[Abstract] Understanding Characteristics of User Adherence to Optimize the Use of Home Hand Rehabilitation Technology

Abstract:

Home-based rehabilitation can serve as an adjunct to in-clinic rehabilitation, encouraging users to engage in more practice. However, conventional home-based rehabilitation programs suffer from low adherence and high drop-out rates. Wearable movement sensors coupled with computer games can be more engaging, but have highly variable adherence rates. Here we examined characteristics of user adherence by analyzing unsupervised, wearable grip sensor-based home-hand rehabilitation data from 1,587 users. We defined three different classes of users based on activity level: low users (<2 days), medium users (2 – 9 days), and power users (> 9 days). The probability of using the device more than two days was positively correlated with first day game success (p = 0.91, p<. 001), and number of sessions played on the first day (p = 0.87, p<. 001) but negatively correlated with parameter exploration (total number of game adjustments / total number of sessions played) on the first day (p = – 0.31, p= 0.05). Compared to low users, power users on the first day had more game success (65.18 ± 25.76 %vs. 54.94 ± 30.31 %,p <. 001), parameter exploration (25.47 ± 22.78 % vs. 12.05 ± 20.56 %, p <. 001), and game sessions played (7.60 ± 6.59 sessions vs. 4.04 ± 3.56 sessions, p <. 001). These observations support the premise that initial game success which is modulated by strategically adjusting parameters when necessary is a key determinant of adherence to rehabilitation technology.

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[ARTICLE] Application of Home-Based Wearable Technologies in Physical Rehabilitation for Stroke: A Scoping Review – Full Text

Abstract:

Using wearable technologies in the home setting is an emerging option for self-directed rehabilitation. A comprehensive review of its application as a treatment in home-based stroke rehabilitation is lacking. This review aimed to 1) map the interventions that have used wearable technologies in home-based physical rehabilitation for stroke, and 2) provide a synthesis of the effectiveness of wearable technologies as a treatment choice. Electronic databases of the Cochrane Library, MEDLINE, CINAHL, and Web of Science were systematically searched for work published from their inception to February 2022. This scoping review adopted Arksey and O’Malley’s framework in the study procedure. Two independent reviewers screened and selected the studies. Twenty-seven were selected in this review. These studies were summarized descriptively, and the level of evidence was assessed. This review identified that most research focused on improving the hemiparetic upper limb (UL) function and a lack of studies applying wearable technologies in home-based lower limb (LL) rehabilitation. Virtual reality (VR), stimulation-based training, robotic therapy, and activity trackers are the interventions identified that apply wearable technologies. Among the UL interventions, “strong” evidence was found to support stimulation-based training, “moderate” evidence for activity trackers, “limited” evidence for VR, and “inconsistent evidence” for robotic training. Due to the lack of studies, understanding the effects of LL wearable technologies remains “very limited.” With newer technologies like soft wearable robotics, research in this area will grow exponentially. Future research can focus on identifying components of LL rehabilitation that can be effectively addressed using wearable technologies.

SECTION I.

Introduction

Stroke survivors sustain multiple impairments in physical, cognitive, and sensory functions, significantly impeding their participation in daily activities and impacting their quality of life. For example, 80 percent of stroke survivors face motor impairments affecting one side of their body [1]. Previous research [2] indicated that only 15 percent of stroke survivors achieve full functional recovery in both limbs. In comparison, 33 to 60 percent have significant residual impairments in their hemiplegic arm at the chronic phase [3]. Though there have been recent advances in the medical management of stroke, most post-stroke recovery relies heavily on rehabilitation interventions [4].

Intensive rehabilitation has been shown to enhance motor recovery after stroke [5], and rehabilitation is necessary until maximum recovery is achieved [6]. Nevertheless, such extensive training is not sustainable in the long run due to the high cost of post-stroke care, such as rehabilitation services (i.e., therapist salary, rehabilitation site) and hospitalization [7]. Therefore, the self-management paradigm has been adopted to facilitate home-based self-directed training to reduce the burden placed on existing healthcare resources [8]. Self-directed rehabilitation is conducted independently by patients and carers without direct supervision from a healthcare professional. This sort of training at home offers several advantages, such as providing contextual learning in real-life environments that promotes generalization [9], [10] and reduces the cost of supervised therapy [11].

