Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study

Han Shi Jocelyn Chew; Palakorn Achananuparp; Mayank Dalakoti; Nicholas W S Chew; Yip Han Chin; Yujia Gao; Bok Yan Jimmy So; Asim Shabbir; Lim Ee Peng; Kee Yuan Ngiam

doi:10.3389/fnut.2024.1287156

Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study

Front Nutr. 2024 Feb 7:11:1287156. doi: 10.3389/fnut.2024.1287156. eCollection 2024.

Authors

Affiliations

¹ Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
² School of Computing and Information Systems, Singapore Management University, Singapore, Singapore.
³ Department of Cardiology, National University Heart Centre, Singapore, Singapore.
⁴ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
⁵ Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, National University Hospital, Singapore, Singapore.
⁶ Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore, Singapore.

Abstract

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity.

Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression.

Results: 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m² (24.2-28.4 kg/m²) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity.

Conclusion: UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.

Keywords: UTAUT; acceptability; artificial intelligence; behavior; implementation; obesity; perception; weight management.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the National University Health System (NUHS) ref.: NUHSRO/2021/063/RO5+6/FMPCHSRG-Mar21/01.