Physiological Sensors Equipped in Wearable Devices for Management of Long COVID Persisting Symptoms: Scoping Review (Preprint) DOI
Shikha Kukreti,

Meng-Ting Lu,

Chun‐Yin Yeh

et al.

Published: Dec. 2, 2024

BACKGROUND Wearable technology has evolved in managing COVID-19, offering early monitoring of key physiological parameters. However, the role wearables tracking and long COVID is less understood requires further exploration their potential. OBJECTIVE This review assessed application effectiveness wearable devices symptoms, focusing on commonly used sensors potential for improving long-term patient care. METHODS A literature search was conducted across databases including PubMed, Embase, Web Science, Cochrane Central, adhering to PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses extension Scoping Reviews) reporting guidelines. The updated regularly throughout 2024. Abstract full-text screening selection were facilitated using Rayyan software developed by Qatar Computing Research Institute. Quality appraisal Joanna Briggs Institute (JBI) critical tool ensure methodological rigor included studies. Data extracted study characteristics, devices, used, monitored parameters, results synthesized a narrative format. RESULTS total 1186 articles identified, after duplicate removal screening, 15 studies initially included, with 11 meeting criteria final data synthesis. varied design, ranging from observational interventional trials, involved sample sizes 3 17,667 participants different countries. In total, 10 monitor capturing metrics such as heart rate variability, body temperature, sleep, physical activity. Smartwatches most fitness trackers electrocardiography photoplethysmography rate, oxygen saturation, respiratory rate. Of 4 Food Drug Administration–approved, emphasizing reliability validation collected. Studies primarily United States Europe, reflecting significant regional research interest management. CONCLUSIONS highlights providing continuous personalized patients. Although show promise persistent needed improve usability, validate efficacy, enhance engagement.

Language: Английский

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors DOI Creative Commons
Carlijn I R Braem, Utku Ş. Yavuz, Hermie Hermens

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(5), P. 1526 - 1526

Published: Feb. 27, 2024

Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing data get causal insight into the mechanisms leading data. Methods: Two-week-long from a continuous glucose monitor and Fitbit activity tracker recording heart rate (HR) step count free-living patients with type 2 mellitus were used. The gap size distribution was fitted Planck test for not at random (MNAR) difference between distributions tested Chi-squared test. Significant dispersion over time Kruskal–Wallis Dunn post hoc analysis. Results: 77 subjects resulted 73 cleaned glucose, 70 HR 68 recordings. sizes followed distribution. frequency differed significantly (p < 0.001), therefore MNAR. In more found night (23:00–01:00), count, measurement days 6 7 0.001). both cases, caused by insufficient of synchronization. Conclusions: Our novel approach investigating statistics revealed CGM

Language: Английский

Citations

4
Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems DOI Creative Commons
Jialin Liu, Yi‐Tao Wang, Yiwei Liu

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(7), P. 2180 - 2180

Published: March 28, 2024

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety applications people's daily lives, not only requiring devices have excellent flexibility and biocompatibility, but also taking into account redundant data communication delays due use large number sensors. Fortunately, emerging paradigms near-sensor in-sensor computing, together with proposal flexible neuromorphic devices, provides viable solution for application intelligent low-power devices. Therefore, based on new computing are great research value. This review discusses status five-sense sensing system architectures, considering material design, structural design circuit design. Furthermore, we summarize challenging problems that need solved provide an outlook potential

Language: Английский

Citations

4
Physiological Sensors Equipped in Wearable Devices for Management of Long COVID Persisting Symptoms: Scoping Review DOI Creative Commons
Shikha Kukreti,

Meng-Ting Lu,

Chun‐Yin Yeh

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e69506 - e69506

Published: March 26, 2025

Background Wearable technology has evolved in managing COVID-19, offering early monitoring of key physiological parameters. However, the role wearables tracking and long COVID is less understood requires further exploration their potential. Objective This review assessed application effectiveness wearable devices symptoms, focusing on commonly used sensors potential for improving long-term patient care. Methods A literature search was conducted across databases including PubMed, Embase, Web Science, Cochrane Central, adhering to PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses extension Scoping Reviews) reporting guidelines. The updated regularly throughout 2024. Abstract full-text screening selection were facilitated using Rayyan software developed by Qatar Computing Research Institute. Quality appraisal Joanna Briggs Institute (JBI) critical tool ensure methodological rigor included studies. Data extracted study characteristics, devices, used, monitored parameters, results synthesized a narrative format. Results total 1186 articles identified, after duplicate removal screening, 15 studies initially included, with 11 meeting criteria final data synthesis. varied design, ranging from observational interventional trials, involved sample sizes 3 17,667 participants different countries. In total, 10 monitor capturing metrics such as heart rate variability, body temperature, sleep, physical activity. Smartwatches most fitness trackers electrocardiography photoplethysmography rate, oxygen saturation, respiratory rate. Of 4 Food Drug Administration–approved, emphasizing reliability validation collected. Studies primarily United States Europe, reflecting significant regional research interest management. Conclusions highlights providing continuous personalized patients. Although show promise persistent needed improve usability, validate efficacy, enhance engagement.

Language: Английский

Citations

0
Predicting Blood Glucose Levels with Organic Neuromorphic Micro‐Networks DOI Creative Commons

Ibrahim Kurt,

Imke Krauhausen, Simone Spolaor

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(27)

Published: April 29, 2024

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results reliable predictions, offering timely warnings fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality life diabetic patients. transforming this field, potentially leading to implantable smart devices fully pancreas. However, transition presents several challenges, including need specialized, compact, lightweight, low-power hardware. Organic polymer-based electronics solution as they have ability implement behavior networks, operate at low voltage, possess key attributes like flexibility, stretchability, biocompatibility. Here, study focuses on implementing systems. How minimize network requirements, downscale architecture, integrate with electrochemical neuromorphic organic devices, meeting strict demands implants in-body computation investigated.

Language: Английский

Citations

3
Wearable Devices in Colorectal Surgery: A Scoping Review DOI Open Access
Konstantinos Kavallieros, Lampros Karakozis, R Hayward

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(13), P. 2303 - 2303

Published: June 22, 2024

Wearable devices are increasingly utilised to monitor patients perioperatively, allowing for continuous data collection and early complication detection. There is considerable variability in the types usage settings of wearables, particularly within colorectal surgery. To address this, a scoping review was conducted investigate current utilisation wearable A systematic search across MEDLINE Embase following PRISMA Scoping Review guidelines. Results were synthesised narratively, categorised by perioperative phase (preoperative; postoperative; combination), supplemented with descriptive statistics tables. Out 1525 studies initially identified, 20 included, reporting on 10 different devices. Use varied those used preoperatively tending focus baseline physical status or prehabilitation, while postoperative use centred around monitoring identification complications. can enhance monitoring, enable proactive interventions, promote personalised care improved patient outcomes

Language: Английский

Citations

3
Machine Learning for Breast Cancer Detection with Dual-Port Textile UWB MIMO Bra-Tenna System DOI Creative Commons

Azza H. Elnaggar,

Anwer S. Abd El-Hameed,

Mohamed Yakout

et al.

Information, Journal Year: 2024, Volume and Issue: 15(8), P. 467 - 467

Published: Aug. 6, 2024

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work to investigate which type of polarization most effective BC detection, using the combination orthogonal signals with machine learning (ML) techniques enhance accuracy. have a bandwidth ranging from 2–12 GHz. To complement proposed system, algorithms (MLAs) developed and tested its functionality. Using scattered at different polarizations, uses MLAs predict early stages. Classification are highly data classification, especially biomedical field. Two scenarios considered: Scenario 1, where detects tumor or non-tumor, 2, three classes one, two, non-tumors. This confirms that can detect tumors as small 10 mm. ML techniques, including eight such Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme (XG Boost), Logistic Regression (LR), applied balanced dataset. For optimal analysis BC, performance evaluation performed. Notably, SVM achieves outstanding both scenarios, metrics F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), precision all exceeding 90%, helping doctors effectively BC. Furthermore, Horizontal-Horizontal (HH) sensor configuration achieved highest accuracy 98% 99% SVMs two respectively.

Language: Английский

Citations

1
Strategic management of employee churn: Leveraging machine learning for sustainable development and competitive advantage in emerging markets DOI
Poorva Agrawal,

Seema Ghangale,

Bablu Kumar Dhar

et al.

Business Strategy & Development, Journal Year: 2024, Volume and Issue: 7(4)

Published: Nov. 5, 2024

Abstract Employee churn or attrition presents significant challenges, especially in emerging markets, where it can disrupt business operations and inflate recruitment costs. This research leverages machine learning techniques to predict employee churn, focusing on developing sustainable inclusive retention strategies that enhance competitiveness. By analyzing a range of predictive algorithms key variables associated with the study identifies most effective models for predicting attrition. A comprehensive exploratory data analysis was conducted using an indigenous model, offering practical insights human resource management markets. The findings align development goals (SDGs), promoting decent work, economic growth. contributes strategy by proposing data‐driven solutions workforce stability development.

Language: Английский

Citations

1
Potenzial von Wearable-Technologie in der Knieendoprothetik DOI
Ricardo Smits Serena, Matthias Cotic, Florian Hinterwimmer

et al.

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

Published: Sept. 28, 2024

Wearable technology has developed rapidly in recent years and offers promising possibilities for supporting optimizing orthopaedic procedures, especially pre- postoperatively. The continuous monitoring precise analysis of movement patterns, as well the individual adaptation rehabilitation processes are just some potential benefits wearable technology. aim this paper is to evaluate knee arthroplasty provide an overview evidence that currently available.

Language: Английский

Citations

0
Identifying the high-risk surgical patient DOI
Javier Ripollés‐Melchor, César Aldecoa

Current Opinion in Critical Care, Journal Year: 2024, Volume and Issue: 30(6), P. 624 - 628

Published: Nov. 6, 2024

aInfanta Leonor University Hospital bUniversidad Complutense de Madrid; Madrid cRío Hortega dUniversidad Valladolid, Spain Correspondence to Prof. Javier Ripollés-Melchor, Universitario Infanta Leonor: Leonor, Madrid, Spain. E-mail: [email protected]

Language: Английский

Citations

0
Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment DOI Creative Commons
Aleš Procházka, Daniel Martynek,

Marie Vitujová

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7330 - 7330

Published: Nov. 16, 2024

Individual physiotherapy is crucial in treating patients with various pain and health issues, significantly impacts abdominal surgical outcomes further medical problems. Recent technological artificial intelligent advancements have equipped healthcare professionals innovative tools, such as sensor systems telemedicine equipment, offering groundbreaking opportunities to monitor analyze patients' physical activity. This paper investigates the potential applications of mobile accelerometers evaluating symmetry specific rehabilitation exercises using a dataset 1280 tests on 16 individuals age range between 8 75 years. A comprehensive computational methodology introduced, incorporating traditional digital signal processing, feature extraction both time transform domains, advanced classification techniques. The study employs machine learning methods, including support vector machines, Bayesian analysis, neural networks, evaluate balance activities. proposed approach achieved high accuracy 90.6% distinguishing left- right-side motion patterns by employing features from frequency domains two-layer network. These findings demonstrate promising precise monitoring increase probability successful recovery, highlighting enhance patient care treatment outcomes.

Language: Английский

Citations

0