The use of wearable technology is a promising option for providing home-based self-directed rehabilitation while keeping costs low [7]. Using wearable devices offers several advantages over conventional approaches. For example, some devices are portable, low-cost, and flexible [12], [13]. Wearable technologies are electronic hand-free devices worn externally on the body and monitor activities without limiting users’ movements [14], [15]. In rehabilitation, wearable technologies are applied to measure body kinematics outside the laboratory and augment posture and motion correction by providing real-time feedback to users [13], [16] or assistance (passive or active assisted) in movements [17].

Some wearable devices provide real-time augmented feedbacks by emitting auditory, visual or tactile cues to the user, which is critical for motor relearning [16] and sustains motivation during training [18]. This feedback increases the awareness of correct posture and movement patterns during task execution [13], [16] in stroke individuals whose intrinsic feedback mechanisms (e.g., proprioceptive cues) are weakened or impaired [18]. Traditionally, the therapist provides extrinsic feedback to facilitate motor relearning in persons with stroke [13]. However, this training method is very time-consuming and manpower-intensive to carry out at home [13]. Alternatively, these wearable devices initiate augmented feedback to prompt individuals to perform self-directed training in the home setting. Unlike traditional methods of monitoring therapy adherence such as using an activity logbook or checklist, the wearable device increases treatment adherence in the home setting by providing objective feedback on the type and amount of upper limb training and trigger sensory reminders to increase the frequency of upper limb practice [19].

Increasing publication trends on the use of wearable technologies in stroke rehabilitation highlight the growing interest in this area [19], [20]. Maceira-Elvira et al. [7] and Kim et al. [19] conducted scoping reviews on using UL wearable sensors for assessment and treatment in the stroke population. They found that several studies had focused on hemiparetic UL measurement with sensors, but few focused on treatment approaches, and there is a lack of large-scale studies to prove the clinical efficacy of wearable sensors for home use [19]. Another study by Peter et al. [20] focused on reviewing the evidence of wearable sensors for gait assessment and did not look at other types of wearable devices or treatment uses. All these studies [7], [19], [20] narrowed their scope to wearable sensors; other wearable devices, such as stimulators and robotics, were not explored. Finally, two systematic reviews by Parker et al. [14] and Powell et al. [21] investigated the evidence of wearable devices for upper and lower limb rehabilitation, respectively. These reviews included other wearable devices for poststroke rehabilitation, such as electrical stimulation and robotics. Both studies [14], [21] revealed a paucity of high-quality evidence supporting using upper and lower-limb wearable technologies to improve activity and participation. Nevertheless, both narrowed their scope to select randomized controlled studies that used wearable devices to improve activity and participation. Other study designs and outcomes, such as motor impairment and function, were not addressed.

The effects of wearable devices in home-based stroke rehabilitation remained unclear from the analysis of previous reviews [14], [19], [20], [21], as most focus on wearable sensors, which are predominantly used for assessment rather than treatment [7], [19], [20]. Augmented feedback from a wearable device may make it an effective tool in motor training for stroke survivors beyond its measurement capabilities. In addition, the current evidence from previous reviews seems to skew toward using wearable technologies in care institutions or laboratories requiring the supervision of a rehabilitation specialist [15], [20], which eliminates stroke survivors’ ability to self-direct their training [20].

Although earlier studies [7], [14], [19], [20], [21] contributed valuable knowledge to wearable technologies research, a comprehensive review of their application in home-based stroke rehabilitation remains scarce. To the best of our knowledge, no review has investigated the effectiveness of wearable devices as a treatment option in home-based rehabilitation for persons with stroke. A scoping review method is commonly used for new research areas because emerging and diverse evidence clarifies key concepts and characteristics and identifies research gaps [22]. This scoping review aimed to (1) map interventions that use wearable technologies in home-based physical rehabilitation for stroke, and (2) provide a synthesis of their effectiveness as treatment options. The findings of this review shed light on the research gap and aid researchers and clinicians by providing valuable knowledge to translate the use of wearable technologies into clinical practice.[…]

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Fig. 2. Level of evidence in applying wearable technologies to different body regions.

[Editorial] How can wearable robotic and sensor technology advance neurorehabilitation?

