The Role of Wearable Devices in Chronic Disease Monitoring and Patient Care: A Comprehensive Review

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 8, 2024

Wearable health devices are becoming vital in chronic disease management because they offer real-time monitoring and personalized care. This review explores their effectiveness challenges across medical fields, including cardiology, respiratory health, neurology, endocrinology, orthopedics, oncology, mental health. A thorough literature search identified studies focusing on wearable devices' impact patient outcomes. In wearables have proven effective for hypertension, detecting arrhythmias, aiding cardiac rehabilitation. these enhance asthma continuous of critical parameters. Neurological applications include seizure detection Parkinson's management, with showing promising results improving technology advances thyroid dysfunction monitoring, fertility tracking, diabetes management. Orthopedic improved postsurgical recovery rehabilitation, while help early complication oncology. Mental benefits anxiety detection, post-traumatic stress disorder reduction through biofeedback. conclusion, transformative potential managing illnesses by enhancing engagement. Despite significant improvements adherence outcomes, data accuracy privacy persist. However, ongoing innovation collaboration, we can all be part the solution to maximize technologies healthcare.

Language: Английский

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Movement Disorders and Smart Wrist Devices: A Comprehensive Study

Sensors, Journal Year: 2025, Volume and Issue: 25(1), P. 266 - 266

Published: Jan. 5, 2025

In the medical field, there are several very different movement disorders, such as tremors, Parkinson’s disease, or Huntington’s disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in modern era, use smart wrist devices, smartwatches, wristbands, bracelets spreading among all categories people. This diffusion justified by limited costs, ease use, less invasiveness (and consequently greater acceptability) than other types sensors used for health status monitoring. systematic review aims to synthesize research studies using devices a specific class disorders. Following PRISMA-S guidelines, 130 were selected analyzed. For each study, information provided relating smartwatch/wristband/bracelet model (whether it commercial not), number end-users involved experimentation stage, finally characteristics benchmark dataset possibly testing. Moreover, some articles also reported type raw data extracted from device, implemented designed algorithmic pipeline, classification methodology. turned out most have been published last ten years, showing growing interest scientific community. The mainly investigate relationship between Epilepsy seizure detection topics interest, while few papers analyzing gait Disease, ataxia, Tourette Syndrome. However, results this highlight difficulties still present identified despite advantages these technologies could bring dissemination low-cost solutions usable directly within living environments without need caregivers personnel.

Language: Английский

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Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

Frontiers in Neurology, Journal Year: 2021, Volume and Issue: 12

Published: Aug. 23, 2021

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management epilepsy. recognition activity is essential for diagnostic, forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work revealed accurate capture electrographic possible with an implantable intracranial device, less invasive electroencephalography (EEG) recording systems would optimal. Here, we present preliminary results detection a minimally sub-scalp device that continuously records EEG. Five participants refractory epilepsy who experience at least two clinically identifiable monthly implanted devices (Minder ® ), providing channels data from hemispheres brain. Data captured via behind-the-ear system, which also powers transferred wirelessly mobile phone, where it accessible remotely cloud storage. EEG recordings were compared recorded conventional system during 1-week ambulatory video-EEG monitoring session. Suspect epileptiform (EA) was detected using machine learning algorithms reviewed by trained neurophysiologists. Seizure demonstrated retrospectively utilizing cycles EA previous times. The procedures well-tolerated no significant complications reported. Seizures accurately identified on as visually confirmed periods concurrent scalp recordings. acquired allowed successfully undertaken. area under receiver operating characteristic curve (AUC score) achieved (0.88), comparable best score recent, state-of-the-art

Language: Английский

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Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: Nov. 9, 2021

Abstract The ability to forecast seizures minutes hours in advance of an event has been verified using invasive EEG devices, but not previously demonstrated noninvasive wearable devices over long durations ambulatory setting. In this study we developed a seizure forecasting system with short-term memory (LSTM) recurrent neural network (RNN) algorithm, wrist-worn research-grade physiological sensor device, and tested the patients epilepsy field, concurrent confirmation via implanted recording device. achieved performance significantly better than random predictor for 5 6 studied, mean AUC-ROC 0.80 (range 0.72–0.92). These results provide first clear evidence that direct forecasts are possible setting many epilepsy.

