year 7, Issue 4 (Winter 2019)
Ann Appl Sport Sci 2019, 7(4): 61-71 |
Back to browse issues page
Vitaliy Epishev1 
, Anna Nenasheva * 2, Yulia Korableva3 , Alexander Belenkov1 , Alina Episheva1 , Seyed Morteza Tayebi4
, Anna Nenasheva * 2, Yulia Korableva3 , Alexander Belenkov1 , Alina Episheva1 , Seyed Morteza Tayebi4
1- Theory and Methods of Physical Education and Sport Department, the Institute of Sports, Tourism and Service, South Ural State University (National Research University), Chelyabinsk, Russia
2- Theory and Methods of Physical Education and Sport Department, the Institute of Sports, Tourism and Service, South Ural State University (National Research University), Chelyabinsk, Russia , isaeva-susu@yandex.ru
3- Sports Science Research Centre, the Institute of Sports, Tourism and Service, South Ural State University (National Research University), Chelyabinsk, Russia
4- Core Research of Health Physiology and Physical Activity, Department of Exercise Physiology, Faculty of Sport Science, Allameh Tabataba'i University, Tehran, Iran
2- Theory and Methods of Physical Education and Sport Department, the Institute of Sports, Tourism and Service, South Ural State University (National Research University), Chelyabinsk, Russia , isaeva-susu@yandex.ru
3- Sports Science Research Centre, the Institute of Sports, Tourism and Service, South Ural State University (National Research University), Chelyabinsk, Russia
4- Core Research of Health Physiology and Physical Activity, Department of Exercise Physiology, Faculty of Sport Science, Allameh Tabataba'i University, Tehran, Iran
Abstract: (3893 Views)
Background. Skin temperature is an important indicator of the functional status of the body. Infrared thermal images of the body surface or its separate parts could be the indicator of body composition and, probably, the criterion of the functional activity of muscles.
Objectives. This study aims to find a correlation between the average values of skin temperature in different parts of the body and the components of body composition in young women with low values of adipose tissue (FAT % = 20.73±5.50; BMI = 20.23±2.44).
Methods. The study involved 69 healthy women aged 18-20 (BMI = 20.23±2.44). Participants were subjected to a 15-minute temperature adaptation in the room with a temperature of 22–24°С and humidity of 45-50%. We measured body composition using bioelectrical impedance analysis and taking eight photos of different body areas with the help of the thermal infrared camera.
Results. Correlation analysis allowed us to reveal temperature correlations with BMI and FAT%. The most significant values were registered between FAT% and tmean (r=-0.36), FAT% and tshinBL (r=-0.39), FAT% and tshin BR (r=-0.38).
Conclusion. To forecast FAT% for this sample, the skin temperature of the shin is the most informative parameter (decrease by 1% results in the increase in FAT% by 1-1.5%). Moreover, we made a hypothesis that the differences between tmean of shins and hips indicate the postural balance: tshinmean ˃ t hipmean is responsible for the shin strategy; tshinmean ˂ thipmean indicates the hip strategy.
Objectives. This study aims to find a correlation between the average values of skin temperature in different parts of the body and the components of body composition in young women with low values of adipose tissue (FAT % = 20.73±5.50; BMI = 20.23±2.44).
Methods. The study involved 69 healthy women aged 18-20 (BMI = 20.23±2.44). Participants were subjected to a 15-minute temperature adaptation in the room with a temperature of 22–24°С and humidity of 45-50%. We measured body composition using bioelectrical impedance analysis and taking eight photos of different body areas with the help of the thermal infrared camera.
Results. Correlation analysis allowed us to reveal temperature correlations with BMI and FAT%. The most significant values were registered between FAT% and tmean (r=-0.36), FAT% and tshinBL (r=-0.39), FAT% and tshin BR (r=-0.38).
Conclusion. To forecast FAT% for this sample, the skin temperature of the shin is the most informative parameter (decrease by 1% results in the increase in FAT% by 1-1.5%). Moreover, we made a hypothesis that the differences between tmean of shins and hips indicate the postural balance: tshinmean ˃ t hipmean is responsible for the shin strategy; tshinmean ˂ thipmean indicates the hip strategy.
Keywords: Young Women, Body Composition, Skin Temperature, Temperature Distribution, Thermal Imaging
Full-Text [PDF 749 kb]
(809 Downloads)
APPLICABLE REMARKS
APPLICABLE REMARKS
- The authors suggest that the correlations established between FAT and skin temperature, combined with the results of further studies, will allow developing a brand-new algorithm and state-of-the-art equipment for monitoring human health with IR thermography.
