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Wearable and digital devices to monitor and treat metabolic diseases

Abstract

Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.

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Fig. 1: Illustration of the integration and analysis of data from multiple sources of wearable and digital devices, with varying temporalities.

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References

  1. Dagher, L., Shi, H., Zhao, Y. & Marrouche, N. F. Wearables in cardiology: here to stay. Heart Rhythm 17, 889–895 (2020).

    Article  PubMed  Google Scholar 

  2. González, S. et al. Features and models for human activity recognition. Neurocomputing 167, 52–60 (2015).

    Article  Google Scholar 

  3. Pannurat, N., Thiemjarus, S. & Nantajeewarawat, E. A hybrid temporal reasoning framework for fall monitoring. IEEE Sens. J. 17, 1749–1759 (2017).

    Article  Google Scholar 

  4. Brown, S. A. et al. Overnight closed-loop control improves glycemic control in a multicenter study of adults with type 1 diabetes. J. Clin. Endocrinol. Metab. 102, 3674–3682 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Dudde, R., Vering, T., Piechotta, G. & Hintsche, R. Computer-aided continuous drug infusion: setup and test of a mobile closed-loop system for the continuous automated infusion of insulin. IEEE Trans. Inf. Technol. Biomed. 10, 395–402 (2006).

    Article  PubMed  Google Scholar 

  6. Turakhia, M. P. et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am. Heart J. 207, 66–75 (2019).

    Article  PubMed  Google Scholar 

  7. Shashikumar, S. P., Shah, A. J., Li, Q., Clifford, G. D. & Nemati, S. A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. in 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) 141–144 (IEEE, 2017).

  8. Nemati, S. et al. Monitoring and detecting atrial fibrillation using wearable technology. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016, 3394–3397 (2016).

    Google Scholar 

  9. Hall, H. et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 16, e2005143 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tao, R. et al. Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes. BMJ Open Diabetes Res. Care 9, e001869 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lu, L. et al. Wearable health devices in health care: narrative systematic review. JMIR Mhealth Uhealth 8, e18907 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bayoumy, K. et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18, 581–599 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Costello, K. R. & Schones, D. E. Chromatin modifications in metabolic disease: potential mediators of long-term disease risk. Wiley Interdiscip. Rev. Syst. Biol. Med. 10, e1416 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Guariguata, L., Whiting, D., Weil, C. & Unwin, N. The international diabetes federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults. Diabetes Res. Clin. Pract. 94, 322–332 (2011).

    Article  PubMed  Google Scholar 

  15. Lin, C.-F., Chang, Y.-H., Chien, S.-C., Lin, Y.-H. & Yeh, H.-Y. Epidemiology of dyslipidemia in the Asia Pacific region. Int. J. Gerontol. 12, 2–6 (2018).

    Article  Google Scholar 

  16. Hirode, G. & Wong, R. J. Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA 323, 2526–2528 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bruce, K. D. & Byrne, C. D. The metabolic syndrome: common origins of a multifactorial disorder. Postgrad. Med. J. 85, 614–621 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Lann, D. & LeRoith, D. Insulin resistance as the underlying cause for the metabolic syndrome. Med. Clin. North Am. 91, 1063–77 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Roberts, C. K., Hevener, A. L. & Barnard, R. J. Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. Compr. Physiol. 3, 1–58 (2013).

    PubMed  PubMed Central  Google Scholar 

  20. Rodbard, D. Continuous glucose monitoring: a review of successes, challenges, and opportunities. Diabetes Technol. Ther. 18, S3–S13 (2016).

    Article  PubMed  Google Scholar 

  21. Wang, Y., Xue, H., Huang, Y., Huang, L. & Zhang, D. A systematic review of application and effectiveness of mHealth interventions for obesity and diabetes treatment and self-management. Adv. Nutr. 8, 449–462 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dunn, J. et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat. Med. 27, 1105–1112 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Selvin, E., Crainiceanu, C. M., Brancati, F. L. & Coresh, J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch. Intern. Med. 167, 1545–1551 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Danne, T. et al. International consensus on use of continuous glucose monitoring. Diabetes Care 40, 1631–1640 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lee, S. et al. Predictions of diabetes complications and mortality using HbA1c variability: a 10-year observational cohort study. Acta Diabetol. 58, 171–180 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Chehregosha, H., Khamseh, M. E., Malek, M., Hosseinpanah, F. & Ismail-Beigi, F. A view beyond HbA1c: role of continuous glucose monitoring. Diabetes Ther. 10, 853–863 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rohlfing, C., Hanson, S. & Little, R. R. Measurement of hemoglobin A1c in patients with sickle cell trait. JAMA 317, 2237 (2017).

