James Wai Kit Lee

Background: Just-in-time adaptive interventions (JITAIs) use real-time monitoring to deliver personalized support at optimal moments, demonstrating potential for improving lifestyle behaviors in weight management. Objective: This study provides an overview of how JITAIs have been used or developed for weight management in adults with excess body weight. Methods: This scoping review followed Arksey and O’Malley’s 5-step framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist to ensure methodological rigor. Eight electronic databases (PubMed, Cochrane Library, Embase, CINAHL, PsycINFO, IEEE Xplore, Scopus, and Web of Science) were searched from journal inception to November 13, 2024, along with gray literature and hand-searched references. Two independent reviewers conducted data extraction for all included studies. Descriptive statistics were used to summarize study characteristics, followed by a nonlinear, inductive qualitative content analysis of the extracted data to identify and synthesize recurring concepts and characteristics of JITAI-based weight management interventions. Results: Thirty-five studies on JITAIs for weight management were included, focusing on dietary behavior (25/35, 71.4%), physical activity (20/35, 57.1%), and self-weighing (17/35, 48.6%). Types of support included prompts (n=33), feedback (n=24), recommendations of coping strategies (n=7), and educational information (n=5). A total of 31.4% of studies used machine learning for decision-making, while the rest used rule-based algorithms. Retention rates varied from 74% to 100%, and compliance from 15.1% to 94.6%. Greater user engagement was associated with improved weight loss outcomes. Across interventions, significant improvements were observed in weight, waist circumference, BMI, and blood pressure, alongside increased physical activity, healthier dietary behaviors, and reductions in sedentary time. Conclusions: While JITAIs show potential for improving lifestyle habits by providing the right intervention at the right time and in the right setting, most studies lacked theoretical grounding and were not conceptualized as JITAIs. Furthermore, terminology and reporting were inconsistent, which hindered evaluation and comparison across studies. Nevertheless, most studies incorporated varied distal and proximal outcomes, behavioral theories, intervention delivery methods, and data acquisition methods, and demonstrated positive outcomes in weight, physical activity, and dietary behaviors. This review demonstrates JITAIs’ potential in weight management but highlights the field’s early stage of development. Future research should focus on improving reporting standards, optimizing JITAI components such as the integration of behavioral theories and machine learning, and enhancing user engagement and long-term effectiveness by incorporating passive sensing, personalization, and adaptive feedback mechanisms.

Type 2 diabetes mellitus (T2DM), hypertension and hyperlipidaemia (collectively DHL) are longstanding cardiometabolic risk factors contributing to premature morbidity and mortality globally.1-3 Despite clear guidelines on early screening, coverage remains suboptimal in many healthcare systems, with disproportionate gaps in lower socioeconomic groups.4-6 Socioeconomic status (SES) is a multifaceted construct that often influences how much access an individual or community has to essentials such as healthcare, housing, transportation and nutritious food, as well as social resources like political influence, social engagement and autonomy.7 In this letter, the authors present key findings from a recent cross-sectional analysis of over 1.1 million residents in western Singapore that highlight missed opportunities for prevention and propose pragmatic implications for clinical practice and health policy.

