Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

User-Centered Design of a Preference-Driven Patient Activation Tool for Optimizing Depression Treatment in Integrated Primary Care Settings (The Transform DepCare Study).

BACKGROUND: Few patient engagement tools incorporate the complex patient experiences, contexts, and workflows that limit depression treatment implementation. OBJECTIVE: Describe a user-centered design (UCD) process for operationalizing a preference-driven patient activation tool. DESIGN: Informed by UCD and behavior change/implementation science principles, we designed a preference-driven patient activation prototype for engaging patients in depression treatment. We conducted three usability cycles using different recruitment/implementation approaches: near live/live testing in primary care waiting rooms (V1-2) and lab-based think aloud testing (V3) oversampling older, low-literacy, and Spanish-speaking patients in the community and via EHR algorithms. We elicited clinician and "heuristic" expert input. MAIN MEASURES: We administered the system usability scale (SUS) all three cycles and pre-post V3, the patient activation measure, decisional conflict scale, and depression treatment barriers. We employed descriptive statistics and thematically analyzed observer notes and transcripts for usability constructs. RESULTS: Overall, 43 patients, 3 clinicians, and 5 heuristic (a usability engineering method for identifying usability problems) experts participated. Among patients, 41.9% were ≥ 65 years old, 79.1% female, 23.3% Black, 62.8% Hispanic, and 55.8% Spanish-speaking and 46.5% had ≤ high school education. We described V1-3 usability (67.2, 77.3, 81.8), treatment seeking (92.3%, 87.5%, 92.9%), likelihood/comfort discussing with clinician (76.9%, 87.5%, 100.0%), and pre vs. post decisional conflict (23.7 vs. 15.2), treatment awareness (71.4% vs. 92.9%), interest in antidepressants (7.1% vs. 14.3%), and patient activation (66.8 vs. 70.9), with fewer barriers pertaining to cost/insurance, access/coordination, and self-efficacy/stigma/treatment efficacy. Key themes included digital literacy, understandability, high acceptability for aesthetics, high usefulness of patient/clinician videos, and workflow limitations. We adapted manual entry/visibility/content; added patient activation and a personalized algorithm; and proposed flexible, care manager delivery leveraging clinic screening protocols. DISCUSSION: We provide an example of leveraging UCD to design/adapt a real-world, patient experience and workflow-aligned patient activation tool in diverse populations.

Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.

Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to identify latent profiles ("phenotypes") based on daily PA trends among adults living in. This was a secondary analysis of 724 person-level days of accelerometry data from 133 urban-dwelling adults (89% Latinx, age = 19-77 years). We used Actigraph accelerometers and the Actilife software to collect and process 24-hour PA data. We implemented a probabilistic clustering technique based on functional mixture models. Multiple days of data per person were averaged for entry into the models. We evaluated step counts, moderate-intensity PA (MOD), total activity and sedentary minutes as potential model variables. Bayesian Information Criterion (BIC) index was used to select the model that provided the best fit for the data. A 4-cluster resolution provided the best fit for the data (i.e., BIC=-3257, improvements of Δ = 13 and Δ = 7 from 3- and 5-cluster models, respectively). MOD provided the greatest between-cluster discrimination. Phenotype 1 (N = 61) was characterized by a morning peak in PA that declined until bedtime. Later bedtimes and the highest daily PA volume were distinct for phenotype 2 (N = 18), along with a similar peak pattern. Phenotype 3 (N = 29) membership was associated with the lowest PA levels throughout the day. Phenotype 4 was characterized by a more evenly distributed PA during the day, and later waking/bedtimes. Our findings point to distinct, interpretable PA phenotypes based on temporal patterns. Functional clustering of PA data could provide additional actionable points for tailoring behavioral interventions.

Feasibility and Acceptability of the Adherence Connection Counseling, Education, and Support (ACCESS) Proof of Concept: A Peer-Led, Mobile Health (mHealth) Cognitive Behavioral Antiretroviral Therapy (ART) Adherence Intervention for HIV-Infected (HIV+) Adolescents and Young Adults (AYA).

