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Chang CH, Lien WC, Chiu TP, Yang TH, Wei CC, Kuo YL, Yeh CH, Liu B, Chen PJ, Lin YC. A novel smart somatosensory wearable assistive device for older adults' home rehabilitation during the COVID-19 pandemic. Front Public Health 2023; 11:1026662. [PMID: 37790724 PMCID: PMC10544986 DOI: 10.3389/fpubh.2023.1026662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 08/29/2023] [Indexed: 10/05/2023] Open
Abstract
Background Due to the Coronavirus disease 19 (COVID-19) related social distancing measures and health service suspension, physical activity has declined, leading to increased falling risk and disability, and consequently, compromising the older adult health. How to improve the quality of older adult life has become a crucial social issue. Objective In traditional rehabilitation, manual and repetitive muscle training cannot identify the patient's rehabilitation effect, and increasing the willingness to use it is not easy. Therefore, based on the usability perspective, this study aims to develop a novel smart somatosensory wearable assistive device (called SSWAD) combined with wireless surface electromyography (sEMG) and exergame software and hardware technology. The older adult can do knee extension, ankle dorsiflexion, and ankle plantar flexion rehabilitation exercises at home. Meanwhile, sEMG values can be digitally recorded to assist physicians (or professionals) in judgment, treatment, or diagnosis. Methods To explore whether the novel SSWAD could improve the older adult willingness to use and motivation for home rehabilitation, 25 frail older adult (12 males and 13 females with an average age of 69.3) perform the rehabilitation program with the SSWAD, followed by completing the system usability scale (SUS) questionnaire and the semi-structured interview for the quantitative and qualitative analyses. In addition, we further investigate whether the factor of gender or prior rehabilitation experience would affect the home rehabilitation willingness or not. Results According to the overall SUS score, the novel SSWAD has good overall usability performance (77.70), meaning that the SSWAD makes older adult feel interested and improves their willingness for continuous rehabilitation at home. In addition, the individual item scores of SUS are shown that female older adult with prior rehabilitation experience perform better in "Learnability" (t = 2.35, p = 0.03) and "Confidence" (t = -3.24, p = 0.01). On the contrary, male older adult without rehabilitation experience are more willing to adopt new technologies (t = -2.73, p = 0.02), and perform better in "Learnability" (t = 2.18, p = 0.04) and "Confidence" (t = -3.75, p < 0.001) with the SSWAD. In addition, the result of the semi-structured interview shows that the operation of the SSWAD is highly flexible, thus reducing older adult burden during the rehabilitation exercise and using them long-term. Conclusion This novel SSWAD receives consistently positive feedback regardless of the gender or prior rehabilitation experience of elders. The SSWAD could be used as a novel way of home rehabilitation for elders, especially during the COVID-19 pandemic. Older adult can do rehabilitation exercises at home, and physicians could make proper judgments or adjust suitable treatments online according to the sEMG data, which older adult can know their rehabilitation progress at the same time. Most importantly, older adult do not have to go to the hospital every time for rehabilitation, which significantly reduces time and the risk of infection.
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Affiliation(s)
- Chien-Hsiang Chang
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
| | - Wei-Chih Lien
- Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tseng-Ping Chiu
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
| | - Tai-Hua Yang
- Department of Orthopaedic Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Taipei, Taiwan
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Chun-Chun Wei
- Department of Digital Multimedia Design, National Taipei University of Business, Taipei, Taiwan
| | - Yu-Liang Kuo
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Hsing Yeh
- Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Bo Liu
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
| | - Pin-Jun Chen
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
| | - Yang-Cheng Lin
- Department of Industrial Design, National Cheng Kung University, Tainan, Taiwan
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de Beukelaar TT, Mantini D. Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions. Bioengineering (Basel) 2023; 10:1085. [PMID: 37760187 PMCID: PMC10525173 DOI: 10.3390/bioengineering10091085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.
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Affiliation(s)
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium;
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Bin KJ, De Pretto LR, Sanchez FB, De Souza E Castro FPM, Ramos VD, Battistella LR. Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study. JMIR Form Res 2023; 7:e47388. [PMID: 37698916 PMCID: PMC10523215 DOI: 10.2196/47388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. OBJECTIVE This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. METHODS A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. RESULTS In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. CONCLUSIONS On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.
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Affiliation(s)
- Kaio Jia Bin
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Lucas Ramos De Pretto
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Fábio Beltrame Sanchez
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Vinicius Delgado Ramos
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Linamara Rizzo Battistella
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
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Baniya P, Tebyani M, Asefifeyzabadi N, Nguyen T, Hernandez C, Zhu K, Li H, Selberg J, Hsieh HC, Pansodtee P, Yang HY, Recendez C, Keller G, Hee WS, Aslankoohi E, Isseroff RR, Zhao M, Gomez M, Rolandi M, Teodorescu M. A system for bioelectronic delivery of treatment directed toward wound healing. Sci Rep 2023; 13:14766. [PMID: 37679425 PMCID: PMC10485133 DOI: 10.1038/s41598-023-41572-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023] Open
Abstract
The development of wearable bioelectronic systems is a promising approach for optimal delivery of therapeutic treatments. These systems can provide continuous delivery of ions, charged biomolecules, and an electric field for various medical applications. However, rapid prototyping of wearable bioelectronic systems for controlled delivery of specific treatments with a scalable fabrication process is challenging. We present a wearable bioelectronic system comprised of a polydimethylsiloxane (PDMS) device cast in customizable 3D printed molds and a printed circuit board (PCB), which employs commercially available engineering components and tools throughout design and fabrication. The system, featuring solution-filled reservoirs, embedded electrodes, and hydrogel-filled capillary tubing, is assembled modularly. The PDMS and PCB both contain matching through-holes designed to hold metallic contact posts coated with silver epoxy, allowing for mechanical and electrical integration. This assembly scheme allows us to interchange subsystem components, such as various PCB designs and reservoir solutions. We present three PCB designs: a wired version and two battery-powered versions with and without onboard memory. The wired design uses an external voltage controller for device actuation. The battery-powered PCB design uses a microcontroller unit to enable pre-programmed applied voltages and deep sleep mode to prolong battery run time. Finally, the battery-powered PCB with onboard memory is developed to record delivered currents, which enables us to verify treatment dose delivered. To demonstrate the functionality of the platform, the devices are used to deliver H[Formula: see text] in vivo using mouse models and fluoxetine ex vivo using a simulated wound environment. Immunohistochemistry staining shows an improvement of 35.86% in the M1/M2 ratio of H[Formula: see text]-treated wounds compared with control wounds, indicating the potential of the platform to improve wound healing.
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Affiliation(s)
- Prabhat Baniya
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
| | - Maryam Tebyani
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Narges Asefifeyzabadi
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Tiffany Nguyen
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Cristian Hernandez
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Kan Zhu
- Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, 95816, USA
- Department of Ophthalmology and Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - Houpu Li
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - John Selberg
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Hao-Chieh Hsieh
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Pattawong Pansodtee
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Hsin-Ya Yang
- Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, 95816, USA
| | - Cynthia Recendez
- Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, 95816, USA
- Department of Ophthalmology and Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - Gordon Keller
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Wan Shen Hee
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Elham Aslankoohi
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Roslyn Rivkah Isseroff
- Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, 95816, USA
| | - Min Zhao
- Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, 95816, USA
- Department of Ophthalmology and Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - Marcella Gomez
- Department of Applied Mathematics, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Marco Rolandi
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
| | - Mircea Teodorescu
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, USA.
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Stern RM, Yoma NB. Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios. SENSORS (BASEL, SWITZERLAND) 2023; 23:7590. [PMID: 37688044 PMCID: PMC10490721 DOI: 10.3390/s23177590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Laura Mendoza
- Hospital Clínico Universidad de Chile, Santiago 8380420, Chile;
- Clínica Alemana, Santiago 7630000, Chile
| | - Richard M. Stern
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
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Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
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Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
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Żyliński M, Nassibi A, Mandic DP. Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller. SENSORS (BASEL, SWITZERLAND) 2023; 23:7521. [PMID: 37687975 PMCID: PMC10490693 DOI: 10.3390/s23177521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023]
Abstract
At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.
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Affiliation(s)
- Marek Żyliński
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.); (D.P.M.)
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Brandwood BM, Naik GR, Gunawardana U, Gargiulo GD. Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database. SENSORS (BASEL, SWITZERLAND) 2023; 23:7401. [PMID: 37687857 PMCID: PMC10490584 DOI: 10.3390/s23177401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.
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Affiliation(s)
- Benjamin M. Brandwood
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia;
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
- The MARCS Institute, Westmead, NSW 2145, Australia
- Translational Research Health Institute, Westmead, NSW 2145, Australia
- The Ingam Institute for Medical Research, Liverpool, NSW 2170, Australia
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Cunha B, Ferreira R, Sousa ASP. Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview. SENSORS (BASEL, SWITZERLAND) 2023; 23:7100. [PMID: 37631637 PMCID: PMC10459225 DOI: 10.3390/s23167100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.
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Affiliation(s)
- Bruno Cunha
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
| | - Ricardo Ferreira
- Institute for Systems and Computer Engineering, Technology and Science—Telecommunications and Multimedia Centre, FEUP, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Andreia S. P. Sousa
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
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Coyle-Asbil HJ, Habegger J, Oliver M, Vallis LA. Enabling the ActiGraph GT9X Link's Idle Sleep Mode and Inertial Measurement Unit Settings Directly Impacts Data Acquisition. SENSORS (BASEL, SWITZERLAND) 2023; 23:5558. [PMID: 37420725 PMCID: PMC10305544 DOI: 10.3390/s23125558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 07/09/2023]
Abstract
The ActiGraph GT9X has been implemented in clinical trials to track physical activity and sleep. Given recent incidental findings from our laboratory, the overall aim of this study was to notify academic and clinical researchers of the idle sleep mode (ISM) and inertial measurement unit (IMU)'s interaction, as well as their subsequent effect on data acquisition. Investigations were undertaken using a hexapod robot to test the X, Y and Z sensing axes of the accelerometers. Seven GT9X were tested at frequencies ranging from 0.5 to 2 Hz. Testing was performed for three sets of setting parameters: Setting Parameter 1 (ISMONIMUON), Setting Parameter 2 (ISMOFFIMUON), Setting Parameter 3 (ISMONIMUOFF). The minimum, maximum and range of outputs were compared between the settings and frequencies. Findings indicated that Setting Parameters 1 and 2 were not significantly different, but both were significantly different from Setting Parameter 3. Upon inspection, it was discovered that the ISM was only active during Setting Parameter 3 testing, despite it being enabled in Setting Parameter 1. Researchers should be aware of this when conducting future research using the GT9X.
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Affiliation(s)
- Hannah J. Coyle-Asbil
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Janik Habegger
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.H.); (M.O.)
| | - Michele Oliver
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.H.); (M.O.)
| | - Lori Ann Vallis
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
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Shim J, Fleisch E, Barata F. Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014. Sci Rep 2023; 13:9326. [PMID: 37291134 PMCID: PMC10250365 DOI: 10.1038/s41598-023-36062-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/29/2023] [Indexed: 06/10/2023] Open
Abstract
Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.
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Affiliation(s)
- Jinjoo Shim
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Elgar Fleisch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Filipe Barata
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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63
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Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics 2023; 22:100561. [PMID: 37119971 PMCID: PMC10220275 DOI: 10.1016/j.mcpro.2023.100561] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/20/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023] Open
Abstract
The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
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Abir FF, Chowdhury MEH, Tapotee MI, Mushtak A, Khandakar A, Mahmud S, Hasan MA. PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106130. [PMID: 37006447 PMCID: PMC10047244 DOI: 10.1016/j.engappai.2023.106130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing.
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Affiliation(s)
- Farhan Fuad Abir
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
| | | | - Malisha Islam Tapotee
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Md Anwarul Hasan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
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Dhingra LS, Aminorroaya A, Oikonomou EK, Nargesi AA, Wilson FP, Krumholz HM, Khera R. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA Netw Open 2023; 6:e2316634. [PMID: 37285157 PMCID: PMC10248745 DOI: 10.1001/jamanetworkopen.2023.16634] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/16/2023] [Indexed: 06/08/2023] Open
Abstract
Importance Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities. Objective To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020. Design, Setting, and Participants This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022. Exposures Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking). Main Outcomes and Measures Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey). Results Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care. Conclusions and Relevance Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.
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Affiliation(s)
- Lovedeep S. Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Arash Aghajani Nargesi
- Heart and Vascular Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Francis Perry Wilson
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
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Alhaddad AY, Aly H, Gad H, Elgassim E, Mohammed I, Baagar K, Al-Ali A, Sadasivuni KK, Cabibihan JJ, Malik RA. Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115003. [PMID: 37299733 DOI: 10.3390/s23115003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/09/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.
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Affiliation(s)
- Ahmad Yaser Alhaddad
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
| | - Hussein Aly
- KINDI Center for Computing Research, Qatar University, Doha 2713, Qatar
| | - Hoda Gad
- Weill Cornell Medicine-Qatar, Doha 24144, Qatar
| | | | - Ibrahim Mohammed
- Weill Cornell Medicine-Qatar, Doha 24144, Qatar
- Department of Internal Medicine, Albany Medical Center Hospital, Albany, NY 12208, USA
| | | | - Abdulaziz Al-Ali
- KINDI Center for Computing Research, Qatar University, Doha 2713, Qatar
| | | | - John-John Cabibihan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
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Newman SJ. Early-life physical performance predicts the aging and death of elite athletes. SCIENCE ADVANCES 2023; 9:eadf1294. [PMID: 37205754 DOI: 10.1126/sciadv.adf1294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/14/2023] [Indexed: 05/21/2023]
Abstract
Athleticism and the mortality rates begin a lifelong trajectory of decline during early adulthood. Because of the substantial follow-up time required, however, observing any longitudinal link between early-life physical declines and late-life mortality and aging remains largely inaccessible. Here, we use longitudinal data on elite athletes to reveal how early-life athletic performance predicts late-life mortality and aging in healthy male populations. Using data on over 10,000 baseball and basketball players, we calculate age at peak athleticism and rates of decline in athletic performance to predict late-life mortality patterns. Predictive capacity of these variables persists for decades after retirement, displays large effect sizes, and is independent of birth month, cohort, body mass index, and height. Furthermore, a nonparametric cohort-matching approach suggests that these mortality rate differences are associated with differential aging rates, not just extrinsic mortality. These results highlight the capacity of athletic data to predict late-life mortality, even across periods of substantial social and medical change.
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Affiliation(s)
- Saul Justin Newman
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
- The Research School of Biology, Australian National University, Canberra, ACT, Australia
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68
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VanDyk T, Meyer B, DePetrillo P, Donahue N, O'Leary A, Fox S, Cheney N, Ceruolo M, Solomon AJ, McGinnis RS. Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2279-2286. [PMID: 37115839 PMCID: PMC10408384 DOI: 10.1109/tnsre.2023.3271601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of ∼ 6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.
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69
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Sheikh AB, Sobotka PA, Garg I, Dunn JP, Minhas AMK, Shandhi MMH, Molinger J, McDonnell BJ, Fudim M. Blood Pressure Variability in Clinical Practice: Past, Present and the Future. J Am Heart Assoc 2023; 12:e029297. [PMID: 37119077 PMCID: PMC10227216 DOI: 10.1161/jaha.122.029297] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Recent advances in wearable technology through convenient and cuffless systems will enable continuous, noninvasive monitoring of blood pressure (BP), heart rate, and heart rhythm on both longitudinal 24-hour measurement scales and high-frequency beat-to-beat BP variability and synchronous heart rate variability and changes in underlying heart rhythm. Clinically, BP variability is classified into 4 main types on the basis of the duration of monitoring time: very-short-term (beat to beat), short-term (within 24 hours), medium-term (within days), and long-term (over months and years). BP variability is a strong risk factor for cardiovascular diseases, chronic kidney disease, cognitive decline, and mental illness. The diagnostic and therapeutic value of measuring and controlling BP variability may offer critical targets in addition to lowering mean BP in hypertensive populations.
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Affiliation(s)
- Abu Baker Sheikh
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Paul A. Sobotka
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
| | - Ishan Garg
- Department of Internal MedicineUniversity of New Mexico Health Sciences CenterAlbuquerqueNMUSA
| | - Jessilyn P. Dunn
- Department of Biomedical EngineeringDuke UniversityDurhamNCUSA
- Department of Biostatistics & BioinformaticsDuke UniversityDurhamNCUSA
| | | | | | | | - Barry J. McDonnell
- Department of Biomedical ResearchCardiff Metropolitan UniversitySchool of Sport and Health SciencesCardiffUnited Kingdom
| | - Marat Fudim
- Division of CardiologyDuke University Medical CenterDurhamNCUSA
- Duke Clinical Research InstituteDurhamNCUSA
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Hirst Y, Stoffel ST, Brewer HR, Timotijevic L, Raats MM, Flanagan JM. Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom. JMIR Public Health Surveill 2023; 9:e40814. [PMID: 36951929 PMCID: PMC10131900 DOI: 10.2196/40814] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce. OBJECTIVE This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors. METHODS We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs. RESULTS Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data-51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations. CONCLUSIONS This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.
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Affiliation(s)
- Yasemin Hirst
- Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
- Department of Behavioural Science and Health, University College London, London, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Sandro T Stoffel
- Department of Behavioural Science and Health, University College London, London, United Kingdom
- Institute of Pharmaceutical Medicine, University of Basel, Basel, Switzerland
| | - Hannah R Brewer
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Lada Timotijevic
- School of Psychology, University of Surrey, Guildford, United Kingdom
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford, United Kingdom
- Institute for Sustainability, University of Surrey, Guildford, United Kingdom
| | - Monique M Raats
- School of Psychology, University of Surrey, Guildford, United Kingdom
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford, United Kingdom
- Institute for Sustainability, University of Surrey, Guildford, United Kingdom
| | - James M Flanagan
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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Ang BWK, Yeow CH, Lim JH. A Critical Review on Factors Affecting the User Adoption of Wearable and Soft Robotics. SENSORS (BASEL, SWITZERLAND) 2023; 23:3263. [PMID: 36991974 PMCID: PMC10051244 DOI: 10.3390/s23063263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/06/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
In recent years, the advent of soft robotics has changed the landscape of wearable technologies. Soft robots are highly compliant and malleable, thus ensuring safe human-machine interactions. To date, a wide variety of actuation mechanisms have been studied and adopted into a multitude of soft wearables for use in clinical practice, such as assistive devices and rehabilitation modalities. Much research effort has been put into improving their technical performance and establishing the ideal indications for which rigid exoskeletons would play a limited role. However, despite having achieved many feats over the past decade, soft wearable technologies have not been extensively investigated from the perspective of user adoption. Most scholarly reviews of soft wearables have focused on the perspective of service providers such as developers, manufacturers, or clinicians, but few have scrutinized the factors affecting adoption and user experience. Hence, this would pose a good opportunity to gain insight into the current practice of soft robotics from a user's perspective. This review aims to provide a broad overview of the different types of soft wearables and identify the factors that hinder the adoption of soft robotics. In this paper, a systematic literature search using terms such as "soft", "robot", "wearable", and "exoskeleton" was conducted according to PRISMA guidelines to include peer-reviewed publications between 2012 and 2022. The soft robotics were classified according to their actuation mechanisms into motor-driven tendon cables, pneumatics, hydraulics, shape memory alloys, and polyvinyl chloride muscles, and their pros and cons were discussed. The identified factors affecting user adoption include design, availability of materials, durability, modeling and control, artificial intelligence augmentation, standardized evaluation criteria, public perception related to perceived utility, ease of use, and aesthetics. The critical areas for improvement and future research directions to increase adoption of soft wearables have also been highlighted.
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Affiliation(s)
- Benjamin Wee Keong Ang
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore; (B.W.K.A.); (C.-H.Y.)
| | - Chen-Hua Yeow
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore; (B.W.K.A.); (C.-H.Y.)
| | - Jeong Hoon Lim
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
- Division of Rehabilitation Medicine, University Medicine Cluster, National University Hospital, Singapore 119077, Singapore
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Moeller T, Moehler F, Krell-Roesch J, Dežman M, Marquardt C, Asfour T, Stein T, Woll A. Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3032. [PMID: 36991743 PMCID: PMC10057915 DOI: 10.3390/s23063032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.
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Affiliation(s)
- Tobias Moeller
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Felix Moehler
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Janina Krell-Roesch
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Miha Dežman
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Charlotte Marquardt
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Tamim Asfour
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Thorsten Stein
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Alexander Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
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Zhou ZB, Cui TR, Li D, Jian JM, Li Z, Ji SR, Li X, Xu JD, Liu HF, Yang Y, Ren TL. Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16062133. [PMID: 36984013 PMCID: PMC10057755 DOI: 10.3390/ma16062133] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.
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Affiliation(s)
- Zi-Bo Zhou
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
| | - Tian-Rui Cui
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Ding Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jin-Ming Jian
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Zhen Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Shou-Rui Ji
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xin Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jian-Dong Xu
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Hou-Fang Liu
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yi Yang
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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74
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Frade MCM, Beltrame T, Gois MDO, Pinto A, Tonello SCGDM, Torres RDS, Catai AM. Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data. PLoS One 2023; 18:e0282398. [PMID: 36862737 PMCID: PMC9980797 DOI: 10.1371/journal.pone.0282398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
Cardiopulmonary exercise testing (CPET) is a non-invasive approach to measure the maximum oxygen uptake ([Formula: see text]), which is an index to assess cardiovascular fitness (CF). However, CPET is not available to all populations and cannot be obtained continuously. Thus, wearable sensors are associated with machine learning (ML) algorithms to investigate CF. Therefore, this study aimed to predict CF by using ML algorithms using data obtained by wearable technologies. For this purpose, 43 volunteers with different levels of aerobic power, who wore a wearable device to collect unobtrusive data for 7 days, were evaluated by CPET. Eleven inputs (sex, age, weight, height, and body mass index, breathing rate, minute ventilation, total hip acceleration, walking cadence, heart rate, and tidal volume) were used to predict the [Formula: see text] by support vector regression (SVR). Afterward, the SHapley Additive exPlanations (SHAP) method was used to explain their results. SVR was able to predict the CF, and the SHAP method showed that the inputs related to hemodynamic and anthropometric domains were the most important ones to predict the CF. Therefore, we conclude that the cardiovascular fitness can be predicted by wearable technologies associated with machine learning during unsupervised activities of daily living.
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Affiliation(s)
| | - Thomas Beltrame
- Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil
- Samsung R&D Institute Brazil–SRBR, Campinas, São Paulo, Brazil
- * E-mail:
| | | | - Allan Pinto
- Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil
| | | | - Ricardo da Silva Torres
- Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, NTNU—Norwegian University of Science and Technology, Ålesund, Norway
| | - Aparecida Maria Catai
- Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil
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Alizadehsani R, Roshanzamir M, Izadi NH, Gravina R, Kabir HMD, Nahavandi D, Alinejad-Rokny H, Khosravi A, Acharya UR, Nahavandi S, Fortino G. Swarm Intelligence in Internet of Medical Things: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031466. [PMID: 36772503 PMCID: PMC9920579 DOI: 10.3390/s23031466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 05/13/2023]
Abstract
Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Correspondence:
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Vali asr Blvd, Fasa 74617-81189, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Daneshgah e Sanati Hwy, Isfahan 84156-83111, Iran
| | - Raffaele Gravina
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
| | - H. M. Dipu Kabir
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Darius Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia
- UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia
- Health Data Analytics Program, AI-Enabled Processes (AIP) Research Centre, Macquarie University, Sydney, NSW 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036 Cosenza, Italy
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Van Ooteghem K, Godkin FE, Thai V, Beyer KB, Cornish BF, Weber KS, Bernstein H, Kheiri SO, Swartz RH, Tan B, McIlroy WE, Roberts AC. User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease. Digit Health 2023; 9:20552076231179031. [PMID: 37312943 PMCID: PMC10259132 DOI: 10.1177/20552076231179031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/12/2023] [Indexed: 06/15/2023] Open
Abstract
Objective There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables. Methods A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n = 15) and persons living with neurodegenerative disease (NDD) (n = 25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7-10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status. Results Participants (n = 40) were 60% female (median 72 (60-87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility. Conclusions The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.
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Affiliation(s)
- Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin F Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Hannah Bernstein
- Department of Nanotechnology Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Soha O Kheiri
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Richard H Swartz
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela C Roberts
- School of Communication Sciences and Disorders, Western University, London, ON, Canada
- Department of Computer Science, Western University, London, ON, Canada
- Canadian Centre for Activity and Aging, Western University, London, ON, Canada
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77
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Sandic Spaho R, Uhrenfeldt L, Fotis T, Kymre IG. Wearable devices in palliative care for people 65 years and older: A scoping review. Digit Health 2023; 9:20552076231181212. [PMID: 37426582 PMCID: PMC10328013 DOI: 10.1177/20552076231181212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/24/2023] [Indexed: 07/11/2023] Open
Abstract
Objective The objective of this scoping review is to map existing evidence on the use of wearable devices in palliative care for older people. Methods The databases searched included MEDLINE (via Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Google Scholar, which was included to capture grey literature. Databases were searched in the English language, without date restrictions. Reviewed results included studies and reviews involving patients aged 65 years or older who were active users of non-invasive wearable devices in the context of palliative care, with no limitations on gender or medical condition. The review followed the Joanna Briggs Institute's comprehensive and systematic guidelines for conducting scoping reviews. Results Of the 1,520 reports identified through searching the databases, reference lists, and citations, six reports met our inclusion criteria. The types of wearable devices discussed in these reports were accelerometers and actigraph units. Wearable devices were found to be useful in various health conditions, as the patient monitoring data enabled treatment adjustments. The results are mapped in tables as well as a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) chart. Conclusions The findings indicate limited and sparse evidence for the population group of patients aged 65 years and older in the palliative context. Hence, more research on this particular age group is needed. The available evidence shows the benefits of wearable device use in enabling patient-centred palliative care, treatment adjustments and symptom management, and reducing the need for patients to travel to clinics while maintaining communication with healthcare professionals.
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Affiliation(s)
- Rada Sandic Spaho
- Faculty of Nursing and Health Sciences, Nord University, Bodo, Norway
| | - Lisbeth Uhrenfeldt
- Faculty of Nursing and Health Sciences, Nord University, Bodo, Norway
- Danish Centre of Systematic Reviews: An
Affiliate Center of The Joanna Briggs Institute, The Center of Clinical Guidelines –
Clearing House, Aalborg University Denmark, Aalborg, Denmark
- Institute of Regional Health Research,
Lillebaelt University Hospital, Southern Danish University, Kolding, Denmark
| | - Theofanis Fotis
- School of Sport & Health Sciences,
Centre for Secure, Intelligent and Usable Systems, University of Brighton, Brighton, UK
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Kasoju N, Remya NS, Sasi R, Sujesh S, Soman B, Kesavadas C, Muraleedharan CV, Varma PRH, Behari S. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSI TRANSACTIONS ON ICT 2023; 11:11-30. [PMCID: PMC10089382 DOI: 10.1007/s40012-023-00380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/27/2023] [Indexed: 04/12/2024]
Abstract
Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.
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Affiliation(s)
- Naresh Kasoju
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - N. S. Remya
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Renjith Sasi
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - S. Sujesh
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Biju Soman
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. Kesavadas
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - C. V. Muraleedharan
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - P. R. Harikrishna Varma
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
| | - Sanjay Behari
- Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, 695011 Kerala India
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79
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Wang W, Li X, Qiu X, Zhang X, Zhao J, Brusic V. A privacy preserving framework for federated learning in smart healthcare systems. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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80
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Ghosh A, Nag S, Gomes A, Gosavi A, Ghule G, Kundu A, Purohit B, Srivastava R. Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance. MICROMACHINES 2022; 14:121. [PMID: 36677182 PMCID: PMC9862021 DOI: 10.3390/mi14010121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.
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Affiliation(s)
- Arnab Ghosh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sagnik Nag
- Department of Biotechnology, School of Biosciences & Technology, Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Alyssa Gomes
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Apurva Gosavi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Gauri Ghule
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Aniket Kundu
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Buddhadev Purohit
- DTU Bioengineering, Technical University of Denmark, Søltofts Plads 221, 2800 Kongens Lyngby, Denmark
| | - Rohit Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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81
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Rangan ES, Pathinarupothi RK, Anand KJS, Snyder MP. Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning. JAMIA Open 2022; 5:ooac080. [PMID: 36267121 PMCID: PMC9566305 DOI: 10.1093/jamiaopen/ooac080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/07/2022] [Accepted: 09/20/2022] [Indexed: 11/15/2022] Open
Abstract
Objective To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis. Materials and methods By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked. Results The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them. Conclusion It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.
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Affiliation(s)
- Ekanath Srihari Rangan
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | | | - Kanwaljeet J S Anand
- Division of Critical Care, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
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82
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Alamouti SF, Jan J, Yalcin C, Ting J, Arias AC, Muller R. A Sparse Sampling Sensor Front-End IC for Low Power Continuous SpO 2 & HR Monitoring. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:997-1007. [PMID: 36417724 DOI: 10.1109/tbcas.2022.3223971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages.This work presents a sensor IC for continuous SpO 2 and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by ∼ 70% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm 2 and consumes 49.7 μW and 15.2 μW of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified in vivo with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1% mean absolute errors were achieved in both continuous and sparse modes of operation.
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83
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Alam S, Zhang M, Harris K, Fletcher LM, Reneker JC. The Impact of Consumer Wearable Devices on Physical Activity and Adherence to Physical Activity in Patients with Cardiovascular Disease: A Systematic Review of Systematic Reviews and Meta-Analyses. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Sabrina Alam
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Mengna Zhang
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Kisa Harris
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
| | - Lauren M. Fletcher
- Rowland Medical Library; University of Mississippi Medical Center, Jackson, Mississippi, USA
- John D. Rockefeller Library, Brown University, Providence, Rhode Island, USA
| | - Jennifer C. Reneker
- Department of Population Health Science, John D. Bower School of Population Health; Jackson, Mississippi, USA
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84
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Kheirinejad S, Visuri A, Ferreira D, Hosio S. "Leave your smartphone out of bed": quantitative analysis of smartphone use effect on sleep quality. PERSONAL AND UBIQUITOUS COMPUTING 2022; 27:447-466. [PMID: 36405389 PMCID: PMC9643910 DOI: 10.1007/s00779-022-01694-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Smartphones have become an integral part of people's everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one's sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one's sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7-456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87-55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.
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85
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Merle G, Miclau T, Parent-Harvey A, Harvey EJ. Sensor technology usage in orthopedic trauma. Injury 2022; 53 Suppl 3:S59-S63. [PMID: 36182592 DOI: 10.1016/j.injury.2022.09.036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 02/02/2023]
Abstract
Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. Orthopaedic trauma can and will be part of this new wave of medicine. Early sensor products that are now coming to market, or are in early development, will directly change the way we think about surgical diagnosis and outcomes. Sensor development for biometrics is already here. Wellness devices, pressure, temperature, and other parameters are already being measured. Data acquisition and analysis is going to be a fruitful addition to our research armamentarium with the volume of information now available. A combination of broadband internet, micro electrical machine systems (MEMS), and new wireless communication standards is driving this new wave of medicine. The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.
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Affiliation(s)
- Géraldine Merle
- École Polytechnique de Montréal, Université de Montréal, Montréal, Canada
| | - Theodore Miclau
- Orthopaedic Trauma Institute, University of Calfornia, School of Medicine, Department of Orthopaedics, San Francisco, USA
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86
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Alyafei K, Ahmed R, Abir FF, Chowdhury MEH, Naji KK. A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices. Comput Biol Med 2022; 149:106070. [PMID: 36099862 PMCID: PMC9433350 DOI: 10.1016/j.compbiomed.2022.106070] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 08/03/2022] [Accepted: 08/27/2022] [Indexed: 11/30/2022]
Abstract
Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.
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Affiliation(s)
- Khalid Alyafei
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar
| | - Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Department of Biotechnology, Mirpur University of Science and Technology (MUST), Mirpur, 10250, AJK, Pakistan
| | - Farhan Fuad Abir
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
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Faihs V, Figalist C, Bossert E, Weimann K, Berberat PO, Wijnen-Meijer M. Medical Students and Their Perceptions of Digital Medicine: a Question of Gender? MEDICAL SCIENCE EDUCATOR 2022; 32:941-946. [PMID: 36276758 PMCID: PMC9584022 DOI: 10.1007/s40670-022-01594-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 06/02/2023]
Abstract
Digital technologies play an essential role in the medical sector of today and the future. In a cross-sectional online survey at a German medical university, male students more frequently reported keeping themselves informed about digital medicine outside of their studies across all clinical years of study. While female students self-assessed their knowledge in different fields of digital medicine as worse than their male peers in the first clinical years of study, no more gender differences could be found towards the final year. However, students of both genders showed a strong desire for further education on the topic of digital medicine.
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Affiliation(s)
- Valentina Faihs
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- Department of Dermatology and Allergy Biederstein, TUM School of Medicine, Technical University of Munich, Biedersteiner Str. 29, 80802 Munich, Germany
| | - Christina Figalist
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Eileen Bossert
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Katja Weimann
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Pascal O. Berberat
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Marjo Wijnen-Meijer
- TUM Medical Education Center, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
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Huhn S, Matzke I, Koch M, Gunga HC, Maggioni MA, Sié A, Boudo V, Ouedraogo WA, Compaoré G, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. Using wearable devices to generate real-world, individual-level data in rural, low-resource contexts in Burkina Faso, Africa: A case study. Front Public Health 2022; 10:972177. [PMID: 36249225 PMCID: PMC9561896 DOI: 10.3389/fpubh.2022.972177] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Background Wearable devices may generate valuable data for global health research for low- and middle-income countries (LMICs). However, wearable studies in LMICs are scarce. This study aims to investigate the use of consumer-grade wearables to generate individual-level data in vulnerable populations in LMICs, focusing on the acceptability (quality of the devices being accepted or even liked) and feasibility (the state of being workable, realizable, and practical, including aspects of data completeness and plausibility). Methods We utilized a mixed-methods approach within the health and demographic surveillance system (HDSS) to conduct a case study in Nouna, Burkina Faso (BF). All HDSS residents older than 6 years were eligible. N = 150 participants were randomly selected from the HDSS database to wear a wristband tracker (Withings Pulse HR) and n = 69 also a thermometer patch (Tucky thermometer) for 3 weeks. Every 4 days, a trained field worker conducted an acceptability questionnaire with participants, which included questions for the field workers as well. Descriptive and qualitative thematic analyses were used to analyze the responses of study participants and field workers. Results In total, n = 148 participants were included (and n = 9 field workers). Participant's acceptability ranged from 94 to 100% throughout the questionnaire. In 95% of the cases (n = 140), participants reported no challenges with the wearable. Most participants were not affected by the wearable in their daily activities (n = 122, 83%) and even enjoyed wearing them (n = 30, 20%). Some were concerned about damage to the wearables (n = 7, 5%). Total data coverage (i.e., the proportion of the whole 3-week study duration covered by data) was 43% for accelerometer (activity), 3% for heart rate, and 4% for body shell temperature. Field workers reported technical issues like faulty synchronization (n = 6, 1%). On average, participants slept 7 h (SD 3.2 h) and walked 8,000 steps per day (SD 5573.6 steps). Acceptability and data completeness were comparable across sex, age, and study arms. Conclusion Wearable devices were well-accepted and were able to produce continuous measurements, highlighting the potential for wearables to generate large datasets in LMICs. Challenges constituted data missingness mainly of technical nature. To our knowledge, this is the first study to use consumer-focused wearables to generate objective datasets in rural BF.
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Affiliation(s)
- Sophie Huhn
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany,*Correspondence: Sophie Huhn
| | - Ina Matzke
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Mara Koch
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Hanns-Christian Gunga
- Charité – Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany
| | - Martina Anna Maggioni
- Charité – Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Berlin, Germany,Department of Biomedical Sciences for Health, Università Degli Studi di Milano, Milano, Italy
| | - Ali Sié
- Centre de Recherche en Santé, Nouna, Burkina Faso
| | | | | | | | - Aditi Bunker
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Rainer Sauerborn
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
| | - Till Bärnighausen
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany,Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States,Africa Health Research Institute (AHRI), KwaZulu-Natal, South Africa
| | - Sandra Barteit
- Faculty of Medicine and University Hospital, Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany
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Bin KJ, De Pretto LR, Sanchez FB, Battistella LR. Digital Platform to Continuously Monitor Patients Using a Smartwatch: Preliminary Report. JMIR Form Res 2022; 6:e40468. [PMID: 36107471 PMCID: PMC9523529 DOI: 10.2196/40468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/01/2022] [Accepted: 08/23/2022] [Indexed: 11/28/2022] Open
Abstract
Background Monitoring vital signs such as oximetry, blood pressure, and heart rate is important to follow the evolution of patients. Smartwatches are a revolution in medicine allowing the collection of such data in a continuous and organic way. However, it is still a challenge to make this information available to health care professionals to make decisions during clinical follow-up. Objective This study aims to build a digital solution that displays vital sign data from smartwatches, collected remotely, continuously, reliably, and from multiple users, with trigger warnings when abnormal results are identified. Methods This is a single-center prospective study following the guidelines “Evaluating digital health products” from the UK Health Security Agency. A digital platform with 3 different applications was created to capture and display data from the mobile phones of volunteers with smartwatches. We selected 80 volunteers who were followed for 24 weeks each, and the synchronization interval between the smartwatch and digital solution was recorded for each vital sign collected. Results In 14 weeks of project progress, we managed to recruit 80 volunteers, with 68 already registered in the digital solution. More than 2.8 million records have already been collected, without system downtime. Less than 5% of continuous heart rate measurements (bpm) were synchronized within 2 hours. However, approximately 70% were synchronized in less than 24 hours, and 90% were synchronized in less than 119 hours. Conclusions The digital solution is working properly in its role of displaying data collected from smartwatches. Vital sign values are being monitored by the research team as part of the monitoring of volunteers. Although the digital solution proved unsuitable for monitoring urgent events, it is more than suitable for use in outpatient clinical use. This digital solution, which is based on cloud technology, can be applied in the future for telemonitoring in regions lacking health care professionals. Accuracy and reliability studies still need to be performed at the end of the 24-week follow-up.
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Affiliation(s)
- Kaio Jia Bin
- Instituto de Medicina Física e Reabilitação do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Lucas Ramos De Pretto
- Instituto de Medicina Física e Reabilitação do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Fabio Beltrame Sanchez
- Instituto de Medicina Física e Reabilitação do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Linamara Rizzo Battistella
- Instituto de Medicina Física e Reabilitação do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
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Huang Y, Upadhyay U, Dhar E, Kuo LJ, Syed-Abdul S. A Scoping Review to Assess Adherence to and Clinical Outcomes of Wearable Devices in the Cancer Population. Cancers (Basel) 2022; 14:4437. [PMID: 36139602 PMCID: PMC9496886 DOI: 10.3390/cancers14184437] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/20/2022] Open
Abstract
The use of wearable devices (WDs) in healthcare monitoring and management has attracted increasing attention. A major problem is patients' adherence and acceptance of WDs given that they are already experiencing a disease burden and treatment side effects. This scoping review explored the use of wrist-worn devices in the cancer population, with a special focus on adherence and clinical outcomes. Relevant articles focusing on the use of WDs in cancer care management were retrieved from PubMed, Scopus, and Embase from 1 January 2017 to 3 March 2022. Studies were independently screened and relevant information was extracted. We identified 752 studies, of which 38 met our inclusion criteria. Studies focused on mixed, breast, colorectal, lung, gastric, urothelial, skin, liver, and blood cancers. Adherence to WDs varied from 60% to 100%. The highest adherence was reported in the 12-week studies. Most studies focused on physical activity, sleep analysis, and heart vital signs. Of the 10 studies that described patient-reported outcomes using questionnaires and personal interviews, 8 indicated a positive correlation between the patient-reported and wearable outcomes. The definitions of the outcome measures and adherence varied across the studies. A better understanding of the intervention standards in terms of the clinical outcomes could improve adherence to wearables.
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Affiliation(s)
- Yaoru Huang
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Umashankar Upadhyay
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Eshita Dhar
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Li-Jen Kuo
- Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei 110, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
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91
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Kamecka K, Foti C, Gawiński Ł, Matejun M, Rybarczyk-Szwajkowska A, Kiljański M, Krochmalski M, Kozłowski R, Marczak M. Telemedicine Technologies Selection for the Posthospital Patient Care Process after Total Hip Arthroplasty. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11521. [PMID: 36141791 PMCID: PMC9517262 DOI: 10.3390/ijerph191811521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/05/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
For many years, the importance of using telematic technologies in medicine has been growing, especially in the period of the coronavirus pandemic, when direct contact and supervision of medical personnel over the patient is difficult. The existing possibilities of modern information and communication technologies (ICTs) are not fully used. The aim of the study is to identify the telemedicine technologies that can be used in future implementation projects of the posthospital patient care process after total hip arthroplasty (THA). The literature search is reported according to PRISMA 2020. The search strategy included databases and gray literature. In total, 28 articles (EMBASE, PubMed, PEDro) and 24 records from gray literature (Google Search and Technology presentations) were included in the research. This multi-source study analyzes the possibilities of using different technologies useful in the patient care process. The conducted research resulted in defining visual and wearable types of telemedicine technologies for the original posthospital patient care process after THA. As the needs of stakeholders in the posthospital patient care process after THA differ, the awareness of appropriate technologies selection, information flow, and its management importance are prerequisites for effective posthospital patient care with the use of telemedicine technologies.
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Affiliation(s)
- Karolina Kamecka
- Department of Management and Logistics in Healthcare, Medical University of Lodz, 90-131 Lodz, Poland
| | - Calogero Foti
- Physical and Rehabilitation Medicine, Clinical Sciences and Translational Medicine Department, Tor Vergata University, 00133 Rome, Italy
| | - Łukasz Gawiński
- Department of Management and Logistics in Healthcare, Medical University of Lodz, 90-131 Lodz, Poland
| | - Marek Matejun
- Department of Entrepreneurship and Industrial Policy, Faculty of Management, University of Lodz, 90-237 Lodz, Poland
| | | | - Marek Kiljański
- Polish Association of Physiotherapy Specialists, 95-200 Pabianice, Poland
- Medical Magnus Clinic, 90-552 Lodz, Poland
| | - Marek Krochmalski
- Medical Magnus Clinic, 90-552 Lodz, Poland
- Polish Muscles, Ligaments and Tendons Society, 90-552 Lodz, Poland
| | - Remigiusz Kozłowski
- Center of Security Technologies in Logistics, Faculty of Management, University of Lodz, 90-237 Lodz, Poland
| | - Michał Marczak
- Department of Management and Logistics in Healthcare, Medical University of Lodz, 90-131 Lodz, Poland
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92
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Liverani M, Ir P, Perel P, Khan M, Balabanova D, Wiseman V. Assessing the potential of wearable health monitors for health system strengthening in low- and middle-income countries: a prospective study of technology adoption in Cambodia. Health Policy Plan 2022; 37:943-951. [PMID: 35262172 PMCID: PMC9469886 DOI: 10.1093/heapol/czac019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/24/2022] [Accepted: 02/26/2022] [Indexed: 11/15/2022] Open
Abstract
Wearable health monitors are a rapidly evolving technology that may offer new opportunities for strengthening health system responses to cardiovascular and other non-communicable diseases (NCDs) in low- and middle-income countries (LMICs). In light of this, we explored opportunities for, and potential challenges to, technology adoption in Cambodia, considering the complexity of contextual factors that may influence product uptake and sustainable health system integration. Data collection for this study involved in-depth interviews with national and international stakeholders and a literature review. The analytical approach was guided by concepts and categories derived from the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework-an evidence-based framework that was developed for studying health technology adoption and the challenges to scale-up, spread and sustainability of such technologies in health service organizations. Three potential applications of health wearables for the prevention and control of NCDs in Cambodia were identified: health promotion, follow-up and monitoring of patients and surveys of NCD risk factors. However, several challenges to technology adoption emerged across the research domains, associated with the intended adopters, the organization of the national health system, the wider infrastructure, the regulatory environment and the technology itself. Our findings indicate that, currently, wearables could be best used to conduct surveys of NCD risk factors in Cambodia and in other LMICs with similar health system profiles. In the future, a more integrated use of wearables to strengthen monitoring and management of patients could be envisaged, although this would require careful consideration of feasibility and organizational issues.
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Affiliation(s)
- Marco Liverani
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
- School of Tropical Medicine and Global Health, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
- Faculty of Public Health, Mahidol University, 420/1 Rajvithi Road, Bangkok 10400, Thailand
| | - Por Ir
- National Institute of Public Health, Street 289, Phnom Penh, Cambodia
| | - Pablo Perel
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
- Centre for Global Chronic Conditions, London School of Hygiene & Tropical Medicine, London, UK
| | - Mishal Khan
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
| | - Dina Balabanova
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
| | - Virginia Wiseman
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK
- The Kirby Institute, University of New South Wales, Sydney NSW 2052, Australia
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93
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Wearables in Cardiovascular Disease. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10314-0. [PMID: 36085432 DOI: 10.1007/s12265-022-10314-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/29/2022] [Indexed: 10/14/2022]
Abstract
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.
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94
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Kuosmanen E, Visuri A, Risto R, Hosio S. Comparing consumer grade sleep trackers for research purposes: A field study. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.971793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.
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Colombo T, Todeschini LB, Orlandini M, Nascimento HD, Gabriel FC, Alves RJV, Stein AT. Low-Risk Antenatal Care Enhanced by Telemedicine: A Practical Guideline Model. REVISTA BRASILEIRA DE GINECOLOGIA E OBSTETRÍCIA 2022; 44:845-853. [PMID: 35853473 PMCID: PMC9948049 DOI: 10.1055/s-0042-1753505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/06/2022] [Indexed: 10/17/2022] Open
Abstract
OBJECTIVE To develop a protocol for hybrid low-risk prenatal care adapted to Brazilian guidelines, merging reduced face-to-face consultations and remote monitoring. METHODS The PubMed, Embase, and Cochrane Library databases were systematically searched on telemedicine and antenatal care perspectives and adaptation of the low-risk prenatal care protocols recommended by the Ministry of Health and by the Brazilian Federation of Gynecology and Obstetrics Associations. RESULTS Five relevant articles and three manuals were included in the review, for presented criteria to develop this clinical guideline. We identified, in these studies, that the schedule of consultations is unevenly distributed among the gestational trimesters, and ranges from 7 to 14 appointments. In general, the authors propose one to two appointments in the first trimester, two to three appointments in the second trimester, and two to six appointments in the third trimester. Only three studies included puerperal evaluations. The routine exams recommended show minimal variations among authors. To date, there are no validated Brazilian protocols for prenatal care by telemedicine. The included studies showed that pregnant women were satisfied with this form of care, and the outcomes of interest, except for hypertensive diseases, were similar between the groups exposed to traditional and hybrid prenatal care. CONCLUSION The presented guideline comprises the Ministry of Health recommendations for low-risk prenatal care and reduces exposure to the hospital environment and care costs. A randomized clinical trial, to be developed by this group, will provide real-world data on safety, effectiveness, satisfaction, and costs.
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Affiliation(s)
- Talita Colombo
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Lorenza Bridi Todeschini
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Mariana Orlandini
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Hallana do Nascimento
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Franciele Cordeiro Gabriel
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Rafael José Vargas Alves
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
| | - Airton Tetelbom Stein
- Postgraduate Program in Health Sciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil
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96
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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97
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Liu X, Huang S, Ma L, Ye H, Lin J, Cai X, Shang Q, Zheng C, Xu R, Zhang D. Recent advances in wearable medical diagnostic sensors and new therapeutic dosage forms for fever in children. J Pharm Biomed Anal 2022; 220:115006. [PMID: 36007307 DOI: 10.1016/j.jpba.2022.115006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/05/2022] [Accepted: 08/13/2022] [Indexed: 11/17/2022]
Abstract
Fever in children is one of the most common symptoms of pediatric diseases and the most common complaint in pediatric clinics, especially in the emergency department. Diseases such as pneumonia, sepsis, and meningitis are leading causes of death in children, and the early manifestations of these diseases are accompanied by fever symptoms. Accurate diagnosis and real-time monitoring of the status of febrile children, rapid and effective identification of the cause, and treatment can have a positive impact on relieving their symptoms and improving their quality of life. In recent years, wearable diagnostic sensors have attracted special attention for their high flexibility, real-time monitoring, and sensitivity. Temperature sensors and heart rate sensors have provided new advances in detecting children's body temperature and heart rate. Furthermore, some novel formulations have also received wide attention for addressing bottlenecks in medication administration for febrile children, such as difficulty in swallowing and inaccurate dosing. In this context, the present review provides recent advances of novel wearable medical sensor devices for diagnosing fever. Moreover, the application progress of innovative dosage forms of classical antipyretic drugs for children is presented. Finally, challenges and prospects of wearable sensor-based diagnostics and novel agent-based treatment of fever in children are discussed in brief.
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Affiliation(s)
- Xuemei Liu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
| | - Shengjie Huang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
| | - Lele Ma
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
| | - Hui Ye
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China
| | - Junzhi Lin
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, PR China
| | - Xinfu Cai
- Sichuan Guangda Pharmaceutical Co. Ltd., Pengzhou 611930, PR China; National Engineering Research Center for Modernization of Traditional Chinese Medicine, Pengzhou 611930, PR China
| | - Qiang Shang
- Sichuan Guangda Pharmaceutical Co. Ltd., Pengzhou 611930, PR China; National Engineering Research Center for Modernization of Traditional Chinese Medicine, Pengzhou 611930, PR China
| | - Chuan Zheng
- TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, PR China.
| | - Runchun Xu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China.
| | - Dingkun Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, PR China.
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98
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Chan PY, Ryan NP, Chen D, McNeil J, Hopper I. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis. Anaesthesia 2022; 77:1268-1280. [PMID: 35947876 DOI: 10.1111/anae.15834] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2022] [Indexed: 11/28/2022]
Abstract
We performed a systematic review and meta-analysis to identify, classify and evaluate the body of evidence on novel wearable and contactless devices that measure heart rate, respiratory rate and oxygen saturations in the clinical setting. We included any studies of hospital inpatients, including sleep study clinics. Eighty-four studies were included in the final review. There were 56 studies of wearable devices and 29 of contactless devices. One study assessed both types of device. A high risk of patient selection and rater bias was present in proportionally more studies assessing contactless devices compared with studies assessing wearable devices (p = 0.023 and p < 0.0001, respectively). There was high but equivalent likelihood of blinding bias between the two types of studies (p = 0.076). Wearable device studies were commercially available devices validated in acute clinical settings by clinical staff and had more real-time data analysis (p = 0.04). Contactless devices were more experimental, and data were analysed post-hoc. Pooled estimates of mean (95%CI) heart rate and respiratory rate bias in wearable devices were 1.25 (-0.31-2.82) beats.min-1 (pooled 95% limits of agreement -9.36-10.08) and 0.68 (0.05-1.32) breaths.min-1 (pooled 95% limits of agreement -5.65-6.85). The pooled estimate for mean (95%CI) heart rate and respiratory rate bias in contactless devices was 2.18 (3.31-7.66) beats.min-1 (pooled limits of agreement -6.71-10.88) and 0.30 (-0.26-0.87) breaths.min-1 (pooled 95% limits of agreement -3.94-4.29). Only two studies of wearable devices measured Sp O2 ; these reported mean measurement biases of 3.54% (limits of agreement -5.65-11.45%) and 2.9% (-7.4-1.7%). Heterogeneity was observed across studies, but absent when devices were grouped by measurement modality and reference standard. We conclude that, while studies of wearable devices were of slightly better quality than contactless devices, in general all studies of novel devices were of low quality, with small (< 100) patient datasets, typically not blinded and often using inappropriate statistical techniques. Both types of devices were statistically equivalent in accuracy and precision, but wearable devices demonstrated less measurement bias and more precision at extreme vital signs. The statistical variability in precision and accuracy between studies is partially explained by differences in reference standards.
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Affiliation(s)
- P Y Chan
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Vic., Australia
| | - N P Ryan
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Vic., Australia
| | - D Chen
- Department of Intensive Care Medicine, Eastern Health, Melbourne, Vic., Australia
| | - J McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic., Australia
| | - I Hopper
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic., Australia
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99
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Duan J, Wang Q, Zhang B, Liu C, Li C, Wang L. Accurate detection of atrial fibrillation events with R-R intervals from ECG signals. PLoS One 2022; 17:e0271596. [PMID: 35925979 PMCID: PMC9352004 DOI: 10.1371/journal.pone.0271596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022] Open
Abstract
Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.
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Affiliation(s)
- Junbo Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
- * E-mail:
| | - Qing Wang
- School of Electronic Engineering, Xidian University, Xi’an, China
| | - Bo Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Chen Liu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Chenrui Li
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Lei Wang
- Cardiovascular Medicine, Weinan Central Hospital, Weinan, China
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Matabuena M, Karas M, Riazati S, Caplan N, Hayes PR. Estimating Knee Movement Patterns of Recreational Runners Across Training Sessions Using Multilevel Functional Regression Models. AM STAT 2022. [DOI: 10.1080/00031305.2022.2105950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Marcos Matabuena
- Centro Singular de Investigación en Tecnologías Intelixentes, Universidad de Santiago de Compostela, Santiago de Compostela, Spain
| | - Marta Karas
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Sherveen Riazati
- Department of Kinesiology, San José State University, CA
- Department of Sport Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Nick Caplan
- Department of Sport Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Philip R. Hayes
- Department of Sport Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
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