On the prediction of clinical outcomes using Heart Rate Variability estimated from wearable devices

This thesis explores the use of Heart Rate Variability as a tool for predicting health outcomes, focusing on data derived from photoplethysmography (PPG) sensors in wrist-worn wearable devices such as smartwatches. These devices offer a unique opportunity for cost-effective, continuous, and unobtrusive monitoring of heart health. However, PPG data is susceptible to motion artefacts, challenging the reliability of Heart Rate Variability metrics derived from it. A critical finding of this research is the unreliability of specific frequency-domain Heart Rate Variability features, such as the Sympathovagal Balance Index (SVI), due to low signal-to-noise ratio in certain frequency bands. Conversely, the thesis demonstrates that most HRV features, including Root Mean Square of Successive Differences between normal heartbeats (RMSSD) and Standard Deviation of Normal heartbeats (SDNN), can be reliably extracted under conditions of motion, such as during physical activity or recovery from exercise. This is achieved by employing accelerometry data from wearable devices to filter out unreliable PPG data. The thesis also addresses the issue of missing data in Heart Rate Variability analysis, a consequence of motion artefacts and the energy-saving strategies of wearable devices. By exploring different interpolation methods and their effects on Heart Rate Variability features, this research identifies the best approaches for handling missing data. Particularly, it recommends operating on timestamp time-series over duration time-series, contradicting traditional Heart Rate Variability preprocessing practices. Quadratic interpolation in the time domain was identified as the most effective method, introducing minimal error across numerous Heart Rate Variability features, contrary to interpolation in the duration domain. The research presented in this thesis evaluates Heart Rate Variability features derived from ultra-short measurement windows, demonstrating the feasibility of accurately estimating RMSSD and SDNN using 30-second and 1-minute time windows, respectively. This study, unique in assessing the effect of missing values on ultra-short Heart Rate Variability data, reveals that missing values significantly impact SDNN estimations while moderately affecting RMSSD. The analysis highlights that ultra-short inter-beat interval time series limit the assessment of very low frequency (VLF) components, increasing bias in SDNN estimates. This finding is particularly significant in light of the prevalent use of SDNN in commercial wearables, underscoring its importance for continuous heart health monitoring. The study notes that the shorter the measurement window and the greater the amount of missing values, the larger the bias observed in SDNN. A novel aspect of the thesis is the creation of an innovative mathematical model designed to estimate the impact of circadian rhythms on resting heart rate. This model stands out for its computational efficiency, making it particularly suitable for data obtained from wearable devices. It surpasses the single component cosinor model in accuracy, demonstrated by a lower root mean square error (RMSE) in predicting future heart rate values. Additionally, it retains the advantage of providing easily interpretable parameters, such as MESOR, Acrophase, and Amplitude, which are essential for assessing changes in heart activity. The thesis demonstrates that Heart Rate data can accurately estimate SDNN24 (the Standard Deviation of NN intervals over 24 hours), with a difference of about 0.22±11.47 (RMSE = 53.81 and $r^2 = 0.97$). This finding indicates that despite being fragmentary, 24-hour HR data from wrist-worn fitness devices is adequate for estimating SDNN24 and assessing health status, as evidenced by an F1 score of 0.97. The robustness of SDNN24 estimation against noisy data suggests that wrist-worn wearables are capable of reliably monitoring cardiovascular health on a continuous basis, thus facilitating early interventions in response to changes in Sinoatrial Node activity. The final part of the thesis introduces an innovative approach to health outcome prediction, employing Heart Rate Variability data gathered during exercise alongside Electronic Health Record data. Employing Large Language Models to process EHR data and Convolutional AutoEncoders for Heart Rate Variability analysis, this approach reveals the untapped potential of exercise Heart Rate Variability data in health monitoring and prediction. Deep Learning models incorporating Heart Rate Variability data demonstrated enhanced predictive accuracy for cardiovascular diseases (CVD), coronary heart disease (CHD), and Angina, evidenced by higher Area Under the Curve (AUC) scores compared to models using only Electronic Health Records and demographic/behavioural data. The highest AUC scores achieved were 0.71 for CVD, 0.74 for CHD, and 0.73 for Angina. In conclusion, this thesis contributes to the field of biomedical engineering by enhancing the understanding and application of HRV analysis in health outcome prediction using wearable device data. It offers insights for future work in continuous, unobtrusive health monitoring and underscores the need for further research in this rapidly evolving domain

Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults' Physical Function: Observational Study.

