Using Patient Decision Aids for Cardiology Care in Diverse Populations.

PURPOSE OF REVIEW: Patient decision aids (PDAs) are tools that help guide treatment decisions and support shared decision-making when there is equipoise between treatment options. This review focuses on decision aids that are available to support cardiac treatment options for underrepresented groups. RECENT FINDINGS: PDAs have been developed to support multiple treatment decisions in cardiology related to coronary artery disease, valvular heart disease, cardiac arrhythmias, heart failure, and cholesterol management. By considering the unique needs and preferences of diverse populations, PDAs can enhance patient engagement and promote equitable healthcare delivery in cardiology. In this review, we examine the benefits, challenges, and current trends in implementing PDAs, with a focus on improving decision-making processes and outcomes for patients from underrepresented racial and ethnic groups. In addition, the article highlights key considerations when implementing PDAs and potential future directions in the field.

Exploring Depressive Symptoms and Anxiety Among Patients With Atrial Fibrillation and/or Flutter at the Time of Cardioversion or Ablation.

BACKGROUND: Depression and anxiety in patients with atrial fibrillation (AF) and/or atrial flutter may influence the effectiveness of cardioversion and ablation. There is a lack of knowledge related to depressive symptoms and anxiety at the time of these procedures. OBJECTIVE: We aimed to describe the prevalence and explore potential covariates of depressive symptoms and anxiety in patients with AF at the time of cardioversion or ablation. We further explored the influence of depressive symptoms and anxiety on quality of life at the time of procedure and 6-month AF recurrence. METHODS: Depressive symptoms, anxiety, and quality of life were collected at the time of cardioversion or ablation using the Patient Health Questionnaire-9, State-Trait Anxiety Inventory, and Atrial Fibrillation Effect on Quality of Life questionnaire. Presence of AF recurrence within 6 months post procedure was evaluated. RESULTS: Participants (N = 171) had a mean (SD) age of 61.20 (11.23) years and were primarily male (80.1%) and white, non-Hispanic (81.4%). Moderate to severe depressive symptoms (17.2%) and clinically significant state (30.2%) and trait (23.6%) anxiety were reported. Mood/anxiety disorder diagnosis was associated with all 3 symptoms. Atrial fibrillation symptom severity was associated with both depressive symptoms and trait anxiety. Heart failure diagnosis and digoxin use were also associated with depressive symptoms. Trends toward significance between state and trait anxiety and participant race/ethnicity as well as depressive symptoms and body mass index were observed. Study findings support associations between symptoms and quality of life, but not 6-month AF recurrence. CONCLUSION: Depressive symptoms and anxiety are common in patients with AF. Healthcare providers should monitor patients with AF for depressive symptoms and anxiety at the time of procedures and intervene when indicated. Additional investigations on assessment, prediction, treatment, and outcome of depressive symptoms and anxiety in patients with AF are warranted.

Mobile Electrocardiogram Monitoring and Health-Related Quality of Life in Patients With Atrial Fibrillation: Findings From the iPhone Helping Evaluate Atrial Fibrillation Rhythm Through Technology (iHEART) Study.

BACKGROUND: Atrial fibrillation (AF) is associated with high recurrence rates and poor health-related quality of life (HRQOL) but few effective interventions to improve HRQOL exist. OBJECTIVE: The aim of this study was to examine the impact of the "iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology" (iHEART) intervention on HRQOL in patients with AF. METHODS: We randomized English- and Spanish-speaking adult patients with AF to receive either the iHEART intervention or usual care for 6 months. The iHEART intervention used smartphone-based electrocardiogram monitoring and motivational text messages. Three instruments were used to measure HRQOL: the Atrial Fibrillation Effect on Quality of Life (AFEQT), the 36-item Short-Form Health survey, and the EuroQol-5D. We used linear mixed models to compare the effect of the iHEART intervention on HRQOL, quality-adjusted life-years, and AF symptom severity. RESULTS: A total of 238 participants were randomized to the iHEART intervention (n = 115) or usual care (n = 123). Of the participants, 77% were men and 76% were white. More than half (55%) had an AF recurrence. Both arms had improved scores from baseline to follow-up for AFEQT and AF symptom severity scores. The global AFEQT score improved 18.5 and 11.2 points in the intervention and control arms, respectively (P < .05). There were no statistically significant differences in HRQOL, quality-adjusted life-years, or AF symptom severity between groups. CONCLUSIONS: We found clinically meaningful improvements in AF-specific HRQOL and AF symptom severity for both groups. Additional research with longer follow-up should examine the influence of smartphone-based interventions for AF management on HRQOL and address the unique needs of patients diagnosed with different subtypes of AF.

