Digital Technologies in Hereditary Coagulation Disorders: A Systematic Review.

Background:  This systematic review aims to comprehensively survey digital technologies used in the prevention, diagnosis, and treatment of hereditary blood coagulation disorders.

Methods:  The systematic review was performed according to the PRISMA guidelines. A systematic search was conducted on PubMed on January 29, 2024.

Monitoring individualized glucose levels predicts risk for bradycardia in type 2 diabetes patients with chronic kidney disease: a pilot study.

Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) exhibit an elevated risk for cardiac arrhythmias, such as bradycardia, which may potentially lead to sudden cardiac death (SCD). While hypoglycemia, defined as a critical drop in glucose levels below the normal range, has long been associated with adverse cardiovascular events, recent studies have highlighted the need for a comprehensive reevaluation of its direct impact on cardiovascular outcomes, particularly in high-risk populations such as those with DM and CKD. In this study, we investigated the association between glucose levels and bradycardia by simultaneously monitoring interstitial glucose (IG) and ECG for 7 days in insulin-treated patients with DM and CKD.

Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder.

Wearable technology enables the unsupervised recording of electrocardiogram (ECG) signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical approaches and explainability. This work investigates the feasibility of medically explainable anomaly detection through disentangled representational learning of ECGs and personalization to mitigate inter-subject variations.

Evaluation of the gamified application KIJANI to promote physical activity in children and adolescents: A multimethod study.

Objective: Digital approaches have the potential to make activity promotion attractive and age-appropriate for children and adolescents. KIJANI is a mobile application aiming to increase physical activity (PA) in youth via gamification and augmented reality. This study investigates the user experience with KIJANI through a multimethod approach.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

Background And Objectives: A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs.

User-centered development of an mHealth app for cardiovascular prevention.

Background: Mobile health (mHealth) apps can be used for cardiovascular disease (CVD) prevention. User-centered design, evidence-based content and user testing can be applied to ensure a high level of usability and adequate app access.

Objective: To develop and evaluate an mHealth app () for CVD prevention.

KIJANI App to Promote Physical Activity in Children and Adolescents: Protocol for a Mixed Method Evaluation.

Background: The prevalence of physical inactivity among children and adolescents is alarmingly high despite the well-documented and comprehensive benefits of regular physical activity (PA). Therefore, PA promotion should start early in childhood and adolescence. Although reducing recreational screen time in children and adolescents is an urgent concern, digital approaches have the potential to make activity promotion attractive and age appropriate for the target group.

The German version of the mHealth App Usability Questionnaire (GER-MAUQ): Translation and validation study in patients with cardiovascular disease.

Objective: In Germany, only a few standardized evaluation tools for assessing the usability of mobile Health apps exist so far. This study aimed to translate and validate the English patient version for standalone apps of the mHealth App Usability Questionnaire (MAUQ) into a German version.

Methods: Following scientific guidelines for translation and cross-cultural adaptation, the patient version for standalone apps was forward and back-translated from English into German by an expert panel.

Guideline-Based Cardiovascular Risk Assessment Delivered by an mHealth App: Development Study.

Background: Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary.

Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen.

Virtual reality in the diagnostic and therapy for mental disorders: A systematic review.

Background: Virtual reality (VR) technologies are playing an increasingly important role in the diagnostics and treatment of mental disorders.

Objective: To systematically review the current evidence regarding the use of VR in the diagnostics and treatment of mental disorders.

Data Source: Systematic literature searches via PubMed (last literature update: 9 of May 2022) were conducted for the following areas of psychopathology: Specific phobias, panic disorder and agoraphobia, social anxiety disorder, generalized anxiety disorder, posttraumatic stress disorder (PTSD), obsessive-compulsive disorder, eating disorders, dementia disorders, attention-deficit/hyperactivity disorder, depression, autism spectrum disorder, schizophrenia spectrum disorders, and addiction disorders.

mBalance: Detect Postural Imbalance with Mobile Devices.

Background: Postural imbalance can be adopted for the early detection of age-related diseases or monitoring the course of the disease treatment; especially in monitoring, frequent balance measurement is crucial. This is mainly done through regular in-person examinations by a physician currently. Feedback in between examinations is often missing.

Interrater reliability in the assessment of physiotherapy students.

Background: Reliable and objective assessment of psychomotor skills in physiotherapy students' education is essential for direct feedback and skill improvement. The aim of this study is to determine the interrater reliability in the assessment process of physiotherapy students and to analyse the assessment behaviour of the examiners.

Methods: Physiotherapy teachers from two different schools assessed students from two different schools performing proprioceptive neuromuscular facilitation (PNF) patterns.

Tracking of Mental Workload with a Mobile EEG Sensor.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load.

Introducing a Navigation Algorithm for Reducing the Spread of Diseases in Public Transport Networks.

Reducing passenger flow through highly frequented bottlenecks in public transportation networks is a well-known urban planning problem. This issue has become even more relevant since the outbreak of the SARS-CoV-2 pandemic and the necessity for minimum distances between passengers. We propose an approach that allows to dynamically navigate passengers around dangerously crowded stations to better distribute the passenger load across an entire urban public transport network.

Developing an App for Cardiovascular Prevention and Scientific Data Collection.

Background: Mobile apps may encourage a lifestyle that avoids unhealthy behaviors, such as smoking or poor nutrition, which promotes cardiovascular diseases (CVD). Yet, little data is available on the utilization, perception, and long-term effects of such apps to prevent CVD.

Objectives: To develop a mobile app concept to reduce the individual CVD risk and collect information addressing research questions on CVD prevention while preserving data privacy and security.

Mobile Motion Tracking for Disease Prevention and Rehabilitation Using Apple ARKit.

Background: Physical activity helps improve the overall quality of life. The correct execution of physical activity is crucial both in sports as well as disease prevention and rehabilitation. Little to no automated commodity solutions for automated analysis and feedback exist.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation.

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system.

360-degree Delphi: addressing sociotechnical challenges of healthcare IT.

Background: IT systems in the healthcare field can have a marked sociotechnical impact: they modify communication habits, alter clinical processes and may have serious ethical implications. The introduction of such systems involves very different groups of stakeholders because of the inherent multi-professionalism in medicine and the role of patients and their relatives that are often underrepresented. Each group contributes distinct perspectives and particular needs, which create specific requirements for IT systems and may strongly influence their acceptance and success.

Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection.

Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context.

Author Correction: Visualizing flow in an intact CSF network using optical coherence tomography: implications for human congenital hydrocephalus.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial.

Introduction: are the most commonly identified pathogens in bloodstream infections. Identification of in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases.