Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis

Heart rate variability (HRV) is an objective, non-invasive tool to assessing autonomic dysfunction in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). People with CFS/ME tend to have lower HRV; however, in the literature there are only a few previous studies (most of them inconclusive) on their association with illness-related complaints. To address this issue, we assessed the value of different diurnal HRV parameters as potential biomarker in CFS/ME and also investigated the relationship between these HRV indices and self-reported symptoms in individuals with CFS/ME.Peer ReviewedPostprint (published version

Validity of telemetric-derived measures of heart rate variability: a systematic review

Heart rate variability (HRV) is a widely accepted indirect measure of autonomic function with widespread application across many settings. Although traditionally measured from the 'gold standard' criterion electrocardiography (ECG), the development of wireless telemetric heart rate monitors (HRMs) extends the scope of the HRV measurement. However, the validity of telemetric-derived data against the criterion ECG data is unclear. Thus, the purpose of this study was twofold: (a) to systematically review the validity of telemetric HRM devices to detect inter-beat intervals and aberrant beats; and (b) to determine the accuracy of HRV parameters computed from HRM-derived inter-beat interval time series data against criterion ECG-derived data in healthy adults aged 19 to 62 yrs. A systematic review of research evidence was conducted. Four electronic databases were accessed to obtain relevant articles (PubMed, EMBASE, MEDLINE and SPORTDiscus. Articles published in English between 1996 and 2016 were eligible for inclusion. Outcome measures included temporal and power spectral indices (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). The review confirmed that modern HRMs (Polar® V800™ and Polar® RS800CX™) accurately detected inter-beat interval time-series data. The HRV parameters computed from the HRM-derived time series data were interchangeable with the ECG-derived data. The accuracy of the automatic in-built manufacturer error detection and the HRV algorithms were not established. Notwithstanding acknowledged limitations (a single reviewer, language bias, and the restricted selection of HRV parameters), we conclude that the modern Polar® HRMs offer a valid useful alternative to the ECG for the acquisition of inter-beat interval time series data, and the HRV parameters computed from Polar® HRM-derived inter-beat interval time series data accurately reflect ECG-derived HRV metrics, when inter-beat interval data are processed and analyzed using identical protocols, validated algorithms and software, particularly under controlled and stable conditions

Associations Between Heart Rate Variability and Metabolic Syndrome Risk Factors

Metabolic syndrome (MetS) is a clustering of risk factors for cardiovascular disease (CVD) and type 2 diabetes (T2D) – two major causes of morbidity and mortality worldwide. Heart rate variability (HRV) is a non-invasive measure of cardiac autonomic regulation that predicts mortality and morbidity. Additionally, HRV is reduced in CVD, T2D and MetS. As such, HRV has potential to be a novel cardiometabolic risk factor to be included in clinical risk assessment. Therefore, the purpose of this thesis was to examine the relationships between MetS and HRV. A systematic review of cross-sectional studies examining relationships between HRV and MetS was completed to consolidate existing evidence and to guide future studies. This was followed by a cross-sectional investigation of time and frequency domain and nonlinear HRV in a population with MetS risk factors to determine which MetS risk factors were associated with HRV parameters. A pilot study was then conducted to study the feasibility of conducting a mobile health (mHealth) and exercise intervention in a rural population, which was followed by a 24-week randomized clinical trial to examine the effects of the interactive mHealth exercise intervention compared to standard of care exercise in participants with MetS risk factors. Overall, HRV was reduced in women with MetS compared to those without, though there were no differences in men. Waist circumference and lipid profiles were most commonly related to HRV parameters when studied cross-sectionally. The changes in waist circumference and fasting plasma glucose were associated with the change in HRV parameters when studied longitudinally. Following the intervention period, waist circumference and blood pressure were improved with no other changes in MetS risk factors. HRV parameters indicative of vagal activity were reduced over the intervention period, but there were no changes in other HRV parameters. There were no differences in changes between the intervention and control groups. In conclusion, MetS and HRV are associated in women but not men. However, there were no clear associations between MetS and HRV to suggest that HRV would be a valuable clinical risk factor

Communication system for a tooth-mounted RF sensor used for continuous monitoring of nutrient intake

In this Thesis, the communication system of a wearable device that monitors the user’s diet is studied. Based in a novel RF metamaterial-based mouth sensor, different decisions have to be made concerning the system’s technologies, such as the power source options for the device, the wireless technology used for communications and the method to obtain data from the sensor. These issues, along with other safety rules and regulations, are reviewed, as the first stage of development of the Food-Intake Monitoring projectOutgoin

A photoplethysmography smartphone-based method for heart rate variability assessment: device model and breathing influences

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A measurement method of heart rate and heart rate variability (HRV) based on smartphone has been developed and validated. The method is based on photoplethysmography (PPG) acquired with the smartphone camera (SPPG). SPPG was compared with the electrocardiogram (ECG), used as the gold standard, and with an external PPG sensor. Twenty-three healthy subjects were measured using two different smartphone models in three different breathing conditions. The error of the first differentiation between SPPG and ECG series is minimized with the fiducial point at maximum first derivative of the SPPG. The obtained standard deviation of error (SDE) between SPPG and ECG is around 5.4 ms and it is similar to SDE between PPG and ECG. Good agreement between SPPG and ECG for NN, SDNN and RMSSD have been found but it is insufficient agreement for LF/HF. Similar levels of agreement for SPPG-ECG and PPG-ECG have been obtained for the HRV indices. Finally, the differences between smartphone models for HRV indices are slight. Therefore, the smartphone can be used for measuring accurately the following HRV indices: NN, SDNN and RMSSD.Peer ReviewedPostprint (published version

Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology

Síndrome de fatiga crònica; Diferències de gènere; Variabilitat de la freqüència cardíacaSíndrome de fatiga crónica; Diferencias de género; Variabilidad del ritmo cardíacoChronic fatigue syndrome; Gender differences; Heart rate variabilityIn a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS.This research was funded by “Ministerio de Ciencia e Innovación” of the Spanish Government, grant number PID2019-107473RB-C2

Continuous IoT-based maternal monitoring: system design, evaluation, opportunities, and challenges

Maternal care encompasses health care services for pregnant women during pregnancy, childbirth, and the postpartum period. Maternity care providers aim to ensure a healthy pregnancy, safe delivery, and smooth transition to motherhood. Traditional maternal care is offered through regular check-ups by health care professionals. In recent years, the emergence of Internet-of-Things (IoT)-based systems has transformed the way health care services are provided. These systems offer low-cost ubiquitous monitoring in everyday life settings and can be used for maternal monitoring. However, IoT-based maternal monitoring systems lack a comprehensive approach in maternal care because they are limited by sensing capabilities, specific health problems, and short periods of monitoring. Moreover, the use of IoT-based systems formaternal health monitoring requires addressing critical quality attributes, such as feasibility, energy efficiency, and reliability and validity of the collected physiological parameters. Quality assessment methods also must be integrated with such systems to discard the noisy part of collected parameters and improve the data quality. Furthermore, long-term, continuous IoT-based maternal monitoring by collecting data that was not traditionally available provides new opportunities, including analyzing the trend of physiological parameters during pregnancy and postpartum, as well as detecting maternal health issues. This thesis presents an IoT-based maternal monitoring system and explores its potential in maternal care. We evaluate the system’s feasibility, reliability, and energy efficiency. We also discuss the practical challenges of implementing the system. Then, we validate the heart rate (HR) and heart rate variability (HRV) parameters that the system collects while the user is asleep and awake. In addition, we propose a deep-learning-based method for quality assessment of HR and HRV parameters to discard unreliable data and improve health decisions. We use the system to collect data from 62 pregnant women during pregnancy and three-months postpartum. Then, the reliable HR and HRV parameters are used to track the trends during pregnancy and postpartum. Finally, we investigate maternal loneliness as a major mental health problem. We develop two predictive models to detect maternal loneliness during late pregnancy and the postpartum period. The models use the objective health parameters passively collected by the system and achieve high performance (weighted F1 scores > 0.87)

The Application of Physiological Metrics in Validating User Experience Evaluation on Automotive Human Machine Interface Systems

Automotive in-vehicle information systems have seen an era of continuous development within the industry and are recognised as a key differentiator for prospective customers. This presents a significant challenge for designers and engineers in producing effective next generation systems which are helpful, novel, exciting, safe and easy to use. The usability of any new human machine interface (HMI) has an implicit cost in terms of the perceived aesthetic perception and associated user experience. Achieving the next engaging automotive interface, not only has to address the user requirements but also has to incorporate established safety standards whilst considering new interaction technologies. An automotive (HMI) evaluation may combine a triad of physiological, subjective and performance-based measurements which are employed to provide relevant and valuable data for product evaluation. However, there is also a growing interest and appreciation that determining real-time quantitative metrics to drivers’ affective responses provide valuable user affective feedback. The aim of this research was to explore to what extent physiological metrics such as heart rate variability could be used to quantify or validate subjective testing of automotive HMIs. This research employed both objective and subjective metrics to assess user engagement during interactions with an automotive infotainment system. The mapping of both physiological and self-report scales was examined over a series of studies in order to provide a greater understanding of users’ responses. By analysing the data collected it may provide guidance within the early stages of in-vehicle design evaluation in terms of usability and user satisfaction. This research explored these metrics as an objective, quantitative, diagnostic measure of affective response, in the assessment of HMIs. Development of a robust methodology was constructed for the application and understanding of these metrics. Findings from the three studies point towards the value of using a combination of methods when examining user interaction with an in-car HMI. For the next generation of interface systems, physiological measures, such as heart rate variability may offer an additional dimension of validity when examining the complexities of the driving task that drivers perform every day. There appears to be no boundaries on technology advancements and with this, comes extra pressure for car manufacturers to produce similar interactive and connective devices to those that are already in use in homes. A successful in-car HMI system will be intuitive to use, aesthetically pleasing and possess an element of pleasure however, the design components that are needed for a highly usable HMI have to be considered within the context of the constraints of the manufacturing process and the risks associated with interacting with an in-car HMI whilst driving. The findings from the studies conducted in this research are discussed in relation to the usability and benefits of incorporating physiological measures that can assist in our understanding of driver interaction with different automotive HMIs

Metabolic Modulation Predicts Heart Failure Tests Performance

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction \u3c 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio