PCORnet's Collaborative Research Groups.

The Patient-Centered Outcomes Research Institute (PCORI) launched a multi-institutional "network of networks" in 2013 - Patient-Centered Clinical Research Network (PCORnet) - that is designed to conduct clinical research that is faster, less expensive, and more responsive to the information needs of patients and clinicians. To enhance cross-network and cross-institutional collaboration and catalyze the use of PCORnet, PCORI has supported formation of 11 Collaborative Research Groups focusing on specific disease types (e.g., cardiovascular health and cancer) or particular patient populations (e.g., pediatrics and health disparities). PCORnet's Collaborative Research Groups are establishing research priorities within these focus areas, establishing relationships with potential funders, and supporting development of specific research projects that will use PCORnet resources. PCORnet remains a complex, multilevel, and heterogeneous network that is still maturing and building a diverse portfolio of observational and interventional people-centered research; engaging with PCORnet can be daunting, particularly for outside investigators. We believe the Collaborative Research Groups are stimulating interest and helping investigators navigate the complexity, but only time will tell if these efforts will bear fruit in terms of funded multicenter PCORnet projects

Volunteer Participation in the Health eHeart Study: A Comparison with the US Population.

Direct volunteer "eCohort" recruitment can be an efficient way of recruiting large numbers of participants, but there is potential for volunteer bias. We compared self-selected participants in the Health eHeart Study to participants in the National Health And Nutrition Examination Survey (NHANES) 2013-14, a cross-sectional survey of the US population. Compared with the US population (represented by 5,769 NHANES participants), the 12,280 Health eHeart participants with complete survey data were more likely to be female (adjusted odds ratio (ORadj) = 3.1; 95% confidence interval (CI) 2.9-3.5); less likely to be Black, Hispanic, or Asian versus White/non-Hispanic (ORadj's = 0.4-0.6, p < 0.01); more likely to be college-educated (ORadj = 15.8 (13-19) versus ≤high school); more likely to have cardiovascular diseases and risk factors (ORadj's = 1.1-2.8, p < 0.05) except diabetes (ORadj = 0.8 (0.7-0.9); more likely to be in excellent general health (ORadj = 0.6 (0.5-0.8) for "Good" versus "Excellent"); and less likely to be current smokers (ORadj = 0.3 (0.3-0.4)). While most self-selection patterns held for Health eHeart users of Bluetooth blood pressure cuff technology, there were some striking differences; for example, the gender ratio was reversed (ORadj = 0.6 (0.4-0.7) for female gender). Volunteer participation in this cardiovascular health-focused eCohort was not uniform among US adults nor for different components of the study

Long-term Glycemic Control and Dementia Risk in Type 1 Diabetes.

ObjectiveIndividuals with type 1 diabetes have experienced an increase in life expectancy, yet it is unknown what level of glycemic control is ideal for maintaining late-life brain health. We investigated the association of long-term glycemic control with dementia in older individuals with type 1 diabetes.Research design and methodsWe followed 3,433 members of a health care system with type 1 diabetes, aged ≥50 years, from 1996 to 2015. Repeated measurements of hemoglobin A1c (HbA1c), dementia diagnoses, and comorbidities were ascertained from health records. Cox proportional hazards models were fit to evaluate the association of time-varying glycemic exposure with dementia, with adjustment for age, sex, race/ethnicity, baseline health conditions, and frequency of HbA1c measurement.ResultsOver a mean follow-up of 6.3 years, 155 individuals (4.5%) were diagnosed with dementia. Patients with ≥50% of HbA1c measurements at 8-8.9% (64-74 mmol/mol) and ≥9% (≥75 mmol/mol) had 65% and 79% higher risk of dementia, respectively, compared with those with <50% of measurements exposed (HbA1c 8-8.9% adjusted hazard ratio [aHR] 1.65 [95% CI 1.06, 2.57] and HbA1c ≥9% aHR 1.79 [95% CI 1.11, 2.90]). By contrast, patients with ≥50% of HbA1c measurements at 6-6.9% (42-52 mmol/mol) and 7-7.9% (53-63 mmol/mol) had a 45% lower risk of dementia (HbA1c 6-6.9% aHR 0.55 [95% CI 0.34, 0.88] and HbA1c 7-7.9% aHR 0.55 [95% CI 0.37, 0.82]).ConclusionsAmong older patients with type 1 diabetes, those with majority exposure to HbA1c 8-8.9% and ≥9% had increased dementia risk, while those with majority exposure to HbA1c 6-6.9% and 7-7.9% had reduced risk. Currently recommended glycemic targets for older patients with type 1 diabetes are consistent with healthy brain aging

Evaluating the Clinical Utility of a Biomarker: A Review of Methods for Estimating Health Impact

Biomarkers, broadly defined, are markers of a biological process or state.1 Biomarkers are often used in research studies, but they may also be useful for clinicians and patients if they provide information about current status or future risk of disease. It is not always clear, however, when a novel biomarker provides enough useful information to justify measuring it in the context of clinical care. Evaluating the clinical utility of a novel biomarker requires a phased approach.2 Early-phase studies must prove that the biomarker is associated statistically with the clinical state of interest and adds information about presence or risk of disease above and beyond established markers. Midphase studies describe how often this incremental information might alter physician prescribing decisions. Early- and mid-phase studies are useful because they help investigators compare biomarker performance in terms that are generic (ie, not dependent on the specifics of the disease state being studied). Generic measures of biomarker performance have been reviewed previously2,–,16 and are described in Table 1 along with relevant published examples.17,–,32 View this table: Table 1. Generic Measures of Biomarker Performance Measuring biomarker performance in generic terms, however, is not sufficient for demonstrating clinical utility.6 The decision to use a biomarker in clinical practice should be based on an expectation that it will have a positive net health impact, and measuring health impact, by definition, requires use of measurements that consider the specific disease state being studied and its consequences. The goal of this review is to describe the methods by which evidence about the health impact of measuring a biomarker may be generated (late-phase evidence2) using examples relevant to cardiovascular disease and with a focus on the use of randomized clinical trials and modeling for estimating health impact. ### Mechanisms by Which Biomarker Measurement Can Impact Health There are 3 fundamental

Setting and motivation in the decision to participate: An approach to the engagement of diverse samples in mobile research.

Internet and mobile based research are powerful tools in the creation of large, cohort studies (eCohort). However, recent analysis indicates that an underrepresentation of minority and low income groups in these studies might exceed that found in traditional research [1-5]. In this report, we present findings from an experiment in research engagement using the Eureka Research Platform developed to enroll diverse populations in support of biomedical clinical research. This experiment involved the recruitment of African American and Latino participants in a smartphone based survey at a temporary, charitable, dental event sponsored, in part, by the research team, in order to explore the impact of setting and approach on recruitment outcomes. 211 participants enrolled including a significant representation of African Americans (51%) and Latinos (31%) and those with education levels at high school or less (37%). Interviews conducted after the study confirmed that our recruitment efforts within the context of a service event affected the decision to participate. While further research is necessary, this experiment holds promise for the engagement of underrepresented groups in research

Using mobile technology to engage sexual and gender minorities in clinical research.

IntroductionHistorical and current stigmatizing and discriminatory experiences drive sexual and gender minority (SGM) people away from health care and clinical research. Being medically underserved, they face numerous disparities that make them vulnerable to poor health outcomes. Effective methods to engage and recruit SGM people into clinical research studies are needed.ObjectivesTo promote health equity and understand SGM health needs, we sought to design an online, national, longitudinal cohort study entitled The PRIDE (Population Research in Identity and Disparities for Equality) Study that enabled SGM people to safely participate, provide demographic and health data, and generate SGM health-related research ideas.MethodsWe developed an iPhone mobile application ("app") to engage and recruit SGM people to The PRIDE Study-Phase 1. Participants completed demographic and health surveys and joined in asynchronous discussions about SGM health-related topics important to them for future study.ResultsThe PRIDE Study-Phase 1 consented 18,099 participants. Of them, 16,394 provided data. More than 98% identified as a sexual minority, and more than 15% identified as a gender minority. The sample was diverse in terms of sexual orientation, gender identity, age, race, ethnicity, geographic location, education, and individual income. Participants completed 24,022 surveys, provided 3,544 health topics important to them, and cast 60,522 votes indicating their opinion of a particular health topic.ConclusionsWe developed an iPhone app that recruited SGM adults and collected demographic and health data for a new national online cohort study. Digital engagement features empowered participants to become committed stakeholders in the research development process. We believe this is the first time that a mobile app has been used to specifically engage and recruit large numbers of an underrepresented population for clinical research. Similar approaches may be successful, convenient, and cost-effective at engaging and recruiting other vulnerable populations into clinical research studies

Exploring Meaningful Patient Engagement in ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness).

BackgroundGenuine patient engagement can improve research relevance, impact and is required for studies using the National Patient-Centered Clinical Research Network including major multicenter research projects. It is unclear, however, how best to integrate patients into governance of such projects.MethodsADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness) is the first major multicenter research project to be conducted in National Patient-Centered Clinical Research Network. Here, we provide a description of how we implemented patient engagement in ADAPTABLE thus far, including a description of committee structures and composition, first-hand patient testimonials, specific contributions, and lessons learned during the planning and early implementation of ADAPTABLE.ResultsWe recruited 1 patient leader from 6 of the 7 enrolling networks to serve on a Patient Review Board for ADAPTABLE, supported the Board with an experienced patient engagement team including an "investigator-advocate" not otherwise involved in the trial, and facilitated bidirectional communication between the Board and ADAPTABLE Coordinating Center. The Board has reviewed and provided substantial input on the informed consent procedure, recruitment materials, patient portal design, and study policy including compensation of participants. Although it was "too late" for some suggested modifications, most modifications suggested by the patient leaders have been implemented, and they are enthusiastic about the study and their role. The patient leaders also attend Steering and Executive Committee calls; these experiences have been somewhat less productive.ConclusionsWith adequate support, a cadre of committed patient leaders can provide substantial value to design and implementation of a major multicenter clinical trial

Validation of a consumer-grade activity monitor for continuous daily activity monitoring in individuals with multiple sclerosis.

Background:Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. Objectives:To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. Methods:Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. Results:Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p < 0.01), although the ICC was high (0.98) indicating that the devices were likely measuring the same kind of activity. Conclusions:Steps from Flex and Flex2 can be used interchangeably. Differences in total step count between ActiGraph and Flex devices can make cross-device comparisons of numerical step-counts challenging particularly for faster walkers