Introduction

Current rehabilitation rests on two approaches: restorative and compensatory. The restorative approach aims to re-establish lost functions via a specific mechanism called neuroplasticity, defined as the ability of the nervous system to regenerate and reorganize its structure, functions, and connections. Neuroplasticity is activity-dependent, where repetitive, targeted, consistently challenging exercise facilitates specific functional recovery via restoring impaired neural circuits, growing new neural circuits, and generalization to real-world settings (Kelly et al., 2006; Dietz and Fouad, 2014; Hylin et al., 2017). On the other hand, the compensatory approach is based on the principle of adaptability, which does not focus on changing and restoring the physical structure or body function. Instead, it is designed to minimize the effect of deficits and recover a degree of function by developing internal and external strategies in which the individuals use residual, intact ability to adapt to the changes in their body functions post injuries or diseases. Assistive rehabilitation technology offers an opportunity to enhance the user’s experience by repeatedly practicing specific tasks that are precisely controlled via real-time kinetic or kinematic feedback and assistance as needed to minimize functional deficits and environmental barriers (Grimm et al., 2016). Hence, the use of rehabilitation technology can change the individual’s experience impacting the ability of functional recovery via the mechanism of neuroplasticity and minimizing the deficits via adapting changes to their body structures and functions post-disease or injury (Grimm et al., 2016).

Modern technological advances in wearable technologies in health care have made it possible to augment patient clinical outcomes (Afzal et al., 2020; Terranova et al.; Edwards et al., 2022; Werner et al.), create an effective continuum of rehabilitation (Ahmed et al., 2022; Garnier-Villarreal et al., 2022), and promote independence (Bützer et al., 2021; Liu et al., 2022) for persons after neurological injuries. In particular, the technology allows more intensive and tailored patient rehabilitation activities and services (increasing the amount and quality of therapy that can be administered and supervised) and enable all the involved actors in the team (e.g., physicians, therapists, bioengineers and others) to design patient-centered and custom intervention collaboratively. Moreover, technology can transform rehabilitation from a one-on-one human resource intensive treatment that can only be provided in specialized centers to a technology-driven, remotely-supervised and widely accessible enterprise (Moulaei et al., 2022). Given the increased costs associated with long-term rehabilitation and the difficulty in providing appropriate duration and intensity of rehabilitation services required to manage disability, cost-effective development of robotic rehabilitation is greatly warranted.

In this special issue, we have collected ten articles on the following themes.

Neuromuscular adaptation mechanisms using wearable robotics

Le et al. investigated the cortical activity associated with executive resource allocation during a motor task with an assistive upper limb exoskeleton robot.

Design and experimentation of novel robotics

Zhang and Collins explored the possibility of optimizing the post-stabilization real-time control performance on lower-limb legged robots driven by series elastic actuators. Chen et al. provided a novel proof-in-conception prosthesis myocontrol approach focused on hip disarticulation amputees and investigated the performance of the gait phase decoder fusing bilateral neuromechanical signals. Porciuncula et al. investigated the feasibility and rehabilitative potential of applying propulsion-augmenting exosuits as part of an individualized and progressive training program to retrain faster walking and the underlying propulsive strategy. Wang et al. investigated different assistance patterns’ effectiveness in improving the walking economy on a lightweight cable-driven ankle exoskeleton.

Evidence of clinical trials using robotics

Brinkemper et al. investigated whether an improvement in physiological gait can be demonstrated in addition to the functional parameters after treatment with a robot suit in persons with acute and chronic spinal cord injury (SCI). Zieriacks et al. investigated the impact of exoskeleton-assisted body weight-supported treadmill training on functional and motor recovery in post-acute neurorehabilitation in persons with acute and chronic SCI. Treviño et al. investigated which factors predict the improvement in Functional Independence Measure score after robotic exoskeleton rehabilitation in persons with cerebrovascular accidents or traumatic brain injuries. Finally, Casas et al. investigated the effectiveness of 8-week home-based therapy using an upper limb robot in persons with chronic stroke.

Clinical and administrative experience in the clinical implementation of wearable robotics in healthcare settings

Nolan et al. presented a multicenter investigation on the utilization of a robotic exoskeleton for overground walking in persons with acute and chronic stroke.

Conclusion

These articles added much knowledge in supporting the feasibility and safety of wearable and robotic technology, i.e., wearable upper and lower limbs exoskeletons to facilitate neuromotor and functional recovery in persons with sensor-motor impairments and disabilities. Moreover, this special issue illustrates not only the broad efforts of adapting technology into rehabilitation care but also the future research direction of the field. The articles reflect the current emphasis on the hardware and software design and early phases of clinical trials in using the technology. While the field needs more multidisciplinary conceptual and collaborative efforts (i.e., the team with clinicians and engineers), we hope the knowledge will further be developed into areas including patient-centered, value-based, and accessible rehabilitation care, considered an exciting future trajectory for neurorehabilitation.