    Article  PubMed  Google Scholar 

  28. Vigersky, R. A. The benefits, limitations, and cost-effectiveness of advanced technologies in the management of patients with diabetes mellitus. J. Diabetes Sci. Technol. 9, 320–330 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dobreanu, D. et al. Current practice for diagnosis and management of silent atrial fibrillation: results of the European Heart Rhythm Association survey. Europace 15, 1223–1225 (2013).

    Article  PubMed  Google Scholar 

  30. Dagres, N. et al. Influence of the duration of Holter monitoring on the detection of arrhythmia recurrences after catheter ablation of atrial fibrillation. Int. J. Cardiol. 139, 305–306 (2010).

    Article  PubMed  Google Scholar 

  31. Bouzid, Z., Al-Zaiti, S. S., Bond, R. & Sejdić, E. Remote and wearable ECG devices with diagnostic abilities in adults: a state-of-the-science scoping review. Heart Rhythm 19, 1192–1201 (2022).

    Article  PubMed  Google Scholar 

  32. Ginsberg, H. N. Insulin resistance and cardiovascular disease. J. Clin. Invest. 106, 453–458 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Paneni, F., Costantino, S. & Cosentino, F. Insulin resistance, diabetes, and cardiovascular risk. Curr. Atheroscler. Rep. 16, 419 (2014).

    Article  PubMed  Google Scholar 

  34. Wilcox, G. Insulin and insulin resistance. Clin. Biochem. Rev. 26, 19–39 (2005).

    PubMed  PubMed Central  Google Scholar 

  35. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Wyatt, P. et al. Postprandial glycaemic dips predict appetite and energy intake in healthy individuals. Nat. Metab. 3, 523–529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cosson, E. et al. Multicentre, randomised, controlled study of the impact of continuous sub-cutaneous glucose monitoring (GlucoDay) on glycaemic control in type 1 and type 2 diabetes patients. Diabetes Metab. 35, 312–318 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Galindo, R. J. & Aleppo, G. Continuous glucose monitoring: the achievement of 100 years of innovation in diabetes technology. Diabetes Res. Clin. Pract. 170, 108502 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim, S., Malik, J., Seo, J. M., Cho, Y. M. & Bien, F. Subcutaneously implantable electromagnetic biosensor system for continuous glucose monitoring. Sci. Rep. 12, 17395 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xu, J., Yan, Z. & Liu, Q. Smartphone-based electrochemical systems for glucose monitoring in biofluids: a review. Sensors 22, 5670 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee, I., Wakako, T., Ikebukuro, K. & Sode, K. In vitro continuous 3 months operation of direct electron transfer type open circuit potential based glucose sensor: heralding the next CGM sensor. J. Diabetes Sci. Technol. 16, 1107–1113 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kokkinos, P. Physical activity, health benefits, and mortality risk. ISRN Cardiol. 2012, 718789 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rhodes, R. E., Janssen, I., Bredin, S. S. D., Warburton, D. E. R. & Bauman, A. Physical activity: health impact, prevalence, correlates and interventions. Psychol. Health 32, 942–975 (2017).

    Article  PubMed  Google Scholar 

  44. Lakka, T. A. & Laaksonen, D. E. Physical activity in prevention and treatment of the metabolic syndrome. Appl. Physiol. Nutr. Metab. 32, 76–88 (2007).

