Mobile apps for self-management in pregnancy: a systematic review

Complications during pregnancy is a major problem affecting healthcare systems which requires the efforts of both patients and healthcare practitioners. For this reason, mobile apps have been increasingly sought to support self-management during pregnancy. Although many benefits have been claimed for the inclusion of self-management mobile apps in supporting care, the domains already explored, functionalities and impacts of mobile apps for self-management in pregnancy is still not clear. A clear understanding of the health domains already explored functionalities of existing apps which have been evaluated as well as the effectiveness of these apps can help researchers and health practitioners identify areas of future needs for self-management mobile apps during pregnancy. The objective of this systematic review was to provide a narrative synthesis of the literature on the evaluation of mobile apps for self-management during pregnancy. The search was conducted on four databases: PubMed, CINAHL, Scopus and EMBASE. 18 articles met the inclusion criteria. Nine randomised controlled trials (RCTs), one non-randomised controlled trial (NRCT) and eight observation studies evaluating self-management mobile apps among pregnant women were identified. Mobile apps for self-management have been developed with different functionalities addressing various areas of complications during pregnancy including gestational diabetes, preeclampsia and high blood pressure. These apps have also been evaluated in countries mostly in the developed context. We conclude that there have been positive impacts of mobile apps for self-management during pregnancy; however, future research should focus on evaluating mobile apps for self-management during pregnancy within developing countries as well as the use of mobile apps for the identification of sexually transmitted infections, early warning signs of potential still birth, miscarriage and management of anaemia during pregnancy

Digital Twins for Precision Healthcare

Precision healthcare is an emerging concept that will see technology-driven digital transformation of the health service. It enables customised patient outcomes via the development of novel, targeted medical approaches with a focus on intelligent, data-centric smart healthcare models. Currently, precision healthcare is seen as a challenging model to apply due to the complexity of the healthcare ecosystem, which is a multi-level and multifaceted environment with high real-time interactions among disciplines, practitioners, patients and discrete computer systems. Digital Twins (DT) pairs individual physical artefacts with digital models reflecting their status in real-time. Creating a live-model for healthcare services introduces new opportunities for patient care including better risk assessment and evaluation without disturbing daily activities. In this article, to address design and management in this complexity, we examine recent work in Digital Twins (DT) to investigate the goals of precision healthcare at a patient and healthcare system levels. We further discuss the role of DT to achieve precision healthcare, proposed frameworks, the value of active participation and continuous monitoring, and the cyber-security challenges and ethical implications for this emerging paradigm