Continuous Health Data Research Articles

Intelligent Health Monitoring Device Data Analysis: From Data Collection to Health Insights

The integration of smart wearable and home health monitoring devices marks a new epoch in health management, offering deep insights through real-time data. This study seeks to convert this collected health data into actionable personalized assessments and recommendations, enhancing health management efficacy. Utilizing literature review, case analysis, and data analysis, the research initially describes smart devices’ capabilities and data gathering techniques. It then concentrates on data processing methodologies, statistical analysis, and machine learning, examining the translation of these into usable health insights. The study reveals that smart devices offer continuous health data, enabling the identification of individual trends and risks through data analysis. Personalized assessments can boost engagement and adherence to health plans. Yet, it acknowledges challenges in data privacy, technological synthesis, and user adoption, proposing solutions like fortified security, interface optimization, and enhanced analytical accuracy. In conclusion, the research highlights the potential of data analysis in advancing personalized health management and recommends future explorations, such as refining algorithms, bolstering privacy safeguards, and setting regulatory benchmarks.

An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security

This study proposes an optimized hybrid encryption framework combining ECC-256r1 with AES-128 in EAX mode, tailored for smart home healthcare environments, and conducts a comprehensive investigation to validate its performance. Our framework addresses current limitations in securing sensitive health data and demonstrates resilience against emerging quantum computing threats. Through rigorous experimental evaluation, we show that the proposed configuration outperforms existing solutions by delivering unmatched security, processing speed, and energy efficiency. It employs a robust yet streamlined approach, meticulously designed to ensure simplicity and practicality, facilitating seamless integration into existing systems without imposing undue complexity. Our investigation affirms the framework's capability to resist common cybersecurity threats like MITM, replay, and Sybil attacks while proactively considering quantum resilience. The proposed method excels in processing speed (0.006 seconds for client and server) and energy efficiency (3.65W client, 95.4W server), offering a quantum-resistant security level comparable to AES-128. This represents a security-efficiency ratio of 21.33 bits per millisecond, a 25.6% improvement in client-side processing speed, and up to 44% reduction in server-side energy consumption compared to conventional RSA-2048 methods. These improvements enable real-time encryption of continuous health data streams in IoT environments, making it ideal for IoT devices where AES-128′s smaller footprint is advantageous. By prioritizing high-grade encryption alongside ease of use and implementation, the proposed framework presents a future-proof solution that anticipates the trajectory of cryptographic standards amid advancing quantum computing technologies, signifying a pivotal advancement in safeguarding IoT-driven healthcare data.

Embroidered Interdigitated Electrodes (IDTs) with Wireless Readout for Continuous Biomarker Monitoring.

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at a certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement, and interpretation of data toward non-invasive molecular sensors that can be integrated into daily wearable items. Toward this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8 GHz to 2 GHz and respond to the change in glucose concentration with a sensitivity of 0.17 MHz/(mg/dL). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by an investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.

Public health surveillance involves the collection, analysis and dissemination of data to improve population health. The main sources of data for public health decision-making are surveys, typically comprised of self-report which may be subject to biases, costs and delays. To complement subjective data, objective measures from sensors could potentially be used. Specifically, advancements in personal mobile and wearable technologies enable the collection of real-time and continuous health data. In this context, the goal of this work is to apply a mobile health platform (MHP) that extracts health data from the Apple Health repository to collect data in daily-life scenarios and use it for the prediction of stress, a major public health issue. A pilot study was conducted with 45 participants over 2 weeks, using the MHP to collect stress-related data from Apple Health and perceived stress self-reports. Apple, Withings and Empatica devices were distributed to participants and collected a wide range of data, including heart rate, sleep, blood pressure, temperature, and weight. These were used to train random forests and support vector machines. The SMOTE technique was used to handle imbalanced datasets. Accuracy and f1-macro scores were in line with state-of-the-art models for stress prediction above 60% for the majority of analyses and samples analysed. Apple Watch sleep features were particularly good predictors, with most models with these data achieving results around 70%. A system such as the MHP could be used for public health data collection, complementing traditional self-reporting methods when possible. The data collected with the system was promising for monitoring and predicting stress in a population.

