Digital Biomarkers最新文献

{"title":"Digital Measures Development: Lessons Learned from an Expert Workshop Addressing Cross-Therapeutic Area Measures of Sleep.","authors":"Piper Fromy, Michael Kremliovsky, Emmanuel Mignot, Mark Aloia, Jonathan Berent, Farah Hasan, Dennis Hwang, Jiat-Ling Poon, Rebecca Malcolm, Christopher Miller, Womba Nawa, Jessie Bakker","doi":"10.1159/000539253","DOIUrl":"10.1159/000539253","url":null,"abstract":"<p><strong>Introduction: </strong>The Digital Measures Development: Core Measures of Sleep project, led by the Digital Medicine Society (DiMe), emphasizes the importance of sleep as a cornerstone of health and the need for standardized measurements of sleep and its disturbances outside the laboratory. This initiative recognizes the complex relationship between sleep and overall health, addressing it as both a symptom of underlying conditions and a consequence of therapeutic interventions. It aims to fill a crucial gap in healthcare by promoting the development of accessible, nonintrusive, and cost-effective digital tools for sleep assessment, focusing on factors important to patients, caregivers, and clinicians.</p><p><strong>Methods: </strong>A central feature of this project was an expert workshop conducted on April 19th, 2023. The workshop convened stakeholders from diverse backgrounds, including regulatory, payer, industry, academic, and patient groups, to deliberate on the project's direction. This gathering focused on discussing the challenges and necessities of measuring sleep across various therapeutic areas, aiming to identify broad areas for initial focus while considering the feasibility of generalizing these measures where applicable. The methodological emphasis was on leveraging expert consensus to guide the project's approach to digital sleep measurement.</p><p><strong>Results: </strong>The workshop resulted in the identification of seven key themes that will direct the DiMe Core Digital Measures of Sleep project and the broader field of sleep research moving forward. These themes underscore the project's innovative approach to sleep health, highlighting the complexity of omni-therapeutic sleep measurement and identifying potential areas for targeted research and development. The discussions and outcomes of the workshop serve as a roadmap for enhancing digital sleep measurement tools, ensuring they are relevant, accurate, and capable of addressing the nuanced needs of diverse patient populations.</p><p><strong>Conclusion: </strong>The Digital Medicine Society's Core Measures of Sleep project represents a pivotal effort to advance sleep health through digital innovation. By focusing on the development of standardized, patient-centric, and clinically relevant digital sleep assessment tools, the project addresses a significant need in healthcare. The expert workshop's outcomes underscore the importance of collaborative, multi-stakeholder engagement in identifying and overcoming the challenges of sleep measurement. This initiative sets a new precedent for the integration of digital tools into sleep health research and practice, promising to improve outcomes for patients worldwide by enhancing our understanding and measurement of sleep.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"132-139"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626281","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 Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students.","authors":"Mikaela Irene Fudolig, Laura S P Bloomfield, Matthew Price, Yoshi M Bird, Johanna E Hidalgo, Julia N Kim, Jordan Llorin, Juniper Lovato, Ellen W McGinnis, Ryan S McGinnis, Taylor Ricketts, Kathryn Stanton, Peter Sheridan Dodds, Christopher M Danforth","doi":"10.1159/000539487","DOIUrl":"10.1159/000539487","url":null,"abstract":"<p><strong>Introduction: </strong>Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health.</p><p><strong>Methods: </strong>As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached.</p><p><strong>Results: </strong>Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females.</p><p><strong>Conclusion: </strong>Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"120-131"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626283","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":"Self-Recording of Eye Movements in Amyotrophic Lateral Sclerosis Patients Using a Smartphone Eye-Tracking App.","authors":"Pouya Barahim Bastani, Ali S Saber Tehrani, Shervin Badihian, Hector Rieiro, David Rastall, Nathan Farrell, Max Parker, Jorge Otero-Millan, Ahmed Hassoon, David Newman-Toker, Lora L Clawson, Alpa Uchil, Kristen Riley, Steven R Zeiler","doi":"10.1159/000538992","DOIUrl":"10.1159/000538992","url":null,"abstract":"<p><strong>Introduction: </strong>Amyotrophic lateral sclerosis (ALS) can affect various eye movements, making eye tracking a potential means for disease monitoring. In this study, we evaluated the feasibility of ALS patients self-recording their eye movements using the \"EyePhone,\" a smartphone eye-tracking application.</p><p><strong>Methods: </strong>We prospectively enrolled ten participants and provided them with an iPhone equipped with the EyePhone app and a PowerPoint presentation with step-by-step recording instructions. The goal was for the participants to record their eye movements (saccades and smooth pursuit) without the help of the study team. Afterward, a trained physician administered the same tests using video-oculography (VOG) goggles and asked the participants to complete a questionnaire regarding their self-recording experience.</p><p><strong>Results: </strong>All participants successfully completed the self-recording process without assistance from the study team. Questionnaire data indicated that participants viewed self-recording with EyePhone favorably, considering it easy and comfortable. Moreover, 70% indicated that they prefer self-recording to being recorded by VOG goggles.</p><p><strong>Conclusion: </strong>With proper instruction, ALS patients can effectively use the EyePhone to record their eye movements, potentially even in a home environment. These results demonstrate the potential for smartphone eye-tracking technology as a viable and self-administered tool for monitoring disease progression in ALS, reducing the need for frequent clinic visits.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"8 1","pages":"111-119"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626282","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":"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}