Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Introduction: The aim of this retrospective clinical study was to compare the accuracy of 36 commonly used weight estimation formulae in macrosomic fetuses. Material and Methods: Ultrasound estimation of fetal weight (FW) was carried out within 7 days up to delivery in 350 singleton fetuses with a birth weight (BW) of ≧4,000 g. The accuracy of the different formulae for FW estimation was compared by, firstly, the mean percentage (MPE) and mean absolute percentage error (MAPE), secondly, by the frequency distribution of differences between estimated FW and fetal BW, and thirdly by comparing detection and false positive rates in screening for fetuses with a BW of 4,000, 4,300 and 4,500 g or more. Results: MPE ranged from –62.2 to 9.6% and was closest to 0 with the Hart formula. With 12 of 36 weight estimation formulae, MAPE was 10% or less, and was smallest with the Hart formulae (3.9%). The mean detection rate among all formulae for fetuses with a BW ≧4,000, ≧4,300 and ≧4,500 g was 29, 24 and 22%, respectively, and the false positive rate was 12% (for ≧4,300 g) and 7% (≧4,500 g). Discussion: Some formulae showed advantages as far as mean and absolute percentage errors were concerned, but none reached a detection rate and false positive rate for fetuses ≧4,500 g that could lead to clinical recommendation.

Uterus transplantation (UTx) can provide a route to motherhood for women with Mayer–Rokitansky–Küster–Hauser syndrome (MRKHS), a congenital disorder characterized by uterovaginal aplasia, but with functional ovaries. Based on our four successful living-donor transplantations and two resulting births, this analysis presents parameters relevant to standardizing recipient/donor selection, UTx surgery, and postoperative treatment, and their implementation in routine settings. We descriptively analyzed prospectively collected observational data from our four uterus recipients, all with MRKHS, their living donors, and the two newborns born to two recipients, including 1-year postnatal follow-ups. Analysis included only living-donor/recipient pairs with completed donor/recipient surgery. Two recipients, both requiring ovarian restimulation under immunosuppression after missed pregnancy loss in one case and no pregnancy in the other, each delivered a healthy boy by cesarean section. We conclude that parameters crucial to successful transplantation, pregnancy, and childbirth include careful selection of donor/recipient pairs, donor organ quality, meticulous surgical technique, a multidisciplinary team approach, and comprehensive follow-up. Surgery duration and blood vessel selection await further optimization, as do the choice and duration of immunosuppression, which are crucial to timing the first embryo transfer. Data need to be collected in an international registry due to the low prevalence of MRKHS.

As in other healthcare professions, artificial intelligence will influence midwifery education. To prepare midwifes for a future where AI plays a significant role in healthcare, educational requirements need to be adapted. This scoping review aims to outline the current state of research regarding the impact of AI on midwifery education. The review follows the framework of Arksey and O’Malley and the PRISMA-ScR. Two databases (Academic Search Premier and PubMed) were searched for different search strings, following defined inclusion criteria, and six articles were included. The results indicate that midwifery practice and education is faced with several challenges as well as opportunities when integrating AI. All articles see the urgent need to implement AI technologies into midwifery education for midwives to actively participate in AI initiatives and research. Midwifery educators need to be trained and supported to use and teach AI technologies in midwifery. In conclusion, the integration of AI in midwifery education is still at an early stage. There is a need for multidisciplinary research. The analysed literature indicates that midwifery curricula should integrate AI at different levels for graduates to be prepared for their future in healthcare.

Background: Pregnant women are increasingly using mobile apps to access health information during the antenatal period. Therefore, digital health solutions can potentially be used as monitoring instruments during pregnancy. However, a main factor of success is high user engagement. Objective: The aim of this study was to analyze engagement and factors influencing compliance in a longitudinal study targeting pregnant women using a digital health app with self-tracking. Methods: Digitally collected data concerning demographics, medical history, technical aspects, and mental health from 585 pregnant women were analyzed. Patients filling out ≥80% of items at every study visit were considered to be highly compliant. Factors associated with high compliance were identified using logistic regression. The effect of a change in mental and physical well-being on compliance was assessed using a one-sample t test. Results: Only 25% of patients could be considered compliant. Overall, 63% left at least one visit blank. Influential variables for higher engagement included higher education, higher income, private health insurance, nonsmoking, and German origin. There was no relationship between a change in the number of physical complaints or depressive symptoms and study dropout. Conclusions: Maintaining high engagement with digital monitoring devices over a long time remains challenging. As cultural and socioeconomic background factors had the strongest influence, more effort needs to be directed toward understanding the needs of patients from different demographic backgrounds to ensure high-quality care for all patients. More studies need to report on compliance to disclose potential demographic bias.