Optimal exercise modalities and dosages for improving depression in middle-aged and older adults with Parkinson's disease: A Bayesian Dose–response network meta-analysis
Abstract
Objective To characterise the dose response relationship between exercise and depressive symptoms in middle aged and older adults with Parkinson disease using a continuous modelling framework and to compare structural differences across exercise modalities. Methods PubMed, Embase, Web of Science, Cochrane Library and APA PsycINFO were searched to 19 February 2026. Exercise dose was standardised as MET-minutes per week. A Bayesian network meta-regression model was used to estimate continuous dose–response relationships. Evidence certainty was assessed using GRADE. Results Twenty-five RCTs were included. Overall exercise dose showed a significant non-linear association with reduced depressive symptoms in middle-aged and older adults with Parkinson disease. Beneficial effects were sustained between 110 and 890 MET-minutes per week, peaking at 560 MET-minutes per week (effect size 0.48, 95% credible interval 0.22–0.80), with an optimal range of 440–670 MET-minutes per week. Effects were not stable at 1000 MET-minutes per week. Modality analyses indicated that exercise combined with cognitive training and mind–body exercise demonstrated consistent benefits within moderate to moderately high dose ranges, both peaking at 560 MET-minutes per week. Aerobic cycling, functional training, and multicomponent training showed no stable significant dose range, whereas walking reached significance only at ≥670 MET-minutes per week with wide credible intervals. Conclusion Exercise in middle-aged and older adults with Parkinson disease and comorbid depression exhibits a structured non-linear dose–response pattern with a defined optimal range, and modality-specific differences indicate that therapeutic effects depend on both quantitative dose and qualitative task characteristics, providing a quantitative basis for precision exercise prescription.
Article Details
Authors (3)
Yiang Lyu
Jiawei Chen
State Key Laboratory of Advanced Materials for Intelligent Sensing and Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Institute of Molecular Plus, Department of Chemistry
Siqin Zeng