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Ghazizadeh E, Naseri Z, Deigner HP, Rahimi H, Altintas Z. Approaches of wearable and implantable biosensor towards of developing in precision medicine. Front Med (Lausanne) 2024; 11:1390634. [PMID: 39091290 PMCID: PMC11293309 DOI: 10.3389/fmed.2024.1390634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/30/2024] [Indexed: 08/04/2024] Open
Abstract
In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.
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Affiliation(s)
- Elham Ghazizadeh
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Naseri
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Furtwangen University, Villingen-Schwenningen, Germany
- Fraunhofer Institute IZI (Leipzig), Rostock, Germany
- Faculty of Science, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Hossein Rahimi
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zeynep Altintas
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
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2
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Coppi F, Bucciarelli V, Solodka K, Selleri V, Zanini G, Pinti M, Nasi M, Salvioli B, Nodari S, Gallina S, Mattioli AV. The Impact of Stress and Social Determinants on Diet in Cardiovascular Prevention in Young Women. Nutrients 2024; 16:1044. [PMID: 38613078 PMCID: PMC11013318 DOI: 10.3390/nu16071044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024] Open
Abstract
The prevention of cardiovascular diseases is a fundamental pillar for reducing morbidity and mortality caused by non-communicable diseases. Social determinants, such as socioeconomic status, education, neighborhood, physical environment, employment, social support networks, and access to health care, play a crucial role in influencing health outcomes and health inequities within populations. Social determinants and stress in women are interconnected factors that can significantly impact women's health and well-being. Pregnancy is a good time to engage young women and introduce them to beneficial behaviors, such as adopting essential life skills, especially diet, and learning stress management techniques. Stress influences diet, and women are more likely to engage in unhealthy eating behaviors such as emotional eating or coping with stress with food. Strong action is needed to improve women's lifestyle starting at a young age considering that this lays the foundation for a lower cardiovascular risk in adults and the elderly. The objective of this review is to examine cardiovascular primary prevention in young healthy women, focusing particularly on unresolved issues and the influence of social determinants, as well as the correlation with stressors and their influence on diet.
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Affiliation(s)
- Francesca Coppi
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, 41121 Modena, Italy;
| | - Valentina Bucciarelli
- Cardiovascular Sciences Department, Azienda Ospedaliero-Universitaria delle Marche, 60166 Ancona, Italy;
| | - Kateryna Solodka
- Istituto Nazionale per le Ricerche Cardiovascolari, 40126 Bologna, Italy (M.P.); (S.G.)
| | - Valentina Selleri
- Department of Life Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (V.S.); (G.Z.)
| | - Giada Zanini
- Department of Life Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (V.S.); (G.Z.)
| | - Marcello Pinti
- Istituto Nazionale per le Ricerche Cardiovascolari, 40126 Bologna, Italy (M.P.); (S.G.)
- Department of Life Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy; (V.S.); (G.Z.)
| | - Milena Nasi
- Department of Surgical, Medical and Dental Sciences, University of Modena and Reggio Emilia, 41121 Modena, Italy;
| | - Beatrice Salvioli
- Department of Quality of Life Sciences, University of Bologna, 40126 Bologna, Italy;
| | - Savina Nodari
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy;
| | - Sabina Gallina
- Istituto Nazionale per le Ricerche Cardiovascolari, 40126 Bologna, Italy (M.P.); (S.G.)
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University of Chieti-Pescara, 66013 Chieti, Italy
| | - Anna Vittoria Mattioli
- Istituto Nazionale per le Ricerche Cardiovascolari, 40126 Bologna, Italy (M.P.); (S.G.)
- Department of Quality of Life Sciences, University of Bologna, 40126 Bologna, Italy;
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Kumar R, Fu J, Ortiz BL, Cao X, Shedden K, Choi SW. Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers. Bioengineering (Basel) 2024; 11:95. [PMID: 38247972 PMCID: PMC10813060 DOI: 10.3390/bioengineering11010095] [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: 12/20/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest. METHODS a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables. RESULTS Four distinct factors of dyadic covariation in circadian HR were found, contributing approximately 7% to overall circadian HR variation. These factors, along with non-dyadic factors reflecting individual variation exhibited diverse and statistically robust patterns of association with explanatory variables of interest. CONCLUSIONS Both dyadic and individual anomalies are present in the 24 h HR patterns of cancer patients and their caregivers. These patterns are largely synchronous, and their presence robustly associates with multiple explanatory variables. One notable finding is that higher mood scores in cancer patients correspond to an earlier HR nadir in the morning and higher HR during the afternoon.