Bridging the digital health divide-patient experiences with mobile integrated health and facilitated telehealth by community-level indicators of health disparity.

OBJECTIVE: Evaluate the impact of community tele-paramedicine (CTP) on patient experience and satisfaction relative to community-level indicators of health disparity. MATERIALS AND METHODS: This mixed-methods study evaluates patient-reported satisfaction and experience with CTP, a facilitated telehealth program combining in-home paramedic visits with video visits by emergency physicians. Anonymous post-CTP visit survey responses and themes derived from directed content analysis of in-depth interviews from participants of a randomized clinical trial of mobile integrated health and telehealth were stratified into high, moderate, and low health disparity Community Health Districts (CHD) according to the 2018 New York City (NYC) Community Health Survey. RESULTS: Among 232 CTP patients, 55% resided in high or moderate disparity CHDs but accounted for 66% of visits between April 2019 and October 2021. CHDs with the highest proportion of CTP visits were more adversely impacted by social determinants of health relative to the NYC average. Satisfaction surveys were completed in 37% of 2078 CTP visits between February 2021 and March 2023 demonstrating high patient satisfaction that did not vary by community-level health disparity. Qualitative interviews conducted with 19 patients identified differing perspectives on the value of CTP: patients in high-disparity CHDs expressed themes aligned with improved health literacy, self-efficacy, and a more engaged health system, whereas those from low-disparity CHDs focused on convenience and uniquely identified redundancies in at-home services. CONCLUSIONS: This mixed-methods analysis suggests CTP bridges the digital health divide by facilitating telehealth in communities negatively impacted by health disparities.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry.

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

A RE-AIM Evaluation of a Visualization-Based Electronic Patient-Reported Outcome System.

OBJECTIVES: Health care systems are primarily collecting patient-reported outcomes (PROs) for research and clinical care using proprietary, institution- and disease-specific tools for remote assessment. The purpose of this study was to conduct a Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) evaluation of a scalable electronic PRO (ePRO) reporting and visualization system in a single-arm study. METHODS: The "mi.symptoms" ePRO system was designed using gerontechnological design principles to ensure high usability among older adults. The system enables longitudinal reporting of disease-agnostic ePROs and includes patient-facing PRO visualizations. We conducted an evaluation of the implementation of the system guided by the RE-AIM framework. Quantitative data were analyzed using basic descriptive statistics, and qualitative data were analyzed using directed content analysis. RESULTS: Reach-the total reach of the study was 70 participants (median age: 69, 31% female, 17% Black or African American, 27% reported not having enough financial resources). Effectiveness-half (51%) of participants completed the 2-week follow-up survey and 36% completed all follow-up surveys. Adoption-the desire for increased self-knowledge, the value of tracking symptoms, and altruism motivated participants to adopt the tool. Implementation-the predisposing factor was access to, and comfort with, computers. Three enabling factors were incorporation into routines, multimodal nudges, and ease of use. Maintenance-reinforcing factors were perceived usefulness of viewing symptom reports with the tool and understanding the value of sustained symptom tracking in general. CONCLUSION: Challenges in ePRO reporting, particularly sustained patient engagement, remain. Nonetheless, freely available, scalable, disease-agnostic systems may pave the road toward inclusion of a more diverse range of health systems and patients in ePRO collection and use.

A Structured Review of Commercially Available Cardiac Rehabilitation mHealth Applications Using the Mobile Application Rating Scale.

PURPOSE: This study systematically evaluated the quality and functionalities of patient-facing, commercially available mobile health (mHealth) apps for cardiac rehabilitation (CR). METHODS: We performed our search in two of the most widely used commercial mobile app stores: Apple iTunes Appstore and Google Play Store (Android apps). Six search terms were used to query relevant CR apps: "cardiac rehabilitation," "heart disease and remote therapy," "heart failure exercise," "heart therapy and cardiac recovery," "cardiac recovery," and "heart therapy." App quality was evaluated using the Mobile Application Rating Scale (MARS). App functionality was evaluated using the IQVIA functionality scale, and app content was evaluated against the American Heart Association guidelines for CR. Apps meeting our inclusion criteria were downloaded and evaluated by two to three reviewers, and interclass correlations between reviewers were calculated. RESULTS: We reviewed 3121 apps and nine apps met our inclusion criteria. On average, the apps scored a 3.0 on the MARS (5-point Likert scale) for overall quality. The two top-ranking mHealth apps for CR for all three quality, functionality, and consistency with evidence-based guidelines were My Cardiac Coach and Love My Heart for Women, both of which scored ≥4.0 for behavior change. CONCLUSION: Overall, the quality and functionality of free apps for mobile CR was high, with two apps performing the best across all three quality categories. High-quality CR apps are available that can expand access to CR for patients with cardiovascular disease.

