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Record W4403600649 · doi:10.1016/j.prdoa.2024.100277

Coupling neurologic music therapy with immersive virtual reality to improve executive functions in individuals with Parkinson’s disease: A Quasi-Randomized Clinical Trial

2024· article· en· W4403600649 on OpenAlexaff
Federica Impellizzeri, Maria Grazia Maggio, Paolo De Pasquale, Mirjam Bonanno, Lilla Bonanno, Rosaria De Luca, Giuseppe Paladina, Angela Alibrandi, Demetrio Milardi, Michael H. Thaut, Corene Hurt, Angelo Quartarone, Rocco Salvatore Calabrò

Bibliographic record

VenueClinical Parkinsonism & Related Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsToronto Rehabilitation Institute
FundersMinistero della Salute
KeywordsMusic therapyRandomized controlled trialParkinson's diseaseDiseaseVirtual realityPhysical medicine and rehabilitationCoupling (piping)PsychologyMedicinePhysical therapyPsychotherapistHuman–computer interactionComputer scienceSurgeryPathologyEngineering

Abstract

fetched live from OpenAlex

Introduction: Parkinson's disease (PD) is one the most common neurodegenerative movement disorder, leading to motor and non-motor symptoms, including deficits in executive functions (EF), memory, visuospatial abilities, and psychomotor speed. Current treatments are primarily symptomatic, involving pharmacological, surgical strategies. Neurologic Music Therapy (NMT) has gained recognition for its effectiveness in neurorehabilitation of PD patients and improving motor and cognitive domains, such as EF. This study combines NMT with the virtual reality (VR) platform Computer-Assisted Rehabilitation Environment (CAREN), offering customizable environments for rhythmic cue practice to provide an innovative approach to Parkinson's rehabilitation. Methods: In our single-blind quasi-randomized controlled trial, forty patients were assigned to either an experimental group (EG = 20) or a control group (CG = 20). Both groups underwent two months of training with CAREN scenarios (three times a week for 24 sessions). The experimental group additionally received NMT applied to the selected scenarios. Participants were evaluated by a neuropsychologist at baseline and immediately after training. Results: Intra-group analysis showed significant improvements in the EG for MOCA (p = 0.007), FAB (p = 0.008), Stroop Error (p = 0.003), Stroop Time (p < 0.001), and Visual Search (p < 0.001). The CG showed a significant difference only in Stroop Error (p = 0.02). Conclusions: This pilot study is the first to combine NMT with CAREN in PD patients. Our findings suggest that NMT, within an immersive VR environment, effectively improves cognitive and EF in PD. Music structured within NMT techniques, coupled with advanced audio-visual feedback from VR, offers an innovative and potentially more effective approach for managing cognitive and executive deficits associated with 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 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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.063
GPT teacher head0.392
Teacher spread0.329 · 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 designNon-randomized trial
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

Citations10
Published2024
Admission routes1
Has abstractyes

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