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Annual Review of Medicine

Volume 75, 2024

Wearable Devices: Implications for Precision Medicine and the Future of Health Care

Abstract

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Keyword(s): COVID-19, early detection, longitudinal, precision medicine, wearables
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2024-01-29
2024-05-01
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Literature Cited

  1. 1.
    Bouchard C, Rankinen T. 2001. Individual differences in response to regular physical activity. Med. Sci. Sports Exerc. 33:6 Suppl.S446–51
     [Google Scholar]
  2. 2.
    Lewis DA, Kamon E, Hodgson JL. 1986. Physiological differences between genders. Implications for sports conditioning. Sports Med. 3:5357–69
     [Google Scholar]
  3. 3.
    Witt D, Kellogg R, Snyder M et al. 2019. Windows into human health through wearables data analytics. Curr. Opin. Biomed. Eng. 9:28–46
     [Google Scholar]
  4. 4.
    Gambhir SS, Ge TJ, Vermesh O et al. 2021. Continuous health monitoring: an opportunity for precision health. Sci. Transl. Med. 13:597eabe5383
     [Google Scholar]
  5. 5.
    Dunn J, Runge R, Snyder M. 2018. Wearables and the medical revolution. Per. Med. 15:5429–48
     [Google Scholar]
  6. 6.
    Ates HC, Nguyen PQ, Gonzalez-Macia L et al. 2022. End-to-end design of wearable sensors. Nat. Rev. Mater. 7:11887–907
     [Google Scholar]
  7. 7.
    Li X, Dunn J, Salins D et al. 2017. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLOS Biol. 15:1e2001402
     [Google Scholar]
  8. 8.
    Lim WK, Davila S, Teo JX et al. 2018. Beyond fitness tracking: the use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research. PLOS Biol. 16:2e2004285
     [Google Scholar]
  9. 9.
    Tison GH, Sanchez JM, Ballinger B et al. 2018. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol. 3:5409–16
     [Google Scholar]
  10. 10.
    Price ND, Magis AT, Earls JC et al. 2017. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35:8747–56
     [Google Scholar]
  11. 11.
    Ballinger B, Hsieh J, Singh A et al. 2018. DeepHeart: semi-supervised sequence learning for cardiovascular risk prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 321 https://doi.org/10.1609/aaai.v32i1.11891
    [Google Scholar]
  12. 12.
    Mishra T, Wang M, Metwally AA et al. 2020. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat. Biomed. Eng. 4:121208–20
     [Google Scholar]
  13. 13.
    Quer G, Radin JM, Gadaleta M et al. 2021. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat. Med. 27:173–77
     [Google Scholar]
  14. 14.
    Alavi A, Bogu GK, Wang M et al. 2022. Real-time alerting system for COVID-19 and other stress events using wearable data. Nat. Med. 28:1175–84
     [Google Scholar]
  15. 15.
    Stamatakis E, Ahmadi MN, Gill JMR et al. 2022. Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality. Nat. Med. 28:122521–29
     [Google Scholar]
  16. 16.
    Master H, Annis J, Huang S et al. 2022. Association of step counts over time with the risk of chronic disease in the All of Us research program. Nat. Med. 28:112301–8
     [Google Scholar]
  17. 17.
    Brickwood K-J, Watson G, O'Brien J et al. 2019. Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR mHealth uHealth 7:4e11819
     [Google Scholar]
  18. 18.
    Hall H, Perelman D, Breschi A et al. 2018. Glucotypes reveal new patterns of glucose dysregulation. PLOS Biol. 16:7e2005143
     [Google Scholar]
  19. 19.
    Hickey BA, Chalmers T, Newton P et al. 2021. Smart devices and wearable technologies to detect and monitor mental health conditions and stress: a systematic review. Sensors 21:103461
     [Google Scholar]
  20. 20.
    Lautman Z, Lev-Ari S. 2022. The use of smart devices for mental health diagnosis and care. J. Clin. Med. Res. 11:185359
     [Google Scholar]
  21. 21.
