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Objective: To design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset.Materials and Methods: Design requirements were identified through a conceptual framework on DQ, literature review, and interviews. The prototype of the tool was developed based on the requirements gathered and was further refined by domain experts. The Fitness-for-Use Tool was evaluated through a within-subjects controlled experiment comparing it with a baseline tool that provides information on missing data based on intrinsic DQ measures. The tools were evaluated on task performance and perceived usability.Results: The Fitness-for-Use Tool allows users to define data completeness by customizing the measures and its thresholds to fit their research task and provides a data summary based on the customized definition. Using the Fitness-for-Use Tool, study participants were able to accurately complete fitness-for-use assessment in less time than when using the Intrinsic DQ Tool. The study participants perceived that the Fitness-for-Use Tool was more useful in determining the fitness-for-use of a dataset than the Intrinsic DQ Tool.Discussion: Incorporating fitness-for-use measures in a DQ characterization tool could provide data summary that meets researchers needs. The design features identified in this study has potential to be applied to other biomedical data types.Conclusion: A tool that summarizes a dataset in terms of fitness-for-use dimensions and measures specific to a research question supports dataset assessment better than a tool that only presents information on intrinsic DQ measures. [ABSTRACT FROM AUTHOR]

The Electronic Health Record has failed to meet its intended purpose. We propose a new approach focusing on the use of data for health and health care. The first step is to create a repository of all patient data with data storage independent of data use. All use functionality is external to data storage. We propose the development of a common data model in which data elements have a rich set of attributes including actionable knowledge. Finally, functionality is provided through a series of application program interfaces (API). New APIs will address directly new methods for using data to increase the effectiveness of data application to improve management of the health and care of a patient. Together these components will open a pathway to finally accomplish the goals of a better future health system. [ABSTRACT FROM AUTHOR]

Background: The Pfizer-BioNTech COVID-19 vaccine uses a novel messenger RNA technology to elicit a protective immune response. Short-term physiologic responses to the vaccine have not been studied using wearable devices. Objective: We aim to characterize physiologic changes in response to COVID-19 vaccination in a small cohort of participants using a wearable device (WHOOP Strap 3.0). This is a proof of concept for using consumer-grade wearable devices to monitor response to COVID-19 vaccines. Methods: In this prospective observational study, physiologic data from 19 internal medicine residents at a single institution that received both doses of the Pfizer-BioNTech COVID-19 vaccine was collected using the WHOOP Strap 3.0. The primary outcomes were percent change from baseline in heart rate variability (HRV), resting heart rate (RHR), and respiratory rate (RR). Secondary outcomes were percent change from baseline in total, rapid eye movement, and deep sleep. Exploratory outcomes included local and systemic reactogenicity following each dose and prophylactic analgesic use. Results: In 19 individuals (mean age 28.8, SD 2.2 years; n=10, 53% female), HRV was decreased on day 1 following administration of the first vaccine dose (mean-13.44%, SD 13.62%) and second vaccine dose (mean-9.25%, SD 22.6%). RHR and RR showed no change from baseline after either vaccine dose. Sleep duration was increased up to 4 days post vaccination, after an initial decrease on day 1. Increased sleep duration prior to vaccination was associated with a greater change in HRV. Local and systemic reactogenicity was more severe after dose two. Conclusions: This is the first observational study of the physiologic response to any of the novel COVID-19 vaccines as measured using wearable devices. Using this relatively small healthy cohort, we provide evidence that HRV decreases in response to both vaccine doses, with no significant changes in RHR or RR. Sleep duration initially decreased following each dose with a subsequent increase thereafter. Future studies with a larger sample size and comparison to other inflammatory and immune biomarkers such as antibody response will be needed to determine the true utility of this type of continuous wearable monitoring in regards to vaccine responses. Our data raises the possibility that increased sleep prior to vaccination may impact physiologic responses and may be a modifiable way to increase vaccine response. These results may inform future studies using wearables for monitoring vaccine responses. [ABSTRACT FROM AUTHOR]

Introduction: Personalized medicine has exposed wearable sensors as new sources of biomedical data which are expected to accrue annual data storage costs of approximately $7.2 trillion by 2020 (>2000 exabytes). To improve the usability of wearable devices in healthcare, it is necessary to determine the minimum amount of data needed for accurate health assessment. Methods: Here, we present a generalizable optimization framework for determining the minimum necessary sampling rate for wearable sensors and apply our method to determine optimal optical blood volume pulse sampling rate. We implement t-tests, Bland–Altman analysis, and regression-based visualizations to identify optimal sampling rates of wrist-worn optical sensors. Results: We determine the optimal sampling rate of wrist-worn optical sensors for heart rate and heart rate variability monitoring to be 21–64 Hz, depending on the metric. Conclusions: Determining the optimal sampling rate allows us to compress biomedical data and reduce storage needs and financial costs. We have used optical heart rate sensors as a case study for the connection between data volumes and resource requirements to develop methodology for determining the optimal sampling rate for clinical relevance that minimizes resource utilization. This methodology is extensible to other wearable sensors. [ABSTRACT FROM AUTHOR]

Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth 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 conditions. There are many challenges faced by 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. [ABSTRACT FROM AUTHOR]

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field. [ABSTRACT FROM AUTHOR]

Recording in high resolution: Recording large number of neural signals in real time with high definition for long periods of time is necessary for understanding brain physiology and disease pathophysiology. Unfortunately, current brain interface devices only allow recordings of small brain areas with limited number of electrodes. Here, Chiang et al. developed a neural interface device, called Neural Matrix, that allowed stable in vivo neural recordings with high throughput in rodents and nonhuman primates. The system provided stable recordings projected to last for 6 years after implantation. The Neural Matrix will be useful for the study of brain physiology in preclinical setting and might be scalable to humans for clinical purposes. Long-lasting, high-resolution neural interfaces that are ultrathin and flexible are essential for precise brain mapping and high-performance neuroprosthetic systems. Scaling to sample thousands of sites across large brain regions requires integrating powered electronics to multiplex many electrodes to a few external wires. However, existing multiplexed electrode arrays rely on encapsulation strategies that have limited implant lifetimes. Here, we developed a flexible, multiplexed electrode array, called "Neural Matrix," that provides stable in vivo neural recordings in rodents and nonhuman primates. Neural Matrix lasts over a year and samples a centimeter-scale brain region using over a thousand channels. The long-lasting encapsulation (projected to last at least 6 years), scalable device design, and iterative in vivo optimization described here are essential components to overcoming current hurdles facing next-generation neural technologies. [ABSTRACT FROM AUTHOR]

: Studies have shown that the hemodynamic measurements taken during a Left Ventricular Assist Device (LVAD) ramp study can be used to optimize LVAD speed and improve clinical outcomes, yet these studies lack external validation. : 470 patients, including 3 LVAD subtypes, were retrospectively analyzed at Penn State Hershey Medical Center from January 2015 to September 2020 and 53 ramp studies were identified. Measurements (RA, mean PA, PCWP, and CI) were taken at speeds +/- 20% of manufacturer recommended set speed for HM2, HM3, and HVAD devices. Primary outcomes were all-cause and heart failure (HF) hospitalizations in the 6-month period pre- and post-ramp study. Secondary outcomes included final LVAD speed and hemodynamic measurements following ramp study. Subgroup analysis was performed on LVAD type and subjects who underwent LVAD speed change. Time to first hospitalization and cumulative incidence rate of all-cause and HF hospitalization were reported. : 53 ramp studies were analyzed. Baseline characteristics included: mean age of 60.1 (+/- 10.9), 84.9% male, 56.6% ischemic etiology, 71.7% destination therapy, and average NYHA class and INTERMACS of 2.5 (+/- 0.7) and 5.9 (+/- 1), respectively. 38 of 53 studies (71.7%) showed PCWP decompression >20%, with average PCWP decompression of 51.2% (+/- 21.0%), and CI increase of 22.5% (+/- 18.7%). Optimal LVAD speeds were chosen to maintain CI > 2.2, PCWP < 15 and minimize RAP, in the absence of suction events. 31 (58.5%) of studies resulted in an LVAD speed change and 16 (30.2%) of studies resulted in diuretic change. All-cause and HF hospitalizations were significantly decreased in the 6-months following ramp studies compared the 6-months pre-ramp (total days hospitalized for all causes: 12.0 vs 26.6, p=0.0002; total days hospitalized for HF, 4.8 vs 22.1, p=0.00003). Time to first hospitalization was decreased in the subgroup of studies who underwent a speed change during ramp procedure, yet these differences were not statistically significant. : This data externally validates previous work showing that LVAD hemodynamic ramp studies decrease hospitalizations. Future, prospective studies with a larger patient cohort and longer follow up time may elucidate specific hemodynamic targets to improve mortality and further reduce rehospitalizations. [ABSTRACT FROM AUTHOR]

