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Personalized and adaptive physical activity-based social comparison opportunities for women with health risks

Insights from a real-time test of the identification/contrast model

Bibliographic Data

ID4863596
AuthorsDanielle Arigo (0000-0002-7807-5913, Rowan University), Iris Bercovitz (0000-0002-2015-0618, Rowan University), Anisha Satish (Rowan University), Emmanuel Lapitan (0000-0003-4986-8524, Rowan University), Amanda L Folk (0000-0001-8424-9265, Rowan University), Andrea F Lobo (0000-0002-2797-2868, Rowan University)
Year2025
Volume30
Issue13
Pages3927-3945
Publication date2025-03-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Health Psychology (JOURNAL)
Journal identifiersISSN: 1359-1053 • E-ISSN: 1461-7277
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/13591053251326629
PMID40159126
OpenAlexW4408985960
LanguageEN
References cited57

Women in midlife (ages 40-60) experience unique risk for cardiovascular disease, in part due to insufficient physical activity (PA). Although social comparison can motivate PA, how best to harness this process is unclear. The Identification/Contrast Model proposes a useful framework but has not been tested in real time. We tested this model among 88 women ( M Age = 51.6 years, M BMI = 31.9 kg/m 2 ) who viewed comparison targets via personalized and adaptive peer profiles, once per day for 7 days. They also wore PA monitors, responded to profiles in real time, and completed subsequent qualitative interviews. Results from multilevel models support the Identification/Contrast Model for upward comparison, though responses to downward comparison were complex; narrative feedback highlighted the benefits of affiliative responses. Findings demonstrate within-person variability in comparison response that is often overlooked and support the utility of the Identification/Contrast Model for improving the use of comparison processes to promote PA

Biology · Contrast (vision · Developmental psychology · Identification (biology · Test (biology · Behavioral Health and Interventions · Computer Science · Mobile Health and mHealth Applications · Physical Activity and Health · Psychology · Artificial Intelligence · Social Psychology

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