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Affiliation(s)
- Rajnish Kumar
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Junhan Fu
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA; (J.F.); (K.S.)
| | - Bengie L. Ortiz
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Xiao Cao
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Kerby Shedden
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA; (J.F.); (K.S.)
| | - Sung Won Choi
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
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4
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Satashia PH, Franco PM, Rivas AL, Isha S, Hanson A, Narra SA, Singh K, Jenkins A, Bhattacharyya A, Guru P, Chaudhary S, Kiley S, Shapiro A, Martin A, Thomas M, Sareyyupoglu B, Libertin CR, Sanghavi DK. From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets. Front Med (Lausanne) 2023; 10:1240426. [PMID: 38020180 PMCID: PMC10664024 DOI: 10.3389/fmed.2023.1240426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery. Materials and methods Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources. Results Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission. Discussion/conclusion Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.
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Affiliation(s)
| | - Pablo Moreno Franco
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Ariel L. Rivas
- Center for Global Health-Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, United States
| | - Shahin Isha
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Abby Hanson
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sai Abhishek Narra
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Kawaljeet Singh
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Anna Jenkins
- Mayo Clinic Alix School of Medicine, Jacksonville, FL, United States
| | | | - Pramod Guru
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sanjay Chaudhary
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Sean Kiley
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Anna Shapiro
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Archer Martin
- Division of Cardiovascular and Thoracic Anesthesiology, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Jacksonville, FL, United States
| | - Mathew Thomas
- Department of Cardiothoracic Surgery, Mayo Clinic, Jacksonville, FL, United States
| | - Basar Sareyyupoglu
- Department of Cardiothoracic Surgery, Mayo Clinic, Jacksonville, FL, United States
| | - Claudia R. Libertin
- Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, United States
| | - Devang K. Sanghavi
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States
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5
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Lennon MJ, Harmer C. Machine learning prediction will be part of future treatment of depression. Aust N Z J Psychiatry 2023; 57:1316-1323. [PMID: 36823974 DOI: 10.1177/00048674231158267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.
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Affiliation(s)
- Matthew J Lennon
- Department of Psychiatry, University of Oxford, Oxford, UK
- Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Catherine Harmer
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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6
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Prasad S, Farella M. Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future. Clin Oral Investig 2023; 27:4861-4867. [PMID: 37410151 DOI: 10.1007/s00784-023-05127-7] [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: 09/13/2022] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.
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Affiliation(s)
- Sabarinath Prasad
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University, Dubai, United Arab Emirates.
| | - Mauro Farella
- Discipline of Orthodontics, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
- Discipline of Orthodontics and Pediatric Dentistry, Department of Surgical Science, University of Cagliari, Cagliari, Italy
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7
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Busnatu ȘS, Niculescu AG, Bolocan A, Andronic O, Pantea Stoian AM, Scafa-Udriște A, Stănescu AMA, Păduraru DN, Nicolescu MI, Grumezescu AM, Jinga V. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment. J Pers Med 2022; 12:jpm12101656. [PMID: 36294795 PMCID: PMC9604784 DOI: 10.3390/jpm12101656] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022] Open
Abstract
With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.
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Affiliation(s)
- Ștefan Sebastian Busnatu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
| | - Alexandra Bolocan
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Octavian Andronic
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Alexandru Scafa-Udriște
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | | | - Dan Nicolae Păduraru
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Mihnea Ioan Nicolescu
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Politehnica University of Bucharest, 011061 Bucharest, Romania
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 050044 Bucharest, Romania
- Correspondence:
| | - Viorel Jinga
- Department of Cardiology, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania
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8
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Yeung AWK, Kulnik ST, Parvanov ED, Fassl A, Eibensteiner F, Völkl-Kernstock S, Kletecka-Pulker M, Crutzen R, Gutenberg J, Höppchen I, Niebauer J, Smeddinck JD, Willschke H, Atanasov AG. Research on Digital Technology Use in Cardiology: Bibliometric Analysis. J Med Internet Res 2022; 24:e36086. [PMID: 35544307 PMCID: PMC9133979 DOI: 10.2196/36086] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/11/2022] Open
Abstract
Background Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area. Objective We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field. Methods The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization. Results The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues. Conclusions Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.
