Estimating Personal Resting Heart Rate from Wearable Biosensor Data

Jiang, Chentian; Faroqi, Lida; Palaniappan, Latha; Dunn, Jessilyn

doi:10.1109/bhi.2019.8834554

Cited by 4 publications

(5 citation statements)

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“…We have developed this module in order to increase the accessibility and transparency of RHR as a digital biomarker and to move toward a standardized and consistent RHR calculation method. This novel estimation model [30] (1) considers an individual’s heart rate data, (2) finds the subset of low exercise/step intensity heart rate data corresponding to a minimal rolling sum of steps ( n ) within a window size of m minutes, (3) searches for optimal values of parameters n and m by minimizing a standard deviation penalty function which quantifies the difference in variation between the distributions, and (4) outputs the RHR estimate as the median of the optimal low exercise/step intensity subset of personal heart rate data. This method has been validated against RHR clinical data through the STRONG-D study using Fitbits for type II diabetic adults (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…4.RHR module available in the DBDP. Clinical validation of our RHR algorithm against clinical data [8,30]. …”

Section: Resultsmentioning

confidence: 99%

“…RHR module available in the DBDP. Clinical validation of our RHR algorithm against clinical data[8,30].Journal of Clinical and Translational Science 5domain knowledge from entering the field. Creating the DBDP, an open-source software platform for collaborative efforts in the digital biomarker space, with detailed documentation and user training, supports exploration and validation of digital biomarkers and enables multidisciplinary collaboration.…”

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confidence: 99%

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The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Bent

Wang

Grzesiak

et al. 2020

J. Clin. Trans. Sci.

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Introduction Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mHealth and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mobile Health and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions1. There are many challenges facing digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). Results Here we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions The clear need for such a platform will accelerate the DBDP’s adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

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Section: Resultsmentioning

confidence: 99%

“…4.RHR module available in the DBDP. Clinical validation of our RHR algorithm against clinical data [8,30]. …”

Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

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Wang

Grzesiak

et al. 2020

J. Clin. Trans. Sci.

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“…Despite their high accuracy in measuring HR at rest and the ubiquity of these devices in modern life, an in-clinic ECG is still the gold standard method to determine RHR [17]. In clinical research, there are a variety of methodologies used depending on the availability of data and clinical feasibility [10,11,18,19]. In this study, we investigated the viability of the VSW in determining RHR by comparing it with RHR determined by ECG.…”

Section: Associations With Vsw Rhrs By Domainmentioning

confidence: 99%

Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study (Preprint)

Feng,

Short,

Saeb

et al. 2024

Preprint

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BACKGROUND Though widely used, resting heart rate (RHR) as measured by a wearable device has not been previously evaluated in a large cohort against a variety of important baseline characteristics. OBJECTIVE This study aimed to assess the validity of the RHR measured by a wearable device compared against the gold standard of ECG, and assess the relationships between device-measured RHR and a broad range of clinical characteristics. METHODS The Project Baseline Health Study (PHBS) captured detailed demographic, occupational, social, lifestyle, and clinical data to generate a deeply phenotyped cohort. We selected an analysis cohort within it, which included participants who had RHR determined by both electrocardiogram (ECG) and by the Verily Study Watch (VSW). We examined the correlation between these simultaneous RHR measures, and assessed the relationship between VSW RHR and a range of baseline characteristics including demographic, clinical, laboratory, and functional assessments. RESULTS From the overall PBHS cohort (N=2502), 875 (35%) participants entered the analysis cohort, 519 (59%) female and 356 (41%) male participants. The mean and standard deviation of VSW RHR was 66.6 ± 11.2 beats per minute (bpm) for female participants, and 64.4 ± 12.3 bpm for male participants. There was excellent reliability between the two measures of RHR (ECG and VSW) with an intraclass correlation coefficient of 0.946. By univariate analyses, female and male participants had similar baseline characteristics that trended with higher VSW RHR: lack of healthcare insurance (both p <0.05), higher BMI (both p<0.0001), higher C-reactive protein (both p<0.0001), presence of type 2 diabetes mellitus (both p<0.0001), and higher World Health Organization Disability Assessment Schedule (WHODAS) 2.0 score (both p<0.0001) were associated with higher RHR. By regression analyses, within each domain of baseline characteristics, different characteristics were most associated with VSW RHR in female compared to male participants: demographics and socioeconomic status (unemployment vs. lack of health insurance), medical conditions (chronic obstructive pulmonary disease with emphysema vs. type 2 diabetes mellitus), laboratory assessments (C-reactive protein vs. blood glucose), and patient reported outcomes (eWHO Disability Assessment Schedule 2.0 score vs. Behavior Risk Factor Surveillance System score). Diastolic blood pressure was the most associated characteristic with higher VSW RHR for both sexes within the vitals and physical function domain. CONCLUSIONS RHR determined by the VSW had excellent correlation with RHR determined by ECG. Participants with higher VSW RHR had similar trends in socioeconomic status, medical conditions, vitals, laboratory assessments, physical function, and patient reported outcomes irrespective of sex. However, within each domain of baseline characteristics, different characteristics were most associated with VSW RHR in female versus male participants. CLINICALTRIAL clinicaltrials.gov identifier NCT03154346

