Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach.

The concept of Quality of Life (QoL) refers to a holistic measurement of an individual's well-being, incorporating psychological and social aspects. Pregnant women, especially those with obesity and stress, often experience lower QoL. Physical activity (PA) has shown the potential to enhance the QoL.

Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients.

Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses.

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection.

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications.

Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation.

Wearable technology has expanded the applications of photoplethysmography (PPG) in remote health monitoring, enabling real-time measurement of various physiological parameters, such as heart rate (HR), heart rate variability (HRV), and respiration rate (RR). While existing studies mainly focus on individual parameters derived from PPG, they often overlook the shared characteristics among these physiological parameters. Multitask learning (MTL) offers a promising solution by training a single model to perform multiple related tasks, leveraging their interdependencies.

Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.

Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk.

Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications.

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way.

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers.

Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings?

Sleep is crucial for physical, mental, and emotional well-being. Physical activity and sleep are known to be interrelated; however, limited research has been performed to investigate their interactions in long-term. Conventional studies have presented sleep quality prediction, focusing on a single sleep quality aspect, such as sleep efficiency.

Exploring Lin28 proteins: Unravelling structure and functions with emphasis on nervous system malignancies.

Cancer and stem cells share many characteristics related to self-renewal and differentiation. Both cell types express the same critical proteins that govern cellular stemness, which provide cancer cells with the growth and survival benefits of stem cells. LIN28 is an example of one such protein.

ZotCare: a flexible, personalizable, and affordable mhealth service provider.

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation.

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study.

Background: Maternal loneliness is associated with adverse physical and mental health outcomes for both the mother and her child. Detecting maternal loneliness noninvasively through wearable devices and passive sensing provides opportunities to prevent or reduce the impact of loneliness on the health and well-being of the mother and her child.

Objective: The aim of this study is to use objective health data collected passively by a wearable device to predict maternal (social) loneliness during pregnancy and the postpartum period and identify the important objective physiological parameters in loneliness detection.

Exploring Access as a Process of Adaptation in a Self-Monitoring Perinatal eHealth System: Mixed Methods Study From a Sociomaterial Perspective.

Background: The development and quality assurance of perinatal eHealth self-monitoring systems is an upcoming area of inquiry in health science. Building patient engagement into eHealth development as a core component has potential to guide process evaluation. Access, 1 attribute of patient engagement, is the focus of study here.

Objective Prediction of Next-Day's Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study.

Background: Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time.

Objective: Previous attempts to model an individual's mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones).

Comparing prenatal and postpartum stress among women with previous adverse pregnancy outcomes and normal obstetric histories: A longitudinal cohort study.

Objective: The aim of this study was to compare subjectively and objectively measured stress during pregnancy and the three months postpartum in women with previous adverse pregnancy outcomes and women with normal obstetric histories.

Methods: We recruited two cohorts in southwestern Finland for this longitudinal study: (1) pregnant women (n = 32) with histories of preterm births or late miscarriages January-December 2019 and (2) pregnant women (n = 30) with histories of full-term births October 2019-March 2020. We continuously measured heart rate variability (HRV) using a smartwatch from 12 to 15 weeks of pregnancy until three months postpartum, and subjective stress was assessed with a smartphone application.

Supporting lifestyle change in obese pregnant mothers through the wearable internet-of-things (SLIM) -intervention for overweight pregnant women: Study protocol for a quasi-experimental trial.

Objectives: To assess, in terms of self-efficacy in weight management, the effectiveness of the SLIM lifestyle intervention among overweight or obese women during pregnancy and after delivery, and further to exploit machine learning and event mining approaches to build personalized models. Additionally, the aim is to evaluate the implementation of the SLIM intervention.

Methods: This prospective trial, which is a non-randomized, quasi-experimental, pre-post intervention, includes an embedded mixed-method process evaluation.

LIN28 expression and function in medulloblastoma.

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Current treatment modalities are not completely effective and can lead to severe neurological and cognitive adverse effects. In addition to urgently needing better treatment approaches, new diagnostic and prognostic biomarkers are required to improve the therapy outcomes of MB patients.

A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability.

Background: Photoplethysmography (PPG) is a low-cost and easy-to-implement method to measure vital signs, including heart rate (HR) and pulse rate variability (PRV) which widely used as a substitute of heart rate variability (HRV). The method is used in various wearable devices. For example, Samsung smartwatches are PPG-based open-source wristbands used in remote well-being monitoring and fitness applications.

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being.

Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual's holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing.

PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network.

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series analysis, signal decomposition, and machine learning methods to reconstruct PPG signals or reduce noise.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks.

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact.

Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context.

Background: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.

T-Type Calcium Channel Inhibitors Induce Apoptosis in Medulloblastoma Cells Associated with Altered Metabolic Activity.

Medulloblastoma (MB) is the most common malignant paediatric brain tumour. In our previous studies, we developed a novel 3D assay for MB cells that was used to screen a panel of plasma membrane calcium channel modulators for their effect on the 3D growth of D341 MB cells. These studies identified T-type (CaV3) channel inhibitors, mibefradil and NNC-55-0396 (NNC) as selective inhibitors of MB cell growth.

Comparison of Oura Smart Ring Against ActiGraph Accelerometer for Measurement of Physical Activity and Sedentary Time in a Free-Living Context.

Smart rings, such as the Oura ring, might have potential in health monitoring. To be able to identify optimal devices for healthcare settings, validity studies are needed. The aim of this study was to compare the Oura smart ring estimates of steps and sedentary time with data from the ActiGraph accelerometer in a free-living context.