Language: Английский

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Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

EBioMedicine, Journal Year: 2021, Volume and Issue: 72, P. 103619 - 103619

Published: Oct. 1, 2021

Abstract

Background

Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, immunology. In people with epilepsy, epileptic brain activity seizure occurrence have been to follow circadian, weekly, monthly rhythms. Understanding the relationship between these cycles of excitability other physiological systems can provide new insight into causes cycles. The brain-heart link has previously considered in epilepsy research, potential implications for forecasting, therapy, mortality (i.e., sudden unexpected death epilepsy).

Methods

We report results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought examine heart rate seizures adults diagnosed uncontrolled (N=31) healthy adult controls (N=15) using wearable smartwatches mobile diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles were detected continuous wavelet transform. Relationships measured distributions likelihood respect underlying cycle phase.

Findings

Heart all 46 participants (people controls), circadian (N=46), about-weekly (N=25) about-monthly (N=13) being most prevalent. Of 19 had 20 reported seizures, 10 significantly phase locked their

Interpretation

showed similarities may be comodulated likelihood. is relevant also cardiovascular disease. More broadly, understanding shed light on endogenous humans.

Funding

research received funding Australian Government National Health Medical Research Council (investigator grant 1178220), BioMedTech Horizons program, Epilepsy Foundation America's ‘My Gauge' grant.

Language: Английский

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IoT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 413, P. 137506 - 137506

Published: May 16, 2023

Language: Английский

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In‐hospital and home‐based long‐term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience

Epilepsia, Journal Year: 2023, Volume and Issue: 64(4), P. 937 - 950

Published: Jan. 22, 2023

Abstract Objective The aim is to report the performance of an electroencephalogram (EEG) seizure‐detector algorithm on data obtained with a wearable device (WD) in patients focal refractory epilepsy and their experience. Methods Patients used WD, Sensor Dot (SD), measure two channels EEG using dry electrode patches during presurgical evaluation at home for up 8 months. An automated seizure detection flagged regions possible seizures, which we reviewed evaluate algorithm's diagnostic yield. In addition, collected usability, side effects, patient satisfaction electronic diary application (Helpilepsy). Results Sixteen inpatients SD 5 days had 21 seizures. outpatients months reported 101 impaired awareness seizures periods selected analysis. Focal sensitivity based behind‐the‐ear was 52% 23% outpatients. False detections/h, positive predictive value (PPV), F1 scores were 7.13%, .11%, .002% 7.77%, .04%, .001% Artifacts low signal quality contributed poor metrics. detector identified 19 nonreported sleep, when better. Regarding patients' experience, likelihood 6 62%, effects main reason dropping out. Finally, daily monthly questionnaire completion rates 33% 65%, respectively. Significance outpatients, high false alarm PPV scores. This unobtrusive well received but effects. current workflow limit its implementation clinical practice. We suggest different steps improve these metrics

Language: Английский

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Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: Individualized intrapatient models

Epilepsia, Journal Year: 2022, Volume and Issue: 64(S4)

Published: April 8, 2022

One of the most disabling aspects living with chronic epilepsy is unpredictability seizures. Cumulative research in past decades has advanced our understanding dynamics seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed assess whether patient-specific forecasting using remote, minimally invasive ultra-long-term subcutaneous EEG.

Language: Английский

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Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: March 27, 2024

This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges monitoring and management due the unpredictable nature of seizures. Wearable devices offer continuous real-time data collection, providing insights into seizure patterns trends. is important epilepsy because it enables early detection, prediction, personalized intervention, empowering patients healthcare providers. Key findings highlight potential improve detection accuracy, enhance patient empowerment through monitoring, facilitate data-driven decision-making clinical practice. However, further research needed validate accuracy reliability these across diverse populations settings. Collaborative efforts between researchers, clinicians, developers, are essential drive innovation for management, ultimately improving outcomes quality life individuals with this neurological condition.

Language: Английский

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Prospective validation of a seizure diary forecasting falls short

Epilepsia, Journal Year: 2024, Volume and Issue: 65(6), P. 1730 - 1736

Published: April 12, 2024

Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm.

Language: Английский

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