Digital Biomarkers最新文献

{"title":"Smartwatch-Based Screening to Identify Candidates for Deep Brain Stimulation in Parkinson's Disease.","authors":"Rabie Fadil, Benjamin L Walter, Greg Kuhlman, Syed A Zaidi, Dustin A Heldman","doi":"10.1159/000553397","DOIUrl":"https://doi.org/10.1159/000553397","url":null,"abstract":"<p><strong>Introduction: </strong>Identifying deep brain stimulation (DBS) candidates, particularly those without access to an advanced specialty center, presents ongoing challenges. This study evaluates the feasibility of a smartwatch system for identifying DBS candidates in Parkinson's disease (PD).</p><p><strong>Methods: </strong>We recruited adults diagnosed with PD and motor complications. Participants wore a consumer smartwatch for at least 4 days per month for 8 months. The smartwatch continuously recorded motion data from its internal motion sensors. Previously validated algorithms used motion data to measure tremor, slowness, and dyskinesia. We compared various metrics in participants who were and were not recommended for DBS and developed an artificial intelligence (AI) model to predict DBS candidacy using features extracted from the motion data.</p><p><strong>Results: </strong>Twenty-three participants were included in the data analysis. Sixteen participants were considered DBS candidates; among them, ten initiated DBS during the study, and six did not due to age or personal preference. Seven participants were not DBS candidates. Bad time (presence of tremor, slowness, and/or dyskinesia as measured by the smartwatch) occurred more often in DBS candidates (3.46 ± 2.23 vs. 1.24 ± 1.23 h/day; <i>p</i> < 0.001) compared to those who were not. Additionally, the system captured a significant reduction in bad time (3.46 ± 2.23 vs. 2.25 ± 2.76 h/day; <i>p</i> < 0.001) after receiving DBS. The AI model achieved an area under the receiver operating characteristic curve of 0.96 for identifying DBS candidates.</p><p><strong>Conclusions: </strong>The results suggest that sensors in commercial smartwatches and AI can help identify DBS candidates and detect improvements resulting from the therapy. This type of remote patient monitoring could expand access to patients who might not otherwise have considered DBS.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"165-177"},"PeriodicalIF":0.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13485297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789521","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":"Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative.","authors":"Mégane Pizzimenti, Ayush Kalia, Jamie A Toghranegar, Mohamed Ebraheem, Nicholas Cummins, Satrajit S Ghosh, James T Anibal, Rhoda Au, Arian Azarang, Ruth H Bahr, Sybille Barvaux, Steven D Bedrick, Hugo Botha, Oita C Coleman, Abir Elbeji, Lampros C Kourtis, Anaïs Rameau, Jaskanwal Deep Singh Sara, Stephanie W Watts, Daria Hemmerling, Jiri Mekyska, Marisha L Speights, Jean-Christophe Bélisle-Pipon, Yael E Bensoussan, Guy Fagherazzi","doi":"10.1159/000553327","DOIUrl":"10.1159/000553327","url":null,"abstract":"<p><strong>Introduction: </strong>Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. The objective of this work was to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL (Vocal Biomarker Guidelines for Ontology, Classification, Application, and Logistics) initiative, a structured, international consensus-based framework that aims to provide standards and guidelines.</p><p><strong>Methods: </strong>VOCAL is a rigorous, international, multistage consensus-building study conducted in 2024-2025. It is a multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), involving a group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, and ethics. VOCAL's iterative process involved 5 rounds of review and feedback and an in-person workshop at the 2025 Bridge2AI Voice Symposium.</p><p><strong>Results: </strong>Consensus-based definitions for vocal biomarkers were developed, spanning from broad concepts to domain-specific measures. A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (level 0: biomarker, digital biomarker, vocal biomarker) to more specific physiological and cognitive domains (level 1: cardio-respiratory acoustic; level 2: voice; level 3: speech/articulatory; level 4: cognitive/language, including linguistic and paralinguistic subtypes).