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Park SH, Pinto-Powell R, Thesen T, Lindqwister A, Levy J, Chacko R, Gonzalez D, Bridges C, Schwendt A, Byrum T, Fong J, Shasavari S, Hassanpour S. Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. MEDICAL EDUCATION ONLINE 2024; 29:2315684. [PMID: 38351737 PMCID: PMC10868429 DOI: 10.1080/10872981.2024.2315684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.
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Affiliation(s)
- Soo Hwan Park
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Thomas Thesen
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Joshua Levy
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Rachael Chacko
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Connor Bridges
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Adam Schwendt
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Travis Byrum
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Justin Fong
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Yue JJ, Gilligan CJ, Falowski S, Jameson J, Desai MJ, Moeschler S, Pilitsis J, Heros R, Tavel E, Wahezi S, Funk R, Buchanan P, Christopher A, Weisbein J, Patterson D, Levy R, Antony A, Miller N, Scarfo K, Kreiner S, Wilson D, Lim C, Braun E, Dickerson D, Duncan J, Xu J, Candido K, Mohab I, Michael F, Blomme B, Okaro U, Deer T. Surgical treatment of refractory low back pain using implanted BurstDR spinal cord stimulation (SCS) in a cohort of patients without options for corrective surgery: Findings and results from the DISTINCT study, a prospective randomized multi-center-controlled trial. NORTH AMERICAN SPINE SOCIETY JOURNAL 2024; 19:100508. [PMID: 39139617 PMCID: PMC11321325 DOI: 10.1016/j.xnsj.2024.100508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 06/08/2024] [Accepted: 06/11/2024] [Indexed: 08/15/2024]
Abstract
Background Low back pain (LBP) is a highly prevalent, disabling condition affecting millions of people. Patients with an identifiable anatomic pain generator and resulting neuropathic lower extremity symptoms often undergo spine surgery, but many patients lack identifiable and/or surgically corrective pathology. Nonoperative treatment options often fail to provide sustained relief. Spinal cord stimulation (SCS) is sometimes used to treat these patients, but the lack of level 1 evidence limits its widespread use and insurance coverage. The DISTINCT RCT study evaluates the efficacy of passive recharge burst SCS compared to conventional medical treatment (CMM) in alleviating chronic, refractory axial low back pain. Methods This prospective, multicenter, randomized, study with an optional 6-month crossover involved patients who were not candidates for lumbar spine surgery. The primary and secondary endpoints evaluated improvements in low back pain intensity (NRS), back pain-related disability (ODI), pain catastrophizing (PCS), and healthcare utilization. Patients were randomized to SCS therapy or CMM at 30 US study sites. Results The SCS arm reported an 85.3% NRS responder rate (≥ 50% reduction) compared to 6.2% (5/81) in the CMM arm. After the 6M primary endpoint, SCS patients elected to remain on assigned therapy and 66.2% (49/74) of CMM patients chose to trial SCS (crossover). At the 12M follow-up, SCS and crossover patients reported 78.6% and 71.4% NRS responder rates. Secondary outcomes indicated significant improvements in ODI, PCS, and reduced healthcare utilization. Six serious adverse events were reported and resolved without sequelae. Conclusion DISTINCT chronic low back pain patients with no indication for corrective surgery experienced a significant and sustained response to burst SCS therapy for up to 12 months. CMM patients who crossed over to the SCS arm reported profound improvements after 6 months. This data advocates for a timely consideration of SCS therapy in patients unresponsive to conservative therapy.
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Affiliation(s)
- James J. Yue
- Connecticut Orthopaedics, Hamden, CT, United States
| | | | - Steven Falowski
- Center for Interventional Pain and Spine, Lancaster, PA, United States
| | | | - Mehul J. Desai
- International Spine, Pain and Performance Center, Washington, DC, United States
| | | | - Julie Pilitsis
- Florida Atlantic University, Boca Raton, FL, United States
| | | | - Edward Tavel
- Clinical Trials of South Carolina, Charleston, SC, United States
| | - Sayed Wahezi
- Montefiore Montefiore Medical Center, Bronx, NY, United States
| | - Robert Funk
- Indiana Spine Group, Indianapolis, IN United States
| | - Patrick Buchanan
- Spanish Hills Interventional Pain Specialists, Camarillo, CA United States
| | | | | | | | - Robert Levy
- Anesthesia Pain Care Consultants, Tamarac, FL United States
| | - Ajay Antony
- The Orthopaedic Institute, Gainesville, FL United States
| | - Nathan Miller
- Coastal Pain & Spinal Diagnostics Medical Group, Carlsbad, CA United States
| | - Keith Scarfo
- Rhode Island Hospital, Providence, RI United States
| | - Scott Kreiner
- Barrow Brain and Spine—Ahwatukee, Phoenix, AZ United States
| | - Derron Wilson
- Goodman Campbell Brain and Spine, Greenwood, IN United States
| | - Chi Lim
- Carolina Orthopaedic and Neurosurgical Associates, Spartanburg, SC United States
| | - Edward Braun
- Kansas University Medical Center, Kansas City, KS United States
| | | | - Jonathan Duncan
- Burkhart Research Institute for Orthopaedics, San Antonio, TX United States
| | - Jijun Xu
- The Cleveland Clinic Foundation, Cleveland, OH United States
| | - Kenneth Candido
- Chicago Anesthesia Associates, SC, Chicago, IL United States
| | - Ibrahim Mohab
- Banner University Medical Center, Tucson, AZ United States
| | | | | | | | - Timothy Deer
- The Spine and Nerve Center of the Virginias, Charleston, WV United States
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3
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Xiao X, Yin J, Xu J, Tat T, Chen J. Advances in Machine Learning for Wearable Sensors. ACS NANO 2024; 18:22734-22751. [PMID: 39145724 DOI: 10.1021/acsnano.4c05851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.
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Affiliation(s)
- Xiao Xiao
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Junyi Yin
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jing Xu
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Trinny Tat
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
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Kim G, Chishty HA, Sergi F. Using Dynamic Bayesian Optimization to Induce Desired Effects in the Presence of Motor Learning: a Simulation Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.13.607783. [PMID: 39185228 PMCID: PMC11343104 DOI: 10.1101/2024.08.13.607783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship between control parameters and user response does not change over time. BO can be modified to account for the dynamics of the user response by implementing time into the kernel function, a method known as Dynamic Bayesian Optimization (DBO). However, it is unknown if DBO outperforms BO when the human response is characterized by models of human motor learning. In this work, we simulated runs of HIL optimization using BO and DBO towards establishing if DBO is a suitable paradigm for HIL optimization in the presence of motor learning. Simulations were conducted assuming either purely time-dependent participant responses, or assuming that responses would arise from state-space models of motor learning capable of describing both adaptation and use-dependent learning behavior. Statistical comparisons indicated that DBO was never inferior to BO, and, after a certain number of iterations, generally outperformed BO in convergence to optimal inputs and outputs. The number of iterations beyond which DBO was superior to BO occurred earlier when the input-output relationship of the simulated responses was more dynamic. Our results suggest that DBO may improve the performance of HIL optimization over BO when a sufficient number of iterations can be evaluated to accurately distinguish between unstructured variability (noise) and learning.
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Affiliation(s)
- GilHwan Kim
- Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Haider A. Chishty
- Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA
| | - Fabrizio Sergi
- Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA
- Department of Biomedical Engineering, University of Delaware, Newark DE, 19713, USA
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Rolo P, Vidal JV, Kholkin AL, Soares Dos Santos MP. Self-adaptive rotational electromagnetic energy generation as an alternative to triboelectric and piezoelectric transductions. COMMUNICATIONS ENGINEERING 2024; 3:105. [PMID: 39085411 PMCID: PMC11291956 DOI: 10.1038/s44172-024-00249-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
Triboelectric and piezoelectric energy harvesters can hardly power most microelectronic systems. Rotational electromagnetic harvesters are very promising alternatives, but their performance is highly dependent on the varying mechanical sources. This study presents an innovative approach to significantly increase the performance of rotational harvesters, based on dynamic coil switching strategies for optimization of the coil connection architecture during energy generation. Both analytical and experimental validations of the concept of self-adaptive rotational harvester were carried out. The adaptive harvester was able to provide an average power increase of 63.3% and 79.5% when compared to a non-adaptive 16-coil harvester for harmonic translation and harmonic swaying excitations, respectively, and 83.5% and 87.2% when compared to a non-adaptive 8-coil harvester. The estimated energy conversion efficiency was also enhanced from ~80% to 90%. This study unravels an emerging technological approach to power a wide range of applications that cannot be powered by other vibrationally driven harvesters.
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Affiliation(s)
- Pedro Rolo
- Department of Mechanical Engineering and TEMA - Centre for Mechanical Technology & Automation, University of Aveiro, 3810-193, Aveiro, Portugal.
- Department of Physics and CICECO - Aveiro Institute of Materials, University of Aveiro, 3810-193, Aveiro, Portugal.
| | - João V Vidal
- Department of Physics and CICECO - Aveiro Institute of Materials, University of Aveiro, 3810-193, Aveiro, Portugal.
- Department of Physics and I3N, University of Aveiro, 3810-193, Aveiro, Portugal.
| | - Andrei L Kholkin
- Department of Physics and CICECO - Aveiro Institute of Materials, University of Aveiro, 3810-193, Aveiro, Portugal.
| | - Marco P Soares Dos Santos
- Department of Mechanical Engineering and TEMA - Centre for Mechanical Technology & Automation, University of Aveiro, 3810-193, Aveiro, Portugal.
- LASI - Intelligent Systems Associate Laboratory, 4800-058, Guimarães, Portugal.
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Metwally AA, Perelman D, Park H, Wu Y, Jha A, Sharp S, Celli A, Ayhan E, Abbasi F, Gloyn AL, McLaughlin T, Snyder M. Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.20.24310737. [PMID: 39108516 PMCID: PMC11302614 DOI: 10.1101/2024.07.20.24310737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D: muscle insulin resistance, β-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in β-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, β-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and β-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and β-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and β-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.
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Affiliation(s)
- Ahmed A. Metwally
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Dalia Perelman
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Heyjun Park
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Yue Wu
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Alokkumar Jha
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA
| | - Seth Sharp
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Ekrem Ayhan
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Fahim Abbasi
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA
- Stanford Diabetes Research Centre, Stanford University, Stanford, CA 94305, USA
| | - Tracey McLaughlin
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
- These authors contributed equally
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- These authors contributed equally
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7
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Esmaeilpour Z, Natarajan A, Su HW, Faranesh A, Friel C, Zanos TP, D'Angelo S, Heneghan C. Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study. JMIR Form Res 2024; 8:e53716. [PMID: 39018555 PMCID: PMC11292157 DOI: 10.2196/53716] [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: 10/17/2023] [Revised: 02/12/2024] [Accepted: 06/17/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.
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Affiliation(s)
| | | | - Hao-Wei Su
- Google LLC, San Francisco, CA, United States
| | | | - Ciaran Friel
- Northwell Health, New Hyde Park, NY, United States
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, United States
| | - Theodoros P Zanos
- Northwell Health, New Hyde Park, NY, United States
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, United States
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8
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Aslam MS, Altaf A, Iqbal F, Nigar N, Galán JC, Aray DG, Díez IDLT, Ashraf I. Novel model to authenticate role-based medical users for blockchain-based IoMT devices. PLoS One 2024; 19:e0304774. [PMID: 38985779 PMCID: PMC11236171 DOI: 10.1371/journal.pone.0304774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/19/2024] [Indexed: 07/12/2024] Open
Abstract
The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization.
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Affiliation(s)
- Muhammad Shehzad Aslam
- Department of Computer Science, University of Engineering & Technology, (UET), Lahore, Pakistan
| | - Ayesha Altaf
- Department of Computer Science, University of Engineering & Technology, (UET), Lahore, Pakistan
| | - Faiza Iqbal
- Department of Computer Science, University of Engineering & Technology, (UET), Lahore, Pakistan
| | - Natasha Nigar
- Department of Computer Science, University of Engineering & Technology, (UET), Lahore, Pakistan
| | - Juan Castanedo Galán
- Universidad Europea del Atlántico, Santander, Spain
- Universidade Internacional do Cuanza, Cuito, Bié, Angola
- Fundación Universitaria Internacional de Colombia, Bogotá, Colombia
| | - Daniel Gavilanes Aray
- Universidad Europea del Atlántico, Santander, Spain
- Universidad Internacional Iberoamericana Campeche, Campeche, México
- Universidad Internacional Iberoamericana Arecibo, Arecibo, Puerto Rico, United States of America
| | - Isabel de la Torre Díez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South Korea
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9
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Poursharifi N, Hassanpouramiri M, Zink A, Ucuncu M, Parlak O. Transdermal Sensing of Enzyme Biomarker Enabled by Chemo-Responsive Probe-Modified Epidermal Microneedle Patch in Human Skin Tissue. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2403758. [PMID: 38733567 DOI: 10.1002/adma.202403758] [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: 03/13/2024] [Revised: 05/07/2024] [Indexed: 05/13/2024]
Abstract
Wearable bioelectronics represents a significant breakthrough in healthcare settings, particularly in (bio)sensing which offers an alternative way to track individual health for diagnostics and therapy. However, there has been no notable improvement in the field of cancer, particularly for skin cancer. Here, a wearable bioelectronic patch is established for transdermal sensing of the melanoma biomarker, tyrosinase (Tyr), using a microneedle array integrated with a surface-bound chemo-responsive smart probe to enable target-specific electrochemical detection of Tyr directly from human skin tissue. The results presented herein demonstrate the feasibility of a transdermal microneedle sensor for direct quantification of enzyme biomarkers in an ex vivo skin model. Initial performance analysis of the transdermal microneedle sensor proves that the designed methodology can be an alternative for fast and reliable diagnosis of melanoma and the evaluation of skin moles. The innovative approach presented here may revolutionize the landscape of skin monitoring by offering a nondisruptive means for continuous surveillance and timely intervention of skin anomalies, such as inflammatory skin diseases or allergies and can be extended to the screening of multiple responses of complementary biomarkers with simple modification in device design.
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Affiliation(s)
- Nazanin Poursharifi
- Department of Medicine, Solna, Division of Dermatology and Venereology, Karolinska Institutet, Stockholm, 171 77, Sweden
| | - Morteza Hassanpouramiri
- Department of Medicine, Solna, Division of Dermatology and Venereology, Karolinska Institutet, Stockholm, 171 77, Sweden
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, 80802, Munich, Germany
| | - Alexander Zink
- Department of Medicine, Solna, Division of Dermatology and Venereology, Karolinska Institutet, Stockholm, 171 77, Sweden
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, 80802, Munich, Germany
| | - Muhammed Ucuncu
- Department of Analytical Chemistry, Faculty of Pharmacy, İzmir Katip Çelebi University, İzmir, 35620, Türkiye
| | - Onur Parlak
- Department of Medicine, Solna, Division of Dermatology and Venereology, Karolinska Institutet, Stockholm, 171 77, Sweden
- Department of Dermatology and Allergy, TUM School of Medicine and Health, Technical University of Munich, 80802, Munich, Germany
- Center for the Advancement of Integrated Medical and Engineering Sciences, Karolinska Institutet and KTH Royal Institute of Technology, Stockholm, 171 77, Sweden
- Centre for Molecular Medicine, Karolinska University Hospital, Stockholm, 171 64, Sweden
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10
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Doheny EP, Renerts K, Braun A, Werth E, Baumann C, Baumgartner P, Morgan-Jones P, Busse M, Lowery MM, Jung HH. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med 2024; 20:1163-1171. [PMID: 38450553 PMCID: PMC11217637 DOI: 10.5664/jcsm.11098] [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: 01/24/2024] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/08/2024]
Abstract
STUDY OBJECTIVES Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. METHODS Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data. RESULTS FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data. CONCLUSIONS Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. CITATION Doheny EP, Renerts K, Braun A, et al. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med. 2024;20(7):1163-1171.
