Effects of a patient-centered digital health intervention in patients referred to cardiac rehabilitation: the Smart HEART clinical trial.

BACKGROUND: Cardiac rehabilitation (CR) improves outcomes in heart disease yet remains vastly underutilized. Remote CR enhanced with a digital health intervention (DHI) may offer higher access and improved patient-centered outcomes over non-technology approaches. We sought to pragmatically determine whether offering a DHI improves CR access, cardiac risk profile, and patient-reported outcome measures. METHODS: Adults referred to CR at a tertiary VA medical center between October 2017 and December 2021 were offered enrollment into a DHI alongside other CR modalities using shared decision-making. The DHI consisted of remote CR with a structured, 3-month home exercise program enhanced with multi-component coaching, a commercial smartphone app, and wearable activity tracker. We measured completion rates among DHI participants and evaluated changes in 6-min walk distance, cardiovascular risk factors, and patient-reported outcomes from pre- to post-intervention. RESULTS: Among 1,643 patients referred to CR, 258 (16%) consented to the DHI where the mean age was 60 ± 9 years, 93% were male, and 48% were black. A majority (90%) of the DHI group completed the program. Over 3-months, significant improvements were seen in 6MWT (mean difference [MD] -29 m; 95% CI, 10 to 49; P < 0.01) and low-density lipoprotein cholesterol (MD -11 mg/dL; 95% CI, -17 to -5; P < 0.01), and the absolute proportion of patients who reported smoking decreased (10% vs 15%; MD, -5%; 95% CI, -8% to -2%; P < 0.01) among DHI participants with available data. No adverse events were reported. CONCLUSIONS: The addition of a DHI-enhanced remote CR program was delivered in 16% of referred veterans and associated with improved CR access, markers of cardiovascular risk, and healthy behaviors in this real-world study. These findings support the continued implementation of DHIs for remote CR in real-world clinical settings. TRIAL REGISTRATION: This trial was registered on ClinicalTrials.gov: NCT02791685 (07/06/2016).

Lights, Facts, and Goals: A Novel Framework to Enhance Community Health Messaging Campaign Design, Implementation, and Assessment.

The spread of health misinformation has made the task of health communicators more difficult. However, the success of health messaging hinges not only on meaningful message content but also on the credibility of who is delivering the message. "Trusted messengers," like local leaders and community-based organizations, have a greater ability to influence improvements in community health, due to their shared cultural experience with their communities. Health communication agencies should empower trusted messengers with the tools they need to succeed in health communication. One tool critical for their success is a succinct health messaging framework to plan and implement health messaging. Marketing has "See, Think, Do"-a simple, practical framework used to influence consumer purchases. As a more trustworthy corollary, we propose the "Lights, Facts, and Goals" framework, a concise, authentic, and transparent method for planning, implementing, and assessing health messaging campaigns that influence health improvements. "Lights" refers to different methods of reaching communities like trusted messengers, advertisements, and text messages. "Facts" refers to key sourced scientific information relevant to a specific aspect of community health. "Goals" refers to actions community members can take to improve their health in connection with the communicated health facts. This article describes how the "Lights, Facts, and Goals" framework both simplifies the creation and communication of scientifically sound health messaging and strengthens the partnership between health agencies and trusted messengers in the community. Through "Lights, Facts, and Goals," community-based organizations, community leaders, and their partners will be more effective at improving community health through messaging.

Exploring the Discursive Emphasis on Patients and Coaches Who Participated in Technology-Assisted Diabetes Self-management Education: Clinical Implementation Study of Health360x.

BACKGROUND: A critical unmet need for underserved patients with diabetes is regular access to sufficient support for diabetes self-management. Although advances in digital technologies have made way for eHealth applications that provide a scalable path for tailored interventions for self-management of chronic conditions, health and digital literacy has remained an obstacle to leveraging these technologies for effective diabetes self-management education. Studies have shown that the availability of coaches helps to maintain engagement in internet-based studies and improves self-efficacy for behavior change. However, little is known about the substances involved in these interactions. OBJECTIVE: This study aims to compare the content of conversations between patient-coach pairs that achieved their self-management goals and those that did not. The context is a clinical implementation study of diabetes self-management behavior change using Health360x within the practices of the Morehouse Choice Accountable Care Organization in the Atlanta metro area. Health360x is a coach-assisted consumer health information technology designed to support self-management skills acquisition and behavior among underserved, high-risk patients with diabetes. METHODS: We provide a novel analysis of the discursive emphasis on patients and coaches. We examined transcripts of visits using a structural topic model to estimate topic content and prevalence as a function of patient and coach characteristics. We compared topics between patient-coach pairs that achieved diabetes-related self-management goals and those who did not. We also estimated a regression in which utterances are the units, the dependent variable is the proportion of an utterance that is about a given topic, and the independent variables are speaker types and explored other themes. RESULTS: Transcripts from 50 patients who were recruited and consented, starting in February 2015, were analyzed. A total of 44 topics were estimated for patient-coach pairs that achieved their intended health goals and 50 topics for those who did not. Analysis of the structural topic model results indicated that coaches in patient-coach pairs that were able to achieve self-management goals provided more contextual feedback and probed into patients' experience with technology and trust in consumer information technologies. We also found that discussions around problem areas and stress, support (ßCoach=.015; P<.001), initial visits (ßCoach=.02; P<.001), problems with technology (ßCoach=.01; P<.001), health eating goals (ßCoach=.01; P=.04), diabetes knowledge (ßCoach=.02; P<.001), managing blood sugar (ßCoach=.03; P<.001), and using Health360x (ßCoach=.003; P=.03) were dominated by coaches. CONCLUSIONS: Coach-facilitated, technology-based diabetes self-management education can help underserved patients with diabetes. Our use of topic modeling in this application sheds light on the actual dynamics in conversations between patients and coaches. Knowledge of the key elements for successful coach-patient interactions based on the analysis of transcripts could be applied to understanding everyday patient-provider encounters, given the recent paradigm shift around the use of telehealth.

Quantifying Clinical Trial Diversity in Pivotal Registration Trials of FDA Novel Drug Approvals.

BACKGROUND: Health care inequity includes the lack of adequate representation of various populations in clinical trials. Government, academic and industry organizations have highlighted these issues and committed to actions to improve. In order to assess the current status and future success of these initiatives a quantitative objective measure to assess the state of clinical trial diversity is needed. METHODS: FDA review documents for all novel drug approvals from January 2022 through March 2023 were assessed using a scorecard that considers diversity across different demographic subgroups including age (≥ 65 years old), sex (female), race (Black and Asian) and ethnicity (Hispanic/Latino). The scorecard assigns each drug a letter grade, between A and F, for each subgroup (and overall) based on (1) the percent of each sub-population included in the trials and grades relative to the percent of the US population per the 2020 Census, (2) the number of participants from each subpopulation that received the novel new drug in the trials, (3) the incidence or prevalence of the disease/condition in each of the sub-populations. RESULTS: The FDA approved 49 novel new drugs for 50 indications (one drug was simultaneously approved for two indications). There was good representation of elderly and females with only two drugs receiving a D grade in either of these sub-populations. In contrast, Black (5 F grades) and Hispanic (4 F grades) representation was often inadequate. There were 10 drugs (20.0%) where there were no Black participants receiving the novel new drug and 16 (32.0%) approvals where there were 1-9 Black participants receiving the novel drug. In the Hispanic/Latino population there were 4 (8.0%) approvals with no Hispanic participants receiving the novel drug and 15 (30.0%) approvals where there were 1-9 Hispanic participants receiving the drug. CONCLUSIONS: This scorecard provides an objective quantitative approach to assess the current state of diversity in clinical trials supporting new drug approvals. Substantial improvement in racial and ethnic representation is needed. Meaningful change will require actions and cooperation among all stakeholders to address this multifaceted issue and will take commitment, perseverance, and appropriate incentives.

Race Adjustment of Pulmonary Function Tests in the Diagnosis and Management of COPD: A Scoping Review.

Aim: Increasing evidence suggests that the inclusion of self-identified race in clinical decision algorithms may perpetuate longstanding inequities. Until recently, most pulmonary function tests utilized separate reference equations that are race/ethnicity based. Purpose: We assess the magnitude and scope of the available literature on the negative impact of race-based pulmonary function prediction equations on relevant outcomes in African Americans with COPD. Methods: We performed a scoping review utilizing an English language search on PubMed/Medline, Embase, Scopus, and Web of Science in September 2022 and updated it in December 2023. We searched for publications regarding the effect of race-specific vs race-neutral, race-free, or race-reversed lung function testing algorithms on the diagnosis of COPD and COPD-related physiologic and functional measures. Joanna Briggs Institute (JBI) guidelines were utilized for this scoping review. Eligibility criteria: The search was restricted to adults with COPD. We excluded publications on other lung disorders, non-English language publications, or studies that did not include African Americans. The search identified publications. Ultimately, six peer-reviewed publications and four conference abstracts were selected for this review. Results: Removal of race from lung function prediction equations often had opposite effects in African Americans and Whites, specifically regarding the severity of lung function impairment. Symptoms and objective findings were better aligned when race-specific reference values were not used. Race-neutral prediction algorithms uniformly resulted in reclassifying severity in the African Americans studied. Conclusion: The limited literature does not support the use of race-based lung function prediction equations. However, this assertion does not provide guidance for every specific clinical situation. For African Americans with COPD, the use of race-based prediction equations appears to fall short in enhancing diagnostic accuracy, classifying severity of impairment, or predicting subsequent clinical events. We do not have information comparing race-neutral vs race-based algorithms on prediction of progression of COPD. We conclude that the elimination of race-based reference values potentially reduces underestimation of disease severity in African Americans with COPD.

Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells.

Low-level lead exposure in children is a major public health issue. Higher-resolution spatial targeting would significantly improve county and state-wide policies and programs for lead exposure prevention that generally intervene across large geographic areas. We use stack-ensemble machine learning, including an elastic net generalized linear model, gradient-boosted machine, and deep neural network, to predict the number of children with venous blood lead levels (BLLs) ≥2 to <5 µg/dL and ≥5 µg/dL in ~1 km2 raster cells in the metro Atlanta region using a sample of 92,792 children ≤5 years old screened between 2010 and 2018. Permutation-based predictor importance and partial dependence plots were used for interpretation. Maps of predicted vs. observed values were generated to compare model performance. According to the EPA Toxic Release Inventory for air-based toxic release facility density, the percentage of the population below the poverty threshold, crime, and road network density was positively associated with the number of children with low-level lead exposure, whereas the percentage of the white population was inversely associated. While predictions generally matched observed values, cells with high counts of lead exposure were underestimated. High-resolution geographic prediction of lead-exposed children using ensemble machine learning is a promising approach to enhance lead prevention efforts.

The Role of Mock Reviewing Sessions in the National Research Mentoring Network Strategic Empowerment Tailored for Health Equity Investigators: A Randomized Controlled Study.

The National Research Mentoring Network (NRMN) Strategic Empowerment Tailored for Health Equity Investigators (SETH) study evaluates the value of adding Developmental Network to Coaching in the career advancement of diverse Early-Stage Investigators (ESIs). Focused NIH-formatted Mock Reviewing Sessions (MRS) prior to the submission of grants can significantly enhance the scientific merits of an ESI's grant application. We evaluated the most prevalent design, analysis-related factors, and the likelihood of grant submissions and awards associated with going through MRS, using descriptive statistics, Chi-square, and logistic regression methods. A total of 62 out of 234 applications went through the MRS. There were 69.4% that pursued R grants, 22.6% career development (K) awards, and 8.0% other grant mechanisms. Comparing applications that underwent MRS versus those that did not (N = 172), 67.7% vs. 38.4% were submitted for funding (i.e., unadjusted difference of 29.3%; OR = 4.8, 95% CI = (2.4, 9.8), p-value < 0.0001). This indicates that, relative to those who did not undergo MRS, ESIs who did, were 4.8 times as likely to submit an application for funding. Also, ESIs in earlier cohorts (1-2) (a period that coincided with the pre COVID-19 era) as compared to those who were recruited at later cohorts (3-4) (i.e., during the peak of COVID-19 period) were 3.8 times as likely to submit grants (p-value < 0.0001). The most prevalent issues that were identified included insufficient statistical design considerations and plans (75%), conceptual framework (28.3%), specific aims (11.7%), evidence of significance (3.3%), and innovation (3.3%). MRS potentially enhances grant submissions for extramural funding and offers constructive feedback allowing for modifications that enhance the scientific merits of research grants.

Impact of COVID-19 on the Research Career Advancement of Health Equity Scholars from Diverse Backgrounds.

