Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

[This corrects the article DOI: 10.2196/46036.].

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

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.

Effects of Plant-Based Diets on Anthropometric and Cardiometabolic Markers in Adults: An Umbrella Review.

We conducted an umbrella review to consolidate the evidence of adopting plant-based diets on anthropometric and cardiometabolic outcomes. Six electronic databases (CINAHL, EMBASE, PubMed, Scopus, the Cochrane Library, and Web of Science) were searched for systematic reviews with meta-analysis (SRMAs) published from each journal's inception until 1 October 2022. Effect sizes from SRMAs and primary studies were pooled separately using random effects models. Overlapping primary studies were removed for primary studies' analyses. Seven SRMAs representing 51 primary studies were included, suggesting significant benefits of plant-based diets on weight (-2.09 kg, 95% CI: -3.56, -0.62 kg, p = 0.01, I2 = 95.6%), body mass index (-0.95 kg/m2, 95% CI: -1.26, -0.63 kg/m2, p = 0.002; I2 = 45.1%), waist circumference (-2.20 cm, 95% CI: -0.08, 0.00 cm, p = 0.04; I2 = 88.4%), fasting blood glucose (-0.11 mmol/L, 95% CI: -0.13, -0.09 mmol/L, p < 0.001, I2 = 18.2%), and low-density lipoprotein cholesterol (-0.31 mmol/L, 95% CI: -0.41, -0.20 mmol/L, p < 0.001, I2 = 65.6%). Changes in high-density lipoprotein cholesterol, triglycerides, and blood pressure were not statistically significant. Generally, plant-based diets were recommended to improve anthropometry, lipid profile, and glucose metabolism. However, findings should be interpreted with caution, because most of the reviews were rated to be of low credibility of evidence and were largely based on Western eating habits and traditions, which may limit the generalizability of findings.

Socio-Demographic, Behavioral and Psychological Factors Associated with High BMI among Adults in a Southeast Asian Multi-Ethnic Society: A Structural Equation Model.

While various influencing factors of overweight and obesity have been identified, the underlying mechanism remains unclear. We examined the relationships among sociodemographic, behavioral, and psychological factors on anthropometry in a multi-ethnic population with overweight and obesity. Participants (N = 251) were recruited from January to October 2022. Mean age and self-reported BMI were 31.7 ± 10.1 years and 29.2 ± 7.2 kg/m2. Participants were mostly female (52.4%) and overweight (58.2%). Multivariate multiple regression was performed using maximum likelihood estimation. Body mass index was associated with waist circumference, age, sex, race, marital status, education level, residential region, overeating habit, immediate thinking, self-regulation, and physical activity, but not anxiety, depression, or the intention to change eating habits. Final model indicated good fit: χ2 (30, N = 250) = 33.5, p = 0.32, CFI = 0.993, TLI = 0.988, RMSEA = 0.022, and SRMR = 0.041. Direct effects were found between BMI and overeating (ß = 0.10, p = 0.004), race (ß = -0.82, p < 0.001), marital status (ß = -0.42, p = 0.001), and education level (ß = -0.28, p = 0.019). Crisps (68.8%), cake (66.8%) and chocolate (65.6%) were identified as the most tempting foods. Immediate thinking indirectly increased overeating habits through poor self-regulation, although sociodemographic characteristics better predicted anthropometry than psycho-behavioral constructs.