ArticleJournal of medical Internet research2024
Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04833803 (Development of the Eating Behaviour Lapse Inventory Survey Singapore), which is not on this map. Cited by 29 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS):An Exploratory Mixed-method Study
Who cites it
29 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.Obesity surgery · 2026Pooled it
- Meta-analysis of smartphone applications targeting eating behaviour for weight loss.Scientific reports · 2026Pooled it
- Pooled it
- Mobile and Web Apps for Weight Management in Overweight and Obese Adults: An Updated Umbrella Review and Meta-Analysis.International journal of environmental research and public health · 2025Pooled it
- Systematic review exploring human, AI, and hybrid health coaching in digital health interventions: trends, engagement, and lifestyle outcomes.Frontiers in digital health · 2025Pooled it
- Body composition phenotypes are key predictors of cardiovascular-kidney-metabolic multimorbidity and long-term mortality: insights from a population-based cohort study.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- The global cardiovascular-kidney-liver-metabolic burden from 1990 to 2023.EClinicalMedicine · 2026Article
- Artificial Intelligence as a Personal Coach: A Narrative Review of Benefits and Risks in Educational and Health Contexts.Behavioral sciences (Basel, Switzerland) · 2026Review
- Article
- Artificial Intelligence-Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- Long-term prognosis of coexistent metabolic dysfunction-associated steatotic liver disease and chronic kidney disease following acute myocardial infarction.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Neuropsychological Mechanisms Associated with the Effectiveness of AI-Delivered Health Promotion Programs: A Comprehensive Meta-Analysis.Brain sciences · 2026Review
- A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
- A multimodal tableware system for supporting mindful eating in solo dining: a pilot study.Frontiers in public health · 2026Article
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- An Artificial Intelligence-Assisted Smartphone Application for Improving Dietary Quality Among Frail Older Adults: A Quasi-Experimental Study.Geriatrics (Basel, Switzerland) · 2025Article
- AI powered dietary proportion assessment for improving accuracy and practicality of the balanced meal plate model.Scientific reports · 2025Article
- The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.Nutrients · 2025Article
- Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.Journal of obesity & metabolic syndrome · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA 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.
objectiveThis 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.
methodsA 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.
resultsThe mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m
conclusionseTRIP 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 registrationClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.
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