Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Behavior, Psychology and Sociology

Weight variability during self-monitored weight loss predicts future weight loss outcome

International Journal of Obesity volume 44, pages 1360–1367 (2020)Cite this article

Subjects

Abstract

Background

Obesity treatments often do not produce long-term results. It is therefore critical to better understand biological and behavioral correlates or predictors of future weight change.

Objective

We tested the hypothesis that greater weight variability, independent of total body weight change, during early weight loss would predict degree of long-term success.

Subjects/Methods

We included 24,009 American users of the Withings smart scale with over a year’s worth of self-monitored weight data. Multilevel modeling was used to calculate weight variability as the root mean square error around participants’ weight trajectory regression line, using weekly average weights from the first 12 weeks of weight loss. Linear regressions were then used to examine whether weight variability predicted weight change from week 12 to week 48, 72, and 96.

Results

Greater weight variability predicted less weight loss/more weight regain at week 48 (b ± SE: 1.18 ± 0.17, p < 0.001), week 72 (b ± SE: 1.45 ± 0.21, p < 0.001), and week 96 (b ± SE: 1.45 ± 0.23, p < 0.001), controlling for baseline BMI and overall weight change during the first 12 weeks. An interaction effect was found between weight variability and baseline BMI such that the relationship between weight variability and later weight change was stronger in individuals with lower baseline BMI.

Conclusions

This study found that in a large population sample, weight variability early on during weight loss significantly predicted longer term weight loss outcomes. The results provide further support that weight variability be considered an important predictor of future weight change. Research is needed to understand the mechanisms underlying this effect.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mean weight over time.
Fig. 2: Examples of individual regression curves used to calculate RMSE (weight variability), independent of weight trajectory.
Fig. 3: Baseline BMI significantly moderated the relationship between weight variability and future weight change from weeks 12 to 48.

Similar content being viewed by others

References

  1. Anastasiou CA, Karfopoulou E, Yannakoulia M. Weight regaining: From statistics and behaviors to physiology and metabolism. Metabolism. 2015;64:1395–407.

    CAS  PubMed  Google Scholar 

  2. Franz MJ, VanWormer JJ, Crain AL, Boucher JL, Histon T, Caplan W, et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc. 2007;107:1755–67.

    PubMed  Google Scholar 

  3. Lowe MR, Feig EH, Winter SR, Stice E. Short-term variability in body weight predicts long-term weight gain. Am J Clin Nutr. 2015;102:995–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Montani JP, Schutz Y, Dulloo AG. Dieting and weight cycling as risk factors for cardiometabolic diseases: who is really at risk? Obes Rev. 2015;16(Suppl 1):7–18.

    PubMed  Google Scholar 

  5. Lissner L, Odell PM, D’Agostino RB, Stokes J 3rd, Kreger BE, Belanger AJ, et al. Variability of body weight and health outcomes in the Framingham population. N Engl J Med. 1991;324:1839–44.

    CAS  PubMed  Google Scholar 

  6. Lowe MR. Dieting: proxy or cause of future weight gain? Obes Rev. 2015;16(Suppl 1):19–24.

    PubMed  Google Scholar 

  7. Winter SR, Yokum S, Stice E, Osipowicz K, Lowe MR. Elevated reward response to receipt of palatable food predicts future weight variability in healthy-weight adolescents. Am J Clin Nutr. 2017;105:781–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Tamakoshi K, Yatsuya H, Kondo T, Hirano T, Hori Y, Yoshida T, et al. The accuracy of long-term recall of past body weight in Japanese adult men. Int J Obes Relat Metab Disord. 2003;27:247–52.

    CAS  PubMed  Google Scholar 

  9. French SA, Jeffery RW, Folsom AR, Williamson DF, Byers T. Relation of weight variability and intentionality of weight loss to disease history and health-related variables in a population-based sample of women aged 55–69 years. Am J Epidemiol. 1995;142:1306–14.

    CAS  PubMed  Google Scholar 

  10. French SA, Folsom AR, Jeffery RW, Zheng W, Mink PJ, Baxter JE. Weight variability and incident disease in older women: the Iowa Women’s Health Study. Int J Obes Relat Metab Disord. 1997;21:217–23.

    CAS  PubMed  Google Scholar 

  11. Lissner L, Andres R, Muller DC, Shimokata H. Body weight variability in men: metabolic rate, health and longevity. Int J Obes. 1990;14:373–83.

    CAS  PubMed  Google Scholar 

  12. Feig EH, Lowe MR. Variability in Weight Change Early in Behavioral Weight Loss Treatment: Theoretical and Clinical Implications. Obesity. 2017;25:1509–15.

    PubMed  Google Scholar 

  13. Lowe MR, Butryn ML, Zhang F. Evaluation of meal replacements and a home food environment intervention for long-term weight loss: a randomized controlled trial. Am J Clin Nutr. 2018;107:12–9.

    PubMed  PubMed Central  Google Scholar 

  14. Eldredge KL, Agras WS, Arnow B. The last supper: emotional determinants of pretreatment weight fluctuation in obese binge eaters. Int J Eat Disord. 1994;16:83–8.

    CAS  PubMed  Google Scholar 

  15. Kroke A, Liese AD, Schulz M, Bergmann MM, Klipstein-Grobusch K, Hoffmann K, et al. Recent weight changes and weight cycling as predictors of subsequent two year weight change in a middle-aged cohort. Int J Obes Relat Metab Disord. 2002;26:403–9.

    CAS  PubMed  Google Scholar 

  16. Lowe MR, Doshi SD, Katterman SN, Feig EH. Dieting and restrained eating as prospective predictors of weight gain. Front Psychol. 2013;4:577.

    PubMed  PubMed Central  Google Scholar 

  17. Stubbs J, Whybrow S, Teixeira P, Blundell J, Lawton C, Westenhoefer J, et al. Problems in identifying predictors and correlates of weight loss and maintenance: implications for weight control therapies based on behaviour change. Obes Rev. 2011;12:688–708.

    CAS  PubMed  Google Scholar 

  18. Elfhag K, Rossner S. Initial weight loss is the best predictor for success in obesity treatment and sociodemographic liabilities increase risk for drop-out. Patient Educ Couns. 2010;79:361–6.

    PubMed  Google Scholar 

  19. Astrup A, Rossner S. Lessons from obesity management programmes: greater initial weight loss improves long-term maintenance. Obes Rev. 2000;1:17–9.

    CAS  PubMed  Google Scholar 

  20. Bangalore S, Fayyad R, Laskey R, DeMicco DA, Messerli FH, Waters DD. Body-Weight Fluctuations and Outcomes in Coronary Disease. N Engl J Med. 2017;376:1332–40.

    PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the Withings company, employees and users for sharing the data with us.

Disclosure

Dr. Wilkinson is an employee of Novo Nordisk Inc.

Author information

Authors and Affiliations

  1. Department of Psychology, Drexel University, Philadelphia, PA, USA

    Leora Benson, Fengqing Zhang, Hallie Espel-Huynh & Michael R. Lowe

  2. Novo Nordisk Inc., 800 Scudders Mill Road, Plainsboro, NJ, 08536, USA

    Lua Wilkinson

Authors
  1. Leora Benson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fengqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hallie Espel-Huynh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lua Wilkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael R. Lowe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leora Benson.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benson, L., Zhang, F., Espel-Huynh, H. et al. Weight variability during self-monitored weight loss predicts future weight loss outcome. Int J Obes 44, 1360–1367 (2020). https://doi.org/10.1038/s41366-020-0534-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-020-0534-6

This article is cited by

Search

Advanced search

Quick links