Non-pharmacologic interventions to treat apathy in Parkinson’s disease: A realist review
Bibliographic record
Abstract
INTRODUCTION: There is a diverse body of evidence investigating non-pharmacological treatment options for apathy in Parkinson's disease (PD). We aimed to better understand the context and mechanisms by which non-pharmacological interventions may improve apathy in persons with PD. METHODS: We conducted a realist review of the body of evidence investigating treatment options for apathy in PD. Study authors used findings from a preceding scoping review to identify initial program theory. We then update the scoping review, which was originally conducted in 2017. Two authors independently reviewed and extracted data from studies that discussed non-pharmacological treatment options for apathy in PD. Any data concerning context, mechanisms, and outcomes of interventions for apathy in PD were extracted, synthesized, and analyzed. RESULTS: Our review included nine studies. We categorized studies into two categories, exercise and mindfulness. There were seven exercise interventions included. Exercise interventions evaluated group exercise compared to individual exercise, aerobic exercise, dance, Nordic walking, and an equine program. There were two mindfulness interventions. CONCLUSION: Exercise interventions work best for persons with PD and apathy who are not significantly physically or cognitively impaired, and who have access to transportation, adapted programs, and specialized coaches. Exercise may improve apathy through goal-directed behaviour change and engagement in social interactions. Mindfulness interventions work best for persons with PD and apathy who are not significantly cognitively impaired, have caregiver support, and may improve apathy by targeting the emotional, cognitive, and goal-directed domains that define apathy.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".