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.

Enhanced heart sound classification using Mel frequency cepstral coefficients and comparative analysis of single vs. ensemble classifier strategies.

This paper seeks to enhance the performance of Mel Frequency Cepstral Coefficients (MFCCs) for detecting abnormal heart sounds. Heart sounds are first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs are used for heart sound classification.

Predicting Sleep Quality in Family Caregivers of Dementia Patients From Diverse Populations Using Wearable Sensor Data.

This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness.

Active Remote Focus Stabilization in Oblique Plane Microscopy.

Light-sheet fluorescence microscopy (LSFM) has demonstrated great potential in the life sciences owing to its efficient volumetric imaging capabilities. For long term imaging, the light-sheet typically needs to be stabilized to the detection focal plane for the best imaging results. Current light-sheet stabilization methods rely on fluorescence emission from the sample, which may interrupt the scientific imaging and add to sample photobleaching.

Physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: An intensive, longitudinal dataset.

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (=3.

The Effect of COVID-19 Public Health Measures on Mental Health in California.

The goal of this study was to examine the influence of the COVID-19 pandemic, including public health measures (mitigation and containment efforts), on new onset mental health diagnoses by age group. This study was a longitudinal retrospective cohort study. Data on new mental health diagnoses were extracted from the University of California Health System Electronic Health Records (EHR) that contained five academic health centers in California.

Community health workers supporting diverse family caregivers of persons with dementia: Preliminary qualitative results from a randomized home-based study.

Background And Objectives: Culturally diverse informal caregivers of community-dwelling persons with dementia face challenges in accessing dementia care resources due to language barriers and cultural stigmas surrounding dementia. This study presents the perceived intervention experiences of a home-based approach which considers the cultural and linguistic needs of diverse family caregivers in dementia care. The intervention model includes home visits by trained bilingual, non-licensed community health workers (CHWs) whose cultural histories and understandings reflect that of the caregivers.

Effectiveness of supporting lifestyle change in pregnant mothers with obesity through the wearable internet-of-things (SLIM) -intervention on self-efficacy in weight management in pregnant women: A quasi-experimental trial.

Background: Half of women with overweight gain excessive weight during pregnancy. Perceived self-efficacy plays a significant role in adherence to healthy behaviours, especially in turning points of life, such as a pregnancy.

Objectives: To assess the effectiveness of the Supporting lifestyle change in pregnant mothers with obesity through the wearable internet-of-things (SLIM)-intervention in terms of improving self-efficacy in eating and physical activity and preventing excessive weight gain of pregnant women with overweight.

Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study.

Background: An estimated 6.7 million persons are living with dementia in the United States, a number expected to double by 2060. Persons experiencing moderate to severe dementia are 4 to 5 times more likely to fall than those without dementia, due to agitation and unsteady gait.

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.

Finnish Immigrants' Affective Attitude Towards the IMPMS for Loneliness Detection: A Descriptive Qualitative Study.

Loneliness is a global health concern that contributes to morbidity in immigrant populations. However, traditional treatments for loneliness focus on symptom management rather than prevention. Technology-related solutions for preventing and assessing loneliness among immigrants are crucial.

Blockchain technology and raft consensus for secure physician prescriptions and improved diagnoses in electronic healthcare systems.

With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector.

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.

Astigmatism-based active focus stabilisation with universal objective lens compatibility, extended operating range and nanometer precision.

Focus stabilisation is vital for long-term fluorescence imaging, particularly in the case of high-resolution imaging techniques. Current stabilisation solutions either rely on fiducial markers that can be perturbative, or on beam reflection monitoring that is limited to high-numerical aperture objective lenses, making multimodal and large-scale imaging challenging. We introduce a beam-based method that relies on astigmatism, which offers advantages in terms of precision and the range over which focus stabilisation is effective.

Feasibility of continuous smart health monitoring in pregnant population: A mixed-method approach.

The utilization of smart monitoring technology offers potential for enhancing health outcomes, yet its feasibility and acceptance among Hispanic pregnant individuals remain uncertain. This is particularly crucial to investigate within the context of apparently healthy individuals identified as low risk, who still face a 10% likelihood of complications. Given their frequent underrepresentation in healthcare services and relative lack of attention, improving the feasibility of remote monitoring in this population could yield significant benefits.

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.

The effect of SARS-COV-2 variant on non-respiratory features and mortality among vaccinated and non-fully vaccinated patients.

Objective: To determine the effect of SARS-CoV-2 variants on non-respiratory features of COVID-19 in vaccinated and not fully vaccinated patients using a University of California database.

Methods: A longitudinal retrospective review of medical records (n = 63,454) from 1/1/2020-4/26/2022 using the UCCORDS database was performed to compare non-respiratory features, vaccination status, and mortality between variants. Chi-square tests were used to study the relationship between categorical variables using a contingency matrix.

Using Causal Inference to Estimate the Effect of Pre-Exposure Prophylaxis (PrEP) for HIV Prevention on COVID-19 Mortality.

Traditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.

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.

Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction.

The autonomic nervous system (ANS) plays a central role in dynamic adaptation during pregnancy in accordance with the pregnancy demands which otherwise can lead to various pregnancy complications. Despite the importance of understanding the ANS function during pregnancy, the literature lacks sufficiency in the ANS assessment. In this study, we aimed to identify the heart rate variability (HRV) function during the second and third trimesters of pregnancy and 1 week after childbirth and its relevant predictors in healthy pregnant Latina individuals in Orange County, CA.

Designing secure PUF-based authentication protocols for constrained environments.

Physical Unclonable Functions (PUFs) are widely used in cryptographic authentication and key-agreement protocols due to their unique physical properties. This article presents a comprehensive cryptanalysis of two recently developed authentication protocols, namely PLAKE and EV-PUF, both relying on PUFs. Our analysis reveals significant vulnerabilities in these protocols, including susceptibility to impersonation and key leakage attacks, which pose serious threats to the security of the underlying systems.

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare.

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes.