Who needs what (features) when? Personalizing engagement with data-driven self-management to improve health equity.

OBJECTIVES: To examine the feasibility of promoting engagement with data-driven self-management of health among individuals from minoritized medically underserved communities by tailoring the design of self-management interventions to individuals' type of motivation and regulation in accordance with the Self-Determination Theory. METHODS: Fifty-three individuals with type 2 diabetes from an impoverished minority community were randomly assigned to four different versions of an mHealth app for data-driven self-management with the focus on nutrition, Platano; each version was tailored to a specific type of motivation and regulation within the SDT self-determination continuum. These versions included financial rewards (external regulation), feedback from expert registered dietitians (RDF, introjected regulation), self-assessment of attainment of one's nutritional goals (SA, identified regulation), and personalized meal-time nutrition decision support with post-meal blood glucose forecasts (FORC, integrated regulation). We used qualitative interviews to examine interaction between participants' experiences with the app and their motivation type (internal-external). RESULTS: As hypothesized, we found a clear interaction between the type of motivation and Platano features that users responded to and benefited from. For example, those with more internal motivation reported more positive experience with SA and FORC than those with more external motivation. However, we also found that Platano features that aimed to specifically address the needs of individuals with external regulation did not create the desired experience. We attribute this to a mismatch in emphasis on informational versus emotional support, particularly evident in RDF. In addition, we found that for participants recruited from an economically disadvantaged community, internal factors, such as motivation and regulation, interacted with external factors, most notably with limited health literacy and limited access to resources. CONCLUSIONS: The study suggests feasibility of using SDT to tailor design of mHealth interventions for promoting data-driven self-management to individuals' motivation and regulation. However, further research is needed to better align design solutions with different levels of self-determination continuum, to incorporate stronger emphasis on emotional support for individuals with external regulation, and to address unique needs and challenges of underserved communities, with particular attention to limited health literacy and access to resources.

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.

Comparative Effectiveness of Behavioral Interventions for Cardiovascular Risk Reduction in Latinos: A Systematic Review.

BACKGROUND: Latinos, the fastest growing ethnic minority group in the United States, are at a high risk for cardiovascular disease (CVD). However, little is known about effective strategies to reduce CVD risk in this population. OBJECTIVE: The aim of this study was to systematically review and synthesize evidence from randomized controlled trials that examined the effectiveness of behavioral interventions to reduce CVD risk in Latinos living in the United States. METHODS: Four electronic databases were searched for relevant peer-reviewed English- and Spanish-language articles published between January 1, 2000, and December 31, 2019. Four reviewers independently completed article screening, data abstraction, and quality appraisal. At least 2 reviewers completed data abstraction and quality appraisal for each article, and a third reviewer was assigned to settle disagreements. Data on study characteristics and outcomes were abstracted. RESULTS: We retrieved 1939 articles. After applying inclusion/exclusion criteria, 17 articles were included. Most interventions were led by community health workers (n = 10); 2 family-based interventions were identified. None of the included studies was nurse led. Behavioral factors were assessed across all included studies, whereas only 4 studies reported on psychosocial outcomes. Improvements were observed in dietary habits and psychosocial outcomes. Findings for physical activity and biological outcomes were mixed. We identified no differences in outcomes based on intervention modalities used or the role of those who led the interventions. CONCLUSION: Existing evidence is mixed. Future research should assess the effectiveness of understudied treatment modalities (including nurse-led, mobile health, and family-based interventions) in reducing CVD risk in Latinos.

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.

Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes.

Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals' engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants' attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one's life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual's proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive-relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals' engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.

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.

Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.

The electronic health record contains valuable patient data and offers opportunities to administer and analyze patients' individual needs longitudinally. However, most information in the electronic health record is currently stored in unstructured text notations. Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, interpret, and generate human language, can be used to delve into unstructured text data to uncover valuable insights and knowledge. This article discusses different types of NLP, the potential of NLP for cardiovascular nursing, and how to get started with NLP as a clinician.

Characterizing atrial fibrillation symptom improvement following de novo catheter ablation.

AIMS: Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but AF symptom resolution is not well characterized. The study objective was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms. METHODS AND RESULTS: We conducted a retrospective cohort study using EHRs of patients with AF (n = 1293), undergoing ablation in a large, urban health system from 2010 to 2020. We extracted symptom data from clinical notes using a natural language processing algorithm (F score: 0.81). We used Cochran's Q tests with post-hoc McNemar's tests to determine differences in symptom prevalence pre- and post-ablation. We used logistic regression models to estimate the adjusted odds of symptom resolution by personal or clinical characteristics at 6 and 12 months post-ablation. In fully adjusted models, at 12 months post-ablation patients, patients with heart failure had significantly lower odds of dyspnoea resolution [odds ratio (OR) 0.38, 95% confidence interval (CI) 0.25-0.57], oedema resolution (OR 0.37, 95% CI 0.25-0.56), and fatigue resolution (OR 0.54, 95% CI 0.34-0.85), but higher odds of palpitations resolution (OR 1.90, 95% CI 1.25-2.89) compared with those without heart failure. Age 65 and older, female sex, Black or African American race, smoking history, and antiarrhythmic use were also associated with lower odds of resolution of specific symptoms at 6 and 12 months. CONCLUSION: The post-ablation symptom patterns are heterogeneous. Findings warrant confirmation with larger, more representative data sets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief.

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness. MATERIALS AND METHODS: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing. RESULTS: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers. DISCUSSION AND CONCLUSION: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation.

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.

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.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.

OBJECTIVES: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes). MATERIALS AND METHODS: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome. RESULTS: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%). DISCUSSION AND CONCLUSION: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Health literacy, numeracy, graph literacy, and digital literacy: An overview of definitions, evaluation methods, and best practices.

Health literacy is an important skill for people receiving care. Those with limited literacy face disparities in their care and health outcomes when strategies for addressing literacy are not used when delivering health information. In this article, we introduce the importance of considering health literacy, defining it and related concepts including numeracy, graph literacy, and digital literacy, and discuss open questions about measuring health literacy in clinical care. Finally, we present best practices, including assuming "universal precautions," carefully considering wording, leveraging visualizations, recognizing cultural differences in interpretation, guidance on pilot testing, and considering digital literacy when developing electronic materials.

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.

Characterising symptom clusters in patients with atrial fibrillation undergoing catheter ablation.

OBJECTIVE: This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters. METHODS: We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward's hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher's exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status. RESULTS: A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients. CONCLUSIONS: We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients' symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes.

'Replace uncertainty with information': shared decision-making and decision quality surrounding catheter ablation for atrial fibrillation.

AIMS: As a first step in developing a decision aid to support shared decision-making (SDM) for patients with atrial fibrillation (AF) to evaluate treatment options for rhythm and symptom control, we aimed to measure decision quality and describe decision-making processes among patients and clinicians involved in decision-making around catheter ablation for AF. METHODS AND RESULTS: We conducted a cross-sectional, mixed-methods study guided by an SDM model outlining decision antecedents, processes, and outcomes. Patients and clinicians completed semi-structured interviews about decision-making around ablation, feelings of decision conflict and regret, and preferences for the content, delivery, and format of a hypothetical decision aid for ablation. Patients also completed surveys about AF symptoms and aspects of decision quality. Fifteen patients (mean age 71.1 ± 8.6 years; 27% female) and five clinicians were recruited. For most patients, decisional conflict and regret were low, but they also reported low levels of information and agency in the decision-making process. Most clinicians report routinely providing patients with information and encouraging engagement during consultations. Patients reported preferences for an interactive, web-based decision aid that clearly presents evidence regarding outcomes using data, visualizations, videos, and personalized risk assessments, and is available in multiple languages. CONCLUSION: Disconnects between clinician efforts to provide information and bolster agency and patient experiences of decision-making suggest decision aids may be needed to improve decision quality in practice. Reported experiences with current decision-making practices and preferences for decision aid content, format, and delivery can support the user-centred design and development of a decision aid.

Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant (P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women.

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.

Mobile Integrated Health Interventions for Older Adults: A Systematic Review.

Background and Objectives: Mobile integrated health (MIH) interventions have not been well described in older adult populations. The objective of this systematic review was to evaluate the characteristics and effectiveness of MIH programs on health-related outcomes among older adults. Research Design and Methods: We searched Ovid MEDLINE, Ovid EMBASE, CINAHL, AgeLine, Social Work Abstracts, and The Cochrane Library through June 2021 for randomized controlled trials or cohort studies evaluating MIH among adults aged 65 and older in the general community. Studies were screened for eligibility against predefined inclusion/exclusion criteria. Using at least 2 independent reviewers, quality was appraised using the Downs and Black checklist and study characteristics and findings were synthesized and evaluated for potential bias. Results: Screening of 2,160 records identified 15 studies. The mean age of participants was 67 years. The MIH interventions varied in their focus, community paramedic training, types of assessments and interventions delivered, physician oversight, use of telemedicine, and post-visit follow-up. Studies reported significant reductions in emergency call volume (5 studies) and immediate emergency department (ED) transports (3 studies). The 3 studies examining subsequent ED visits and 4 studies examining readmission rates reported mixed results. Studies reported low adverse event rates (5 studies), high patient and provider satisfaction (5 studies), and costs equivalent to or less than usual paramedic care (3 studies). Discussion and Implications: There is wide variability in MIH provider training, program coordination, and quality-based metrics, creating heterogeneity that make definitive conclusions challenging. Nonetheless, studies suggest MIH reduces emergency call volume and ED transport rates while improving patient experience and reducing overall health care costs.