BACKGROUND:Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults' physical function. OBJECTIVE:This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Our primary hypothesis was that lower HRV would be associated with lower physical function. In addition, we expected that HRV would explain a significant proportion of variance in measures of physical health status. METHODS:We evaluated 77 participants from an ongoing study of older adults aged between 65 and 95 years. The assessments encompassed a thorough examination of domains typically included in a geriatric health evaluation. We collected HRV data with the Empatica E4 device and examined bivariate correlations between HRV quantified with the triangular index (HRV TI) and 3 widely used and validated measures of physical functioning-the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), and Medical Outcomes Study Short Form 36 (SF-36) physical composite scores. We further investigated the additional predictive power of HRV TI on physical health status, as characterized by SF-36 physical composite scores and Cumulative Illness Rating Scale for Geriatrics (CIRS-G) scores, using generalized estimating equation regression analyses with backward elimination. RESULTS:We observed significant associations of HRV TI with SPPB (n=52; Spearman ρ=0.41; P=.003), TUG (n=51; ρ=-0.40; P=.004), SF-36 physical composite scores (n=49; ρ=0.37; P=.009), and CIRS-G scores (n=52, ρ=-0.43; P=.001). In addition, the HRV TI explained a significant proportion of variance in SF-36 physical composite scores (R2=0.28 vs 0.11 without HRV) and CIRS-G scores (R2=0.33 vs 0.17 without HRV). CONCLUSIONS:The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults

Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters:A comparison to electrocardiography (ECG)

Wearable monitoring devices are an innovative way to measure heart rate (HR) and heart rate variability (HRV), however, there is still debate about the validity of these wearables. This study aimed to validate the accuracy and predictive value of the Empatica E4 wristband against the VU University Ambulatory Monitoring System (VU-AMS) in a clinical population of traumatized adolescents in residential care. A sample of 345 recordings of both the Empatica E4 wristband and the VU-AMS was derived from a feasibility study that included fifteen participants. They wore both devices during two experimental testing and twelve intervention sessions. We used correlations, cross-correlations, Mann-Whitney tests, difference factors, Bland-Altman plots, and Limits of Agreement to evaluate differences in outcomes between devices. Significant correlations were found between Empatica E4 and VU-AMS recordings for HR, SDNN, RMSSD, and HF recordings. There was a significant difference between the devices for all parameters but HR, although effect sizes were small for SDNN, LF, and HF. For all parameters but RMSSD, testing outcomes of the two devices led to the same conclusions regarding significance. The Empatica E4 wristband provides a new opportunity to measure HRV in an unobtrusive way. Results of this study indicate the potential of the Empatica E4 as a practical and valid tool for research on HR and HRV under non-movement conditions. While more research needs to be conducted, this study could be considered as a first step to support the use of HRV recordings provided by wearables

Wearable and app-based resilience modelling in employees:exploring the possibilities to model psychological resilience using wearable-measured heart rate variability and sleep

Stress has a major impact on both an individual and a societal level. Early recognition of the negative impact of stress or reduced resilience can be used in personalized interventions that enable the user to break the identified pattern through timely feedback, and thus limit the emergence of stress-related problems. The emergence of wearable sensor technology makes it possible to continuously monitor relevant behavioral and physical parameters such as sleep and heart rate variability (HRV). Sleep and HRV have been linked to stress and resilience in population studies, but knowledge on whether these relationships also apply within individuals, which is necessary for the aforementioned personalization, is lacking. This thesis introduces a cyclical conceptual model for resilience and four observational studies that test relationships between sleep, HRV and subjective resilience-related outcomes within participants using different types of data analysis at different timeframes. The relationships from the conceptual model and the related hypotheses are broadly confirmed in these studies. Participants tended to have more favorable subjective stress- and resilience-related outcomes on days with a relatively high resting HRV or long total sleep duration. Also, having a resting HRV that fluctuates relatively little from day to day was related to less stress and somatization. However, the strength of the relationships found was modest. The current findings can therefore not yet be directly implemented to initiate meaningful feedback, but they do provide starting points for future research and take a relevant step towards the possible future development of automated resilience interventions

