Cognitive and Motor Recovery and Predictors of Long-Term Outcome in Patients With Traumatic Brain Injury

Zarshenas, Sareh; Colantonio, Angela; Horn, Susan D.; Jaglal, Susan; Cullen, Nora

doi:10.1016/j.apmr.2018.11.023

Cited by 21 publications

(19 citation statements)

References 57 publications

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“…However, choosing the most effective intervention among the myriad of available rehabilitation approaches is challenging 10 – 12 . High variability in response to interventions aimed to restore upper-limb function is observed across patients 13 – 15 , hence pointing to the need for designing “precision rehabilitation” interventions that account for the unique characteristics of each individual. The need for developing patient-specific interventions is paramount in the broad field of medicine 16 – 18 and is gradually emerging as a topic of great interest in the field of rehabilitation as investigators explore approaches relying on patients’ genotype 19 – 21 and motor phenotype 22 – 24 to develop subject-specific interventions.…”

Section: Introductionmentioning

confidence: 99%

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

et al. 2020

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“…The GPR model utilized in this study to generate FAS score estimates from baseline clinical data performed better than traditional regression models utilized in previous work to predict FMA-UE scores in stroke survivors [ 5 ], [ 7 ] and Functional Independence Measure scores in TBI survivors [ 8 ]. While it remains to be seen if this is a specific characteristic of the FAS, the analysis of the estimation error presented in Fig.…”

Section: Discussionmentioning

confidence: 93%

“…5 ). Prior work investigated the relationship between rehabilitation outcomes and several clinical variables, including the intervention dosage (i.e., number of treatment sessions or hours of therapy) [ 7 ], [ 10 ], [ 11 ], the chronicity level (i.e., time since acquired brain injury) [ 5 ]–[ 8 ], and the outcome of baseline clinical assessments [ 7 ], [ 8 ]. Age (because of the decrease in neural plasticity with aging [ 52 ]) and length of treatment (because of the proportional recovery observed in the early phases post-injury [ 53 ]) have also been considered as potential predictive factors of rehabilitation outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…They often result in significant motor impairments with negative impact on patients’ independence and quality of life [ 3 ], [ 4 ]. The recovery of motor abilities in these patients is marked by two phases: in the subacute phase, patients typically display a spontaneous biological recovery [ 5 ], [ 6 ]; in the chronic phase, they show limited and rather unpredictable motor gains in response to rehabilitation [ 7 ], [ 8 ]. These observations have been interpreted by some as evidence that traditional rehabilitation interventions have a modest impact on the recovery trajectory, and thus, new intervention approaches should be explored [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

Lee

Adans-Dester

O’Brien

et al. 2021

IEEE Trans. Biomed. Eng.

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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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“…They are the future professional medical staff. They are very familiar with the establishment of rehabilitation departments and the types of patients admitted to the department [9] . Our study has the limitations of sampling from only one region and the exclusion of some influencing factors.…”

Section: Analysis Of Factors Influencing Awareness Of Sports Rehabimentioning

confidence: 99%

Analysis of cognitive characteristics and influencing factors of mass sports rehabilitation in Taiyuan

Wang

Zhao³

2019

Preprint

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Background: To assess the awareness regarding sports rehabilitation among residents of Taiyuan. Method: From September 27, 2018 to March 29, 2019, 1200 residents who met the inclusion/ criteria were selected using convenient sampling method. The population was surveyed by self-designed questionnaires, and single factor and two-category logistic regression analysis (stepwise forward method) was used to identify the factors influencing awareness of mass sports rehabilitation in Taiyuan. Results: A total of 1200 questionnaires were issued, of which 1167 were collected and 1101 were valid. The corresponding recovery and effective recovery rates were 97.25% and 94.34% respectively. The overall rate of awareness of exercise rehabilitation was 80.7%, and education level, occupation, income and health status were significant influencing factors (R<0.05). The results of two-class logistic regression analysis showed that age, occupation, education level, income level and health status were the influencing factors affecting the public's perception of the sports rehabilitation concept (R<0.05), whereas gender, occupation, education level and health status influenced understanding of the establishment of the rehabilitation department in Taiyuan (R<0.05), and gender, age, education level and health status affected understanding of the types of patients receiving rehabilitation (R<0.05). Conclusion: There is a high general awareness regarding sports rehabilitation, and is influenced by various socio-economic factors.

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Cognitive and Motor Recovery and Predictors of Long-Term Outcome in Patients With Traumatic Brain Injury

Cited by 21 publications

References 57 publications

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

Analysis of cognitive characteristics and influencing factors of mass sports rehabilitation in Taiyuan

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