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Objective: While the American Diabetes Association (ADA) screening guidelines have been used widely, the way they are implemented and adapted to a particular setting can impact their practical application and usage. Our primary objective was to validate a best practice advisory (BPA) screening algorithm informed by the ADA guidelines to identify patients eligible for hemoglobin a1c (HbA1c) testing in the emergency department (ED). Methods: This cross-sectional study included adults presenting to a large urban medical center’s ED in May 2021. We used sensitivity, specificity, likelihood ratios, and predictive values to estimate the algorithm’s ability to correctly identify patients eligible for diabetes screening, with manual chart review as the reference standard. Eligibility criteria targeted patients at risk for diabetes who were likely unaware of their elevated HbA1c. We also calculated the area under the receiver operating characteristic curve (AUC).  Results: In May 2021, 2,963 (77%) of the 3,850 adults admitted to the ED had a routine lab ordered. Among those, 796 (27%) had a BPA triggered, and of those 631 (79%) had an HbA1c test completed. The algorithm had acceptable sensitivity (0.69, 95% confidence interval [CI] 0.66-0.72), specificity (0.91, CI 0.89-0.92), positive predictive value (0.75, CI 0.72-0.78) and negative predictive value (0.88, CI 0.86-0.89). The positive likelihood ratio (7.39, CI 6.35-8.42 ) was adequate, and the negative likelihood ratio (0.34, CI 0.30-0.37) was informative. The AUC of 0.74 (CI 0.72-0.77) suggests that the algorithm had acceptable accuracy. Conclusion: Findings suggest that an electronic health record-based algorithm informed by the ADA guidelines is a valid tool for identifying patients presenting to the ED who are eligible for HbA1c testing and may be unaware of having prediabetes or diabetes. The ease of workflow integration and high yield of potentially undiagnosed diabetes and prediabetes makes the BPA algorithm an appealing method for diabetes screening within the ED.

Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit’s ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through “trial and error.” This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals.

BackgroundAggressive management of blood glucose, blood pressure, and cholesterol through medication and lifestyle adherence is necessary to minimize the adverse health outcomes of type 2 diabetes. However, numerous psychosocial and environmental barriers to adherence prevent low-income, urban, and ethnic minority populations from achieving their management goals, resulting in diabetes complications. Health coaches working with clinical pharmacists represent a promising strategy for addressing common diabetes management barriers. Mobile health (mHealth) tools may further enhance their ability to support vulnerable minority populations in diabetes management. ObjectiveThe aim of this study is to evaluate the impact of an mHealth clinical pharmacist and health coach–delivered intervention on hemoglobin A1c (HbA1c, primary outcome), blood pressure, and low-density lipoprotein (secondary outcomes) in African-Americans and Latinos with poorly controlled type 2 diabetes. MethodsA 2-year, randomized controlled crossover study will evaluate the effectiveness of an mHealth diabetes intervention delivered by a health coach and clinical pharmacist team compared with usual care. All patients will receive 1 year of team intervention, including lifestyle and medication support delivered in the home with videoconferencing and text messages. All patients will also receive 1 year of usual care without team intervention and no home visits. The order of the conditions received will be randomized. Our recruitment goal is 220 urban African-American or Latino adults with uncontrolled type 2 diabetes (HbA1c ≥8%) receiving care from a largely minority-serving, urban academic medical center. The intervention includes the following: health coaches supporting patients through home visits, phone calls, and text messaging and clinical pharmacists supporting patients through videoconferences facilitated by health coaches. Data collection includes physiologic (HbA1c, blood pressure, weight, and lipid profile) and survey measures (medication adherence, diabetes-related behaviors, and quality of life). Data collection during the second year of study will determine the maintenance of any physiological improvement among participants receiving the intervention during the first year. ResultsParticipant enrollment began in March 2017. We have recruited 221 patients. Intervention delivery and data collection will continue until November 2021. The results are expected to be published by May 2022. ConclusionsThis is among the first trials to incorporate health coaches, clinical pharmacists, and mHealth technologies to increase access to diabetes support among urban African-Americans and Latinos to achieve therapeutic goals. International Registered Report Identifier (IRRID)DERR1-10.2196/17170

Introduction: Treatment advances for hematologic malignancies (HM) have dramatically improved life expectancy, necessitating greater focus on long-term cancer pain management. This study explored real-world patterns of opioid use among patients with HM. Methods: This retrospective cohort study identified adults diagnosed with HM from January 1, 2013 through December 31, 2019 using the Truven MarketScan Commercial Claims and Encounters database. Across several HM types, we described rates of high-risk opioid use (based on Pharmacy Quality Alliance measures) and opioid-related harms, including incident opioid use disorder (OUD) diagnoses and opioid-related hospitalizations or emergency department (ED) visits. We used multivariable Cox regression to generate adjusted hazard ratios and 95% confidence intervals comparing the risk of opioid-related harms between patients with versus without high-risk opioid use. Results: Our sample included 43,190 patients with HM. Median age at HM diagnosis was 54 years (interquartile range = 44–60). Most patients (61.9%) were diagnosed with lymphoma. Approximately half (49.2%) had an opioid dispensed in the follow-up period. Among all patients, 20.0% met criteria for high-risk opioid use, 0.9% had an OUD diagnosis, and 0.3% experienced an opioid-related hospitalization/ED visit in follow-up. High-risk opioid use increased the risk of an OUD diagnosis by 3.3 times (p < 0.0001) and an opioid-related hospitalization/ED visit 4.2 times (p < 0.0001). Conclusion: High-risk opioid use was prevalent among patients with HM and significantly increased the risk of opioid-related harms. However, rates of opioid-related harms were low. These findings highlight the importance of continually monitoring pain and opioid use throughout HM survivorship to provide safe, effective HM pain management. [ABSTRACT FROM AUTHOR]

Physical activity is not only important for preventing noncommunicable diseases but also for managing and recovering from them. Climate change poses a particular risk to individuals with clinical conditions that are worsened by environmental hazards, such as asthma. More research is needed to understand the role of physical activity in disease management and climate resilience for these vulnerable populations. Climate change can limit people's ability to be physically active due to natural disasters, extreme weather events, and worsening air quality. People with respiratory conditions and other chronic illnesses are especially vulnerable to the health impacts of climate change. The physical activity research community is called upon to study the impacts of climate change on clinical populations and incorporate climate resilience strategies into interventions and policies. Equity and justice should guide these efforts to ensure that physical activity promotion supports climate resilience for all. [Extracted from the article]