Effective antiretroviral therapy (ART) adherence strategies for HIV+ adolescents and young adults (AYA) are needed to prevent HIV-related morbidity, mortality, and onward transmission. In the Adherence Connection for Counseling, Education, and Support (ACCESS) pilot, an exploratory sequential mixed-methods design was used to develop and test a peer-led, mobile health (mHealth) cognitive behavioral ART adherence intervention. HIV+ AYA (ages 16-29 years) with unsuppressed plasma HIV RNA (HIV viral load) were eligible for this five-session intervention directed to improving ART adherence and HIV viral load. A total of 78 peer-led remote videoconferencing sessions (via WebEx) were delivered to 16 participants. High completion rates (97.5%) and client satisfaction scores (mean = 29.13 of 32; SD = 2.45) were observed. Self-reported ART adherence improved (32% increase in doses taken; 95th CI 11.2-53.3) with an annualized average rate of 47.5% (0.28 log10) reduction in HIV viral load. We established proof of concept for the ACCESS peer-led, mHealth cognitive behavioral ART adherence intervention, with promising adherence and virologic outcome data.

RESUMEN: Se necesitan estrategias efectivas de adherencia a la terapia antirretroviral (TAR) para adolescentes y adultos jóvenes (AAJ) VIH+ para prevenir la morbilidad, la mortalidad y las transmisiones futuras relacionadas con el VIH. En el proyecto piloto Adherence Connection for Counseling, Education, and Support (ACCESS), se utilizó un diseño exploratorio secuencial de métodos mixtos para desarrollar y testear una intervención de adherencia cognitiva conductual de salud móvil (mHealth) dirigida por pares a la TAR. AAJ VIH+ (de 16 a 29 años de edad) con ARN del VIH (carga viral del VIH) en plasma no suprimido fueron elegibles para esta intervención de cinco sesiones dirigida a mejorar la adherencia a la TAR y la carga viral del VIH. Se dictaron un total de 78 sesiones de videoconferencias remotas dirigidas por pares (a través de WebEx) a 16 participantes. Fueron observadas tasas altas de finalización (97.5%) y puntuaciones de satisfacción del cliente (media=29.13 de 32; SD=2.45). La adherencia autoinformada a la TAR mejoró (aumento del 32% en las dosis tomadas; IC del 95=11.2 a 53.3) con una tasa promedio anualizada de reducción en la carga viral del VIH del 47.5% (0.28 log 10). Establecimos una prueba de concepto para ACCESS, la intervención de adherencia a la TAR cognitivo conductual mHealth dirigida por pares, con datos prometedores sobre la adherencia y los resultados virológicos.

Older Adult Engagement With Symptom Reporting in a COVID-19 Citizen Science Application.

The current study examined the frequency and predictors of older adults' engagement with symptom reporting in COVIDWATCHER, a mobile health (mHealth) citizen science application. Citizen science is a type of participatory research that leverages information provided by community members. There were 1,028 COVIDWATCHER participants who engaged with symptom reporting between April 2020 and January 2021. Approximately 13.5% (n = 139) were adults aged ≥65 years. We used a Wilcoxon test to compare the mean frequency of engagement with symptom reporting by older adults (i.e., aged ≥65 years) to younger adults (i.e., aged ≤64 years) and multivariable linear regression to explore the predictors of engagement with symptom reporting. There was a significant difference in engagement with symptom reporting between adults aged ≥65 years compared to those aged ≤64 years (p < 0.001). In our final model, age (ß = 26.0; 95% confidence interval [14.8, 34.2]) was a significant predictor for engagement with symptom reporting. These results help further our understanding of older adult engagement with mHealth-enabled citizen science for symptom reporting. [Journal of Gerontological Nursing, 49(4), 6-11.].

Feasibility and acceptability of using information visualizations to improve HIV-related communication in a limited-resource setting: a short report.

Infographics (visualizations that present information) can assist clinicians to offer health information to patients with low health literacy in an accessible format. In response, we developed an infographic intervention to enhance clinical, HIV-related communication. This study reports on its feasibility and acceptability at a clinical setting in the Dominican Republic. We conducted in-depth interviews with physicians who administered the intervention and patients who received it. We conducted audio-recorded interviews in Spanish using semi-structured interview guides. Recordings were professionally transcribed verbatim then analyzed using descriptive content analysis. Physician transcripts were deductively coded according to constructs of Bowen et al.'s feasibility framework and patient transcripts were inductively coded. Three physicians and 26 patients participated. Feasibility constructs endorsed by physicians indicated that infographics were easy to use, improved teaching, and could easily be incorporated into their workflow. Coding of patient transcripts identified four categories that indicated the intervention was acceptable and useful, offered feedback regarding effective clinical communication, and recommended improvements to infographics. Taken together, these data indicate our intervention was a feasible and acceptable way to provide clinical, HIV-related information and provide important recommendations for future visualization design as well as effective clinical communication with similar patient populations.