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Affiliation(s)
- Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.,Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Stefan Tino Kulnik
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Emil D Parvanov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Department of Translational Stem Cell Biology, Research Institute of the Medical University of Varna, Varna, Bulgaria
| | - Anna Fassl
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Fabian Eibensteiner
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Sabine Völkl-Kernstock
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Rik Crutzen
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.,Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Johanna Gutenberg
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.,Department of Health Promotion, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Isabel Höppchen
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.,Center for Human Computer Interaction, Paris Lodron University Salzburg, Salzburg, Austria
| | - Josef Niebauer
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.,University Institute of Sports Medicine, Prevention and Rehabilitation, Paracelsus Medical University Salzburg, Salzburg, Austria.,REHA Zentrum Salzburg, Salzburg, Austria
| | - Jan David Smeddinck
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
| | - Atanas G Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland
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Singh S, Melnik R. Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures. CHEMOSENSORS (BASEL, SWITZERLAND) 2022; 10:157. [PMID: 35909810 PMCID: PMC9171916 DOI: 10.3390/chemosensors10050157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/22/2022] [Indexed: 12/20/2022]
Abstract
Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or all three spatial dimensions, leading to their unique properties. New advancements in nanosensors, based on low-dimensional nanostructures, permit their functioning at scales comparable with biological processes and natural systems, allowing their efficient functionalization with chemical and biological molecules. In this article, we provide details of such sensors, focusing on their several important classes, as well as the issues of their designs based on mathematical and computational models covering a range of scales. Such multiscale models require state-of-the-art techniques for their solutions, and we provide an overview of the associated numerical methodologies and approaches in this context. We emphasize the importance of accounting for coupling between different physical fields such as thermal, electromechanical, and magnetic, as well as of additional nonlinear and nonlocal effects which can be salient features of new applications and sensor designs. Our special attention is given to nanowires and nanotubes which are well suited for nanosensor designs and applications, being able to carry a double functionality, as transducers and the media to transmit the signal. One of the key properties of these nanostructures is an enhancement in sensitivity resulting from their high surface-to-volume ratio, which leads to their geometry-dependant properties. This dependency requires careful consideration at the modelling stage, and we provide further details on this issue. Another important class of sensors analyzed here is pertinent to sensor and actuator technologies based on smart materials. The modelling of such materials in their dynamics-enabled applications represents a significant challenge as we have to deal with strongly nonlinear coupled problems, accounting for dynamic interactions between different physical fields and microstructure evolution. Among other classes, important in novel sensor applications, we have given our special attention to heterostructures and nucleic acid based nanostructures. In terms of the application areas, we have focused on chemical and biomedical fields, as well as on green energy and environmentally-friendly technologies where the efficient designs and opportune deployments of sensors are both urgent and compelling.
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Affiliation(s)
- Sundeep Singh
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada;
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada;
- BCAM-Basque Centre for Applied Mathematics, E-48009 Bilbao, Spain
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10
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eHealth generated patient data in an outpatient setting after stem cell transplantation: a scoping review. Transplant Cell Ther 2022; 28:463-471. [DOI: 10.1016/j.jtct.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/24/2022]
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11
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Mayer C, Tyler J, Fang Y, Flora C, Frank E, Tewari M, Choi SW, Sen S, Forger DB. Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Rep Med 2022; 3:100601. [PMID: 35480626 PMCID: PMC9017023 DOI: 10.1016/j.xcrm.2022.100601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 11/04/2021] [Accepted: 03/20/2022] [Indexed: 11/29/2022]
Abstract
Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables. We separate wearable heart rate into cardiopulmonary, circadian, and other signals Parameters from different physiological systems enable disease tracking Individual signals change in distinct ways around COVID-19 symptom onset Together, the parameter changes can distinguish healthy from infection periods
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Affiliation(s)
- Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Tyler
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.,Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher Flora
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elena Frank
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Muneesh Tewari
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.,Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Won Choi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.,Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel B Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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12
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Erickson ML, Wang W, Counts J, Redman LM, Parker D, Huebner JL, Dunn J, Kraus WE. Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. J Biol Rhythms 2022; 37:260-271. [PMID: 35416084 DOI: 10.1177/07487304221087068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.
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Affiliation(s)
| | - Will Wang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Julie Counts
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Leanne M Redman
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana
| | - Daniel Parker
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Janet L Huebner
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina.,Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | - William E Kraus
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina
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13
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Gupta V. Post-transplant dynamic risk prediction. NATURE COMPUTATIONAL SCIENCE 2022; 2:144-145. [PMID: 38177450 DOI: 10.1038/s43588-022-00220-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Vibhuti Gupta
- Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, USA.