</p><p><strong>Conclusion: </strong>This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"178-190"},"PeriodicalIF":0.0,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988431","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":"Multimodal Machine Learning to Personalize Transcutaneous Spinal Cord Stimulation for Stroke Rehabilitation.","authors":"Ameen Kishta, Rushmin Khazanchi, Nicole Veit, Mitchell Tillman, Kelly McKenzie, Shreya Aalla, Jasmine Glenn-Hunt, Chen Yang, Luigi Privitera, Xin S Yu, Jai Chundi, Francesco Lanotte, Richard L Lieber, Elliot Roth, Arun Jayaraman","doi":"10.1159/000552236","DOIUrl":"10.1159/000552236","url":null,"abstract":"<p><strong>Introduction: </strong>Several studies have identified transcutaneous spinal cord stimulation (tSCS) as a noninvasive neuromodulation technique for improving motor function in individuals with neurological disorders, including stroke. Despite a plethora of preliminary findings, there remains no standardized protocol regarding the optimal tSCS parameters tailored to patients with stroke. The objective of this study was to employ a supervised machine learning (ML) approach to determine the optimal tSCS frequency and intensity parameters for patients with chronic stroke by leveraging data from single-day interventions that systematically varied in frequency and intensity across four stimulation conditions.</p><p><strong>Methods: </strong>Twenty adults with chronic hemiparetic stroke (mean age 53.3 ± 10.8 years; 13 males, 7 females) who were ≥6 months post-stroke was enrolled, excluding individuals with multiple strokes, severe spasticity, or implanted devices. Each participant participated in a baseline session followed by five intervention sessions, during which the frequency and intensity of stimulation were varied randomly. Multimodal sensors, including surface EMG, inertial measurement unit-based kinematics, and spatiotemporal gait parameters, were used to quantify acute changes in gait symmetry, and optimal stimulation frequency and intensity were defined as those yielding the greatest improvement in the combined gait asymmetry metric. A supervised machine-learning classifier was trained using nested leave-one-subject-out cross-validation to predict the optimal stimulation frequency and intensity to maximize differences from the baseline data alone. AUROC values were calculated for the frequency and intensity predictions.</p><p><strong>Results: </strong>The ML models achieved AUROC values of 0.86 [0.75-0.94] for frequency prediction and 0.82 [0.69-0.94] for intensity prediction. The top ten predictive features for each model spanned with spinal motor evoked potentials, wearable sensors, and demographic domains, highlighting multimodal contributions to stimulation optimization.</p><p><strong>Conclusion: </strong>These findings demonstrate that supervised learning can predict individualized tSCS parameters from demographic data and baseline sensor features that yield the greatest improvement in gait symmetry after stroke, representing a promising step toward the data-driven personalization of neuromodulation therapy in neurorehabilitation.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"151-164"},"PeriodicalIF":0.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419498","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":"Cervical Dilation Classification from Electrohysterography and Clinical Features: A Machine-Learning-Derived Digital Biomarker.","authors":"Otniel Portillo-Rodríguez, Jorge Escalante-Gaytán, Oscar Osvaldo Sandoval-González, Paula Romina Soria, Hugo Mendieta-Zerón, Juan Carlos Echeverría, Miguel A Peña-Castillo, Eric Alonso Abarca-Castro, José Javier Reyes-Lagos","doi":"10.1159/000552288","DOIUrl":"10.1159/000552288","url":null,"abstract":"<p><strong>Introduction: </strong>Noninvasive tracking of cervical dilation could reduce discomfort and infection risk from repeated digital examinations during labor. We present an electrohysterography (EHG)-based model framed as a digital biomarker of labor progression that leverages objective physiological signals with minimal clinical context.</p><p><strong>Methods: </strong>We analyzed 72 ten-minute single-channel EHG recordings from low-risk labor cases, yielding 648 segments of 120 s. Signals were filtered into three sub-bands. Twenty-one linear and nonlinear EHG descriptors were combined with two clinical variables, maternal age and gestational age, and two EHG-derived contraction-count features, namely counts of low (LC) and high (HC) uterine contractions, to form 25 predictors. Segments were labeled as low (1-4 cm), moderate (5-6 cm), or advanced (7-10 cm) dilation. Data were split 70/30 into training (<i>n</i> = 454) and independent test (<i>n</i> = 194) sets. Feature importance was estimated using χ², ANOVA, and Kruskal-Wallis ranking. Thirty-three classifiers were evaluated using five-fold cross-validation within the training set, with the 10 top-ranked features. Among all models, a bagged tree ensemble achieved the highest macro-averaged F1 score and was therefore selected as the baseline classifier for this study. We then used a \"Genetic Algorithm Ensemble Bagged Tree (GA-EBT)\" approach, in which a binary-encoded genetic algorithm optimizes the bagged tree classifier's feature combination using stratified five-fold cross-validation with 50 repetitions on the training set.</p><p><strong>Results: </strong>The best cross-validated model was a bagged tree ensemble. Performance plateaued at 17 predictors (median macro-F1 = 0.898) under progressive inclusion. The GA-EBT identified a four-feature subset - maternal age, gestational age, LC count, and HC count - that achieved F1, recall, precision, specificity, and accuracy of 1.000 on the independent test set for classifying cervical dilation stage (low, moderate, advanced).</p><p><strong>Conclusion: </strong>An EHG-derived digital biomarker combining a minimal set of clinical variables and EHG-derived contraction-count features enables accurate classification of cervical dilation stages from single-channel recordings. This pilot-stage classification approach showed maximal internal and independent test performance and may support real-time, noninvasive intrapartum monitoring while potentially reducing repeated digital examinations.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"139-150"},"PeriodicalIF":0.0,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148390215","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":"Impact of Video Quality on Video-Based Digital Assessment of Parkinson's Disease.","authors":"Atefeh Irani, Kye Won Park, Maryam S Mirian, Mahdi Shakouri, Michael Grundy, Juana Ayala, Reshad Hosseini, Hadi Moradi, Martin J McKeown","doi":"10.1159/000551752","DOIUrl":"10.1159/000551752","url":null,"abstract":"<p><strong>Introduction: </strong>Vision-based digital biomarkers have emerged as promising tools for objectively (and possibly remotely) assessing Parkinson's disease (PD) motor signs, addressing inherent limitations of traditional clinical scales like the Unified Parkinson's Disease Rating Scale (UPDRS). However, real-world deployment is hindered by variability in video quality, particularly in uncontrolled home environments. This study aims to quantify the impact of video quality parameters on the accuracy of human pose estimation (HPE) and downstream clinical assessments, including finger-tapping event detection and UPDRS scoring.</p><p><strong>Methods: </strong>We analyzed videos of PD patients performing finger-tapping tasks across two settings: high-resolution recordings collected in controlled clinical environments (<i>n</i> = 227: \"clinical dataset\") and patient-recorded videos from home settings (<i>n</i> = 88: \"home-recorded dataset\"). To evaluate the effect of video quality on assessment accuracy, we introduced systematic degradations and assessed key parameters, including resolution, frame rate, lighting conditions, and hand visibility. Performance was measured using mean per joint position error, percentage of correct keypoints, and estimated landmark failed frames.</p><p><strong>Results: </strong>Low frame rates and inadequate hand coverage within the frame significantly reduced the accuracy of HPE-based assessments. In contrast, video resolution had a less pronounced effect than expected.</p><p><strong>Conclusion: </strong>Frame rate and proper visibility of body parts are more critical than resolution for reliable at-home motor signs evaluation in PD. We establish practical thresholds for video quality in remote PD assessments and provide actionable guidelines for optimizing remote monitoring systems.