Wearables in Chronomedicine and Interpretation of Circadian Health

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 327 - 327

Published: Jan. 30, 2025

Wearable devices have gained increasing attention for use in multifunctional applications related to health monitoring, particularly research of the circadian rhythms cognitive functions and metabolic processes. In this comprehensive review, we encompass how wearables can be used study disease. We highlight importance these as markers well-being potential predictors outcomes. focus on wearable technologies sleep research, medicine, chronomedicine beyond domain emphasize actigraphy a validated tool monitoring sleep, activity, light exposure. discuss various mathematical methods currently analyze actigraphic data, such parametric non-parametric approaches, linear, non-linear, neural network-based applied quantify non-circadian variability. also introduce novel actigraphy-derived markers, which personalized proxies status, assisting discriminating between disease, offering insights into neurobehavioral status. lifestyle factors physical activity exposure modulate brain health. establishing reference standards measures further refine data interpretation improve clinical The review calls existing tools methods, deepen our understanding health, develop healthcare strategies.

Language: Английский

---

Prodromal Parkinson’s Disease

Neurologic Clinics, Journal Year: 2025, Volume and Issue: 43(2), P. 209 - 228

Published: Jan. 22, 2025

Language: Английский

---

Predictive Modeling of Social Frailty in Older Adults through Digital Biomarkers: Insights from Fitbit-Derived Data on Circadian Rhythm and Heart Rate Changes (Preprint)

Published: Feb. 8, 2025

Social frailty poses a potential risk even for relatively healthy older adults, necessitating development of early detection and prevention strategies. Recently, consumer-grade wearable devices have gained attention their ability to provide accurate sensor data, digital biomarkers social screening could be calculated from these data. The objective this study was explore associated with using data recorded by Fitbit evaluate relationship health outcomes in adults. This cross-sectional conducted 102 community-dwelling Participants attending programs wore the Inspire series on non-dominant wrist at least seven consecutive days, during which step count heart rate were collected. Standardized questionnaires used assess physical functions, cognitive frailty, based scores, participants categorized into three groups: robust, pre-frailty, frailty. analyzed calculate nonparametric extended cosinor rhythm metrics, along rate-related metrics. final sample included 86 who as robust (n = 28), pre-frailty 39), 19). mean age 77.14 years (SD 5.70), 90.6% women 78). Multinomial logistic regression analysis revealed that step-based metric, Intradaily Coefficient Variation (ICV.st), significantly pre-frailty. including delta resting (dRHR) UpMesor.hr, showed significant associations both Furthermore, standard deviation (HR.sd) alpha.hr predictors Specifically, dRHR, defined difference between overall average (RHR), exhibited negative (odds ratio [OR] 0.82, 95% confidence interval [CI] 0.68-0.97, p 0.024) (OR 0.74, CI 0.58-0.94, 0.015). linear model association ICV.st Word List Memory (WM) score, measure decline (β -0.04, 0.024). identified novel metrics These findings suggest devices, are low-cost accessible, hold promise tools evaluating its factors through enabling calculation biomarkers. Future research should include larger sizes focus clinical applications findings. UMIN-CTR

Language: Английский

---

Detecting Sleep/Wake Rhythm Disruption Related to Cognition in Older Adults With and Without Mild Cognitive Impairment Using the myRhythmWatch Platform: Feasibility and Correlation Study

JMIR Aging, Journal Year: 2025, Volume and Issue: 8, P. e67294 - e67294

Published: April 7, 2025

Abstract Background Consumer wearable devices could, in theory, provide sufficient accelerometer data for measuring the 24-hour sleep/wake risk factors dementia that have been identified prior research. To our knowledge, no study older adults has demonstrated feasibility and acceptability of accessing consumer to compute rhythm measures. Objective We aimed establish characterizing measures using gathered from Apple Watch with without mild cognitive impairment (MCI), examine correlations these neuropsychological test performance. Methods Of 40 enrolled (mean [SD] age 67.2 [8.4] years; 72.5% female), 19 had MCI 21 disorder (NCD). Participants were provided devices, oriented software (myRhythmWatch or myRW), asked use system a week. The primary outcome was whether participants collected enough assess (ie, ≥3 valid continuous days). extracted standard nonparametric extended-cosine based metrics. Neuropsychological tests gauged immediate delayed memory (Hopkins Verbal Learning Test) as well processing speed set-shifting (Oral Trails Parts A B). Results All meet providing (≥3 days) mean (SD) recording length somewhat shorter group at 6.6 (1.2) days compared NCD 7.2 (0.6) days. Later activity onset times associated worse performance ( β =−.28). More fragmented rhythms =.40). Conclusions Using Watch-based myRW gather raw is feasible MCI. Sleep/wake variables generated this correlated function, suggesting future studies can approach evaluate novel, scalable, factor characterization targeted therapy approaches.

Language: Английский

---

Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data

PLOS Digital Health, Journal Year: 2025, Volume and Issue: 4(4), P. e0000795 - e0000795

Published: April 25, 2025

Neurodegenerative diseases, such as Alzheimer’s and Parkinson’s Disease, pose a significant healthcare burden to the aging population. Structural MRI brain parameters accelerometry data from wearable devices have been proven be useful predictors for these diseases but separately examined in prior literature. This study aims determine whether combination of may improve detection prognostication disease, compared with alone. A cohort 19,793 participants free neurodegenerative disease at time imaging capture UK Biobank longitudinal follow-up was derived test this hypothesis. Relevant structural parameters, collected devices, standard polygenic risk scores lifestyle information were obtained. Subsequent development among recorded (mean 5.9 years), positive cases defined those diagnosed least one year after imaging. machine learning algorithm (XGBoost) employed create prediction models disease. model consisting all factors, including data, PRS, information, achieved highest AUC value (0.819) out tested models. that excluded lowest (0.688). Feature importance analyses revealed 18 20 most important features while 2 data. Our demonstrates potential utility combining predict incidence diseases. Future prospective studies across different populations should conducted confirm results look differences predictive ability various types

Language: Английский

---

Adverse effects of late sleep on physical health in a large cohort of community-dwelling adults

European Journal of Internal Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

---

Monitoring Wearable Devices for Elderly People with Dementia: A Review

Designs, Journal Year: 2024, Volume and Issue: 8(4), P. 75 - 75

Published: July 29, 2024

The growth in the prevalence of dementias is associated with a phenomenon that challenges 21st century, population aging. Dementias require physical and mental effort on part caregivers, making it difficult to promote controlled active care. This review aims explore usability integration wearable devices designed measure daily activities elderly people dementia. A survey was carried out following databases: LILACS, Science Direct PubMed, between 2018 2024 methodologies as well selection criteria are briefly described. total 27 articles were included met inclusion answered research question. As main conclusions, various monitoring measurements interaction aspects critically important, demonstrating their significant contributions controlled, adequate monitoring, despite incomplete compliance key which could guarantee solutions economically accessible institutions or other organizations through application design requirements. Future should not only focus development follow essential requirements but also further studying needs adversities dementia face pillar for feasible device.

Language: Английский

---

Examining resilience to Alzheimer’s disease through the lens of monoaminergic neuromodulator systems

Trends in Neurosciences, Journal Year: 2024, Volume and Issue: 47(11), P. 892 - 903

Published: Oct. 4, 2024

Language: Английский

---

Contactless longitudinal monitoring of ageing and dementia-related sleep trajectories in the home

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 29, 2024

Disturbed sleep is common in ageing and dementia, but objectively quantifying it over time challenging. We validated a contactless under-mattress pressure sensor developed data analysis method to assess patterns the home long periods. Data from 13,588 individuals (3.7 million nights) general population were compared dementia cohort of 93 patients (>40,000 nights). Dementia was associated with heterogeneous disturbances primarily characterised by advanced delayed timing, longer bed, more bed exits. Explainable machine learning used derive Research Institute Sleep Index (DRI-SI), digital biomarker their evolution. The DRI-SI can detect effects acute clinical events progression at individual level. This approach bridges gap care providing feasible for monitoring health events, disease risk.

Language: Английский

---

Multidimensional Sleep Profiles via Machine learning and Risk of Dementia and Cardiovascular Disease

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Abstract Importance Sleep health comprises several dimensions such as duration and fragmentation of sleep, circadian activity, daytime behavior. Yet, most research has focused on individual sleep characteristics. Studies are needed to identify profiles incorporating multiple assess how different may be linked adverse outcomes. Objective To actigraphy-based 24-hour sleep/circadian in older men investigate whether these associated with the incidence dementia cardiovascular disease (CVD) events over 12 years. Design Data came from a prospective study participants recruited between 2003-2005 followed until 2015-2016. Setting Multicenter population-based cohort study. Participants Among 3,135 enrolled, we excluded 331 missing or invalid actigraphy data 137 significant cognitive impairment at baseline, leading sample 2,667 participants. Exposures Leveraging 20 actigraphy-derived activity rhythm variables, determined using an unsupervised machine learning technique based coalesced generalized hyperbolic mixture modeling. Main Outcomes Measures Incidence CVD events. Results We identified three distinct profiles: active healthy sleepers (AHS; n=1,707 (64.0%); characterized by normal duration, higher quality, stronger rhythmicity, during wake periods), fragmented poor (FPS; n=376 (14.1%); lower fragmentation, shorter weaker rhythmicity), long frequent nappers (LFN; n=584 (21.9%); longer more naps, rhythmicity). Over 12-year follow-up, compared AHS, FPS had increased risks (Hazard Ratio (HR)=1.35, 95% confidence interval (CI)=1.02-1.78 HR=1.32, CI=1.08-1.60, respectively) after multivariable adjustment, whereas LFN showed marginal association risk (HR=1.16, CI=0.98-1.37) but not (HR=1.09, 95%CI=0.86-1.38). Conclusion Relevance multidimensional health. Compared sleepers, overall rhythms exhibited worse incident These results highlight potential targets for interventions need comprehensive screening Key Points Question: Are there men, if so, they years? Findings: Three were identified: [AHS], [FPS], [LFN]. Meaning: Older events, suggesting their target populations

Language: Английский

---