Introduction: Actigraphy-quantified physical activity (PA) allows for continuous measurements of physical activity 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-DCM (NCT03439514) clinical trial in LMNA-related dilated cardiomyopathy. Methods: Between 2020 and 2022, REALM-DCM randomized 37 patients with actigraphy worn on the non-dominant wrist continuously to monitor daily physical activity. Of those 35 participants had analyzable data for this analysis. Results: 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 ten hours of awake wear time for at least four days within the two-week monitoring period prior to and after clinic visits. Kansas City Cardiomyopathy Questionnaire (KCCQ) physical limitation scores was 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). 6 minute walk time (6MWT) distance was positively associated with time spent in MVPA and moderate activity, and total step counts. Patient Global Impression (PGI) Symptom Heart Failure Severity were 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. Conclusion: This is the largest and most longitudinal dataset of LMNA-DCM patients collected and reported to date using wearable sensors to gain understanding of physical activity patterns in these patients. These data help to understand the potential use of actigraphy monitoring and wearable technologies in genetic cardiomyopathy and heart failure clinical trials.

Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

Background: 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. Objective: 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. Methods: Retrospective secondary analysis of an open-source pilot. Ten men ≥65 years with metastatic prostate cancer completed weekly PRO-CTCAE items and self-rated health over 12 weeks. Symptom-triggered (or random) 48-h, 10-Hz wrist-accelerometry sessions were aggregated to 60-s counts-per-minute (CPM) and vector-magnitude change (VMC). From these, 98 pre-specified statistical and spectral features were extracted. Associations with a weekly Symptom-Burden + SRH composite (SBSI) were assessed using linear mixed-effects models (days + random intercept), Spearman correlations across five 30-day bins, penalized mixed-effects regression LASSO (λ=0.5, 1), and a 500-tree random forest. Results: 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; p=0.012), while an overall rhythm balance measure (CPM_median_freq) tended to shift lower (ρ=−0.88; p=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 rhythm balance showed individual variability. Conclusion: 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.

Introduction: Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease. Patients with MG are typically evaluated by neuromuscular experts through in-person neurologic examinations. These assessments are time-consuming, require significant disease expertise, and capture only a snapshot of disease. Methods: Given this need, we developed a multimodal digital health technology (DHT) called BioDigit MG, for monitoring MG symptoms and objectively measuring disease severity. BioDigit MG includes tablet-guided speech and video-based assessments, electronic patient-reported outcomes relevant to MG, and a wearable sensor to measure physical activity and posture during activities of daily living. Results: We assessed the feasibility and acceptability of BioDigit MG by conducting a clinical study with 20 participants with MG who used the DHT. During the study, a total of 219 speech tasks and 119 videos were collected by the DHT, achieving 100% reliability in data collection and transfer. To evaluate technology acceptance and usability, we conducted face-to-face interviews with the 20 MG patients and 5 expert clinicians. Participants found the DHT highly effective, easy to use, and well-suited to their needs. Efficient and reliable data transfer capabilities of BioDigit MG ensured that patient-generated data were promptly and securely delivered to healthcare providers. Conclusion: These feasibility findings demonstrate that BioDigit MG is capable of reliable multimodal data collection and is acceptable to both patients and clinicians, supporting its potential for use in future larger scale validation studies.

Introduction: Incorporating outcome measures that assess the most impactful symptoms is a priority for clinical trials. We qualitatively examined whether caregivers of individuals with Rett syndrome deemed breathing dysfunction as a meaningful and measurable aspect of health. Methods: We conducted semi-structured interviews (N = 13) with caregivers of individuals with Rett syndrome followed by thematic analysis grounded in theory to examine themes. Results: Themes and subthemes for experiences with breathing dysfunction emerged: (1) meaningfulness; (2) impact; and (3) connecting with other symptoms. Two themes for preferences for digitally measuring breathing dysfunction emerged: (1) conditional willingness and (2) benefits of digital measurement. Conclusion: Caregivers reported that breathing dysfunction was meaningful and measurable and had significant impacts on their child’s lives as well as theirs and their families. This study lays the groundwork for guiding the development of novel measures and outcomes within future clinical trials managing breathing dysfunction in Rett syndrome.

