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Record W4229372900 · doi:10.3389/fnhum.2022.863930

Immersive Technology for Cognitive-Motor Training in Parkinson’s Disease

2022· article· en· W4229372900 on OpenAlexaboutno aff
Justin Kai‐Chi Lau, Claude Regis, Christina Burke, MaryJo Kaleda, Raymond McKenna, Lisa M. Muratori

Bibliographic record

VenueFrontiers in Human Neuroscience · 2022
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersThomas Hartman Center for Parkinson' Research, Stony Brook University
KeywordsPhysical medicine and rehabilitationCognitionGaitCognitive trainingParkinson's diseaseRehabilitationPsychologyGait trainingPhysical therapyMedicineRandomized controlled trialTreadmillDiseaseNeuroscience

Abstract

fetched live from OpenAlex

Background: Parkinson’s disease (PD) is a neurodegenerative disease in which the progressive loss of dopaminergic neurons (DA) leads to initially sporadic and eventually widespread damage of the nervous system resulting in significant musculoskeletal and cognitive deterioration. Loss of motor function alongside increasing cognitive impairment is part of the natural disease progression. Gait is often considered an automatic activity; however, walking is the result of a delicate balance of multiple systems which maintain the body’s center of mass over an ever-changing base of support. It is a complex motor behavior that requires components of attention and memory to prevent falls and injury. In addition, evidence points to the critical role of salient visual information to gait adaptability. There is a growing understanding that treatment for PD needs to address movement as it occurs naturally and walking needs to be practiced in more complex environments than traditional therapy has provided. Methods In this single-blinded randomized-controlled pilot study, an immersive treadmill training was piloted to determine feasibility and preliminary efficacy on gait and cognition in people with PD. Eighteen participants with Hoehn and Yahr stages I-III PD were randomized to either an intervention or a waitlist control group. Following baseline data collection, the intervention group trained for 30 min, three times/week for 4 weeks on a split belt treadmill combined with a first-person immersive video game targeting visuospatial skills and working memory. Assessment was repeated after 4 weeks of training for the experimental group and 1-month after baseline for the control group. Primary motor outcomes were captured with the APDM Opal sensors during 6 MWT, TUG, and TUG Cognitive. Secondary outcomes of cognition were measured with the Montreal Cognitive Assessment (MoCA), Verbal Fluency (Fruit, Vegetable, and Animal) and the Symbol Digit Modality Test (SDMT). Within subject differences were calculated using the Wilcoxon Signed Ranked Test and between subject comparisons were analyzed using the Mann Whitney U -test. Results This novel treadmill training program was well-tolerated with all participants in the intervention group completing 4 weeks of training three times a week without any adverse effects. After immersive cognitive motor training, the experimental group made clinically relevant improvements in gait speed and walking distance during the 6 MWT while members of the control group showed no change or decreased gait speed and walking distance over the 1-month trial. In addition, the experimental group demonstrated significant improvement for the TUG Cognitive ( p = 0.05) and those changes were greater than the control group (between group p = 0.040). The experimental group also improved scores on MoCA ( p = 0.007) and SDMT ( p = 0.01) cognitive outcome measures while the control group did not. Conclusion The use of immersive gaming technology to engage specific areas of cognition related to gait is feasible in PD. The treadmill training program paired with a customized interactive video game improved walking velocity in addition to non-significant but consistent improvements in other gait measures and cognitive performance in participants with early to mid-stage PD.

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 score0.040
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.361
Teacher spread0.316 · 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

Citations21
Published2022
Admission routes1
Has abstractyes

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