Dietary treatment is important in management of type 2 diabetes or prediabetes, but uncertainty exists about the optimal diet. We randomized adults (n = 34) with glycated hemoglobin (HbA1c) > 6.0% and elevated body weight (BMI > 25) to a very low-carbohydrate ketogenic (LCK) diet (n = 16) or a moderate-carbohydrate, calorie-restricted, low-fat (MCCR) diet (n = 18). All participants were encouraged to be physically active, get sufficient sleep, and practice behavioral adherence strategies based on positive affect and mindful eating. At 12 months, participants in the LCK group had greater reductions in HbA1c levels (estimated marginal mean (EMM) at baseline = 6.6%, at 12 mos = 6.1%) than participants in MCCR group (EMM at baseline = 6.9%, at 12 mos = 6.7%), p = .007. Participants in the LCK group lost more weight (EMM at baseline = 99.9 kg, at 12 mos = 92.0 kg) than participants in the MCCR group (EMM at baseline = 97.5 kg, at 12 mos = 95.8 kg), p < .001. The LCK participants experienced larger reductions in diabetes-related medication use; of participants who took sulfonylureas or dipeptidyl peptidase-4 inhibitors at baseline, 6/10 in the LCK group discontinued these medications compared with 0/6 in the MCCR group (p = .005). In a 12-month trial, adults with elevated HbA1c and body weight assigned to an LCK diet had greater reductions in HbA1c, lost more weight, and reduced more medications than those instructed to follow an MCCR diet.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.