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14
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How to Assess the Measurement Performance of Mobile/Wearable Point-of-Care Testing Devices? A Systematic Review Addressing Sweat Analysis. ELECTRONICS 2022. [DOI: 10.3390/electronics11050761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent advances in technologies for biosensor integration in mobile or wearable devices have highlighted the need for the definition of proper validation procedures and technical standards that enable testing, verification and validation of the overall performance of these solutions. Thus, reliable assessment—in terms of limits of detection/quantitation, linearity, range, analytical and diagnostic sensitivity/specificity, accuracy, repeatability, reproducibility, cross-reactivity, diagnostic efficiency, and positive/negative prediction—still represents the most critical and challenging aspect required to progress beyond the status of feasibility studies. Considering this picture, this work aims to review and discuss the literature referring to the available methods and criteria reported in the assessment of the performance of point-of-care testing (PoCT) devices within their specific applications. In particular, without losing generality, we focused on mobile or wearable systems able to analyze human sweat. In performing this review, the focus was on the main challenges and trends underlined in the literature, in order to provide specific hints that can be used to set shared procedures and improve the overall reliability of the identified solutions, addressing the importance of sample management, the sensing components, and the electronics. This review can contribute to supporting an effective validation of mobile or wearable PoCT devices and thus to spreading the use of reliable approaches outside hospitals and clinical laboratories.
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15
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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16
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Raj M, Gupta V, Hoodin F, Clingan C, Roslin C, Yahng L, Braun T, Choi SW. Evaluating mobile Health technology use among cancer caregivers in the digital era. Digit Health 2022; 8:20552076221109071. [PMID: 35769358 PMCID: PMC9234853 DOI: 10.1177/20552076221109071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/10/2022] [Accepted: 06/06/2022] [Indexed: 01/29/2023] Open
Abstract
Introduction Digital health technology-based interventions have the potential to support cancer caregivers in caregiving responsibilities and in managing their own health and well-being. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used in a national sample of caregivers of patients undergoing hematopoietic cell transplantation (HCT). Methods We conducted an online, cross-sectional survey of 948 HCT caregivers. Results Spousal caregivers comprised nearly one-third of respondents (27.1%) with a median age of 59 years (range: 18-80 years), compared with parents (32.9%: 38 years), adult children (28.9%: 38 years), and other (11.1%; e.g. friend, other family member: 36 years). Almost two-thirds (65.4%) of all respondents reported using an app for fitness or step counting and 41.3% reported using a smartwatch. However, spousal caregivers were the least likely group to use mobile apps (0.72; P < 0.005) or smartwatches (OR = 0.46; P < 0.005) compared with parent caregivers in models adjusted for demographics and coping style. Caregiving for six months or greater was associated with the use of fewer apps compared with caregiving for less than six months in adjusted models (OR = 0.80, P < 0.005). Caregivers of patients receiving an allogeneic transplant (i.e. non-self-donor) used more apps on average than caregivers of patients receiving an autologous transplant (i.e. self-donor) in adjusted models (OR = 1.36, P < 0.005). Conclusion Digital health technologies reflect promising avenues for supporting cancer caregivers. While digital technologies are becoming increasingly pervasive, older caregivers remain an underserved population. Future research should integrate older adult caregivers in the co-design and development activities of technology-driven caregiver support products.
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Affiliation(s)
- Minakshi Raj
- Department of Kinesiology and Community Health, University of Illinois at Urbana Champaign, Champaign, IL, 61820, USA
| | - Vibhuti Gupta
- Department of Computer Science & Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, USA
| | - Flora Hoodin
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Caroline Clingan
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - Chloe Roslin
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - Lilian Yahng
- Center for Survey Research, Indiana University, Bloomington, IN, USA
| | - Thomas Braun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sung Won Choi
- Department of Computer Science & Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, USA
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17
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Mirjalali S, Peng S, Fang Z, Wang C, Wu S. Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of Covid-19. ADVANCED MATERIALS TECHNOLOGIES 2022; 7:2100545. [PMID: 34901382 PMCID: PMC8646515 DOI: 10.1002/admt.202100545] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 07/22/2021] [Indexed: 05/11/2023]
Abstract
Wearable sensors are emerging as a new technology to detect physiological and biochemical markers for remote health monitoring. By measuring vital signs such as respiratory rate, body temperature, and blood oxygen level, wearable sensors offer tremendous potential for the noninvasive and early diagnosis of numerous diseases such as Covid-19. Over the past decade, significant progress has been made to develop wearable sensors with high sensitivity, accuracy, flexibility, and stretchability, bringing to reality a new paradigm of remote health monitoring. In this review paper, the latest advances in wearable sensor systems that can measure vital signs at an accuracy level matching those of point-of-care tests are presented. In particular, the focus of this review is placed on wearable sensors for measuring respiratory behavior, body temperature, and blood oxygen level, which are identified as the critical signals for diagnosing and monitoring Covid-19. Various designs based on different materials and working mechanisms are summarized. This review is concluded by identifying the remaining challenges and future opportunities for this emerging field.