BACKGROUND Just-in-time adaptive interventions (JITAIs) use real-time monitoring to deliver personalized support at optimal moments, demonstrating potential for improving lifestyle behaviors in weight management. OBJECTIVE This study provides an overview of how JITAIs have been used or developed for weight management in adults with excess body weight. METHODS This scoping review followed Arksey and O’Malley’s 5-step framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist to ensure methodological rigor. Eight electronic databases (PubMed, Cochrane Library, Embase, CINAHL, PsycINFO, IEEE Xplore, Scopus, and Web of Science) were searched from journal inception to November 13, 2024, along with gray literature and hand-searched references. Two independent reviewers conducted data extraction for all included studies. Descriptive statistics were used to summarize study characteristics, followed by a nonlinear, inductive qualitative content analysis of the extracted data to identify and synthesize recurring concepts and characteristics of JITAI-based weight management interventions. RESULTS Thirty-five studies on JITAIs for weight management were included, focusing on dietary behavior (25/35, 71.4%), physical activity (20/35, 57.1%), and self-weighing (17/35, 48.6%). Types of support included prompts (n=33), feedback (n=24), recommendations of coping strategies (n=7), and educational information (n=5). A total of 31.4% of studies used machine learning for decision-making, while the rest used rule-based algorithms. Retention rates varied from 74% to 100%, and compliance from 15.1% to 94.6%. Greater user engagement was associated with improved weight loss outcomes. Across interventions, significant improvements were observed in weight, waist circumference, BMI, and blood pressure, alongside increased physical activity, healthier dietary behaviors, and reductions in sedentary time. CONCLUSIONS While JITAIs show potential for improving lifestyle habits by providing the right intervention at the right time and in the right setting, most studies lacked theoretical grounding and were not conceptualized as JITAIs. Furthermore, terminology and reporting were inconsistent, which hindered evaluation and comparison across studies. Nevertheless, most studies incorporated varied distal and proximal outcomes, behavioral theories, intervention delivery methods, and data acquisition methods, and demonstrated positive outcomes in weight, physical activity, and dietary behaviors. This review demonstrates JITAIs’ potential in weight management but highlights the field’s early stage of development. Future research should focus on improving reporting standards, optimizing JITAI components such as the integration of behavioral theories and machine learning, and enhancing user engagement and long-term effectiveness by incorporating passive sensing, personalization, and adaptive feedback mechanisms. CLINICALTRIAL

BACKGROUND Large Language Models (LLM) are AI models that can generate conversational content based on a trained specified source of information (corpus). OBJECTIVE The aim is to use these corpus-trained LLMs to limit the content offered by LLM, then using prompt engineering to teach using Socratic methods. METHODS Two chatbots were created and deployed, powered by OpenAI’s GPT-3.5 model, with a medical-school textbook corpus. The first chatbot generates a brief summary and open-ended question. The second chatbot generates a case vignette from its pre-trained clinical cases, prompting users for a diagnosis. Both chatbots reply to the user’s response, commenting on the accuracy and asks further questions to encourage critical thinking. A randomised controlled trial was conducted on two groups comprising third year medical students. One group used both chatbots for 10 minutes while the other read the medical textbook. A 15-question test was administered to both groups before and after the intervention. RESULTS Forty students participated in the study. The average of the group before and after reading the textbook (n=20) are 3.9 +/- 1.0 and 7.6 +/- 1.5 respectively (p<0.001). The average of the group before and after using the bot (n=20) are 3.9 +/- 0.9 and 12.8 +/- 1.6 respectively (p<0.001). The respective increase in results was 3.7 and 8.9. CONCLUSIONS Medical students’ learning showed a better performance using a LLM based chatbot compared to self-reading of medical information assessed using a standardised test. More studies are required to determine if LLM-based pedagogical methods are superior to standard education.

Medical education in the pandemic has been challenging owing to various physical and technological constraints in the current education landscape. This has resulted in reduced patient contact and opportunities for clinical exposure. In utilizing various platforms to supplement teaching, we adopted the use of Telegram, a cloud-based messaging application as an education aid for 3 cohorts of medical students in 1 medical school in Singapore. Herein, we share our experience with Telegram as a novel platform to augment medical education and to supplement clinical training amidst the various constraints. We believe that the circumstances have allowed us to find a method that may serve as an effective adjunct in education. Qualitative feedback has been positive and generally in line with our goals. We believe that further work could involve utilizing other features of the application, or by developing specialized applications to serve the same purpose. More needs to be done to consider applicability in different cultural and socioeconomic contexts.