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Affiliation(s)
- Emer P. Doheny
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Klavs Renerts
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Andreas Braun
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Esther Werth
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Christian Baumann
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | | | - Philippa Morgan-Jones
- Centre for Trials Research, Cardiff University, Cardiff, Wales, United Kingdom
- School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, Wales, United Kingdom
| | - Madeleine M. Lowery
- School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Hans H. Jung
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
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11
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Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. NPJ Digit Med 2024; 7:161. [PMID: 38890529 PMCID: PMC11189504 DOI: 10.1038/s41746-024-01151-3] [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: 11/02/2023] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Department of Behavioral and Social Sciences, Brown University, Providence, USA
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Jürgen Bernard
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computer Science, University of Zurich, Zurich, Switzerland
| | | | - Viktor von Wyl
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
- Digital Society Initiative, University of Zurich, Zurich, Switzerland.
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
- Swiss School of Public Health (SSPH+), Zurich, Switzerland.
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12
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Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, Narayan SM, Slotwiner DJ, Wiley KK, Khera R. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e000095. [PMID: 38779844 DOI: 10.1161/hcg.0000000000000095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.
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13
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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14
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Narayan SM, Wan EY, Andrade JG, Avari Silva JN, Bhatia NK, Deneke T, Deshmukh AJ, Chon KH, Erickson L, Ghanbari H, Noseworthy PA, Pathak RK, Roelle L, Seiler A, Singh JP, Srivatsa UN, Trela A, Tsiperfal A, Varma N, Yousuf OK. Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:37-49. [PMID: 38765620 PMCID: PMC11096652 DOI: 10.1016/j.cvdhj.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Affiliation(s)
| | - Elaine Y Wan
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | | | | | | | | | | | - Ki H Chon
- University of Connecticut, Storrs, Connecticut
| | | | | | | | | | - Lisa Roelle
- Washington University School of Medicine, Saint Louis, Missouri
| | | | - Jagmeet P Singh
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Anthony Trela
- Lucile Packard Children's Hospital, Palo Alto, California
| | - Angela Tsiperfal
- Stanford Arrhythmia Service, Stanford Healthcare, Palo Alto, California
| | | | - Omair K Yousuf
- Inova Heart and Vascular Institute; Carient Heart and Vascular; and University of Virginia Health, Fairfax, Virginia
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15
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Bloomfield LSP, Fudolig MI, Kim J, Llorin J, Lovato JL, McGinnis EW, McGinnis RS, Price M, Ricketts TH, Dodds PS, Stanton K, Danforth CM. Predicting stress in first-year college students using sleep data from wearable devices. PLOS DIGITAL HEALTH 2024; 3:e0000473. [PMID: 38602898 PMCID: PMC11008774 DOI: 10.1371/journal.pdig.0000473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 02/16/2024] [Indexed: 04/13/2024]
Abstract
Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.
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Affiliation(s)
- Laura S. P. Bloomfield
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Mikaela I. Fudolig
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Julia Kim
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Jordan Llorin
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Juniper L. Lovato
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Ellen W. McGinnis
- Department of Social Science and Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
- Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Ryan S. McGinnis
- Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Matt Price
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Psychological Science, University of Vermont, Burlington, Vermont, United States of America
| | - Taylor H. Ricketts
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont, United States of America
| | - Peter Sheridan Dodds
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America
| | - Kathryn Stanton
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Christopher M. Danforth
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
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16
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Friesner ID, Feng J, Kalnicki S, Garg M, Ohri N, Hong JC. Machine Learning-Based Prediction of Hospitalization During Chemoradiotherapy With Daily Step Counts. JAMA Oncol 2024:2816984. [PMID: 38546697 PMCID: PMC10979356 DOI: 10.1001/jamaoncol.2024.0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/21/2023] [Indexed: 04/01/2024]
Abstract
Importance Toxic effects of concurrent chemoradiotherapy (CRT) can cause treatment interruptions and hospitalizations, reducing treatment efficacy and increasing health care costs. Physical activity monitoring may enable early identification of patients at high risk for hospitalization who may benefit from proactive intervention. Objective To develop and validate machine learning (ML) approaches based on daily step counts collected by wearable devices on prospective trials to predict hospitalizations during CRT. Design, Setting, and Participants This study included patients with a variety of cancers enrolled from June 2015 to August 2018 on 3 prospective, single-institution trials of activity monitoring using wearable devices during CRT. Patients were followed up during and 1 month following CRT. Training and validation cohorts were generated temporally, stratifying for cancer diagnosis (70:30). Random forest, neural network, and elastic net-regularized logistic regression (EN) were trained to predict short-term hospitalization risk based on a combination of clinical characteristics and the preceding 2 weeks of activity data. To predict outcomes of activity data, models based only on activity-monitoring features and only on clinical features were trained and evaluated. Data analysis was completed from January 2022 to March 2023. Main Outcomes and Measures Model performance was evaluated in terms of the receiver operating characteristic area under curve (ROC AUC) in the stratified temporal validation cohort. Results Step counts from 214 patients (median [range] age, 61 [53-68] years; 113 [52.8%] male) were included. EN based on step counts and clinical features had high predictive ability (ROC AUC, 0.83; 95% CI, 0.66-0.92), outperforming random forest (ROC AUC, 0.76; 95% CI, 0.56-0.87; P = .02) and neural network (ROC AUC, 0.80; 95% CI, 0.71-0.88; P = .36). In an ablation study, the EN model based on only step counts demonstrated greater predictive ability than the EN model with step counts and clinical features (ROC AUC, 0.85; 95% CI, 0.70-0.93; P = .09). Both models outperformed the EN model trained on only clinical features (ROC AUC, 0.53; 95% CI, 0.31-0.66; P < .001). Conclusions and Relevance This study developed and validated a ML model based on activity-monitoring data collected during prospective clinical trials. Patient-generated health data have the potential to advance predictive ability of ML approaches. The resulting model from this study will be evaluated in an upcoming multi-institutional, cooperative group randomized trial.
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Affiliation(s)
- Isabel D. Friesner
- Bakar Computational Health Sciences Institute, University of California, San Francisco
| | - Jean Feng
- Bakar Computational Health Sciences Institute, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Shalom Kalnicki
- Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
| | - Madhur Garg
- Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
| | - Nitin Ohri
- Department of Radiation Oncology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
| | - Julian C. Hong
- Bakar Computational Health Sciences Institute, University of California, San Francisco
- Department of Radiation Oncology, University of California, San Francisco
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17
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Zhao C, Wang Y, Chen C, Zhu Y, Miao Z, Mou X, Yuan W, Zhang Z, Li K, Chen M, Liang W, Zhang M, Miao W, Dong Y, Deng D, Wu J, Ke B, Bao R, Geng J. Direct and Continuous Monitoring of Multicomponent Antibiotic Gentamicin in Blood at Single-Molecule Resolution. ACS NANO 2024; 18:9137-9149. [PMID: 38470845 DOI: 10.1021/acsnano.4c00302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Point-of-care monitoring of small molecules in biofluids is crucial for clinical diagnosis and treatment. However, the inherent low degree of recognition of small molecules and the complex composition of biofluids present significant obstacles for current detection technologies. Although nanopore sensing excels in the analysis of small molecules, the direct detection of small molecules in complex biofluids remains a challenge. In this study, we present a method for sensing the small molecule drug gentamicin in whole blood based on the mechanosensitive channel of small conductance in Pseudomonas aeruginosa (PaMscS) nanopore. PaMscS can directly detect gentamicin and distinguish its main components with only a monomethyl difference. The 'molecular sieve' structure of PaMscS enables the direct measurement of gentamicin in human whole blood within 10 min. Furthermore, a continuous monitoring device constructed based on PaMscS achieved continuous monitoring of gentamicin in live rats for approximately 2.5 h without blood consumption, while the drug components can be analyzed in situ. This approach enables rapid and convenient drug monitoring with single-molecule level resolution, which can significantly lower the threshold for drug concentration monitoring and promote more efficient drug use. Moreover, this work also lays the foundation for the future development of continuous monitoring technology with single-molecule level resolution in the living body.
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Affiliation(s)
- Changjian Zhao
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Yu Wang
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Chen Chen
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Yibo Zhu
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Center of Infectious Diseases, Division of Infectious Diseases in State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Zhuang Miao
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xingyu Mou
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Weidan Yuan
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Zhihao Zhang
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Kaiju Li
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Mutian Chen
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Weibo Liang
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Ming Zhang
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Wenqian Miao
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuhan Dong
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
| | - Dong Deng
- Division of Obstetrics, Key Laboratory of Birth Defects and Related Disease of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041 China
| | - Jianping Wu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Bowen Ke
- Department of Anesthesiology, Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Rui Bao
- Center of Infectious Diseases, Division of Infectious Diseases in State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jia Geng
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy and Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu 610500, China
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Lai KY, Kumari S, Gallacher J, Webster CJ, Sarkar C. Association between Residential Greenness and Allostatic Load: A Cohort Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4884-4893. [PMID: 38437596 DOI: 10.1021/acs.est.3c04792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
The association between residential greenness and allostatic load (AL), a marker of composite physiological burden and predictor of chronic disease, remains understudied. This study comprised 212,600 UK Biobank participants recruited over 2007 and 2010 at the baseline. Residential greenness was modeled as the normalized difference vegetation index (NDVI) from high spatial resolution (0.50 m) color infrared imagery and measured within a 0.5 km radial catchment. AL was measured as a composite index from 13 biomarkers comprising three physiological systems (metabolic, cardiovascular, and inflammatory systems) and two organ systems (liver and kidney). Multilevel mixed-effects generalized linear models with a random intercept for UK Biobank assessment centers were employed to examine the association between residential greenness and AL. Each interquartile range (IQR = 0.24) increment in NDVI greenness was associated with lower AL (beta (β) = -0.28, 95% confidence interval (CI) = -0.55, -0.01). Consistently, relative to the lowest NDVI greenness quintile, participants in the highest quintile had lower AL (β = -0.64, 95% CI = -1.02, -0.26). The proportion of the association between greenness and AL mediated by the physical activity was 3.2%. In conclusion, residential greenness was protectively associated with AL, a composite marker of wear and tear and general health.
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Affiliation(s)
- Ka Yan Lai
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Urban Planning & Design, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Sarika Kumari
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
| | - Christopher John Webster
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Urban Planning & Design, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Urban Systems Institute, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
| | - Chinmoy Sarkar
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Urban Planning & Design, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
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19
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Syversen A, Dosis A, Jayne D, Zhang Z. Wearable Sensors as a Preoperative Assessment Tool: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:482. [PMID: 38257579 PMCID: PMC10820534 DOI: 10.3390/s24020482] [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: 11/23/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.
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Affiliation(s)
- Aron Syversen
- School of Computing, University of Leeds, Leeds LS2 9JT, UK
| | - Alexios Dosis
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - David Jayne
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - Zhiqiang Zhang
- School of Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK;
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20
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Shen X, Kellogg R, Panyard DJ, Bararpour N, Castillo KE, Lee-McMullen B, Delfarah A, Ubellacker J, Ahadi S, Rosenberg-Hasson Y, Ganz A, Contrepois K, Michael B, Simms I, Wang C, Hornburg D, Snyder MP. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat Biomed Eng 2024; 8:11-29. [PMID: 36658343 PMCID: PMC10805653 DOI: 10.1038/s41551-022-00999-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/14/2022] [Indexed: 01/21/2023]
Abstract
Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.
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Affiliation(s)
- Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ryan Kellogg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Daniel J Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Nasim Bararpour
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kevin Erazo Castillo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Brittany Lee-McMullen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Alireza Delfarah
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Jessalyn Ubellacker
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sara Ahadi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Yael Rosenberg-Hasson
- Human Immune Monitoring Center, Microbiology and Immunology, Stanford University Medical Center, Stanford, CA, USA
| | - Ariel Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Basil Michael
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ian Simms
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Chuchu Wang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
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21
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Dynamic monitoring of thousands of biochemical analytes using microsampling. Nat Biomed Eng 2024; 8:5-6. [PMID: 36697922 DOI: 10.1038/s41551-023-01005-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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22
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Kang JY, Bae YS, Chie EK, Lee SB. Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. SENSORS (BASEL, SWITZERLAND) 2023; 23:9597. [PMID: 38067970 PMCID: PMC10708735 DOI: 10.3390/s23239597] [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: 11/07/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023]
Abstract
Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.
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Affiliation(s)
- Jin-Yeong Kang
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
- Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
| | - Ye Seul Bae
- Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
- Department of Future Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
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23
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Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [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: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
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24
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Hirai K, Fujimoto Y, Bamba Y, Kageyama Y, Ima H, Ichise A, Sasaki H, Nakagawa R. Continuous Monitoring of Changes in Heart Rate during the Periprocedural Course of Carotid Artery Stenting Using a Wearable Device: A Prospective Observational Study. Neurol Med Chir (Tokyo) 2023; 63:526-534. [PMID: 37648537 PMCID: PMC10725827 DOI: 10.2176/jns-nmc.2023-0093] [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: 04/26/2023] [Accepted: 07/03/2023] [Indexed: 09/01/2023] Open
Abstract
This prospective observational study will evaluate the change in heart rate (HR) during the periprocedural course of carotid artery stenting (CAS) via continuous monitoring using a wearable device. The participants were recruited from our outpatient clinic between April 2020 and March 2023. They were instructed to continuously wear the device from the last outpatient visit before admission to the first outpatient visit after discharge. The changes in HR of interest throughout the periprocedural course of CAS were assessed. In addition, the Bland-Altman analysis was adopted to compare the HR measurement made by the wearable device during CAS with that made by the electrocardiogram (ECG). A total of 12 patients who underwent CAS were included in the final analysis. The time-series analysis revealed that a percentage change in HR decrease occurred on day 1 following CAS and that the most significant HR decrease rate was 12.1% on day 4 following CAS. In comparing the measurements made by the wearable device and ECG, the Bland-Altman analysis revealed the accuracy of the wearable device with a bias of -1.12 beats per minute (bpm) and a precision of 3.16 bpm. Continuous HR monitoring using the wearable device indicated that the decrease in HR following CAS could persist much longer than previously reported, providing us with unique insights into the physiology of carotid sinus baroreceptors.
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Affiliation(s)
| | | | - Yohei Bamba
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Yu Kageyama
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Hiroyuki Ima
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Ayaka Ichise
- Department of Neurosurgery, Osaka Rosai Hospital
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25
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Nazaret A, Tonekaboni S, Darnell G, Ren SY, Sapiro G, Miller AC. Modeling personalized heart rate response to exercise and environmental factors with wearables data. NPJ Digit Med 2023; 6:207. [PMID: 37968567 PMCID: PMC10651837 DOI: 10.1038/s41746-023-00926-4] [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: 03/31/2023] [Accepted: 09/20/2023] [Indexed: 11/17/2023] Open
Abstract
Heart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy-but ubiquitous-data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout intensity. Our approach efficiently fits the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces fitness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4-8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory fitness, such as VO2 max (explained variance 0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with flexible neural networks can yield interpretable, robust, and expressive models for health applications.
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26
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Tang W, Sun Q, Wang ZL. Self-Powered Sensing in Wearable Electronics─A Paradigm Shift Technology. Chem Rev 2023; 123:12105-12134. [PMID: 37871288 PMCID: PMC10636741 DOI: 10.1021/acs.chemrev.3c00305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/25/2023]
Abstract
With the advancements in materials science and micro/nanoengineering, the field of wearable electronics has experienced a rapid growth and significantly impacted and transformed various aspects of daily human life. These devices enable individuals to conveniently access health assessments without visiting hospitals and provide continuous, detailed monitoring to create comprehensive health data sets for physicians to analyze and diagnose. Nonetheless, several challenges continue to hinder the practical application of wearable electronics, such as skin compliance, biocompatibility, stability, and power supply. In this review, we address the power supply issue and examine recent innovative self-powered technologies for wearable electronics. Specifically, we explore self-powered sensors and self-powered systems, the two primary strategies employed in this field. The former emphasizes the integration of nanogenerator devices as sensing units, thereby reducing overall system power consumption, while the latter focuses on utilizing nanogenerator devices as power sources to drive the entire sensing system. Finally, we present the future challenges and perspectives for self-powered wearable electronics.
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Affiliation(s)
- Wei Tang
- CAS
Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy
and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- School
of Nanoscience and Technology, University
of Chinese Academy of Sciences, Beijing 100049, China
- Institute
of Applied Nanotechnology, Jiaxing, Zhejiang 314031, P.R. China
| | - Qijun Sun
- CAS
Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy
and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- School
of Nanoscience and Technology, University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Lin Wang
- CAS
Center for Excellence in Nanoscience, Beijing Institute of Nanoenergy
and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Yonsei
Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
- Georgia
Institute of Technology, Atlanta, Georgia 30332-0245, United States
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27
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Hosseinzadeh S, Sajadi Tabar SS. Smartwatches in healthcare medicine: assistance and monitoring; a scoping review. BMC Med Inform Decis Mak 2023; 23:248. [PMID: 37924029 PMCID: PMC10625201 DOI: 10.1186/s12911-023-02350-w] [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: 07/12/2023] [Accepted: 10/22/2023] [Indexed: 11/06/2023] Open
Abstract
Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed/Medline, Scopus, Embase, Web of Science, ERIC and Google Scholar. In our search strategy, 761 articles were returned. The exclusion/inclusion criteria were applied. Finally, 35 articles were selected for extracting data. These included six studies on stress monitoring, six on movement disorders, three on sleep tracking, three on blood pressure, two on heart disease, six on covid pandemic, three on safety and six on validation. The use of smartwatches has been found to be effective in diagnosing the symptoms of various diseases. In particular, smartwatches have shown promise in detecting heart diseases, movement disorders, and even early signs of COVID-19. Nevertheless, it should be emphasised that there is an ongoing discussion concerning the reliability of smartwatch diagnoses within healthcare systems. Despite the potential advantages offered by utilising smartwatches for disease detection, it is imperative to approach their data interpretation with prudence. The discrepancies in detection between smartwatches and their algorithms have important implications for healthcare use. The accuracy and reliability of the algorithms used are crucial, as well as high accuracy in detecting changes in health status by the smartwatches themselves. This calls for the development of medical watches and the creation of AI-hospital assistants. These assistants will be designed to help with patient monitoring, appointment scheduling, and medication management tasks. They can educate patients and answer common questions, freeing healthcare providers to focus on more complex tasks.
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Affiliation(s)
- Mohsen Masoumian Hosseini
- Department of E-Learning in Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- CyberPatient Research Affiliate, Interactive Health International, Department of the surgery, University of British Columbia, Vancouver, Canada
| | - Seyedeh Toktam Masoumian Hosseini
- CyberPatient Research Affiliate, Interactive Health International, Department of the surgery, University of British Columbia, Vancouver, Canada.
- Department of Nursing, School of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran.
| | - Karim Qayumi
- Professor at Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Shahriar Hosseinzadeh
- CyberPatient Research Coordinator, Interactive Health International, Department of Surgery, University of British Columbia, Vancouver, Canada
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Faria M, Zin STP, Chestnov R, Novak AM, Lev-Ari S, Snyder M. Mental Health for All: The Case for Investing in Digital Mental Health to Improve Global Outcomes, Access, and Innovation in Low-Resource Settings. J Clin Med 2023; 12:6735. [PMID: 37959201 PMCID: PMC10649112 DOI: 10.3390/jcm12216735] [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: 08/11/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Mental health disorders are an increasing global public health concern that contribute to morbidity, mortality, disability, and healthcare costs across the world. Biomedical and psychological research has come a long way in identifying the importance of mental health and its impact on behavioral risk factors, physiological health, and overall quality of life. Despite this, access to psychological and psychiatric services remains widely unavailable and is a challenge for many healthcare systems, particularly those in developing countries. This review article highlights the strengths and opportunities brought forward by digital mental health in narrowing this divide. Further, it points to the economic and societal benefits of effectively managing mental illness, making a case for investing resources into mental healthcare as a larger priority for large non-governmental organizations and individual nations across the globe.
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Affiliation(s)
- Manuel Faria
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Stella Tan Pei Zin
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Roman Chestnov
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Anne Marie Novak
- Department of Health Promotion, Tel Aviv University School of Medicine, Tel Aviv 6997801, Israel;
| | - Shahar Lev-Ari
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Department of Health Promotion, Tel Aviv University School of Medicine, Tel Aviv 6997801, Israel;
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
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Belsare P, Bartolome A, Stanger C, Prioleau T. Understanding temporal changes and seasonal variations in glycemic trends using wearable data. SCIENCE ADVANCES 2023; 9:eadg2132. [PMID: 37738344 PMCID: PMC10516495 DOI: 10.1126/sciadv.adg2132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/18/2023] [Indexed: 09/24/2023]
Abstract
Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year's Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.
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Affiliation(s)
- Prajakta Belsare
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
| | - Abigail Bartolome
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
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Li H, Yuan J, Fennell G, Abdulla V, Nistala R, Dandachi D, Ho DKC, Zhang Y. Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19. BIOPHYSICS REVIEWS 2023; 4:031302. [PMID: 38510705 PMCID: PMC10903389 DOI: 10.1063/5.0140900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/19/2023] [Indexed: 03/22/2024]
Abstract
The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.
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Affiliation(s)
- Huijie Li
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Jianhe Yuan
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Gavin Fennell
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Vagif Abdulla
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
| | - Ravi Nistala
- Division of Nephrology, Department of Medicine, University of Missouri-Columbia, Columbia, Missouri 65212, USA
| | - Dima Dandachi
- Division of Infectious Diseases, Department of Medicine, University of Missouri-Columbia, 1 Hospital Drive, Columbia, Missouri 65212, USA
| | - Dominic K. C. Ho
- Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA
| | - Yi Zhang
- Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA
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31
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Volkova E, Perchik A, Pavlov K, Nikolaev E, Ayuev A, Park J, Chang N, Lee W, Kim JY, Doronin A, Vilenskii M. Multispectral sensor fusion in SmartWatch for in situ continuous monitoring of human skin hydration and body sweat loss. Sci Rep 2023; 13:13371. [PMID: 37591885 PMCID: PMC10435441 DOI: 10.1038/s41598-023-40339-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 08/09/2023] [Indexed: 08/19/2023] Open
Abstract
Post-pandemic health operations have become a near-term reality, discussions around wearables are on the rise. How do wearable health solutions effectively deploy and use this opportunity to fill the gap between wellness and healthcare? In this paper, we will talk about wearable healthcare diagnosis, with a particular focus on monitoring skin hydration using optical multi-wavelength sensor fusion. Continuous monitoring of human skin hydration is a task of paramount importance for maintaining water loss dynamics for fitness lovers as well as for skin beauty, integrity and the health of the entire body. Preserving the appropriate levels of hydration ensures consistency of weight, positively affects psychological state, and proven to result in a decrease in blood pressure as well as the levels of "bad" cholesterol while slowing down the aging processes. Traditional methods for determining the state of water content in the skin do not allow continuous and non-invasive monitoring, which is required for variety of consumer, clinical and cosmetic applications. We present novel sensing technology and a pipeline for capturing, modeling and analysis of the skin hydration phenomena and associated changes therein. By expanding sensing capabilities built into the SmartWatch sensor and combining them with advanced modeling and Machine Learning (ML) algorithms, we identified several important characteristics of photoplethysmography (PPG) signal and spectral sensitivity corresponding to dynamics of skin water content. In a hardware aspect, we newly propose the expansion of SmartWatch capabilities with InfraRed light sources equipped with wavelengths of 970 nm and 1450 nm. Evaluation of the accuracy and characteristics of PPG sensors has been performed with biomedical optics-based simulation framework using Monte Carlo simulations. We performed rigorous validation of the developed technology using experimental and clinical studies. The developed pipeline serves as a tool in the ongoing studies of the next generation of optical sensing technology.
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Affiliation(s)
- Elena Volkova
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia.
| | - Alexey Perchik
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia
| | - Konstantin Pavlov
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia
| | - Evgenii Nikolaev
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia
| | - Alexey Ayuev
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia
| | - Jaehyuck Park
- Health H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea
| | - Namseok Chang
- Health H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea
| | - Wonseok Lee
- Health H/W R&D Group, Samsung Electronics, Suwon, 16678, Korea
| | | | - Alexander Doronin
- School of Engineering and Computer Science, Victoria University of Wellington, 6140, Wellington, New Zealand
| | - Maksim Vilenskii
- Sensor Solutions Laboratory, Samsung R&D Institute Russia, 127018, Moscow, Russia
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32
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Romero-Tapiador S, Lacruz-Pleguezuelos B, Tolosana R, Freixer G, Daza R, Fernández-Díaz CM, Aguilar-Aguilar E, Fernández-Cabezas J, Cruz-Gil S, Molina S, Crespo MC, Laguna T, Marcos-Zambrano LJ, Vera-Rodriguez R, Fierrez J, Ramírez de Molina A, Ortega-Garcia J, Espinosa-Salinas I, Morales A, Carrillo de Santa Pau E. AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence. Database (Oxford) 2023; 2023:baad049. [PMID: 37465917 PMCID: PMC10354505 DOI: 10.1093/database/baad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/24/2023] [Accepted: 06/21/2023] [Indexed: 07/20/2023]
Abstract
The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.
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Affiliation(s)
- Sergio Romero-Tapiador
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Blanca Lacruz-Pleguezuelos
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Ruben Tolosana
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Gala Freixer
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Roberto Daza
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Cristina M Fernández-Díaz
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Elena Aguilar-Aguilar
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
- Department of Nursing and Nutrition, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Calle Tajo s/n, Villaviciosa de Odon, Madrid 28670, Spain
| | - Jorge Fernández-Cabezas
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Silvia Cruz-Gil
- Molecular Oncology and Nutritional Genomics of Cancer Group, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Susana Molina
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Maria Carmen Crespo
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Teresa Laguna
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Ruben Vera-Rodriguez
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Julian Fierrez
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Ana Ramírez de Molina
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Javier Ortega-Garcia
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Isabel Espinosa-Salinas
- GENYAL Platform on Nutrition and Health, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
| | - Aythami Morales
- Biometrics and Data Pattern Analytics Laboratory, Universidad Autonoma de Madrid, Calle Francisco Tomas y Valiente, 11, Campus de Cantoblanco, Madrid 28049, Spain
| | - Enrique Carrillo de Santa Pau
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Carretera de Cantoblanco, 8, Madrid 28049, Spain
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Payette J, Vaussenat F, Cloutier S. Deep learning framework for sensor array precision and accuracy enhancement. Sci Rep 2023; 13:11237. [PMID: 37433852 DOI: 10.1038/s41598-023-38290-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/06/2023] [Indexed: 07/13/2023] Open
Abstract
In the upcoming years, artificial intelligence is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between [Formula: see text]. 800 vectors are extracted, covering a range from to 30 to [Formula: see text]. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10[Formula: see text] on the training set and 1.22x10[Formula: see text] on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.
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Affiliation(s)
- Julie Payette
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada
| | - Fabrice Vaussenat
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada
| | - Sylvain Cloutier
- Department of Electrical Engineering, École de technologie supérieure, Montréal, H3C 1K3, Canada.
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Lychagov V, Semenov V, Volkova E, Chernakov D, Ahn J, Kim JY. Non-invasive hemoglobin concentration measurements with multi-wavelength reflectance mode PPG sensor and CNN data processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082910 DOI: 10.1109/embc40787.2023.10341173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Possibility of non-invasive hemoglobin concentration measurements with wearable devices have been evaluated. The proposed solution is based on the assumption that PPG waveform shape measured at various wavelengths in the reflectance mode carries information about in-depth distribution of optical pathlength in the tissue. Decomposition of temporal and spectral features of PPG signal have been applied to correct estimation of hemoglobin concentration. The dataset including 840 PPG waveforms from 170 volunteers have been collected for the purpose of neural network training and validation. The achieved performance (MAE~13.6 g/l, R~0.62) is confirmed with the invasive blood test.Clinical Relevance - This paper establishes possibility of non-invasive real time hemoglobin concentration measurements by means of low-cost wearable sensor with accuracy comparable to non-invasive clinical instruments.
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35
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Chen C, Ding S, Wang J. Digital health for aging populations. Nat Med 2023; 29:1623-1630. [PMID: 37464029 DOI: 10.1038/s41591-023-02391-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 05/09/2023] [Indexed: 07/20/2023]
Abstract
Growing life expectancy poses important societal challenges, placing an increasing burden on ever more strained health systems. Digital technologies offer tremendous potential for shifting from traditional medical routines to remote medicine and transforming our ability to manage health and independence in aging populations. In this Perspective, we summarize the current progress toward, and challenges and future opportunities of, harnessing digital technologies for effective geriatric care. Special attention is given to the role of wearables in assisting older adults to monitor their health and maintain independence at home. Challenges to the widespread future use of digital technologies in this population will be discussed, along with a vision for how such technologies will shape the future of healthy aging.
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Affiliation(s)
- Chuanrui Chen
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Shichao Ding
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Joseph Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
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Shiwani MA, Chico TJA, Ciravegna F, Mihaylova L. Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies. SENSORS (BASEL, SWITZERLAND) 2023; 23:5752. [PMID: 37420916 DOI: 10.3390/s23125752] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.
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Affiliation(s)
- Muhammad Ali Shiwani
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
| | - Timothy J A Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Sheffield S10 2RX, UK
| | - Fabio Ciravegna
- Dipartimento di Informatica, Università di Torino, 10124 Turin, Italy
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
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37
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Giurgiu M, Ketelhut S, Kubica C, Nissen R, Doster AK, Thron M, Timm I, Giurgiu V, Nigg CR, Woll A, Ebner-Priemer UW, Bussmann JBJ. Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions. Int J Behav Nutr Phys Act 2023; 20:68. [PMID: 37291598 DOI: 10.1186/s12966-023-01473-7] [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: 01/14/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. METHODS We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. RESULTS Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk". CONCLUSION Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.
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Affiliation(s)
- Marco Giurgiu
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany.
- Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany.
| | - Sascha Ketelhut
- Health Science Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Claudia Kubica
- Health Science Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Rebecca Nissen
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Ann-Kathrin Doster
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Maximiliane Thron
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Irina Timm
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Valeria Giurgiu
- Baden-Wuerttemberg Cooperative State University (DHBW), Karlsruhe, Germany
| | - Claudio R Nigg
- Sport Pedagogy Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Alexander Woll
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Ulrich W Ebner-Priemer
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Johannes B J Bussmann
- Erasmus MC, Department of Rehabilitation medicine, University Medical Center Rotterdam, Rotterdam, Netherlands
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38
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Jacobsen M, Gholamipoor R, Dembek TA, Rottmann P, Verket M, Brandts J, Jäger P, Baermann BN, Kondakci M, Heinemann L, Gerke AL, Marx N, Müller-Wieland D, Möllenhoff K, Seyfarth M, Kollmann M, Kobbe G. Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies. NPJ Digit Med 2023; 6:105. [PMID: 37268734 DOI: 10.1038/s41746-023-00847-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/19/2023] [Indexed: 06/04/2023] Open
Abstract
Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at -24 h and 0.88 at -48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.
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Affiliation(s)
- Malte Jacobsen
- Faculty of Health, University Witten/Herdecke, 58448, Witten, Germany.
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany.
| | - Rahil Gholamipoor
- Department of Computer Science, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, 50937, Cologne, Germany
| | - Pauline Rottmann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Marlo Verket
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Julia Brandts
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Paul Jäger
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Ben-Niklas Baermann
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Mustafa Kondakci
- Department of Oncology and Hematology, St. Lukas Hospital Solingen, 42697, Solingen, Germany
| | | | - Anna L Gerke
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Nikolaus Marx
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Dirk Müller-Wieland
- Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074, Aachen, Germany
| | - Kathrin Möllenhoff
- Mathematical Institute, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Melchior Seyfarth
- Faculty of Health, University Witten/Herdecke, 58448, Witten, Germany
- Department of Cardiology, Helios University Hospital Wuppertal, 42117, Wuppertal, Germany
| | - Markus Kollmann
- Department of Biology, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Germany.
| | - Guido Kobbe
- Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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Kooman JP. Detecting chronic kidney disease by electrocardiography. COMMUNICATIONS MEDICINE 2023; 3:74. [PMID: 37237030 DOI: 10.1038/s43856-023-00306-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Affiliation(s)
- Jeroen P Kooman
- Department of Internal Medicine, Division of Nephrology, Maastricht University Medical Centre, Maastricht, The Netherlands.
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40
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Zhu Y, Li J, Kim J, Li S, Zhao Y, Bahari J, Eliahoo P, Li G, Kawakita S, Haghniaz R, Gao X, Falcone N, Ermis M, Kang H, Liu H, Kim H, Tabish T, Yu H, Li B, Akbari M, Emaminejad S, Khademhosseini A. Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare. Biomaterials 2023; 296:122075. [PMID: 36931103 PMCID: PMC10085866 DOI: 10.1016/j.biomaterials.2023.122075] [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: 10/26/2022] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023]
Abstract
Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices.
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Affiliation(s)
- Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
| | - Jinghang Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Jinjoo Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Shaopei Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Yichao Zhao
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Jamal Bahari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Payam Eliahoo
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90007, United States
| | - Guanghui Li
- The Centre of Nanoscale Science and Technology and Key Laboratory of Functional Polymer Materials, Institute of Polymer Chemistry, College of Chemistry, Nankai University, Tianjin, 300071, China; Renewable Energy Conversion and Storage Center (RECAST), Nankai University, Tianjin, 300071, China
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Reihaneh Haghniaz
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Xiaoxiang Gao
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, 92093, United States
| | - Natashya Falcone
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Menekse Ermis
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Heemin Kang
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hao Liu
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China
| | - HanJun Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; College of Pharmacy, Korea University, Sejong, 30019, Republic of Korea
| | - Tanveer Tabish
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Haidong Yu
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Bingbing Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Department of Manufacturing Systems Engineering and Management, California State University, Northridge, CA, 91330, United States
| | - Mohsen Akbari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Laboratory for Innovation in Microengineering (LiME), Department of Mechanical Engineering, Center for Biomedical Research, University of Victoria, Victoria, BC V8P 2C5, Canada
| | - Sam Emaminejad
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Ali Khademhosseini
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
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Hicks JL, Boswell MA, Althoff T, Crum AJ, Ku JP, Landay JA, Moya PML, Murnane EL, Snyder MP, King AC, Delp SL. Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annu Rev Public Health 2023; 44:131-150. [PMID: 36542772 PMCID: PMC10523351 DOI: 10.1146/annurev-publhealth-060220-041643] [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: 12/24/2022]
Abstract
Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.
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Affiliation(s)
- Jennifer L Hicks
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Melissa A Boswell
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Tim Althoff
- Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA
| | - Alia J Crum
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - James A Landay
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Paula M L Moya
- Department of English and the Center for Comparative Studies in Race and Ethnicity, Stanford University, Stanford, California, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Abby C King
- Department of Epidemiology and Population Health, and Department of Medicine (Stanford Prevention Research Center), Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Scott L Delp
- Department of Bioengineering and Department of Mechanical Engineering, Stanford University, Stanford, California, USA
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42
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Keshet A, Reicher L, Bar N, Segal E. Wearable and digital devices to monitor and treat metabolic diseases. Nat Metab 2023; 5:563-571. [PMID: 37100995 DOI: 10.1038/s42255-023-00778-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 03/07/2023] [Indexed: 04/28/2023]
Abstract
Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.
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Affiliation(s)
- Ayya Keshet
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Lee Reicher
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Lis Maternity and Women's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv University (affiliated with Sackler Faculty of Medicine), Tel Aviv, Israel
| | - Noam Bar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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43
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Smith AA, Li R, Tse ZTH. Reshaping healthcare with wearable biosensors. Sci Rep 2023; 13:4998. [PMID: 36973262 PMCID: PMC10043012 DOI: 10.1038/s41598-022-26951-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/22/2022] [Indexed: 03/29/2023] Open
Abstract
Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit, which analyzes the data and provides feedback of the health value to the user through the computer. This is the working principle of wearable sensors for health. This article focuses on wearable biosensors used for healthcare monitoring in different situations, as well as the development, technology, business, ethics, and future of wearable sensors for health monitoring.
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Affiliation(s)
- Aaron Asael Smith
- College of Engineering, University of Georgia, Athens, GA, 30602, USA
| | - Rui Li
- Tandon School of Engineering, New York University, New York, NY, 11201, USA
| | - Zion Tsz Ho Tse
- Department of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, UK.
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44
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Abstract
Wearable devices, such as smartwatches and activity trackers, are commonly used by patients in their everyday lives to manage their health and well-being. These devices collect and analyze long-term continuous data on measures of behavioral or physiologic function, which may provide clinicians with a more comprehensive view of a patients' health compared with the traditional sporadic measures captured by office visits and hospitalizations. Wearable devices have a wide range of potential clinical applications ranging from arrhythmia screening of high-risk individuals to remote management of chronic conditions such as heart failure or peripheral artery disease. As the use of wearable devices continues to grow, we must adopt a multifaceted approach with collaboration among all key stakeholders to effectively and safely integrate these technologies into routine clinical practice. In this Review, we summarize the features of wearable devices and associated machine learning techniques. We describe key research studies that illustrate the role of wearable devices in the screening and management of cardiovascular conditions and identify directions for future research. Last, we highlight the challenges that are currently hindering the widespread use of wearable devices in cardiovascular medicine and provide short- and long-term solutions to promote increased use of wearable devices in clinical care.
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Affiliation(s)
- Andrew Hughes
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Hiral Master
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
| | - Evan Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN
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45
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Yuan M, Zhang X, Wang J, Zhao Y. Recent Progress of Energy-Storage-Device-Integrated Sensing Systems. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:nano13040645. [PMID: 36839014 PMCID: PMC9964226 DOI: 10.3390/nano13040645] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 06/12/2023]
Abstract
With the rapid prosperity of the Internet of things, intelligent human-machine interaction and health monitoring are becoming the focus of attention. Wireless sensing systems, especially self-powered sensing systems that can work continuously and sustainably for a long time without an external power supply have been successfully explored and developed. Yet, the system integrated by energy-harvester needs to be exposed to a specific energy source to drive the work, which provides limited application scenarios, low stability, and poor continuity. Integrating the energy storage unit and sensing unit into a single system may provide efficient ways to solve these above problems, promoting potential applications in portable and wearable electronics. In this review, we focus on recent advances in energy-storage-device-integrated sensing systems for wearable electronics, including tactile sensors, temperature sensors, chemical and biological sensors, and multifunctional sensing systems, because of their universal utilization in the next generation of smart personal electronics. Finally, the future perspectives of energy-storage-device-integrated sensing systems are discussed.
