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Exploration of rehabilitation through the use of virtual reality interventions for patients with upper limb conditions: protocol for scoping review. v1

2021· preprint· en· W3156877185 on OpenAlexaboutno aff
Stefanie F Andrew, Carol Clark, Sheppard Z.A., Matthew J. Evans, Julie A. Hutt, Timothy Bernard Crook

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityTelerehabilitationRehabilitationNeurorehabilitationPhysical medicine and rehabilitationPopulationExoskeletonMedicineHaptic technologyStroke (engine)Psychological interventionTelemedicinePsychologyPhysical therapyComputer scienceHuman–computer interactionSimulationHealth careEngineeringNursing

Abstract

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Background The use of virtual reality to aid rehabilitation of upper-limb conditions has been an emerging field over the past decade. The majority of research seems to focus on post-stroke therapies with major advances in improving hand function through various robotic and digital techniques (1), however it is still not standard practice. With the recent crisis of COVID 19, home therapy has been thrust into the forefront of rehabilitation. At present a wide variety of technology has been developed to target rehabilitation in the upper limb.This includes hand tracking technology (e.g. Leap Motion) where various prototypes have been tested using haptics (2), robotic-assisted movement devices which predominantly target stroke or neurorehabilitation by means of exoskeleton prototypes (3,4), and exercise programmes through telerehabilitation and virtual reality (5). The majority of hand devices that have been developed are bulky and can impact on the accuracy of functional measurements (6). In terms of virtual reality, studies suggest that patients find it enjoyable and are as, if not more, motivated in these rehabilitation sessions when compared to traditional exercises, even in the older population (7). Hoffman et al in 2020 (8) found that patients undergoing hand rehabilitation with virtual reality felt that their pain was reduced, they had increased levels of motivation and therefore felt more able to perform exercises. This has been matched by studies looking at pain and anxiety levels whilst using virtual reality during wide awake anaesthesia with a significant reduction in both (9). Hand rehabilitation outside of stroke and neurological disorders, for example post-surgery or trauma, involves intensive hand rehabilitation by the hand therapy team but there is a global shortage of this service. Current practice is one-to-one patient-to-therapist and involves intensive rehabilitation, assessment and documentation. Virtual reality could prove a vital technology to develop to enable patients to undertake immersive therapy within their own homes whilst allowing remote assessment of their progress and function from the hand therapy and clinical teams. Aims The overarching aim of this review is to identify the range and variety of virtual reality tools that are available and effective for upper limb conditions. Review objectives 1) What is the “extent (size), range (variety) and nature (characteristics) of the evidence” of virtual tools to enhance hand rehabilitation? 2) Do these tools have the potential to be used in the field of hand surgery and hand rehabilitation? 3) What are the barriers and facilitators to hand rehabilitation using virtual tools? Design The scoping review will be conducted in accordance with PRISMA-ScR guidelines (10) and the frameworks developed by Arksey and O’Malley (11) and Levac et al. (12). Preliminary searches will be conducted and two librarians will be consulted to create a comprehensive search strategy. Screening of titles and abstracts will be undertaken by three independent reviewers, with conflicts resolved by discussions and consensus. Full-text screening will be undertaken following this same approach. A charting form will be developed based on the objectives of the review and refined by the research team. Data will be collated and summarised, with quantitative sources described descriptively and qualitative data analysed thematically (13). Results will be presented using summary tables and/or using pictorial/flow charts, if appropriate. References 1) Saposnik G, Levin M, Outcome Research Canada (SORCan) Working Group. Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians. Stroke. 2011 May;42(5):1380-1386. DOI: 10.1161/strokeaha.110.605451. 2) Placidi G, Avola D, Iacoviello D, Cinque L. Overall design and implementation of the virtual glove. Comput Biol Med. 2013 Nov;43(11):1927-40. doi: 10.1016/j.compbiomed.2013.08.026. Epub 2013 Sep 25. PMID: 24209938. 3) Ghassemi M, Ochoa JM, Yuan N, Tsoupikova D, Kamper D. Development of an Integrated Actuated Hand Orthosis and Virtual Reality System for Home-Based Rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1689-1692. doi: 10.1109/EMBC.2018.8512704. PMID: 30440720. 4) Osuagwu BAC, Timms S, Peachment R, Dowie S, Thrussell H, Cross S, Shirley R, Segura-Fragoso A, Taylor J. Home-based rehabilitation using a soft robotic hand glove device leads to improvement in hand function in people with chronic spinal cord injury:a pilot study. J Neuroeng Rehabil. 2020 Mar 5;17(1):40. doi: 10.1186/s12984-020-00660-y. PMID: 32138780; PMCID: PMC7057671. 5) Levanon Y. The advantages and disadvantages of using high technology in hand rehabilitation. J Hand Ther. 2013 Apr-Jun;26(2):179-83. doi: 10.1016/j.jht.2013.02.002. PMID: 23598084. 6) Rose CG, Pezent E, Kann CK, Deshpande AD, O'Malley MK. Assessing Wrist Movement With Robotic Devices. IEEE Trans Neural Syst Rehabil Eng. 2018 Aug;26(8):1585-1595. doi: 10.1109/TNSRE.2018.2853143. Epub 2018 Jul 5. PMID: 29994401. 7) Van Schaik P, Blake J, Pernet F, Spears I, Fencott C. Virtual augmented exercise gaming for older adults. Cyberpsychol Behav. 2008 Feb;11(1):103-6. doi: 10.1089/cpb.2007.9925. PMID: 18275322. 8) Hoffman HG, Boe DA, Rombokas E, Khadra C, LeMay S, Meyer WJ, Patterson S, Ballesteros A, Pitt SW. Virtual reality hand therapy: A new tool for nonopioid analgesia for acute procedural pain, hand rehabilitation, and VR embodiment therapy for phantom limb pain. J Hand Ther. 2020 Apr-Jun;33(2):254-262. doi: 10.1016/j.jht.2020.04.001. Epub 2020 May 30. PMID: 32482376; PMCID: PMC7719341. 9) Hoxhallari E, Behr IJ, Bradshaw JS, Morkos MS, Haan PS, Schaefer MC, Clarkson JHW. Virtual Reality Improves the Patient Experience during Wide-Awake Local Anesthesia No Tourniquet Hand Surgery: A Single-Blind, Randomized, Prospective Study. Plast Reconstr Surg. 2019 Aug;144(2):408-414. doi: 10.1097/PRS.0000000000005831. PMID: 31348351. 10) Tricco, AC, Lillie, E, Zarin, W, O'Brien, KK, Colquhoun, H, Levac, D, Moher, D, Peters, MD, Horsley, T, Weeks, L, Hempel, S et al. (2018b) PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018,169 (7):467-473. doi:10.7326/M18-0850. 11) Arksey H and O’Malley L (2005) Scoping studies: towards a methodological framework International Journal of Social Research Methodology 8(1): 19-32 12) Levac D, Colquhoun H and O’Brien K (2010) Scoping studies: advancing the methodology Implementation Science 5: 69 13) Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. doi:10.1191/1478088706qp063oa 14) World Health Organisation (2018) Classification of digital health interventions v1.0: A shared language to describe the uses of digital technology for health. WHO/RHR/19.06. Pages 1-20. https://www.who.int/reproductivehealth/publications/mhealth/classification-digital-health-interventions/en/

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.077
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.048
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0140.012
Bibliometrics0.0120.010
Science and technology studies0.0030.003
Scholarly communication0.0070.005
Open science0.0040.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0770.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.186
GPT teacher head0.453
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2021
Admission routes1
Has abstractyes

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