The COVID-19 pandemic has significantly taxed scientific research and seems to have exacerbated existing inequities within the research field, particularly for early-stage investigators (ESIs). This study examines the effects of the COVID-19 pandemic on traditionally underrepresented ESIs enrolled in an NIH-supported study evaluating the effectiveness of developmental networks, grant writing coaching, and mentoring on research career advancement. The survey consisted of 24 closed-ended (quantitative) and 4 open-ended questions (qualitative) linked to a participant's ability to meet grant submission deadlines, research and professional development disruptions, stress level, career transition level, self-efficacy and management of scholarly tasks, and familial responsibilities. Results from 32 respondents (53%) suggest that COVID-19 adversely impacted the continuity of research (81%) and grant submissions (63%). On average, grant submissions were delayed by 6.69 months (i.e., greater than one grant cycle). We also conducted additional analyses characterizing nonresponse and found that there were no significant predictors of nonresponse, indicating a limited threat to the validity of our findings. The disruption caused by COVID-19 to the careers of ESIs from underrepresented groups in the biomedical workforce has been profound in the short term. The long-term consequences to the future success of these groups are unknown but is a worthwhile area of research and potential innovation.

Individual and Institutional Factors Contribute to Research Capacity Building for Early-Stage Investigators from Groups Underrepresented in Biomedical Research: A Qualitative Comparative Analysis.

BACKGROUND: Enhancement of diversity within the U.S. research workforce is a recognized need and priority at a national level. Existing comprehensive programs, such as the National Research Mentoring Network (NRMN) and Research Centers in Minority Institutions (RCMI), have the dual focus of building institutional research capacity and promoting investigator self-efficacy through mentoring and training. METHODS: A qualitative comparative analysis was used to identify the combination of factors that explain the success and failure to submit a grant proposal by investigators underrepresented in biomedical research from the RCMI and non-RCMI institutions. The records of 211 participants enrolled in the NRMN Strategic Empowerment Tailored for Health Equity Investigators (NRMN-SETH) program were reviewed, and data for 79 early-stage, underrepresented faculty investigators from RCMI (n = 23) and non-RCMI (n = 56) institutions were included. RESULTS: Institutional membership (RCMI vs. non-RCMI) was used as a possible predictive factor and emerged as a contributing factor for all of the analyses. Access to local mentors was predictive of a successful grant submission for RCMI investigators, while underrepresented investigators at non-RCMI institutions who succeeded with submitting grants still lacked access to local mentors. CONCLUSION: Institutional contexts contribute to the grant writing experiences of investigators underrepresented in biomedical research.

The law of non-usage attrition in a technology-based behavioral intervention for black adults with poor cardiovascular health.

Digital health innovations, such as telehealth and remote monitoring, have shown promise in addressing patient barriers to accessing evidence-based programs and providing a scalable path for tailored behavioral interventions that support self-management skills, knowledge acquisition and promotion of relevant behavioral change. However, significant attrition continues to plague internet-based studies, a result we believe can be attributed to characteristics of the intervention, or individual user characteristics. In this paper, we provide the first analysis of determinants of non usage attrition in a randomized control trial of a technology-based intervention for improving self-management behaviors among Black adults who face increased cardiovascular risk factors. We introduce a different way to measure nonusage attrition that considers usage over a specific period of time and estimate a cox proportional hazards model of the impact of intervention factors and participant demographics on the risk of a nonusage event. Our results indicated that not having a coach (compared to having a coach) decreases the risk of becoming an inactive user by 36% (HR = .63, P = 0.04). We also found that several demographic factors can influence Non-usage attrition: The risk of nonusage attrition amongst those who completed some college or technical school (HR = 2.91, P = 0.04) or graduated college (HR = 2.98, P = 0.047) is significantly higher when compared to participants who did not graduate high school. Finally, we found that the risk of nonsage attrition among participants with poor cardiovascular from "at-risk" neighborhoods with higher morbidity and mortality rates related to CVD is significantly higher when compared to participants from "resilient" neighborhoods (HR = 1.99, P = 0.03). Our results underscore the importance of understanding challenges to the use of mhealth technologies for cardiovascular health in underserved communities. Addressing these unique barriers is essential, because a lack of diffusion of digital health innovations exacerbates health disparities.

Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science.

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.

The Research Centers in Minority Institutions (RCMI) Consortium: A Blueprint for Inclusive Excellence.

The Research Centers in Minority Institutions, (RCMI) Program was established by Congress to address the health research and training needs of minority populations, by preparing future generations of scientists at these institutions, with a track record of producing minority scholars in medicine, science, and technology. The RCMI Consortium consists of the RCMI Specialized Centers and a Coordinating Center (CC). The RCMI-CC leverages the scientific expertise, technologies, and innovations of RCMI Centers to accelerate the delivery of solutions to address health disparities in communities that are most impacted. There is increasing recognition that the gap in representation of racial/ethnic groups and women is perpetuated by institutional cultures lacking inclusion and equity. The objective of this work is to provide a framework for inclusive excellence by developing a systematic evaluation process with common data elements that can track the inter-linked goals of workforce diversity and health equity. At its core, the RCMI Program embodies the trinity of diversity, equity, and inclusion. We propose a realist evaluation framework and a logic model that integrates the institutional context to develop common data metrics for inclusive excellence. The RCMI-CC will collaborate with NIH-funded institutions and research consortia to disseminate and scale this model.

Impact of Technology-Based Intervention for Improving Self-Management Behaviors in Black Adults with Poor Cardiovascular Health: A Randomized Control Trial.

Cardiovascular disease (CVD) is the number one killer of adults in the U.S., with marked ethnic/racial disparities in prevalence, risk factors, associated health behaviors, and death rates. In this study, we recruited and randomized Blacks with poor cardiovascular health in the Atlanta Metro area to receive an intervention comparing two approaches to engagement with a behavioral intervention technology for CVD. Generalized Linear Mixed Models results from a 6-month intervention indicate that 53% of all participants experienced a statistical improvement in Life's Simple 7 (LS7), 54% in BMI, 61% in blood glucose, and 53% in systolic blood pressure. Females demonstrated a statistically significant improvement in BMI and diastolic blood pressure and a reduction in self-reported physical activity. We found no significant differences in changes in LS7 or their constituent parts but found strong evidence that health coaches can help improve overall LS7 in participants living in at-risk neighborhoods. In terms of clinical significance, our result indicates that improvements in LS7 correspond to a 7% lifetime reduction of incident CVD. Our findings suggest that technology-enabled self-management can be effective for managing selected CVD risk factors among Blacks.

Randomized Controlled Study to Test the Effectiveness of Developmental Network Coaching in the Career Advancement of Diverse Early-Stage Investigators (ESIs): Implementation Challenges and Lessons Learned.

Introduction: Adding developmental networks (DN) to grant-writing coaching can significantly enhance ESIs' research careers. Herein, we present study design, ESIs' characteristics and encountered challenges/lessons learned and their resolutions when deploying/implementing (a) NCR algorithm(s), (b) recruitment/retention and (c) implementing DN intervention. Methods: Nested Cluster Randomization (NCR) design governs this study implementation. The sample size is 220 ESIs intending to submit an NIH K, R, U, and/or Minority Supplement application(s). Primary outcome: intensity/sustainability of grant submission(s)/funding(s), measured by time to/between application(s). Outcome(s) analyses modes: summaries, Kaplan Meir and Cox proportional hazard models as a function of randomization groups and other predictors of outcomes. Results: In the present study, we recruited two cohorts of ESIs (N = 85): 39% African Americans, 18% Latinx, 18% Whites, 20% Asians and 6% Hawaiian/Pacific Islander/other ethnicities; 65% are women; 73% are assistant professors, 4% are Associate Professors and 23% are instructors/scientists/post-doctoral. Participants' disciplines: 32% basic/biomedical, 36% clinical/translational and 32% social/behavioral. Proposal(s) mechanisms: 61% research grants (R series), 31% career development (K series), 7% support of competitive research (SCORE) and 1% National Science Foundation applications. NCR did produce balance in the distribution of ESIs' demographics, sex at birth, ethnicity, professional appointments, background disciplines, and mechanism of sought funding. Lessons learned/challenges: NCR implementation was methodologically challenged during implementation by added constraints (e.g., assigning coaches to the same randomization arm of their participants as well as blinding them to ESIs' randomization group). Recruitment and retention were hampered by the COVID-19 pandemic and more progressive and innovative strategies were needed to heighten the visibility and outreach of this program. DN delivery was also affected by the pandemic and monitoring of ESIs' engagement and facilitation of communications interventions were needed. Resolution of these challenges effectively reconfigured NCR algorithms, recruitment/retention plans, and DN intervention delivery. We intend to recruit an additional 135 ESIs focusing on underrepresented scholars from RCMIs, CTSAs, and other programs. COVID-19 rendered this program 100% virtual, with recruitment/retention challenges and substantial disruption of ESIs' research. We may extend the grant writing period, coaching, and Mock Study Section support.