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“…A growing area in medicine, and one of the most promising ones, is bioelectronics electronics. Such medical devices can measure vital parameters of a patient noninvasively, such as heart rate (Asada et al, 2003;Schwartz et al, 2013;Jiang et al, 2019;Coffen et al, 2020), blood pressure (Pang et al, 2015;Wang et al, 2017;Chung, 2019a, 2019b), body temperature (Seungyong Goeckenjan et al, 2020), brain activity (Borisova et al, 2018;Ganesana et al, 2019) and specific biomarkers (pH, glucose, lactate) (Murat et al, 2020;Wiorek et al, 2020), to name a few. These measurements take place in real time, so they can alert individuals and physicians of the situation in order to minimize the decision-making time of a possible or necessary intervention.…”

Section: Bioelectronicsmentioning

confidence: 99%

Flexible Electronics: Status, Challenges and Opportunities

2020

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The concept of flexible electronics has been around for several decades. In principle, anything thin or very long can become flexible. While cables and wiring are the prime example for flexibility, it was not until the space race that silicon wafers used for solar cells in satellites were thinned to increase their power per weight ratio, thus allowing a certain degree of warping. This concept permitted the first flexible solar cells in the 1960s (Crabb and Treble, 1967). The development of conductive polymers (Shirakawa et al., 1977), organic semiconductors, and amorphous silicon (Chittick et al., 1969; Okaniwa et al., 1983) in the following decades meant huge strides toward flexibility and processability, and thus these materials became the base for electronic devices in applications that require bending, rolling, folding, and stretching, among other properties that cannot be fulfilled by conventional electronics (Cheng and Wagner, 2009) (Figure 1). Presently there is great interest in new materials and fabrication techniques which allow for highperformance scalable electronic devices to be manufactured directly onto flexible substrates. This interest has also extended to not only flexibility but also properties like stretchability and healability which can be achieved by utilizing elastomeric substrates with strong molecular interactions (Oh et al., 2016; Kang et al., 2018). Likewise, biocompatibility and biodegradability has been achieved through polymers that do not cause adverse effect to the body and can be broken down into smaller constituent pieces after utilization (Bettinger and Bao, 2010; Irimia-Vladu et al., 2010; Liu H. et al., 2019). This new progress is now enabling devices which can conform to complex and dynamic surfaces, such as those found in biological systems and bioinspired soft robotics. These next-generation flexible electronics open up a wide range of exciting new applications such as flexible lighting and display technologies for consumer electronics, architecture, and textiles, wearables with sensors that help monitor our health and habits, implantable electronics for improved medical imaging and diagnostics, as well as extending the functionality of robots and unmanned aircraft through lightweight and conformable energy harvesting devices and sensors. While conventional electronics are very capable of these functions, flexible electronics are intended to expand the mechanical features to adhere to novel form factors through hybrid strategies, or as standalone solutions where the application does not require high computation power, intended to be highly robust to deformation, low cost, thin, or disposable. The definition of flexibility differs from application to application. From bending and rolling for easier handling of large area photovoltaics, to conforming onto irregular shapes, folding, twisting, stretching, and deforming required for devices in electronic skin, all while maintaining device performance and reliability. While early progress and many important innovations have alre...

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Estimating Personal Resting Heart Rate from Wearable Biosensor Data

Cited by 4 publications

References 11 publications

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study (Preprint)

Flexible Electronics: Status, Challenges and Opportunities

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