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During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.

Introduction Among those who experience trauma, some individuals develop Adverse Posttraumatic Neuropsychiatric Sequelae (APNS). Identifying biomarkers associated with APNS is critical for predicting trajectories of recovery and informing interventions to reduce the impact of trauma. In this study, we examined objective 24-hour activity patterns and APNS in the weeks following a traumatic event. Methods Participants (n = 2,021) were recruited from emergency departments after experiencing trauma. Over 8 weeks, they wore wrist accelerometry devices to measure 24-hour activity patterns, and completed surveys assessing 10 Research Domain Criteria (RDoC) constructs associated with APNS. We aimed to 1) examine the relationship between 24-hour activity patterns and APNS symptoms, and 2) examine how 24-hour activity patterns changed over time in relation to changes in APNS over the 8-week period. A bivariate linear mixed model approach was used to model the cross-sectional and longitudinal associations with each of the 10 RDoC constructs. Results Overall, participants reporting more pain in the weeks following trauma also showed objectively less 24-hour activity variance (Pearson correlation = -0.14, p = 0.001). An improving pain trajectory over the 8 weeks was associated with increases in daily activity (Pearson correlation = -0.14, p < 0.001) variability in activity (Pearson correlation = -0.12, p < 0.001), and increases in sleep consolidation (Pearson correlation = 0.013, p < 0.001). Within subjects, an increasing number of transitions between sleep and wake over the study period was also associated with worsening self-reported anxiety (Pearson correlation = 0.06, p = 0.003) and sleep problems (Pearson correlation = 0.10, p = 0.003) Conclusion After a traumatic event, several 24-hour activity biomarkers were associated with APNS. Results suggest pain, activity, and sleep highly influence each other in the weeks following a traumatic event, and 24-hour activity patterns should be considered when making predictions about who is likely to recover from trauma. Targeted interventions for sleep and pain may promote recovery in the other domains. Support (If Any) NIMH U01MH110925, US Army Medical Research and Material Command, the One Mind Foundation, The Mayday Fund, Verily Life Sciences. LDS is supported by a Department of Veterans Affairs Career Development Award (IK2CX002032).

BACKGROUND Cardiovascular disease (CVD) disparities are a particularly devastating manifestation of health inequity. Despite advancements in prevention and treatment, CVD is still the leading cause of death in the United States. Additionally, research indicates that African American (AA) and other ethnic-minority populations are affected by CVD at earlier ages than white Americans. Given that AAs are the fastest-growing population of smartphone owners and users, mobile health (mHealth) technologies offer the unparalleled potential to prevent or improve self-management of chronic disease among this population. OBJECTIVE To address the unmet need for culturally tailored primordial prevention CVD–focused mHealth interventions, the MOYO app was cocreated with the involvement of young people from this priority community. The overall project aims to develop and evaluate the effectiveness of a novel smartphone app designed to reduce CVD risk factors among urban-AAs, 18-29 years of age. METHODS The theoretical underpinning will combine the principles of community-based participatory research and the agile software development framework. The primary outcome goals of the study will be to determine the usability, acceptability, and functionality of the MOYO app, and to build a cloud-based data collection infrastructure suitable for digital epidemiology in a disparity population. Changes in health-related parameters over a 24-week period as determined by both passive (eg, physical activity levels, sleep duration, social networking) and active (eg, use of mood measures, surveys, uploading pictures of meals and blood pressure readings) measures will be the secondary outcome. Participants will be recruited from a majority AA “large city” school district, 2 historically black colleges or universities, and 1 urban undergraduate college. Following baseline screening for inclusion (administered in person), participants will receive the beta version of the MOYO app. Participants will be monitored during a 24-week pilot period. Analyses of varying data including social network dynamics, standard metrics of activity, percentage of time away from a given radius of home, circadian rhythm metrics, and proxies for sleep will be performed. Together with external variables (eg, weather, pollution, and socioeconomic indicators such as food access), these metrics will be used to train machine-learning frameworks to regress them on the self-reported quality of life indicators. RESULTS This 5-year study (2015-2020) is currently in the implementation phase. We believe that MOYO can build upon findings of classical epidemiology and longitudinal studies like the Jackson Heart Study by adding greater granularity to our knowledge of the exposures and behaviors that affect health and disease, and creating a channel for outreach capable of launching interventions, clinical trials, and enhancements of health literacy. CONCLUSIONS The results of this pilot will provide valuable information about community cocreation of mHealth programs, efficacious design features, and essential infrastructure for digital epidemiology among young AA adults. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/16699