We present our efforts toward enabling a wearable sensor system that allows for the correlation of individual environmental exposures with physiologic and subsequent adverse health responses. This system will permit a better understanding of the impact of increased ozone levels and other pollutants on chronic asthma conditions. We discuss the inefficiency of existing commercial off-the-shelf components to achieve continuous monitoring and our system-level and nano-enabled efforts toward improving the wearability and power consumption. Our system consists of a wristband, a chest patch, and a handheld spirometer. We describe our preliminary efforts to achieve a submilliwatt system ultimately powered by the energy harvested from thermal radiation and motion of the body with the primary contributions being an ultralow-power ozone sensor, an volatile organic compounds sensor, spirometer, and the integration of these and other sensors in a multimodal sensing platform. The measured environmental parameters include ambient ozone concentration, temperature, and relative humidity. Our array of sensors also assesses heart rate via photoplethysmography and electrocardiography, respiratory rate via photoplethysmography, skin impedance, three-axis acceleration, wheezing via a microphone, and expiratory airflow. The sensors on the wristband, chest patch, and spirometer consume 0.83, 0.96, and 0.01 mW, respectively. The data from each sensor are continually streamed to a peripheral data aggregation device and are subsequently transferred to a dedicated server for cloud storage. Future work includes reducing the power consumption of the system-on-chip including radio to reduce the entirety of each described system in the submilliwatt range. [ABSTRACT FROM AUTHOR]

Background: The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating digital medicine into practice. In order to understand how to mitigate challenges in digital medicine research and practice, this community must understand the landscape of digital medicine professionals, which digital medicine tools are being used and how, and user perspectives on current challenges in the field of digital medicine. Objective: The primary objective of this study is to provide information to the digital medicine community that is working to establish frameworks and best practices for interoperability in digital medicine. We sought to learn about the background of digital medicine professionals and determine which sensors and file types are being used most commonly in digital medicine research. We also sought to understand perspectives on digital medicine interoperability. Methods: We used a web-based survey to query a total of 56 digital medicine professionals from May 1, 2020, to July 10, 2020, on their educational and work experience, the sensors, file types, and toolkits they use professionally, and their perspectives on interoperability in digital medicine. Results: We determined that the digital medicine community comes from diverse educational backgrounds and uses a variety of sensors and file types. Sensors measuring physical activity and the cardiovascular system are the most frequently used, and smartphones continue to be the dominant source of digital health information collection in the digital medicine community. We show that there is not a general consensus on file types in digital medicine, and data are currently handled in multiple ways. There is consensus that interoperability is a critical impediment in digital medicine, with 93% (52) of survey respondents in agreement. However, only 36% (20) of respondents currently use tools for interoperability in digital medicine. We identified three key interoperability needs to be met: integration with electronic health records, implementation of standard data schemas, and standard and verifiable methods for digital medicine research. We show that digital medicine professionals are eager to adopt new tools to solve interoperability problems, and we suggest tools to support digital medicine interoperability. Conclusions: Understanding the digital medicine community, the sensors and file types they use, and their perspectives on interoperability will enable the development and implementation of solutions that fill critical interoperability gaps in digital medicine. The challenges to interoperability outlined by this study will drive the next steps in creating an interoperable digital medicine community. Establishing best practices to address these challenges and employing platforms for digital medicine interoperability will be essential to furthering the field of digital medicine. JMIR Mhealth Uhealth 2021;9(2):e24570 doi:10.2196/24570 [ABSTRACT FROM AUTHOR]

Glucose variability (GV) is an established risk factor for hypoglycemia and diabetes complications. Over 20 GV metrics have been identified, but there is no consensus on which metrics are most effective for determining glucose control. A1c is the clinical standard of glucose control. Two of the most common GV metrics include intraday standard deviation (iSD) and intraday coefficient of variation (iCV), which are calculated for each day and then averaged over multiple days. Thus, the current metrics of iSD and iCV are aggregate means, which are influenced by outliers and do not consider variations over multiple days. To reduce the influence of outliers and examine variation in these metrics over multiple days, we propose expanding the definition of iSD and iCV to include median and standard deviation of iSD and iCV over multiple days. We examined iSD and iCV in a population of 14 high normoglycemic or prediabetic participants (A1c 5.5-6.4%). Participants wore a continuous glucose monitor for 8-10 days. In addition to iSD and iCV, 23 metrics of glucose and GV were calculated. To determine the strength of the relationship between each GV metric and A1c, Pearson correlation coefficients (PCC) were calculated. There were higher PCC between A1c and the standard deviation of iSD and iCV than the traditional metric of iSD and iCV (traditional: 0.296, SD: -0.387). Interestingly, all metrics of iSD and iCV calculated (mean, median, and standard deviation) were more closely correlated with A1c than mean glucose (0.296, 0.138, -0.387, and 0.220, respectively). We demonstrate that expanding the definitions of iSD and iCV can provide a more comprehensive view of GV. Additionally, these metrics may provide more insight into glucose control than traditionally utilized metric, mean glucose. Disclosure: P.J. Cho: None. B.M. Bent: None. A.H. Wittmann: None. R.M. Merwin: None. C.R. Thacker: None. M.N. Feinglos: None. J.P. Dunn: None. Funding: MEDx (451-1746) [ABSTRACT FROM AUTHOR]

The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks. [ABSTRACT FROM AUTHOR]