A Fully Online Clustering Approach for Enhanced Performance of Health Information System

Health Data clustering is a significant research direction aiming to extract knowledge from a continuous health data flow to support online health decisions. However, processing health data clusters is still a challenging task. Existing clustering approaches are subject to various limitations in terms of considering the neighbor clusters and conducting multiple operations during the maintenance process. In this paper, we model, design and implement a novel framework called IClustMaint for efficiently clustering and maintaining health data clusters incrementally. A two-phase algorithm is embedded in the framework. We first employ the Principal Component Analysis (PCA) method to efficiently reduce the high costs of the initial clustering phase. Next, in the maintenance phase, we propose the incremental Cluster maintenance (ICM) approach for managing the generated cluster during a period of time. Technically, when the data clusters are evolving over time and need to be maintained frequently, the ICM approach improves the performance of cluster maintenance by only tracking the edge points. The experimental results on a real medical dataset verify the efficiency of the proposed approaches.

Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study.

Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data. We sought to pilot the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants. Data were stratified into socio-demographic classes to further explore our prediction model. Overall, the RF model performed slightly better than SVM, with results having an accuracy within the low end of state-of-the-art. Our models showed specificity in their capacity to assess "no stress" states but were less successful at capturing "stress" states. Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.

The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data. Typically, the field of precision medicine deals with a huge amount of data from few individuals; public health, on the other hand, deals with limited data from a population. With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health. We also discuss how these data types and use cases can converge toward precision public health, as well as challenges and opportunities provided by research and analyses of health data.

Abstract 15845: Heart Heroes: A Gamified Mhealth Platform to Measure Continuous Cardiac Health Data in the Outpatient Setting of Adolescent Patients With Known and Suspected Heart Disease

Introduction: Adolescents with heart disease report difficulty in communication about their health as a major inhibiting factor in their care. MHealth technologies collect health data in daily life and enable health data sharing between the provider and patient. The adolescent population has a high level of engagement with mobile devices and a willingness to use them for health-related activities. Hypothesis: We hypothesized that our novel gamified mHealth platform Heart Hero can engage adolescent patients in the collection of cardiac health data in their daily life. Methods: We designed the research app using ResearchKit to collect continuous physiological data from the Apple Watch and daily survey data on well-being, stress, medical adherence, and cardiac symptoms. Patients were provided the app, iPhone, and Apple Watch and enrolled for 27 days. A final in-app survey was provided to assess feedback. We enrolled 28 patients total who were scheduled for outpatient cardiopulmonary exercise testing. Results: Mean age was 14.3 years old (SD +/-3.08) with a 1:1 M:F ratio. 61% of patients were ≥ 15 years of age. 94% of patients completed the final survey. Subjects on average completed 64% (SEM +/-5) of the daily quizzes with an average daily adherence of over 50% wearing the watch. 100% reported they liked using the watch and app, and 89% would like to continue wearing the Apple Watch. 53% reported the study encouraged them to exercise more while 21% reported encouragement to walk more. Fig 1 demonstrates A) Apple Watch and b) daily survey data collected from a patient throughout the study. Conclusions: In conclusion, Heart Hero is a mHealth platform which can successfully be used to collect continuous health data from the adolescent population with high engagement characterized by adherence and positive patient feedback. Adherence to the app was notably superior to the initial 5 ResearchKit applications enrolling adult patients.

SCRHM: A Secure Continuous Remote Health Monitoring System

With the growing need for the remote caring at home and the everincreasing popularity of mobile devices, more and more applications are being developed to enable remote health monitoring. Combining cloud computing with mobile technologies has enabled the healthcare service provider to offer continuous health data collection and physicians to conveniently monitor and assess users’ health while they are at home. However, users’ health data contain highly confidential information, and servers in the cloud are out of users’ trusted domain. Therefore, users may be reluctant to take advantage of remote health monitoring systems before they make sure their data are properly protected. In this paper, we propose a secure continuous remote health monitoring system named SCRHM with the main component, a searchable encryption scheme supporting range searches for remote health monitoring system. With this scheme, remote health monitoring service provider is able to detect outliers over encrypted health parameters. Using analysis, we prove the correctness and security of the proposed scheme on privacy protection of users’ health data. Via simulation experiments, we validate the performance of the proposed scheme in terms of computation and communication overhead.