Comparison of mobile application and bioelectrical impedance analysis in body composition measurement
Introduction: Smartphone-based anthropometry offers a low-cost, high-throughput approach to body composition assessment, but agreement with established field methods requires careful evaluation.
Objectives: We assessed agreement between an image-based mobile application (LeanScreen within PostureScreen Mobile) and a multi-frequency, segmental bioelectrical impedance analyzer (TANITA MC‑180) under standardized pre-test conditions in sedentary young adults.
Methods: In a cross-sectional study, 40 participants (57.5% women; aged 18–30 years) underwent same-day assessments by both methods following strict pre-analytic controls. Outcomes assessed included waist-to-hip ratio (WHR), body fat percentage (%BF), fat-free percentage (%FF), fat body mass (FBM), fat-free mass (FFM), and basal metabolic rate (BMR). Agreement was evaluated using paired t-tests, Pearson correlations (95% confidence intervals), Bland–Altman analyses, and intraclass correlation coefficients (ICCs) with a two-way mixed-effects absolute agreement model.
Results: Mean paired differences were small and not statistically significant for all outcomes. Linear associations were very high (r = 0.943–0.995; p < 0.001), with the highest for BMR (r = 0.995) and FFM (r = 0.993). Bland–Altman analyses showed small mean biases and clinically acceptable limits of agreement (LoA) for WHR, %BF, %FF, FBM, and FFM, with no evidence of proportional bias. For BMR, bias was near-zero with acceptable LoA (−74.1 to 72.7 kcal/day), but a small proportional bias was detected (β1 = 0.068; p < 0.001). Absolute agreement was excellent, as indicated by the ICC (3,1) values: BMR (0.993), FFM (0.990), FBM (0.989), %FF (0.967), %BF (0.967), and WHR (0.938).
Conclusion: Under controlled conditions, LeanScreen and BIA TANITA showed high agreement for body composition estimates, supporting their use for population-level comparisons and screening. For individual-level decisions near clinical cut points, corroboration with repeated measures or a reference method is advisable. These findings support protocolized smartphone anthropometry as a practical, field-based approach for body composition assessment in young adults, while criterion-validation studies remain necessary to determine absolute accuracy.
- The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. New Engl J Med. 2017;377(1):13-27. doi: 10.1056/NEJMoa1614362
- Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr. 2004;23(5):1226-1243. doi: 10.1016/j.clnu.2004.06.004
- Riebe D, Ehrman JK, Liguori G, Magal M. ACSM’s guidelines for exercise testing and prescription. American College of Sports Medicine; 2018.
- Wells J, Fewtrell M. Measuring body composition. Arch Dis Child. 2006;91(7):612-617. doi: 10.1136/adc.2005.085522
- MacDonald EZ. Validity and Reliability of a Photographic Method of Assessing Body Composition. Brigham Young University; 2016.
- Marx R, Porcari JP, Doberstein S, Mikat R, Ryskey A, Foster C. Ability of the LeanScreen App to Accurately Assess Body Composition. Med Sci Sports Exerc. 2018;50(5S):159. doi: 10.1249/01.mss.0000535610.04677.17
- Shaw MP, Robinson J, Peart DJ. Comparison of a mobile application to estimate percentage body fat to other non-laboratory based measurements. Biomed Hum Kinet. 2017;9(1):94-98. doi: 10.1515/bhk-2017-0014
- Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis—part II: utilization in clinical practice. Clin Nutr. 2004;23(6):1430-1453. doi: 10.1016/j.clnu.2004.09.012
- Pietrobelli A, Rubiano F, St-Onge M, Heymsfield S. New bioimpedance analysis system: improved phenotyping with whole-body analysis. Eur J Clin Nutr. 2004;58(11):1479-1484. doi: 10.1038/sj.ejcn.1601993
- Ling CH, de Craen AJ, Slagboom PE, et al. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr. 2011;30(5):610-615. doi: 10.1016/j.clnu.2011.04.001
- Dixon CB, Deitrick RW, Pierce JR, Cutrufello PT, Drapeau LL. Evaluation of the BOD POD and leg-to-leg bioelectrical impedance analysis for estimating percent body fat in National Collegiate Athletic Association Division III collegiate wrestlers. J Strength Cond Res. 2005;19(1):85-91. doi: 10.1519/14053.1
- Saglam M, Arikan H, Savci S, et al. International physical activity questionnaire: reliability and validity of the Turkish version. Percept Mot Ski. 2010;111(1):278-284. doi: 10.2466/06.08.PMS.111.4.278-284
- Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381-1395. doi: 10.1249/01.MSS.0000078924.61453.FB
- Franco-Villoria M, Wright CM, McColl JH, et al. Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values. BMJ Open. 2016;6(1):e008922. doi: 10.1136/bmjopen-2015-008922
- Marx R, Porcari JP, Doberstein S, Mikat R, Foster C. Can the LeanScreen App Accurately Assess Percent Body Fat and Waist-to-Hip Ratio? ACE. 2018;1-6.
- Farina GL, Spataro F, De Lorenzo A, Lukaski H. A smartphone application for personal assessments of body composition and phenotyping. Sensors. 2016;16(12):2163. doi: 10.3390/s16122163
- Foysal KH, Chang H-J, Bruess F, Chong J-W. Body size measurement using a smartphone. Electronics. 2021;10(11):1338. doi: 10.3390/electronics10111338
- Arlindo de Sousa C, de Macedo B, Coutinho de Azevedo L, et al. Bioelectrical impedance analysis and skinfold thickness for the estimation of body fat: a population-based study in southern Brazil. Rev Assoc Médica Bras. 2025;71(1):e20240406. doi: 10.1590/1806-9282.20240406
- Nana A, Staynor J, Arlai S, El-Sallam A, Dhungel N, Smith M. Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods. Obes Res Clin Pract. 2022;16(1):37-43. doi: 10.1016/j.orcp.2021.12.006
- MacDonald EZ, Vehrs PR, Fellingham GW, Eggett D, George JD, Hager R. Validity and reliability of assessing body composition using a mobile application. Med Sci Sports Exerc. 2017;49(12):2593-2599. doi: 10.1249/MSS.0000000000001378
- Neufeld EV, Seltzer RA, Sazzad T, Dolezal BA. A multidomain approach to assessing the convergent and concurrent validity of a mobile application when compared to conventional methods of determining body composition. Sensors. 2020;20(21):6165. doi: 10.3390/s20216165
- Farina GL, Orlandi C, Lukaski H, Nescolarde L. Digital single-image smartphone assessment of total body fat and abdominal fat using machine learning. Sensors. 2022;22(21):8365. doi: 10.3390/s22218365
- Majmudar MD, Chandra S, Yakkala K, et al. Smartphone camera based assessment of adiposity: a validation study. NPJ Digit Med. 2022;5(1):79. doi: 10.1038/s41746-022-00628-3
- Jacobs JV, Horak F. Cortical control of postural responses. J Neural Transm. 2007;114(10):1339-1348. doi: 10.1007/s00702-007-0657-0
- Ostojic S. Estimation of body fat in athletes: skinfolds vs bioelectrical impedance. J Sports Med Phys Fit. 2006;46(3):442-446.