Digital Biomarkers最新文献

{"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":"https://doi.org/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":"https://doi.org/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":"https://doi.org/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":"The Digital Physical Exam: A Pilot Exploring the Utility of Smartphone and Smartwatch Wearable Data for Hospital Medicine.","authors":"Ujwal Srivastava, Ekanath Shrihari Rangan, Wasan Kumar, Michael Snyder, Thomas Savage","doi":"10.1159/000550798","DOIUrl":"https://doi.org/10.1159/000550798","url":null,"abstract":"<p><strong>Introduction: </strong>Wearable devices such as smartphones and smartwatches collect large volumes of biometric data on patients, yet their utility for admitting inpatient providers remains largely unexplored.</p><p><strong>Methods: </strong>This exploratory pilot screened patients admitted to Stanford University Hospital for use of smartphones or smartwatches. For patients reporting wearable use, preadmission biometric data were collected and analyzed in relation to inpatient outcomes, including admitting diagnosis and discharge disposition.</p><p><strong>Results: </strong>Among 137 screened patients, 27 reported smartphone or smartwatch use, and 16 had adequate preadmission wearable data for analysis. Step count was the most consistently recorded metric. Among those analyzed, a steep decline in step count prior to hospitalization was associated with discharge to a skilled nursing facility or need for home health services. Patients with cardiac diagnoses also exhibited more pronounced declines in step count compared to those with noncardiac conditions.</p><p><strong>Conclusion: </strong>Despite low rates of wearable usage, our findings suggest that trends in prehospital biometric data may offer early insights into patient functional status and discharge needs. As wearable adoption increases, such data could enhance inpatient decision-making and discharge planning.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"74-78"},"PeriodicalIF":0.0,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13065307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671299","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 a Speech-Based Model of Premanifest Huntington's Disease Progression Using Deep Neural Networks.","authors":"Luis A Sierra, Japleen Kaur, Namhee Kwon, Vinod Subramanian, Raymond Brueckner, Nate Blaylock, Henry O'Connell, Samuel A Frank, Jody Corey-Bloom, Simon Laganiere","doi":"10.1159/000549327","DOIUrl":"https://doi.org/10.1159/000549327","url":null,"abstract":"<p><strong>Introduction: </strong>Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by motor, cognitive, and psychiatric decline. The Unified Huntington's Disease Rating Scale Total Motor Score (UHDRS-TMS) is standard for staging manifest disease, but is relatively insensitive to subtle premanifest changes. Speech abnormalities are emerging as candidate digital biomarkers; however, reliably separating premanifest HD (preHD) from healthy controls remains challenging. Here, we assess the feasibility of a speech-only approach by training and comparing multiple classifiers across diverse feature sets and structured tasks to determine whether speech alone can discriminate preHD from controls.</p><p><strong>Methods: </strong>Speech samples were collected from 94 individuals with HD (38 premanifest, 56 manifest) and 36 controls using a standardized six-task protocol administered via tablet. From these recordings, 188 lexical and prosodic features were automatically extracted. We trained 4 machine learning classifiers: random forest, support vector machine, XGBoost, and deep neural networks (DNNs), within 10-fold cross-validation using three feature configurations: (1) all tasks (188 features), (2) the top 30 ANOVA-ranked features, and (3) 22 features from the Caterpillar passage alone.</p><p><strong>Results: </strong>Traditional classifiers showed limited accuracy. A DNN using only the Caterpillar task achieved 81% unweighted accuracy for classifying preHD versus controls. Accuracy increased to 83% for prodromal HD and 87% when all HD participants were compared to controls. Adding features from additional tasks did not improve performance.</p><p><strong>Conclusion: </strong>A brief, structured speech task combined with deep learning enabled accurate classification of preHD. These findings support speech analysis as a scalable, objective tool for early disease detection and monitoring.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"53-62"},"PeriodicalIF":0.0,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147270122","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":"Facial Expression and Predicting and Monitoring Response to Depression Treatment: A Systematic Review.","authors":"David Plevin, Simon Hartmann, K Oliver Schubert, Leo Chen, Scott R Clark","doi":"10.1159/000550413","DOIUrl":"https://doi.org/10.1159/000550413","url":null,"abstract":"<p><strong>Introduction: </strong>The identification of biomarkers for treatment response in major depression is critical to the further development of personalized treatment. There is a recognized relationship between facial expression and depression of mood, and previous literature also indicates that facial expression is associated with treatment outcomes in depression. This suggests that facial expression may have use as a biomarker for treatment response. There is no previous synthesis of related research to drive the development of new digital approaches.</p><p><strong>Methods: </strong>We conducted a systematic review using three databases (MEDLINE, Scopus, and PsycINFO), identifying English-language publications (journal articles or books) that assessed either facial muscle activity or expression as predictors of treatment response or correlates of treatment outcome in depression. Risk of bias was assessed using a National Institutes of Health quality assessment tool.</p><p><strong>Results: </strong>We identified 12 studies, involving a total of 389 participants, which used a variety of different assessment methods and thus assessment outcomes, including electromyography, observer-related assessments (including Facial Action Coding System), and automated tools of facial expression assessment. Depression treatment response correlated with an increase in facial expressivity. Greater activity in the corrugator and zygomatic muscles, and lower levels of lip tightening and downward lip movement, may predict treatment response.</p><p><strong>Conclusions: </strong>Included studies were limited by heterogeneity in facial expression assessment tools and outcomes, along with demographic homogeneity. The findings of the review suggest that facial expression analysis may offer an avenue for biomarkers of depression status and treatment response prediction.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"41-52"},"PeriodicalIF":0.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225800","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":"Actigraphy-Quantified Physical Activity Measurements in Patients with Symptomatic <i>LMNA</i>-Related Dilated Cardiomyopathy in REALM-Dilated Cardiomyopathy.","authors":"Pablo Garcia-Pavia, Xuemei Cai, Junrui Di, Huihua Li, Charmaine Demanuele, Jonathan Bruno, Tina Marie Hinnershitz, Franca S Angeli, Calum A MacRae","doi":"10.1159/000550259","DOIUrl":"10.1159/000550259","url":null,"abstract":"<p><strong>Introduction: </strong>Actigraphy-quantified physical activity (PA) allows for continuous measurements of PA that are reflective of real-world day-to-day functioning and morbidity in persons living with cardiomyopathy. This analysis reports the results of actigraphy monitoring and relates these to other clinical outcome assessments in the phase 3, multinational REALM-dilated cardiomyopathy (DCM) (NCT03439514) clinical trial in <i>LMNA</i>-related DCM.</p><p><strong>Methods: </strong>Between 2020 and 2022, REALM-DCM randomized 37 patients with actigraphy worn on the nondominant wrist continuously to monitor daily PA. Of those, 35 participants had analyzable data for this analysis.</p><p><strong>Results: </strong>The median duration of actigraphy monitoring for all participants was 293 days across 120 patient visits. Over 85% of the visits met a predefined threshold of wear-time compliance of 10 h of awake wear time for at least 4 days within the 2-week monitoring period prior to and after clinic visits. Kansas City Cardiomyopathy Questionnaire (KCCQ) physical limitation scores were positively associated with several actigraphy-quantified PA metrics, including moderate-to-vigorous physical activity (MVPA), moderate activity, non-sedentary behavior, total step counts, total activity counts (all 3 axes and their vector magnitude). Six-minute walk time distance was positively associated with time spent in MVPA and moderate activity, and total step counts. Patient Global Impression (PGI) Symptom Heart Failure Severity was negatively associated with non-sedentary behavior, total activity counts (vector magnitude, X- and Y-axes), and light activity. Actigraphy endpoints also distinguished between NYHA class II and class III patients. Actigraphy endpoints did not correlate with the KCCQ total score.</p><p><strong>Conclusion: </strong>This is the largest and most longitudinal dataset of <i>LMNA</i>-DCM patients collected and reported to date using wearable sensors to gain understanding of PA patterns in these patients. These data help understand the potential use of actigraphy monitoring and wearable technologies in genetic cardiomyopathy and heart failure clinical trials.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"32-40"},"PeriodicalIF":0.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12904659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146200368","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":"3DeepVOG: An Open-Source Framework for Real-Time, Accurate 3D Gaze Tracking with Deep Learning.","authors":"Jingkang Zhao, Seyed-Ahmad Ahmadi, Julian Decker, Ken Möhwald, Peter Zu Eulenburg, Andreas Zwergal, Virginia L Flanagin, Max Wuehr","doi":"10.1159/000549948","DOIUrl":"10.1159/000549948","url":null,"abstract":"<p><strong>Introduction: </strong>Eye movements are key biomarkers for diagnosing and monitoring neuro-otological, neuro-ophthalmological and neurodegenerative disorders. Video-oculography (VOG) systems enable detection of small, rapid eye movements and subtle oculomotor pathologies that may be missed during clinical exams. However, they rely on high-quality input, struggle with torsional movements, and are often limited by high costs in clinical and research settings.</p><p><strong>Methods: </strong>To overcome these limitations, we developed 3DeepVOG, a deep learning-based framework for three-dimensional monocular gaze tracking (horizontal, vertical, and torsional rotation) that operates robustly across varied imaging conditions, including low-light and noisy environments. The method combines automated pupil and iris segmentation with geometrically interpretable estimation using a two-sphere anatomical eyeball model with corneal refraction correction. Torsion is tracked in real time using a novel mini-patch template matching approach. The system was trained on over 24,000 annotated samples obtained across multiple devices and clinical scenarios. Application was tested against a gold-standard VOG system in healthy controls.</p><p><strong>Results: </strong>3DeepVOG operates in real time (>300 fps) and achieves gaze errors of ∼0.1° in all three dimensions. Oculomotor measures - saccadic peak velocity, smooth pursuit gain, and optokinetic nystagmus slow-phase velocity - show good-to-excellent agreement with a clinical gold-standard system. As proof of concept, we present a case of acute unilateral vestibular failure where 3DeepVOG reliably captures 3D nystagmus.</p><p><strong>Conclusions: </strong>3DeepVOG enables accurate, quantitative eye movement tracking across three dimensions under diverse conditions. As an open-source framework, it provides an accessible and scalable tool for advancing research and clinical assessment in neurological oculomotor disorders.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"21-31"},"PeriodicalIF":0.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12880844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141389","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":"Putting Theory into Practice by Developing a Novel Digital Health Technology-Derived Endpoint in Sleep Quality.","authors":"Frank Kramer, Jonas Krauss, Jaya Pal, John Batchelor, Parla Yuksel, Kathleen O Sullivan, Marta Stepien, Amy Bertha","doi":"10.1159/000549984","DOIUrl":"10.1159/000549984","url":null,"abstract":"<p><strong>Introduction: </strong>Sleep disturbances associated with menopause (SDM) are common and bothersome, but there are currently no specifically licensed treatments, and studies thus far have used different methodologies to measure sleep quality. Among those, digital health technologies (DHTs) present an innovative approach that supports patient-centric drug development by providing insights into how a patient responds to treatment in real-world settings. DHTs therefore may offer a solution to provide unobtrusive objective measurement of SDM. Here we describe the joint development of a novel DHT-derived endpoint for assessing sleep quality in menopausal women through a collaborative approach from evidence generation to analytical, clinical, and usability validation based on regulatory guidance.</p><p><strong>Methods: </strong>To demonstrate the fit-for-purpose of the novel DHT-derived endpoint, Bayer (drug developer), Sleepiz AG (DHT provider), and DEEP Measures (collaboration platform provider) partnered and applied established frameworks to leverage prior work while compiling comprehensive data, conducting a gap analysis, and curating evidence in the DEEP Measures collaboration platform based on and in preparation for discussions with health authorities. Initial regulatory feedback from health authorities provided useful input and supported the study design on the incorporation of the DHT-derived endpoint into the clinical development program of elinzanetant. Through collaborative efforts between the drug developer and the DHT provider, the novel DHT-derived endpoint (Sleepiz One+ for continuous, home-based measurement of wake after sleep onset in SDM and other sleep parameters) was implemented as an exploratory endpoint in a phase 2 pilot study where data to demonstrate fit-for-purpose were generated and validated against polysomnography, the gold-standard objective measure for sleep. The study outcomes alongside the results of the gap analyses and leveraging prior work were then structured systematically in the DEEP Measures platform. Data were organized according to the DEEP Stack model (which included information on the measurement definition, target solution profile, and instrumentation), and these facilitated the integration of our outputs directly into the regulatory package used for following health authority interactions to drive the acceptance of the novel endpoint.</p><p><strong>Conclusion: </strong>We outline how various stakeholders collaborated to leverage prior evidence, interacted with regulatory authority, and incorporated a novel DHT-derived endpoint into clinical development programs. Evidence and data generated in the present project have the potential to build the basis for further endpoint and DHT development and validation.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12830005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050837","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":"Tolerability and the Accelerome: Open-Source Wrist Accelerometry Relates to Symptom Burden in Androgen Ablated Older Men with Prostate Cancer.","authors":"Nabiel Mir, Konstantinos Ameranis, Yan Che, Russell Z Szmulewitz, Megan Huisingh-Scheetz","doi":"10.1159/000549704","DOIUrl":"10.1159/000549704","url":null,"abstract":"<p><strong>Introduction: </strong>Older men on androgen suppression for prostate cancer experience substantial symptom burden that is often missed between clinic visits. In prior work from our group, frequency-domain features ranked highly for predicting geriatric impairment, motivating a focus on interpretable spectral measures from open-source wrist accelerometry. Our overall objective was to identify accelerometry features from a pre-specified library that track weekly symptom burden in older men on ADT, and to characterize the temporal scale of the top candidates; spectral features were of particular interest.</p><p><strong>Methods: </strong>A retrospective secondary analysis of an open-source pilot was performed. Ten men ≥65 years with metastatic prostate cancer completed weekly symptom burden and self-rated health over ∼100 days. Symptom-triggered (and random) 48-h, 10-Hz wrist-accelerometry sessions were aggregated to 60-s counts-per-minute (CPM) and vector-magnitude change. From these, 98 pre-specified statistical and spectral features were extracted. Associations with a weekly Symptom Burden + Self-Rated Health Index composite (SBSI) were assessed using linear mixed-effects models (days + random intercept), Spearman correlations across five 30-day bins, penalized mixed-effects regression least absolute shrinkage and selection operator (λ = 0.5, 1), and a 500-tree random forest.</p><p><strong>Results: </strong>Nine participants provided 44 monitoring windows (14-48 h). In mixed-effects models, two CPM features were nominally associated with SBSI but did not survive false-discovery-rate adjustment. Across 30-day bins, a minute-scale restlessness pattern (CPM_top_15_freq3) rose with higher SBSI (ρ = +0.95; <i>p</i> = 0.012), while an overall rhythm balance measure (CPM_median_freq) tended to shift lower (ρ = -0.88; <i>p</i> = 0.049). Penalized models (λ = 1) retained both features, and random-forest importance ranked them highest. Within-participant plots showed restlessness increased during higher symptom weeks, while slower rhythm balance showed individual variability.</p><p><strong>Conclusion: </strong>Two interpretable CPM spectral features - restlessness (CPM_top_15_freq3) and global rhythm balance (CPM_median_freq) - were consistently associated with weekly symptom burden in this cohort. Findings are preliminary and warrant prospective validation for remote symptom monitoring.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"10 1","pages":"11-20"},"PeriodicalIF":0.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12863745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112451","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}