References

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PubMed Abstract | CrossRef Full Text | Google Scholar

Ahmed, S., Archambault, P., Auger, C., Durand, A., Fung, J., Kehayia, E., et al. (2022). Biomedical research and informatics living laboratory for innovative advances of new technologies in community mobility rehabilitation: Protocol for evaluation and rehabilitation of mobility across continuums of care. JMIR Res. Protocol. 11, e12506. doi: 10.2196/12506

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Bützer, T., Lambercy, O., Arata, J., and Gassert, R. (2021). Fully wearable actuated soft exoskeleton for grasping assistance in everyday activities. Soft Robot. 8, 128–143. doi: 10.1089/soro.2019.0135

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Dietz, V., and Fouad, K. (2014). Restoration of sensorimotor functions after spinal cord injury. Brain 137, 654–667. doi: 10.1093/brain/awt262

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Moulaei, K., Sheikhtaheri, A., Nezhad, M. S., Haghdoost, A., Gheysari, M., and Bahaadinbeigy, K. (2022). Telerehabilitation for upper limb disabilities: a scoping review on functions, outcomes, and evaluation methods. Arch. Public Health. 80, 196. doi: 10.1186/s13690-022-00952-w

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[ARTICLE] Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: a multi-stage approach

Abstract

Purpose

Post-stroke survivors report that feedback helps to increase training motivation. A wearable system (M-MARK), comprising movement and muscle sensors and providing feedback when performing everyday tasks was developed. The objective reported here was to create an evidence-based set of upper-limb tasks for use with the system.

Materials and methods

Data from two focus groups with rehabilitation professionals, ten interviews with stroke survivors and a review of assessment tests were synthesized. In a two-stage process, suggested tasks were screened to exclude non-tasks and complex activities. Remaining tasks were screened for suitability and entered into a categorization matrix.

Results

Of 83 suggestions, eight non-tasks, and 42 complex activities were rejected. Of the remaining 33 tasks, 15 were rejected: five required fine motor control; eight were too complex to standardize; one because the role of hemiplegic hand was not defined and one involved water. The review of clinical assessment tests found no additional tasks. Eleven were ultimately selected for testing with M-Mark.

Conclusions

Using a task categorization matrix, a set of training tasks was systematically identified. There was strong agreement between data from the professionals, survivors and literature. The matrix populated by tasks has potential for wider use in upper-limb stroke rehabilitation.

• IMPLICATIONS FOR REHABILITATION
• Rehabilitation technologies that provide feedback on quantity and quality of movements can support independent home-based upper limb rehabilitation.
• Rehabilitation technology systems require a library of upper limb tasks at different levels for people with stroke and therapists to choose from.
• A user-defined and evidence-based set of upper limb tasks for use within a wearable sensor device system have been developed.

Introduction

Approximately two-thirds of patients who survive a stroke have upper limb limitations, with only 5 to 20% demonstrating full recovery at 6 months post stroke [1] contributing to reduced quality of life for survivors and caregivers [2]. Principles of neuroplasticity from animal studies [3] and motor learning from human studies suggest that repetition, challenge and feedback are some of the main facilitators for recovery of function [4]. Studies comparing large differences in amount of therapy have shown greater functional improvement than those with smaller differences, for example [5]. Furthermore, best practice guidelines recommend a prolonged period of rehabilitation [7] but economic pressures have led to reduction in therapy time [8] and intensity of practise in the clinic rarely reaches a number of repetitions that appears optimal based on animal studies [6]. Stroke survivors therefore need to train at home to sustain and augment their rehabilitation. According to stroke survivors, themselves, training at home is not motivating unless accompanied by therapist input and a source of feedback [9]. Assisting and optimizing self-management of rehabilitation is, consequently, a goal for both clinicians and researchers.

To address this problem we designed, built, and tested a novel device to support home-based rehabilitation that provided feedback on performance to both patients and therapists. M-MARK, an acronym for Mechanical Muscle Activity with Real time Kinematics [10], comprises a garment with embedded inertial measurement unit (IMU) sensors to detect movement of trunk, shoulder, elbow, wrist (but not finger) coupled with mechanomyography (MMG) to detect synchronous mechanical muscle activity in biceps brachii, triceps brachii, wrist/finger extensors and flexors [11]. Data are transmitted via Bluetooth from the device to a computer tablet with a user interface for system set up and to display feedback (Figure 1).

Figure 1. M-MARK system including garment, sensors and user interface.

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[Abstract] The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review

Abstract

Purpose

To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment.

Materials and methods

The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis.

Results

We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE.

Conclusions

Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.