Background: Endoscopic retrograde cholangiopancreatography is a common procedure performed for choledocholithiasis and gallstone pancreatitis. Although a relatively low risk procedure, it is not without its complications. Cases of impacted Dormia baskets during stone retrieval have been reported, but these are usually retrieved surgically during the same setting.Case summary: A 40-year-old man presented to our hospital with an episode of epigastric pain and discomfort. He has a prior background of recurrent episodes of pancreatitis of which he underwent prior endoscopic therapy in his home country. Initial investigations revealed a metallic object seen on abdominal x-ray, computer tomographic scan of the abdomen and pelvis, and magnetic resonance imaging of the pancreas. Further evaluation was done with endoscopy, which revealed a retained stone extraction basket from a previous endoscopic retrograde pancreatography, resulting in recurrent episodes of acute chronic pancreatitis. Although the retained foreign body was removed, he subsequently developed further complications of portal vein thrombosis as a result of recurrent acute chronic pancreatitis, which required anticoagulation.Conclusion: This case highlights the importance of retrieving any foreign body from the pancreas, especially on the head, to prevent the development of further complications.

Introduction Online multiple-choice question (MCQ) quizzes are popular in medical education due to their ease of access and ability for test-enhanced learning. However, a general lack of motivation among students often results in decreasing usage over time. We aim to address this limitation by developing Telegram Education for Surgical Learning and Application Gamified (TESLA-G), an online platform for surgical education that incorporates game elements into conventional MCQ quizzes. Methods and analysis This online, pilot randomised control trial will be conducted over 2 weeks. Fifty full-time undergraduate medical students from a medical school in Singapore will be recruited and randomised into an intervention group (TESLA-G) and an active control group (non-gamified quizzing platform) with a 1:1 allocation ratio, stratified by year of study. We will evaluate TESLA-G in the area of endocrine surgery education. Our platform is designed based on Bloom’s taxonomy of learning domains: questions are created in blocks of five questions per endocrine surgery topic, with each question corresponding to one level on Bloom’s taxonomy. This structure promotes mastery while boosting student engagement and motivation. All questions are created by two board-certified general surgeons and one endocrinologist, and validated by the research team. The feasibility of this pilot study will be determined quantitatively by participant enrolment, participant retention and degree of completion of the quizzes. The acceptability of the intervention will be assessed quantitatively by a postintervention learner satisfaction survey consisting of a system satisfaction questionnaire and a content satisfaction questionnaire. The improvement of surgical knowledge will be assessed by comparing the scores of preintervention and postintervention knowledge tests, which consist of separately created questions on endocrine surgery. Retention of surgical knowledge will be measured using a follow-up knowledge test administered 2 weeks postintervention. Finally, qualitative feedback from participants regarding their experience will be obtained and thematically analysed. Ethics and dissemination This research is approved by Singapore Nanyang Technological University (NTU) Institutional Review Boards (Reference Number: IRB-2021-732). All participants will be expected to read and sign a letter of informed consent before they are considered as recruited into the study. This study poses minimal risk to participants. Study results will be published in peer-reviewed open-access journals and presented in conference presentations. Trial registration number NCT05520671.

Objective Thyroid nodules are extremely common, with prevalence rate up to 68%, yet only 7–15% of these are malignant. Many nodules require surveillance and 2-dimensional ultrasound (2D US) is used. Issues include the huge workload of obtaining and labeling images and difficulty comparing sizes of nodules over time due to large inter-operator variability. Inaccuracies may result in unnecessary FNAC or missed diagnosis of malignant nodules. Methods We compared two techniques: freehand plain 2D US against freehand 2D US with gyroscopic guidance, both followed by 3D reconstruction using software. We measured the volume of nodules and a normal thyroid gland. Results We found 2D US with gyroscopic guidance to be superior to plain 2D US as 3D reconstructions of greater accuracy are produced. The volume of the thyroid lobe measured 8.42 cm³ ± 0.94 was reasonably close to the normal average volume. However, the measured volume of the ellipsoidal nodule by the software is 8.69 cm³ ± 0.97 while the measured volume of the spherical nodule is 7.09 cm³ ± 0.79. As the expected volume of the nodules were 4.24cm³ and 4.19 cm³ respectively, the measured volume of the nodule was not accurate. The time taken to characterise nodules was reduced greatly from over 30 min in usual procedure to less than 10 min. Conclusion We find 3D US promising for evaluating size of thyroid nodules, with potential to study other TIRAD characteristics. Freehand 2D US with gyroscopic guidance shows the most promise for producing reliable, accurate and faster 3D reconstructions of thyroid nodules.