Implementation of health risk assessments with family health history: barriers and benefits

Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching.

OBJECTIVE

This pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management.

METHODS

This study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss.

RESULTS

On average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%).

CONCLUSIONS

The study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

Predicting personal cardiovascular disease risk based on family health history: Development of expert-based family criteria for the general population

In inherited and familial cardiovascular diseases (CVDs), relatives without current symptoms can still be at risk for early and preventable cardiovascular events. One way to help people evaluate their potential risk of CVD is through a risk-assessment tool based on family health history. However, family criteria including inherited CVD risk to be used by laypersons are non-existent. In this project, we employed a qualitative study design to develop expert-based family criteria for use in individual risk assessment. In the first phase of the project, we identified potential family criteria through an online focus group with physicians with expertise in monogenic and/or multifactorial CVDs. The family criteria from phase one were then used as input for a three-round Delphi procedure carried out in a larger group of expert physicians to reach consensus on appropriate criteria. This led to consensus on five family criteria that focus on cardiovascular events at young age (i.e., sudden death, any CVD, implantable cardioverter-defibrillator, aortic aneurysm) and/or an inherited CVD in one or more close relatives. We then applied these family criteria to a high-risk cohort from a clinical genetics department and demonstrated that they have substantial diagnostic accuracy. After further evaluation in a general population cohort, we decided to only use the family criteria for first-degree relatives. We plan to incorporate these family criteria into a digital tool for easy risk assessment by the public and, based on expert advice, will develop supporting information for general practitioners to act upon potential risks identified by the tool. Results from an expert focus group, a Delphi method in a larger group of experts, and evaluation in two cohorts were used to develop family criteria for assessing cardiovascular disease risk based on family health history for a digital risk-prediction tool for use by the general population. CVD Cardiovascular disease, ICD Implantable cardioverter defibrillator, TAA Thoracic aortic aneurysm, AAA Abdominal aortic aneurysm.

FHH Quick App Review: How Can a Quality Review Process Assist Primary Care Providers in Choosing a Family Health History App for Patient Care?

Family health history (FHH) is a data type serving risk assessment, diagnosis, research, and preventive health. Despite technological leaps in genomic variant detection, FHH remains the most accessible, least expensive, and most practical assessment tool for assessing risks attributable to genetic inheritance. The purpose of this manuscript is to outline a process to assist primary care professionals in choosing FHH digital tools for patient care based on the new ISO/TS 82304-2 Technical Specification (TS), which is a recently developed method to determine eHealth app quality. With a focus on eHealth in primary care, we applied the quality label concept to FHH, and how a primary care physician can quickly review the quality and reliability of an FHH app. Based on our review of the ISO TS’s 81 questions, we compiled a list of 25 questions that are recommended to be more succinct as an initial review. We call this process the FHH Quick App Review. Our ‘informative-only’ 25 questions do not produce a quality score, but a guide to complete an initial review of FHH apps. Most of the questions are straight from the ISO TS, some are modified or de novo. We believe the 25 questions are not only relevant to FHH app reviews but could also serve to aid app development and clinical implementation.

Experience and Perceptions of a Family Health History Risk Assessment Tool among Multi-Ethnic Asian Breast Cancer Patients

A family health history-based risk assessment is particularly valuable for guiding cancer screening and treatment strategies, yet an optimal implementation depends upon end-users' values and needs. This is not only true prior to disease development, but also for those already affected. The aim of this study is to explore perceptions of the value of knowing one's family health history (FHH)-based risk, experience using a patient-facing FHH tool and the potential of the tool for wider implementation. Twenty multi-ethnic Asian patients undergoing breast cancer treatment in Singapore completed an FHH-based risk assessment. Semi-structured one-on-one interviews were conducted and data were thematically analyzed. All participants were female and slightly more than half were Chinese. The acceptance and usage of an FHH risk assessment tool for cancers and its broader implementation was affected by a perceived importance of personal control over early detection, patient concerns of anxiety for themselves and their families due to risk results, concerns for genetic discrimination, adequacy of follow-up care plans and Asian cultural beliefs toward disease and dying. This study uniquely sheds light on the factors affecting Asian breast cancer patients' perceptions about undergoing an FHH-based risk assessment, which should inform steps for a broader implementation in Asian healthcare systems.

Modernizing family health history: achievable strategies to reduce implementation gaps

Family health history (FHH) is a valuable yet underused healthcare tool for assessing health risks for both prevalent disorders like diabetes, cancer, and cardiovascular diseases, and for rare, monogenic disorders. Full implementation of FHH collection and analysis in healthcare could improve both primary and secondary disease prevention for individuals and, through cascade testing, make at risk family members eligible for pre-symptomatic testing and preventative interventions. In addition to risk assessment in the clinic, FHH is increasingly important for interpreting clinical genetic testing results and for research connecting health risks to genomic variation. Despite this value, diverse implementation gaps in clinical settings undermine its potential clinical value and limit the quality of connected health and genomic data. The NHGRI Family Health History Group, an open-membership, US-based group with international members, believes that integrating FHH in healthcare and research is more important than ever, and that achievable implementation advances, including education, are urgently needed to boost the pace of translational utility in genomic medicine. An inventory of implementation gaps and proposed achievable strategies to address them, representing a consensus developed in meetings from 2019-2020, is presented here. The proposed measures are diverse, interdisciplinary, and are guided by experience and ongoing implementation and research efforts.

