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2017.12.005.][This corrects the article DOI 10.1016/j.jctube.2018.02.001.][This corrects the article DOI 10.1016/j.jctube.2018.06.007.].COVID-19 required innovative approaches to educating health professions students who could no longer attend in-person classes or clinical rotations. Interprofessional education (IPE) activities were similarly impacted. To replace an in-person IPE activity slated for this spring, nursing and medical students with similar levels of clinical experience came together to attend a synchronous virtual session focused on discharge planning. The class objectives focused on the IPEC competencies of Role/Responsibility and Interprofessional Communication. Discussion revolved around the discharge planning process for an elderly patient with multiple medical problems, as this is a time when interprofessional collaboration has a clear benefit to patients. Twenty-eight nursing students and eleven medical students attended a 90 min session via Zoom. Students received pre-readings, the day's agenda, learning objectives, and discussion questions in advance. The session had three sections introduction/welcome, breakout sessions, and debrief and evaluation. Four faculty leaders and four students who participated in a similar in-person session in the past served as facilitators. They received a supplemental facilitator guide for use if students were not able to sustain their discussions for the allotted time. Materials can be accessed by contacting the corresponding author (BR). Students completed a post-session survey, and qualitative analysis demonstrated that they had addressed the two relevant IPEC competencies in their groups and showed evidence of touching on the additional two IPEC competencies as well. Overall, they enjoyed the experience. This virtual experience made scheduling simpler than planning an in-person session and allowed this activity to occur despite restrictions secondary to the pandemic. This might remain a useful format for similar sessions in the future.The aggregation of Latino subgroups in national studies creates an overly simplistic narrative that Latinos are at lower risk of mental illness and that foreign nativity seems protective against mental illness (i.e., immigrant paradox). This broad generalization does not hold up as the Latino population ages. Given that social inequalities for risk appear to widen with age, the social disadvantages of being Latino in the United States increase the risk for mental illness across the life span. This review focuses on the mental health of older Latinos, specifically the 3 subgroups with the longest residential history in the United States-Mexicans, Puerto Ricans, and Cubans. We examine relevant epidemiological and clinical psychopathology studies on aging in these Latino populations and present evidence of the heterogeneity of the older Latino population living in the United States, thus illustrating a limitation in this field-combining Latino subgroups despite their diversity because of small sample sizes. We address the migration experience-how intraethnic differences and age of migration affect mental health-and discuss social support and discrimination as key risk and protective factors. We conclude with a discussion on meeting the mental health needs of older Latinos with a focus on prevention, a promising approach to addressing mental illness in older Latinos, and future directions for mental health research in this population. Success in this endeavor would yield a substantial reduction in the burden of late-life depression and anxiety and a positive public health impact.

Visual speed of processing training had clinically and statistically significant beneficial effects on health-related quality of life among 2,802 healthy community-dwelling adults aged 65-94 years at 2 and 5 years post-training in the Advanced Cognitive Training for Independent and Vital Elderly randomized controlled trial. We examined whether that effect would be found among older adults in assisted and independent living communities.

We conducted a two-arm, parallel randomized controlled trial stratified by assisted versus independent settings in 31 senior living communities and enrolled 351 adults aged 55-102 years. The targeted intervention dose was 10 hr at baseline with 4-hr boosters at 5 and 11 months. The intervention group received computerized visual speed of processing training, while the attention control group solved computerized crossword puzzles. The health-related quality of life outcomes were the Short-Form 36-item Health Survey's mental and physical component

scores. Linear mixed-effem-sized harmful effect of visual speed of processing training among those in the assisted living communities, caution is advised when using these two visual speed of processing training modalities in assisted living communities until further research verifies or refutes our findings and the underlying etiological pathways.Digital health applications (apps) have the potential to improve health behaviors and outcomes. We aimed to examine the effectiveness of a consumer web-based app linked to primary care electronic health records (EHRs). CONNECT was a multicenter randomized controlled trial involving patients with or at risk of cardiovascular disease (CVD) recruited from primary care (Clinical Trial registration ACTRN12613000715774). Intervention participants received an interactive app which was pre-populated and refreshed with EHR risk factor data, diagnoses and, medications. Interactive risk calculators, motivational messages and lifestyle goal tracking were also included. Control group received usual health care. Primary outcome was adherence to guideline-recommended medications (≥80% of days covered for blood pressure (BP) and statin medications). Secondary outcomes included attainment of risk factor targets and eHealth literacy. In total, 934 patients were recruited; mean age 67.6 (±8.1) years. At 12 months, the proportion with >80% days covered with recommended medicines was low overall and there was no difference between the groups (32.8% vs. 29.9%; relative risk [RR] 1.07 [95% CI, 0.88-1.20] p = 0.49). There was borderline improvement in the proportion meeting BP and LDL targets in intervention vs. control (17.1% vs. 12.1% RR 1.40 [95% CI, 0.97-2.03] p = 0.07). The intervention was associated with increased attainment of physical activity targets (87.0% intervention vs. 79.7% control, p = 0.02) and e-health literacy scores (72.6% intervention vs. 64.0% control, p = 0.02). In conclusion, a consumer app integrated with primary health care EHRs was not effective in increasing medication adherence. Borderline improvements in risk factors and modest behavior changes were observed.Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. selleck chemical We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p  less then  0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.Background Contextual factors such as an intervention's setting are key to understanding how interventions to change behaviour have their effects and patterns of generalisation across contexts. The intervention's setting is not consistently reported in published reports of evaluations. Using ontologies to specify and classify intervention setting characteristics enables clear and reproducible reporting, thus aiding replication, implementation and evidence synthesis. This paper reports the development of a Setting Ontology for behaviour change interventions as part of a Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods The Intervention Setting Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project 1) Defining the ontology's scope, 2) Identifying key entities by reviewing existing classification systems (top-down) and 100 published behaviour change intervention reports (bottptable) for those unfamiliar with it. Conclusion The Intervention Setting Ontology can be used to code information from diverse sources, annotate the setting characteristics of existing intervention evaluation reports and guide future reporting.Tuberculous meningitis (TBM) is the most devastating form of tuberculosis (TB) but diagnosis is difficult and delays in initiating therapy increase mortality. All currently available tests are imperfect; culture of Mycobacterium tuberculosis from the cerebrospinal fluid (CSF) is considered the most accurate test but is often negative, even when disease is present, and takes too long to be useful for immediate decision making. Rapid tests that are frequently used are conventional Ziehl-Neelsen staining and nucleic acid amplification tests such as Xpert MTB/RIF and Xpert MTB/RIF Ultra. While positive results will often confirm the diagnosis, negative tests frequently provide insufficient evidence to withhold therapy. The conventional diagnostic approach is to determine the probability of TBM using experience and intuition, based on prevalence of TB, history, examination, analysis of basic blood and CSF parameters, imaging, and rapid test results. Treatment decisions may therefore be both variable and inaccurate, depend on the experience of the clinician, and requests for tests may be inappropriate.