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.). Targeted emmetropia was achieved in all patients, no distance and near correction was needed. Retrospective IOL calculations were performed utilizing 7 formulas (SRK/T, Holladay 1 and 2, Haigis, Hoffer Q, Barrett Universal 2, Olsen) and Pentacam keratometry data: Holladay equivalent K-readings, total optical power by ray tracing (TCRP) centered on the apex and pupil in 10 zones (from 0.5 to 5 mm in 0.5 mm increments). For each formula/zone/map combination: postoperative predicted refraction (PPRs), mean absolute errors (MAEs), and median absolute errors (MedAEs) were analyzed. RESULTS: According to EKR, the Haigis formula showed the lowest error in the central zones up to 3.5 mm, the TCRP zone for Holladay I and II formulas 4.0-4.5 mm, for HofferQ and SRK/T formulas 4.5-5.0 mm, and for Olsen and Barrett II Universal-5 mm. CONCLUSION: The use of keratometry data (EKR, TCRP) in the formulas adapted to SimK, with the correct choice of the evaluation zone of keratometric data, will increase the chance of hitting the refractive target.

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. Methods: A systematic review of the literature on DiGA using PubMed, Scopus, and Web of Science was started on February 4, 2023. Papers addressing the evidence for applications listed in the directory were included, while duplicates and mere study protocols not reporting on data were removed. The remaining publications were used to assess the quality of the evidence or potential gaps in this regard. Results were aggregated in tabular form. Results: The review identified fourteen relevant publications. Six studies suggested inadequate scientific evidence, five mentioned shortcomings of tools for validating DiGA-related evidence, and four publications described a high potential for bias, potentially influencing the validity of the results. Concerns about limited external generalizability were also raised. Conclusions: The literature review found evidence-related gaps that must be addressed with adequate measures. Our findings can serve as a basis for a plea for a more detailed examination of the quality of evidence in the DiGA context.

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. MATERIALS AND METHODS: An automated total read-out of the "Medical" category of Apple's German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using a semiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered. RESULTS: A total of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided a telemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status. CONCLUSION: Through the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have a wide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality.

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.

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.

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.

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. Women need to get new knowledge and change their diet very quickly. Usually, these patients require additional frequent visits to healthcare professionals. Recommender systems based on artificial intelligence could partially substitute healthcare professionals in the process of educating and controlling women with GDM, thus reducing the burden on the women and healthcare systems. We have developed a mobile-based personalized recommendation system DiaCompanion I with data-driven real time personal recommendations focused primarily on postprandial glycaemic response prediction. The study aims to clarify the effect of using DiaCompanion I on glycaemic levels and pregnancy outcomes in women with GDM. Methods: Women with GDM are randomized to 2 treatment groups: utilizing and not utilizing DiaCompanion I. The app provides women in the intervention group the resulting data-driven prognosis of 1-hour postprandial glucose level every time they input their meal data. Based on the predicted glucose level, they can adjust their current meal so that the predicted glucose level falls within the recommended range below 7 mmol/L. The app also provides reminders and recommendations on diet and lifestyle to the participants of the intervention group. All the participants are required to perform 6 blood glucose measurements a day. Capillary glucose values are retrieved from the glucose meter and if not available, from the woman's diary. Additionally, data on glycaemic levels during the study and consumption of major macro- and micronutrients will be collected using the mobile app with electronic report forms in the intervention group. Women from the control group receive standard care without the mobile app. All participants are prescribed with insulin therapy if needed and modifications in their lifestyle. A total of 216 women will be recruited. The primary outcome is the percentage of postprandial capillary glucose values above target (>7.0 mmol/L). Secondary outcomes include the percentage of patients requiring insulin therapy during pregnancy, maternal and neonatal outcomes, glycaemic control using glycated hemoglobin (HbA1c), continuous glucose monitoring data and other blood glucose metrics, the number of patient visits to endocrinologists and acceptance/satisfaction of the two strategies assessed using a questionnaire. Discussion: We believe that the approach including DiaCompanion I will be more effective in patients with GDM for improving glycaemic levels and pregnancy outcomes. We also expect that the use of the app will help reduce the number of clinic visits. Trial registration number: ClinicalTrials.gov, Identifier NCT05179798.

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.

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.

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. We conducted a case−control study (including 318 pregnant women) nested on two previous studies. In 2012−2014, a total of 214 women (83 GDM and 131 controls) were enrolled before 15 weeks of gestation and maternal serum 25(OH)D concentrations were measured twice: at 8th−14th week of gestation and simultaneously with two-hour 75 g oral glucose tolerance test (OGTT) at 24th−32nd week of gestation. In the period of 2018−2021, 104 women (56 GDM and 48 controls) were included after OGTT and 25(OH)D concentrations were measured at 24th−32nd week of gestation. Median 25(OH)D levels were 20.0 [15.1−25.7] vs. 20.5 [14.5−27.5] ng/mL (p = 0.565) in GDM and control group in the first half of pregnancy and 25.3 [19.8−33.0] vs. 26.7 [20.8−36.8] ng/mL (p = 0.471) in the second half of pregnancy, respectively. The prevalence rates for vitamin D deficiency (25(OH)D levels < 20 ng/mL) were 49.4% and 45.8% (p = 0.608) in the first half of pregnancy and 26.2% vs. 22.1% (p = 0.516) in the second half of pregnancy in women who developed GDM and in women without GDM, respectively. The frequency of vitamin D supplements intake during pregnancy increased in 2018−2021 compared to 2012−2014 (p = 0.001). However, the third trimester 25(OH)D levels and prevalence of vitamin D deficiency (25.5 vs. 23.1, p = 0.744) did not differ in women examined in the periods of 2012−2014 and 2018−2021. To conclude, there was no association between gestational diabetes risk and maternal 25(OH)D measured both in the first and in the second halves of pregnancy. The increased prevalence of vitamin D supplements intake during pregnancy by 2018−2021 did not lead to higher levels of 25(OH)D.

