Digital Biomarkers最新文献

{"title":"Beyond the Therapist's Office: Merging Measurement-Based Care and Digital Medicine in the Real World.","authors":"Emil Chiauzzi,&nbsp;Paul Wicks","doi":"10.1159/000517748","DOIUrl":"https://doi.org/10.1159/000517748","url":null,"abstract":"<p><p>This viewpoint focuses on the ways in which digital medicine and measurement-based care can be utilized in tandem to promote better assessment, patient engagement, and an improved quality of psychiatric care. To date, there has been an underutilization of digital measurement in psychiatry, and there is little discussion of the feedback and patient engagement process in digital medicine. Measurement-based care is a recognized evidence-based strategy that engages patients in an understanding of their outcome data. When implemented as designed, providers review the scores and trends in outcome immediately and then provide feedback to their patients. However, the process is typically confined to office visits, which does not provide a complete picture of a patient's progress and functioning. The process is labor intensive, even with digital feedback systems, but the integration of passive metrics obtained through wearables and apps can supplement office-based observations. This enhanced measurement-based care process can provide a picture of real-world patient functioning through passive metrics (activity, sleep, etc.). This can potentially engage patients more in their health data and involve a critically needed therapeutic alliance component in digital medicine.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 2","pages":"176-182"},"PeriodicalIF":0.0,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9e/a9/dib-0005-0176.PMC8460973.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39580571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study.","authors":"Megan K O'Brien,&nbsp;Olivia K Botonis,&nbsp;Elissa Larkin,&nbsp;Julia Carpenter,&nbsp;Bonnie Martin-Harris,&nbsp;Rachel Maronati,&nbsp;KunHyuck Lee,&nbsp;Leora R Cherney,&nbsp;Brianna Hutchison,&nbsp;Shuai Xu,&nbsp;John A Rogers,&nbsp;Arun Jayaraman","doi":"10.1159/000517144","DOIUrl":"https://doi.org/10.1159/000517144","url":null,"abstract":"<p><strong>Introduction: </strong>Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.</p><p><strong>Methods: </strong>Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).</p><p><strong>Results: </strong>These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (<i>p</i> < 0.037).</p><p><strong>Discussion: </strong>Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 2","pages":"167-175"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000517144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39685067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study.","authors":"Noah Balestra, Gaurav Sharma, Linda M Riek, Ania Busza","doi":"10.1159/000516619","DOIUrl":"10.1159/000516619","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 2","pages":"158-166"},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339513/pdf/dib-0005-0158.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39328724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Real-World Data Challenges Radar: A Review on the Challenges and Risks regarding the Use of Real-World Data.","authors":"Frank Grimberg,&nbsp;Petra Maria Asprion,&nbsp;Bettina Schneider,&nbsp;Enkelejda Miho,&nbsp;Lmar Babrak,&nbsp;Ali Habbabeh","doi":"10.1159/000516178","DOIUrl":"https://doi.org/10.1159/000516178","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Summary: </strong>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.</p><p><strong>Key messages: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 2","pages":"148-157"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000516178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39330837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVIDENCE Publication Checklist for Studies Evaluating Connected Sensor Technologies: Explanation and Elaboration.","authors":"Christine Manta,&nbsp;Nikhil Mahadevan,&nbsp;Jessie Bakker,&nbsp;Simal Ozen Irmak,&nbsp;Elena Izmailova,&nbsp;Siyeon Park,&nbsp;Jiat-Ling Poon,&nbsp;Santosh Shevade,&nbsp;Sarah Valentine,&nbsp;Benjamin Vandendriessche,&nbsp;Courtney Webster,&nbsp;Jennifer C Goldsack","doi":"10.1159/000515835","DOIUrl":"https://doi.org/10.1159/000515835","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 2","pages":"127-147"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000515835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39115370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Digital Neurologic Examination.","authors":"Adam B Cohen,&nbsp;Brain V Nahed","doi":"10.1159/000515577","DOIUrl":"https://doi.org/10.1159/000515577","url":null,"abstract":"<p><p>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 <i>digital neurologic examination</i>. 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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"114-126"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000515577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38966377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Cardiac Safety Monitoring through the Lens of the FDA Biomarker Qualification Evidentiary Criteria Framework: A Case Study Analysis.","authors":"Elena S Izmailova,&nbsp;William A Wood,&nbsp;Qi Liu,&nbsp;Vadim Zipunnikov,&nbsp;Daniel Bloomfield,&nbsp;Jason Homsy,&nbsp;Steven C Hoffmann,&nbsp;John A Wagner,&nbsp;Joseph P Menetski","doi":"10.1159/000515110","DOIUrl":"https://doi.org/10.1159/000515110","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"103-113"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000515110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38966375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Devices: Current Status and Opportunities in Pain Assessment and Management.","authors":"Andrew Leroux, Rachael Rzasa-Lynn, Ciprian Crainiceanu, Tushar Sharma","doi":"10.1159/000515576","DOIUrl":"10.1159/000515576","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"89-102"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138140/pdf/dib-0005-0089.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38966378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Future of Digital Health: Meeting Report.","authors":"Priya Kumar,&nbsp;Ieuan Clay","doi":"10.1159/000515355","DOIUrl":"https://doi.org/10.1159/000515355","url":null,"abstract":"<p><p>At the end of 2020, Karger's <i>Digital Biomarkers</i>, 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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"74-77"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000515355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38954761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice.","authors":"Guy Fagherazzi, Aurélie Fischer, Muhannad Ismael, Vladimir Despotovic","doi":"10.1159/000515346","DOIUrl":"10.1159/000515346","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"5 1","pages":"78-88"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138221/pdf/dib-0005-0078.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38954762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}