Translating Cancer Risk Prediction Models into Personalized Cancer Risk Assessment Tools: Stumbling Blocks and Strategies for Success

Cancer risk prediction models such as those published in Cancer Epidemiology, Biomarkers, and Prevention are a cornerstone of precision medicine and public health efforts to improve population health outcomes by tailoring preventive strategies and therapeutic treatments to the people who are most likely to benefit. However, there are several barriers to the effective translation, dissemination, and implementation of cancer risk prediction models into clinical and public health practice. In this commentary, we discuss two broad categories of barriers. Specifically, we assert that the successful use of risk-stratified cancer prevention and treatment strategies is particularly unlikely if risk prediction models are translated into risk assessment tools that (i) are difficult for the public to understand or (ii) are not structured in a way to engender the public's confidence that the results are accurate. We explain what aspects of a risk assessment tool's design and content may impede understanding and acceptance by the public. We also describe strategies for translating a cancer risk prediction model into a cancer risk assessment tool that is accessible, meaningful, and useful for the public and in clinical practice.

Applying theory to characterize impediments to dissemination of community-facing family health history tools: a review of the literature

Uptake of community-facing family health history (FHH) tools to identify those at highest risk of disease and target prevention efforts has been consistently low. This review uses the diffusion of innovations (DOI) as a framework to organize the FHH evidence base and identify potential strategies to improve uptake of community-facing FHH tools. Methods for this literature review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We completed systematic searches in PubMed, Embase, and Web of Science databases for studies from 2009 to 2017 and hand searched bibliographies of relevant literature to identify additional articles. We abstracted and synthesized results, which were then organized by the DOI including the innovation-decision making process and characteristics of the innovation likely to influence diffusion (e.g., compatibility, relative advantage, complexity, trialability, observa/bility). Of the 290 unique articles identified, 65 were eligible for full-text review after title and abstract screening; a total of 27 were included in the final abstraction. Eleven unique tools were identified. The most commonly used tool was the Surgeon General's My Family Health Portrait (n =9/27). Only six studies directly evaluated participant perceptions shown to be associated with tool uptake. Studies tended to focus on improving compatibility by customizing tools to the target population's needs and use of educational interventions. Results from this review suggest the need to evaluate strategies to increase the pace of uptake of community-facing FHH tools. These include pragmatic trials that compare different approaches to engage and evaluate participant perceptions of the relative advantage and complexity of tools. Ancillary support strategies may include collaborations with community networks to facilitate use and implementation study designs for rigorous evaluation.

Primary care physician experiences utilizing a family health history tool with electronic health record-integrated clinical decision support: an implementation process assessment

Family health history (FHH) screening plays a key role in disease risk identification and tailored disease prevention strategies. Primary care physicians (PCPs) are in a frontline position to provide personalized medicine recommendations identified through FHH screening; however, adoption of FHH screening tools has been slow and inconsistent in practice. Information is also lacking on PCP facilitators and barriers of utilizing family history tools with clinical decision support (CDS) embedded in the electronic health record (EHR). This study reports on PCPs' initial experiences with the Genetic and Wellness Assessment (GWA), a patient-administered FHH screening tool utilizing the EHR and CDS. Semi-structured interviews were conducted with 24 PCPs who use the GWA in a network of community-based practices. Four main themes regarding GWA implementation emerged: benefits to clinical care, challenges in practice, CDS-specific issues, and physician-recommended improvements. Sub-themes included value in improving patient access to genetic services, inadequate time to discuss GWA recommendations, lack of patient follow-through with recommendations, and alert fatigue. While PCPs valued the GWA's clinical utility, a number of challenges were identified in the administration and use of the GWA in practice. Based on participants' recommendations, iterative changes have been made to the GWA and workflow to increase efficiency, upgrade the CDS process, and provide additional education to PCPs and patients. Future studies are needed to assess a diverse sample of physicians' and patients' perspectives on the utility of FHH screening utilizing EHR-based genomics recommendations.

Opportunities, resources, and techniques for implementing genomics in clinical care

Advances in technologies for assessing genomic variation and an increasing understanding of the effects of genomic variants on health and disease are driving the transition of genomics from the research laboratory into clinical care. Genomic medicine, or the use of an individual's genomic information as part of their clinical care, is increasingly gaining acceptance in routine practice, including in assessing disease risk in individuals and their families, diagnosing rare and undiagnosed diseases, and improving drug safety and efficacy. We describe the major types and measurement tools of genomic variation that are currently of clinical importance, review approaches to interpreting genomic sequence variants, identify publicly available tools and resources for genomic test interpretation, and discuss several key barriers in using genomic information in routine clinical practice.