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PurposeThe appalling abuse healthcare workers have endured from patients is long documented in the popular press and social media. Less explored in the healthcare management literature is workplace abuse that professional nurses experience from their coworkers.Design/methodology/approachThe authors use text-based first-hand accounts from nurses posting on Reddit (N = 75) to better understand the types and context of abusive acts endured by their coworkers in the contemporary healthcare setting. Each account is content analyzed using two raters, and thematic analysis is utilized to summarize findings.FindingsFindings indicate that nurse workplace abuse frequently targets new entrants to a work unit (e.g. recent grads), typically is ongoing, takes verbal and nonverbal forms, mainly stems from coworkers (i.e. lateral mistreatment), and frequently takes place in front of other coworkers, mainly in hospital settings.Practical implicationsBy applying the lens of mindfulness, healthcare organizations can transform these harmful interactions within the nursing profession. The authors offer administrators and frontline workers practical implications for mitigating workplace abuse, including reshaping the culture, bystander interventions and explicit leadership support.Originality/valueFirst-hand accounts from nurses in the frontlines of healthcare provide a rich voice that reveals the reality of ongoing verbal and nonverbal peer abuse in hospitals and healthcare settings.

Introduction: Food insecurity (FI), defined as “limited or uncertain availability of nutritionally adequate and safe foods, or limited or uncertain ability to acquire acceptable foods in socially acceptable ways,” affects over 12% of US households. Embarrassment persists for patients with FI, and due to the potential consequences of FI, including increased utilization of the health care system, it is important to find causes and potential interventions for FI. The purpose of this project was to better understand FI from the patient perspective, including contributing factors, perceived health effects, and helpful interventions. Methods: Interviews (N=21) were conducted with suburban community residency clinic patients who screened positive for FI in the last 12 months. Six open-ended questions and a ranking question examined contributors to FI, effects of FI, perceptions of clinic intervention helpfulness, and ideas for novel interventions. Results: Patients identified lack of income (85.7%) as the primary issue they faced. Secondary identified issues were lack of transportation (38.1%), too much debt (33.3%), and food assistance programs not providing for all needs (33.3%). FI effects on patients’ health included difficulty adhering to specialized diets and the need to modify eating patterns due to lack of food. Surprisingly, 28.6% perceived no FI related-health effects. Patients felt that the most valuable clinic intervention was provision of urgent need food boxes, followed by FI screening and referrals to community food resources. Conclusions: Frequent FI screening is in itself useful to patients. Screening paired with community food resource referrals and urgent-need food boxes are the most helpful interventions according to patients.

Evaluate whether structured BGM testing (BGM) or real-time CGM (CGM) lead to improved glucose control (A1c). Determine which approach optimized glucose control more effectively. METHODS-MULTI-ARM PARALLEL: trial of three type 2 diabetes (T2D) therapies ± metformin: (1) sulfonylurea (SU), (2) incretin (DPP4 inhibitor or GLP-1 agonist), or (3) insulin. After a baseline CGM, 114 adult subjects were randomized to either BGM (4 times daily) or CGM (24/7) for 16 weeks with therapies adjusted every 4 weeks.A1c means decreased from 8.19 to 7.07 (1.12% difference) with CGM (n = 59) and 7.85 to 7.03 (0.82% difference) with BGM (n = 55) (p 0.001). BGM and CGM groups showed significant improvements in time in range and glucose variability-with no significant difference between the two groups. Clinically important hypoglycemia (50 mg/dL) was significantly reduced for the CGM group compared with BGM (p 0.01), particularly in subjects taking insulin or therapies with higher hypoglycemic risk (SU).In T2D, structured, consistent use of glucose data regardless of device (structured BGM or CGM) leads to improvements in A1c control. CGM is more effective than BGM in minimizing hypoglycemia particularly for those using higher hypoglycemic risk therapies.

Aim: Determine how patients review CGM and insulin pump data simultaneously to determine self-treatment adjustments. Methods: Subjects (n=21) verbally reported their interpretation/thoughts while examining an AGP (CGM + Insulin Pump) Report in a cognitive interview. Interviews lasted 45-90 minutes. Results: Subjects overwhelmingly prefer phone/watch interfaces; 75% do not have computers at home. Users report looking at their phones/CGM readings on average 16-60 times a day (1-4 times/hour); with several reporting high frequency of 15-45 times/hour. All subjects prefer colored graphs rather than numerical data on a single page report, with heavy preference to focus on CGM data with only occasional review of insulin pump data. Subjects felt the AGP CGM + Pump Report was “helpful” (86%), yet only 57% felt it was easy to understand due to the basal vs. bolus graphs. When asked to identify glucose data patterns: 95% noted trends, 67% noted hypoglycemia, 95% noted hyperglycemia, and 100% identified “missed” opportunities to treat high daily glucose levels. Bolus insulin graph responses included: bolus dose times of day helpful (100%), average meal bolus helpful (71%), and 73% understood the graph after an explanation, but only 38% could teach back offering specific advice. Subjects felt basal insulin graph made sense (90%). Subject confidence using AGP report data for self-care was high (95%) and would allow independent decision making about their DM care including identifying treatment next steps and determining how insulin dosing affects glucose trends. Seventy percent would feel confident making changes to their care by themselves using this report including adjustment of their pump settings, while 90% felt it would help with basal rate adjustments. Conclusion: CGM metrics and pattern visualization have recently been standardized in an AGP CGM report. Our patient interview reinforces that the same efforts are needed worldwide to standardize CGM and insulin dose (pump and connected pen) reporting. Disclosure D.M. Mullen: Consultant; Self; International Diabetes Center DBA HealthPartners Institute. R.M. Bergenstal: Consultant; Self; Ascensia Diabetes Care, Johnson & Johnson. Other Relationship; Self; Abbott, Dexcom, Inc., Hygieia, Lilly Diabetes, Medtronic, Novo Nordisk A/S, Onduo, Roche Diabetes Care, Sanofi, UnitedHealth Group. M.L. Johnson: Consultant; Self; Jaeb Center for Health Research. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation, JDRF, Lilly Diabetes, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (3UC4DK10861101S1)

HbA1c is a valuable metric for comparing treatment groups in a randomized trial, for assessing glycemic trends in a population over time, or for cross-sectional comparisons of glycemic control in different populations. However, what is not widely appreciated is that HbA1c may not be a good indicator of an individual patient’s glycemic control because of the wide range of mean glucose concentrations and glucose profiles that can be associated with a given HbA1c level. To illustrate this point, we plotted mean glucose measured with continuous glucose monitoring (CGM) versus central laboratory–measured HbA1c in 387 participants in three randomized trials, showing that not infrequently HbA1c may underestimate or overestimate mean glucose, sometimes substantially. Thus, if HbA1c is to be used to assess glycemic control, it is imperative to know the patient’s actual mean glucose to understand how well HbA1c is an indicator of the patient’s glycemic control. With knowledge of the mean glucose, an estimated HbA1c (eA1C) can be calculated with the formula provided in this article to compare with the measured HbA1c. Estimating glycemic control from HbA1c alone is in essence applying a population average to an individual, which can be misleading. Thus, a patient’s CGM glucose profile has considerable value for optimizing his or her diabetes management. In this era of personalized, precision medicine, there are few better examples with respect to the fallacy of applying a population average to a specific patient rather than using specific information about the patient to determine the optimal approach to treatment.

Introduction Electronic cigarettes (e-cigarettes) are gaining in popularity as an alternative to regular cigarettes, as they are viewed as potentially less harmful. However, it remains unclear how awareness about e-cigarettes is permeating through the general U.S. population. This study seeks to extend previous research and examine trends in e-cigarette awareness and perceived harmfulness, and their association with smoking-cessation efforts. Methods Data from three cycles (2012, 2013, and 2014) of the Health Information National Trends Survey were combined into a single data set. Controlling for survey year, multivariate logit models were used to determine the association between demographic characteristics and e-cigarette awareness, perceived harmfulness, quit attempts, and quit intentions. Data were analyzed in 2015. Results Awareness of e-cigarettes increased from 77.1% in 2012 to 94.3% in 2014. Controlling for demographic characteristics, e-cigarette awareness significantly increased in both 2013 and 2014, relative to 2012. Perception that e-cigarettes were less harmful than regular cigarettes declined from 50.7% in 2012 to 43.1% in 2014. Among smokers, no relationship was observed between e-cigarette awareness and past-year quit attempts or quit intentions, but those that viewed e-cigarettes as less harmful were less likely to have a past-year quit attempt. Conclusions These analyses reveal a continued increase in overall public awareness of e-cigarettes and shifting harm perceptions relative to regular cigarettes. New regulatory oversight by the U.S. Food and Drug Administration may have major effects on both dimensions, which are worth continued monitoring.

