Remote monitoring of amyotrophic lateral sclerosis using wearable sensors detects differences in disease progression and survival: a prospective cohort study

EBioMedicine, Journal Year: 2024, Volume and Issue: 103, P. 105104 - 105104

Published: April 6, 2024

BackgroundThere is an urgent need for objective and sensitive measures to quantify clinical disease progression gauge the response treatment in trials amyotrophic lateral sclerosis (ALS). Here, we evaluate ability of accelerometer-derived outcome detect differential assess its longitudinal associations with overall survival patients ALS.MethodsPatients ALS wore accelerometer on hip 3–7 days, every 2–3 months during a multi-year observation period. An outcome, Vertical Movement Index (VMI), was calculated, together predicted rates, jointly analysed survival. The utility VMI evaluated using comparisons patient-reported functionality, while impact various monitoring schemes empirical power explored through simulations.FindingsIn total, 97 (70.1% male) 1995 total 27,701 h. highly discriminatory revealing faster rates decline worse prognosis compared those better (p < 0.0001). strongly associated hazard death (HR 0.20, 95% CI: 0.09–0.44, p 0.0001), where decrease 0.19–0.41 unit reduced ambulatory status. Recommendations future studies accelerometery are provided.InterpretationThe results serve as motivation incorporate outcomes trials, which essential further validation these markers meaningful endpoints.FundingStichting Nederland (TRICALS-Reactive-II).

Language: Английский

---

Smartphone-based measures as real-world indicators of functional status in advanced cancer patients

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

ABSTRACT Objective This study evaluated the feasibility of using smartphone-based metrics to monitor physical functioning and quality life in patients with advanced gynecological cancers. We analyzed associations between gait (step count, cadence, stride acceleration) measures mobility (home time, distance traveled, number significant locations visited) patient-reported outcomes (PROMs). Methods studied raw accelerometer GPS data from smartphones over 180-days for 85 gynecologic computed smartphone sensor data, PROMs (performance status, health-related life, functioning) surveys at baseline, 30, 90, 180 days. assessed longitudinal digital linear mixed-effects models, attention adherence temporal trends. Results Smartphone was high: 83% participants reported daily usage >1 hour; 74.1% had >16 hours use. Gait measures, particularly step count acceleration, were statistically associated PROMs. Worsening ECOG performance status corresponded reduced (ECOG 3 vs. 0: -1837 steps/day, p <0.001), while higher PROMIS Physical Function increase (+72.64 steps/day per one-point increase, <0.001). Mobility less strongly but provided complementary insights into patients’ behavioral patterns. Conclusion Smartphone-based offer robust, real-world individuals’ health statuses, providing a scalable, low-burden alternative wearable devices. The high levels use among underscore integrating this technology routine oncology care.

Language: Английский

---

Using wearable sensors and machine learning to assess upper limb function in Huntington’s disease

Communications Medicine, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 25, 2025

Huntington's disease, a neurodegenerative disorder, impairs both upper and lower limb function, typically assessed in clinical settings. However, wearable sensors offer the opportunity to monitor real-world data that complements assessments, providing more comprehensive understanding of disease symptoms. In this study, we function individuals with (HD, n = 16), prodromal HD (pHD, 7), controls (CTR, 16) using wrist-worn sensor over 7-day period. Goal-directed hand movements are detected through deep learning model, kinematic features each movement analyzed. The collected is used predict groups scores statistical machine models. Here show significant differences goal-directed exist between groups. Additionally, several these strongly correlate scores. Classification models accurately distinguish HD, pHD, CTR individuals, achieving balanced accuracy 67% recall 0.72 for group. Regression effectively This study demonstrates potential offering tool early detection, remote monitoring, assessing treatment efficacy trials. People can have difficulty moving, experiencing involuntary limbs. aimed better understand how affects whether devices be this. Individuals those at risk it, healthy participants wore small device on their wrist week track during daily activities. We advanced computer analyze severity. main finding was could clearly people risk, people, helping research shows technology effect treatments future. Nunes et al. score by applying readings from obtained 7 day Differences seen score.

Language: Английский

---

The use of digital devices to monitor physical behaviour in motor neuron disease: a systematic review (Preprint)

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e68479 - e68479

Published: March 1, 2025

Background Motor neuron disease (MND) is a progressive and incurable neurodegenerative disease. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) the primary clinical tool for assessing severity progression in MND. However, despite its widespread use, it does not adequately capture extent of physical function decline. There an urgent need sensitive measures that can be used to robustly evaluate new treatments. Measures derived from digital devices are beginning assess progression. value establishing consensus approach standardizing use such devices. Objective We aimed explore how being quantify free-living behavior evaluated feasibility assessed implications monitoring future trials practice. Methods Systematic searches 4 databases were performed October 2023 June 2024. Peer-reviewed English-language articles (including preprints) examined people living with MND their included. Study reporting quality was using 22-item checklist (maximum possible score=44 points). Results In total, 12 met inclusion criteria data extraction. All studies longitudinal observational design, but collection, analysis, protocols varied. Quality assessment scores ranged between 19 40 points. Sample sizes 10 376 at baseline, declining over course study. Most accelerometer device worn on wrist, chest, hip, or ankle. Participants typically asked continuously wear 1 8 days 1- 4-month intervals, running weeks 24 months. Some participants full duration. Studies traditional end points focusing duration, intensity, frequency activity nontraditional features individual’s movement patterns. correlation coefficients (r) ALSFRS-R 0.31 0.78. Greater frequencies improved point sensitivity shown provide smaller sample size requirements shorter durations hypothetical trials. People found acceptable reported low burden. Adherence (67%) good, ranging approximately 86% 96%, differences evident locations. perspectives other users practice explored. Conclusions Remote infancy has potential function. It essential develop statement, working toward agreed standardized methods reporting.

Language: Английский

---

AI‐Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference

Clinical and Translational Science, Journal Year: 2025, Volume and Issue: 18(4)

Published: April 1, 2025

ABSTRACT Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, trials, patient care. This review summarizes the key takeaways from AI preconference at American Society for Clinical Pharmacology Therapeutics (ASCPT) 2024 Annual Meeting Colorado Springs, where experts academia, industry, regulatory bodies discussed how streamlining discovery, dosing strategies, outcome assessment, The theme of was centered around can empower pharmacologists researchers make informed decisions translate research findings into practice. also looked impact large language models biomedical these are democratizing data analysis empowering researchers. application explainable predicting efficacy safety, ethical considerations that should be applied when integrating were touched upon. By sharing diverse perspectives real‐world examples, this shows used bring efficiency accelerate discovery development address patients' unmet needs.

Language: Английский

---

The use of digital devices to monitor physical behaviour in motor neuron disease: a systematic review (Preprint)

Published: Nov. 6, 2024

Motor neuron disease (MND) is a progressive and incurable neurodegenerative disease. There an urgent need for sensitive measures of progression that can be used to robustly evaluate new treatments. Measures physical function, derived from digital devices, are beginning assess progression. Given MND relatively rare, there value in establishing consensus approach standardizing use such devices. This systematic review explored how devices being quantify free-living behaviour people living with (plwMND). We evaluated the feasibility using assessed implications monitoring future design clinical trials. Systematic searches four databases were performed October 2023 June 2024. Peer-reviewed articles (including pre-prints) written English language plwMND included. Twelve met inclusion criteria data extraction. Studies traditional endpoints focusing on duration, intensity, frequency activity or non-traditional features individual’s movement patterns. Greater frequencies improved endpoint sensitivity was shown provide smaller sample size requirements shorter durations hypothetical PlwMND found acceptable reported low burden. The perspectives other end-users practice not explored. Remote its infancy but has exciting potential function MND. It essential develop statement within community, working towards agreed standardised methods collection, analysis reporting.

Language: Английский

---

Novel Digital Wearable Sensors for Drug Development in Pharmaceutical Industry

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 28, 2024

As clinical trials evolve with technological advancements, wearable sensors and digital health technologies (DHTs) have significantly enhanced data collection by providing continuous, near real-time measurements. Traditional methods, constrained infrequent site visits subjective measures, often result in sparse, low-resolution that limits understanding of patient outcomes. The adoption wearables drug development has led to the growth novel endpoints across multiple therapeutic areas, such as stride velocity Duchenne Muscular Dystrophy physical activity heart failure. Regulatory bodies issued guidance supporting integration DHTs, emphasizing objective endpoints. US Food Drug Administration’s Digital Health Center Excellence guidelines on remote acquisition exemplify this support. Additionally, frameworks Medicine Society’s “V3+” standardize validation fit-for-purpose Emerging analytical approaches for sensor data, including functional analysis handling missing further bolster utility trials. Collectively, these advancements allow a more comprehensive nuanced health, improving both precision applicability trial Ultimately, revolutionizes monitoring, enhancing regulatory decision-making.

Language: Английский

---