Informatics and data science perspective on Future of Nursing 2020-2030: Charting a pathway to health equity.

The Future of Nursing 2020 to 2030 report explicitly addresses the need for integration of nursing expertise in designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health (SDOH) and health equity. The metrics necessary to enable and evaluate progress on all recommendations require harnessing existing data sources and developing new ones, as well as transforming and integrating data into information systems to facilitate communication, information sharing, and decision making among the key stakeholders. We examine the recommendations of the 2021 report through an interdisciplinary lens that integrates nursing, biomedical informatics, and data science by addressing three critical questions: (a) what data are needed?, (b) what infrastructure and processes are needed to transform data into information?, and (c) what information systems are needed to "level up" nurse-led interventions from the micro-level to the meso- and macro-levels to address social determinants of health and advance health equity?

Efficacy, Use, and Usability of the VIP-HANA App for Symptom Self-management in PLWH with HANA Conditions.

The purpose of this study was to evaluate the efficacy of the VIP-HANA application (app) for improving symptom burden in a randomized control trial of 100 people living with HIV (PLWH) who have non-AIDS conditions associated with HIV. The intervention group received the VIP-HANA app which allowed them to report their symptoms every week and receive self-management strategies tailored to their symptoms. The control arm received an app to report their symptoms every week but did not receive any strategies. The results of our study suggest that symptom burden improved in the participants of both study arms. Although these findings do not support the efficacy of VIP-HANA in improving symptom burden in PLWH who have HIV-associated non-AIDS (HANA) conditions, this could be a function of the study design. Findings suggest that PLWH are interested in monitoring their symptoms, which could have implications for the wider use of digital health for patient surveillance.

RESUMEN: El propósito de este estudio fue evaluar la eficacia de la aplicación VIP-HANA para mejorar la carga de síntomas en una prueba controlada aleatorizada de 100 personas que viven con VIH con condiciones no de SIDA asociadas al VIH. El grupo de intervención recibió la aplicación VIP-HANA que les permitió reportar sus síntomas cada semana y recibir estrategias de autogestión personalizadas. El brazo de control recibió una aplicación para reportar sus síntomas cada semana, pero no recibió ninguna estrategia. Los resultados de nuestro estudio sugieren que la carga general de los síntomas mejoro entre los participantes en ambos brazos del estudio. Aunque estos hallazgos no apoyan la eficacia de la aplicación VIP-HANA para mejorar la carga de síntomas en PVVS con condiciones de HANA, esto puede ser una función del diseño del estudio. Estos hallazgos sugieren que PVVS están interesados en monitorear sus síntomas, lo que puede tener implicaciones para el uso más amplio de salud digital para la vigilancia de pacientes.

Clinician Use of HIV-Related Infographics During Clinic Visits in the Dominican Republic is Associated with Lower Viral Load and Other Improvements in Health Outcomes.

We designed an infographic intervention to help clinicians provide health information to persons living with HIV. In this study, we assessed the extent to which our intervention may improve objectively and subjectively measured health outcomes (CD4 count, viral load, and engagement with clinician among others) when integrated into routine visits in the Dominican Republic. In this pretest-posttest study, we followed participants for 9 months at 3-month intervals. Physicians administered the intervention during participants' first 3 visits. Outcome measures, selected using a conceptual model, were assessed at 4 time points. We assessed changes in outcomes over time with general linear regressions and Wilcoxon Signed-Rank tests. Participants (N = 50) were mostly female (56%) and had been living with HIV for a mean of 6.3 years (SD = 6.1). All outcomes, except CD4 count, demonstrated statistically significant improvements by study end. This provides preliminary evidence our intervention may improve outcomes, but further testing is needed.

