JOURNAL ARTICLE

A Protection Motivation Theory Approach to Understanding How Fear of Falling Affects Physical Activity Determinants in Older Adults.

  • Published In: Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 2023, v. 78, n. 1. P. 30 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Preissner, Christian Erik; Kaushal, Navin; Charles, Kathleen; Knäuper, Bärbel 3 of 3

Abstract

This study investigated the role of fear of falling (FoF) within an extended Protection Motivation Theory (PMT) framework to understand motivational and intentional determinants of physical activity (PA) among older U.S. adults aged 65 and older. Using survey data from 667 participants, the research found that coping appraisal constructs—self-efficacy and response efficacy—along with autonomous motivation and past PA behavior, significantly predicted intentions to engage in PA, whereas FoF and threat appraisal (perceived vulnerability and severity) did not directly influence PA intention. Physical health and gender influenced FoF and appraisal pathways, with women and those in poorer health reporting higher FoF. The findings suggest that while FoF relates to threat perception, it may be less critical than established PA habits and coping appraisals in shaping older adults' intentions to remain active, highlighting the importance of fostering self-efficacy and motivation in interventions aimed at promoting PA in this population.

Additional Information

  • Source:Journals of Gerontology Series B: Psychological Sciences & Social Sciences. 2023/01, Vol. 78, Issue 1, p30
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2023
  • ISSN:1079-5014
  • DOI:10.1093/geronb/gbac105
  • Accession Number:161878268
  • 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.