Digital Biomarkers最新文献

Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.

Background: Contactless symptom tracking is essential for the diagnosis of COVID-19 cases that need hospitalization. Indications from sensors and user descriptions have to be combined in order to make the right decisions.

Methods: The proposed multipurpose platform Coronario combines sensory information from different sources for a valid diagnosis following a dynamically adaptable protocol. The information exchanged can also be exploited for the advancement of research on COVID-19. The platform consists of mobile and desktop applications, sensor infrastructure, and cloud services. It may be used by patients in pre- and post-hospitalization stages, vulnerable populations, medical practitioners, and researchers.

Results: The supported audio processing is used to demonstrate how the Coronario platform can assist research on the nature of COVID-19. Cough sounds are classified as a case study, with 90% accuracy.

Discussion/conclusions: The dynamic adaptation to new medical protocols is one of the main advantages of the developed platform, making it particularly useful for several target groups of patients that require different screening methods. A medical protocol determines the structure of the questionnaires, the medical sensor sampling strategy and, the alert rules.

Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently.

Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30).

Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups.

Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = -4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different.

Conclusion: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

Background: With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research.

Summary: This article synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients. We propose next steps to drive forward the science of high-quality patient engagement in support of measures of health that matter in the era of digital medicine.

Key messages: All measures of health should be meaningful, regardless of the product's regulatory classification, type of measure, or context of use. To evaluate meaningfulness of signals derived from digital sensors, the following four-level framework is useful: Meaningful Aspect of Health, Concept of Interest, Outcome to be measured, and Endpoint (exclusive to research). Incorporating patient input is a dynamic process that requires more than a single, transactional touch point but rather should be conducted continuously throughout the measurement selection process. We recommend that developers, clinicians, and researchers reevaluate processes for more continuous patient engagement in the development, deployment, and interpretation of digital measures of health.

The internet of healthcare things aims at connecting biosensors, clinical information systems and electronic health dossiers. The resulting data expands traditionally available diagnostics with digital biomarkers. In this technical note, we report the implementation and pilot operation of a device- and analytics-agnostic automated monitoring platform for in-house patients at hospitals. Any available sensor, as well as any analytics tool can be integrated if the application programming interface is made available. The platform consists of a network of Bluetooth gateways communicating via the hospital's secure Wi-Fi network, a server application (Device Hub) and associated databases. Already existing access points or low-cost hardware can be used to run the gateway software. The platform can be extended to a remote patient monitoring solution to close the gap between in-house treatments and follow-up patient monitoring.

Aims: Heart failure (HF) affects approximately 26 million people worldwide. With an aging global population, innovative approaches to HF evaluation and management are needed to cope with the worsening HF epidemic. The aim of the Real-Life Multimarker Monitoring in Patients with Heart Failure (REALIsM-HF) study (NCT03507439) is to evaluate a composite instrument comprising remote, real-time, activity-monitoring devices combined with daily electronic patient-reported outcome (ePRO) items in patients who have been hospitalized for HF and are undergoing standard HF assessment (e.g., 6-min walking distance [6MWD], blood biomarkers, Kansas City Cardiomyopathy Questionnaire [KCCQ], and echocardiography).

Methods: REALIsM-HF is an ongoing, 12-week, observational study enrolling 80-100 patients aged ≥45 years with HF with preserved ejection fraction (HFpEF; EF ≥45%) or reduced EF (HFrEF; EF ≤35%). Statistical analyses will include examining the association between data from wearables (the AVIVO^© mobile patient management patch or VitalPatch^© biosensor, and the DynaPort MoveMonitor^©), daily ePROs, and conventional HF metrics (e.g., serum/plasma biomarkers, 6MWD, KCCQ, and echocardiographic parameters). The feasibility of and patient compliance with at-home devices will be documented, and the data captured for the purpose of establishing reference values in patients with HFpEF or HFrEF will be summarized.

Conclusions: The REALIsM-HF study is to evaluate the longitudinal daily activity profiles of patients with HF and correlate these with changes in serum/plasma biomarker profiles, symptoms, quality of life, and cardiac function and morphology to inform the use of wearable activity monitors for developing novel therapies and managing patients.

Background: Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population.

Objective: To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients.

Methods: Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application.

Results: Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and increased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (p < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (p < 0.03).

Conclusion: These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.

Digital therapeutics is a newly described concept in healthcare which is proposed to change patient behavior and treat medical conditions using a variety of digital technologies. However, the term is rarely defined with criteria that make it distinct from simply digitizedversions of traditional therapeutics. Our objective is to describe a more valuable characteristic of digital therapeutics, which is distinct from traditional medicine or therapy: that is, the utilization of artificial intelligence and machine learning systems to monitor and predict individual patient symptom data in an adaptive clinical feedback loop via digital biomarkers to provide a precision medicine approach to healthcare. Artificial intelligence platforms can learn and predict effective interventions for individuals using a multitude of personal variables to provide a customized and more tailored therapy regimen. Digital therapeutics coupled with artificial intelligence and machine learning also allows more effective clinical observations and management at the population level for various health conditions and cohorts. This vital differentiation of digital therapeutics compared to other forms of therapeutics enables a more personalized form of healthcare that actively adapts to patients' individual clinical needs, goals, and lifestyles. Importantly, these characteristics are what needs to be emphasized to patients, physicians, and policy makers to advance the entire field of digital healthcare.

We conducted a survey about recent surgical procedures on a large connected population and requested each individual's permission to access data from commercial wearable devices they may have been wearing around the time of the procedure. For subcohorts of 66-118 patients who reported having a weight loss procedure and who had dense Fitbit data around their procedure date, we examined several daily measures of behavior and physiology in the 12 weeks leading up to and the 12 weeks following their procedures. We found that the weeks following weight loss operations were associated with fewer daily total steps, smaller proportions of the day spent walking, lower resting and 95th percentile heart rates, more total sleep time, and greater sleep efficiency. We demonstrate that consumer-grade activity trackers can capture behavioral and physiological changes resulting from weight loss surgery and these devices have the potential to be used to develop measures of patients' postoperative recovery that are convenient, sensitive, scalable, individualized, and continuous.