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What drives college students to use ChatGPT for fitness planning? A health belief and technology acceptance perspective with necessary condition analysis

Year: 2026

Type: article

Abstract: Physical inactivity among college students remains a public health concern, and generative AI tools like ChatGPT present a scalable option for personalized fitness planning. However, the psychological and motivational factors that constitute minimum prerequisites for students to adopt ChatGPT for this purpose remain poorly understood. This study determined the necessary conditions for college students’ behavioral intention to use ChatGPT for physical fitness planning by testing a hybrid Health Belief Model (HBM) and Technology Acceptance Model (TAM) framework. Health Motivation (HM), Perceived Value (PV), and Social Cues (SC) were examined in relation to Behavioral Intention (BI), with Prior ChatGPT Fitness Use (PCFU) included as a contextual variable. A cross-sectional survey was administered to 380 college students from a higher education institution in the Philippines. Pearson correlations and Necessary Condition Analysis (NCA) were used as the primary analytical methods to examine bivariate associations and identify non-compensatory prerequisites for high adoption intention. All three constructs were significantly correlated with BI, with PV showing the strongest association (r = .839), followed by SC (r = .541) and HM (r = .349). NCA revealed that all three constructs functioned as large necessary conditions for BI, with SC showing the most binding ceiling effect (d = .944), followed by PV (d = .759) and HM (d = .728). The subgroup analysis confirmed that these necessary conditions held for both prior users and non-users of ChatGPT for fitness planning. These findings suggest that while PV shows the strongest association with adoption intention, health motivation and social exposure to fitness-related ChatGPT content are non-compensatory prerequisites that must be minimally present before high adoption intention can form.

Source: Multidisciplinary Reviews

Authors: Sibug, Vanessa, Miller, Jumel, Liwanag, Ivan, Fernando, Emerson, Sunga, Jan Henry, Bansil, Joseph Alexander, Miranda, John Paul

Institutions: Pampanga State University, Central Luzon State University

Language: English

Topic: Technology Adoption and User Behaviour

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7220180000

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