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Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?

ABSTRACT: Motor-training software on tablets or smartphones (Apps) offer a low-cost, widely-available solution to supplement arm physiotherapy after stroke. We assessed the proportions of hemiplegic stroke patients who, with their plegic hand, could meaningfully engage with mobile-gaming devices using a range of standard control-methods, as well as by using a novel wireless grip-controller, adapted for neurodisability. We screened all newly-diagnosed hemiplegic stroke patients presenting to a stroke centre over 6 months. Subjects were compared on their ability to control a tablet or smartphone cursor using: finger-swipe, tap, joystick, screen-tilt, and an adapted handgrip. Cursor control was graded as: no movement (0); less than full-range movement (1); full-range movement (2); directed movement (3). In total, we screened 345 patients, of which 87 satisfied recruitment criteria and completed testing. The commonest reason for exclusion was cognitive impairment. Using conventional controls, the proportion of patients able to direct cursor movement was 38-48%; and to move it full-range was 55-67% (controller comparison: p>0.1). By comparison, handgrip enabled directed control in 75%, and full-range movement in 93% (controller comparison: p<0.001). This difference between controllers was most apparent amongst severely-disabled subjects, with 0% achieving directed or full-range control with conventional controls, compared to 58% and 83% achieving these two levels of movement, respectively, with handgrip. In conclusion, hand, or arm, training Apps played on conventional mobile devices are likely to be accessible only to mildly-disabled stroke patients. Technological adaptations such as grip-control can enable more severely affected subjects to engage with self-training software.

SUBMITTER: Rinne P

PROVIDER: S-EPMC5051962 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStroke is a frequent cause of adult disability that can lead to enduring impairments. However, given the life-long plasticity of the brain one could assume that recovery could be facilitated by the harnessing of mechanisms underlying neuronal reorganization. Currently it is not clear how this reorganization can be mobilized. Novel technology based neurorehabilitation techniques hold promise to address this issue. Here we describe a Virtual Reality (VR) based system, the Rehabilitation Gaming System (RGS) that is based on a number of hypotheses on the neuronal mechanisms underlying recovery, the structure of training and the role of individualization. We investigate the psychometrics of the RGS in stroke patients and healthy controls.MethodsWe describe the key components of the RGS and the psychometrics of one rehabilitation scenario called Spheroids. We performed trials with 21 acute/subacute stroke patients and 20 healthy controls to study the effect of the training parameters on task performance. This allowed us to develop a Personalized Training Module (PTM) for online adjustment of task difficulty. In addition, we studied task transfer between physical and virtual environments. Finally, we assessed the usability and acceptance of the RGS as a rehabilitation tool.ResultsWe show that the PTM implemented in RGS allows us to effectively adjust the difficulty and the parameters of the task to the user by capturing specific features of the movements of the arms. The results reported here also show a consistent transfer of movement kinematics between physical and virtual tasks. Moreover, our usability assessment shows that the RGS is highly accepted by stroke patients as a rehabilitation tool.ConclusionsWe introduce a novel VR based paradigm for neurorehabilitation, RGS, which combines specific rehabilitative principles with a psychometric evaluation to provide a personalized and automated training. Our results show that the RGS effectively adjusts to the individual features of the user, allowing for an unsupervised deployment of individualized rehabilitation protocols.

Project description:BackgroundBaseline physical activity in patients when they initiate cardiac rehabilitation is poorly understood. We used mobile health technology to understand baseline physical activity of patients initiating cardiac rehabilitation within a clinical trial to potentially inform personalized care.MethodsThe VALENTINE (Virtual Application-Supported Environment to Increase Exercise During Cardiac Rehabilitation Study) is a prospective, randomized-controlled, remotely administered trial designed to evaluate a mobile health intervention to supplement cardiac rehabilitation for low- and moderate-risk patients. All participants receive a smartwatch and usual care. Baseline physical activity was assessed remotely after enrollment and included (1) 6-minute walk distance, (2) daily step count, and (3) daily exercise minutes, both over 7 days and for compliant days, defined by >8 hours of watch wear time. Multivariable linear regression identified patient-level features associated with these 3 measures of baseline physical activity.ResultsFrom October 2020 to March 2022, 220 participants enrolled in the study. Participants are mostly White (184 [83.6%]); 67 (30.5%) are female and 84 (38.2%) are >65 years old. Most participants enrolled in cardiac rehabilitation after percutaneous coronary intervention (105 [47.7%]) or coronary artery bypass surgery (39 [17.7 %]). Clinical diagnoses include coronary artery disease (78.6%), heart failure (17.3%), and valve repair or replacement (26.4%). Baseline mean 6-minute walk distance was 489.6 (SD, 143.4) meters, daily step count was 6845 (SD, 3353), and exercise minutes was 37.5 (SD, 33.5). In a multivariable model, 6-minute walk distance was significantly associated with age and sex, but not cardiac rehabilitation indication. Sex but not age or cardiac rehabilitation indication was significantly associated with daily step count and exercise minutes.ConclusionsBaseline physical activity varies substantially in low- and moderate-risk patients enrolled in cardiac rehabilitation. Future studies are warranted to explore whether personalizing cardiac rehabilitation programs using mobile health technologies could optimize recovery.RegistrationURL: https://www.Clinicaltrialsgov; Unique identifier: NCT04587882.

Project description:Practicing clinicians in neurorehabilitation continue to lack a systematic evidence base to personalize rehabilitation therapies to individual patients and thereby maximize outcomes. Computational modeling- collecting, analyzing, and modeling neurorehabilitation data- holds great promise. A key question is how can computational modeling contribute to the evidence base for personalized rehabilitation? As representatives of the clinicians and clinician-scientists who attended the 2023 NSF DARE conference at USC, here we offer our perspectives and discussion on this topic. Our overarching thesis is that clinical insight should inform all steps of modeling, from construction to output, in neurorehabilitation and that this process requires close collaboration between researchers and the clinical community. We start with two clinical case examples focused on motor rehabilitation after stroke which provide context to the heterogeneity of neurologic injury, the complexity of post-acute neurologic care, the neuroscience of recovery, and the current state of outcome assessment in rehabilitation clinical care. Do we provide different therapies to these two different patients to maximize outcomes? Asking this question leads to a corollary: how do we build the evidence base to support the use of different therapies for individual patients? We discuss seven points critical to clinical translation of computational modeling research in neurorehabilitation- (i) clinical endpoints, (ii) hypothesis- versus data-driven models, (iii) biological processes, (iv) contextualizing outcome measures, (v) clinical collaboration for device translation, (vi) modeling in the real world and (vii) clinical touchpoints across all stages of research. We conclude with our views on key avenues for future investment (clinical-research collaboration, new educational pathways, interdisciplinary engagement) to enable maximal translational value of computational modeling research in neurorehabilitation.

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Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?

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