can be used with Android and iOS operating systems. These aims build on our current technologies to generate resources that will be of immense value to the rehabilitation research community.
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1 |
2021 |
Jayaraman, Arun |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Locomotor Function Following Transcutaneous Electrical Spinal Cord Stimulation in Individuals With Hemiplegic Stroke @ Rehabilitation Institute of Chicago D/B/a Shirley Ryan Abilitylab
Project Abstract Approximately 70% of the more than 7.2 million U.S. stroke survivors experience persistent gait deficits, including reduced walking speed, asymmetrical walking patterns, and reduced lower limb coordination, which limit their capacity for community ambulation. Current rehabilitation approaches are based on the assumption that stroke impairs motor cortex function while the spinal cord is preserved and thus focus on stimulating the ipsilateral or contralateral motor cortex during gait training to activate dormant or new pathways. Although animal models of stroke reveal secondary degeneration of the cervical and lumbar spinal cord, suggesting that damage to the spinal cord may effect functional recovery, little or no research has been done to elucidate spinal cord changes in humans after stroke. Our objective is to evaluate the effects of spinal stimulation combined with gait training after stroke and to investigate mechanisms underlying these effects. In preliminary work, we measured spinally evoked motor potentials (sEMPs) generated by non-invasive, transcutaneous electrical spinal stimulation in 10 stroke survivors, 10 age-matched healthy controls, and 10 young healthy subjects. Stimulation thresholds were significantly higher in stroke survivors than in controls and latency was significantly delayed in the paretic side compared to the non-paretic side, indicating secondary effects of stroke on downstream spinal circuitry and descending pathways. We also showed that spinal stimulation + symmetry-focused gait training (n=4) compared to gait training alone (n=4), significantly improved step-length symmetry, walking speed (10-meter walk test, 10MWT), and walking endurance (6-minute walk test, 6MWT); these improvements exceeded the minimal clinically important difference for chronic stroke. These results support our hypothesis that spinal stimulation may increase gait training efficacy. In Aim 1, we will evaluate the short-term effects of spinal stimulation and sham stimulation, with or without symmetry-focused gait training, on gait function (primary outcome: step-length symmetry) and corticospinal circuitry in 25 stroke survivors. In Aim 2, we will conduct a randomized clinical trial to evaluate the long-term effects of symmetry-focused gait training with stim or sham stimulation in stroke survivors (n=25 per group). The primary outcome will be step-length symmetry; secondary outcomes include temporal gait symmetry, speed (10MWT), muscle activation (electromyography), walking endurance (6MWT), energy expenditure (Cosmed K4B2), upper and lower limb function (Fugl-Meyer Assessment), health status (Stroke Impact Scale-16), and community activity (wearable sensors, Actigraph LLC). We will also investigate mechanisms underlying the effects of spinal stimulation by examining sEMPs elicited in lower limb muscles by cortical/subcortical stimulation of corticospinal axons and intracortical inhibition. This work will (i) identify short- and long-term effects of spinal stimulation, (ii) validate spinal stimulation as a non-invasive method to restore gait in chronic stroke, (iii) identify clinical measures that may determine response to spinal stimulation, and (iv) identify underlying neuromodulatory mechanisms, which may provide additional treatment options.
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1 |