Background: Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps.Objective: This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations.Methods: We first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility.Results: We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m2 (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities.Conclusions: The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives.

Bariatric surgery is effective in remission of obesity comorbidities. This study was aimed at comparing CVD risk between morbidly obese patients with type 2 diabetes and pre-diabetes before and after bariatric surgery as well as assessing comorbidities. This is a retrospective observational study with 105 patients with type 2 diabetes (DMbaseline) and prediabetes (preDMbaseline) who underwent Roux-en-Y gastric bypass. Data were collected preoperative and then at 3,6,12,18,24,36,48, and 60 months after surgery. Anthropometric, cardiovascular and glycemic parameters were assessed. CVD risk was calculated using the Framingham Risk Score. Prior to surgery, 48 patients had type 2 diabetes, while 57 had pre-diabetes. Mean age was 48 (9.2) and mean BMI was 52 (7.4). 26.1% of patients had a high CVD risk. CVD risk decreased in patients with type 2 diabetes and prediabetes at month 12 after surgery compared to the baseline risk (p < 0.001). BMI, body fat percentage, fasting plasma glucose, HbA1c, c-peptide, HOMA-IR, LDL-c, systolic blood pressure, and diastolic blood pressure decreased during the first year after surgery. From the 12th month until the 60th, they showed a flat trend, or a very mild increase in some cases. 3.2% of patients maintained high CVD risk at 60 months. Type 2 diabetes remission was 92%. No patient of the preDMbaseline group developed type 2 diabetes. Bariatric surgery reduces CVD risk in type 2 diabetes and pre-diabetes. Given that patients with type 2 diabetes benefit the most, more studies are necessary to consider pre-diabetes as a criterion for metabolic surgery in patients with BMI ≥ 35 kg/m2.

Background: Continuous glucose monitoring (CGM) may overcome the limitations of intermittent point-of-care (POC) testing by providing real-time glucose trends and reducing treatment delays. This study aimed to evaluate the efficacy of CGM versus POC capillary testing in improving glycemic control among hospitalized non-Intensive Care Unit (non-ICU) adults with type 2 diabetes mellitus (T2DM). Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. We searched PubMed for randomized controlled trials published in English or Spanish that compared CGM with POC testing in hospitalized non-ICU adults ≥ 18 years old with T2DM and assessed risk of bias using the Cochrane RoB2 tool. The primary outcome was time in range (TIR). Secondary outcomes included time below range (TBR), time above range (TAR), mean glucose (MG), and glycemic variability (GV). Results: Seven randomized controlled trials (RCTs) including 1106 patients were analyzed. CGM significantly improved TIR (mean difference [MD] +8.15%; 95% confidence interval [CI]: +5.76, +10.55; p < 0.001) and reduced TAR > 180 mg/dL (MD −7.11%; 95% CI: −9.43, −4.78; p < 0.001) and TAR > 250 mg/dL (MD −3.96%; 95% CI: −5.29, −2.62; p < 0.001) compared with POC testing. MG also decreased with CGM (MD −11.27 mg/dL; 95% CI: −14.74, −7.81; p < 0.001). A modest reduction in TBR <70 mg/dL was observed (MD −0.29%; p < 0.001), whereas no significant differences were found for TBR < 54 mg/dL or GV. Conclusions: CGM improves inpatient glycemic control in non-ICU adults with type 2 diabetes, demonstrating advantages over POC testing across multiple randomized trials. However, further multicenter research is needed to clarify workflow implications, cost-effectiveness, and optimal implementation strategies.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a multisystemic disease with overlapping metabolic, renal, and cardiovascular effects. Within the Diabetic@ project, which aims to characterize individuals with T2DM using real-world data extracted from electronic health records (EHRs), this substudy sought to develop a predictive model for two-year heart failure (HF) risk. Methods: Multicenter, retrospective study including T2DM individuals across eight Spanish hospitals (2013–2018). Data were extracted exclusively from EHRs’ unstructured free text using clinical natural language processing (cNLP) and mapped to SNOMED CT. At inclusion, individuals were categorized as having or not prevalent HF (pHF). Predictive modeling was performed in non-pHF to assess two-year risk of developing HF, termed incident HF (iHF). Logistic regression (LR), decision trees, random forest, and XGBoost were compared, selecting for accuracy and interpretability. Results: Of 588,756 individuals with T2DM, 84,197 (14.3%) had pHF. Among non-pHF, 353,371 (60%) were used for model development (90.7% training, 9.3% validation). iHF occurred in 13.6% of the training set and 11.4% of the validation set. Ischemic heart disease was present in 16.2% overall, 37.9% in pHF, and 12.6% in non-pHF. Glycosylated hemoglobin data was rarely reported (<15%). LR achieved the best performance (AUC-ROC 0.73) using 27 predictors. Reduced 12- and clinically refined 9-predictor models performed similarly, with the latter implemented in a web-based tool. Conclusions: Unstructured data from EHRs enabled development of a two-year HF risk model for individuals with T2DM, underscoring the potential of cNLP for risk stratification across the cardiovascular–renal–metabolic spectrum.