Activity Trackers, Wearables, Noninvasive Technologies for Early Detection, and Management of Cardiometabolic Risks

Akshay R. Tate, Gundu H. R. Rao

 
International Journal of Biomedicine. 2020;10(3):189-197.
DOI: 10.21103/Article10(3)_RA2
Originally published September 10, 2020

Abstract: 

According to the World Health Organization (WHO), cardiovascular diseases (CVD) are the number one cause of death globally, taking an estimated 18 million lives each year. Framingham Heart Study Group in Boston developed, the very first 10-year CVD risk prediction model. The WHO recently derived a 10-year risk prediction model, for fatal and non-fatal CVD events using individual participant data, from the Emerging Risk Factor Collaboration. This CVD risk model, derivation involved participation of 376,177 individuals from 85 cohorts, and 19,333 incident CVD events. Cardiometabolic diseases in general, - and metabolic diseases like hypertension, excess weight, obesity, type-2 diabetes, and vascular diseases collectively, contribute to the overall morbidity and mortality related to CVDs. Of the 18 million CVD-related deaths in 2017, more than three quarters were in low-income and middle-income countries. Risk stratification, management, and prevention models can be very important components of disease prevention and control efforts. In view of this very important role the risk assessment plays,  we have reviewed and discussed three specific areas of integration of emerging diagnostic technologies,-population level, individual level, and novel ways of collecting, collating, derivation, and validation of individual risks, risk scores for cluster of risks, and for the use of such data for treatment management. For collection of massive data from general population, we have discussed the National Institutes of Health’s flagship program, -All of Us. For empowering individuals in risk assessment and management of identified risks, we have discussed ambulatory diagnostic devices, blood pressure monitors, and continuous glucose monitors. As an example of futuristic approach for the integration of diagnostic tools, we have discussed the products developed by LD-Technologies, Miami, Florida by Dr. Albert Maarek and associates. There are thousands of mHealth apps available for download, and not all are of great use in developing, a seamless diagnostic platform, that we at AayuSmart are trying to put together for cardiometabolic risk stratification, risk prediction, and risk management. We have provided, just a brief glimpse of available tracking, computing, collating, and the software analytical capabilities that exists, and discussed how innovators are trying to use them to build novel diagnostic tools. Despite the important role of telemedicine and internet of things (IOT) in disease management, and healthcare delivery, we have not discussed those applications. In this overview, we have discussed the use of emerging diagnostic tools, for obtaining data at the population level, personal level, as well as for clinical settings. Having said that, we must inform the readers, that there is a great difference between the consumer devices (wrist-worn activity trackers), medical devices (FDA cleared or CE Marked), and a variety of technologies that are in use, but not yet considered clinical testing devices.

Keywords: 
cardiovascular diseases • noninvasive technologies • cardiometabolic risk
References: 

1. NCD Risk Factor Collaboration (NCD-RisC): Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants. Lancet. 2017;389(10064):37-55. doi:10.1016/S0140-6736(16)31919-5
2. Global Burden of Metabolic Risk Factors for Chronic Disease Collaboration: Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 2014;2(8):634-647. doi:10.1016/S2213-8587(14)70102-0
3. NCD Risk Factor Collaboration: Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants [published correction appears in Lancet. 2017 Feb 4;389(10068):e2]. Lancet. 2016;387(10027):1513-1530. doi:10.1016/S0140-6736(16)00618-8
4. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365(9455):217-223. doi:10.1016/S0140-6736(05)17741-1
5. Evans A, Tolonen H, Hense HW et al: Trends in coronary artery disease risk factors in the WHO MONICA project. Int J Epidemiol. 2001;30 Suppl 1:S35-S40. doi:10.1093/ije/30.suppl_1.s35
6. Juonala M, Viikari JS, Hutri-Kähönen N, et al. The 21-year follow-up of the Cardiovascular Risk in Young Finns Study: risk factor levels, secular trends and east-west difference. J Intern Med. 2004;255(4):457-468. doi:10.1111/j.1365-2796.2004.01308.x
7. Reilly JJ, El-Hamdouchi A, Diouf A, Monyeki A, Somda SA. Determining the worldwide prevalence of obesity. Lancet. 2018;391(10132):1773-1774. doi:10.1016/S0140-6736(18)30794-3
8. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627-2642. doi:10.1016/S0140-6736(17)32129-3
9. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants [published correction appears in Lancet. 2016 May 14;387(10032):1998]. Lancet. 2016;387(10026):1377-1396. doi:10.1016/S0140-6736(16)30054-X
10. Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects), Lu Y, Hajifathalian K, et al. Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383(9921):970-983. doi:10.1016/S0140-6736(13)61836-X
11. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793-795. doi:10.1056/NEJMp1500523
12. All of Us Research Program Investigators, Denny JC, Rutter JL, et al. The "All of Us" Research Program. N Engl J Med. 2019;381(7):668-676. doi:10.1056/NEJMsr1809937
13. Feehan LM, Geldman J, Sayre EC, et al. Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR Mhealth Uhealth. 2018;6(8):e10527. Published 2018 Aug 9. doi:10.2196/10527
14. Saunders TJ, Atkinson HF, Burr J, MacEwen B, Skeaff CM, Peddie MC. The Acute Metabolic and Vascular Impact of Interrupting Prolonged Sitting: A Systematic Review and Meta-Analysis. Sports Med. 2018;48(10):2347-2366. doi:10.1007/s40279-018-0963-8
15. Biswas A, Oh PI, Faulkner GE, et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis [published correction appears in Ann Intern Med. 2015 Sep 1;163(5):400]. Ann Intern Med. 2015;162(2):123-132. doi:10.7326/M14-1651
16. Lynch BM, Owen N. Too much sitting and chronic disease risk: steps to move the science forward. Ann Intern Med. 2015;162(2):146-147. doi:10.7326/M14-2552
17. Berlin JA, Colditz GA. A meta-analysis of physical activity in the prevention of coronary heart disease. Am J Epidemiol. 1990;132(4):612-628. doi:10.1093/oxfordjournals.aje.a115704
18. Eaton CB. Relation of physical activity and cardiovascular fitness to coronary heart disease, Part I: A meta-analysis of the independent relation of physical activity and coronary heart disease. J Am Board Fam Pract. 1992;5(1):31-42.
19. Manson JE, Greenland P, LaCroix AZ, et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N Engl J Med. 2002;347(10):716-725. doi:10.1056/NEJMoa021067
20. Tanasescu M, Leitzmann MF, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Exercise type and intensity in relation to coronary heart disease in men. JAMA. 2002;288(16):1994-2000. doi:10.1001/jama.288.16.1994
21. Wannamethee SG, Shaper AG, Alberti KG. Physical activity, metabolic factors, and the incidence of coronary heart disease and type 2 diabetes. Arch Intern Med. 2000;160(14):2108-2116. doi:10.1001/archinte.160.14.2108
22. Swain DP, Franklin BA. Comparison of cardioprotective benefits of vigorous versus moderate intensity aerobic exercise. Am J Cardiol. 2006;97(1):141-147. doi:10.1016/j.amjcard.2005.07.130
23. Yu S, Yarnell JW, Sweetnam PM, Murray L; Caerphilly study. What level of physical activity protects against premature cardiovascular death? The Caerhilly study. Heart. 2003;89(5):502-506. doi:10.1136/heart.89.5.502
24. Bove AA. Exercise and Heart Disease. Methodist Debakey Cardiovasc J. 2016;12(2):74-75. doi:10.14797/mdcj-12-2-74
25. Di Cesare M, Bennett JE, Best N, Stevens GA, Danaei G, Ezzati M. The contributions of risk factor trends to cardiometabolic mortality decline in 26 industrialized countries. Int J Epidemiol. 2013;42(3):838-848. doi:10.1093/ije/dyt063
26. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937-952. doi:10.1016/S0140-6736(04)17018-9
27. Khera AV, Emdin CA, Drake I, et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med. 2016;375(24):2349-2358. doi:10.1056/NEJMoa1605086
28. Rao GHR. Fitness, Lifestyle changes, Wellness: Cardiometabolic Health. J Cardiol (OAJC). 2018;2(4):000132.
29. Sullivan AN, Lachman ME. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Front Public Health. 2017;4:289. Published 2017 Jan 11. doi:10.3389/fpubh.2016.00289
30. Rao GHR. Integration of emerging technologies: Management of cardiometabolic diseases; Review. Ind J Cardio Biol & Clin Sci. 2018;5(1):111.
31. Rao GHR. Risk prediction, assessment, and management of type-2 diabetes. Point of View. EC Endocrinol Metab Res. 2018;3:30-41.
32. Rao GHR. Integration of novel emerging technologies for the management of type-2 diabetes. Arch Dia. & Obesity. 2018;1(1). MIS ID: 000102.
33. Rao GHR. Predictive and Preventive Healthcare: Integration of emerging technologies. J Clin Res In Diab & Endocrinol. 2018;1(1):1-8.
34. Malmefeldt E, Rao GHR. Noninvasive Diagnostic Tools: Cardiometabolic Risk Assessment and Prediction. J Clin Cardiol Diag. 2019;2(1):1-10.
35. Framingham Heart Study: Three Generation of Dedication. Available at https://framinghamheartstudy.org/
36.  Brindle P, Emberson J, Lampe F, et al. Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ. 2003;327(7426):1267. doi:10.1136/bmj.327.7426.1267
37. D'Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-753. doi:10.1161/CIRCULATIONAHA.107.699579
38. Bosomworth NJ. Practical use of the Framingham risk score in primary prevention: Canadian perspective. Can Fam Physician. 2011;57(4):417-423..
39. Cohn JN. Cardiovascular Disease Progression: A Target for Therapy?. Am J Med. 2018;131(10):1170-1173. doi:10.1016/j.amjmed.2018.03.032
40. Cohn JN, Duprez DA, Grandits GA. Arterial elasticity as part of a comprehensive assessment of cardiovascular risk and drug treatment. Hypertension. 2005;46(1):217-220. doi:10.1161/01.HYP.0000165686.50890.c3

