Purpose of review-To advance integration of shared decision making (SDM) into mental health care service delivery, researchers have outlined several priorities for future research [2][3]. These include: 1) SDM and its role in mental health care; 2) Patient and provider perspectives on SDM; 3) The degree to which SDM is practice in mental health settings; and 4) Outcomes of SDM in mental health populations. This article will review recent advances in these areas.Recent findings-The current literature shows that 1) SDM can play a role in the mental health treatment process from entry into care to recovery; 2) Patients and providers find SDM acceptable and express a willingness to engage in SDM for reasons that are multifactorial; 3) Barriers to SDM exist in mental health decision making including patient preferences and provider level biases; and 4) Outcomes research provide encouraging preliminary evidence for feasibility and effectiveness of SDM during the mental health encounter.Summary-Although there have not been a great number of SDM studies in mental health to date, the positive effects of SDM are comparable to those documented in general non-mental health patient groups, suggesting that future research is likely to be helpful for patients with psychiatric disorders.

This study explored preferences for treatment decision making using the Control Preferences Scale and Problem Solving Decision Making Scale among a sample of ethnically diverse adults (N = 60) seeking treatment for anxiety and depression. Most participants expressed a desire for participation in shared decision making. Being Hispanic was significantly associated with a more passive role in decision making. Participants preferred more involvement in decision making versus problem solving tasks for both mental and general health vignettes, and more involvement in mental health versus general health decision-making. More research is needed to confirm tentative results on the influence of sociodemographic variables on preferences for role and participation in treatment decision making and the variation in these preferences. Treatment seeking individuals with anxiety and depression have identifiable preferences for participation in decision making. Asking about patient preferences and a better understanding of variability in preferences may improve patientprovider communication.

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.

BackgroundDepression 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.ObjectiveWe 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.MethodsDepressive 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.ResultsParticipants (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.ConclusionDepressive 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.

The Ambulatory and Hospital Care Statistics Branch is pleased to release the most current nationally representative data on ambulatory care visits to physician offices in the United States. Statistics are presented on physician practices as well as patient and visit characteristics using data collected in the 2015 National Ambulatory Medical Care Survey (NAMCS). NAMCS is an annual nationally representative sample survey of visits to nonfederal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists. Visit estimates for the following 16 states that were targeted for separate estimation are included in the summary tables:

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

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

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 (β = 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.]