Digital Health Monitoring Research Articles

Design and Analysis of Micro-Vibration Isolation System for Digital Image Correlation System-Based Structural Health Monitoring

Design and Analysis of Micro-Vibration Isolation System for Digital Image Correlation System-Based Structural Health Monitoring

Asthma diagnosis using patient-reported outcome measures and objective diagnostic tests: now and into the future.

The global prevalence of asthma continues to increase; however, asthma remains under-diagnosed and under-treated. This results in a significant burden on the healthcare system and preventable patient morbidity and mortality. Over-diagnosis of asthma based on clinical history alone also complicates patient management. This heightens the importance of a prompt and accurate asthma diagnosis. Therefore, a review of the literature was performed regarding both objective diagnostic testing for asthma and using patient-reported outcome measures. The cornerstone of asthma diagnosis remains spirometry with testing for bronchodilator reversibility testing for pediatric and adult populations. This test may need to be repeated at multiple time points due to its low sensitivity. Peak flow measurement, fractional exhaled nitric oxide testing, and allergy testing are useful adjuncts to the diagnosis and phenotyping of asthma. Bronchoprovocation testing is reserved for people with high clinical suspicion for asthma, but negative spirometry. Novel noninvasive testing modalities may play a diagnostic role in the future. The advent of remote digital health monitoring technology has resulted in revisiting patient-reported outcome measures for the diagnosis and monitoring of asthma. Overall, improved diagnostic tools for asthma are crucial for earlier recognition and treatment of the disease and improved patient care outcomes worldwide.

Outlook of Digital Health and Non-Contact Vital Monitoring

Outlook of Digital Health and Non-Contact Vital Monitoring

An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease.

The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.

The Implementation of a Primary Care-Based Integrated Mobile Health Intervention for Stroke Management in Rural China: Mixed-Methods Process Evaluation.

Background: There is a lack of evidence concerning the effective implementation of strategies for stroke prevention and management, particularly in resource-limited settings. A primary-care-based integrated mobile health intervention (SINEMA intervention) has been implemented and evaluated via a 1-year-long cluster-randomized controlled trial. This study reports the findings from the trial implementation and process evaluation that investigate the implementation of the intervention and inform factors that may influence the wider implementation of the intervention in the future.Methods: We developed an evaluation framework by employing both the RE-AIM framework and the MRC process evaluation framework to describe the implementation indicators, related enablers and barriers, and illustrate some potential impact pathways that may influence the effectiveness of the intervention in the trial. Quantitative data were collected from surveys and extracted from digital health monitoring systems. In addition, we conducted quarterly in-depth interviews with stakeholders in order to understand barriers and enablers of program implementation and effectiveness. Quantitative data analysis and thematic qualitative data analysis were applied, and the findings were synthesized based on the evaluation framework.Results: The SINEMA intervention was successfully implemented in 25 rural villages, reached 637 patients with stroke in rural Northern China during the 12 months of the trial. Almost 90% of the participants received all follow-up visits per protocol, and about half of the participants received daily voice messages. The majority of the intervention components were adopted by village doctors with some adaptation made. The interaction between human-delivered and technology-enabled components reinforced the program implementation and effectiveness. However, characteristics of the participants, doctor-patient relationships, and the healthcare system context attributed to the variation of program implementation and effectiveness.Conclusion: A comprehensive evaluation of program implementation demonstrates that the SINEMA program was well implemented in rural China. Findings from this research provide additional information for program adaptation, which shed light on the future program scale-up. The study also demonstrates the feasibility of combining RE-AIM and MRC process evaluation frameworks in process and implementation evaluation in trials.Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03185858.

BrainWear: Longitudinal, objective assessment of physical activity in 42 High Grade Glioma (HGG) patients

Abstract Aims In patients with HGG, we know that QoL and physical function decline with progressive disease (PD) and fatigue is a strong predictor of survival in recurrent disease. Despite notable technical advances in therapy for in the past decade, survival has not improved. The role of physical function as a predictor of QoL, treatment tolerance and as an early indicator of worsening morbidity (e.g. tumour recurrence) is an area of growing importance. Recent advancements in wearable technology allow us the opportunity to gather high-quality, continuous and objective data BrainWear is a feasibility study collecting longitudinal physical activity (PA) data from patients with primary and secondary brain tumours and we hypothesise changes in PA over time, are a potentially sensitive biomarker for PD both at diagnosis and relapse. Method Here we show early analysis of this novel dataset of 42 HGG patients and will present: 1) feasibility and acceptability 2) how digitally captured PA changes through treatment and at PD/hospitalization 3) the correlation between patient reported outcomes (PRO) and PA data 4) how PA in HGG patients compares with healthy UK Biobank participants. PA data is collected via a wrist-worn accelerometer. Raw accelerometer data is processed using the UK Biobank Accelerometer Analysis pipeline in python 3.7, and evaluated for good quality wear-time. Overall activity is represented as vector magnitude in milligravity units(mg) and a machine-learning classifier classifies daily activity into 5 separate groups (walking, tasks-light, moderate, sedentary and sleep). Descriptive statistics summarise baseline characteristics and unadjusted mean used to present vector magnitude and accelerometer-predicted functional behaviours (in h/day) by age, sex, radiotherapy and weekend days. Mixed effect models for repeated measures are used for longitudinal data evaluation of PA. Results Between October 2018 and March 2021, 42 patients with a suspected HGG were recruited; 16 females and 26 males with a median age of 59. 40 patients had surgery and 35 patients had adjuvant primary radiotherapy, 23 of whom had a 6-week course. They have provided 3458 days of accelerometer data, 80% of which has been classified as good quality wear-time. There are no statistical differences in mean activity between gender, patients >60 years show statistical difference in time spent doing moderate activity compared to those <60 years, and there are significant differences in mean vector magnitude and walking between radiotherapy and non-radiotherapy days. In patients having a 6-week RT course, time spent in daily moderate activity falls 4-fold between week 1 and the second week following RT completion (70 minutes to 16 minutes). HGG versus healthy UK Biobank participants shows significant differences in all measures of PA. Conclusion Here we present preliminary analysis of this highly novel dataset in adult high grade glioma patients, and show digital remote health monitoring is feasible and acceptable with 80% of data classified as high quality wear-time suggesting good patient adherence. We are able to objectively describe how PA changes through standard treatments and understand the inter and intra-patient variation in PA, and whether there are correlates with patient-centred measures, clinical measures and early indicators of worsening disease. We will present further data on changes in PA prior to hospitalisation and at disease progression, and discuss some of the challenges of running a digital health trial. The passive and objective nature of wearable activity monitors gives clinicians the opportunity to evaluate and monitor the patient in motion, rather than the episodic snapshot we currently see, and in turn has the potential to improve our clinical decision making and potentially outcomes.

A36 IMPACT OF DIGITAL HEALTH MONITORING IN THE MANAGEMENT OF INFLAMMATORY BOWEL DISEASE

Abstract Background Inflammatory bowel disease (IBD) affects over 270,000 Canadians and costs the healthcare system $1.28 billion dollars annually. With advancements in technology, a shift from the traditional ‘reactive’ approach to IBD management to a ‘proactive’ approach that integrates self-management strategies using digital health monitoring platforms could greatly benefit patient care. Aims The purpose of this study was to investigate the effect of implementing the IBD health monitoring platform, HealthPROMISE, in clinical practice and to evaluate whether its use leads to better quality of care, improved health outcomes, and reduce resource consumption in patients with IBD. Methods IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/short inflammatory bowel disease questionnaire (SIBDQ) scores were flagged for their physicians to follow up with. After one-year, patient outcome metrics were compared to baseline values. Results Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p=0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p=0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p=0.48). Conclusions Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients. Future studies evaluating acceptability and costs with a larger sample size would help determine the feasibility and generalizability of widely implementing mobile health applications in the management of IBD. Funding Agencies CCC

Impact of Digital Health Monitoring in the Management of Inflammatory Bowel Disease.

Technological advances now permit self-management strategies using mobile applications which could greatly benefit patient care. The purpose of this study was to investigate whether the use of the inflammatory bowel disease (IBD) digital health monitoring platform, HealthPROMISE, leads to better quality of care and improved health outcomes in IBD patients. IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/SIBDQ scores were flagged for their physicians to follow up. After one-year, patient outcome metrics were compared to baseline values. Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p = 0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p = 0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p = 0.48). Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10916-021-01706-x.

Development of smart wearable sensors for life healthcare

Abstract Wearable biosensors are gaining tremendous interest in the clinical and biological medical fields for their potential in providing patients with real-time diagnostic tools and time-sensitive information, non-invasive measurements of biochemical markers distributed in body fluids throughout the body. These sensors replace part of the equipment that can only be installed in hospitals and become a new choice for assessing human health. Herein, critical perspectives are put forward regarding wearable sensors in the future digital health monitoring industry. Moreover, the materials and processing technologies involved have also been discussed in recent years, along with their monitoring schemes and system-level integration technologies. Finally, the probability of wearable sensors being used in early disease detection is considerable. Thus, despite potential challenges, it is still quite promising for wearable sensors to come into production.

Multi-Fault Bearing Classification Using Sensors and ConvNet-Based Transfer Learning Approach

The development of sensor technology and modern computing allows the fault diagnosis of rotating machinery to exploitunder differentworkingconditions.As an effect, digital health monitoring of machine is performed based on the actual data values obtained from the multisensor, and also, it supports to design the data-rich datasets using multi-sensor. Thus, it motivates to develop the datadriven approaches for fault diagnosis based on multi-sensor data. However, they require a large amount of historical evidence for the development, and in a real-world situation, usually sufficient data is not available for building the datadriven model. As a result, they become less competent to perform the task of fault diagnosis. Therefore, in this paper, the transfer of knowledge from data-rich datasets is proposed to identifying the fault in new working conditions of the related domain. To transfer the experience, firstly the data has transformed to kurtogram, which is the fusion ofmulti-level kurtosis values and then convolutional neural network-based transfer learning approach has presented for fault diagnosis under various operating conditions. Also, the simultaneous change in speed and load in source and target domain has analyzed to explore the effect of the proposed method on performance of fault diagnosis. Further, the issue of negative transfer of knowledge has investigated by determining the association of speed as well as a load between the source and target domain. The proposed method is tested using two vibration datasets, and results demonstrate the decent performance of fault diagnosis using transfer learning approach.

Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study.

BackgroundHeart failure (HF) is a condition that affects approximately 6.2 million people in the United States and has a 5-year mortality rate of approximately 42%. With the prevalence expected to exceed 8 million cases by 2030, projections estimate that total annual HF costs will increase to nearly US $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management.ObjectiveThe goal of our program, named the Heart Health Program, was to examine the feasibility of using digital health monitoring in real-world home settings, ascertain patient adoption, and evaluate impact on 30-day readmission rate.MethodsA digital medicine software platform developed at Mount Sinai Health System, called RxUniverse, was used to prescribe a digital care pathway including the HealthPROMISE digital therapeutic and iHealth mobile apps to patients’ personal smartphones. Vital sign data, including blood pressure (BP) and weight, were collected through an ambulatory remote monitoring system that comprised a mobile app and complementary consumer-grade Bluetooth-connected smart devices (BP cuff and digital scale) that send data to the provider care teams. Care teams were alerted via a Web-based dashboard of abnormal patient BP and weight change readings, and further action was taken at the clinicians’ discretion. We used statistical analyses to determine risk factors associated with 30-day all-cause readmission.ResultsOverall, the Heart Health Program included 58 patients admitted to the Mount Sinai Hospital for HF. The 30-day hospital readmission rate was 10% (6/58), compared with the national readmission rates of approximately 25% and the Mount Sinai Hospital’s average of approximately 23%. Single marital status (P=.06) and history of percutaneous coronary intervention (P=.08) were associated with readmission. Readmitted patients were also less likely to have been previously prescribed angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (P=.02). Notably, readmitted patients utilized the BP and weight monitors less than nonreadmitted patients, and patients aged younger than 70 years used the monitors more frequently on average than those aged over 70 years, though these trends did not reach statistical significance. The percentage of the 58 patients using the monitors at least once dropped from 83% (42/58) in the first week after discharge to 46% (23/58) in the fourth week.ConclusionsGiven the increasing burden of HF, there is a need for an effective and sustainable remote monitoring system for HF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we demonstrated that interventions driven by real-time vital sign data may greatly aid in reducing hospital readmissions and costs while improving patient outcomes.

Dual-Core Capacitive Microfiber Sensor for Smart Textile Applications.

Wearable sensors for smart textile applications have garnered tremendous interest in recent years and can have enormous potential for human machine interfaces and digital health monitoring. Here, we report a soft capacitive microfiber sensor that can be woven seamlessly into textiles for strain measurement. Comprising a dual-lumen elastomeric microtube and liquid metallic alloy, the microfiber sensor enables continual strain perception even after being completely severed. In addition, our microfiber sensor is highly stretchable and flexible and exhibits tunable sensitivity, excellent linearity, a fast response, and negligible hysteresis. More importantly, the microfiber sensor is minimally affected by train rate and compression during strain sensing. Even under drastic environmental changes, the microfiber sensor presents good electrical stability. By integrating the microfiber sensor imperceptibly with textiles, we devise smart textile wearables to interpret hand gestures, detect limb motion, and monitor respiration rate. We believe that this sensor presents enormous potential in unobtrusive continuous health monitoring.

Flexible Hybrid Sensors for Health Monitoring: Materials and Mechanisms to Render Wearability.

Wearable electronics have revolutionized the way physiological parameters are sensed, detected, and monitored. In recent years, advances in flexible and stretchable hybrid electronics have created emergent properties that enhance the compliance of devices to our skin. With their unobtrusive attributes, skin conformable sensors enable applications toward real-time disease diagnosis and continuous healthcare monitoring. Herein, critical perspectives of flexible hybrid electronics toward the future of digital health monitoring are provided, emphasizing its role in physiological sensing. In particular, the strategies within the sensor composition to render flexibility and stretchability while maintaining excellent sensing performance are considered. Next, novel approaches to the functionalization of the sensor for physical or biochemical stimuli are extensively covered. Subsequently, wearable sensors measuring physical parameters such as strain, pressure, temperature, as well as biological changes in metabolites and electrolytes are reported. Finally, their implications toward early disease detection and monitoring are discussed, concluding with a future perspective into the challenges and opportunities in emerging wearable sensor designs for the next few years.

Principles for the design of digital occupational health systems

There is a growing body of literature that addresses the importance of health and wellbeing in the workplace, and the effectiveness of corporate wellness programs. Following advancements in low-cost and unobtrusive computing technology, an emerging trend in corporate wellness programs is to offer wearable devices to employees. These devices monitor employees' physiological and environmental conditions in order to improve their awareness of their personal health. In addition, organizations can harness the aggregated anonymized data provided by such technology to investigate ways of improving the work environment. However, promoting digital health monitoring systems introduces new dynamic interactions between the social actors and technology. Three main categories of strain caused by the use of these systems in a work environment are value tensions (privacy vs. wellbeing); action tensions (work vs. leisure activities), and role tensions (leisure vs. work roles). Based on an analysis of these tensions, design principles for digital occupational health systems are derived that minimize strain and have much bigger chances to be accepted and thus to create value for all stakeholders. Consequently, this study follows the design science research paradigm to derive design principles.

Inside Front Cover Picture: Validation of a novel digital health monitoring system to measure the volume of voided urine (Neurourology and Urodynamics 38/2019)

Inside Front Cover Picture: Validation of a novel digital health monitoring system to measure the volume of voided urine (Neurourology and Urodynamics 38/2019)

Validation of a novel digital health monitoring system to measure the volume of voided urine.

To validate a novel digital health monitoring system to measure the volume of voided urine. Micturition volume was calculated using our novel digital self-health monitoring system of urine excretion (s-HMSU) in 18 participants (16 women and 2 men; average age, 40.8 years), without a history of voiding symptoms. Participants completed a self-reported questionnaire regarding their medical history and water intake during the period of observation, as well as the Core Lower Urinary Tract Symptom Score (CLSS) questionnaire. To assess the reliability of the voided volumes measured using the s-HMSU, the intraclass correlation coefficient (ICC) was calculated between the volume and the change in body weight before and after micturition. The CLSS questionnaire confirmed the absence of urinary system diseases in all participants. The medical history was also negative with the exception of hypertension in one participant. The ICC (1,1) between the measured volume of urine excretion using the s-HMSU and the change in body weight was 0.972 (95% confidence interval, 0.957-0.982). The s-HMSU system provides a reliable measure of voiding volume and is appropriate for home use. It has the potential to facilitate large-scale clinical research to examine the relationship between medical diseases and voiding dysfunction.

Integration and Adoption Analysis of Digital Health Monitoring Devices: Reflections of a Pilot Project

Background: Congestive heart failure (CHF) is a disease that affects about 6.5 million people in the United States with a mortality rate of around 30%. With the incidence rate projected to rise by 46% to exceed 8 million cases by 2030, projections estimate that total CHF costs will increase about to nearly $70 billion. Recently, the advent of remote monitoring technology has significantly broadened the scope of the physician’s reach in chronic disease management. Using remotely monitored health data, providers may be able to better manage and predict their patients’ outcomes, leading to reduced incidence and hospital admission costs.

Digital Health Monitoring of Physical and Emotional Recovery After LVAD Placement

Recovery of functional status after left ventricular assist device (LVAD) implantation is difficult to measure. Formal exercise testing and quality of life questionnaires are cumbersome. Digital health technology has been used to monitor patients suffering from other cardiovascular conditions; we sought to determine whether a digital health approach could reliably measure psychologic and functional status after LVAD placement.

Design and Development of Intelligent Electrodes for Future Digital Health Monitoring: A Review

Electrodes are sensors used in electrocardiography (ECG) monitoring system to diagnose heart diseases. Over the years, diverse types of electrodes have been designed and developed to improve ECG monitoring system. However, more recently, with the technological advances and capabilities from the Internet of Things (IoT), cloud computing and data analytics in personalized healthcare, researchers are attempting to design and develop more effective as well as flexible ECG devices by using intelligent electrodes. This paper reviews previous works on electrodes used in electrocardiography (ECG) monitoring devices to identify the key ftures for designing and developing intelligent electrodes in digital health monitoring devices.

Wearable sweat detector device design for health monitoring and clinical diagnosis

Miniaturized sensor is necessary part for wearable detector for biomedical applications. Wearable detector device is indispensable for online health care. This paper presents a concept of an wearable digital health monitoring device design for sweat analysis. The flexible sensor is developed to quantify the amount of hydrogen ions in sweat and skin temperature in real time. The detection system includes pH sensor, temperature sensor, signal processing module, power source, microprocessor, display module and so on. The sweat monitoring device is designed for sport monitoring or clinical diagnosis.