JOURNAL ARTICLE

Music Engagement and Episodic Memory Among Middle-Aged and Older Adults: A National Longitudinal Analysis.

  • Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2023, v. 78, n. 9. P. 1484 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Rouse, Hillary J; Doyle, Cassidy; Hueluer, Gizem; Torres, Mia D; Peterson, Lindsay J; Pan, Xi; Dobbs, Debra; Du, Yan; Conner, Kyaien; Meng, Hongdao 3 of 3

Abstract

This study examines the longitudinal relationship between music engagement and episodic memory over more than 12 years in a nationally representative sample of 5,021 cognitively normal middle-aged and older adults in the United States, using data from the Health and Retirement Study (2006–2018). Music engagement was categorized as none, passive (listening), active (singing or playing an instrument), or both passive and active. Results indicate that participants who engaged with music both passively and actively demonstrated better episodic memory performance at baseline and experienced slower memory decline over time compared to those with no music engagement, while passive engagement alone was associated with a slower decline but not baseline differences. No significant cognitive benefits were observed for active engagement alone. The findings suggest that combined passive and active music engagement may help mitigate age-related episodic memory decline, highlighting music engagement as a potential modifiable lifestyle factor for cognitive health in aging populations.

Additional Information

  • Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2023/09, Vol. 78, Issue 9, p1484
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2023
  • ISSN:1079-5014
  • DOI:10.1093/geronb/gbad058
  • Accession Number:170744889
  • Copyright Statement:Copyright of Journals of Gerontology Series B: Psychological Sciences & Social Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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