Knowledge Hub

Knowledge Hub

Sources for Body Composition & Bioimpedance and Data for Further Research 

1. Sergi, G., Bussolotto, M., Perini, P., Calliari, I., Giantin, V., Ceccon, A., Scanferla, F., Bressan, M., Moschini, G., & Enzi, G. (1994). Accuracy of bioelectrical impedance analysis in estimation of extracellular space in healthy subjects and in fluid retention states. Annals of nutrition & metabolism, 38(3), 158–165. https://doi.org/10.1159/000177806
2. Stahn, Alexander & Terblanche, Elmarie & Gunga, Hanns-Christian. (2012). Use of Bioelectrical Impedance: General Principles and Overview. 10.1007/978-1-4419-1788-1_3
3. Samouda, H., Dutour, A., Chaumoitre, K., Panuel, M., Dutour, O., & Dadoun, F. (2013). VAT=TAAT-SAAT: an innovative anthropometric model to predict visceral adipose tissue without resort to CT-Scan or DXA. Obesity (Silver Spring, Md.), 21(1), E41–E50. https://doi.org/10.1002/oby.20033
4. Soria, D.I. (2008). Implementation of an Electrical Bioimpedance Monitoring System and a Tool for Bioimpedance Vector Analysis
5. Jang, H. Y., Choi, H. J., Lee, K. B., Cho, S. B., Im, I. J., & Kim, H. J. (2016). The Association between Muscle Mass Deficits Estimated from Bioelectrical Impedance Analysis and Lumbar Spine Bone Mineral Density in Korean Adults. Journal of bone metabolism, 23(2), 95–100. https://doi.org/10.11005/jbm.2016.23.2.95
6. Tanaka, S., Ando, K., Kobayashi, K., Hida, T., Ito, K., Tsushima, M., Morozumi, M., Machino, M., Ota, K., Seki, T., Ishiguro, N., Hasegawa, Y., & Imagama, S. (2018). A low phase angle measured with bioelectrical impedance analysis is associated with osteoporosis and is a risk factor for osteoporosis in community-dwelling people: the Yakumo study. Archives of osteoporosis, 13(1), 39. https://doi.org/10.1007/s11657-018-0450-8
7. Fujimoto, K., Inage, K., Eguchi, Y., Orita, S., Suzuki, M., Kubota, G., Sainoh, T., Sato, J., Shiga, Y., Abe, K., Kanamoto, H., Inoue, M., Kinoshita, H., Norimoto, M., Umimura, T., Koda, M., Furuya, T., Akazawa, T., Toyoguchi, T., Terakado, A., … Ohtori, S. (2018). Use of Bioelectrical Impedance Analysis for the Measurement of Appendicular Skeletal Muscle Mass/Whole Fat Mass and Its Relevance in Assessing Osteoporosis among Patients with Low Back Pain: A Comparative Analysis Using Dual X-ray Absorptiometry. Asian spine journal, 12(5), 839–845. https://doi.org/10.31616/asj.2018.12.5.839
8. Pop-Busui, R., Evans, G. W., Gerstein, H. C., Fonseca, V., Fleg, J. L., Hoogwerf, B. J., Genuth, S., Grimm, R. H., Corson, M. A., Prineas, R., & Action to Control Cardiovascular Risk in Diabetes Study Group (2010). Effects of cardiac autonomic dysfunction on mortality risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Diabetes care, 33(7), 1578–1584. https://doi.org/10.2337/dc10-0125. 
9. Age-Matched Attenuation of Both Autonomic Branches in Chronic Disease: II. Diabetes Mellitus, Aaron I. Vinik, Rohit R. Arora, Joseph Colombo. Cleveland Clinic Journal of Medicine Aug 2011, 78 (8 suppl 1) S86, Lewis et al. Journal of Diabetes & Metabolic Disorders (2017) 16:26. DOI 10.1186/s40200-017-0307-5
10. Endothelial function testing. Cedars. (n.d.). Retrieved June 22, 2022, from https://www.cedars-sinai.edu/Patients/Programs-and-Services/Womens-Heart-Center/Services/Endothelial-Function-Testing.aspx 

