Digital Biomarkers最新文献

{"title":"Changes in Exhaled Carbon Dioxide during the Menstrual Cycle and Menopause.","authors":"Tomer Cramer, Shlomo Yeshurun, Merav Mor","doi":"10.1159/000539126","DOIUrl":"10.1159/000539126","url":null,"abstract":"<p><strong>Introduction: </strong>The menstrual cycle (MC) reflects multifaceted hormonal changes influencing women's metabolism, making it a key aspect of women's health. Changes in hormonal levels throughout the MC have been demonstrated to influence various physiological parameters, including exhaled carbon dioxide (CO₂). Lumen is a small handheld device that measures metabolic fuel usage via exhaled CO₂. This study leverages exhaled CO₂ patterns measured by the Lumen device to elucidate metabolic variations during the MC, which may hold significance for fertility management. Additionally, CO₂ changes are explored in menopausal women with and without hormonal replacement therapy (HRT).</p><p><strong>Methods: </strong>This retrospective cohort study analyzed exhaled CO₂ data from 3,981 Lumen users, including eumenorrheal women and menopausal women with and without HRT. Linear mixed models assessed both CO₂ changes of eumenorrheal women during the MC phases and compared between menopausal women with or without HRT.</p><p><strong>Results: </strong>Eumenorrheic women displayed cyclical CO₂ patterns during the MC, characterized by elevated levels during the menstrual, estrogenic and ovulation phases and decreased levels during post-ovulation and pre-menstrual phases. Notably, despite variations in cycle length affecting the timing of maximum and minimum CO₂ levels within a cycle, the overall pattern remained consistent. Furthermore, CO₂ levels in menopausal women without HRT differed significantly from those with HRT, which showed lower levels.</p><p><strong>Conclusion: </strong>This study reveals distinct CO₂ patterns across MC phases, providing insights into hormonal influences on metabolic activity. Menopausal women exhibit altered CO₂ profiles in relation to the use or absence of HRT. CO₂ monitoring emerges as a potential tool for tracking the MC and understanding metabolic changes during menopause.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"102-110"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626280","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":"Clustering Approaches for Gait Analysis within Neurological Disorders: A Narrative Review.","authors":"Jonas Hummel, Michael Schwenk, Daniel Seebacher, Philipp Barzyk, Joachim Liepert, Manuel Stein","doi":"10.1159/000538270","DOIUrl":"10.1159/000538270","url":null,"abstract":"<p><strong>Background: </strong>The prevalence of neurological disorders is increasing, underscoring the importance of objective gait analysis to help clinicians identify specific deficits. Nevertheless, existing technological solutions for gait analysis often suffer from impracticality in daily clinical use, including excessive cost, time constraints, and limited processing capabilities.</p><p><strong>Summary: </strong>This review aims to evaluate existing techniques for clustering patients with the same neurological disorder to assist clinicians in optimizing treatment options. A narrative review of thirteen relevant studies was conducted, characterizing their methods, and evaluating them against seven criteria. Additionally, the results are summarized in two comprehensive tables. Recent approaches show promise; however, our results indicate that, overall, only three approaches display medium or high process maturity, and only two show high clinical applicability.</p><p><strong>Key messages: </strong>Our findings highlight the necessity for advancements, specifically regarding the use of markerless optical tracking systems, the optimization of experimental plans, and the external validation of results. This narrative review provides a comprehensive overview of existing clustering techniques, bridging the gap between instrumented gait analysis and its real-world clinical utility. We encourage researchers to use our findings and those from other medical fields to enhance clustering techniques for patients with neurological disorders, facilitating the identification of disparities within groups and their extent, ultimately improving patient outcomes.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"93-101"},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11078540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140890552","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":"A Study of Pupil Response to Light as a Digital Biomarker of Recent Cannabis Use.","authors":"Suneeta Godbole, Andrew Leroux, Ashley Brooks-Russell, Prem S Subramanian, Michael J Kosnett, Julia Wrobel","doi":"10.1159/000538561","DOIUrl":"https://doi.org/10.1159/000538561","url":null,"abstract":"<p><strong>Introduction: </strong>Given the traffic safety and occupational injury prevention implications associated with cannabis impairment, there is a need for objective and validated measures of recent cannabis use. Pupillary light response may offer an approach for detection.</p><p><strong>Method: </strong>Eighty-four participants (mean age: 32, 42% female) with daily, occasional, and no-use cannabis use histories participated in pupillary light response tests before and after smoking cannabis ad libitum or relaxing for 15 min (no use). The impact of recent cannabis consumption on trajectories of the pupillary light response was modeled using functional data analysis tools. Logistic regression models for detecting recent cannabis use were compared, and average pupil trajectories across cannabis use groups and times since light test administration were estimated.</p><p><strong>Results: </strong>Models revealed small, significant differences in pupil response to light after cannabis use comparing the occasional use group to the no-use control group, and similar statistically significant differences in pupil response patterns comparing the daily use group to the no-use comparison group. Trajectories of pupillary light response estimated using functional data analysis found that acute cannabis smoking was associated with less initial and sustained pupil constriction compared to no cannabis smoking.</p><p><strong>Conclusion: </strong>These analyses show the promise of pairing pupillary light response and functional data analysis methods to assess recent cannabis use.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"83-92"},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11052563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140853275","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":"Toward Personalized Orthopedic Care: Validation of a Smart Knee Brace","authors":"Annah McPherson, Andrew J. McDaid, Sarah Ward","doi":"10.1159/000538487","DOIUrl":"https://doi.org/10.1159/000538487","url":null,"abstract":"Abstract Introduction Wearable technology offers a promising solution to advance current rehabilitation strategies for post-operative orthopedic care. The aim of this study was to determine the level of agreement and concurrent validity of a smart knee brace compared to the gold standard measurement system GAITRite® for assessing lower limb gait parameters. Methods Thirty-four healthy participants were fitted with the smart knee brace (Digital Knee®) on their dominant limb. Gait parameters (stride length, stride time, and gait velocity) were measured simultaneously using the Digital Knee® and the GAITRite® electronic walkway. Two walks were performed at a comfortable speed and two at a fast-walking speed. Results At a comfortable walking speed, stride time was moderately valid (ICC2,1 = 0.66 s), and stride length and gait velocity demonstrated poor validity (ICC2,1 = 0.29; ICC2,1 = 0.41). All gait parameters demonstrated poor validity at a fast-walking speed (ICC2,1 = −0.16 to −0.01). Bias ranged from −0.08 to 0.28, with more clinically acceptable percentage errors at a comfortable walking speed (14.1–30%) versus at a fast-walking speed (26.4–42.6%). Gait velocity and stride length had substantially higher biases in the fast-walking speed compared to the comfortable walking speed (0.28 ± 0.39 m s−1 vs. 0.02 ± 0.21 m s−1; 0.15 ± 0.23 m vs. −0.04 ± 0.17 m). Limits of agreement were considered narrower for stride time compared to stride length and gait velocity. Conclusion The Digital Knee® is a promising approach to improving post-operative rehabilitation outcomes in patients with osteoarthritis. The Digital Knee® demonstrated good agreement and moderate concurrent validity for measuring gait metrics at a comfortable walking speed. These findings highlight the opportunity of the wearable sensor as an intervention for post-operative orthopedic care. This was a laboratory-based study; thus, further research is required to validate the wearable sensor in real-world contexts and in patients with knee pathologies. Further, refinement of the algorithm for measuring gait metrics at slow- and fast-walking speed with the Digital Knee® is warranted.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"26 5","pages":"75 - 82"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio","authors":"Aya Hassouneh, Bradley Bazuin, A. Danna-dos-Santos, Ilgin Acar, I. Abdel-Qader","doi":"10.1159/000538486","DOIUrl":"https://doi.org/10.1159/000538486","url":null,"abstract":"Abstract Introduction Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"46 5","pages":"59 - 74"},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vestibulo-Ocular Reflex Suppression: Clinical Relevance and Assessment in the Digital Age","authors":"Patrik Theodor Nerdal, Florin Gandor, Maximilian Uwe Friedrich, Laurin Schappe, Georg Ebersbach, Walter Maetzler","doi":"10.1159/000537842","DOIUrl":"https://doi.org/10.1159/000537842","url":null,"abstract":"Abstract Background Visual acuity and image stability are crucial for daily activities, particularly during head motion. The vestibulo-ocular reflex (VOR) and its suppression (VORS) support stable fixation of objects of interest. The VOR drives a reflexive eye movement to counter retinal slip of a stable target during head motion. In contrast, VORS inhibits this countermovement when the target stimulus is in motion. The VORS allows for object fixation when it aligns with the direction of the head’s movement, or when an object within or outside the peripheral vision needs to be focused upon. Summary Deficits of the VORS have been linked to age-related diseases such as balance deficits associated with an increased fall risk. Therefore, the accurate assessment of the VORS is of particular clinical relevance. However, current clinical assessment methods for VORS are mainly qualitative and not sufficiently standardised. Recent advances in digital health technology, such as smartphone-based videooculography, offer a promising alternative for assessing VORS in a more accessible, efficient, and quantitative manner. Moreover, integrating mobile eye-tracking technology with virtual reality environments allows for the implementation of controlled VORS assessments with different visual inputs. These assessment approaches allow the extraction of novel parameters with potential pathomechanistic and clinical relevance. Key Messages We argue that researchers and clinicians can obtain a more nuanced understanding of this ocular stabilisation reflex and its associated pathologies by harnessing digital health technology for VORS assessment. Further research is warranted to explore the technologies’ full potential and utility in clinical practice.