Advances in Remote Monitoring for Stroke Recovery

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Efficacy of Robot-Assisted and Virtual Reality Interventions on Balance, Gait, and Daily Function in Patients With Stroke: A Systematic Review and Network Meta-analysis

OBJECTIVE

This study aimed to evaluate the comparative effectiveness and ranking of robot-assisted training, virtual reality, and robot-assisted rehabilitation combined with virtual reality in improving balance, gait, and daily function in patients with stroke.

DATA SOURCES

PubMed, EMBASE, the Cochrane Library, Physiotherapy Evidence Database, CINAHL, Web of Science, and ProQuest Dissertations and Theses abstracting and indexing databases were comprehensively searched to include randomized controlled trials published through August 31, 2022.

STUDY SELECTION

Randomized controlled trials comparing robot-assisted training, virtual reality, robot-assisted rehabilitation combined with virtual reality, and conventional therapy to assess the effects on balance, gait, and daily function of patients with stroke.

DATA EXTRACTION

The risk of bias was assessed using the Cochrane Risk of Bias tool and the methodological quality of the studies was assessed using the Physiotherapy Evidence Database scale. A network meta-analysis of random effects models was performed for direct and indirect effects. Data were analyzed using Stata SE 17.0 and R 4.2.1.

DATA SYNTHESIS

A total of 52 randomized controlled trials involving 1,559 participants were included in this study. Based on the ranking probabilities, robot-assisted rehabilitation combined with virtual reality was most effective in improving balance (surface under the cumulative ranking curve [SUCRA]=82.0%; mean difference [MD]=4.10; 95% confidence interval [CI], 0.43 to 7.67). Virtual reality was most effective in improving velocity (SUCRA=97.8%; MD=-0.15; 95% CI, -0.24 to -0.06) and daily function (SUCRA=92.1%; MD=-7.85; 95% CI, -15.18 to -1.07).

CONCLUSIONS

Compared to robot-assisted training and conventional therapy, robot-assisted training combined virtual reality was most likely the best intervention for balance, and virtual reality might be the most helpful in improving daily function for patients after stroke. Further studies are needed to clarify the specific efficacy of robot-assisted training combined with virtual reality and virtual reality on gait.

Health Professional- and Volunteer-partnered Self-management Support (COMBO-KEY) to Promote Self-efficacy and Self-management Behaviors in People with Stroke: A Randomized Controlled Trial

BACKGROUND

Multiple systematic reviews have reported that self-management interventions are associated with positive impacts on self-efficacy and health-related quality of life (HRQoL) of people with stroke.

PURPOSE

This article reports the effects of an enhanced stroke self-management program.

METHODS

Eligible adults with stroke were recruited from community-based organizations and a support group for a two-arm, assessor-blinded randomized controlled trial. Participants in the control group received usual care, while those in the intervention group also received the 8-week self-management program, Coaching Ongoing Momentum Building On stroKe rEcovery journeY (COMBO-KEY), consisting of four individual home visits and five follow-up phone calls, delivered by healthcare professionals and trained volunteers. Assessments were conducted at baseline and after the intervention, for outcomes of self-efficacy, satisfaction with the performance of self-management behaviors, HRQoL, and community reintegration, which were analyzed using generalized estimating equations (GEEs).

RESULTS

134 Participants were recruited [mean age = 64.1 years, standard deviation (SD) = 12.7]. Over 80% of the participants had a first-ever stroke [mean years after first stroke: 4.2 (SD = 5.1)]. The GEE analysis revealed that the intervention group participants showed significantly greater improvements in self-efficacy, satisfaction with their performance of self-management behaviors, HRQoL, and community reintegration at 8-week follow-up with respect to their baseline levels.

CONCLUSIONS

The COMBO-KEY program showed effective improvements in recovery outcomes of people with stroke. Future research should explore the inclusion of virtual/hybrid sessions, strategies to assess health conditions of people with stroke via online modes, and assessment of goal attainment and actual performance of self-management behaviors.

ICTs and interventions in telerehabilitation and their effects on stroke recovery

Telerehabilitation (TR) is a new model to provide rehabilitation services to stroke survivors. It is a promising approach to deliver mainstream interventions for movement, cognitive, speech and language, and other disorders. TR has two major components: information and communication technologies (ICTs) and stroke interventions. ICTs provide a platform on which interventions are delivered and subsequently result in stroke recovery. In this mini-review, we went over features of ICTs that facilitate TR, as well as stroke interventions that can be delivered via TR platforms. Then, we reviewed the effects of TR on various stroke disorders. In most studies, TR is a feasible and effective solution in delivering interventions to patients. It is not inferior to usual care and in-clinic therapy with matching dose and intensity. With new technologies, TR may result in better outcomes than usual care for some disorders. One the other hand, TR also have many limitations that could lead to worse outcomes than traditional rehabilitation. In the end, we discussed major concerns and possible solutions related to TR, and also discussed potential directions for TR development.

A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review

Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed.