Introduction: 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. Methods: We analyzed videos of PD patients performing finger-tapping tasks across two settings: high-resolution recordings collected in controlled clinical environments (n = 227: “clinical dataset”) and patient-recorded videos from home settings (n = 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. Results: 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. Conclusion: 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.
Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.
Introduction: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. Methods: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. Results: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. Conclusion: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.
Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.
Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.
Introduction: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 LMNA-related DCM. Methods: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. 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 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. 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 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.
IMPORTANCE:Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. OBJECTIVE:The objective of this work is 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. DESIGN:VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. SETTING:Multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), culminating in an in person workshop at the 2025 Bridge2AI Voice Symposium. PARTICIPANTS:A group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. METHODS:VOCAL's iterative process involved five rounds of review, feedback, and an in-person workshop at the international 2025 Bridge2AI Voice Symposium, ensuring the incorporation of diverse perspectives and achieving a robust agreement on the proposed definitions. MAIN OUTCOMES AND MEASURES:Consensus-based definitions for vocal biomarkers, spanning from broad concepts (biomarker, digital biomarker, vocal biomarker) to domain-specific measures (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). RESULTS: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). CONCLUSIONS AND RELEVANCE: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.
Introduction: 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). Methods: 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. Results: 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; p < 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; p < 0.001) after receiving DBS. The AI model achieved an area under the receiver operating characteristic curve of 0.96 for identifying DBS candidates. Conclusions: 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.
Introduction: 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). Methods: 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. Results: 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). Conclusion: 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.
Introduction: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. Methods: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. Conclusion: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.
Introduction: 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. Methods: 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. 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 slower 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: 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.
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: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: 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.
Introduction: A 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. This study aimed to develop and validate a digital framework using GPS-enabled smartphones and inertial sensors to monitor community mobility and estimate clinical function in individuals with chronic stroke or lower limb amputation (LLA). Methods: Ninety individuals with chronic stroke or LLA underwent remote monitoring for 3–9 months. Participants completed standard clinical assessments, and daily mobility data were extracted from GPS and step count features. We conducted four analyses: (1) characterization of group- and individual-level community mobility, (2) evaluation of mobility changes following a mobility-targeted intervention in a single case participant, (3) development of machine-learned models to predict clinical gait outcomes using community data, and (4) estimation of the minimum number of days needed to reliably predict functional outcomes. Results: Community mobility measures revealed substantial variability both across and within individuals, reflecting diverse functional profiles. In a case study, a participant with LLA demonstrated increased activity and movement diversity following a personalized intervention. Machine-learned models estimated 6-Minute Walk Test and 10-Meter Walk Test scores with clinically acceptable error margins (7–10%) using as few as 14 days of community data. Reliable predictions were achievable with just 3–6 days of monitoring. Conclusions: GPS- and smartphone-based monitoring offer a feasible and scalable approach to assess real-world mobility. This approach could close a critical gap in the care continuum and enable us to fully evaluate the real-world impact of treatment interventions while also reducing reliance on frequent in-person evaluations.
Introduction: Gait is a critical indicator of neurological health, with changes often signaling underlying decline. We developed a remote gait monitoring protocol using off-the-shelf shoe-based sensors (RunScribe) to assess gait parameters in real-world home settings. This protocol, known as Gait Assessment with Innovative Technologies – Home-based Use and Benefit (GAIT-HUB), was tested in individuals with multiple sclerosis (MS), a population at high risk for gait impairment due to the disease’s variable progression. Methods: Participants with MS completed an in-clinic baseline gait assessment using a validated sensor (G-Sensor®) and three weekly, remotely supervised gait assessments at home using the RunScribe sensors. Gait parameters were compared across devices using intra-class correlation coefficients (ICCs) and Bland-Altman analyses. Longitudinal reliability of remote assessments and system usability score (SUS) were evaluated. Results: Twenty-nine participants (76% women, ages 19–67, PDDS range 0–5) successfully completed the home-based assessments. High agreement between devices was observed for gait speed, stride length, and cadence (ICCs >0.90), though phases like stance and swing showed more variability. Bland-Altman analyses indicated minimal bias in most parameters. Longitudinal assessments demonstrated strong reliability (ICCs >0.87) for key metrics, and SUS indicated good-to-excellent usability of the remote protocol. Conclusion: The GAIT-HUB protocol enables reliable and feasible home-based gait monitoring using wearable sensors that patients can easily self-apply. This approach provides valuable insights into daily mobility patterns beyond clinical visits, supporting more precise and timely assessments of functional status between appointments and offering the potential for seamless integration into telemedicine routine care.
Introduction:Hypertension is the leading risk factor for cardiovascular disorders. Early detection and initiation of treatment have been identified as the most effective ways to reduce the burden of hypertension. The most common method for detecting hypertension is blood pressure measurement, typically performed with cuff-based devices, where systolic pressure (SBP) and diastolic pressure (DBP) are measured through Korotkoff sounds. Although this method is accurate and non-invasive, it requires technical expertise and is often inaccessible in rural and remote areas. In this study, we investigated the feasibility of using overt speech (random speech corpora) through multiple short recordings for hypertension screening based on two hypertension guidelines: (1) SBP ≥135 mm Hg OR DBP ≥85 mm Hg, and (2) SBP ≥140 mm Hg OR DBP ≥90 mm Hg. Methods:We incorporated speech recordings from 573 participants (197 women) with diverse ages and body mass index and extracted temporal, spectral, and nonlinear acoustic features through three different frameworks, all of which are based on classical and boosted machine learning models. The models were evaluated using a leave-one-subject-out cross-validation scheme. Results:Our proposed pipeline achieved a balanced accuracy (BACC) of 61% for males and 70% for females under the relaxed criterion (SBP ≥135 OR DBP ≥85), and a BACC of 71% for males and 78% for females under the stricter European Society of Hypertension (ESH) guidelines (SBP ≥140 OR DBP ≥90). Conclusion:These results demonstrate the potential of employing overt speech alongside acoustic analysis for hypertension screening.
Introduction:Preoperative physical functional assessments (i.e., assessments that measure capability to perform physical activity) are integral to estimate perioperative risk for older adults. However, these assessments are not routinely performed in-clinic prior to surgery. Walking cadence, or the number of steps walked in a specified amount of time (i.e., steps/min), measures activity intensity and may be able to identify high-risk patients prior to surgery. Smartphones can measure walking characteristics and guide patients through remote functional assessments. Here, we assess feasibility, acceptability, and accuracy of Walk Test, a smartphone application designed to measure walking cadence. Methods:We performed a prospective cohort study of older adults prior to abdominal surgery and enrolled them remotely to perform at-home usual- and fast-paced walks with subsequent validation in-clinic. Each walk (usual- and fast-paced) was 2 min in duration. Feasibility was assessed if 80% of patients could perform all study procedures; acceptability was measured using the Post-Study Survey Usability Questionnaire (PSSUQ); accuracy of our approach was assessed with Lin's concordance coefficient (CCC). activPAL thigh worn accelerometer worn during the in-clinic walk served as a gold standard comparison. We used the CCC to compare the at-home and in-clinic walks as performed by Walk Test. Results:We enrolled 41 participants (mean age 69 ± 5 years, 26 (63%) female); 88% (36/41) successfully completed entire study protocol including independent installation of the application, walk tests (at-home and in-clinic) and questionnaires. Median (interquartile range) overall score of PSSUQ was 1 (1, 1) indicating strong acceptability and usability. The Lin's CCC between the in-clinic activPAL and Walk Test for usual-paced walk was 0.97 (95% CI: 0.96, 0.99, p < 0.001) and for fast-paced walks 0.96 (95% CI: 0.93, 0.98, p < 0.001). The CCC between the at-home and in-clinic walks for usual-paced walks was 0.70 (95% CI: 0.53, 0.86) and for fast-paced walks was 0.46 (95% CI: 0.21, 0.72). Conclusion:We successfully demonstrated the feasibility, acceptability and accuracy of Walk Test to measure walking cadence. Future work is needed to standardize walk test performance at-home to ensure consistency between in-clinic and at-home measures.
Introduction:Impaired walking performance significantly impacts the quality of life in individuals with Parkinson's disease (PD). This study aimed to examine the effects of medication "on" and "off" periods on walking performance, focusing on an alternative aspect of traditional gait analysis by assessing movement components or synergies (i.e., principal movements, PMs). Methods:Principal component analysis was used to decompose kinematic marker data from 22 PD patients (64.1 ± 10.5 years) during self-selected speed overground walking into a set of PMs that cooperatively contribute to the locomotion task. Gait adaptation between medication periods was assessed using two PM-based variables: relative explained variance (rVAR) of the PM's position, reflecting movement structure, and root mean square (RMS) of the PM's acceleration, indicating movement acceleration magnitude and reflecting changes in force or speed. Results:The on-medication condition increased the contribution (greater rVAR) of PM2, representing the swing-phase movement component (p = 0.001), and enhanced movement acceleration magnitudes (greater RMS) in PM4, characterizing the single-leg support phase coupled with trunk rotation (p = 0.026). Conclusion:Although medication enhances propulsion by increasing the contribution of swing-phase movement components, thereby improving forward movement and walking efficiency, it may also lead to instability during the single-leg stance phase.