RESUMEN: Diseñamos una intervención infográfica para ayudar a los médicos brindar información médica a personas viviendo con el VIH. En este estudio, evaluamos en qué medida nuestra intervención puede mejorar los resultados de salud (conteo de CD4, carga viral, y compromiso con el médico entre otros), medidos de una manera objetiva y subjetiva, cuando se incorpora en las visitas médicas de rutina en la República Dominicana. En este estudio de prueba previo y posterior, seguimos los participantes durante 9 meses a intervalos de 3 meses. Los médicos administraron la intervención durante las primeras 3 visitas de los participantes. Seleccionamos las medidas de resultado utilizando un marco conceptual y las evaluamos en los 4 puntos de tiempo. Evaluamos cambios a lo largo del tiempo usando regresiones lineales generales y pruebas de asociación de Wilcoxon Signed-Rank. Los participantes (N = 50) fueron mayormente mujeres (56%) y habían estado viviendo con el VIH durante una media de 6,3 años (DE = 6,1). Todos los resultados, aparte del conteo de CD4, demostraron mejoras estadísticamente significativas al final del estudio. Esto proporciona evidencia preliminar de que nuestra intervención puede mejorar los resultados de la salud, pero se justifican pruebas adicionales.

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes. OBJECTIVES: We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations. METHODS: First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types. RESULTS: Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent. DISCUSSION: Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

GeneLiFT: A novel test to facilitate rapid screening of genetic literacy in a diverse population undergoing genetic testing.

With the broader introduction of genomic medicine in research and clinical care, an increasing number of persons are offered genetic testing. Many factors, including genetic literacy, may impact the utilization of genetic results by patients and their families. We developed a rapid, self-administered measure of genetic literacy, called Genetic Literacy Fast Test (GeneLiFT). We next evaluated the association of GeneLiFT scores with the comprehension of limitations of genomic medicine in participants undergoing genetic testing in the NIH-sponsored eMERGE III study at Columbia University Irving Medical Center, New York. All participants underwent genetic screening for variants in 74 actionable genes associated with adult-onset disorders. A diverse cohort of 724 participants completed the survey (60% women, 45% less than 40 years old, and 53% self-reported White non-Hispanic ancestry). The GeneLiFT was validated using known group differences based on education, health literacy, and numeracy, and with questions assessing genetic knowledge. GeneLiFT identified multiple standard genetics terms, that is, jargon, not recognized by more than 50% of participants (including actionability and pathogenicity). Low genetic literacy, identified in 210 participants (29%), was significantly associated with poor understanding of the limitations of genetic testing (p-values < 10-9 ). This association was independent of education, health literacy, and numeracy levels, highlighting the importance of directly measuring genetic literacy. Low genetic literacy was also associated with low satisfaction with the informed consent process. GeneLiFT is a practical tool for rapid assessment of genetic literacy in large studies or clinical care. GeneLiFT will allow future research to efficiently assess the role of genetic literacy on the clinical impact of genetic testing.

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.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.

AIM: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). METHODS: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. IMPLICATIONS FOR NURSING: Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. CONCLUSION: There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. IMPACT: We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.

Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions-chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N = 504,395; 133,977 patients) were obtained for the 2016 calendar year from a single medical center. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients (N = 22,657) diagnosed with COPD (n = 3339), heart failure (n = 6587), diabetes (n = 12,139), and cancer (n = 7269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly-identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record-derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.

Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients.

BACKGROUND: Nurses often document patient symptoms in narrative notes. PURPOSE: This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare. METHOD: NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States. FINDINGS: NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission. DISCUSSION: Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.

Adherence Self-Management and the Influence of Contextual Factors Among Emerging Adults With Human Immunodeficiency Virus.

