Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms-A Feasibility Study.

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs.

Digital health applications from a government-regulated directory of reimbursable health apps in Germany-a systematic review for evidence and bias.

Background: The Digital Healthcare Act, passed in November 2019, authorizes healthcare providers in Germany to prescribe digital health applications (DiGA) to patients covered by statutory health insurance. If DiGA meet specific efficacy requirements, they may be listed in a special directory maintained by the German Federal Institute for Drugs and Medical Devices. Due to the lack of well-founded app evaluation tools, the objectives were to assess (I) the evidence quality situation for DiGA in the literature and (II) how DiGA manufacturers deal with this issue, as reflected by the apps available in the aforementioned directory.

The accuracy of the trifocal IOL calculation using equivalent K-readings and total corneal power in different zones.

Purpose: To identify the equivalent K-readings and total keratometry zones that is optimally suitable for calculating the IOL spheroequivalent according to 7 formulas.

Methods: The study included 40 patients (40 eyes) who underwent uneventful femtosecond laser-assisted cataract surgery and refractive lens exchange (RLE) with implantation of a trifocal diffractive IOL (PanOptix, Alcon inc.).

Do Health Apps Stigmatize People with Obesity?

Apps in the "Medicine" category of Apple's App Store were examined concerning the potential stigmatization of people with obesity through word and image language. Only 5/71 potentially stigmatizing apps related to obesity were identified. Stigmatization in this context can occur, for example, through the excessive promotion of very slim people in connection with weight loss-related apps.

Conciliation and Approval of Quality Principles for the Self-Declaration of the Quality of Health Apps.

This poster describes the conciliation and approval process of the unified set of criteria for self-declaration of health app quality. The timeline underlines the necessity of transparency and open communication in regulations.

COVID-19 Mobile Apps Trends Derived from Long-Term Google Play Analysis.

In this paper, we describe the 5-year trends of COVID-related mobile apps in the Google Play platform obtained by retrospectively analyzing app descriptions. Out of 21764 and 48750 unique apps available free of charge in the "medical" and "health and fitness", there were 161 and 143 COVID-related apps, respectively. The prominentrise in apps' prevalence occurred in January 2021.

Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias.

Background: Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms.

Objectives: The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias.

Trial protocol for the study of recommendation system DiaCompanion with personalized dietary recommendations for women with gestational diabetes mellitus (DiaCompanion I).

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy associated with serious adverse outcomes for mothers and their offspring. Achieving glycaemic targets is the mainstream in the treatment of GDM in order to improve pregnancy outcomes. As GDM is usually diagnosed in the third trimester of pregnancy, the time frame for the intervention is very narrow.

Evidence of Digital Health Applications from a State-Regulated Repository for Reimbursable Health Applications in Germany.

17 RCTs for 15 digital health applications (DiGA) permanently listed in the state-regulated register were analyzed descriptively for methodological study aspects relevant to evidence analysis. The analysis revealed that several underlying studies had limitations, at least worthy of discussion, in terms of their power concerning sample size, intervention and control group specifications, drop-out rates, and blinding.

Medical Apps for Android and iOS: Differences and Similarities.

Google Play and Apple's App Store dominate the mobile health app market. We analyzed the metadata and descriptive texts of apps in the medical category using semi-automated retrospective app store analysis (SARASA) and compared the store offerings in terms of their number, descriptive texts, user ratings, medical device status, diseases, and conditions (both keyword-based). Relatively speaking, the store listings for the selected items were comparable.

Vitamin D Status and Gestational Diabetes in Russian Pregnant Women in the Period between 2012 and 2021: A Nested Case-Control Study.

Several meta-analyses found an association between low maternal serum 25-hydroxyvitamin D (25(OH)D) level and gestational diabetes mellitus (GDM). However, some of them reported significant heterogeneity. We examined the association of serum 25(OH)D concentration measured in the first and in the second halves of pregnancy with the development of GDM in Russian women surveyed in the periods of 2012−2014 and 2018−2021.

Improving nutrition for the prevention of gestational diabetes: Current status and perspectives.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy and a serious public health problem. It carries significant risks of short-term and long-term adverse health effects for both mothers and their children. Risk factors, especially modifiable risk factors, must be considered to prevent GDM and its consequences.

Association of Common Genetic Risk Variants With Gestational Diabetes Mellitus and Their Role in GDM Prediction.

Objective: We aimed to explore the associations between common genetic risk variants with gestational diabetes mellitus (GDM) risk in Russian women and to assess their utility in the identification of GDM cases.

Methods: We conducted a case-control study including 1,142 pregnant women (688 GDM cases and 454 controls) enrolled at Almazov National Medical Research Centre. The International Association of Diabetes and Pregnancy Study Groups criteria were used to diagnose GDM.

The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients With Gestational Diabetes.

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney's database and assigned to a food database used in the mobile app DiaCompanion.

Association of tribbles homologue 1 gene expression in human umbilical vein endothelial cells with duration of intrauterine exposure to hyperglycaemia.

Maternal gestational diabetes mellitus (GDM) is considered to be an important factor that epigenetically predisposes offspring to metabolic and cardiovascular diseases. However, the mechanisms of how intrauterine hyperglycaemia affects offspring have not been thoroughly studied. The mammalian tribbles homologue 1 (TRIB1) gene is associated with plasma lipid concentrations and coronary artery disease (CAD).

Effect of gene-lifestyle interaction on gestational diabetes risk.

We hypothesized that the association of certain lifestyle parameters with gestational diabetes mellitus (GDM) risk would depend on susceptibility loci. In total, 278 Russian women with GDM and 179 controls completed questionnaires about lifestyle habits (food consumption, physical activity and smoking). GDM was diagnosed according to the criteria of the International Association of Diabetes and Pregnancy Study Groups.

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus.

Background: Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage.

Evaluation of mHealth Applications Security Based on Application Permissions.

The presented study covers the evaluation of ratings of a set of 1080 applications classified as "top apps" for the two categories "Medicine" and "Health & Fitness" as they are available on Google's Play Store. Within the evaluation, the manifest files and source code of the applications were analyzed in order to reveal whether the requested set of permissions correspond to the ones really utilized by the apps and whether they surpass what is necessary. For many apps, the declarations in the manifest file do not match what is specified in the source code, raising the question of whether this may be an indication of questionable app quality with a potentially negative impact on the safety and reliability of mHealth related apps.

Evaluation of mHealth Applications Quality Based on User Ratings.

The presented study covers the evaluation of ratings of a sample set of 46,430 medical applications available on Google's Play Store. It was discovered that the distribution of user ratings given to applications has a log-normal form and has a correlation with many application characteristics one would not expect to be directly related, among others the time of the last update of the app, the app vocabulary as well as descriptions. Popular applications with a large number of downloads and reviews tend to have average ratings, while the ratings of rarely downloaded apps tend to be either highly positive or negative.