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 2","pages":"52 - 58"},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study","authors":"Le Huang, K. Chun, Lian Yu, Jong Yoon Lee, Alan Soetikno, Hope Chen, Hyoyoung Jeong, Joshua Barrett, Knute L. Martell, Youn Kang, Alpesh A. Patel, Shuai Xu","doi":"10.1159/000536473","DOIUrl":"https://doi.org/10.1159/000536473","url":null,"abstract":"Abstract Introduction Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor; ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients. Methods Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN). Results An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients. Discussion ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"31 1","pages":"40 - 51"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue-Related Changes of Daily Function: Most Promising Measures for the Digital Age.","authors":"Walter Maetzler, Leonor Correia Guedes, Kirsten Nele Emmert, Jennifer Kudelka, Hanna Luise Hildesheim, Emma Paulides, Hayley Connolly, Kristen Davies, Valentina Dilda, Teemu Ahmaniemi, Luisa Avedano, Raquel Bouça-Machado, Michael Chambers, Meenakshi Chatterjee, Peter Gallagher, Johanna Graeber, Corina Maetzler, Hanna Kaduszkiewicz, Norelee Kennedy, Victoria Macrae, Laura Carrasco Marin, Anusha Moses, Alessandro Padovani, Andrea Pilotto, Natasha Ratcliffe, Ralf Reilmann, Madalena Rosario, Stefan Schreiber, Dina De Sousa, Geert Van Gassen, Lori Ann Warring, Klaus Seppi, C Janneke van der Woude, Joaquim J Ferreira, Wan-Fai Ng","doi":"10.1159/000536568","DOIUrl":"10.1159/000536568","url":null,"abstract":"<p><strong>Background: </strong>Fatigue is a prominent symptom in many diseases and is strongly associated with impaired daily function. The measurement of daily function is currently almost always done with questionnaires, which are subjective and imprecise. With the recent advances of digital wearable technologies, novel approaches to evaluate daily function quantitatively and objectively in real-life conditions are increasingly possible. This also creates new possibilities to measure fatigue-related changes of daily function using such technologies.</p><p><strong>Summary: </strong>This review examines which digitally assessable parameters in immune-mediated inflammatory and neurodegenerative diseases may have the greatest potential to reflect fatigue-related changes of daily function.</p><p><strong>Key messages: </strong>Results of a standardized analysis of the literature reporting about perception-, capacity-, and performance-evaluating assessment tools indicate that changes of the following parameters: physical activity, independence of daily living, social participation, working life, mental status, cognitive and aerobic capacity, and supervised and unsupervised mobility performance have the highest potential to reflect fatigue-related changes of daily function. These parameters thus hold the greatest potential for quantitatively measuring fatigue in representative diseases in real-life conditions, e.g., with digital wearable technologies. Furthermore, to the best of our knowledge, this is a new approach to analysing evidence for the design of performance-based digital assessment protocols in human research, which may stimulate further systematic research in this area.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"30-39"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10954320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174078","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":"Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.","authors":"Yunzhao Xing, Sheng Zhong, Samuel L Aronson, Francisco M Rausa, Dan E Webster, Michelle H Crouthamel, Li Wang","doi":"10.1159/000536499","DOIUrl":"10.1159/000536499","url":null,"abstract":"<p><strong>Introduction: </strong>Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists.</p><p><strong>Methods: </strong>An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the \"One-Step PASI\" framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture.</p><p><strong>Results: </strong>The highest-performing model demonstrated a mean absolute error of 3.3, Lin's concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline.</p><p><strong>Conclusion: </strong>This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"13-21"},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10911790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140027681","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":"Why Language Matters in Digital Endpoint Development: Harmonized Terminology as a Key Prerequisite for Evidence Generation","authors":"Lada Leyens, Carrie A. Northcott, Lesley Maloney, Marie McCarthy, Nona Dokuzova, Thomas Pfister","doi":"10.1159/000534954","DOIUrl":"https://doi.org/10.1159/000534954","url":null,"abstract":"Abstract Background Developments in the field of digital measures and digitally derived endpoints demand greater attention on globally aligned approaches to enhance digital measure acceptance by regulatory authorities and health technology assessment (HTA) bodies for decision-making. In order to maximize the value of digital measures in global drug development programs and to ensure study teams and regulators are referring to the same items, greater alignment of concepts, definitions, and terminology is required. This is a fast-moving complex field; every day brings new technologies, algorithms, and possibilities. A common language is particularly important when working in multifunctional teams to ensure that there is a clear understanding of what is meant and understood. Summary In the paper, the EFPIA digital endpoint joint subgroup reviews the challenges facing teams working to advance digital endpoints, where different terms are used to describe the same things, where common terms such as “monitoring” have significantly different meaning for different regulatory agencies, where the preface “e” to denote electronic is still used in some contexts, but the term “digital” is used in other, and where there is significant confusion as to what is understood by “raw” when it comes to data derived from digital health technologies. Key Message The EFPIA subgroup is calling for an aligned lexicon. Alignment provides a more predictable path for development, validation, and use of the tools and measures used to collect digital endpoints supporting standardization and consistency in this new field of research, with the goal of increasing regulatory and payer harmonization and acceptance.","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 5","pages":"1 - 12"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}