BACKGROUND: Maintaining adherence to antiretroviral therapy (ART) is a significant challenge for human immunodeficiency virus (HIV)-infected racial and ethnic minority adolescents and young adults (youth). Given the consequences of suboptimal ART adherence, there is a pressing need for an expanded understanding of adherence behavior in this cohort. OBJECTIVES: As part of an exploratory sequential, mixed-methods study, we used qualitative inquiry to explore adherence information, motivation, and behavioral skills among HIV-infected racial and ethnic minority youth. Our secondary aim was to gain an understanding of the contextual factors surrounding adherence behavior. METHODS: The information-motivation-behavioral skills model (IMB model) was applied to identify the conceptual determinants of adherence behavior in our target population, along with attention to emergent themes. In-depth, individual, semistructured interviews, including open-ended questions with probes, were conducted with a convenience sample of HIV-infected racial and ethnic minority youth (ages 16-29 years), receiving ART and with evidence of virologic failure (i.e., detectable HIV viral load). New participants were interviewed until information redundancy was reached. Qualitative interviews were digitally recorded, transcribed verbatim, and analyzed using Atlas.ti (v8). Directed content analysis was performed to generate categories and broad themes. Coding was initially conceptually driven (IMB model) and shifted to a data-driven approach, allowing for the discovery of key contextual factors that influence adherence behavior in this population. Methodological rigor was ensured by member checks, an audit trail, thick descriptive data, and triangulation of data sources. RESULTS: Twenty racial and ethnic minority participants (mean age = 24.3 years, 55.0% male) completed interviews. We found adherence information was understood in relation to HIV biomarkers; adherence motivation and behavioral skills were influenced by stigma and social context. We identified five primary themes regarding ART self-management: (a) emerging adulthood with a chronic illness, (b) stigma and disclosure concerns, (c) support systems and support deficits, (d) mental and behavioral health risks and challenges, and (e) mode of HIV transmission and perceptions of power and control. DISCUSSION: Key constructs of the IMB model were applicable to participating HIV-infected youth yet did not fully explain the essence of adherence behavior. As such, we recommend expansion of current adherence models and frameworks to include known contextual factors associated with ART self-management among HIV-infected racial and ethnic minority youth.

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.

Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results.

OBJECTIVE: This study evaluated the impact of daily ECG (electrocardiogram) self-recordings on time to documented recurrent atrial fibrillation (AF) or atrial flutter (AFL) and time to treatment of recurrent arrhythmia in patients undergoing catheter radiofrequency ablation (RFA) or direct current cardioversion (DCCV) for AF/AFL. BACKGROUND: AF recurrence rates after RFA and DCCV are 20% to 45% and 60% to 80%, respectively. Randomized trials comparing mobile ECG devices to standard of care have not been performed in an AF/AFL population after treatment. METHODS: Of 262 patients consented, 238 were randomized to either standard of care (123) or to receive the iHEART intervention (115). Patients in the intervention group were provided with and trained to use an AliveCor KardiaMobile ECG monitor, and were instructed to take and transmit daily ECG recordings. Data were collected from transmitted ECG recordings and patients' electronic health records. RESULTS: In a multivariate Cox model, the likelihood of recurrence detection was greater in the intervention group (hazard ratio = 1.56, 95% confidence interval [CI]: 1.06-2.30, P = .024). Hazard ratios did not differ significantly for RFA and DCCV procedures. Recurrence during the first month after ablation strongly predicted later recurrence (hazard ratio = 4.53, 95% CI: 2.05-10.00, P = .0006). Time from detection to treatment was shorter for the control group (hazard ratio = 0.33, 95% CI: 0.57-2.92, P < .0001). CONCLUSIONS: The use of mobile ECG self-recording devices allows for earlier detection of AF/AFL recurrence and may empower patients to engage in shared health decision-making.

Behavioral Interventions Using Consumer Information Technology as Tools to Advance Health Equity.

The digital divide related to consumer information technologies (CITs) has diminished, thus increasing the potential to use CITs to overcome barriers of access to health interventions as well as to deliver interventions situated in the context of daily lives. However, the evidence base regarding the use and impact of CIT-enabled interventions in health disparity populations lags behind that for the general population. Literature and case examples are summarized to demonstrate the use of mHealth, telehealth, and social media as behavioral intervention platforms in health disparity populations, identify challenges to achieving their use, describe strategies for overcoming the challenges, and recommend future directions. The evidence base is emerging. However, challenges in design, implementation, and evaluation must be addressed for the promise to be fulfilled. Future directions include (1) improved design methods, (2) enhanced research reporting, (3) advancement of multilevel interventions, (4) rigorous evaluation, (5) efforts to address privacy concerns, and (6) inclusive design and implementation decisions.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers.

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.