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    More Exercise Isn’t Always Better: New Study Reveals the Surprising Secret to a Younger Brain

    By Health Data Science1 Comment3 Mins Read
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    A groundbreaking analysis of nearly 17,000 UK Biobank participants used wrist-worn accelerometers and advanced MRI-derived metrics to show that brain “age” doesn’t simply improve with more exercise. Credit: Stock

    Moderate exercise may slow brain aging, protecting cognition and brain structure, while too little or too much activity may have the opposite effect.

    A new scientific investigation using data from accelerometers and brain MRI scans suggests that engaging in moderate physical activity could help slow the aging process in the brain. The research, led by Associate Professor Chenjie Xu of the School of Public Health at Hangzhou Normal University, was conducted in collaboration with Tianjin University of Traditional Chinese Medicine and Tianjin Medical University. The findings have been published in the journal Health Data Science.

    The team examined information from 16,972 participants in the UK Biobank. To estimate each person’s “brain age,” they applied a LightGBM machine learning model to more than 1,400 image-based phenotypes. Their results revealed a U-shaped pattern between physical activity (PA) intensity and the brain age gap (BAG). In this pattern, both low and high levels of PA were associated with faster brain aging, while moderate activity appeared to offer the most benefit.

    Accelerometers Measured Physical Activity and Neuroimaging Driven Brain Age
    A ) A brain age prediction model is constructed by leveraging LightGBM algorithm training on 1425 image-derived phenotypes (IDPs) from T1-weighted brain MRI and chronological age. Features initially undergo tree-based feature importance ranking, where top 50 important features are picked out. Next, supervised distance between each feature is calculated then underwent hierarchy clustering to identify redundant feature groups. After removing redundancy, we visually interpret the final selected subset of features using SHAP technique. To deal with bias, predicted brain age was corrected by linear method. B) We first investigate correlations between objectively measured PA and BAG using both nonlinear and linear models. Next, to gain insight into PA and brain structures, we investigate correlations between PA and 1425 IDPs using both nonlinear and linear models. C) To verify whether PA and brain health was mediated by BAG, we conducted mediation analysis. Cognitive function and brain disorders were selected as brain health outcomes of interest. Credit: Chen Han., et al, School of Public Health, Hangzhou Normal University

    Addressing the shortcomings of prior research reliant on self-reported data, this study objectively measured 7-day PA using wrist-worn accelerometers to quantify light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) activity. Results showed that moderate levels of MPA and VPA significantly reduced BAG (e.g., VPA: β = −0.27), suggesting a brain-protective effect.

    Brain Aging and Cognitive Outcomes

    Importantly, BAG was found to partially mediate the effects of PA on cognitive function (e.g., reaction time) and brain-related disorders (e.g., dementia, depression). Neuroanatomical analysis revealed that activity-related reductions in BAG were associated with lower white matter hyperintensities and preserved volume in the cingulate cortex, caudate nuclei, and putamen—regions critical for cerebrovascular integrity and cortico-striatal circuitry.

    “Our study not only confirms a nonlinear relationship between objectively measured PA and brain aging in a large population, but also provides actionable insight: more exercise isn’t always better—moderation is key,” said Xu.

    The team’s next step is to build a multi-scale aging framework incorporating sleep, sedentary behavior, neuroimaging, and omics data. Longitudinal studies will investigate how behavioral interventions reshape brain aging, while genome-wide and proteomic analyses aim to uncover the biological mechanisms underlying these effects.

    Reference: “Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age” by Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang and Chenjie Xu, 2 May 2025, Health Data Science.
    DOI: 10.34133/hds.0257

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