    Article  PubMed  Google Scholar 

  45. Degroote, L., De Bourdeaudhuij, I., Verloigne, M., Poppe, L. & Crombez, G. The accuracy of smart devices for measuring physical activity in daily life: validation study. JMIR Mhealth Uhealth 6, e10972 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Case, M. A., Burwick, H. A., Volpp, K. G. & Patel, M. S. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA 313, 625–626 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Paluch, A. E. et al. Steps per day and all-cause mortality in middle-aged adults in the coronary artery risk development in young adults study. JAMA Netw. Open 4, e2124516 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Althoff, T. et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 336–339 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Althoff, T., Nilforoshan, H., Hua, J. & Leskovec, J. Large-scale diet tracking data reveal disparate associations between food environment and diet. Nat. Commun. 13, 267 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Stephens, J. D., Yager, A. M. & Allen, J. Smartphone technology and text messaging for weight loss in young adults: a randomized controlled trial. J. Cardiovasc. Nurs. 32, 39–46 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Painter, S. L. et al. What matters in weight loss? An in-depth analysis of self-monitoring. J. Med. Internet Res. 19, e160 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Marrone, G. et al. Vegan diet health benefits in metabolic syndrome. Nutrients 13, 817 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Giugliano, D., Ceriello, A. & Esposito, K. The effects of diet on inflammation. J. Am. Coll. Cardiol. 48, 677–685 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Berry, S. et al. Personalised REsponses to DIetary Composition Trial (PREDICT): an intervention study to determine inter-individual differences in postprandial response to foods. Protocol Exchange https://doi.org/10.21203/rs.2.20798/v1 (2020).

  55. Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus mediterranean diet for glycemic control in prediabetes. Diabetes Care 44, 1980–1991 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Ipjian, M. L. & Johnston, C. S. Smartphone technology facilitates dietary change in healthy adults. Nutrition 33, 343–347 (2017).

    Article  PubMed  Google Scholar 

  57. Kwon, B. C. et al. Improving heart disease risk through quality-focused diet logging: pre-post study of a diet quality tracking app. JMIR Mhealth Uhealth 8, e21733 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Peos, J. J., Helms, E. R., Fournier, P. A. & Sainsbury, A. Continuous versus intermittent moderate energy restriction for increased fat mass loss and fat free mass retention in adult athletes: protocol for a randomised controlled trial-the ICECAP trial (intermittent versus continuous energy restriction compared in an athlete population). BMJ Open Sport Exerc. Med. 4, e000423 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kipnis, V., Carroll, R. J., Freedman, L. S. & Li, L. Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies. Am. J. Epidemiol. 150, 642–651 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Prentice, R. L. Measurement error and results from analytic epidemiology: dietary fat and breast cancer. J. Natl Cancer Inst. 88, 1738–1747 (1996).

    Article  CAS  PubMed  Google Scholar 

  61. Kaaks, R. & Riboli, E. Validation and calibration of dietary intake measurements in the EPIC project: methodological considerations. European Prospective Investigation into Cancer and Nutrition. Int. J. Epidemiol. 26, S15–S25 (1997).

    Article  PubMed  Google Scholar 

  62. Ancoli-Israel, S. in Understanding Sleep: The Evaluation and Treatment of Sleep Disorders. (eds. Pressman, M. R. & Orr, W. C.) 177–191 (American Psychological Association, 1997).

  63. Park, K. S. & Choi, S. H. Smart technologies toward sleep monitoring at home. Biomed. Eng. Lett. 9, 73–85 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Wolk, R. & Somers, V. K. Sleep and the metabolic syndrome. Exp. Physiol. 92, 67–78 (2007).

    Article  PubMed  Google Scholar 

  65. Vgontzas, A. N., Bixler, E. O. & Chrousos, G. P. Sleep apnea is a manifestation of the metabolic syndrome. Sleep. Med. Rev. 9, 211–224 (2005).

    Article  PubMed  Google Scholar 

  66. Hoevenaar-Blom, M. P., Spijkerman, A. M. W., Kromhout, D., van den Berg, J. F. & Verschuren, W. M. M. Sleep duration and sleep quality in relation to 12-year cardiovascular disease incidence: the MORGEN study. Sleep 34, 1487–1492 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Newman, A. B. et al. Relation of sleep-disordered breathing to cardiovascular disease risk factors: the Sleep Heart Health Study. Am. J. Epidemiol. 154, 50–59 (2001).