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

Cross-instrument feasibility, validity, and reproducibility of wireless heart rate monitors:Novel opportunities for extended daily life monitoring

Wired ambulatory monitoring of the electrocardiogram (ECG) is an established method used by researchers and clinicians. Recently, a new generation of wireless, compact, and relatively inexpensive heart rate monitors have become available. However, before these monitors can be used in scientific research and clinical practice, their feasibility, validity, and reproducibility characteristics have to be investigated. Therefore, we tested how two wireless heart rate monitors (i.e., the Ithlete photoplethysmography (PPG) finger sensor and the Cortrium C3 ECG monitor perform against an established wired reference method (the VU-AMS ambulatory ECG monitor). Monitors were tested on cross-instrument and test-retest reproducibility in a controlled laboratory setting, while feasibility was evaluated in protocolled ambulatory settings at home. We found that the Cortrium and the Ithlete monitors showed acceptable agreement with the VU-AMS reference in laboratory setting. In ambulatory settings, assessments were feasible with both wireless devices although more valid data were obtained with the Cortrium than with the Ithlete. We conclude that both monitors have their merits under controlled laboratory settings where motion artefacts are minimized and stationarity of the ECG signal is optimized by design. These findings are promising for long-term ambulatory ECG measurements, although more research is needed to test whether the wireless devices' feasibility, validity, and reproducibility characteristics also hold in unprotocolled daily life settings with natural variations in posture and activities

Validity of Estimating the Maximal Oxygen Consumption by Consumer Wearables: A Systematic Review with Meta‑analysis and Expert Statement of the INTERLIVE Network

This research was partly funded by Huawei Technologies Oy (Finland) Co. Ltd. A limited liability company headquartered in Helsinki, Finland.Background Technological advances have recently made possible the estimation of maximal oxygen consumption (VO2max) by consumer wearables. However, the validity of such estimations has not been systematically summarized using metaanalytic methods and there are no standards guiding the validation protocols. Objective The aim was to (1) quantitatively summarize previous studies investigating the validity of the VO2max estimated by consumer wearables and (2) provide best-practice recommendations for future validation studies. Methods First, we conducted a systematic review and meta-analysis of studies validating the estimation of VO2max by wearables. Second, based on the state of knowledge (derived from the systematic review) combined with the expert discussion between the members of the Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) consortium, we provided a set of best-practice recommendations for validation protocols. Results Fourteen validation studies were included in the systematic review and meta-analysis. Meta-analysis results revealed that wearables using resting condition information in their algorithms significantly overestimated VO2max (bias 2.17 ml·kg−1·min−1; limits of agreement − 13.07 to 17.41 ml·kg−1·min−1), while devices using exercise-based information in their algorithms showed a lower systematic and random error (bias − 0.09 ml·kg−1·min−1; limits of agreement − 9.92 to 9.74 ml·kg−1·min−1). The INTERLIVE consortium proposed six key domains to be considered for validating wearable devices estimating VO2max, concerning the following: the target population, reference standard, index measure, testing conditions, data processing, and statistical analysis. Conclusions Our meta-analysis suggests that the estimations of VO2max by wearables that use exercise-based algorithms provide higher accuracy than those based on resting conditions. The exercise-based estimation seems to be optimal for measuring VO2max at the population level, yet the estimation error at the individual level is large, and, therefore, for sport/ clinical purposes these methods still need improvement. The INTERLIVE network hereby provides best-practice recommendations to be used in future protocols to move towards a more accurate, transparent and comparable validation of VO2max derived from wearables.Huawei Technologie

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology