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Record W4220966663 · doi:10.2196/32027

A Virtual Reality Game Suite for Graded Rehabilitation in Patients With Low Back Pain and a High Fear of Movement: Within-Subject Comparative Study

2022· article· en· W4220966663 on OpenAlexvenueno aff
Alexander T. Peebles, Susanne M. van der Veen, Alexander Stamenkovic, Christopher France, Peter E. Pidcoe, James S. Thomas

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersNational Institute of General Medical Sciences
KeywordsVirtual realityLumbarPhysical medicine and rehabilitationRehabilitationKinematicsComputer sciencePsychologyExcursionMovement (music)Repeated measures designMixed realityHuman–computer interactionSimulationPhysical therapyMedicineMathematicsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Complex movement pathologies that are biopsychosocial in nature (eg, back pain) require a multidimensional approach for effective treatment. Virtual reality is a promising tool for rehabilitation, where therapeutic interventions can be gamified to promote and train specific movement behaviors while increasing enjoyment, engagement, and retention. We have previously created virtual reality-based tools to assess and promote lumbar excursion during reaching and functional gameplay tasks by manipulating the position of static and dynamic contact targets. Based on the framework of graded exposure rehabilitation, we have created a new virtual reality therapy aimed to alter movement speed while retaining the movement-promoting features of our other developments. OBJECTIVE: This study aims to compare lumbar flexion excursion and velocity across our previous and newly developed virtual reality tools in a healthy control cohort. METHODS: A total of 31 healthy participants (16 males, 15 females) took part in 3 gamified virtual reality therapies (ie, Reachality, Fishality, and Dodgeality), while whole-body 3D kinematics were collected at 100 Hz using a 14-camera motion capture system. Lumbar excursion, lumbar flexion velocity, and actual target impact location in the anterior and vertical direction were compared across each virtual reality task and between the 4 anthropometrically defined intended target impact locations using separate 2-way repeated measures analysis of variance models. RESULTS: There was an interaction between game and impact height for each outcome (all P<.001). Post-hoc simple effects models revealed that lumbar excursion was reduced during Reachality and Fishality relative to that during Dodgeality for the 2 higher impact heights but was greater during Reachality than during Fishality and Dodgeality for the lowest impact height. Peak lumbar flexion velocity was greater during Dodgeality than during Fishality and Reachality across heights. Actual target impact locations during Dodgeality and Fishality were lower relative to those during Reachality at higher intended impact locations but higher at lower intended impact locations. Finally, actual target impact location was further in the anterior direction for Reachality compared to that for Fishality and for Fishality relative to that for Dodgeality. CONCLUSIONS: Lumbar flexion velocity was reduced during Fishality relative to that during Dodgeality and resembled velocity demands more similar to those for a self-paced reaching task (ie, Reachality). Additionally, lumbar motion and target impact location during Fishality were more similar to those during Reachality than to those during Dodgeality, which suggests that this new virtual reality game is an effective tool for shaping movement. These findings are encouraging for future research aimed at developing an individualized and graded virtual reality intervention for patients with low back pain and a high fear of movement.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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