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How to Improve Mental Health in the Older Adults Through AI‐Enhanced Physical Activity: An Emerging Research Topic

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Preprints.org
DOI
10.20944/preprints202502.2037.v1

Introduction: With the global rise in elderly populations, the demand for healthcare and long-term care services has grown, particularly for addressing mental health. While AI has shown promise for health management of older adults, its effects on physical activity, mental health, and their associations remain largely unexamined. Objective: This study aimed to investigate the role of AI in improving mental health among older adults through the promotion of physical activity and elucidating the underlying mechanisms. Methods: Bibliometric methods, including CiteSpace and VOSviewer, were employed to analyze 1,831 publications, identifying trends, collaborations, hotspots, and potential applications in this domain. Results: AI interventions significantly enhanced mental health in older adults by promoting physical activity. Prominent mechanisms included tailored psychological support, cognitive rehabilitation, and targeted cardiovascular and musculoskeletal interventions. These mechanisms drove better cognitive function, emotional regulation, and physical resilience. These interventions were supported in their effectiveness by biomarkers of BDNF, serotonin, and increased physical activity levels. Conclusion: The present study combines insights on AI, physical activity, and mental health to facilitate emerging research opportunities and practical applications. It highlights the importance of AI for individualized exercise interventions, smart health monitoring, and cognitive and emotional rehabilitation. The insights provide an important compass for scholars and practitioners to harness the power of AI to meet the mental health needs of aging societies by systematically mapping the global research landscape that jointly features the changing demographic profile of older persons and anticipated solutions through the use of AI technologies, delivering evidence-based measures.

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