Introduction: Cognitive performance declines with age and predicts important life outcomes, making it a promising – yet underutilized – biomarker of aging. In this study, we aimed to establish the feasibility and value of game-based digital biomarkers of cognitive aging using data from a home-based cognitive assessment game. Methods: Participants (N = 871; age 18–75) completed Tunnel Runner, a 20–25 min cognitive game measuring reaction speed, response inhibition, interference control, response-rule switching, and decision-making. To assess the game’s out-of-sample predictive accuracy, we trained machine learning models to predict participants’ chronological age based on 17 game-based cognitive metrics and evaluated their performance using nested cross-validation. Cognitive aging scores were calculated as out-of-sample prediction errors from the best-performing model, and then adjusted for age-dependence using generalized additive models. These aging scores were then considered alongside three other variables: depression, ADHD, and gamer identity. Results: The best-performing model, stacked ensemble from the automated machine learning framework AutoGluon, predicted out-of-sample chronological age with a mean absolute error of 6.97 years, a correlation of 0.626, and concordance of 0.698. No evidence of bias in predictive accuracy was found for gender or gaming identity. Prediction patterns and cognitive aging values met several expectations based on previous research: reduced cognitive aging in participants with self-reported ADHD, negative association between cognitive aging and gamer identity, and limited predictive differentiation under age 30. Findings regarding self-reported depression were inconclusive, though consistent with prior work. Conclusion: Game-based assessment can produce accessible digital biomarkers of cognitive aging that reflect meaningful individual differences. This approach enables scalable and low-burden cognitive aging assessment, with potential applications for early detection of cognitive decline, longitudinal tracking, and intervention evaluation.

Plain Language SummaryA primary goal of physical medicine and rehabilitation is restoring community mobility after injury or illness. However, there is no clinically accepted real-world method to measure community mobility, which fundamentally limits our ability to evaluate treatment effectiveness. To address this gap, we adopted a framework using GPS and smartphone technology to extract daily measures of community mobility such as distance traveled, number of locations visited, and step count. As proof-of-concept, we recorded community mobility in 90 individuals with chronic stroke or LLA, resulting in over 4,000 days of data. These data captured a variety of behaviors within and between individuals, as well as responses to a mobility-targeted intervention. Machine-learned models using as few as 14 community days could estimate traditional clinical mobility scores with 7–10% error. This approach could close a critical gap in the care continuum and enable us to fully evaluate the real-world impact of treatment interventions.

Plain Language SummaryWalking ability is an important measure of neurological health, as changes in gait can indicate underlying conditions like multiple sclerosis (MS). However, gait assessments are typically performed in clinics, which may not reflect real-world movement. To address this, we developed GAIT-HUB, a home-based gait monitoring system using commercially available wearable shoe sensors that track walking patterns remotely. In our study, 29 participants with MS completed both in-clinic and home-based gait tests over 3 weeks. We compared data from the shoe sensors with a validated clinic-based sensor to determine accuracy and reliability. The results showed strong agreement between devices for key walking parameters like gait speed, stride length, and cadence, confirming the feasibility of remote gait monitoring. The system was also highly usable, with participants successfully completing assessments independently. By allowing patients to monitor their walking ability at home, GAIT-HUB provides a more complete picture of mobility changes over time, beyond what can be captured in occasional clinic visits. This approach could improve disease management, support telemedicine care, and enhance early detection of mobility decline and response to intervention in neurological conditions.

Introduction: Detecting positional nystagmus is essential for diagnosing benign paroxysmal positional vertigo (BPPV). Therefore, developing methods to streamline this diagnosis can improve timely patient management and help prevent unnecessary emergency department visits. We aimed to evaluate the accuracy of a smartphone eye-tracking application in quantifying eye movements during positional testing to detect positional nystagmus. Methods: We recruited patients with positional dizziness suspected of having BPPV from the vestibular rehabilitation clinic and the consult service for dizzy patients (Tele-Dizzy) at Johns Hopkins Hospital. Using an in-house smartphone app (EyePhone), we recorded eye movements during the Dix-Hallpike and supine roll tests. Two expert clinicians reviewed the videos, and a third one adjudicated the disagreements. Eye position data obtained from the EyePhone app were analyzed with an embedded algorithm to identify positional nystagmus. Using the adjudicated expert review as the reference standard, we evaluated EyePhone’s accuracy in detecting positional nystagmus by calculating the sensitivity, specificity, and predictive values. Results: We recruited ten participants, 60% women, with an average age of 61.8 years. We reviewed 23 positional eye movement videos of participants while undergoing positional testing. The final adjudicated expert review identified positional nystagmus in 3 (13%) videos. The phone application traces indicated nystagmus in all 3 of these videos (sensitivity = 100% [95% CI = 44–100%]) and correctly ruled it out in 20 traces (specificity = 100% [95% CI = 84–100%]). The app demonstrated a positive predictive value of 100% (95% CI = 43–100%) and a negative predictive value of 100% (95% CI = 84–100%). Conclusions: This small pilot study shows proof-of-concept that a smartphone eye-tracking app without special phone attachments can detect positional nystagmus.