41. Gandhi PG, Rao GH. The spectral analysis of photoplethysmography to evaluate an independent cardiovascular risk factor. Int J Gen Med. 2014;7:539-547. Published 2014 Dec 9. doi:10.2147/IJGM.S70892
42. Gandhi PG, Gundu HR. Detection of neuropathy using a sudomotor test in type 2 diabetes [published correction appears in Degener Neurol Neuromuscul Dis. 2015 Jul 16;5:73]. Degener Neurol Neuromuscul Dis. 2015;5:1-7. Published 2015 Jan 9. doi:10.2147/DNND.S75857
43. Maarek AA, Gandhi PG, Rao GHR. Identifying Autonomic Neuropathy and Endothelial Dysfunction in Type 2 Diabetic Patients. EC Neuropathy.2015;2.2:63-78.
44. Duprez DA, Cohn JN. Arterial stiffness as a risk factor for coronary atherosclerosis. Curr Atheroscler Rep. 2007;9(2):139-144. doi:10.1007/s11883-007-0010-y
45. Kelly AS, Wetzsteon RJ, Kaiser DR, Steinberger J, Bank AJ, Dengel DR. Inflammation, insulin, and endothelial function in overweight children and adolescents: the role of exercise. J Pediatr. 2004;145(6):731-736. doi:10.1016/j.jpeds.2004.08.004
46. Zhang H, Jiang L, Yang YJ, et al. Aerobic exercise improves endothelial function and serum adropin levels in obese adolescents independent of body weight loss. Sci Rep. 2017;7(1):17717. Published 2017 Dec 18. doi:10.1038/s41598-017-18086-3
47. Davidson MB. Continuous Glucose Monitoring in Patients With Type 1 Diabetes Taking Insulin Injections. JAMA. 2017;317(4):363-364. doi:10.1001/jama.2016.20327
48. Bhavnani SP, Parakh K, Atreja A, et al. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017;70(21):2696-2718. doi:10.1016/j.jacc.2017.10.018
49. Steinberg JS, Varma N, Cygankiewicz I, et al. 2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry [published correction appears in Heart Rhythm. 2018 Mar 28;:] [published correction appears in Heart Rhythm. 2018 Aug;15(8):1276]. Heart Rhythm. 2017;14(7):e55-e96. doi:10.1016/j.hrthm.2017.03.038
50. Ip JE. Wearable Devices for Cardiac Rhythm Diagnosis and Management. JAMA. 2019;321(4):337-338. doi:10.1001/jama.2018.20437
51. Malmefeldt E, Rao GHR. Changing concepts of healthcare: Physical activity, Fitness and Wellness. EC Endocrinol Metab Rex.2019;4:238-250.
52. Malmefeldt E, Rao GHR. Noninvasive diagnostic tools: Cardiometabolic risk assessment and prediction. J Clin Cardiol Diagn. 2019;2(1):1-10.
53. Murakami H, Kawakami R, Nakae S, et al. Accuracy of Wearable Devices for Estimating Total Energy Expenditure: Comparison With Metabolic Chamber and Doubly Labeled Water Method. JAMA Intern Med. 2016;176(5):702-703. doi:10.1001/jamainternmed.2016.0152
54. Cortez NG, Cohen IG, Kesselheim AS. FDA regulation of mobile health technologies. N Engl J Med. 2014;371(4):372-379. doi:10.1056/NEJMhle1403384
55. Quer G, Gouda P, Galarnyk M, Topol EJ, Steinhubl SR. 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. 2020;15(2):e0227709. Published 2020 Feb 5. doi:10.1371/journal.pone.0227709
56. Tuttor M, von Stengel S, Kohl M, et al. High Intensity Resistance Exercise Training vs High Intensity Training to Fight Cardiometabolic Risk Factors in Overweight Men 30-50 Years Old. Frontiers in Sports and Active Living. June 16, 2020. doi: 10.3389/fspor.2020.0068.
57. Steinhubl SR. The future of individualized health maintenance. Nat Med. 2019;25(5):712-714. doi:10.1038/s41591-019-0443-1
58. Palatini P, Casiglia E, Julius S, Pessina AC. High heart rate: a risk factor for cardiovascular death in elderly men. Arch Intern Med. 1999;159(6):585-592. doi:10.1001/archinte.159.6.585
59. Izmailova ES, Wagner JA, Perakslis ED. Wearable Devices in Clinical Trials: Hype and Hypothesis. Clin Pharmacol Ther. 2018;104(1):42-52. doi:10.1002/cpt.966
60. WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7(10):e1332-e1345. doi:10.1016/S2214-109X(19)30318-3
61. Ueda P, Woodward M, Lu Y, et al. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys. Lancet Diabetes Endocrinol. 2017;5(3):196-213. doi:10.1016/S2213-8587(17)30015-3
62. Pennells L, Kaptoge S, Wood A, et al. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. Eur Heart J. 2019;40(7):621-631. doi:10.1093/eurheartj/ehy653
63. Rowland SP, Fitzgerald JE, Holme T, Powell J, McGregor A. What is the clinical value of mHealth for patients? NPJ Digit Med. 2020;3:4. Published 2020 Jan 13. doi:10.1038/s41746-019-0206-x

Download Article
Received June 28, 2020.
Accepted July 27, 2020.
©2020 International Medical Research and Development Corporation.