Bibliographic Details
| Title: |
Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study |
| Authors: |
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 |
| Source: |
Frontiers in Nutrition, Vol 11 (2024) |
| Publisher Information: |
Frontiers Media S.A., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Nutrition. Foods and food supply |
| Subject Terms: |
artificial intelligence, obesity, implementation, acceptability, weight management, behavior, Nutrition. Foods and food supply, TX341-641 |
| More Details: |
IntroductionWith 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.Methods280 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.Results271 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.ConclusionUTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2296-861X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fnut.2024.1287156/full; https://doaj.org/toc/2296-861X |
| DOI: |
10.3389/fnut.2024.1287156 |
| Access URL: |
https://doaj.org/article/f1560cd2a4d74988b4fec80a8ded60b0 |
| Accession Number: |
edsdoj.f1560cd2a4d74988b4fec80a8ded60b0 |
| Database: |
Directory of Open Access Journals |