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Affiliation(s)
- Sheyda Mirjalali
- School of EngineeringMacquarie University SydneySydneyNSW2109Australia
| | - Shuhua Peng
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
| | | | - Chun‐Hui Wang
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
| | - Shuying Wu
- School of EngineeringMacquarie University SydneySydneyNSW2109Australia
- School of Mechanical and Manufacturing EngineeringUniversity of New South WalesSydneyNSW2052Australia
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18
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Abstract
Digital Palliative Care Abstract. Palliative care is becoming more and more digital. This article illuminates how digital approaches can help identify patients who qualify for palliative care offers and who wish to make use of them. Digital approaches can be used to monitor patients through apps and wearables, but digital methods are also becoming more important in psychosocial and spiritual support. One case demonstrates the therapeutic use of virtual reality. Work organization is digital, and teaching has also become digital during the corona crisis. In spite of all the advantages, however, the potential risks of digitization must also be considered.
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Affiliation(s)
- Manuel Amann
- Kompetenzzentrum Palliative Care, Klinik für Radio-Onkologie, Universitätsspital Zürich, Zürich
| | - David Blum
- Kompetenzzentrum Palliative Care, Klinik für Radio-Onkologie, Universitätsspital Zürich, Zürich
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19
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Ruiz LJL, Zhu J, Fitzgerald L, Quinn D, Lach J. Capacitive Sensing for Monitoring Stent Patency in the Central Airway. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5441-5445. [PMID: 34892357 DOI: 10.1109/embc46164.2021.9630965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Central airway obstruction (CAO) is a respiratory disorder characterized by the blockage of the trachea and/or the main bronchi that can be life-threatening. Airway stenting is a palliative procedure for CAO commonly used given its efficacy. However, mucus impaction, secretion retention, and granulation tissue growth are known complications that can counteract the stent's benefits. To prevent these situations, patients are routinely brought into the hospital to check stent patency, incurring a burden for the patient and the health care system, unnecessarily when no problems are found. In this paper, we introduce a capacitive sensor embedded in a stent that can detect solid and colloidal obstructions in the stent, as such obstructions alter the capacitor's dielectric relative permittivity. In the case of colloidal obstructions (e.g., mucus), volumes as low as 0.1 ml can be detected. Given the small form factor of the sensor, it could be adapted to a variety of stent types without changing the standard bronchoscopy insertion method. The proposed system is a step forward in the development of smart airway stents that overcome the limitations of current stenting technology.Clinical Relevance- This establishes the foundation for smart stent technology to monitor stent patency as an alternative to rutinary bronchoscopies.
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20
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Greshake Tzovaras B, Senabre Hidalgo E, Alexiou K, Baldy L, Morane B, Bussod I, Fribourg M, Wac K, Wolf G, Ball M. Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study. J Med Internet Res 2021; 23:e28116. [PMID: 34505836 PMCID: PMC8463949 DOI: 10.2196/28116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/16/2021] [Accepted: 07/05/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. OBJECTIVE This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. METHODS We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. RESULTS The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. CONCLUSIONS As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates.
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Affiliation(s)
- Bastian Greshake Tzovaras
- Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, Paris, France
- Open Humans Foundation, Sanford, NC, United States
| | - Enric Senabre Hidalgo
- Center for Research & Interdisciplinarity, INSERM U1284, Université de Paris, Paris, France
| | | | | | | | - Ilona Bussod
- Center for Research & Interdisciplinarity, Paris, France
| | | | - Katarzyna Wac
- Quality of Life Technologies, GSEM/CUI, University of Geneva, Geneva, Switzerland
| | - Gary Wolf
- Article 27 Foundation, Berkeley, CA, United States
| | - Mad Ball
- Open Humans Foundation, Sanford, NC, United States
- Center for Research & Interdisciplinarity, Paris, France
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21
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Park J, Tabata H. Gas Sensor Array Using a Hybrid Structure Based on Zeolite and Oxide Semiconductors for Multiple Bio-Gas Detection. ACS OMEGA 2021; 6:21284-21293. [PMID: 34471733 PMCID: PMC8387996 DOI: 10.1021/acsomega.1c01435] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/19/2021] [Indexed: 05/05/2023]
Abstract
Semiconductor-type gas sensors, composed of metal-oxide semiconductors and porous zeolite materials, are attractive devices for bio-gas detection, particularly when used as bio-gas sensors such as electronic nose application. Previous studies have shown such detection can be obtained with a separate gas concentrator and a sensor device using zeolites and oxide semiconductors of WO3 nanoparticles. By applying the gas concentrator, porous molecular structures alter both the gas sensitivity and the selectivity, and even can be used to define the sensor characteristics. Based on such a gas sensor design, we investigated the properties of an array of three sensors made of a layer of WO3 nanoparticles coated with zeolites with different interactions between gas molecule adsorption and desorption. The array was tested with four volatile organic compounds, each measured at different concentrations. The results confirm that the features of individual zeolites combined with the hybrid gas sensor behavior, along with the differences among the sensors, are sufficient for enabling the discrimination of volatile compounds when disregarding their concentration.
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22
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Cislo C, Clingan C, Gilley K, Rozwadowski M, Gainsburg I, Bradley C, Barabas J, Sandford E, Olesnavich M, Tyler J, Mayer C, DeMoss M, Flora C, Forger DB, Cunningham JL, Tewari M, Choi SW. Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study. JMIR Res Protoc 2021; 10:e29561. [PMID: 34115607 PMCID: PMC8386373 DOI: 10.2196/29561] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/03/2021] [Accepted: 06/04/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being. OBJECTIVE Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19. METHODS We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population. RESULTS This study enrolled 2,158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing. CONCLUSIONS This study examined student health and well-being during the COVID-19 pandemic. It also assessed the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19. CLINICALTRIAL ClinicalTrials.gov NCT04766788.
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Affiliation(s)
- Christine Cislo
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
| | - Caroline Clingan
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
| | - Kristen Gilley
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
| | - Michelle Rozwadowski
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Izzy Gainsburg
- Management and Organizations Area, Ross School of Business, University of Michigan, Ann Arbor, US
| | - Christina Bradley
- Management and Organizations Area, Ross School of Business, University of Michigan, Ann Arbor, US
| | - Jenny Barabas
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Erin Sandford
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Mary Olesnavich
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Jonathan Tyler
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
- Department of Mathematics, University of Michigan, Ann Arbor, US
| | - Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, US
| | - Matthew DeMoss
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Christopher Flora
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
| | - Daniel B Forger
- Department of Mathematics, University of Michigan, Ann Arbor, US
| | - Julia Lee Cunningham
- Management and Organizations Area, Ross School of Business, University of Michigan, Ann Arbor, US
| | - Muneesh Tewari
- Division of Hematology Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, US
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, US
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, US
| | - Sung Won Choi
- Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, US
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23
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Raj M, Gupta V, Hoodin F, Yahng L, Braun T, Choi SW. Evaluating health technology engagement among family caregivers of patients undergoing hematopoietic cell transplantation. RESEARCH SQUARE 2021. [PMID: 34013246 PMCID: PMC8132239 DOI: 10.21203/rs.3.rs-427058/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Purpose: Digital health technology-based interventions have the potential to support caregivers in their caregiving responsibilities and in managing their own health and well-being. Designing digital health technologies to support caregivers of patients undergoing hematopoietic cell transplantation requires evaluating their engagement with these technologies. The objective of this study was to examine the association between caregiving characteristics and different types of digital health technologies used. Methods: We conducted an online cross-sectional, national survey of 948 unpaid family caregivers of patients undergoing hematopoietic cell transplantation. Results: Almost two-thirds (65.4%) of respondents reported using an app for fitness or step counting, while 41.3% reported using a smartwatch. The average number of apps used was 3.3 (range 0-9). In adjusted models, adult children who were caregivers (OR=5.82, p<0.005) and caregivers of another relative (OR=2.51, p<0.005) were significantly more likely to use a fitness tracker than caregivers of a child. Caregiving for six months or greater was associated with use of fewer apps compared with caregiving for less than six months in adjusted models (OR=0.80, p<0.005). Caregivers of patients receiving an allogeneic transplant used more apps on average than caregivers of patients receiving an autologous transplant, in adjusted (OR=1.36, p<0.005) models. Conclusion: Digital health technologies may reflect promising avenues for supporting caregivers of patients undergoing HCT. The rapid insurgence of telehealth, propelled by the current COVID-19 pandemic, emphasizes the need for a better understanding of digital health technology for future study design.
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Affiliation(s)
| | | | | | | | | | - Sung Won Choi
- University of Michigan Medicine: University of Michigan Michigan Medicine
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Clingan CA, Dittakavi M, Rozwadowski M, Gilley KN, Cislo CR, Barabas J, Sandford E, Olesnavich M, Flora C, Tyler J, Mayer C, Stoneman E, Braun T, Forger DB, Tewari M, Choi SW. Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study. JMIR Res Protoc 2021; 10:e29562. [PMID: 33945497 PMCID: PMC8117956 DOI: 10.2196/29562] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/03/2021] [Accepted: 05/03/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Health care workers (HCWs) have been working on the front lines of the COVID-19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID-19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self-monitoring of symptoms, and asymptomatic COVID-19 testing may aid in the early detection of COVID-19 in HCWs and may help reduce further transmission among HCWs, patients, and families. OBJECTIVE By using wearable sensors, smartphone-based symptom logging, and biospecimens, this project aims to assist HCWs in self-monitoring COVID-19. METHODS We conducted a prospective, longitudinal study of HCWs at a single institution. The study duration was 1 year, wherein participants were instructed on the continuous use of two wearable sensors (Fitbit Charge 3 smartwatch and TempTraq temperature patches) for up to 30 days. Participants consented to provide biospecimens (ie, nasal swabs, saliva swabs, and blood) for up to 1 year from study entry. Using a smartphone app called Roadmap 2.0, participants entered a daily mood score, submitted daily COVID-19 symptoms, and completed demographic and health-related quality of life surveys at study entry and 30 days later. Semistructured qualitative interviews were also conducted at the end of the 30-day period, following completion of daily mood and symptoms reporting as well as continuous wearable sensor use. RESULTS A total of 226 HCWs were enrolled between April 28 and December 7, 2020. The last participant completed the 30-day study procedures on January 16, 2021. Data collection will continue through January 2023, and data analyses are ongoing. CONCLUSIONS Using wearable sensors, smartphone-based symptom logging and survey completion, and biospecimen collections, this study will potentially provide data on the prevalence of COVID-19 infection among HCWs at a single institution. The study will also assess the feasibility of leveraging wearable sensors and self-monitoring of symptoms in an HCW population. TRIAL REGISTRATION ClinicalTrials.gov NCT04756869; https://clinicaltrials.gov/ct2/show/NCT04756869. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/29562.
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Affiliation(s)
- Caroline A Clingan
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Manasa Dittakavi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Michelle Rozwadowski
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Kristen N Gilley
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Christine R Cislo
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
| | - Jenny Barabas
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Erin Sandford
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Mary Olesnavich
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Christopher Flora
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan Tyler
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
- Department of Mathematics, College of Literature, Arts, and Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Caleb Mayer
- Department of Mathematics, College of Literature, Arts, and Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Emily Stoneman
- Department of Internal Medicine, Division of Infectious Diseases, University of Michigan, Ann Arbor, MI, United States
| | - Thomas Braun
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Daniel B Forger
- Department of Mathematics, College of Literature, Arts, and Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Muneesh Tewari
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Sung Won Choi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, United States
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25
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Kim H, Huh HJ, Park E, Chung DR, Kang M. Multiplex Molecular Point-of-Care Test for Syndromic Infectious Diseases. BIOCHIP JOURNAL 2021; 15:14-22. [PMID: 33613852 PMCID: PMC7883532 DOI: 10.1007/s13206-021-00004-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/04/2020] [Accepted: 12/08/2020] [Indexed: 12/17/2022]
Abstract
Point-of-care (POC) molecular diagnostics for clinical microbiology and virology has primarily focused on the detection of a single pathogen. More recently, it has transitioned into a comprehensive syndromic approach that employs multiplex capabilities, including the simultaneous detection of two or more pathogens. Multiplex POC tests provide higher accuracy to for actionable decisionmaking in critical care, which leads to pathogen-specific treatment and standardized usages of antibiotics that help prevent unnecessary processes. In addition, these tests can be simple enough to operate at the primary care level and in remote settings where there is no laboratory infrastructure. This review focuses on state-of-the-art multiplexed molecular point-of-care tests (POCT) for infectious diseases and efforts to overcome their limitations, especially related to inadequate throughput for the identification of syndromic diseases. We also discuss promising and imperative clinical POC approaches, as well as the possible hurdles of their practical applications as front-line diagnostic tests.