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"125-138"},"PeriodicalIF":0.0,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148327608","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":"Methods for Wearable-Derived Pulse Rate Measures and Their Application to Modeling the Relationship between Pulse Rate and Motor Activity in Narcolepsy Type 1.","authors":"Eena Kosik-Rose, Raul Torres, Brian Tracey, Tina Olsson, Dmitri Volfson, Francesco Onorati, Marta Karas","doi":"10.1159/000551257","DOIUrl":"10.1159/000551257","url":null,"abstract":"<p><strong>Introduction: </strong>Narcolepsy type 1 (NT1) is a chronic, rare neurological disorder caused by loss of orexin neurons that disrupts the sleep-wake cycle. Current research suggests a link between orexin neuropeptide deficiency and heart rate regulation. Wrist-worn devices enable continuous monitoring of physiological data, including heart rate. We aim to effectively utilize wearables in clinical research by developing robust methodologies to address challenges including data artifacts and missing data.</p><p><strong>Methods: </strong>Empatica wrist-worn smartwatch data were collected for 2 weeks at baseline in the TAK-861-2001 randomized, phase 2 clinical trial (NCT05687903) from participants with NT1. Epoch-level (minute-level) pulse rate data were rigorously screened for artifacts. Daily pulse rate measures (24-h, daytime, and nighttime averages) were derived. Numerical experiments were conducted to evaluate the stability of daily measures as a function of the proportion of valid minute-level data within a given day. Mean pulse rate and motor activity daily measures and the association between the two were quantified using linear mixed-effects models.</p><p><strong>Results: </strong>Participants (<i>n</i> = 110) had a mean age of 34.0 years and 51.8% were female. Approximately 23.5% of the epoch-level pulse rate observations were invalid due to non-wear, missing data, or artifacts. With a ≥70% threshold for valid daily wearable data, participants contributed an average of 10.3 days of data. Mean pulse rates were 72.8, 83.3, and 66.2 beats per minute and mean motor activities were 40.6, 61.5, and 10.3 epoch-level activity counts for 24-h, daytime, and nighttime averages, respectively. Pulse rate was significantly positively associated with motor activity daily measures, with up to 80% of pulse rate variance explained by motor activity levels and individual differences in baseline pulse rate and pulse rate-motor activity association.</p><p><strong>Conclusion: </strong>This work proposes a robust framework for preprocessing smartwatch pulse rate data for analyses in clinical research. The findings from our smartwatch data demonstrate a significant positive association between pulse rate and motor activity in participants with NT1 during a 2-week baseline period.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"113-124"},"PeriodicalIF":0.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13175547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962679","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":"Using Digital Outcomes to Measure Meaningful Aspects of Health in Clinical Trials for Parkinson's Disease: A Scoping Review.","authors":"Marijn J van Es, Nienke A Timmermans, Marjan J Meinders, Willem A Nolen, Bastiaan R Bloem, Sooyoon Shin, Marianne Boenink, Luc J W Evers","doi":"10.1159/000550956","DOIUrl":"10.1159/000550956","url":null,"abstract":"<p><strong>Background: </strong>The importance of considering the meaningfulness of digital measures is increasing with the expanding use of sensor-based monitoring in clinical trials. Regulatory guidelines emphasize measuring meaningful aspects of health specific to a patient population.</p><p><strong>Summary: </strong>Translating meaningful aspects of health into objective measures is not a trivial task. We review how researchers justify the use of digital endpoints in Parkinson's disease trials, with emphasis on meaningfulness to patients. A systematic search (ClinicalTrials.gov; ClinicalTrialsRegister.eu) yielded 45 trials using digital measures as primary or secondary endpoints. We performed a qualitative analysis of trial protocols to identify justifications for using digital endpoints. We found that justifications for meaningfulness were often presented for general health domains and rarely for specific digital endpoints. Moreover, explanation for meaningfulness to patients was often not made explicit, indicating a discrepancy between guidelines and research practice.</p><p><strong>Key messages: </strong>Meaningfulness of specific digital measures to patients is often not explicitly justified and there is a disconnect between guidelines and research practice. Because of the complexity of linking meaningfulness to digital measures, there is a need for sharing examples and lessons learned, and for further developed and differentiated guidelines. Moreover, to foster true meaningfulness of digital measures to patients, there is a need for prioritizing measuring meaningful aspects of health as a central requirement for the development and selection of these measures. The lessons learned from the Parkinson's disease context may inspire and inform the adoption of digital endpoints in other disease areas.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"90-102"},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13132522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147812272","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 Signatures of Momentary Psychological Stress in Real-Life Environments: Results from the Colive Voice Study.","authors":"Noémie Topalian, Abir Elbeji, Charline Bour, Hanin Ayadi, Mégane Pizzimenti, Camille Perchoux, Guy Fagherazzi","doi":"10.1159/000550566","DOIUrl":"10.1159/000550566","url":null,"abstract":"<p><strong>Introduction: </strong>Voice is hypothesized to be modulated by stress and thus could be used as a potential stress detection and monitoring solution. In the literature, vocal biomarkers for stress have mostly been developed on experimental data, with limited samples. Therefore, this study aimed to present insights into the effect of momentary psychological stress on voice in real-life recordings, across different languages, genders, and vocal tasks.</p><p><strong>Methods: </strong>Participants from the Colive Voice study reported their stress level on a 1 to 5 Likert scale. Two tasks were performed: a text reading task and an A-vowel phonation. We analyzed the data cross-sectionally. We extracted vocal features with the DisVoice library and performed ordinary least squares regression models to evaluate the association of vocal features with stress. Models were stratified by gender and language (French/English) and controlled for age, smoking status, alcohol consumption, the presence of chronic disease, education level, mother tongue, well-being, fatigue, and depression. Benjamini-Hochberg correction was applied to control for multiple testing.</p><p><strong>Results: </strong>We analyzed a sample of 4,155 participants, 2,011 in French (1,621 women, 390 men) and 2,144 in English (1,105 women, 1,039 men). In the text reading task, we found that stress was associated with two articulatory features for English-speaking women. Among French-speaking women, higher stress was linked with lower pitch and higher shimmer. The duration of pauses and one glottal feature were also associated with stress. In the A-vowel phonation task, pitch and the variability of the pitch perturbation quotient were lower with stress in English-speaking men. French-speaking women had increased voice intensity and loudness with stress.</p><p><strong>Conclusion: </strong>We were able to confirm the association of momentary psychological stress with various vocal features in real-life settings, but not across languages, vocal tasks, or gender. Future research should include longitudinal studies to investigate the potential of using voice as an intraindividual monitoring biomarker for stress.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"63-73"},"PeriodicalIF":0.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147622114","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":"Functional Regression Methods for Estimating 24-Hour Ambulatory Blood Pressure and Heart Rate Parameters in Narcolepsy Type 1.","authors":"Marta Karas, Francesco Onorati, Raul Torres, Lucie Barateau, Yves Dauvilliers, Brian Tracey, Dmitri Volfson","doi":"10.1159/000550314","DOIUrl":"10.1159/000550314","url":null,"abstract":"<p><strong>Introduction: </strong>The nocturnal dip, a physiological drop in nocturnal blood pressure (BP), is driven by the autonomic nervous system. A reduction of <10% during nocturnal sleep versus daytime wakefulness is considered a \"non-dipping\" BP pattern and associated with increased cardiovascular disease risk in the general population. This study aimed to compare different methods for estimating BP and heart rate (HR) nocturnal dip from ambulatory BP monitoring (ABPM) data in individuals with narcolepsy type 1 (NT1).</p><p><strong>Methods: </strong>Baseline ABPM data were from participants with NT1 in the randomized TAK-994 phase 2 clinical trial (NCT04096560). Sleep period time (SPT) windows were estimated from raw accelerometer data overlapping with baseline and week 3 ABPM visits. Three approaches estimated BP and HR dip: (1) fixed-window, with daytime defined as 06:00 to 22:00, nighttime as 00:00 to 06:00, and dip defined as a drop from the daytime to nighttime window average; (2) 24-h pattern employing a two-component cosinor model to estimate a continuous 24-h pattern of BP and HR, and defining dip as a drop from pattern average to its lowest point; and (3) actigraphy-based, with dip defined as a drop from non-SPT to SPT average of BP and HR, utilizing algorithmically identified SPT aiming to best reflect participants' actual sleep periods.</p><p><strong>Results: </strong>The analytic sample consisted of 31 participants with NT1. Comparing actigraphy-based dip with fixed-window and 24-h pattern dips, the 24-h pattern dip had higher Pearson's correlation than the fixed-window dip across all three parameters (0.91 vs. 0.87, 0.88 vs. 0.68, and 0.88 vs. 0.56 for systolic BP [SBP], diastolic BP [DBP], and HR, respectively). We found substantial between- and within-participant variability in SPT timing and duration. A total of 61% of participants had a fixed-window SBP dip <10%, and 41% had a fixed-window DBP dip <10%. The 30th percentile of SBP/DBP dip varied substantially across calculation methods: 3.8%/8.6% (fixed-window), 6.8%/14.1% (24-h pattern), and 6.7%/12.1% (actigraphy-based).</p><p><strong>Conclusion: </strong>Estimated dip values from the 24-h pattern approach with a two-component cosinor model for BP and HR were strongly correlated with actigraphy-based dip values, which utilized an objective algorithm to identify participants' sleep. The 24-h pattern approach offers a robust alternative to the fixed-window method for assessing dipping, especially in populations with sleep timing variations and disturbances, like NT1, and does not require simultaneous actigraphy measurement. The classification of a \"non-dipper\" varies depending on both the dip type (SBP vs. DBP) and the dip estimation method.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"79-89"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13082773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697908","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":"Machine Learning-Based Stepping Filter Improves Estimates of Moderate-to-Vigorous-Intensity Physical Activity from Wrist Actigraphy.","authors":"Josh Cherian, Emily C Hector, Shyh-Huei Chen, Stephanie M George, John M Jakicic, Ryan S McGinnis, Michael E Miller, W Jack Rejeski, Jason Fanning","doi":"10.1159/000551306","DOIUrl":"10.1159/000551306","url":null,"abstract":"<p><strong>Introduction: </strong>In this proof-of-concept study involving older adults with varying levels of function, we sought to answer the question: can the application of a simple stepping classification algorithm reduce non-stepping sources of acceleration from wrist-worn ActiGraph data prior to the application of acceleration cut points to quantify time spent in moderate-to-vigorous-intensity physical activity (MVPA)?</p><p><strong>Methods: </strong>Participants completed a series of known tasks including stepping tasks, cycling tasks, and home chores repeated across 2 days while wearing an ActivPAL (PAL Technologies LLC, Glasgow, Scotland) activity monitor on the thigh - which provides an accurate measurement of stepping behavior - and an ActiGraph GT3X+ wrist worn device. A stepping classifier was trained for each monitor, and we examined time spent in MVPA prior to and following removal of non-stepping behaviors. Thirty participants (72.51 ± 6.93 years; 50% female) completed the study.</p><p><strong>Results: </strong>Across both visits, the ActiGraph reported participants engaged in 48.47 ± 17.15 min of MVPA, and the ActivPAL recorded 14.00 ± 6.44 min, an approximately 3.5-fold difference. After application of the classifier-based filter, the ActiGraph recorded a mean MVPA duration of 20.57 ± 10.27 min of MVPA (a reduction of 60%), and the ActivPAL recorded 11.80 ± 5.27 min of MVPA (a reduction of 14%), a 1.74-fold difference.</p><p><strong>Conclusion: </strong>These results suggest that identifying and removing non-stepping behaviors prior to scoring accelerometer data via intensity thresholds may help to reduce inflated MVPA duration attributable to non-stepping wrist movement. Future work should focus on producing strong population-specific step classification algorithms and explore their application to existing and future studies focused on PA behavior.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"103-112"},"PeriodicalIF":0.0,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147867064","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}