TY  - JOUR
AU  - Chew, Han Shi Jocelyn
AU  - Chew, Nicholas WS
AU  - Loong, Shaun Seh Ern
AU  - Lim, Su Lin
AU  - Tam, Wai San Wilson
AU  - Chin, Yip Han
AU  - Chao, Ariana M
AU  - Dimitriadish, Georgios K
AU  - Gao, Yujia
AU  - So, Jimmy Bok Yan
AU  - Shabbir, Asim
AU  - Ngiam, Kee Yuan
PY  - 2024
DA  - 2024/5/7
TI  - Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation
JO  - J Med Internet Res
SP  - e46036
VL  - 26
KW  - artificial intelligence
KW  - chatbot
KW  - chatbots
KW  - weight
KW  - overweight
KW  - eating
KW  - food
KW  - weight loss
KW  - mHealth
KW  - mobile health
KW  - app
KW  - apps
KW  - applications
KW  - self-regulation
KW  - self-monitoring
KW  - anxiety
KW  - depression
KW  - consideration of future consequences
KW  - mental health
KW  - conversational agent
KW  - conversational agents
KW  - eating behavior
KW  - healthy eating
KW  - food consumption
KW  - obese
KW  - obesity
KW  - diet
KW  - dietary
AB  - Background: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one’s self-regulation over momentary eating impulses. Objective: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence–assisted weight management app in improving eating behaviors in a Southeast Asian cohort. Methods: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. Results: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean –0.32, SD 1.16; P<.001), snacking habits (mean –0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean –0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). Conclusions: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. Trial Registration: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e46036
UR  - https://doi.org/10.2196/46036
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38713909
DO  - 10.2196/46036
ID  - info:doi/10.2196/46036
ER  -