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Validity and reliability of a Wi-Fi smart scale to estimate body composition


With patients depending on ‘at-home’ devices to measure or monitor body composition changes to determine health- risks, there is the need for critical evaluation of these instruments. The purpose of this study was to determine the validity (i.e., accuracy) and reliability (i.e., consistency) of body fat percentage (BF%) estimates using the consumer Fitbit Aria™ foot-to-foot bioelectrical impedance analysis (BIA) Wi-Fi smart scale. Forty-three healthy volunteers [male (n = 22), female (n = 21); mean ± SD, age: 27.9 ± 5.6y; BMI: 23.7 ± 3.3 kg/m2] underwent measures of residual lung volume, hydration status, and BF% via the Aria™ smart scale [‘Regular’ (AR) and ‘Lean’ (AL) modes] vs. [hydrostatic weighing (HW)] on three separate days. Aria™ validity was assessed using Bland-Altman plots identifying mean biases and limits of agreement [mean difference (Aria–HW) ± 1.96SD] and between-day and -week reliability using two-way mixed, average measures absolute agreement intraclass correlation coefficients (ICC). Standard error of estimate (SEE) could not exceed ±3.5% for acceptable non-research agreement between methods. There were no significant differences between HW compared with AR for all participants (−0.3 ± 9.7%), females (1.1 ± 11.3%), and males (−1.6 ± 7.1%). AL also agreed with HW for females (−1.9 ± 8.6%), but significantly underestimated BF% for all participants and males when analyzed separately (p ≤ 0.05). However, in each measure of validity the SEE fell outside ±3.5%, suggesting BF% measurements from HW and this smart scale cannot be used interchangeably. While not accurate for all individuals, the Aria™ Wi-Fi smart scale is a reliable device to measure BF% over time. Health care professionals may consider recommending this technology to empower patients who want to monitor their body composition at home.

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  1. 1.

    Hruby A, Manson JE, Qi L, Malik VS, Rimm EB, Sun Q, et al. Determinants and Consequences of Obesity. Am J Public Health. 2016;106(9):1656–62.

    Article  Google Scholar 

  2. 2.

    Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr. 1999;69(3):373–80.

    Article  Google Scholar 

  3. 3.

    Smith-Ryan AE, Mock MG, Ryan ED, Gerstner GR, Trexler ET, Hirsch KR. Validity and reliability of a 4- compartment body composition model using dual energy x-ray absorptiometry-derived body volume. Clin Nutr. 2017;36(3):825–30.

    Article  Google Scholar 

  4. 4.

    Pietrobelli A, Sb H, Zm W, Gallagher D. Multi-component body composition models: recent advances and future directions. Eur J Clin Nutr. 2001;55(2):69.

    Article  Google Scholar 

  5. 5.

    Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act. 2016;13(1):127.

    Article  Google Scholar 

  6. 6.

    Stolarczyk L, Heyward V, Van Loan M, Hicks V. The fatness-specific bioelectrical impedance analysis equations of Segal et al: Are they generalizable and practical? Am J Clin Nutr. 1997;66(1):8–17.

    Article  Google Scholar 

  7. 7.

    Fitbit Inc. Accessed March 31 2018.

  8. 8.

    American College of Sports Medicine. ACSM's guidelines for exercise testing and prescription. Tenth ed. Philadelphia: Wolters Kluwer; 2018.

  9. 9.

    Gleichauf C, Roe D. The menstrual cycle’s effect on the reliability of bioimpedance measurements for assessing body composition. Am J Clin Nutr. 1989;50:903–7.

    Article  Google Scholar 

  10. 10.

    Moon RJ, Stout RJ, Walter AA, Smith EA, Stock SM, Herda JT, et al. Mechanical Scale and Load Cell Underwater Weighing: A Comparison of Simultaneous Measurements and the Reliability of Methods. J Strength Cond Res. 2011;25(3):652–61.

    Article  Google Scholar 

  11. 11.

    Heyward VH, Wagner DR. Applied body composition assessment. 2nd ed. Champaign: Human Kinetics; 2004.

    Google Scholar 

  12. 12.

    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47(8):931–6.

    Article  Google Scholar 

  13. 13.

    Atkinson G, Nevill AM. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med. 1998;26(4):217–38.

    Article  Google Scholar 

  14. 14.

    Weir PJ. Quantifying Test-Retest Reliability Using the Intraclass Correlation Coefficient and the SEM. J Strength Cond Res. 2005;19(1):231–40.

    Article  Google Scholar 

  15. 15.

    Dittmar M. Comparison of bipolar and tetrapolar impedance techniques for assessing fat mass. Am J Hum Biol. 2004;16(5):593–7.

    Article  Google Scholar 

  16. 16.

    Xie X, Kolthoff N, Bärenholt O, Sp N. Validation of a leg-to-leg bioimpedance analysis system in assessing body composition in postmenopausal women. Int J Obes. 1999;23(10):1079.

    Article  Google Scholar 

  17. 17.

    Swartz AM, Evans MJ, King GA, Thompson DL. Evaluation of a foot-to-foot bioelectrical impedance analysis analyzer in highly active, moderately active, and less active young men. Br J Nutr. 2002;88:205–10.

    Article  Google Scholar 

  18. 18.

    Nuñez C, Gallagher D, Visser M, Pi-Sunyer FX, Wang Z, Heymsfield SB. Bioimpedance analysis: evaluation of leg-to-leg system based on pressure contact footpad electrodes. Med Sci Sports Exerc. 1997;29(4):524–31, %@ 0195-9131.

    Article  Google Scholar 

  19. 19.

    Utter AC, Nieman DC, Ward AN, Butterworth DE. Use of the leg-to-leg bioelectrical impedance method in assessing body-composition change in obese women. Am J Clin Nutr. 1999;69(4):603–7.

    Article  Google Scholar 

  20. 20.

    Kasvis P, Cohen TR, Loiselle S-È, Kim N, Hazell TJ, Vanstone CA, et al. Foot-to-foot bioelectrical impedance accurately tracks direction of adiposity change in overweight and obese 7- to 13-year-old children. Nutr Res. 2015;35(3):206–13.

    Article  Google Scholar 

  21. 21.

    Yee AJ, Fuerst T, Salamone L, Visser M, Dockrell M, Van Loan M, et al. Calibration and validation of an air- displacement plethysmography method for estimating percentage body fat in an elderly population: a comparison among compartmental models. Am J Clin Nutr. 2001;74(5):637–42.

    Article  Google Scholar 

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The authors would like to thank the participants for volunteering their time; Kendrick Uong for helping with data collection; Dulce Gomez for helping coordinate facility access and participant scheduling; Dr. Kent Lorenz for statistical guidance; Greg Strom for technical assistance with Aria™ set up; and Morgan Fine for assistance piloting the protocol.

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Correspondence to James R. Bagley.

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The authors declare no conflict of interest.

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All procedures were done in accordance with the ethical standards of the San Francisco State University Institution Review Board (IRB) and with the Helsinki Declaration of 1975, as revised in 2000 and 2008.

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Informed consent was obtained from all individual participants included in the study.

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Hood, K.M., Marr, C., Kirk-Sorrow, J. et al. Validity and reliability of a Wi-Fi smart scale to estimate body composition. Health Technol. 9, 839–846 (2019).

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  • Bioelectrical impedance analysis
  • Body fat
  • Hydrodensitometry
  • Obesity
  • Overweight