Development of a scale for assessing factors influencing artificial intelligence technology adoption for fitness and nutrition planning among Filipino college students

Vanessa B. Sibug; Jumel C. Miller; Juvy C. Grume; Joseph Alexander Bansil; Madilaine Claire B. Nacianceno; Jordan L. Salenga; Emmanuel B. Parreño; John Paul P. Miranda

doi:10.15561/20755279.2026.0401

Authors

Vanessa B. Sibug Pampanga State University https://orcid.org/0009-0002-9251-2427
Jumel C. Miller Central Luzon State University https://orcid.org/0000-0003-1019-2424
Juvy C. Grume Pampanga State University https://orcid.org/0009-0007-0744-4121
Joseph Alexander Bansil Pampanga State University https://orcid.org/0009-0005-1094-2944
Madilaine Claire B. Nacianceno Pampanga State University https://orcid.org/0009-0000-8219-0626
Jordan L. Salenga Pampanga State University https://orcid.org/0009-0006-6371-3018
Emmanuel B. Parreño Pampanga State University https://orcid.org/0000-0002-6270-683X
John Paul P. Miranda Pampanga State University https://orcid.org/0000-0003-2059-972X

DOI:

https://doi.org/10.15561/20755279.2026.0401

Keywords:

scale development, artificial intelligence, technology acceptance, fitness planning, nutrition planning, college students

Abstract

Background and Study Aim. Artificial intelligence technologies are increasingly being integrated into health promotion and lifestyle management. Their growing use has expanded opportunities for personalized support in physical education and health-related decision-making. Despite their increasing use, the factors influencing the adoption of these technologies remain an important subject of practical interest. The aim of this study was to develop a scale for assessing the factors influencing the adoption of artificial intelligence technologies for fitness and nutrition planning among Filipino college students. Materials and Methods. This study developed a 40-item scale for assessing the factors influencing the adoption of artificial intelligence technologies for fitness and nutrition planning among Filipino college students. The instrument was grounded in the Technology Acceptance Model (TAM). Data were collected from 395 students at a Philippine higher education institution. The survey measured nine constructs: Behavioral Intention, Perceived Usefulness, Ease of Use, Social Media Exposure, Social Influence, Trust in AI, Risk and Privacy Concern, Health Motivation, and Actual Use. Scale development included three sequential stages: exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Results. All sub-scales demonstrated strong internal consistency and convergent validity. Confirmatory factor analysis demonstrated an acceptable model fit (CFI = 0.942, TLI = 0.936, RMSEA = 0.072). Discriminant validity was supported for most construct pairs. The only exception was the Perceived Usefulness–Ease of Use pair, reflecting the conceptual overlap of these TAM constructs. The structural model identified Trust in AI as the strongest predictor of both Perceived Usefulness and Ease of Use. Social Media Exposure, Social Influence, and Health Motivation also showed significant positive effects on both constructs. Risk and Privacy Concern did not significantly predict either TAM construct. Behavioral Intention was the strongest predictor of Actual Use. Conclusions. The developed scale is a reliable and valid instrument for assessing the factors influencing the adoption of artificial intelligence technologies for fitness and nutrition planning among Filipino college students. Trust in AI and social factors were the primary drivers of technology adoption, whereas perceived risk was not associated with the core TAM constructs in this population.

Author Biographies

Vanessa B. Sibug, Pampanga State University

Associate professor; vbsibug@pampangastateu.edu.ph; Institute of Physical Education; Pampanga, Philippines.

Jumel C. Miller, Central Luzon State University

Associate professor; jcmiller@clsu.edu.ph; Institute of Sports, Physical Education and Leisure Studies; Nueva Ecija, Philippines.

Juvy C. Grume, Pampanga State University

Associate professor; jncruz@pampangastateu.edu.ph; College of Education; Pampanga, Philippines.

Joseph Alexander Bansil, Pampanga State University

jabansil@pampangastateu.edu.ph; College of Engineering and Architecture; Pampanga, Philippines.

Madilaine Claire B. Nacianceno, Pampanga State University

mcbnacianceno@pampangastateu.edu.ph; College of Computing Studies; Pampanga, Philippines.

Jordan L. Salenga, Pampanga State University

Assistant professor; jdsalenga@pampangastateu.edu.ph; College of Computing Studies; Pampanga, Philippines.

Emmanuel B. Parreño, Pampanga State University

Associate professor; ebparreno@pampangastateu.edu.ph; College of Engineering and Architecture; Pampanga, Philippines.

John Paul P. Miranda, Pampanga State University

Associate professor; jppmiranda@pampangastateu.edu.ph; College of Computing Studies; Pampanga, Philippines.

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