Virtual Group Music Therapy for Apathy in Parkinson’s Disease: A Pilot Study
Bibliographic record
Abstract
Objective To evaluate the effect of virtual group music therapy on apathy in people with Parkinson’s disease (PD). Introduction Apathy affects 40% of people with PD, lacks effective therapies, and independently predicts poorer quality of life and greater caregiver burden. Music therapy is the clinical application of music to address a person’s physical or emotional needs and is effective in treating apathy in dementia. Methods People with idiopathic PD and apathy (Movement Disorders Society-Unified Parkinson’s Disease Rating Scale, apathy item ≥ 2) and their caregivers participated in twelve, weekly virtual group music therapy sessions, with session attendance signifying adherence. Participants completed pre- and post-intervention assessments of apathy (Apathy Scale (AS)), quality of life (Parkinson’s Disease Questionnaire-short form), functional ability (Schwab & England Activities of Daily Living Scale), depression (Beck Depression Inventory (BDI-II)), and cognition (Montreal Cognitive Assessment-Blind). Among secondary outcomes, we assessed caregiver burden (Zarit Burden Interview-short form) and strain (Multidimensional Caregiver Strain Index). Results Sixteen PD participants (93.8% men, mean age 68.3 ± 8.4 years, median 6 years PD duration) and their caregivers (93.8% women, mean age 62.6 ± 11 years) completed the study. All PD participants and 88% of caregivers were >70% adherent to the intervention. Apathy (AS, effect size = 0.767, P = 0.002) and depression (BDI-II, effect size = 0.542, P = 0.03) improved, with no change in caregiver measures. Conclusion Group music therapy is an effective treatment for apathy in PD and may improve mood. The virtual format is a feasible alternative to in-person sessions with high adherence and satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".