Perceptions of patient-reported outcome data access and sharing among patients with heart failure: ethical implications for research.

AIMS: In the face of growing expectations for data transparency and patient engagement in care, we evaluated preferences for patient-reported outcome (PRO) data access and sharing among patients with heart failure (HF) using an ethical framework. METHODS AND RESULTS: We conducted qualitative interviews with a purposive sample of patients with HF who participated in a larger 8-week study that involved the collection and return of PROs using a web-based interface. Guided by an ethical framework, patients were asked questions about their preferences for having PRO data returned to them and shared with other groups. Interview transcripts were coded by three study team members using directed content analysis. A total of 22 participants participated in semi-structured interviews. Participants were mostly male (73%), White (68%) with a mean age of 72. Themes were grouped into priorities, benefits, and barriers to data access and sharing. Priorities included ensuring anonymity when data are shared, transparency with intentions of data use, and having access to all collected data. Benefits included: using data as a communication prompt to discuss health with clinicians and using data to support self-management. Barriers included: challenges with interpreting returned results, and potential loss of benefits and anonymity when sharing data. CONCLUSION: Our interviews with HF patients highlight opportunities for researchers to return and share data through an ethical lens, by ensuring privacy and transparency with intentions of data use, returning collected data in comprehensible formats, and meeting individual expectations for data sharing.

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.

Quality of Life After Percutaneous Coronary Intervention Versus Coronary Artery Bypass Grafting.

BACKGROUND: Differences in quality of life (QoL) after coronary artery bypass grafting (CABG) compared with percutaneous coronary intervention (PCI) are not well characterized. We aimed to compare the short- and long-term effects of CABG versus PCI on QoL. METHODS AND RESULTS: We performed a systematic review and meta-analysis of randomized controlled trials comparing CABG versus PCI using the Seattle Angina Questionnaire (SAQ)-Angina Frequency, SAQ-QoL, SAQ-Physical Limitations, EuroQoL-5D, and Short-Form Questionnaire. We calculated mean changes within each group from baseline to 1, 6, 12, and 36 to 60 months (latest follow-up) and the weighted mean differences between groups using inverse-variance methods. A total of 10 760 patients were enrolled in 5 trials. From baseline to 12 months and 36 to 60 months, the mean change in SAQ-Angina Frequency was >22 points (95% CI, 21.0-25.6) after both PCI and CABG. The mean difference in SAQ-Angina Frequency was similar between procedures at 1 month and at 36 to 60 months but favored CABG at 12 months (1.97 [95% CI, 0.68-3.26]). SAQ-QoL favored PCI at 1 month (-2.92 [95% CI, -4.66 to -1.18]) and CABG at 6 (2.50 [95% CI, 1.02-3.97]), 12 (3.30 [95% CI, 1.78-4.82]), and 36 to 60 months (3.17 [95% CI, 0.54 5.80). SAQ-Physical Limitations (-12.61 [95% CI, -16.16 to -9.06]) and EuroQoL-5D (-0.07 [95% CI, -0.08 to -0.07) favored PCI at 1 month. Short-Form Questionnaire-Physical Component favored CABG at 12 months (1.18 [95% CI, 0.46-1.90]). CONCLUSIONS: Both PCI and CABG improved long-term disease-specific and generic QoL.

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.

Returning study results to research participants: Data access, format, and sharing preferences.

INTRODUCTION: Research participants have a growing expectation for transparency with their collected information; however, there is little guidance on participant preferences for receiving health information and how researchers should return this information to participants. METHODS: We conducted a cross-sectional online survey with a representative sample of 502 participants in the United States. Participants were asked about their preferences for receiving, sharing, and the formatting of health information collected for research purposes. RESULTS: Most participants wanted their health information returned (84 %) to use it for their own knowledge and to manage their own health. Email was the most preferred format for receiving health data (67 %), followed by online website (44 %), and/or paper copy (32 %). Data format preferences varied by age, education, financial resources, subjective numeracy, and health literacy. Around one third of Generation Z (25 %), Millennials (30 %), and Generation X (29 %) participants preferred to receive their health information with a mobile app. In contrast, very few Baby Boomers (12 %) and none from the Silent Generation preferred the mobile app format. Having a paper copy of the data was preferred by 38 % of participants without a college degree compared to those with a college degree. Preferences were highest for sharing all health information with doctors and nurses (77 %), and some information with friends and family (66 %). CONCLUSION: Study findings support returning research information to participants in multiple formats, including email, online websites, and paper copy. Preferences for whom to share information with varied by stakeholders and by sociodemographic characteristics. Researchers should offer multiple formats to participants and tailor data sharing options to participants' preferences. Future research should further explore combinations of individual characteristics that may further influence data sharing and format preferences.

Faces Scales for Anxiety and Anger: A National Study of Measurement Properties.

Importance: Faces scales are used worldwide to assess pain, but robust faces scales for anxiety and anger do not exist. These scales are urgently needed, because an estimated two-thirds of patients have difficulty reading written questionnaires.Objective: To develop and evaluate measurement properties of faces scales to monitor two mental health symptoms in US adults (anxiety and anger) in accordance with the COnsensus-based Standards for health Measurement INstruments (COSMIN).Methods: The development process included population identification, scale generation, and pretesting. The evaluation process included assessment of content validity, construct validity, criterion validity, test-retest reliability, and measurement error using 5 order-randomized, positively controlled online survey studies conducted between April and June 2020. We recruited national purposive samples of US adults representative on age, gender, and race. For each faces scale, participants assessed relevance, comprehensibility, and comprehensiveness (study 1, n = 300), strength-of-association (study 2, n = 300), convergent validity against the visual analog scale (VAS; study 3, n = 305), convergent validity against the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires (study 4, n = 1,000), and test-retest reliability and measurement error (study 5, n = 853).Results: The anxiety and anger faces scales showed high relevance (95%-96%), comprehensibility (93%-97%), comprehensiveness (94%-97%), and strength-of-association (74%-96%). We found very high agreement with the VAS (ρ = 0.94-0.95) and high agreement with PROMIS questionnaires (ρ = 0.74-0.79). Scales showed adequate test-retest reliability (intraclass correlation = 0.70-0.78) and measurement error (standard error of measurement = 1.14-1.22).Conclusions: Faces scales to monitor anxiety and anger show adequate measurement properties, including content validity, construct validity, criterion validity, test-retest reliability, and measurement error. The recommended use is non-diagnostic monitoring of anxiety and anger, particularly when mental health is an ancillary but important outcome of treatment.

Building trust in research through information and intent transparency with health information: representative cross-sectional survey of 502 US adults.

OBJECTIVE: Participation in healthcare research shapes health policy and practice; however, low trust is a barrier to participation. We evaluated whether returning health information (information transparency) and disclosing intent of data use (intent transparency) impacts trust in research. MATERIALS AND METHODS: We conducted an online survey with a representative sample of 502 US adults. We assessed baseline trust and change in trust using 6 use cases representing the Social-Ecological Model. We assessed descriptive statistics and associations between trust and sociodemographic variables using logistic and multinomial regression. RESULTS: Most participants (84%) want their health research information returned. Black/African American participants were more likely to increase trust in research with individual information transparency (odds ratio (OR) 2.06 [95% confidence interval (CI): 1.06-4.34]) and with intent transparency when sharing with chosen friends and family (3.66 [1.98-6.77]), doctors and nurses (1.96 [1.10-3.65]), or health tech companies (1.87 [1.02-3.40]). Asian, Native American or Alaska Native, Native Hawaiian or Pacific Islander, Multirace, and individuals with a race not listed, were more likely to increase trust when sharing with health policy makers (1.88 [1.09-3.30]). Women were less likely to increase trust when sharing with friends and family (0.55 [0.35-0.87]) or health tech companies (0.46 [0.31-0.70]). DISCUSSION: Participants wanted their health information returned and would increase their trust in research with transparency when sharing health information. CONCLUSION: Trust in research is influenced by interrelated factors. Future research should recruit diverse samples with lower baseline trust levels to explore changes in trust, with variation on the type of information shared.

Monitoring Symptoms of COVID-19: Review of Mobile Apps.

BACKGROUND: Mobile health (mHealth) apps have facilitated symptom monitoring of COVID-19 symptoms globally and have been used to share data with health care professionals and support disease prediction, prevention, management, diagnostics, and improvements in treatments and patient education. OBJECTIVE: The aim of this review is to evaluate the quality and functionality of COVID-19 mHealth apps that support tracking acute and long-term symptoms of COVID-19. METHODS: We systematically reviewed commercially available mHealth apps for COVID-19 symptom monitoring by searching Google Play and Apple iTunes using search terms such as "COVID-19," "Coronavirus," and "COVID-19 and symptoms." All apps underwent three rounds of screening. The final apps were independently assessed using the Mobile Application Rating Scale (MARS), an informatics functionality scoring system, and the Center for Disease Control and World Health Organization symptom guidelines. The MARS is a 19-item standardized tool to evaluate the quality of mHealth apps on engagement, functionality, aesthetics, and information quality. Functionality was quantified across the following criteria: inform, instruct, record (collect, share, evaluate, and intervene), display, guide, remind or alert, and communicate. Interrater reliability between the reviewers was calculated. RESULTS: A total of 1017 mobile apps were reviewed, and 20 (2%) met the inclusion criteria. The majority of the 20 included apps (n=18, 90%) were designed to track acute COVID-19 symptoms, and only 2 (10%) addressed long-term symptoms. Overall, the apps scored high on quality, with an overall MARS rating of 3.89 out of 5, and the highest domain score for functionality (4.2). The most common functionality among all apps was the instruct function (n=19, 95%). The most common symptoms included in the apps for tracking were fever and dry cough (n=18, 90%), aches and pains (n=17, 85%), difficulty breathing (n=17, 85%), tiredness, sore throat, headache, loss of taste or smell (n=16, 80%), and diarrhea (n=15, 75%). Only 2 (10%) apps specifically tracked long-term symptoms of COVID-19. The top 4 rated apps overall were state-specific apps developed and deployed for public use. CONCLUSIONS: Overall, mHealth apps designed to monitor symptoms of COVID-19 were of high quality, but the majority of apps focused almost exclusively on acute symptoms. Future apps should also incorporate monitoring long-term symptoms of COVID-19 and evidence-based educational materials; they should also include a feature that would allow patients to communicate their symptoms to specific caregivers or their own health care team. App developers should also follow updated technical and clinical guidelines from the Center for Disease Control and the World Health Organization.