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46
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Barman SR, Chan SW, Kao FC, Ho HY, Khan I, Pal A, Huang CC, Lin ZH. A self-powered multifunctional dressing for active infection prevention and accelerated wound healing. SCIENCE ADVANCES 2023; 9:eadc8758. [PMID: 36696504 PMCID: PMC9876552 DOI: 10.1126/sciadv.adc8758] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Interruption of the wound healing process due to pathogenic infection remains a major health care challenge. The existing methods for wound management require power sources that hinder their utilization outside of clinical settings. Here, a next generation of wearable self-powered wound dressing is developed, which can be activated by diverse stimuli from the patient's body and provide on-demand treatment for both normal and infected wounds. The highly tunable dressing is composed of thermocatalytic bismuth telluride nanoplates (Bi2Te3 NPs) functionalized onto carbon fiber fabric electrodes and triggered by the surrounding temperature difference to controllably generate hydrogen peroxide to effectively inhibit bacterial growth at the wound site. The integrated electrodes are connected to a wearable triboelectric nanogenerator (TENG) to provide electrical stimulation for accelerated wound closure by enhancing cellular proliferation, migration, and angiogenesis. The reported self-powered dressing holds great potential in facilitating personalized and user-friendly wound care with improved healing outcomes.
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Affiliation(s)
- Snigdha Roy Barman
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- International Intercollegiate Ph.D. Program, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Shuen-Wen Chan
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Fu-Cheng Kao
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Orthopaedic Surgery, Spine Section, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsuan-Yu Ho
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Imran Khan
- Institute of NanoEngineering and Microsystems, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Arnab Pal
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- International Intercollegiate Ph.D. Program, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Chih-Ching Huang
- Department of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung 202301, Taiwan
- Center of Excellence for the Oceans, National Taiwan Ocean University, Keelung 202301, Taiwan
- School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Zong-Hong Lin
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan
- Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan
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Xie L, Zhang Z, Wu Q, Gao Z, Mi G, Wang R, Sun HB, Zhao Y, Du Y. Intelligent wearable devices based on nanomaterials and nanostructures for healthcare. NANOSCALE 2023; 15:405-433. [PMID: 36519286 DOI: 10.1039/d2nr04551f] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Emerging classes of flexible electronic sensors as alternatives to conventional rigid sensors offer a powerful set of capabilities for detecting and quantifying physiological and physical signals from human skin in personal healthcare. Unfortunately, the practical applications and commercialization of flexible sensors are generally limited by certain unsatisfactory aspects of their performance, such as biocompatibility, low sensing range, power supply, or single sensory function. This review intends to provide up-to-date literature on wearable devices for smart healthcare. A systematic review is provided, from sensors based on nanomaterials and nanostructures, algorithms, to multifunctional integrated devices with stretchability, self-powered performance, and biocompatibility. Typical electromechanical sensors are investigated with a specific focus on the strategies for constructing high-performance sensors based on nanomaterials and nanostructures. Then, the review emphasizes the importance of tailoring the fabrication techniques in order to improve stretchability, biocompatibility, and self-powered performance. The construction of wearable devices with high integration, high performance, and multi-functionalization for multiparameter healthcare is discussed in depth. Integrating wearable devices with appropriate machine learning algorithms is summarized. After interpretation of the algorithms, intelligent predictions are produced to give instructions or predictions for smart implementations. It is desired that this review will offer guidance for future excellence in flexible wearable sensing technologies and provide insight into commercial wearable sensors.
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Affiliation(s)
- Liping Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Zelin Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Qiushuo Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Zhuxuan Gao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Gaotian Mi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Renqiao Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Hong-Bin Sun
- Department of Chemistry, Northeastern University, Shenyang, 110819, China
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
| | - Yanan Du
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
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48
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Song Y, Chen J, Zhang R. Heart Rate Estimation from Incomplete Electrocardiography Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:597. [PMID: 36679394 PMCID: PMC9860828 DOI: 10.3390/s23020597] [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: 11/24/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60−120 bpm in the database without significant arrhythmias and a corresponding range of 30−150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.
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Affiliation(s)
- Yawei Song
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
| | - Jia Chen
- School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| | - Rongxin Zhang
- Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Ministry of Education, Xiamen 361005, China
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49
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Chinoy ED, Cuellar JA, Jameson JT, Markwald RR. Daytime Sleep-Tracking Performance of Four Commercial Wearable Devices During Unrestricted Home Sleep. Nat Sci Sleep 2023; 15:151-164. [PMID: 37032817 PMCID: PMC10075216 DOI: 10.2147/nss.s395732] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/20/2023] [Indexed: 04/11/2023] Open
Abstract
Purpose Previous studies have found that many commercial wearable devices can accurately track sleep-wake patterns in laboratory or home settings. However, nearly all previous studies tested devices under conditions with fixed time in bed (TIB) and during nighttime sleep episodes only. Despite its relevance to shift workers and others with irregular sleep schedules, it is largely unknown how devices track daytime sleep. Therefore, we tested the sleep-tracking performance of four commercial wearable devices during unrestricted home daytime sleep. Participants and Methods Participants were 16 healthy young adults (6 men, 10 women; 26.6 ± 4.6 years, mean ± SD) with habitual daytime sleep schedules. Participants slept at home for 1 week under unrestricted conditions (ie, self-selecting TIB) using a set of four commercial wearable devices and completed reference sleep logs. Wearables included the Fatigue Science ReadiBand, Fitbit Inspire HR, Oura Ring, and Polar Vantage V Titan. Daytime sleep episode TIB biases and frequencies of missed and false-positive daytime sleep episodes were examined. Results TIB bias was low in general for all devices on most daytime sleep episodes, but some exhibited large biases (eg, >1 h). Total missed daytime sleep episodes were as follows: Fatigue Science: 3.6%; Fitbit: 4.8%; Oura: 6.0%; Polar: 37.3%. Missed episodes occurred most often when TIB was short (eg, naps <4 h). Conclusion When daytime sleep episodes were recorded, the devices generally exhibited similar performance for tracking TIB (ie, most episodes had low bias). However, the devices failed to detect some daytime episodes, which occurred most often when TIB was short, but varied across devices (especially Polar, which missed over one-third of episodes). Findings suggest that accurate daytime sleep tracking is largely achievable with commercial wearable devices. However, performance differences for missed recordings suggest that some devices vary in reliability (especially for naps), but improvements could likely be made with changes to algorithm sensitivities.
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Affiliation(s)
- Evan D Chinoy
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Joseph A Cuellar
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Jason T Jameson
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc, San Diego, CA, USA
| | - Rachel R Markwald
- Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Correspondence: Rachel R Markwald, Sleep, Tactical Efficiency, and Endurance Laboratory, Warfighter Performance Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA, 92106, USA, Tel +1 619 767 4494, Email
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Jiang P, Gao F, Liu S, Zhang S, Zhang X, Xia Z, Zhang W, Jiang T, Zhu JL, Zhang Z, Shu Q, Snyder M, Li J. Longitudinally tracking personal physiomes for precision management of childhood epilepsy. PLOS DIGITAL HEALTH 2022; 1:e0000161. [PMID: 36812648 PMCID: PMC9931296 DOI: 10.1371/journal.pdig.0000161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/13/2022] [Indexed: 12/24/2022]
Abstract
Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.
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Affiliation(s)
- Peifang Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Gao
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sixing Liu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Sai Zhang
- SensOmics, Inc. Burlingame, California, United States of America
| | - Xicheng Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Zhezhi Xia
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weiqin Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tiejia Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jason L. Zhu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Zhaolei Zhang
- SensOmics, Inc. Burlingame, California, United States of America
- Donnelly Centre, Department of Computer Science and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Qiang Shu
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Michael Snyder
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Jingjing Li
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
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