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

• Main Authors: CHEW, Han Shi Jocelyn, ACHANANUPARP, Palakorn, CHEW, Nicholas W. S., CHIN, Yip Han, GAO, Yujia, SO, Bok Yan Jimmy, SHABBIR, Asim, Ee-peng LIM, NGIAM, Kee Yuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Acceptability; artificial intelligence; behavior; implementation; obesity; perception; UTAUT; weight management; Singapore; Artificial Intelligence and Robotics; Asian Studies; Databases and Information Systems; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/8735; https://ink.library.smu.edu.sg/context/sis_research/article/9738/viewcontent/fnut_11_1287156_pvoa_cc_by.pdf
• Institution: Singapore Management University

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.