Objective: Characterize the effectiveness of insulin glargine alone, exenatide alone, or combined in subjects taking stable doses of metformin and evaluate their impact on hemoglobin A1C, hypoglycemia, weight, and glucose variability. Methods: Open-label, randomized, parallel-arm study of adults with type 2 diabetes naive to both insulin and glucagon-like peptide 1 (GLP-1) agonist who were not at A1C goal despite treatment with metformin. This prospective interventional study employed blinded continuous glucose monitoring ambulatory glucose profile (AGP) reports over 32 weeks. Subjects were randomized to treatment with glargine (Iglar), exenatide (GLP-1), or combination of glargine and exenatide (Iglar + GLP-1). At midpoint, those not at A1C target had the second medication added; those on Iglar + GLP-1 continued therapy optimization. Results: Decreases in A1C were: 7.6 to 6.2% for Iglar + GLP-1, 7.5 to 6.6% for Iglar, and 7.5 to 6.4% for GLP-1. Iglar + GLP-1 achieved A1C targets faster (14 to 16 weeks) but had more hypoglycemia. Hypoglycemia rates increased slightly for all arms. Weight loss was achieved in all regimens including GLP-1. Glucose variability was not reduced to the same extent in the Iglar arm as the GLP-1 arm. Conclusion: Addition of Iglar and/or GLP-1 to metformin for patients not at treatment goal was safe and effective. The order of medication addition needs to consider individualized AGP patterns and goals. Iglar + GLP-1 resulted in rapid A1C lowering, whereas GLP-1 was noted to have less hypoglycemia. Weight loss was most pronounced in GLP-1 monotherapy, suggesting that GLP-1 may mitigate the weight gain of Iglar. Any treatment with GLP-1 showed significant decreases in glucose variability. Abbreviations: A1C = hemoglobin A1c; AGP = ambulatory glucose profile; CGM = continuous glucose monitoring; GLM = general linear model; GLP-1 = glucagon-like peptide 1 (exenatide); Iglar = insulin glargine; SGLT2 = sodium-glucose cotransporter 2; SMBG = self-monitoring blood glucose; SU = sulfonylurea; T2D = type 2 diabetes mellitus.

To compare the effectiveness of 2 insulin protocols to treat glucocorticoid-induced hyperglycemia in the nonintensive care hospital setting.A randomized, open-label, parallel-arm study was conducted comparing standard recommended care of complete insulin orders (CIO) (i.e., 3-part insulin regimen of long-acting basal [background], rapid-acting bolus [mealtime], and rapid-acting correction factor) to an experimental group following a regimen of Neutral Protamine Hagedorn (NPH) plus CIO (NPH-CIO). The primary outcome was mean blood glucose (BG), and the secondary outcome was percent of BG in target range of 70 to 180 mg/dL. Hypoglycemia was also evaluated.Sixty-one patients completed 2 to 5 consecutive inpatient days (31 CIO; 30 NPH-CIO). Baseline mean BG results were 237.2 ± 50.2 and 221.9 ± 35.8 mg/dL (P = .30) in the CIO and NPH-CIO groups, respectively. No significant difference in overall mean BG between the 2 groups was detected; however, a significant difference arose on day 3: mean BG 181.8 ± 32.6 mg/dL (CIO) versus 157.2 ± 6.1 mg/dL (NPH-CIO) (P = .03). Moreover, the total daily doses (TDDs) of insulin did not differ: 34.8 ± 43.0 units (CIO) versus 35.8 ± 25.0 units (NPH-CIO) (P = .13). Percent of BG in target was 54.6% (CIO) and 62% (NPH-CIO) (P = .24). Incidence of severe hypoglycemia (50 mg/dL) was the same in both groups (0.1%).NPH added to 3-part insulin regimen (CIO) may be an effective way to a combat glucocorticoid-induced hyperglycemia, though further research is needed in a larger population.

Background: Diabetes care is predominately done at home by the patient. When clinics do not have a reliable, easy process for obtaining this patient data, clinical decisions must be made with incomplete verbal recall reports. Unused or inaccessible glucose data represent a large information gap affecting clinical decision making. This study’s purpose was to design an optimized glucose device download system with a standardized report and to evaluate its efficiency. Methods: Observations and evaluations of glucose data retrieval occurred at two clinics; an additional clinic utilized the optimized process doing only post process timings. Patients/families and clinicians were surveyed about their experiences with the system and the standardized report (AGP). The study was approved by all the sites’ IRBs. Results: Optimized systems saved staff at least 3 min per patient. Standardized AGP reports and an optimized data system made the work flow of glucose data easier to complete. The AGP report was preferred by patients, families, and clinicians. Conclusions: An optimized system takes advantage of patient lobby downtime to download glucose devices and ensures that diabetes clinical decisions are made utilizing all available data. Staff and patients liked the software lobby system and found it a valuable time-saving tool.