JOURNAL ARTICLE

Is Fitness Technology-Facilitated Social Comparison the Thief of Well-Being? The Mediating Role of Social Comparison on the Relationships Between Passion and Performance Self-Esteem.

  • Published In: Information Systems Research (INFORMS), 2026, v. 37, n. 1. P. 176 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: James, Tabitha L.; Whelan, Eoin; Conboy, Kieran 3 of 3

Abstract

This article examines how exercisers' passion for their activity influences their interpretation of social comparison information provided by popular fitness technologies (e.g., Fitbit, Strava, MyFitnessPal, Apple Fitness+), and how these interpretations affect their performance self-esteem and psychological well-being. Drawing on the Dualistic Model of Passion, it distinguishes between harmonious passion—where exercise is integrated autonomously into identity—and obsessive passion—where exercise is linked to controlled internalization and contingent self-esteem. The study finds that harmoniously passionate exercisers tend to interpret social comparison data adaptively, viewing better performers as attainable goals and worse performers as evidence of their success, which benefits their self-esteem. In contrast, obsessively passionate exercisers are more likely to interpret such comparisons maladaptively, seeing better performers as evidence of their own failure and worse performers as signs of potential decline, leading to reduced self-esteem and well-being. The findings suggest that fitness technology developers should consider user characteristics like passion to tailor social features and minimize negative psychological impacts.

Additional Information

  • Source:Information Systems Research (INFORMS). 2026/03, Vol. 37, Issue 1, p176
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2026
  • ISSN:1047-7047
  • DOI:10.1287/isre.2021.0083
  • Accession Number:192724193
  • Copyright Statement:Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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