Michelle L. Litchman

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: Individuals with diabetes are using online resources to engage in diabetes online communities to find diabetes-related support and information. The benefits and consequences of DOC (diabetes online community) use are unclear. This scoping review aims to map existing research focused on organic DOCs in which individuals affected by diabetes are interacting with peers. METHOD: A scoping review was conducted to comprehensively report and synthesize relevant literature published prior to 2018. Attention was paid to variations in study design, DOC user and platform characteristics, and potential or actual benefits and consequences. RESULTS: Of the 14 486 titles identified, 47 articles met the inclusion criteria and were included in this scoping review. No overt definition of the DOC could be identified. Perceived or actual benefits associated with DOC use can be broadly categorized as clinical, behavioral, psychosocial and community outcomes. Perceived, potential, or actual consequences associated with DOC use were categorized as quality of information, risky behavior exploration, acute concerns, psychosocial, privacy, and inactivity. CONCLUSIONS: The results of this review strongly suggest DOC use is highly beneficial with relatively few negative consequences. DOC use is an emerging area of research and research gaps exist. Future research should seek to identify benefits and consequences to DOC use in experimental trials.

Background: Successful diabetes management requires ongoing lifelong self-care and can require that individuals with diabetes become experts in translating care recommendations into real-life day-to-day diabetes self-care strategies. The diabetes online community comprises multiple websites that include social media sites, blogs, and discussion groups for people with diabetes to chat and exchange information. Online communities can provide disease-specific practical advice and emotional support, allow users to share experiences, and encourage self-advocacy and patient empowerment. However, there has been little research about whether diabetes online community use is associated with better diabetes self-care or quality of life. Objective: The aim of this study was to survey adults with diabetes who participated in the diabetes online community to better understand and describe who is using the diabetes online community, how they are using it, and whether the use of the diabetes online community was associated with health indicators. Methods: We recruited adults diagnosed with diabetes who used at least one of 4 different diabetes-related online communities to complete an online survey. Participants’ demographics, reported glycated hemoglobin (HbA1c), health-related quality of life (SF-12v2), level of diabetes self-care (Self-Care Inventory-Revised), and diabetes online community use (level of intensity and engagement) were collected. We examined the relationships between demographics, diabetes online community use, and health indicators (health-related quality of life, self-care, and HbA1c levels). We used binary logistic regression to determine the extent to which diabetes online community use predicted an HbA1c <7% or ≥7% after controlling statistically for other variables in the model. Results: A total of 183 adults participated in this study. Participants were mostly female (71.6%, 131/183), white (95.1%, 174/183), US citizens (82.5%, 151/183), had type 1 diabetes (69.7%, 129/183), with a mean age of 44.7 years (SD 14) and diabetes duration of 18.2 years (SD 14.6). Participants had higher diabetes self-care (P<.001, mean 72.4, SD 12.1) and better health-related quality of life (physical component summary P<.001, mean 64.8, SD 19; mental component summary P<.001, mean 66.6, SD 21.6) when compared with norms for diabetes. Diabetes online community engagement was a strong predictor of A1c, reducing the odds of having an A1c ≥7% by 33.8% for every point increase in diabetes online community engagement (0-5). Our data also indicated that study participants are oftentimes (67.2%, 123/183) not informing their healthcare providers about their diabetes online community use even though most (91.2%, 161/181) are seeing their healthcare provider on a regular basis. Conclusions: Our results suggest that individuals highly engaged with diabetes online community are more likely to have better glycemic levels compared with those with lower engagement. Furthermore, diabetes online community users have high health-related quality of life and diabetes self-care levels. Supplementing usual healthcare activities with diabetes online community use may encourage knowledge and support among a population that needs to optimize its diabetes self-care. Further studies are needed to determine how diabetes online community engagement may affect health outcomes.

AIMS: To systematically investigate the effect of interventions to overcome therapeutic inertia on glycaemic control in individuals with type 2 diabetes. MATERIALS AND METHODS: We electronically searched for randomized controlled trials or quasi-experimental studies published between January 1, 2004 and December 31, 2019 evaluating the effect of interventions on glycated haemoglobin (HbA1c) control. Characteristics of included studies and HbA1c difference between intervention and control arms (main outcome) were extracted. Interventions were grouped as: care management and patient education; nurse or certified diabetes educator (CDE); pharmacist; or physician-based. RESULTS: Thirty-six studies including 22 243 individuals were combined in nonlinear random-effects meta-regressions; the median (range) duration of intervention was 1 year (0.9 to 36 months). Compared to the control arm, HbA1c reduction ranged from: -17.7 mmol/mol (-1.62%) to -4.4 mmol/mol (-0.40%) for nurse- or CDE-based interventions; -13.1 mmol/mol (-1.20%) to 3.3 mmol/mol (0.30%) for care management and patient education interventions; -9.8 mmol/mol (-0.90%) to -6.6 mmol/mol (-0.60%) for pharmacist-based interventions; and -4.4 mmol/mol (-0.40%) to 2.8 mmol/mol (0.26%) for physician-based interventions. Across the included studies, a reduction in HbA1c was observed only during the first year (6 months: -4.2 mmol/mol, 95% confidence interval [CI] -6.2, -2.2 [-0.38%, 95% CI -0.56, -0.20]; 1 year: -1.6 mmol/mol, 95% CI -3.3, 0.1 [-0.15%, 95% CI -0.30, 0.01]) and in individuals with preintervention HbA1c >75 mmol/mol (9%). CONCLUSIONS: The most effective approaches to mitigating therapeutic inertia and improving HbA1c were those that empower nonphysician providers such as pharmacists, nurses and diabetes educators to initiate and intensify treatment independently, supported by appropriate guidelines.