Comparing the accuracy of ES-BC, EIS-GS, and ES Oxi on body composition, autonomic nervous system activity, and cardiac output to standardized assessments

John E Lewis, Stacey L Tannenbaum, Jinrun Gao, Angelica B Melillo, Evan G Long, Yaima Alonso, Janet Konefal, Judi M Woolger, Susanna Leonard, Prabjot K Singh, Lawrence Chen, Eduard Tiozzo, John E Lewis, Stacey L Tannenbaum, Jinrun Gao, Angelica B Melillo, Evan G Long, Yaima Alonso, Janet Konefal, Judi M Woolger, Susanna Leonard, Prabjot K Singh, Lawrence Chen, Eduard Tiozzo

Abstract

Background and purpose: THE ELECTRO SENSOR COMPLEX (ESC) IS SOFTWARE THAT COMBINES THREE DEVICES USING BIOELECTRICAL IMPEDANCE, GALVANIC SKIN RESPONSE, AND SPECTROPHOTOMETRY: (1) ES-BC (Electro Sensor-Body Composition; LD Technology, Miami, FL) to assess body composition, (2) EIS-GS (Electro Interstitial Scan-Galvanic Skin; LD Technology) to predict autonomic nervous system activity, and (3) ES Oxi (Electro Sensor Oxi; LD Technology) to assess cardiac output. The objective of this study was to compare each to a standardized assessment: ES-BC to dual-energy X-ray absorptiometry (DXA), EIS-GS to heart rate variability, and ES Oxi to BioZ Dx Diagnostic System (BioZ Dx; SonoSite Inc, Bothell, WA).

Patients and methods: The study was conducted in two waves. Fifty subjects were assessed for body composition and autonomic nervous system activity. Fifty-one subjects were assessed for cardiac output.

Results: We found adequate relative and absolute agreement between ES-BC and DXA for fat mass (r = 0.97, P < 0.001) with ES-BC overestimating fat mass by 0.1 kg and for body fat percentage (r = 0.92, P < 0.001) with overestimation of fat percentage by 0.4%. For autonomic nervous system activity, we found marginal relative agreement between EIS-GS and heart rate variability by using EIS-GS as the predictor in a linear regression equation (adjusted R(2) = 0.56, P = 0.03). For cardiac output, adequate relative and absolute agreement was found between ES Oxi and BioZ Dx at baseline (r = 0.60, P < 0.001), after the first exercise stage (r = 0.79, P < 0.001), and after the second exercise stage (r = 0.86, P < 0.001). Absolute agreement was found at baseline and after both bouts of exercise; ES Oxi overestimated baseline and stage 1 exercise cardiac output by 0.3 L/minute and 0.1 L/minute, respectively, but exactly estimated stage 2 exercise cardiac output.

Conclusion: ES-BC and ES Oxi accurately assessed body composition and cardiac output compared to standardized instruments, whereas EIS-GS showed marginal predictive ability for autonomic nervous system activity. The ESC software managing the three devices would be useful to help detect complications related to metabolic syndrome, diabetes, and cardiovascular disease and to noninvasively and rapidly manage treatment follow-up.

Keywords: Electro Sensor Complex; and bioimpedance cardiography; autonomic nervous system activity; dual-energy X-ray absorptiometry; fat mass; heart rate variability.

Figures

Figure 1
Figure 1
Bland–Altman plot of fat mass between ES-BC (Electro Sensor-Body Composition; LD Technology, Miami, FL) and dual-energy X-ray absorptiometry (DXA).
Figure 2
Figure 2
Bland–Altman plot of body fat percentage between ES-BC (Electro Sensor-Body Composition; LD Technology) and dual-energy X-ray absorptiometry (DXA).
Figure 3
Figure 3
Bland–Altman plot of baseline cardiac output between ES Oxi (Electro Sensor Oxi, LD Technology) and BioZ Dx Diagnostic System (BioZ Dx; SonoSite Inc,
Figure 4
Figure 4
Bland–Altman plot of stage 1 exercise cardiac output between ES Oxi (Electro Sensor Oxi, LD Technology) and BioZ Dx Diagnostic System (BioZ Dx; SonoSite Inc).
Figure 5
Figure 5
Bland–Altman plot of stage 2 exercise cardiac output between ES Oxi (Electro Sensor Oxi, LD Technology) and BioZ Dx Diagnostic System (BioZ Dx; SonoSite Inc).

References

    1. Lewis JE, Schneiderman N. Nutrition, physical activity, weight management, and health. Rev Colomb Psiquiatr. 2006;35(Suppl 1):157S–175S.
    1. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288(14):1723–1727.
    1. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. JAMA. 2001;286(10):1195–1200.
    1. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA. 2002;287(3):356–359.
    1. Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the US. Diabetes Care. 2005;28(11):2745–2749.
    1. Duman BS, Turkoglu C, Gunay D, Cagatay P, Demiroglu C, Buyukdevrim AS. The interrelationship between insulin secretion and action in type 2 diabetes mellitus with different degrees of obesity: evidence supporting central obesity. Diabetes Nutr Metab. 2003;16(4):243–250.
    1. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–384.
    1. Romero-Corral A, Somers VK, Sierra-Johnson J, et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J. 2010;31(6):737–746.
    1. Carnethon MR, Prineas RJ, Temprosa M, Zhang ZM, Uwaifo G, Molitch ME. The association among autonomic nervous system function, incident diabetes, and intervention arm in the Diabetes Prevention Program. Diabetes Care. 2006;29(4):914–919.
    1. Sanford T, Treister N, Peters C. Use of noninvasive hemodynamics in hypertension management. Am J Hypertens. 2005;18(2 Pt 2):87S–91S.
    1. Roche AF, Heymsfield SB, Lohman TG, editors. Human body composition. Champaign, IL: Human Kinetics; 1996.
    1. Macleod AF, Smith SA, Cowell T, Richardson PR, Sonksen PH. Non- cardiac autonomic tests in diabetes: use of the galvanic skin response. Diabet Med. 1991;8(Spec No):S67–70.
    1. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1–39.
    1. Brodie D, Moscrip V, Hutcheon R. Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods. Nutrition. 1998;14(3):296–310.
    1. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord. 2002;26(12):1596–1609.
    1. Rigaud B, Morucci JP, Chauveau N. Bioelectrical impedance techniques in medicine. Part I: Bioimpedance measurement. Second section: impedance spectrometry. Crit Rev Biomed Eng. 1996;24(4–6):257–351.
    1. Schoeller DA. Bioelectrical impedance analysis. What does it measure? Ann N Y Acad Sci. 2000;904:159–162.
    1. Guo Y, Franks PW, Brookshire T, Antonio Tataranni P. The intra-and inter-instrument reliability of DXA based on ex vivo soft tissue measurements. Obes Res. 2004;12(12):1925–1929.
    1. Grimnes S, Martinsen ØG. Bioimpedance and bioelectricity basics. San Diego, CA: Academic Press; 2000.
    1. Cottrell FG. Application to the Cottrell equation to chronoamperometry. Z Phys Chem. 1902;42:385.
    1. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J. 1996;17(3):354–381.
    1. Sayers BM. Analysis of heart rate variability. Ergonomics. 1973;16(1):17–32.
    1. Millasseau SC, Ritter JM, Takazawa K, Chowienczyk PJ. Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens. 2006;24(8):1449–1456.
    1. Woltjer HH, Bogaard HJ, de Vries PM. The technique of impedance cardiography. Eur Heart J. 1997;18(9):1396–1403.
    1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310.
    1. Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol. 2003;22(1):85–93.
    1. Romero-Corral A, Lopez-Jimenez F, Sierra-Johnson J, Somers VK. Differentiating between body fat and lean mass-how should we measure obesity? Nat Clin Pract Endocrinol Metab. 2008;4(6):322–323.
    1. Romero-Corral A, Somers VK, Sierra-Johnson J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond) 2008;32(6):959–966.
    1. Pateyjohns IR, Brinkworth GD, Buckley JD, Noakes M, Clifton PM. Comparison of three bioelectrical impedance methods with DXA in overweight and obese men. Obesity (Silver Spring) 2006;14(11):2064–2070.
    1. Valensi P, Smagghue O, Paries J, Velayoudon P, Nguyen TN, Attali JR. Peripheral vasoconstrictor responses to sympathetic activation in diabetic patients: relationship with rheological disorders. Metabolism. 1997;46(3):235–241.
    1. Bers DM, Despa S. Na/K-ATPase – an integral player in the adrenergic fight-or-flight response. Trends Cardiovasc Med. 2009;19(4):111–118.
    1. Brown CM, Stemper B, Welsch G, Brys M, Axelrod FB, Hilz MJ. Orthostatic challenge reveals impaired vascular resistance control, but normal venous pooling and capillary filtration in familial dysautonomia. Clin Sci (Lond) 2003;104(2):163–169.
    1. de Abreu DS. Bioimpedance and chronoamperometry as an adjunct to prostate-specific antigen screening for prostate cancer. Cancer Manag Res. 2011;3:109–116.
    1. Alexeev VG, Kuznecova LV. Bioimpedance in monitoring of effects of selective serotonin reuptake inhibitor treatment. Psychol Res Behav Manag. 2011;4:81–86.
    1. World Health Organization. Cardiovascular Diseases, 2008. Geneva: World Health Organization; 2008.
    1. Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2572.
    1. Lund-Johansen P. Hemodynamic effects of antihypertensive agents. In: Doyle AE, editor. Clinical pharmacology of antihypertensive drugs (handbook of hypertension, volume II) Amsterdam; the Netherlands: Elsevier; 1988. pp. 41–72.
    1. Berlowitz DR, Ash AS, Hickey EC, et al. Inadequate management of blood pressure in a hypertensive population. N Engl J Med. 1998;339(27):1957–1963.
    1. Paramore LC, Halpern MT, Lapuerta P, et al. Impact of poorly controlled hypertension on healthcare resource utilization and cost. Am J Manag Care. 2001;7(4):389–398.
    1. Mathews L, Singh RK. Cardiac output monitoring. Ann Card Anaesth. 2008;11(1):56–68.
    1. Richard R, Lonsdorfer-Wolf E, Charloux A, et al. Non-invasive cardiac output evaluation during a maximal progressive exercise test, using a new impedance cardiograph device. Eur J Appl Physiol. 2001;85(3–4):202–207.
    1. Chowienczyk PJ, Kelly RP, MacCallum H, et al. Photoplethysmographic assessment of pulse wave reflection: blunted response to endothelium-dependent beta2-adrenergic vasodilation in type II diabetes mellitus. J Am Coll Cardiol. 1999;34(7):2007–2014.
    1. McCombie D, Asada H, Reisner A. Identification of vascular dynamics and estimation of the cardiac output waveform from wearable PPG sensors. Conf Proc IEEE Eng Med Biol Soc. 2005;4:3490–3493.
    1. Wang L, Zhang YT. A novel photoplethysmogram index for total peripheral resistance after bicycle exercise. Proceedings of the 5th International Conference on Ubiquitous. Healthcare; 2008 Oct 29–31; Busan, Korea.

Source: PubMed

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