- In particular, the peculiarities revealed in the range of hip and shin temperatures can be used as criteria for diagnosing, for example, flat feet or varicose veins.
- A wide range of temperatures can also be a sign of neurologic, CNS-related, endocrine, and metabolic disorders.
- Early diagnostics of skin temperature with IR thermography can be used as an additional diagnostic tool in orthopedics, nutrition science, and endocrinology.
- In particular, the peculiarities revealed in the range of hip and shin temperatures can be used as criteria for diagnosing, for example, flat feet or varicose veins.
- A wide range of temperatures can also be a sign of neurologic, CNS-related, endocrine, and metabolic disorders.
- Early diagnostics of skin temperature with IR thermography can be used as an additional diagnostic tool in orthopedics, nutrition science, and endocrinology.
Type of Study: Original Article |
Subject:
Exercise, Training and Health
Received: 2019/06/19 | Accepted: 2019/08/23
Received: 2019/06/19 | Accepted: 2019/08/23
References
1. Frim J, Livingstone SD, Reed LD, Nolan RW, Limmer RE. Body composition and skin temperature variation. J Appl Physiol (1985). 1990;68(2):540-3. [DOI:10.1152/jappl.1990.68.2.540] [PMID]
2. Liang MT, Su HF, Lee NY. Skin temperature and skin blood flow affect bioelectric impedance study of female fat-free mass. Med Sci Sports Exerc. 2000;32(1):221-7. [DOI:10.1097/00005768-200001000-00033] [PMID]
3. Prisby R, Glickman-Weiss EL, Nelson AG, Caine N. Thermal and metabolic responses of high and low fat women to cold water immersion. Aviat Space Environ Med. 1999;70(9):887-91.
4. Mozaffarieh M, Fontana Gasio P, Schotzau A, Orgul S, Flammer J, Krauchi K. Thermal discomfort with cold extremities in relation to age, gender, and body mass index in a random sample of a Swiss urban population. Popul Health Metr. 2010;8:17. [DOI:10.1186/1478-7954-8-17] [PMID] [PMCID]
5. Costa CMA, Moreira DG, Sillero-Quintana M, Brito CJ, de Azambuja Pussieldi G, de Andrade Fernandes A, et al. Daily rhythm of skin temperature of women evaluated by infrared thermal imaging. J Therm Biol. 2018;72:1-9. [DOI:10.1016/j.jtherbio.2017.12.002] [PMID]
6. Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr. 2010;29(5):560-6. [DOI:10.1016/j.clnu.2009.12.011] [PMID]
7. Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D. Limits of body mass index to detect obesity and predict body composition. Nutrition. 2001;17(1):26-30. [DOI:10.1016/S0899-9007(00)00471-8]
8. Hung SP, Chen CY, Guo FR, Chang CI, Jan CF. Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults. Obes Res Clin Pract. 2017;11(1):11-8. [DOI:10.1016/j.orcp.2016.02.005] [PMID]
9. Choi JK, Miki K, Sagawa S, Shiraki K. Evaluation of mean skin temperature formulas by infrared thermography. Int J Biometeorol. 1997;41(2):68-75. [DOI:10.1007/s004840050056] [PMID]
10. Fernández-Cuevas I, Bouzas Marins JC, Arnáiz Lastras J, Gómez Carmona PM, Piñonosa Cano S, García-Concepción MÁ, et al. Classification of factors influencing the use of infrared thermography in humans: A review. Infrared Phys Technol. 2015;71:28-55. [DOI:10.1016/j.infrared.2015.02.007]
11. Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: A review. Infrared Phys Technol. 2012;55(4):221-35. [DOI:10.1016/j.infrared.2012.03.007]
12. Ludwig N, Formenti D, Gargano M, Alberti G. Skin temperature evaluation by infrared thermography: Comparison of image analysis methods. Infrared Phys Technol. 2014;62:1-6. [DOI:10.1016/j.infrared.2013.09.011]
13. Kotler DP, Burastero S, Wang J, Pierson RN, Jr. Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease. Am J Clin Nutr. 1996;64(3 Suppl):489S-97S. [DOI:10.1093/ajcn/64.3.489S] [PMID]
14. Shafer KJ, Siders WA, Johnson LK, Lukaski HC. Validity of segmental multiple-frequency bioelectrical impedance analysis to estimate body composition of adults across a range of body mass indexes. Nutrition. 2009;25(1):25-32. [DOI:10.1016/j.nut.2008.07.004] [PMID]
15. Ellis KJ, Bell SJ, Chertow GM, Chumlea WC, Knox TA, Kotler DP, et al. Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference. Nutrition. 1999;15(11-12):874-80. [DOI:10.1016/S0899-9007(99)00147-1]
16. Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG, et al. Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr. 2005;81(1):74-8.
https://doi.org/10.1093/ajcn/86.1.74 [DOI:10.1093/ajcn/81.1.74]
17. Barnes RB. Thermography of the human body. Science. 1963;140(3569):870-7. [DOI:10.1126/science.140.3569.870] [PMID]
18. Hayward MG, Keatinge WR. Roles of subcutaneous fat and thermoregulatory reflexes in determining ability to stabilize body temperature in water. J Physiol. 1981;320:229-51. [DOI:10.1113/jphysiol.1981.sp013946] [PMID] [PMCID]
19. Marins JC, Fernandes AA, Cano SP, Moreira DG, da Silva FS, Costa CM, et al. Thermal body patterns for healthy Brazilian adults (male and female). J Therm Biol. 2014;42:1-8. [DOI:10.1016/j.jtherbio.2014.02.020] [PMID]
20. Leblanc J. Subcutaneous fat and skin temperature. Can J Biochem Physiol. 1954;32(4):354-8.
https://doi.org/10.1139/y54-038 [DOI:10.1139/o54-038] [PMID]
21. Savastano DM, Gorbach AM, Eden HS, Brady SM, Reynolds JC, Yanovski JA. Adiposity and human regional body temperature. Am J Clin Nutr. 2009;90(5):1124-31. [DOI:10.3945/ajcn.2009.27567] [PMID] [PMCID]
22. Chudecka M, Lubkowska A, Kempinska-Podhorodecka A. Body surface temperature distribution in relation to body composition in obese women. J Therm Biol. 2014;43:1-6. [DOI:10.1016/j.jtherbio.2014.03.001] [PMID]
23. Chudecka M, Lubkowska A. Thermal maps of young women and men. Infrared Phys Technol. 2015;69:81-7. [DOI:10.1016/j.infrared.2015.01.012]
24. Neves EB, Salamunes ACC, de Oliveira RM, Stadnik AMW. Effect of body fat and gender on body temperature distribution. J Therm Biol. 2017;70(Pt B):1-8. [DOI:10.1016/j.jtherbio.2017.10.017] [PMID]
25. Fryar CD, Gu Q, Ogden CL. Anthropometric reference data for children and adults; United States, 2007-2010. Vital Health Stat. 2012;11:252-7.
26. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000;72(3):694-701. [DOI:10.1093/ajcn/72.3.694] [PMID]
27. Balaz D, Komornikova A, Kruzliak P, Sabaka P, Gaspar L, Zulli A, et al. Regional differences of vasodilatation and vasomotion response to local heating in human cutaneous microcirculation. Vasa. 2015;44(6):458-65. [DOI:10.1024/0301-1526/a000469] [PMID]
28. Staffa E, Bernard V, Kubicek L, Vlachovsky R, Vlk D, Mornstein V, et al. Infrared thermography as option for evaluating the treatment effect of percutaneous transluminal angioplasty by patients with peripheral arterial disease. Vascular. 2017;25(1):42-9. [DOI:10.1177/1708538116640444] [PMID]
29. Zemkova E, Hamar D. Physiological mechanisms of post-exercise balance impairment. Sports Med. 2014;44(4):437-48. [DOI:10.1007/s40279-013-0129-7] [PMID]
30. Montgomery RE, Hartley GL, Tyler CJ, Cheung SS. Effect of segmental, localized lower limb cooling on dynamic balance. Med Sci Sports Exerc. 2015;47(1):66-73. [DOI:10.1249/MSS.0000000000000379] [PMID]
31. Cieslinska-Swider J, Furmanek MP, Blaszczyk JW. The influence of adipose tissue location on postural control. J Biomech. 2017;60:162-9. [DOI:10.1016/j.jbiomech.2017.06.027] [PMID]
32. Rodriguez-Sanz D, Losa-Iglesias ME, Lopez-Lopez D, Calvo-Lobo C, Palomo-Lopez P, Becerro-de-Bengoa-Vallejo R. Infrared thermography applied to lower limb muscles in elite soccer players with functional ankle equinus and non-equinus condition. PeerJ. 2017;5:e3388. [DOI:10.7717/peerj.3388] [PMID] [PMCID]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |