A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

mHealth and eHealth for Obesity and Types 2 and 1 Diabetes

mHealth and eHealth for Obesity and Types 2 and 1 Diabete

Effect of Information and Communication Technology–Based Self-management System DialBeticsLite on Treating Abdominal Obesity in the Specific Health Guidance in Japan: Randomized Controlled Trial

BackgroundMobile health (mHealth) interventions, a more cost-effective approach compared with traditional methods of delivering lifestyle coaching in person, have been shown to improve physical parameters and lifestyle behavior among overweight populations. In Japan, the Specific Health Checkups and Specific Health Guidance (SHG) started in 2008 to treat obesity and abdominal obesity. However, the effectiveness of SHG is limited owing to its in-person counseling. The effect of mHealth on SHG has yet to be demonstrated. ObjectiveThis study aims to determine whether a mobile self-management app (DialBeticsLite) could make the SHG more beneficial among patients with abdominal obesity to achieve a reduction in visceral fat area (VFA). MethodsThis study was an open-label, 2-arm, parallel-design randomized controlled trial. We recruited 122 people in September 2017 and randomly assigned them into either the intervention or control group. All participants attended an educational group session that delivered information regarding diet and exercise. In addition, participants in the intervention group were asked to use DialBeticsLite for 3 months. DialBeticsLite facilitated the daily recording of several physical parameters and lifestyle behavior and provided feedback to encourage an improvement in behavior. The primary outcome was the change in VFA from baseline to the 3-month follow-up. Secondary outcomes included changes in both physical and metabolic parameters from baseline to the 3-month follow-up. The Welch 2-tailed t test was conducted to analyze the effects of DialBeticsLite on both the primary and secondary outcomes. ResultsOf the 122 participants recruited, 75 (61.5%) were analyzed because 47 (38.5%) were excluded: 37 (30.3%) because of ineligibility and 10 (8.2%) because of withdrawal of consent. The mean age was 49.3 (SD 6.1) years in the intervention group (41/75, 55%) and 48.5 (SD 5.3) years in the control group (34/75, 45%), and all participants were men, although unintentionally. The baseline characteristics did not differ significantly between the intervention and control groups, except for VFA. The average change of VFA was −23.5 (SD 20.6) cm2 in the intervention group and +1.9 (SD 16.2) cm2 in the control group (P<.001). Statistically significant differences were also found for the change of body weight, BMI, and waist circumference. These findings did not change after adjusting for VFA at the baseline. The intervention had no significant effect on any of the metabolic parameters. An exploratory analysis showed significant associations between the change in VFA and steps per day and between the change in VFA and calorie intake per day within the intervention group. ConclusionsOur findings indicate that an mHealth intervention facilitating the daily monitoring of several physical parameters and lifestyle behavior can be highly effective in inducing visceral fat loss and weight loss among adults eligible for SHG. Trial RegistrationUMIN Clinical Trials Registry UMIN000042045; https://tinyurl.com/4vat3v5

Smoking Cessation Increases Short-Term Risk of Type 2 Diabetes Irrespective of Weight Gain: The Japan Public Health Center-Based Prospective Study

Objective: The effect of smoking cessation on the risk of diabetes has been reported previously. However, it is unknown whether the association is influenced by weight gain and other potential risk factors. Methods: The Japan Public Health Center-Based Prospective Study established in 1990 for Cohort I and in 1993 for Cohort II provided data, and 25,875 men and 33,959 women were analyzed. The response rate to the baseline questionnaire was 80.9%, and 68.4% of the respondents participated both the 5- and 10-year follow-up surveys. Smoking cessation was noted during the initial five years and the development of diabetes was reported in the subsequent five years. Results: An increased risk was observed among individuals who newly quit smoking compared with never smokers among men (odds ratio (OR) = 1.42, 95% CI = 1.03–1.94) and women (OR = 2.84, CI = 1.53–5.29). The risk of developing diabetes among male new quitters who gained 3 kg or more during the 5-year follow-up did not substantially differ from the risk among male never smokers with less than 3 kg of weight gain or no weight gain, while an increased risk was observed among male new quitters with less or no weight gain (OR = 1.46, 95%CI 1.00–2.14). An insignificant increased risk was observed among male new quitters with a family history of diabetes compared with male never smokers with a family history of diabetes. The risk was more than twice as high for male new quitters who used to smoke 25 or more cigarettes per day compared with never smokers (OR = 2.15, 95%CI: 1.34–3.47). Discussion: An increased risk of diabetes was implied among individuals who quit smoking. However, the increased risk was not implied among those who gained weight over the 5-years of follow-up. Those who had major risk factors for diabetes or who smoked heavier had a higher risk

The Use of Information and Communication Technology–Based Self-management System DialBeticsLite in Treating Abdominal Obesity in Japanese Office Workers: Prospective Single-Arm Pilot Intervention Study

BackgroundMaking lifestyle changes is an essential element of abdominal obesity (AO) reduction. To support lifestyle modification and self-management, we developed an information and communication technology–based self-management system—DialBeticsLite—with a fully automated dietary evaluation function for the treatment of AO. ObjectiveThe objective of this study was to evaluate the preliminary efficacy and feasibility of DialBeticsLite among Japanese office workers with AO. MethodsA 2- to 3-month prospective single-arm pilot intervention study was designed to assess the effects of the intervention using DialBeticsLite. The information and communication technology system was composed of 4 modules: data transmission (body weight, blood pressure, blood glucose, and pedometer count); data evaluation; exercise input; and food recording and dietary evaluation. Eligible participants were workers who were aged ≥20 years and with AO (waist circumference ≥85 cm for men and ≥90 cm for women). Physical parameters, blood tests, nutritional intake, and self-care behavior were compared at baseline and after the intervention. ResultsA total of 48 participants provided completed data for analysis, which yielded a study retention rate of 100%. The average age was 46.8 (SD 6.8) years, and 92% (44/48) of participants were male. The overall average measurement rate of DialBeticsLite, calculated by dividing the number of days with at least one measurement by the number of days of the intervention, was 98.6% (SD 3.4%). In total, 85% (41/48) of the participants reported that their participation in the study helped them to improve their lifestyle. BMI, waist circumference, and visceral fat area decreased significantly after the intervention (P<.001). In addition, the daily calorie intake reduced significantly (P=.02). There was a significant improvement in self-care behavior in terms of exercise and diet (P=.001). ConclusionsUsing DialBeticsLite was shown to be a feasible and potentially effective method for reducing AO by providing users with a motivational framework to evaluate their lifestyle behaviors

Risk Estimates for Development of Diabetes According to Status of Cigarette Smoking During Follow-Up Between 5-Year Survey and 10-Year Survey in the Japan Public Health Center-Based Prospective Study.

1<p>Who stopped smoking between the baseline and 5-year surveys.</p>2<p>Multivariate adjustment: age, BMI, history of hypertension, alcohol intake, family history of diabetes, weight change between baseline and 5-year surveys, study area, leisure-time physical activity.</p