JOURNAL ARTICLE

Does Future Time Perspective Moderate Associations of Instrumental and Affective Attitude With Exercise Behavior? A Three-Wave Longitudinal Survey Among Japanese Older Adults.

  • Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2023, v. 78, n. 11. P. 1843 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Harada, Kazuhiro; Masumoto, Kouhei; Okada, Shuichi 3 of 3

Abstract

This article investigates whether future time perspective moderates the influence of affective and instrumental attitudes on exercise behavior among older Japanese adults. Using data from a three-wave longitudinal survey of 886 participants, the study found that affective attitude—reflecting enjoyment and pleasure associated with exercise—consistently predicted both behavioral intention and exercise behavior regardless of future time perspective. Instrumental attitude—reflecting goal-oriented evaluations such as health benefits—showed limited predictive value, primarily among those with a lower future time perspective, but this effect was not robust. These findings challenge the socioemotional selectivity theory's assumption that future time perspective differentially influences motivational determinants of exercise, suggesting cultural factors may affect these relationships. The study highlights the importance of fostering positive affective experiences to promote sustained exercise behavior in older adults across varying future time perspectives.

Additional Information

  • Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2023/11, Vol. 78, Issue 11, p1843
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2023
  • ISSN:1079-5014
  • DOI:10.1093/geronb/gbad115
  • Accession Number:173631871
  • 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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