    Chandrasekaran R, Katthula V, Moustakas E. 2020. Patterns of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey. J. Med. Internet Res. 22:10e22443
     [Google Scholar]
  22. 22.
    Mensah GA, Roth GA, Fuster V. 2019. The global burden of cardiovascular diseases and risk factors: 2020 and beyond. J. Am. Coll. Cardiol. 74:202529–32
     [Google Scholar]
  23. 23.
    Roth GA, Mensah GA, Fuster V. 2020. The global burden of cardiovascular diseases and risks: a compass for global action. J. Am. Coll. Cardiol. 76:252980–81
     [Google Scholar]
  24. 24.
    Tsao CW, Aday AW, Almarzooq ZI et al. 2023. Heart disease and stroke statistics—2023 update: a report from the American Heart Association. Circulation 147:8e93–621
     [Google Scholar]
  25. 25.
    Roth GA, Mensah GA, Johnson CO et al. 2020. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76:252982–3021
     [Google Scholar]
  26. 26.
    Zhang D, Wang W, Li F. 2016. Association between resting heart rate and coronary artery disease, stroke, sudden death and noncardiovascular diseases: a meta-analysis. Can. Med. Assoc. J. 188:15e384–92
     [Google Scholar]
  27. 27.
    Fox K, Ford I, Steg PG et al. 2008. Heart rate as a prognostic risk factor in patients with coronary artery disease and left-ventricular systolic dysfunction (BEAUTIFUL): a subgroup analysis of a randomised controlled trial. Lancet 372:9641817–21
     [Google Scholar]
  28. 28.
    Singh N, Moneghetti KJ, Christle JW et al. 2018. Heart rate variability: an old metric with new meaning in the era of using mHealth technologies for health and exercise training guidance. Part two: prognosis and training. Arrhythm. Electrophysiol. Rev. 7:4247–55
     [Google Scholar]
  29. 29.
    Nelson BW, Allen NB. 2019. Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: intraindividual validation study. JMIR mHealth uHealth 7:3e10828
     [Google Scholar]
  30. 30.
    Perez MV, Mahaffey KW, Hedlin H et al. 2019. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 381:201909–17
     [Google Scholar]
  31. 31.
    Lubitz SA, Faranesh AZ, Selvaggi C et al. 2022. Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study. Circulation 146:191415–24
     [Google Scholar]
  32. 32.
    Natarajan A, Pantelopoulos A, Emir-Farinas H et al. 2020. Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study. Lancet Digit. Health 2:12e650–57
     [Google Scholar]
  33. 33.
    Quer G, Gouda P, Galarnyk M et al. 2020. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI, and time of year: retrospective, longitudinal cohort study of 92,457 adults. PLOS ONE 15:2e0227709
     [Google Scholar]
  34. 34.
    Virani SS, Alonso A, Aparicio HJ et al. 2021. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation 143:8e254–743
     [Google Scholar]
  35. 35.
    Turakhia MP, Shafrin J, Bognar K et al. 2018. Estimated prevalence of undiagnosed atrial fibrillation in the United States. PLOS ONE 13:4e0195088
     [Google Scholar]
  36. 36.
    Zhou B, Carrillo-Larco RM, Danaei G, Riley LM, Paciorek CJ et al.(NCD Risk Factor Collab.). 2021. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 398:10304957–80
     [Google Scholar]
  37. 37.
    Burkard T, Mayr M, Winterhalder C et al. 2018. Reliability of single office blood pressure measurements. Heart 104:141173–79
     [Google Scholar]
  38. 38.
    Pickering TG. 1990. The clinical significance of diurnal blood pressure variations. Dippers and nondippers. Circulation 81:2700–2
     [Google Scholar]
  39. 39.
    Kario K, Shimbo D, Tomitani N et al. 2020. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings. J. Clin. Hypertens. 22:2135–41
     [Google Scholar]
  40. 40.
