Digital Biomarkers最新文献

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days.

Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions.

Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets.

Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone.

Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise "dose" in poststroke patients during clinical rehabilitation or clinical trials.

Background: The life science industry has a strong interest in real-world data (RWD), a term that is currently being used in many ways and with varying definitions depending on the source. In this review article, we provide a summary overview of the challenges and risks regarding the use of RWD and its translation into real-world evidence and provide a classification and visualization of RWD challenges by means of the RWD Challenges Radar.

Summary: Based on a systematic literature search, we identified 3 types of challenges - organizational, technological, and people-based - that must be addressed when deriving evidence from RWD to be used in drug approval and other applications. It further demonstrates that numerous different aspects, for example, related to the application field and the associated industry, must be considered. A key finding in our review is that the regulatory landscape must be carefully assessed before utilizing RWD.

Key messages: Establishing awareness and insight into the challenges and risks regarding the use of RWD will be key to taking full advantage of the RWD potential. As a result of this review, an "RWD Challenges Radar" will support the establishment of awareness by providing a comprehensive overview of the relevant aspects to be considered when employing RWD.

The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist was developed by a multidisciplinary group of content experts convened by the Digital Medicine Society, representing the clinical sciences, data management, technology development, and biostatistics. The aim of EVIDENCE is to promote high quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. Here we use the terms digital measurement product and connected sensor technology interchangeably to refer to tools that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function. EVIDENCE is applicable to 5 types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurements products.

Digital health has been rapidly thrust into the forefront of care delivery. Poised to extend the clinician's reach, a new set of examination tools will redefine neurologic and neurosurgical care, serving as the basis for the digital neurologic examination. We describe its components and review specific technologies, which move beyond traditional video-based telemedicine encounters and include separate digital tools. A future suite of these clinical assessment technologies will blur the lines between history taking, examination, and remote monitoring. Prior to full-scale implementation, however, much more investigation is needed. Because of the nascent state of the technologies, researchers, clinicians, and developers should establish digital neurologic examination requirements in order to maximize its impact.

Clinical safety findings remain one of the reasons for attrition of drug candidates during clinical development. Cardiovascular liabilities are not consistently detected in early-stage clinical trials and often become apparent when drugs are administered chronically for extended periods of time. Vital sign data collection outside of the clinic offers an opportunity for deeper physiological characterization of drug candidates and earlier safety signal detection. A working group representing expertise from biopharmaceutical and technology sectors, US Food and Drug Administration (FDA) public-private partnerships, academia, and regulators discussed and presented a remote cardiac monitoring case study at the FNIH Biomarkers Consortium Remote Digital Monitoring for Medical Product Development workshop to examine applicability of the biomarker qualification evidentiary framework by the FDA. This use case examined the components of the framework, including the statement of need, the context of use, the state of the evidence, and the benefit/risk profile. Examination of results from 2 clinical trials deploying 510(k)-cleared devices for remote cardiac data collection demonstrated the need for analytical and clinical validity irrespectively of the regulatory status of a device of interest, emphasizing the importance of data collection method assessment in the context of intended use. Additionally, collection of large amounts of ambulatory data also highlighted the need for new statistical methods and contextual information to enable data interpretation. A wider adoption of this approach for drug development purposes will require collaborations across industry, academia, and regulatory agencies to establish methodologies and supportive data sets to enable data interpretation and decision-making.

Introduction: We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

Methods: We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

Results: Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

Conclusion: There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

At the end of 2020, Karger's Digital Biomarkers, together with Evidation Health, produced a special issue entitled "The Future of Digital Health." This brief meeting report provides an overview of the expert panel and workshop that were held in early 2021 to explore key topics raised in the special issue.

Diseases can affect organs such as the heart, lungs, brain, muscles, or vocal folds, which can then alter an individual's voice. Therefore, voice analysis using artificial intelligence opens new opportunities for healthcare. From using vocal biomarkers for diagnosis, risk prediction, and remote monitoring of various clinical outcomes and symptoms, we offer in this review an overview of the various applications of voice for health-related purposes. We discuss the potential of this rapidly evolving environment from a research, patient, and clinical perspective. We also discuss the key challenges to overcome in the near future for a substantial and efficient use of voice in healthcare.

Introduction: The effect of rhythmic acoustic stimuli on body sway is of increasing interest due to their positive contribution when training or restoring the control of movement. Inertial sensors show promise as a portable, easier, and more affordable method compared to the force plate "gold standard" concerning the evaluation of postural sway. This study examined the concurrent validity of inertially sensed measures of voluntary body sway against those obtained with a force plate, in silence and while exposed to a rhythmic acoustic stimulus.

Methods: Temporal (sway duration and variability) and spatial (trajectory length, variability, range, velocity, and area) body sway variables were extracted using an inertial sensor (at L5) in synchronization with a force plate, during anteroposterior body sway in silence and while exposed to a rhythmic acoustic stimulus (n = 18 young women; two 70-s trials in each condition). Statistics included bivariate correlations between the inertially sensed and the force plate measures, separately, in silence and with a rhythmic acoustic stimulus, as well as for the effect of the rhythmic acoustic stimulus (percentage difference from silence) (p ≤ 0.05, SPSS v25.0).

Results: The inertially sensed measures demonstrated good-to-excellent concurrent validity for all temporal and almost all spatial variables, both in silence and with rhythmic acoustic stimulus (r > 0.75, p = 0.000), as well as for the rhythmic acoustic-stimulus effect (r > 0.75, p ≤ 0.05).

Conclusion: The inertially sensed measures of the voluntary anteroposterior body sway demonstrated an overall good-to-excellent concurrent validity against those obtained with the force plate "gold standard," both in the silence and the rhythmic acoustic stimulus conditions, as well as for the rhythmic acoustic-stimulus effect. The findings of this pilot study allow the recommendation of inertial sensing for the evaluation of postural control alterations when exposed to rhythmic acoustic stimuli, a condition of increasing interest due to the positive contribution of such stimuli when training or restoring the control of movement.

To support the successful adoption of digital measures into internal decision making and evidence generation for medical product development, we present a unified lexicon to aid communication throughout this process, and highlight key concepts including the critical role of participant engagement in development of digital measures. We detail the steps of bringing a successful proof of concept to scale, focusing on key decisions in the development of a new digital measure: asking the right question, optimized approaches to evaluating new measures, and whether and how to pursue qualification or acceptance. Building on the V3 framework for establishing verification and analytical and clinical validation, we discuss strategic and practical considerations for collecting this evidence, illustrated with concrete examples of trailblazing digital measures in the field.