Experiences of care delays and telehealth use during the COVID-19 pandemic among socioeconomically diverse cardiovascular patients and clinicians in an urban hospital.

An understanding of care delays and telehealth experiences during the pandemic among vulnerable patients, such as those with cardiac disease, is needed to inform future telehealth policy. We conducted a cross-sectional survey study with socioeconomically diverse cardiac patients (n=28) and clinicians (n=26). Most patients (89%) preferred to receive some or all of their care in-person during the pandemic and endorsed the lack of in-person visits as the top facilitator to telehealth use. Significantly more clinicians perceived high ease of use of video visits compared to patients (82% vs. 44%). Significantly more patients perceived high ease of learning to use (69% vs. 18%) and using (69% vs. 27%) remote monitoring compared to clinicians. Results suggest that patients are more open to receiving in-person care during the pandemic than clinicians recognize and may need greater support surrounding video visits when in-person care is not feasible or safe.

Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges.

Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.

Phenotypes of engagement with mobile health technology for heart rhythm monitoring.

OBJECTIVES: Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months. MATERIALS AND METHODS: We conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants' weekly use over 6 months were analyzed to identify engagement phenotypes via latent growth mixture model (LGMM). Multinomial logistic regression models were fitted to compute the effects of predictors on LGMM classes. RESULTS: One hundred twenty-eight participants (mean age 61.9 years, 75.8% male) were included in the analysis. Application of LGMM identified 4 distinct engagement phenotypes: "High-High," "Moderate-Moderate," "High-Low," and "Moderate-Low." In multinomial models, older age, less frequent afternoon mHealth use, shorter intervals between mHealth use, more AF episodes measured directly with mHealth, and lower left ventricular ejection fraction were more strongly associated with the High-High phenotype compared to the Moderate-Low phenotype (reference). Older age, more palpitations, and a history of stroke or transient ischemic attack were more strongly associated with the Moderate-Moderate phenotype compared to the reference. DISCUSSION: Engagement phenotypes provide a nuanced characterization of how individuals engage with mHealth over time, and which individuals are more likely to be highly engaged users. CONCLUSION: This study demonstrates that engagement phenotypes are valuable in understanding and possibly intervening upon engagement within a population, and also suggests that engagement is an important variable to be considered in digital phenotyping work more broadly.

Sexual Identity and Racial/Ethnic Differences in Awareness of Heart Attack and Stroke Symptoms: Findings From the National Health Interview Survey.

PURPOSE: Investigate sexual identity and racial/ethnic differences in awareness of heart attack and stroke symptoms. DESIGN: Cross-sectional. SETTING: 2014 and 2017 National Health Interview Survey. SAMPLE: 54 326 participants. MEASURES: Exposure measures were sexual identity (heterosexual, gay/lesbian, bisexual, "something else") and race/ethnicity. Awareness of heart attack and stroke symptoms was assessed. ANALYSIS: Sex-stratified logistic regression analyses to examine sexual identity and racial/ethnic differences in awareness of heart attack and stroke symptoms. RESULTS: Gay men were more likely than heterosexual men to identify calling 911 as the correct action if someone is having a heart attack (adjusted odds ratio [AOR] = 2.16, 95% CI: 1.18-3.96). The majority of racial/ethnic minority heterosexuals reported lower rates of awareness of heart attack and stroke symptoms than White heterosexuals. Hispanic sexual minority women had lower awareness of heart attack symptoms than White heterosexual women (AOR = 0.43, 95% CI: 0.25-0.74), whereas Asian sexual minority women reported lower awareness of stroke symptoms (AOR = 0.25, 95% CI: 0.08-0.80). Hispanic (AOR = 0.52, 95% CI: 0.33-0.84) and Asian (AOR = 0.35, 95% CI: 0.14-0.84) sexual minority men reported lower awareness of stroke symptoms than White heterosexual men. CONCLUSION: Hispanic and Asian sexual minorities had lower rates of awareness of heart attack and stroke symptoms. Health information technology may be a platform for delivering health education and targeted health promotion for sexual minorities of color.

