Motor learning in hemi-Parkinson using VR-manipulated sensory feedback

Ossmy, Ori; Mansano, Lihi; Frenkel‐Toledo, Silvi; Kagan, Eugene; Koren, Shiri; Gilron, Roee; Reznik, Daniel; Soroker, Nachum; Mukamel, Roy

doi:10.1080/17483107.2020.1785561

Cited by 6 publications

(15 citation statements)

References 72 publications

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“…In addition, VR combined with other interventions has potentiated motor recovery. Similar effects have been seen in patients with Parkinson's disease [49,56], cerebral palsy [38,57], multiple sclerosis [25], spinal cord injury [58], and in older individuals [37].…”

Section: Introductionsupporting

confidence: 57%

“…The other five studies were related to other conditions: balance training in older adults [37], Parkinson's disease [49,56], and cerebral palsy [38,57].…”

Section: Sample Descriptionmentioning

confidence: 99%

“…A total of 19 different VR systems were used in the 22 studies. Most of those were non-immersive systems, used in [27-34, 37, 38, 47, 49-54, 57], with only the systems used in [35,36,48,56] being immersive. Also, most studies used their own systems [27, 29, 30, 32-36, 47, 48, 50-54, 56, 57], with two studies using commercial video game consoles, namely, Wii Fit Balance Board (Nintendo, Redmond, WA, USA) [37] and PlayStation3 console connected to a 5DT 5 Ultra Glove [38].…”

Section: Vr Systemsmentioning

confidence: 99%

“…In some cases, haptic feedback was also used [31,54]. used both types of experiment (task and rs-fMRI) [29,53,56].…”

Section: Vr Systemsmentioning

confidence: 99%

“…In task-fMRI studies, block designs were the most common [28, 30-32, 35, 37, 38, 47, 49-52, 57], although some used event-related designs [53,54], and two did not explicit the type of experimental task used [29,56].…”

Section: Vr Systemsmentioning

confidence: 99%

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Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies

et al. 2022

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Background: The use of virtual reality (VR) as a rehabilitation tool has been shown to induce motor and cognitive improvements in different populations. Functional magnetic resonance imaging (fMRI) has been used to investigate neuroplasticity resulting from these treatments. We hypothesize that VR rehabilitation induces functional improvement and brain changes that can be detected by fMRI. Objective: To systematically review the effects of VR intervention on the cortical reorganization measured by fMRI and associated with functional improvement. Methods: We performed a systematic review of studies published between 2005 and 2021. Papers were retrieved from six databases using the following keywords: “motor rehabilitation”, “fMRI” and “virtual reality”. Case studies, pre-post studies, cross-sectional studies, and randomized controlled trials published were included. Manuscripts were assessed by The NIH Study Quality Assessment Tools to determine their quality. Results: Twenty-three articles met our eligibility criteria: 18 about VR rehabilitation in stroke and five on other clinical conditions (older adults, cerebral palsy, and Parkinson's disease). Changes in neural patterns of activation and reorganization were revealed in both the ipsilesional and the contralesional hemispheres. Results were located mainly in the primary motor cortex, sensorimotor cortex and supplementary motor area in post-stroke patients in the acute, subacute, and chronic rehabilitation phases, and were associated with functional improvement after VR intervention. Similar effects were observed in older adults and in patients with other neurological diseases with improved performance. Conclusion: Most stroke-related studies showed either restoration to normal or increase of activation patterns or relateralization at/to the ipsilesional hemisphere, with some also reporting a decrease in activity or extent of activation after VR therapy. In general, VR intervention demonstrated evidence of efficacy both in neurological rehabilitation and in performance improvement of older adults, accompanied by fMRI evidence of brain reorganization.

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Section: Introductionsupporting

confidence: 57%

“…The other five studies were related to other conditions: balance training in older adults [37], Parkinson's disease [49,56], and cerebral palsy [38,57].…”

Section: Sample Descriptionmentioning

confidence: 99%

Section: Vr Systemsmentioning

confidence: 99%

“…In some cases, haptic feedback was also used [31,54]. used both types of experiment (task and rs-fMRI) [29,53,56].…”

Section: Vr Systemsmentioning

confidence: 99%

Section: Vr Systemsmentioning

confidence: 99%

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Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies

et al. 2022

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Technologies in Home-Based Digital Rehabilitation: Scoping Review

Arntz¹,

Weber²,

Handgraaf³

et al. 2023

JMIR Rehabil Assist Technol

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Background Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients’ needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR. Objective In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR. Methods The scoping review followed the framework of Arksey and O’Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR. Results A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies. Conclusions Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients’ needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.

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Technologies in Home-Based Digital Rehabilitation: Scoping Review (Preprint)

Arntz¹,

Weber²,

Handgraaf³

et al. 2022

Preprint

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BACKGROUND Due to population growth, aging, and the increasing number of people with chronic diseases and disabilities, the need for rehabilitation is steadily increasing worldwide and though the pressure on the health care system is rising. Therefore, a shift in rehabilitation to the home setting is essential. For effective home-based rehabilitation, sufficient support must be provided. New digital technologies could help to meet these demands. The COVID-19 pandemic has further exacerbated the challenges on the health care system faced by affected individuals during rehabilitation. To develop and implement digital rehabilitation solutions that meet the clients’ need and ease the growing pressure on the healthcare system it is necessary to give an overview of existing, relevant and future solutions shaping the constantly evolving market of technologies for home-based digital rehabilitation. OBJECTIVE The scoping review aims to identify digital technologies for home-based rehabilitation, to predict emerging trends in digital rehabilitation, and to report the influences of the COVID-19 pandemic on digital rehabilitation. METHODS A scoping review following the framework of Arksey and O’Malley with the improvements of Levac and colleagues were carried out. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO and Cochrane library. The search spanned from January 2015 to January 2022. Every type of English written publication that was existing as full-text and focuses on digital technologies directly related to home-based rehabilitation were included. A bibliometric analysis was performed to give an overview of the included references. A co-occurrence analysis was carried out to identify the technologies for home-based digital rehabilitation. A full-text-analysis of all included reviews was conducted to filter the trends for the home-based digital rehabilitation. A grey literature search was carried out to adjunct the results of the review analysis and to reveal the influences of the COVID-19 pandemic regarding the development of digital rehabilitation. RESULTS A total of 2.437 records was included for the bibliometric analysis, 95 for full-text-analysis and 42 as a result of the grey literature search. Sensors, robotic devices, gamification, virtual and augmented reality (VR/AR), and digital/mobile applications are already widely used in home-based rehabilitation, but AI/machine learning, exoskeletons, and mobile/digital applications represent emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches, however not to the development of new technologies. CONCLUSIONS Multiple tools are available and implemented for home-based rehabilitation. AI and machine learning will be part of particular interest for the redesign of rehabilitation to address future challenges of the rehabilitation sector. The results reflect the need for feasible approaches to implement digital rehabilitation that meet clients’ needs and adhere to framework conditions to be sustained apart from exceptional situations.

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Motor learning in hemi-Parkinson using VR-manipulated sensory feedback

Cited by 6 publications

References 72 publications

Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies

Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies

Technologies in Home-Based Digital Rehabilitation: Scoping Review

Technologies in Home-Based Digital Rehabilitation: Scoping Review (Preprint)

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