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Record W3180392518 · doi:10.1177/13594575211029101

Improvised active music therapy for clients with Parkinson’s disease: A feasibility study

2021· article· en· W3180392518 on OpenAlexaff
Demian Kogutek, Jeffrey D. Holmes, Jessica A. Grahn, Emily A. Ready, Manuel Montero‐Odasso

Bibliographic record

VenueBritish Journal of Music Therapy · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsWestern University
Fundersnot available
KeywordsMusic therapyPhysical medicine and rehabilitationRhythmPsychologyParkinson's diseaseGaitPhysical therapyMedicineAudiologyDisease

Abstract

fetched live from OpenAlex

The purpose of this study was to test the feasibility of delivering Improvised Active Music Therapy sessions in measuring the impact of acquisition of rhythmic complexity levels on gait performance in individuals with Parkinson’s disease. In this single subject multiple baseline design, the study measured the ability of three right-handed participants with Parkinson’s disease to acquire greater density of syncopation, as a measure of rhythmic complexity levels, while playing uninterrupted improvised music on a simplified electronic drum-set. The music content of the sessions was transformed into digital music data in real-time using Musical Instrument Digital Interface. The Musical Instrument Digital Interface data were analyzed to determine the participants’ and the Music Therapist’s density of syncopation (on acoustic guitar) during baseline and treatment conditions. Results from visual analyses and Pearson’s correlations on the outcomes indicated conflicting and inconclusive outcomes about whether higher acquisition of rhythmic complexity levels improves gait performance in individuals with Parkinson’s disease. Despite this, evidence was found to support the overall value of Improvised Active Music Therapy sessions on gait performance. The study design, the intervention, and outcome measures were found to be feasible and could be scaled-up into a larger trial.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.074
GPT teacher head0.311
Teacher spread0.237 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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