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Physical activity and depression: Towards understanding the antidepressant mechanisms of physical activity
Abstract
Physical activity can treat and prevent depressive symptoms, but its antidepressant mechanisms are yet to be established. In this review, we comprehensively assess key biological and psychosocial mechanisms through which physical activity exerts antidepressant effects, with a particular focus on exercise. Exercise, a subset of physical activity, influences a range of biological and psychosocial processes also implicated in the pathophysiology of depression. We focus on the capacity for exercise to elicit changes in neuroplasticity, inflammation, oxidative stress, the endocrine system, self-esteem, social support and self-efficacy. We also discuss how a better understanding of these mechanisms can inform the way we design and implement exercise-based interventions to maximise their antidepressant effects on an individual basis. We conclude by presenting a conceptual framework of the key biological and psychosocial mechanisms underlying the relationship between physical activity and depressive symptoms, and the moderators and confounders that may influence it.
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“Wearable activity trackers also have the potential to improve psychosocial outcomes, such as depression and anxiety, through increases in physical activity, as physical activity is shown to have anti depressant 12 and anxiolytic 13 effects.”
From Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. (2022) · cites this paper as [12]
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Vancampfort, D., & Mugisha, J. (2022) · Pan African Medical Journal · doi:10.11604/pamj.2022.42.210.33928 · Cited by 16
Metadata
- Authors
- Kandola, A., Ashdown-Franks, G., Hendrikse, J., Sabiston, C. M., & Stubbs, B.
- Affiliations
- University College London (GB)
- Monash University (AU)
- University of Toronto (CA)
- National Health Service (GB)
- Corresponding author
- Kandola, Aaron
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 107
- Pages
- 525-539
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W2978644324
- Semantic Scholar
- 670b2192975cd4b76fb8c9b5e099dff17236b86b
- Cited by
- 1,309
- References
- 274
- Citations by year
- 2026: 228 · 2025: 299 · 2024: 241 · 2023: 208 · 2022: 179 · 2021: 100 · 2020: 51 · 2019: 2
- FWCI
- 9.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Research at York St John (York St John University) (acceptedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- ESRC (ES/P000592/1)
- Mitacs Globalink Award
- Canada Research Chairs Program
- RTS scholarship
- Health Education England
- National Institute for Health Research HEE NIHR ICA Program Clinical Lectureship (ICA‐CL‐2017‐03‐001)
- Maudsley Charity and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research
- Care South London at King's College Hospital NHS Foundation Trust
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Life Sciences › Neuroscience › Biological Psychiatry › Tryptophan and brain disorders
- Keywords
- physical activity, depression, antidepressant mechanism, neuroplasticity, inflammation, oxidative stress, endocrine system, self-esteem, social support, self-efficacy, exercise-based interventions
- MeSH terms
- Antidepressive Agents, Depression, Depressive Disorder, Humans, Neuronal Plasticity, Social Support, Exercise
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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