Review of mobile applications for the detection and management of atrial fibrillation.

BACKGROUND: Free mobile applications (apps) that use photoplethysmography (PPG) waveforms may extend atrial fibrillation (AF) detection to underserved populations, but they have not been rigorously evaluated. OBJECTIVE: The purpose of this study was to systematically review and evaluate the quality, functionality, and adherence to self-management behaviors of existing mobile apps for AF. METHODS: We systematically searched 3 app stores for apps that were free, available in English, and intended for use by patients to detect and manage AF. A minimum of 2 reviewers evaluated (1) app quality, using the Mobile Application Rating Scale (MARS); (2) functionality using published criteria; and (3) features that support 4 self-management behaviors (including PPG waveform monitoring) identified using evidence-based guidelines. Interrater reliability between the reviewers was calculated. RESULTS: Of 12 included apps, 5 (42%) scored above average for quality (MARS score ≥3.0). App quality was highest for their ease of use, navigation, layout, and visual appeal (eg, functionality and aesthetics) and lowest for their behavioral change support and subjective impressions of quality. The most common app functionalities were capturing and graphically displaying user-entered data (n = 9 [75%]). Nearly all apps (n = 11 [92%]) supported PPG waveform monitoring, but only 2 (17%) supported all 4 self-management behaviors. Interrater reliability was high (0.75-0.83). CONCLUSION: The reviewed apps had wide variability in quality, functionality, and adherence to self-management behaviors. Given the accessibility of these apps to underserved populations and the tremendous potential they hold for improving AF detection and management, high priority should be given to improving app quality and functionality.

Evaluating Commercially Available Mobile Apps for Depression Self-Management.

Clinical depression affects 17.3 million adults in the U.S. However, 37% of these adults receive no treatment, and many symptoms remain unmanaged. Mobile health apps may complement in-person treatment and address barriers to treatment, yet their quality has not been systematically appraised. We conducted a systematic review of apps for depression by searching in three major app stores. Apps were selected using specific inclusion and exclusion criteria. The final apps were downloaded and independently evaluated using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality score, and six features specific to depression self-management. Mobile health apps for depression self-management exhibit a wide range of quality, but more than half (74%) of the apps had acceptable quality, with 32% having MARS scores ≥ 4.0 out of 5.0. These high scoring apps indicate that mobile apps have the potential to improve patient self-management, treatment engagement, and mental health outcomes.

Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death.

OBJECTIVES: Early hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission. METHODS: We extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients. RESULTS: The Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients. CONCLUSIONS: Patients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.

Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes.

BACKGROUND AND SIGNIFICANCE: Data-driven interventions for health can help to personalize self-management of Type 2 Diabetes (T2D), but lack of sustained engagement with self-monitoring among disadvantaged populations may widen existing health disparities. Prior work developing approaches to increase motivation and engagement with self-monitoring holds promise, but little is known about applicability of these approaches to underserved populations. OBJECTIVE: To explore how low-income, Latino adults with T2D respond to different design concepts for data-driven solutions in health that require self-monitoring, and what features resonate with them the most. MATERIAL AND METHODS: We developed a set of mockups that incorporated different design features for promoting engagement with self-monitoring in T2D. We conducted focus groups to examine individuals' perceptions and attitudes towards mockups. Multiple interdisciplinary researchers analyzed data using directed content analysis. RESULTS: We conducted 14 focus groups with 25 English- and Spanish-speaking adults with T2D. All participants reacted positively to external incentives. Social connectedness and healthcare expert feedback were also well liked because they enhanced current social practices and presented opportunities for learning. However, attitudes were more mixed towards goal setting, and very few participants responded positively to self-discovery and personalized decision support features. Instead, participants wished for personalized recommendations for meals and other health behaviors based on their personal health data. CONCLUSION: This study suggests connections between individuals' degree of internal motivation and motivation for self-monitoring in health and their attitude towards designs of self-monitoring apps. We relate our findings to the self-determination continuum in self-determination theory (SDT) and propose it as a blueprint for aligning incentives for self-monitoring to different levels of motivation.

A Systematic Review of Patient-Facing Visualizations of Personal Health Data.

OBJECTIVES: As personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data. METHODS: We searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized. RESULTS: In 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation. CONCLUSION: In this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.