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. Observational studies have identified several nutritional and lifestyle factors associated with the risk of GDM. The results of intervention studies examining the effects of diet and lifestyle on the prevention of GDM are contradictory. Differences in the study populations, types and intensity of intervention, time frame of the intervention, and diagnostic criteria for GDM may explain the heterogeneity in the results of intervention studies. This review provides an overview of new diets and other factors that may help prevent GDM. The main results of epidemiological studies assessing the risk factors for GDM, as well as the results and methodological problems of intervention studies on the prevention of GDM and their meta-analyses, are discussed. In addition, the evidence that gene and lifestyle interactions influence the development of GDM, as well as prospects for increasing the effectiveness of interventions designed to prevent GDM, including new data on the possible uses of personalized diet therapy, are highlighted.

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. A total of 11 single- nucleotide polymorphisms (SNPs), including those in HKDC1 (rs10762264), GCK (rs1799884), MTNR1B (rs10830963 and rs1387153), TCF7L2 (rs7903146 and rs12255372), KCNJ11 (rs5219), IGF2BP2 (rs4402960), IRS1 (rs1801278), FTO (rs9939609), and CDKAL1 (rs7754840) were genotyped using Taqman assays. A logistic regression model was used to calculate odds ratios (ORs) and their confidence intervals (CIs). A simple-count genetic risk score (GRS) was calculated using 6 SNPs. The area under the receiver operating characteristic curve (c-statistic) was calculated for the logistic regression model predicting the risk of GDM using clinical covariates, SNPs that had shown a significant association with GDM in our study, GRS, and their combinations. Results: Two variants in MTNR1B (rs1387153 and rs10830963) demonstrated a significant association with an increased risk of GDM. The association remained significant after adjustment for age, pre-gestational BMI, arterial hypertension, GDM in history, impaired glucose tolerance, polycystic ovary syndrome, family history of diabetes, and parity (P = 0.001 and P < 0.001, respectively). After being conditioned by each other, the effect of rs1387153 on GDM predisposition weakened while the effect of rs10830963 remained significant (P = 0.004). The risk of GDM was predicted by clinical variables (c-statistic 0.712, 95 % CI: 0.675 - 0.749), and the accuracy of prediction was modestly improved by adding GRS to the model (0.719, 95 % CI 0.682 - 0.755), and more by adding only rs10830963 (0.729, 95 % CI 0.693 - 0.764). Conclusion: Among 11 SNPs associated with T2D and/or GDM in other populations, we confirmed significant association with GDM for two variants in MTNR1B in Russian women. However, these variants showed limited value in the identification of GDM cases.

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. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

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). Our aim was to study the effect of GDM and its treatment terms on the level of TRIB1 gene expression in human umbilical vein endothelial cells (HUVECs) of newborns from women with and without GDM. The study included 50 women with GDM and 25 women without GDM (control group). Women with GDM were divided into three groups according to their gestational age when the treatment of GDM started: 24-28 weeks (GDM1, N = 16), 29-32 weeks (GDM2, N = 25) and >34 weeks (GDM3, N = 9). The levels of TRIB1 gene expression in GDM3, GDM2, GDM1 and control groups were 2.8 ± 1.1, 4.2 ± 2.4, 6.0 ± 3.4 and 8.1 ± 6.1, respectively (p = 0.001). After comparison in pairs the difference was significant for the following pairs: GDM2-control (p = 0.004), GDM3-control (p = 0.002), GDM1-GDM3 (p = 0.012). Notably, if treatment had been started before the 28th week of gestation, the difference in TRIB1 gene expression in HUVECs was not significant (p = 0.320 for comparison between GDM1 and control groups). Our findings support the hypothesis that TRIB1 gene expression in HUVECs depends on the duration of intrauterine exposure to hyperglycaemia.

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. OBJECTIVE: This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. METHODS: A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. RESULTS: The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. CONCLUSIONS: The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.

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. Maternal blood was sampled for genotyping single-nucleotide polymorphisms (SNPs) in MTNR1B (rs10830963 and rs1387153), GCK (rs1799884), KCNJ11 (rs5219), IGF2BP2 (rs4402960), TCF7L2 (rs7903146 and rs12255372), CDKAL1 (rs7754840), IRS1 (rs1801278) and FTO (rs9939609). Binary logistic regression revealed an interaction effect of sausage intake and the number of risk alleles of two SNPs (rs10830963 in MTNR1B and rs1799884 in GCK) on GDM risk (P < 0.001). Among women without risk alleles of these two SNPs, sausage consumption was positively associated with GDM risk (P trend = 0.045). This difference was not revealed in women carrying 1 or more risk alleles. The risk of GDM increased as the number of risk alles increased in participants with low and moderate sausage consumption (P trend <0.001 and 0.006, respectively), while the risk of GDM in women with high sausage consumption remained relatively high, independent of the number of risk alleles. These findings indicate that the association of sausage consumption with GDM risk can be determined based on the number of risk alleles of rs10830963 in MTNR1B and rs1799884 in GCK.

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. Despite the huge diversity and number of applications available in the market, it is highly dominated by just a few apps: 90.7% of the overall number of user ratings assigned to the apps in our sample is distributed among just 1,601 apps, corresponding to 4.1% of all apps provided within the "Medical" and "Health and fitness" categories at the time of our evaluation.

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.