In the past few decades heart sound signals (i.e., phono-cardiograms or PCGs) have been widely studied. Automated heart sound segmentation and classification techniques have the potential to screen for pathologies in a variety of clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of a large and open database of heart sound recordings. The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 addresses this issue by assembling the largest public heart sound database, aggregated from eight sources obtained by seven independent research groups around the world. The database includes 4,430 recordings taken from 1,072 subjects, totalling 233,512 heart sounds collected from both healthy subjects and patients with a variety of conditions such as heart valve disease and coronary artery disease. These recordings were collected using heterogeneous equipment in both clinical and nonclinical (such as in-home visits). The length of recording varied from several seconds to several minutes. Additional data provided include subject demographics (age and gender), recording information (number per patient, body location, and length of recording), synchronously recorded signals (such as ECG), sampling frequency and sensor type used. Participants were asked to classify recordings as normal, abnormal, or not possible to evaluate (noisy/uncertain). The overall score for an entry was based on a weighted sensitivity and specificity score with respect to manual expert annotations. A brief description of a baseline classification method is provided, including a description of open source code, which has been provided in association with the Challenge. The open source code provided a score of 0.71 (Se=0.65 Sp=0.76). During the official phase of the competition, a total of 48 teams submitted 348 open source entries, with a highest score of 0.86 (Se=0.94 Sp=0.78).

BACKGROUND: Recent telehealth studies have demonstrated minor impact on patients affected by long-term conditions. The use of technology does not guarantee the compliance required for sustained collection of high-quality symptom and physiological data. Remote monitoring alone is not sufficient for successful disease management. A patient-centred design approach is needed in order to allow the personalisation of interventions and encourage the completion of daily self-management tasks.METHODS: A digital health system was designed to support patients suffering from chronic obstructive pulmonary disease in self-managing their condition. The system includes a mobile application running on a consumer tablet personal computer and a secure backend server accessible to the health professionals in charge of patient management. The patient daily routine included the completion of an adaptive, electronic symptom diary on the tablet, and the measurement of oxygen saturation via a wireless pulse oximeter.RESULTS: The design of the system was based on a patient-centred design approach, informed by patient workshops. One hundred and ten patients in the intervention arm of a randomised controlled trial were subsequently given the tablet computer and pulse oximeter for a 12-month period. Patients were encouraged, but not mandated, to use the digital health system daily. The average used was 6.0 times a week by all those who participated in the full trial. Three months after enrolment, patients were able to complete their symptom diary and oxygen saturation measurement in less than 1 m 40s (96% of symptom diaries). Custom algorithms, based on the self-monitoring data collected during the first 50 days of use, were developed to personalise alert thresholds.CONCLUSIONS: Strategies and tools aimed at refining a digital health intervention require iterative use to enable convergence on an optimal, usable design. 'Continuous improvement' allowed feedback from users to have an immediate impact on the design of the system (e.g., collection of quality data), resulting in high compliance with self-monitoring over a prolonged period of time (12-month). Health professionals were prompted by prioritisation algorithms to review patient data, which led to their regular use of the remote monitoring website throughout the trial.TRIAL REGISTRATION: Trial registration: ISRCTN40367841 . Registered 17/10/2012.

Moca is a cell phone-facilitated clinical information system to improve diagnostic, screening and therapeutic capabilities in remote resource-poor settings. The software allows transmission of any medical file, whether a photo, x-ray, audio or video file, through a cell phone to (1) a central server for archiving and incorporation into an electronic medical record (to facilitate longitudinal care, quality control, and data mining), and (2) a remote specialist for real-time decision support (to leverage expertise). The open source software is designed as an end-to-end clinical information system that seamlessly connects health care workers to medical professionals. It is integrated with OpenMRS, an existing open source medical records system commonly used in developing countries.