• IMPLICATIONS FOR REHABILITATION
• Sensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.
• Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.
• Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.

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[Abstract] mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties

Abstract

Purpose

We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice.

Materials and methods

Using Arksey and O’Malley’s framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts.

Results

Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill.

Conclusions

This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one’s individual objective.

• Implications for Rehabilitation
• Rehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.
• Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.
• Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.
• Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone application.

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[Abstract] Objective assessments of human motor ability of the upper limb: A systematic review

Abstract

BACKGROUND: Most of the patients who survive stroke, spinal cord or others nervous system injuries, must face different challenges for a complete recovery of physical functional impairment. An accurate and recurrent assessment of the patient rehabilitation progress is very important. So far, wearable sensors (e.g. accelerometers, gyroscopes) and depth cameras have been used in medical rehabilitation for the automation of traditional motor assessments. Combined with machine learning techniques, these sensors are leading to novel metric systems for upper limb mobility assessment.

OBJECTIVE: Review current research for objective and quantitative assessments of the upper limb movement, analyzing sensors used, health issues examined, and data processes applied such as: selected features, feature engineering approach, learning models and data processing techniques.

METHOD: A systematic review conducted according to the PRISMA guidelines. EBSCOHOST discovery service was queried for relevant articles published from January 2014 to December 2018 with English language and scholarly peer reviewed journals limits.

RESULTS: Of the 568 articles identified, 75 were assessed for eligibility and 43 were finally included and weighed for an in-depth analysis according to their ponderation. The reviewed studies show a wide use of sensors to capture raw data for subsequent motion analysis.

CONCLUSION: As the volume of the data captured via these sensors increase, it makes sense to extract useful information about them such as prediction of performance scores, detection of movement impairments and measured progression of recovery.

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[Abstract] Remote Monitoring of Physical Rehabilitation of Stroke Patients using IoT and Virtual Reality

Abstract

The statistics highlights that physical rehabilitation are required nowadays by increased number of people that are affected by motor impairments caused by accidents or aging. Among the most common causes of disability in adults are strokes or cerebral palsy. To reduce the costs preserving the quality of services new solutions based on current technologies in the area of physiotherapy are emerging. The remote monitoring of physical training sessions could facilitate for physicians and physical therapists’ information about training outcome that may be useful to personalize the exercises helping the patients to achieve better rehabilitation results in short period of time process. This research work aims to apply physical rehabilitation monitoring combining Virtual Reality serious games and Wearable Sensor Network to improve the patient engagement during physical rehabilitation and evaluate their evolution. Serious games based on different scenarios of Virtual Reality, allows a patient with motor difficulties to perform exercises in a highly interactive and non-intrusive way, using a set of wearable devices, contributing to their motivational process of rehabilitation. The system implementation, system validation and experimental results are included in the paper.

Source: https://ieeexplore.ieee.org/abstract/document/9183980

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

Abstract

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 estimated 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.

[Abstract] Gait Performance in People with Symptomatic, Chronic Mild Traumatic Brain Injury

Abstract

There is a dearth of knowledge about how symptom severity affects gait in the chronic (>3 months) mild traumatic brain injury (mTBI) population despite up to 53% of people reporting persisting symptoms after mTBI. The aim of this investigation was to determine whether gait is affected in a symptomatic, chronic mTBI group and to assess the relationship between gait performance and symptom severity on the Neurobehavioral Symptom Inventory (NSI). Gait was assessed under single- and dual-task conditions using five inertial sensors in 57 control subjects and 65 persons with chronic mTBI (1.0 year from mTBI). The single- and dual-task gait domains of Pace, Rhythm, Variability, and Turning were calculated from individual gait characteristics. Dual-task cost (DTC) was calculated for each domain. The mTBI group walked (domain z-score mean difference, single-task = 0.70; dual-task = 0.71) and turned (z-score mean difference, single-task = 0.69; dual-task = 0.70) slower (p < 0.001) under both gait conditions, with less rhythm under dual-task gait (z-score difference = 0.21; p = 0.001). DTC was not different between groups. Higher NSI somatic subscore was related to higher single- and dual-task gait variability as well as slower dual-task pace and turning (p < 0.01). Persons with chronic mTBI and persistent symptoms exhibited altered gait, particularly under dual-task, and worse gait performance related to greater symptom severity. Future gait research in chronic mTBI should assess the possible underlying physiological mechanisms for persistent symptoms and gait deficits.

Source: https://www.liebertpub.com/doi/abs/10.1089/neu.2020.6986