    Islam SMS, Cartledge S, Karmakar C et al. 2019. Validation and acceptability of a cuffless wrist-worn wearable blood pressure monitoring device among users and health care professionals: mixed methods study. JMIR mHealth uHealth 7:10e14706
     [Google Scholar]
  41. 41.
    Werhahn SM, Dathe H, Rottmann T et al. 2019. Designing meaningful outcome parameters using mobile technology: a new mobile application for telemonitoring of patients with heart failure. ESC Heart Fail. 6:3516–25
     [Google Scholar]
  42. 42.
    Sherazi S, Kutyifa V, McNitt S et al. 2015. Prognostic significance of heart rate variability among patients treated with cardiac resynchronization therapy: MADIT-CRT (Multicenter Automatic Defibrillator Implantation Trial—Cardiac Resynchronization Therapy). JACC Clin. Electrophysiol. 1:1–274–80
     [Google Scholar]
  43. 43.
    Stavrakis S, Stoner JA, Kardokus J et al. 2017. Intermittent versus Continuous Anticoagulation theRapy in patiEnts with Atrial Fibrillation (iCARE-AF): a randomized pilot study. J. Interv. Cardiac Electrophysiol. 48:151–60
     [Google Scholar]
  44. 44.
    Marvel FA, Spaulding EM, Lee MA et al. 2021. Digital health intervention in acute myocardial infarction. Circ. Cardiovasc. Qual. Outcomes 14:7e007741
     [Google Scholar]
  45. 45.
    Cockrell C, Larie D, An G. 2022. Preparing for the next pandemic: simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents. Front. Immunol. 13:995395
     [Google Scholar]
  46. 46.
    Shahroz M, Ahmad F, Younis MS et al. 2021. COVID-19 digital contact tracing applications and techniques: a review post initial deployments. Transp. Eng. 5:100072
     [Google Scholar]
  47. 47.
    Yang C, Shao H-R. 2015. WiFi-based indoor positioning. IEEE Commun. Mag. 53:3150–57
     [Google Scholar]
  48. 48.
    Lim J-S, Jang W-H, Yoon G-W et al. 2013. Radio map update automation for WiFi positioning systems. IEEE Commun. Lett. 17:4693–96
     [Google Scholar]
  49. 49.
    Mazuelas S, Bahillo A, Lorenzo RM et al. 2009. Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE J. Sel. Top. Signal Process. 3:5821–31
     [Google Scholar]
  50. 50.
    Luoh L. 2013. ZigBee-based intelligent indoor positioning system soft computing. Soft Computing 18:3443–56
     [Google Scholar]
  51. 51.
    Goergen CJ, Tweardy MJ, Steinhubl SR et al. 2022. Detection and monitoring of viral infections via wearable devices and biometric data. Annu. Rev. Biomed. Eng. 24:1–27
     [Google Scholar]
  52. 52.
    Crook H, Raza S, Nowell J et al. 2021. Long covid—mechanisms, risk factors, and management. BMJ 374:n1648
     [Google Scholar]
  53. 53.
    Ates HC, Yetisen AK, Güder F et al. 2021. Wearable devices for the detection of COVID-19. Nat. Electron. 4:113–14
     [Google Scholar]
  54. 54.
    Hirten RP, Danieletto M, Tomalin L et al. 2021. Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: observational study. J. Med. Internet Res. 23:2e26107
     [Google Scholar]
  55. 55.
    Guan G, Mofaz M, Qian G et al. 2022. Higher sensitivity monitoring of reactions to COVID-19 vaccination using smartwatches. NPJ Digit. Med. 5:140
     [Google Scholar]
  56. 56.
    Quer G, Gadaleta M, Radin JM et al. 2022. Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands. NPJ Digit. Med. 5:149
     [Google Scholar]
  57. 57.
    Gepner Y, Mofaz M, Oved S et al. 2022. Utilizing wearable sensors for continuous and highly-sensitive monitoring of reactions to the BNT162b2 mRNA COVID-19 vaccine. Commun. Med. 2:27
     [Google Scholar]
  58. 58.