Sources for Galvanic Skin Response and Data for Further Research 

1. Harmon-Jones, E., & Beer, J. S. (2009). Methods in social neuroscience. Guilford Press. 
2. Tricoche, X. (n.d.). Purdue University – Department of Computer Science. Retrieved June 22, 2022, from https://www.cs.purdue.edu/cgvlab/papers/xmt/flowbio.pdf 
3. Muller, Joop & Pet, Wim & ReatschPet, Ellen & Servaas, Riek & Ansems, Femke & Schwander, Daniel & Firer, Gary & Lothaller, Harald & Endler, P.C.. (2014). Repeatability of Measurements of Galvanic Skin Response – A Pilot Study. The Open Complementary Medicine Journal. 5. 11-17. 10.2174/1876391X01305010011. 
4. Baker L. B. (2019). Physiology of sweat gland function: The roles of sweating and sweat composition in human health. Temperature (Austin, Tex.), 6(3), 211–259. https://doi.org/10.1080/23328940.2019.1632145
5. El-Badawy, M. A., & El Mikkawy, D. M. E. (2016). Sympathetic dysfunction in patients with chronic low back pain and failed back surgery syndrome. The Clinical Journal of Pain, 32(3), 226–231. https://doi.org/10.1097/ajp.0000000000000250 
6. Fujita, T., Fujii, Y., Nakamura, T., Miyauchi, A., & Takagi, Y. (2000). Effect of avicatonin (chicken carbocalcitonin) on galvanic skin response: a randomized, prospective, double-blind, controlled study for an objective assessment of pain. Calcified tissue international, 66(4), 243–247. https://doi.org/10.1007/s002230010049

Sources for Heart Rate Variability and Data for Further Research 

1. Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology.(1996). https://doi.org/10.1161/01.CIR.93.5.1043
2. Tsuji, H., Larson, M. G., Venditti, F. J., Jr, Manders, E. S., Evans, J. C., Feldman, C. L., & Levy, D. (1996). Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation, 94(11), 2850–2855. https://doi.org/10.1161/01.cir.94.11.2850
3. Schroeder, E. B., Chambless, L. E., Liao, D., Prineas, R. J., Evans, G. W., Rosamond, W. D., Heiss, G., & Atherosclerosis Risk in Communities (ARIC) study (2005). Diabetes, glucose, insulin, and heart rate variability: the Atherosclerosis Risk in Communities (ARIC) study. Diabetes care, 28(3), 668–674. https://doi.org/10.2337/diacare.28.3.668
4. Poddubnyĭ, D. A., Gaĭdukova, I. Z., & Rebrov, A. P. (2009). Terapevticheskii arkhiv, 81(6), 56–62.  

Sources for Digital Pulse Wave Analysis and Data for Further Research 

1. Millasseau, S. C., Kelly, R. P., Ritter, J. M., & Chowienczyk, P. J. (2002). Determination of age-related increases in large artery stiffness by digital pulse contour analysis. Clinical science (London, England: 1979), 103(4), 371–377. https://doi.org/10.1042/cs1030371
2. Kannel, W. B., Dawber, T. R., & McGee, D. L. (1980). Perspectives on systolic hypertension. The Framingham study. Circulation, 61(6), 1179–1182. https://doi.org/10.1161/01.cir.61.6.1179
3. Millasseau, S. C., Ritter, J. M., Takazawa, K., & Chowienczyk, P. J. (2006). Contour analysis of the photoplethysmographic pulse measured at the finger. Journal of hypertension, 24(8), 1449–1456. https://doi.org/10.1097/01.hjh.0000239277.05068.87
4. ​​ Jyotsna, M., Mahesh, A., Aditya, M., Mohan, P. R., & Naidu, M. U. R. (2008). Comparison of Invasive vs Noninvasive Pulse Wave Indices in Detection of Significant Coronary Artery Disease: Can We Use Noninvasive Pulse Wave Indices as Screening Test. Clinical Medicine. Cardiology, 2. https://doi.org/10.4137/CMC.S300
5. Otsuka, T., Kawada, T., Katsumata, M., Ibuki, C., & Kusama, Y. (2007). Independent determinants of second derivative of the finger photoplethysmogram among various cardiovascular risk factors in middle-aged men. Hypertension research: official journal of the Japanese Society of Hypertension, 30(12), 1211–1218. https://doi.org/10.1291/hypres.30.1211
6. Bortolotto, L. A., Blacher, J., Kondo, T., Takazawa, K., & Safar, M. E. (2000). Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. American journal of hypertension, 13(2), 165–171. https://doi.org/10.1016/s0895-7061(99)00192-2