Evaluation of a Web Platform to Record Lifestyle Habits in Subjects at Risk of Developing Type 2 Diabetes in a Middle-Income Population: Prospective Interventional Study.

BACKGROUND: Lifestyle is the focus of type 2 diabetes (T2D) prevention strategies. Prevention strategies using mobile health (mHealth)-based therapy have shown positive results for T2D prevention in high-income settings, but little is known about their effectiveness in low- and middle-income populations where the burden of T2D is substantial. "Vida Sana" is a web platform designed to record lifestyle habits and medication use within a lifestyle change program. OBJECTIVE: We sought to identify the barriers, feasibility, usability, and effectiveness of Vida Sana to record lifestyle habits in subjects at risk of developing T2D in a middle-income setting. METHODS: This was a 3-month prospective interventional study in Mexican individuals. A total of 77 subjects at risk of T2D (with prediabetes and BMI between 24 and 40 kg/m2) were selected. Feasibility was assessed by study retention. Usability was evaluated with the System Usability Scale (SUS). Effectiveness measures included changes in weight, body composition, BMI, glycated hemoglobin A1c (HbA1c), and fasting blood glucose from baseline to 3 months. Linear regression models were used to account for covariates. RESULTS: The feasibility of Vida Sana was 42%, with 33 subjects using the platform, and the usability was 48.7 (SD 14.24). Reported barriers to platform usage were; difficulty in accessing the platform from difficulty of use (12 subjects, 36%), lack of time to record their habits (11 subjects, 34%), lack of interest to record their habits (6 subjects, 18%), and lack of resources (4 subjects, 11%). The platform was effective for lowering glucose in fasting (-3.1 mg/dL vs -0.11 [SD 8.08] mg/dL; P=.038) and at 2 hours (-16.9 mg/dL vs 2.5 [SD 26.1] mg/dL; P=.045), body fat percentage (-1.3 [-2.2 to -0.7] vs -1.02 [-1.9 to -0.3]; P=.02), and waist circumference (-3.2 [SD 5.1] cm vs -1.7 [SD 5.0] cm; P=.02) independent of their age, sex, treatment, and education level. CONCLUSIONS: The use of the web platform was effective for improving glycemic and anthropometric parameters in a population at risk of developing diabetes. Improving accessibility and ease of navigation could improve the acceptance of digital health solutions in a middle-income population.

Primary Barriers of Adherence to a Structured Nutritional Intervention in Patients with Dyslipidemia.

PURPOSE: To describe the primary barriers to adequately adhering to a structured nutritional intervention. PATIENTS AND METHODS: A total of 106 participants diagnosed with dyslipidemia and without a medical nutrition therapeutic plan were included in this two-year study conducted at the INCMNSZ dyslipidemia clinic in Mexico City. All patients were treated with the same structured strategies, including three face-to-face visits and two telephone follow-up visits. Diet plan adherence was evaluated at each site visit through a 3-day or 24-h food recall. RESULTS: Barriers to adhere to the nutritional intervention were: lack of time to prepare their meals (23%), eating outside the home (19%), unwillingness to change dietary patterns (14%), and lack of information about a correct diet for dyslipidemias (14%). All barriers decreased significantly at the end of the intervention. Female gender, current smoking, and following a plan of more than 1500 kcal (R2 = 0.18 and p-value = 0.004) were associated with good diet adherence. Participants showed good levels of adherence to total caloric intake at visit 2 and 3, reporting 104.7% and 95.4%, respectively. Adherence to macronutrient intake varied from 65.1% to 126%, with difficulties in adhering to recommended carbohydrate and fat consumption being more notable. CONCLUSION: The study findings confirm that a structured nutritional intervention is effective in reducing barriers and improving dietary adherence and metabolic control in patients with dyslipidemias. Health providers must identify barriers to adherence early on to design interventions that reduce these barriers and improve adherence.

Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk.

BACKGROUND: Impaired fasting glucose (IFG) is a prevalent and potentially reversible intermediate stage leading to type 2 diabetes that increases risk for cardiometabolic complications. The identification of clinical and molecular factors associated with the reversal, or regression, from IFG to a normoglycemia state would enable more efficient cardiovascular risk reduction strategies. The aim of this study was to identify clinical and biological predictors of regression to normoglycemia in a non-European population characterized by high rates of type 2 diabetes. METHODS: We conducted a prospective, population-based study among 9637 Mexican individuals using clinical features and plasma metabolites. Among them, 491 subjects were classified as IFG, defined as fasting glucose between 100 and 125 mg/dL at baseline. Regression to normoglycemia was defined by fasting glucose less than 100 mg/dL in the follow-up visit. Plasma metabolites were profiled by Nuclear Magnetic Resonance. Multivariable cox regression models were used to examine the associations of clinical and metabolomic factors with regression to normoglycemia. We assessed the predictive capability of models that included clinical factors alone and models that included clinical factors and prioritized metabolites. RESULTS: During a median follow-up period of 2.5 years, 22.6% of participants (n = 111) regressed to normoglycemia, and 29.5% progressed to type 2 diabetes (n = 145). The multivariate adjusted relative risk of regression to normoglycemia was 1.10 (95% confidence interval [CI] 1.25 to 1.32) per 10 years of age increase, 0.94 (95% CI 0.91-0.98) per 1 SD increase in BMI, and 0.91 (95% CI 0.88-0.95) per 1 SD increase in fasting glucose. A model including information from age, fasting glucose, and BMI showed a good prediction of regression to normoglycemia (AUC = 0.73 (95% CI 0.66-0.78). The improvement after adding information from prioritized metabolites (TG in large HDL, albumin, and citrate) was non-significant (AUC = 0.74 (95% CI 0.68-0.80), p value = 0.485). CONCLUSION: In individuals with IFG, information from three clinical variables easily obtained in the clinical setting showed a good prediction of regression to normoglycemia beyond metabolomic features. Our findings can serve to inform and design future cardiovascular prevention strategies.