    Keating BJ, Mukhtar EH, Elftmann ED et al. 2021. Early detection of SARS-CoV-2 and other infections in solid organ transplant recipients and household members using wearable devices. Transpl. Int. 34:61019–31
     [Google Scholar]
  59. 59.
    Grzesiak E, Bent B, McClain MT et al. 2021. Assessment of the feasibility of using noninvasive wearable biometric monitoring sensors to detect influenza and the common cold before symptom onset. JAMA Netw. Open 4:9e2128534
     [Google Scholar]
  60. 60.
    Hedayatipour A, Mcfarlane N. 2020. Wearables for the next pandemic. IEEE Access. 8:184457–74
     [Google Scholar]
  61. 61.
    WHO 2022. Mental disorders Fact sheet, World Health Organ. Geneva, Switz: https://www.who.int/news-room/fact-sheets/detail/mental-disorders. Updated Mar. 2023, accessed Mar. 29 2023
  62. 62.
    NIMH 2023. Mental illness. National Institute of Mental Health https://www.nimh.nih.gov/health/statistics/mental-illness. Accessed Mar. 29 2023.
     [Google Scholar]
  63. 63.
    Mental Health America 2023. The state of mental health in America Rep. Mental Health America Alexandria, VA: https://mhanational.org/issues/state-mental-health-america. Accessed Feb. 20, 2023
  64. 64.
    Kessler RC, Berglund P, Demler O et al. 2005. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62:6593–602
     [Google Scholar]
  65. 65.
    Narziev N, Goh H, Toshnazarov K et al. 2020. STDD: short-term depression detection with passive sensing. Sensors 20:51396
     [Google Scholar]
  66. 66.
    Colombo D, Fernández-Álvarez J, Patané A et al. 2019. Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review. J. Clin. Med. Res. 8:4465
     [Google Scholar]
  67. 67.
    Bishop TF, Seirup JK, Pincus HA et al. 2016. Population of US practicing psychiatrists declined, 2003–13, which may help explain poor access to mental health care. Health Aff. 35:71271–77
     [Google Scholar]
  68. 68.
    Gomes N, Pato M, Lourenço AR et al. 2023. A survey on wearable sensors for mental health monitoring. Sensors 23:31330
     [Google Scholar]
  69. 69.
    Long N, Lei Y, Peng L et al. 2022. A scoping review on monitoring mental health using smart wearable devices. Math. Biosci. Eng. 19:87899–919
     [Google Scholar]
  70. 70.
    Can YS, Chalabianloo N, Ekiz D et al. 2019. Continuous stress detection using wearable sensors in real life: algorithmic programming contest case study. Sensors 19:81849
     [Google Scholar]
  71. 71.
    Tsai C-H, Chen P-C, Liu D-S et al. 2022. Panic attack prediction using wearable devices and machine learning: development and cohort study. JMIR Med. Inform. 10:2e33063
     [Google Scholar]
  72. 72.
    Barragán-Montero A, Javaid U, Valdés G et al. 2021. Artificial intelligence and machine learning for medical imaging: a technology review. Phys. Med. 83:242–56
     [Google Scholar]
  73. 73.
    Xu S, Kim J, Walter JR et al. 2022. Translational gaps and opportunities for medical wearables in digital health. Sci. Transl. Med. 14:666eabn6036
     [Google Scholar]
  74. 74.
    Bahmani A, Alavi A, Buergel T et al. 2021. A scalable, secure, and interoperable platform for deep data-driven health management. Nat. Commun. 12:15757
     [Google Scholar]
  75. 75.
    Satija U, Ramkumar B, Manikandan MS. 2018. A review of signal processing techniques for electrocardiogram signal quality assessment. IEEE Rev. Biomed. Eng. 11:36–52
     [Google Scholar]
  76. 76.
    Karimipour A, Homaeinezhad MR. 2014. Real-time electrocardiogram P-QRS-T detection-delineation algorithm based on quality-supported analysis of characteristic templates. Comput. Biol. Med. 52:153–65
     [Google Scholar]
  77. 77.
    Smuck M, Odonkor CA, Wilt JK et al. 2021. The emerging clinical role of wearables: factors for successful implementation in healthcare. NPJ Digit. Med. 4:45
     [Google Scholar]
  78. 78.
    Lebak JW, Yao J, Warren S. 2004. HL7-compliant healthcare information system for home monitoring. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2004:3338–41
     [Google Scholar]
  79. 79.
    Intl. Data Corp. (IDC) 2023. Holiday quarter woes contribute to a full-year decline in shipments of wearable devices, according to IDC tracker News Release Mar. 7. https://www.idc.com/getdoc.jsp?containerId=prUS50462723
  80. 80.
    Lim B, Zohren S. 2021. Time-series forecasting with deep learning: a survey. Philos. Trans. A Math. Phys. Eng. Sci. 379:219420200209
     [Google Scholar]
  81. 81.
    Bolnick HJ, Bui AL, Bulchis A et al. 2020. Health-care spending attributable to modifiable risk factors in the USA: an economic attribution analysis. Lancet Public Health 5:10e525–35
     [Google Scholar]
  82. 82.
    Chiang P-H, Wong M, Dey S. 2021. Using wearables and machine learning to enable personalized lifestyle recommendations to improve blood pressure. IEEE J. Transl. Eng. Health Med. 9:2700513
     [Google Scholar]
  83. 83.
    Beagle AJ, Tison GH, Aschbacher K et al. 2020. Comparison of the physical activity measured by a consumer wearable activity tracker and that measured by self-report: cross-sectional analysis of the Health eHeart Study. JMIR mHealth uHealth 8:12e22090
     [Google Scholar]
  84. 84.
    Prieto-Avalos G, Cruz-Ramos NA, Alor-Hernández G et al. 2022. Wearable devices for physical monitoring of heart: a review. Biosensors 12:5292
     [Google Scholar]
  85. 85.
    Bravo VP, Muñoz JA. 2022. Wearables and their applications for the rehabilitation of elderly people. Med. Biol. Eng. Comput. 60:51239–52
     [Google Scholar]
  86. 86.
    Vilarinho T, Farshchian B, Bajer DG et al. 2015. A combined smartphone and smartwatch fall detection system. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing1443–48. New York: IEEE https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.216
    [Google Scholar]
  87. 87.
    Choi A, Chung K, Chung SP et al. 2022. Advantage of vital sign monitoring using a wireless wearable device for predicting septic shock in febrile patients in the emergency department: a machine learning-based analysis. Sensors 22:187054
     [Google Scholar]
  88. 88.
    Adans-Dester C, Hankov N, O'Brien A et al. 2020. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit. Med. 3:121
     [Google Scholar]
  89. 89.
    van Dijk-Huisman HC, Weemaes ATR, Boymans TAEJ et al. 2020. Smartphone app with an accelerometer enhances patients’ physical activity following elective orthopedic surgery: a pilot study. Sensors 20:154317
     [Google Scholar]
  90. 90.
    Bayoumy K, Gaber M, Elshafeey A et al. 2021. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18:8581–99
     [Google Scholar]
  91. 91.
    Patel V, Orchanian-Cheff A, Wu R. 2021. Evaluating the validity and utility of wearable technology for continuously monitoring patients in a hospital setting: systematic review. JMIR mHealth uHealth 9:8e17411
     [Google Scholar]
  92. 92.
    Soon S, Svavarsdottir H, Downey C et al. 2020. Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field. BMJ Innov. 6:255–71
     [Google Scholar]
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Wearable Devices: Implications for Precision Medicine and the Future of Health Care
Annual Review of Medicine 75, 401 (2024); https://doi.org/10.1146/annurev-med-052422-020437
/content/journals/10.1146/annurev-med-052422-020437
/content/journals/10.1146/annurev-med-052422-020437
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