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.

Gestational diabetes-related gut microbiome dysbiosis is not influenced by different Asian ethnicities and dietary interventions: a pilot study.

Gut microbiome dysbiosis contributes to the pathophysiology of both gestational diabetes mellitus (GDM) and its associated adverse outcomes in the woman and offspring. Even though GDM prevalence, complications, and outcomes vary among different ethnic groups, limited information is available about the influence of ethnicity on gut microbiome dysbiosis in pregnancies complicated by GDM. This pilot prospective cohort study examined the impact of ethnicity on gut dysbiosis in GDM among three Asian ethnic groups (Chinese, Malay, Indian) living in Singapore, and investigated the potential modulatory roles of diet and lifestyle modifications on gut microbiome post-GDM diagnosis. Women with GDM (n = 53) and without GDM (n = 16) were recruited. Fecal samples were collected at 24-28- and 36-40-weeks' gestation and analyzed by targeted 16S rRNA gene-based amplicon sequencing. Permutational multivariate analysis of variance (PERMANOVA) analysis was performed to evaluate differences between groups. Differentially abundant taxa were identified by DeSeq2 based analysis. Functional prediction was performed using the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2). Among women with GDM, gut microbiome from different ethnicities harbored common microbial features. However, among those without GDM, there was contrasting microbiome composition between ethnic groups. Microbial members such as Collinsella, Blautia, Ruminococcus, Ruminococcus gnavus, Ruminococcus torques, and Eubacterium hallii groups were differentially enriched (p < 0.05) in women with GDM compared to those without. Among women with GDM, no differences in alpha- and beta- diversity were observed when comparing 24-28 weeks' samples with 36-40 weeks' samples, a period covering intense dietary and lifestyle modification, suggesting an inability to modulate gut microbiota through classic GDM management. Women with GDM have a distinct gut microbiome profile which harbours common features across different Asian ethnic groups, consistent with the notion that specific microbes are involved in the pathogenesis of insulin resistance, pro-inflammatory conditions, and other metabolic dysregulation known to be present in GDM.

Clinical Service Incorporating Mobile Technology on Weight Loss in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Translation From Research Trial.

BACKGROUND: The prevalence and healthcare cost of metabolic dysfunction-associated steatotic liver disease (MASLD) has increased alongside the epidemic surge in obesity and Type 2 diabetes. Weight loss through lifestyle modification remains the primary effective therapy for MASLD. Incorporation of mobile technology in lifestyle interventions has been previously found to be efficacious and cost-effective in facilitating weight loss. However, there is a paucity of studies that have successfully translated lifestyle research into clinical service for weight loss to alleviate disease burden. Our study aimed to describe the process of translating a mobile technology-enabled trial into a tertiary hospital outpatient dietetics service for patients with MASLD. METHODS: The Iowa Model of Evidence-Based Practice to Improve Quality Care was used as a framework for this paper to guide implementation at the organizational level. RESULTS: Regular engagement of key operational staff and the hospital management team facilitated open discussions of the challenges faced and enabled rapid implementation of strategies that contributed to the smooth piloting of the service. A service adoption rate of 81% was achieved. Preliminary outcome evaluation found that the percentage of patients achieving ≥ 5% weight loss from baseline at 6 months was comparable at 54% and 52% for the service and trial groups, respectively. CONCLUSIONS: Evaluation of the implementation process found that a hybrid model of care (in-person consultation supplemented with app coaching) preserved interpersonal connections while maximizing the convenience and scalability of mobile app-enabled service. Although high digital acceptance and adoption rates propelled by COVID-19-supported telehealth, it is prudent to assess patient's access to technology and digital literacy and offer resources to help them benefit from telehealth services.