Background: A 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. Methods: We 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. Results: The 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. Conclusion: The 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.

Background: There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies. Objective: This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point? Methods: Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically. Results: A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; “use” (102/292, 34.9%) and “engagement” (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies. Conclusions: The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693

OBJECTIVE: Regression from prediabetes to normal glucose regulation (NGR) was associated with reduced incidence of diabetes by 56% over 10 years in participants in the Diabetes Prevention Program Outcomes Study (DPPOS). In an observational analysis, we examined whether regression to NGR also reduced risk for microvascular disease (MVD). RESEARCH DESIGN AND METHODS: Generalized estimating equations were used to examine the prevalence of aggregate MVD at DPPOS year 11 in people who regressed to NGR at least once (vs. never) during the Diabetes Prevention Program (DPP). Logistic regression assessed the relationship of NGR with retinopathy, nephropathy, and neuropathy, individually. Generalized additive models fit smoothing splines to describe the relationship between average A1C during follow-up and MVD (and its subtypes) at the end of follow-up. RESULTS: Regression to NGR was associated with lower prevalence of aggregate MVD in models adjusted for age, sex, race/ethnicity, baseline A1C, and treatment arm (odds ratio [OR] 0.78, 95% CI 0.65–0.78, P = 0.011). However, this association was lost in models that included average A1C during follow-up (OR 0.95, 95% CI 0.78–1.16, P = 0.63) or diabetes status at the end of follow-up (OR 0.92, 95% CI 0.75–1.12, P = 0.40). Similar results were observed in examination of the association between regression to NGR and prevalence of nephropathy and retinopathy, individually. Risk for aggregate MVD, nephropathy, and retinopathy increased across the A1C range. CONCLUSIONS: Regression to NGR is associated with a lower prevalence of aggregate MVD, nephropathy, and retinopathy, primarily due to lower glycemic exposure over time. Differential risk for the MVD subtypes begins in the prediabetes A1C range.

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