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Enregistrement W3156877185 · doi:10.17504/protocols.io.bq6smzee

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 sur OpenAlexaboutno aff
Stefanie F Andrew, Carol Clark, Sheppard Z.A., Matthew J. Evans, Julie A. Hutt, Timothy Bernard Crook

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineMedicine
ThématiqueStroke Rehabilitation and Recovery
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVirtual realityTelerehabilitationRehabilitationNeurorehabilitationPhysical medicine and rehabilitationPopulationExoskeletonMedicineHaptic technologyStroke (engine)Psychological interventionTelemedicinePsychologyPhysical therapyComputer scienceHuman–computer interactionSimulationHealth careEngineeringNursing

Résumé

récupéré en direct d'OpenAlex

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/

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,032
score de la tête « metaresearch » (Gemma)0,048
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Protocole · Signal consensuel: Protocole
Score de désaccord entre enseignants0,077
Score d'incertitude au seuil0,257

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0320,048
Méta-épidémiologie (sens strict)0,0040,003
Méta-épidémiologie (sens large)0,0140,012
Bibliométrie0,0120,010
Études des sciences et des technologies0,0030,003
Communication savante0,0070,005
Science ouverte0,0040,004
Intégrité de la recherche0,0050,004
Charge utile insuffisante (le modèle a refusé de juger)0,0770,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,186
Tête enseignante GPT0,453
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreProtocole

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2021
Routes d'admission1
Résumé présentoui

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