    Article  CAS  PubMed  Google Scholar 

  68. Colilla, S. et al. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am. J. Cardiol. 112, 1142–1147 (2013).

    Article  PubMed  Google Scholar 

  69. Perez, M. V. et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 381, 1909–1917 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Allen, J. Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28, R1–R39 (2007).

    Article  PubMed  Google Scholar 

  71. Ilea, A. et al. Saliva, a magic biofluid available for multilevel assessment and a mirror of general health-a systematic review. Biosensors 9, 27 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Alexeev, V. L., Das, S., Finegold, D. N. & Asher, S. A. Photonic crystal glucose-sensing material for noninvasive monitoring of glucose in tear fluid. Clin. Chem. 50, 2353–2360 (2004).

    Article  CAS  PubMed  Google Scholar 

  73. Bandodkar, A. J., Jeang, W. J., Ghaffari, R. & Rogers, J. A. Wearable sensors for biochemical sweat analysis. Annu Rev. Anal. Chem. 12, 1–22 (2019).

    Article  Google Scholar 

  74. Li, G. & Wen, D. Wearable biochemical sensors for human health monitoring: sensing materials and manufacturing technologies. J. Mater. Chem. B Mater. Biol. Med. 8, 3423–3436 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Gordon, R. S., Thompson, R. H., Muenzer, J. & Thrasher, D. Sweat lactate in man is derived from blood glucose. J. Appl. Physiol. 31, 713–716 (1971).

    Article  CAS  PubMed  Google Scholar 

  76. Vinoth, R., Nakagawa, T., Mathiyarasu, J. & Mohan, A. M. V. Fully printed wearable microfluidic devices for high-throughput sweat sampling and multiplexed electrochemical analysis. ACS Sens. 6, 1174–1186 (2021).

    Article  CAS  PubMed  Google Scholar 

  77. Sharma, A., Badea, M., Tiwari, S. & Marty, J. L. Wearable biosensors: an alternative and practical approach in healthcare and disease monitoring. Molecules 26, 748 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Shilo, S. et al. 10K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36, 1187–1194 (2021).

    Article  CAS  PubMed  Google Scholar 

  79. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. The All of Us Research Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Article  PubMed Central  Google Scholar 

  81. Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).

    Article  PubMed  Google Scholar 

  82. Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Wyatt, K. D., Poole, L. R., Mullan, A. F., Kopecky, S. L. & Heaton, H. A. Clinical evaluation and diagnostic yield following evaluation of abnormal pulse detected using Apple Watch. J. Am. Med. Inform. Assoc. 27, 1359–1363 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Phan, D. T. et al. A flexible, wearable, and wireless biosensor patch with internet of medical things applications. Biosensors 12, 139 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Alavi, A. et al. Real-time alerting system for COVID-19 and other stress events using wearable data. Nat. Med. 28, 175–184 (2022).

    Article  CAS  PubMed  Google Scholar 

  86. Adhikari, S. & Stark, D. E. Video-based eye tracking for neuropsychiatric assessment. Ann. N. Y. Acad. Sci. 1387, 145–152 (2017).

    Article  PubMed  Google Scholar 

  87. Powers, R. et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson’s disease. Sci. Transl. Med. 13, eabd7865 (2021).

    Article  PubMed  Google Scholar 

  88. Kankanhalli, A., Shin, J. & Oh, H. Mobile-Based interventions for dietary behavior change and health outcomes: scoping review. JMIR Mhealth Uhealth 7, e11312 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Tate, A. R. & Rao, G. H. R. Activity trackers, wearables, noninvasive technologies for early detection, and management of cardiometabolic risks. Int. J. Biomed. 10, 189–197 (2020).

    Article  Google Scholar 

  90. Attia, Z. I. et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ. Arrhythm. Electrophysiol. 12, e007284 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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A. K., L. R., N. B. and E. S. wrote the manuscript and approved the final version.

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Keshet, A., Reicher, L., Bar, N. et al. Wearable and digital devices to monitor and treat metabolic diseases. Nat Metab 5, 563–571 (2023). https://doi.org/10.1038/s42255-023-00778-y

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