Health and biosensor technology—A revolution underway for the well-being of the population

Gilberto Bastidas, Roman Iglesias

Article ID: 2618
Vol 4, Issue 1, 2023
DOI: https://doi.org/10.54517/wt.v4i1.2618
VIEWS - 28 (Abstract)

Abstract

This document mentions characteristics of portable devices that highlight their applicability in healthcare because they fundamentally allow monitoring outside hospital institutions and allow the prediction of health events. This writing is achieved with the review of updated information available in the world scientific literature. Additionally, the challenges and disadvantages associated with the implementation of wearable sensors are shown. The aim of the above is to highlight that portable devices improve medical care in a wide variety of environments, but are particularly useful for the remote management of pathologies.


Keywords

remote sensors; biomedical technology; mobile health; digital health

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References

1. Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Personalized Medicine. 2018; 15(5): 429-448. doi: 10.2217/pme-2018-0044

2. Guillodo E, Lemey C, Simonnet M, et al. Clinical Applications of Mobile Health Wearable–Based Sleep Monitoring: Systematic Review. JMIR mHealth and uHealth. 2020; 8(4): e10733. doi: 10.2196/10733

3. Sharma A, Badea M, Tiwari S, et al. Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. Molecules. 2021; 26(3): 748. doi: 10.3390/molecules26030748

4. Vijayan V, Connolly JP, Condell J, et al. Review of Wearable Devices and Data Collection Considerations for Connected Health. Sensors. 2021; 21(16): 5589. doi: 10.3390/s21165589

5. Bastidas-Pacheco G, Bastidas-Delgado D, Bastidas-Delgado G. COVID-19: a challenge for smart cities in the public health decalogue (Spanish). IPSA Scientia, revista científica multidisciplinaria. 2022; 7(Sup. 1): 39-50. doi: 10.25214/27114406.1428

6. Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nature Reviews Cardiology. 2021; 18(8): 581-599. doi: 10.1038/s41569-021-00522-7

7. Tamsin M. Wearable biosensor technologies. International Journal of Innovation and Scientific Research. 2015; 13: 697-703.

8. Bastidas G, Bastidas-Delgado G. Nanobiotechnology in the treatment of Leishmania spp. Biotempo Journal. 2020; 17(2): 321-333.

9. Bastidas G, Bastidas-Delgado G. COVID-19 pandemic in smart cities. Angeles Group Medical Record. 2020; 18(4): 443-444.

10. Lim WK, Davila S, Teo JX, et al. Beyond fitness tracking: The use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research. PLOS Biology. 2018; 16(2): e2004285. doi: 10.1371/journal.pbio.2004285

11. Bastidas G, Rojas A, Bastidas D. Internet of things: an interesting option for the future of public health. EduMecentro Journal. 2022; 14: e2184.

12. Lu L, Zhang J, Xie Y, et al. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR mHealth and uHealth. 2020; 8(11): e18907. doi: 10.2196/18907

13. Bastidas G, Baéz M, Bastidas D. Telehealth in Education and Research in Primary Care in Pandemic: COVID-19 Case. International Journal of Clinical and Experimental Medicine Research. 2021; 5(3): 416-420. doi: 10.26855/ijcemr.2021.07.029

14. Claudio D, Velázquez MA, Bravo-Llerena W, et al. Perceived Usefulness and Ease of Use of Wearable Sensor-Based Systems in Emergency Departments. IIE Transactions on Occupational Ergonomics and Human Factors. 2015; 3(3-4): 177-187. doi: 10.1080/21577323.2015.1040559

15. Kamišalić A, Fister I, Turkanović M, et al. Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review. Sensors. 2018; 18(6): 1714. doi: 10.3390/s18061714

16. Boscari F, Galasso S, Acciaroli G, et al. Head-to-head comparison of the accuracy of Abbott FreeStyle Libre and Dexcom G5 mobile. Nutrition, Metabolism and Cardiovascular Diseases. 2018; 28(4): 425-427. doi: 10.1016/j.numecd.2018.01.003

17. Massa GG, Gys I, Op ‘t Eyndt A, et al. Evaluation of the FreeStyle® Libre Flash Glucose Monitoring System in Children and Adolescents with Type 1 Diabetes. Hormone Research in Paediatrics. 2018; 89(3): 189-199. doi: 10.1159/000487361

18. Heinemann L, Freckmann G, Ehrmann D, Faber-Heinemann G, Guerra S, Waldenmaier D, et al. Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): A multicentre, randomised controlled trial. Lancet. 2018; 391(10128): 1367-1377.

19. Kim J, Kim M, Lee MS, et al. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nature Communications. 2017; 8(1). doi: 10.1038/ncomms14997

20. Park J, Kim J, Kim SY, et al. Soft, smart contact lenses with integrations of wireless circuits, glucose sensors, and displays. Science Advances. 2018; 4(1). doi: 10.1126/sciadv.aap9841

21. Schwartz FL, Marling CR, Bunescu RC. The Promise and Perils of Wearable Physiological Sensors for Diabetes Management. Journal of Diabetes Science and Technology. 2018; 12(3): 587-591. doi: 10.1177/1932296818763228

22. Vandenberk T, Stans J, Mortelmans C, et al. Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study. JMIR mHealth and uHealth. 2017; 5(8): e129. doi: 10.2196/mhealth.7254

23. Solomon MD, Yang J, Sung SH, et al. Incidence and timing of potentially high-risk arrhythmias detected through long term continuous ambulatory electrocardiographic monitoring. BMC Cardiovascular Disorders. 2016; 16(1). doi: 10.1186/s12872-016-0210-x

24. Yin H, Jha NK. A Health Decision Support System for Disease Diagnosis Based on Wearable Medical Sensors and Machine Learning Ensembles. IEEE Transactions on Multi-Scale Computing Systems. 2017; 3(4): 228-241. doi: 10.1109/tmscs.2017.2710194

25. Lee H, Chung H, Ko H, et al. Dedicated cardiac rehabilitation wearable sensor and its clinical potential. PLOS ONE. 2017; 12(10): e0187108. doi: 10.1371/journal.pone.0187108

26. Kim J, Nakamura T, Kikuchi H, et al. Co-Variation of Depressive Mood and Locomotor Dynamics Evaluated by Ecological Momentary Assessment in Healthy Humans. PLoS ONE. 2013; 8(9): e74979. doi: 10.1371/journal.pone.0074979

27. Nwankwo T, Yoon S, Burt V, Gu Q. Hypertension among adults in the United States: National Health and Nutrition Examination Survey, 2011-2012. NCHS Data Brief. 2013; (133): 1-8.

28. Burkard T, Mayr M, Winterhalder C, et al. Reliability of single office blood pressure measurements. Heart. 2018; 104(14): 1173-1179. doi: 10.1136/heartjnl-2017-312523

29. Topouchian J, Agnoletti D, Blacher J, et al. Validation of four devices: Omron M6 Comfort, Omron HEM-7420, Withings BP-800, and Polygreen KP-7670 for home blood pressure measurement according to the European Society of Hypertension International Protocol. Vasc Health Risk Manag. 2014; 10: 33-44.

30. Mukkamala R, Hahn JO, Inan OT, et al. Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. IEEE Transactions on Biomedical Engineering. 2015; 62(8): 1879-1901. doi: 10.1109/tbme.2015.2441951

31. Dua M, Malhotra L, Navalgund A, et al. Monitoring of gastric myoelectric activity after pancreaticoduodenectomy. Gastroenterology. 2017; 152(5): S1293-S1294.

32. Feakins RM. Obesity and metabolic syndrome: pathological effects on the gastrointestinal tract. Histopathology. 2016; 68(5): 630-640. doi: 10.1111/his.12907

33. Dagdeviren C, Javid F, Joe P, et al. Flexible piezoelectric devices for gastrointestinal motility sensing. Nature Biomedical Engineering. 2017; 1(10): 807-817. doi: 10.1038/s41551-017-0140-7

34. Kaplan KA, Hirshman J, Hernandez B, et al. When a gold standard isn’t so golden: Lack of prediction of subjective sleep quality from sleep polysomnography. Biological Psychology. 2017; 123: 37-46. doi: 10.1016/j.biopsycho.2016.11.010

35. Martinez HP, Bengio Y, Yannakakis GN. Learning deep physiological models of affect. IEEE Computational Intelligence Magazine. 2013; 8(2): 20-33. doi: 10.1109/mci.2013.2247823

36. Hashemi J, Tepper M, Vallin Spina T, et al. Computer Vision Tools for Low-Cost and Noninvasive Measurement of Autism-Related Behaviors in Infants. Autism Research and Treatment. 2014; 2014: 1-12. doi: 10.1155/2014/935686

37. Poh MZ, Loddenkemper T, Reinsberger C, et al. Autonomic changes with seizures correlate with postictal EEG suppression. Neurology. 2012; 78(23): 1868-1876. doi: 10.1212/wnl.0b013e318258f7f1

38. Najafi B, Ron E, Enriquez A, et al. Smarter Sole Survival: Will Neuropathic Patients at High Risk for Ulceration Use a Smart Insole-Based Foot Protection System? Journal of Diabetes Science and Technology. 2017; 11(4): 702-713. doi: 10.1177/1932296816689105

39. Gia T, Sarker V, Tcarenko I, et al. Energy efficient wearable sensor node for IoT-based fall detection systems. Microprocessors and Microsystems. 2018; 56: 34-46. doi: 10.1016/j.micpro.2017.10.014

40. Dietz PM, Vesco KK, Callaghan WM, et al. Postpartum Screening for Diabetes After a Gestational Diabetes Mellitus–Affected Pregnancy. Obstetrics & Gynecology. 2008; 112(4): 868-874. doi: 10.1097/aog.0b013e318184db63

41. Nicklas JM, Zera CA, Seely EW, et al. Identifying postpartum intervention approaches to prevent type 2 diabetes in women with a history of gestational diabetes. BMC Pregnancy and Childbirth. 2011; 11(1). doi: 10.1186/1471-2393-11-23

42. Vesco KK, Dietz PM, Bulkley J, et al. A system-based intervention to improve postpartum diabetes screening among women with gestational diabetes. American Journal of Obstetrics and Gynecology. 2012; 207(4): 283.e1-283.e6. doi: 10.1016/j.ajog.2012.08.017

43. Berglund Scherwitzl E, Gemzell Danielsson K, Sellberg JA, et al. Fertility awareness-based mobile application for contraception. The European Journal of Contraception & Reproductive Health Care. 2016; 21(3): 234-241. doi: 10.3109/13625187.2016.1154143

44. Bonafide CP, Jamison DT, Foglia EE. The Emerging Market of Smartphone-Integrated Infant Physiologic Monitors. JAMA. 2017; 317(4): 353. doi: 10.1001/jama.2016.19137

45. Tanigasalam V, Vishnu Bhat B, Adhisivam B, et al. Hypothermia detection in low birth weight neonates using a novel bracelet device. The Journal of Maternal-Fetal & Neonatal Medicine. 2018; 32(16): 2653-2656. doi: 10.1080/14767058.2018.1443072

46. Rhee H, Belyea MJ, Sterling M, et al. Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design. Journal of Medical Internet Research. 2015; 17(10): e234. doi: 10.2196/jmir.4975

47. Pramono RXA, Bowyer S, Rodriguez-Villegas E. Automatic adventitious respiratory sound analysis: A systematic review. PLOS ONE. 2017; 12(5): e0177926. doi: 10.1371/journal.pone.0177926

48. Dieffenderfer J, Goodell H, Mills S, et al. Low-Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic Respiratory Disease. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5): 1251-1264. doi: 10.1109/jbhi.2016.2573286

49. Barajas-Carmona JG, Francisco-Aldana L, Morales-Narváez E. Wearable Nanoplasmonic Patch Detecting Sun/UV Exposure. Analytical Chemistry. 2017; 89(24): 13589-13595. doi: 10.1021/acs.analchem.7b04066

50. Asimina S, Chapizanis D, Karakitsios S, et al. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environmental Monitoring and Assessment. 2018; 190(3). doi: 10.1007/s10661-018-6537-2

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