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Affiliation(s)
- Hanbi Kim
- Biomedical Engineering Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, 06351 South Korea.,Department of Medical Device Management and Research, SAIHST (Samsung Advanced Institute for Health Sciences & Technology), Sungkyunkwan University, Seoul, 06355 South Korea
| | - Hee Jae Huh
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351 South Korea
| | - Eunkyoung Park
- Biomedical Engineering Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, 06351 South Korea.,Department of Medical Device Management and Research, SAIHST (Samsung Advanced Institute for Health Sciences & Technology), Sungkyunkwan University, Seoul, 06355 South Korea
| | - Doo-Ryeon Chung
- Center for Infection Prevention and Control, Samsung Medical Center, Seoul, 06351 South Korea.,Asia Pacific Foundation for Infectious Diseases (APFID), Seoul, 06367 South Korea.,Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351 South Korea
| | - Minhee Kang
- Biomedical Engineering Research Center, Smart Healthcare Research Institute, Samsung Medical Center, Seoul, 06351 South Korea.,Department of Medical Device Management and Research, SAIHST (Samsung Advanced Institute for Health Sciences & Technology), Sungkyunkwan University, Seoul, 06355 South Korea
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26
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Badrick T. Biological variation: Understanding why it is so important? Pract Lab Med 2021; 23:e00199. [PMID: 33490349 PMCID: PMC7809190 DOI: 10.1016/j.plabm.2020.e00199] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 12/23/2020] [Indexed: 12/19/2022] Open
Abstract
This Review will describe the increasing importance of the concepts of biological variation to clinical chemists. The idea of comparison to 'reference' is fundamental in measurement. For the biological measurands, that reference is the relevant patient population, a clinical decision point based on a trial or an individual patient's previous results. The idea of using biological variation to set quality goals was then realised for setting Quality Control (QC) and External Quality Assurance (EQA) limits. The current phase of BV integration into practice is using Patient-Based Real-Time Quality Control (PBRTQC) and Patient Based Quality Assurance (PBQA) to detect a change in assay performance. The challenge of personalised medicine is to determine an individual reference interval. The Athletes Biological Passport may provide the solution.
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Affiliation(s)
- Tony Badrick
- Royal College of Pathologists of Australasia Quality Assurance Programs, St Leonards Sydney, NSW, 2065, Australia
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27
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Bury TM, Bauch CT, Anand M. Detecting and distinguishing tipping points using spectral early warning signals. J R Soc Interface 2020; 17:20200482. [PMID: 32993435 PMCID: PMC7536046 DOI: 10.1098/rsif.2020.0482] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Theory and observation tell us that many complex systems exhibit tipping points—thresholds involving an abrupt and irreversible transition to a contrasting dynamical regime. Such events are commonly referred to as critical transitions. Current research seeks to develop early warning signals (EWS) of critical transitions that could help prevent undesirable events such as ecosystem collapse. However, conventional EWS do not indicate the type of transition, since they are based on the generic phenomena of critical slowing down. For instance, they may fail to distinguish the onset of oscillations (e.g. Hopf bifurcation) from a transition to a distant attractor (e.g. Fold bifurcation). Moreover, conventional EWS are less reliable in systems with density-dependent noise. Other EWS based on the power spectrum (spectral EWS) have been proposed, but they rely upon spectral reddening, which does not occur prior to critical transitions with an oscillatory component. Here, we use Ornstein–Uhlenbeck theory to derive analytic approximations for EWS prior to each type of local bifurcation, thereby creating new spectral EWS that provide greater sensitivity to transition proximity; higher robustness to density-dependent noise and bifurcation type; and clues to the type of approaching transition. We demonstrate the advantage of applying these spectral EWS in concert with conventional EWS using a population model, and show that they provide a characteristic signal prior to two different Hopf bifurcations in data from a predator–prey chemostat experiment. The ability to better infer and differentiate the nature of upcoming transitions in complex systems will help humanity manage critical transitions in the Anthropocene Era.
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Affiliation(s)
- T M Bury
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada ON N2L 3G1.,School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada ON N1G 2W1
| | - C T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada ON N2L 3G1
| | - M Anand
- School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada ON N1G 2W1
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