Randomized comparison of the clinical Outcome of single versus Multiple Arterial grafts: Quality of Life (ROMA:QOL) - Rationale and Study Protocol.

AIMS: The objective of the Randomized comparison of the Outcome of single vs. Multiple Arterial grafts: Quality of Life (ROMA:QOL) trial is to evaluate the impact of coronary artery bypass graft surgery (CABG) on quality of life (QOL). The primary hypothesis of ROMA:QOL is that participants in the multiple arterial graft (MAG) will report time-varying changes in QOL that will be lower in the post-operative recovery period and higher after 12 months compared to patients in the single arterial graft (SAG). The secondary hypotheses are that both groups will have improvements in symptoms at 12 months, and that compared to the SAG group, participants in the MAG group will experience better physical functioning and physical and mental health symptoms. METHODS AND RESULTS: An estimated 2111 participants will be enrolled from the parent ROMA trial from 13 countries. Outcome assessments include the Seattle Angina Questionnaire (SAQ) (primary outcome), Short Form-12v2, EuroQol-5D (EQ-5D)-5L, PROMIS-29, and PROMIS Neuropathic Pain measured at baseline, first post-operative visit, 6, 12, 24, 36, 48, and 60 months. The analysis for the primary outcome, the change in the SAQ from baseline to 12 months, will be compared across all time-points between the two treatment arms. CONCLUSION: The ROMA:QOL trial will answer whether there are differences in QOL, physical and mental health symptoms overall for CABG, by MAG and SAG intervention arms, by sex, and between patients with and without diabetes.

Effect of coronary artery bypass grafting on quality of life: a meta-analysis of randomized trials.

AIMS: We conducted a systematic review and meta-analysis to evaluate temporal trends in quality of life (QoL) after coronary artery bypass grafting (CABG) surgery in randomized clinical trials, and a quantitative comparison from before surgery to up to 5 years after surgery. METHODS AND RESULTS: We searched MEDLINE, CINAHL, EMBASE, Cochrane Library, and PsycINFO from 2010 to 2020 to identify studies that included the measurement of QoL in patients undergoing CABG. The primary outcome was the Seattle Angina Questionnaire (SAQ), and secondary outcomes were the 36-item Short Form Health Survey (SF-36) and EuroQol Questionnaire (EQ-5D). We pooled the means and the weighted mean differences over the follow-up period. In the meta-analysis, 2586 studies were screened and 18 full-text studies were included. There was a significant trend towards higher QoL scores from before surgery to 1 year post-operatively for the SAQ angina frequency (AF), SAQ QoL, SF-36 physical component (PC), and EQ-5D, whereas the SF-36 mental component (MC) did not improve significantly. The weighted mean differences from before surgery to 1 year after was 24 [95% confidence interval (CI): 21.6-26.4] for the SAQ AF, 31 (95% CI: 27.5-34.6) for the SAQ QoL, 9.8 (95% CI: 7.1-12.8) for the SF-36 PC, 7.1 (95% CI: 4.2-10.0) for the SF-36 MC, and 0.1 (95% CI: 0.06-0.14) for the EQ-5D. There was no evidence of publication bias or small-study effect. CONCLUSION: CABG had both short- and long-term improvements in disease-specific QoL and generic QoL, with the largest improvement in angina frequency.

Patient-Reported Outcomes in Cardiovascular Trials.

Patient-reported outcomes (PROs) are reports of a person's health status that provide a global perspective of patient well-being. PROs can be classified into 4 primary domains: global, mental, physical, and social health. In this descriptive review, we focus on how PROs can be used in cardiac clinical trials, with an emphasis on cardiac surgical trials for patients with coronary heart disease and heart failure. We also highlight ongoing challenges and provide specific suggestions and novel opportunities to advance cardiac clinical trials. Current challenges include the long-term measurement of PROs in clinical trials beyond 1 year, inconsistency in the choice of the outcome measures among studies, and the lack of measurement of PROs across multiple domains. Opportunities for advancement include measuring PROs using consumer health informatics tools, including returning information back to participants in formats that they can understand using visualization. Future opportunities include quantifying cohort-specific minimal clinically important differences for PROs.

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from unstructured clinical notes in electronic health record (EHR) systems. Weakly supervised machine learning methods leverage imperfect labels for training, alleviating the burden of creating a large manually annotated dataset. After identifying a cohort of 600 patients at risk for suicidal ideation, we used a rule-based natural language processing approach (NLP) approach to label the training and validation notes (n = 17,978). Using this large corpus of clinical notes, we trained several statistical machine learning models-logistic classifier, support vector machines (SVM), Naive Bayes classifier-and one deep learning model, namely a text classification convolutional neural network (CNN), to be evaluated on a manually-reviewed test set (n = 837). The CNN model outperformed all other methods, achieving an overall accuracy of 94% and a F1-score of 0.82 on documents with "current" suicidal ideation. This algorithm correctly identified an additional 42 encounters and 9 patients indicative of suicidal ideation but missing a structured diagnosis code. When applied to a random subset of 5,000 clinical notes, the algorithm classified 0.46% (n = 23) for "current" suicidal ideation, of which 87% were truly indicative via manual review. Implementation of this approach for large-scale document screening may play an important role in point-of-care clinical information systems for targeted suicide prevention interventions and improve research on the pathways from ideation to attempt.

Correlates of Mental Health Symptoms Among US Adults During COVID-19, March-April 2020.

OBJECTIVES: An understanding of mental health symptoms during the coronavirus disease 2019 (COVID-19) pandemic is critical to ensure that health policies adequately address the mental health needs of people in the United States. The objective of this study was to examine mental health symptoms among US adults in an early stage of the COVID-19 pandemic. METHODS: We conducted a cross-sectional study in late March 2020 with a national sample of 963 US adults using an online research platform. Participants self-reported state of residence, psychosocial characteristics, and levels of anxiety, depression, anger, cognitive function, and fatigue in the context of COVID-19 using validated patient-reported outcomes scales in the Patient-Reported Outcome Measurement Information System measures. We used analysis of variance and multivariate linear regression to evaluate correlates of mental health symptoms. RESULTS: Overall, participants reported high levels of anxiety (mean [SD], 57.2 [9.3]) and depression (mean [SD], 54.2 [9.5]). Levels of anger, anxiety, cognitive function, depression, and fatigue were significantly higher among the Millennial Generation and Generation X (vs Baby Boomers), those with not enough or enough (vs more than enough) financial resources, females vs males), those with self-reported disability (vs no self-reported disability), and those with inadequate (vs adequate) health literacy. In adjusted models, being in Generation X and the Millennial Generation (vs Baby Boomer), having not enough or enough vs more than enough) financial resources, and having inadequate (vs adequate) health literacy were most strongly correlated with worse mental health symptoms. CONCLUSIONS: Results suggest that mental health symptoms during the early stages of the COVID-19 pandemic were prevalent nationally, regardless of state of residence and especially among young, psychosocially vulnerable groups.

Multimodal mental health analysis in social media.

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.

Visual analogies, not graphs, increase patients' comprehension of changes in their health status.

OBJECTIVES: Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs. MATERIALS AND METHODS: We conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4) text plus line graph. Each participant viewed every condition, and we used counterbalancing (systematic randomization) to control for potential order effects. We assessed objective comprehension using the International Organization for Standardization protocol. Secondary outcomes included response times, preferences, risk perceptions, and behavioral intentions. RESULTS: Overall, 63% correctly comprehended the text-only condition and 60% comprehended the line graph condition, compared with 83% for the visual analogy and 70% for the number line (P = .05) conditions. Participants comprehended the visual analogy significantly better than the text-only (P = .02) and line graph (P = .02) conditions. Of participants who comprehended at least 1 condition, 14% preferred a condition that they did not comprehend. Low comprehension was associated with worse cognition (P < .001), lower education level (P = .02), and fewer financial resources (P = .03). CONCLUSIONS: The results support using visual analogies rather than text to display longitudinal PROs but caution against relying on graphs, which is consistent with the known high prevalence of inadequate graph literacy. The discrepancies between comprehension and preferences suggest factors other than comprehension influence preferences, and that future researchers should assess comprehension rather than preferences to guide presentation decisions.

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.

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.