Aerobic Exercise Improves Mood, Cognition, and Language Function in Parkinson’s Disease: Results of a Controlled Study
Bibliographic record
Abstract
OBJECTIVES: Parkinson's disease (PD) results in a range of non-motor deficits that can affect mood, cognition, and language, and many of these issues are unresponsive to pharmacological intervention. Aerobic exercise can improve mood and cognition in healthy older adults, although only a few studies have examined exercise effects on these domains in PD. The current study assesses the effects of aerobic exercise on aspects of cognition, mood, and language production in people with PD. METHODS: This study compares the effects of aerobic exercise to stretch-balance training and a no-contact control group in participants with idiopathic PD. The aerobic and stretch-balance groups trained three times a week for 16 weeks, while controls continued normal activities. Outcome measures included disease severity, mood, cognition (speed of processing, memory, and executive function), and language production (picture descriptions). Cognition and language were assessed in single and dual task conditions. RESULTS: Depressive symptoms increased only in the control group (p<.02). Executive function improved in the aerobic exercise group only in the single task (p=.007) and declined in controls in the dual task. Completeness of picture descriptions improved significantly more in the aerobic group than in the stretch-balance group (p<.02). CONCLUSIONS: Aerobic exercise is a viable intervention for PD that can be protective against increased depressive symptoms, and can improve several non-motor domains, including executive dysfunction and related aspects of language production. (JINS, 2016, 22, 878-889).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".