Sensor networks and personal health data management: software engineering challenges

The advances of 5G, sensors, and information technologies enabled proliferation of smart pervasive sensor networks. 5G mobile networks provide low-power, high-availability, high density, and high-throughput data capturing by sensor networks and continuous streaming of multiple measured variables. Rapid progress in sensors that can measure vital signs, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. The increase in size and complexity of wireless sensor networks and expansion into multiple areas of health monitoring creates challenges for system design and software engineering practices. In this paper, we highlight some of the key software engineering and data-processing issues, along with addressing emerging ethical issues of data management. The challenges associated with ensuring high dependability of sensor network systems can be addressed by metamorphic testing. The proposed conceptual solution combines data streaming, filtering, cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. Integration of blockchain technologies and artificial intelligence offers a solution to the increasing needs for higher accuracy of measurements of vital signs, high-quality decision-making, and dependability, including key medical and ethical requirements of safety and security of the data

A prospective study of artificially sweetened beverage intake and cardiometabolic health among women at high risk

BackgroundArtificially sweetened beverages (ASBs) are commonly consumed and recommended for individuals at high risk for cardiometabolic diseases; however, the health effects of ASBs remain contradictory. Given that cross-sectional analyses are subject to reverse causation, prospective studies with long-term follow-up are needed to evaluate associations between ASBs and cardiometabolic health, especially among high-risk individuals.ObjectiveThe aim of this study was to examine associations of ASB intake and cardiometabolic health among high-risk women with prior gestational diabetes mellitus (GDM).MethodsWe included 607 women with GDM from the Danish National Birth Cohort (DNBC; 1996-2002) who completed a clinical exam 9-16 y after the DNBC pregnancy for the Diabetes & Women's Health (DWH) Study (2012-2014). We assessed ASB intake using FFQs completed during the DNBC pregnancy and at the DWH Study clinical exam. We examined cardiometabolic outcomes at the DWH clinical exam. We estimated percentage differences in continuous cardiometabolic markers and RRs for clinical endpoints in association with ASB intake both during pregnancy and at follow-up adjusted for prepregnancy BMI, diet, and lifestyle factors. Sensitivity analyses to account for reverse causation were performed.ResultsIn pregnancy and at follow-up, 30.4% and 36.4% of women regularly (≥2 servings/wk) consumed ASB, respectively. Consumption of ASBs, both during pregnancy and at follow-up, was associated with higher glycated hemoglobin (HbA1c), insulin, HOMA-IR, triglycerides, liver fat, and adiposity and with lower HDL at follow-up. After adjustment for covariates, particularly prepregnancy BMI, the majority of associations between ASB intake in pregnancy and outcomes at follow-up became null with the exception of HbA1c. ASB intake at follow-up (≥1 serving/d compared with <1 serving/mo) was associated with higher HbA1c (6.5%; 95% CI: 1.9, 11.3; P-trend = 0.007); however, associations were not upheld in sensitivity analyses for reverse causation.ConclusionsAmong Danish women with a history of GDM, ASB intake was not significantly associated with cardiometabolic profiles

The Urban Built Environment and Associations with Women’s Psychosocial Health

The determinants that underlie a healthy or unhealthy pregnancy are complex and not well understood. We assess the relationship between the built environment and maternal psychosocial status using directly observed residential neighborhood characteristics (housing damage, property disorder, tenure status, vacancy, security measures, violent crime, and nuisances) and a wide range of psychosocial attributes (interpersonal support evaluation list, self-efficacy, John Henryism active coping, negative partner support, Perceived Stress Scale, perceived racism, Center for Epidemiologic Studies—Depression) on a pregnant cohort of women living in the urban core of Durham, NC, USA. We found some associations between built environment characteristic and psychosocial health varied by exposure categorization approach, while others (residence in environments with more rental property is associated with higher reported active coping and negative partner support) were consistent across exposure categorizations. This study outlines specific neighborhood characteristics that are modifiable risk markers and therefore important targets for increased research and public health intervention