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Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study


Self-weighing may promote attainment and maintenance of healthy weight; however, the natural temporal patterns and factors associated with self-weighing behavior are unclear. The aims of this secondary analysis were to (1) identify distinct temporal patterns of self-weighing behaviors; (2) explore factors associated with temporal self-weighing patterns; and (3) examine differences in percent weight changes by patterns of self-weighing over time. We analyzed electronically collected self-weighing data from the Health eHeart Study, an ongoing longitudinal research study coordinated by the University of California, San Francisco. We selected participants with at least 12 months of data since the day of first use of a WiFi- or Bluetooth-enabled digital scale. The sample (N = 1041) was predominantly male (77.5%) and White (89.9%), with a mean age of 46.5 ± 12.3 years and a mean BMI of 28.3 ± 5.9 kg/m2 at entry. Using group-based trajectory modeling, six distinct temporal patterns of self-weighing were identified: non-users (n = 120, 11.5%), weekly users (n = 189, 18.2%), rapid decliners (n = 109, 10.5%), increasing users (n = 160, 15.4%), slow decliners (n = 182, 17.5%), and persistent daily users (n = 281, 27.0%). Individuals who were older, female, or self-weighed 6–7 days/week at week 1 were more likely to follow the self-weighing pattern of persistent daily users. Predicted self-weighing trajectory group membership was significantly associated with weight change over time (p < .001). In conclusion, we identified six distinct patterns of self-weighing behavior over the 12-month period. Persistent daily users lost more weight compared with groups with less frequent patterns of scale use.

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  • Brokaw, S. M., Carpenedo, D., Campbell, P., et al. (2015). Effectiveness of an adapted diabetes prevention program lifestyle intervention in older and younger adults. Journal of the American Geriatrics Society, 63, 1067–1074.

    Article  PubMed  Google Scholar 

  • Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111, 92–102.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cameron, E., Ward, P., Mandville-Anstey, S. A., & Coombs, A. (2018). The female aging body: A systematic review of female perspectives on aging, health, and body image. Journal of Women & Aging.

  • Carrard, I., & Kruseman, M. (2016). Qualitative analysis of the role of self-weighing as a strategy of weight control for weight-loss maintainers in comparison with a normal, stable weight group. Appetite, 105, 604–610.

    Article  PubMed  Google Scholar 

  • Diaz-Melean, C. M., Somers, V. K., Rodriguez-Escudero, J. P., et al. (2013). Mechanisms of adverse cardiometabolic consequences of obesity. Curr Atheroscler Rep., 15, 364.

    Article  CAS  PubMed  Google Scholar 

  • Dixit, S., Pletcher, M. J., Vittinghoff, E., et al. (2016). Secondhand smoke and atrial fibrillation: Data from the Health eHeart Study. Heart Rhythm, 13, 3–9.

    Article  PubMed  Google Scholar 

  • Helander, E. E., Vuorinen, A. L., Wansink, B., & Korhonen, I. K. (2014). Are breaks in daily self-weighing associated with weight gain? PLoS ONE, 9, e113164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jones, B. L., & Nagin, D. S. (2007). Advances in group-based trajectory modeling and an SAS procedure for estimating them. Sociological Methods & Research, 35, 542–571.

    Article  Google Scholar 

  • Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research, 29, 374–393.

    Article  Google Scholar 

  • Madigan, C. D., Daley, A. J., Lewis, A. L., Aveyard, P., & Jolly, K. (2015). Is self-weighing an effective tool for weight loss: A systematic literature review and meta-analysis. The International Journal of Behavioral Nutrition and Physical Activity, 12, 104.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nagin, D. S. (1999). Analyzing developmental trajectories: Semi-parametric, groupbased approach. Psychological Methods, 4, 139–157.

    Article  Google Scholar 

  • Nagin, D. S. (2005). Group-based modeling of development. Cambridge: Harvard University Press.

    Book  Google Scholar 

  • O’Neil, P. M., & Brown, J. D. (2005). Weighing the evidence: Benefits of regular weight monitoring for weight control. Journal of Nutrition Education and Behavior., 37, 319–322.

    Article  PubMed  Google Scholar 

  • Sereika, S. M., Zheng, Y., Hu, L., & Burke, L. E. (2017). Modern methods for modeling change in obesity research in nursing. Western Journal of Nursing Research.

  • Shieh, C., Knisely, M. R., Clark, D., & Carpenter, J. S. (2016). Self-weighing in weight management interventions: A systematic review of literature. Obesity Research & Clinical Practice., 10, 493–519.

    Article  Google Scholar 

  • Steinberg, D. M., Bennett, G. G., Askew, S., & Tate, D. F. (2015). Weighing every day matters: Daily weighing improves weight loss and adoption of weight control behaviors. Journal of the Academy of Nutrition and Dietetics., 115, 511–518.

    Article  PubMed  PubMed Central  Google Scholar 

  • Steinberg, D. M., Tate, D. F., Bennett, G. G., Ennett, S., Samuel-Hodge, C., & Ward, D. S. (2013). The efficacy of a daily self-weighing weight loss intervention using smart scales and e-mail. Obesity, 21, 1789–1797.

    Article  PubMed  Google Scholar 

  • Wilkinson, L., Pacanowski, C. R., & Levitsky, D. (2017). Three-year follow-up of participants from a self-weighing randomized controlled trial. Journal of Obesity, 2017, 4956326.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wing, R. R., Hamman, R. F., Bray, G. A., et al. (2004). Achieving weight and activity goals among diabetes prevention program lifestyle participants. Obesity Research, 12, 1426–1434.

    Article  PubMed  Google Scholar 

  • Wing, R. R., Tate, D. F., Gorin, A. A., Raynor, H. A., Fava, J. L., & Machan, J. (2007). STOP regain: are there negative effects of daily weighing? [Erratum appears in Journal of Consulting & Clinical Psychology 2007 75(5):715]. Journal of Consulting & Clinical Psychology, 75(4), 652–656.

  • Zheng, Y., Klem, M. L., Sereika, S. M., Danford, C. A., Ewing, L. J., & Burke, L. E. (2015). Self-weighing in weight management: A systematic literature review. Obesity, 23, 256–265.

    Article  PubMed  Google Scholar 

  • Zheng, Y., Sereika, S. M., Ewing, L. J., Danford, C. A., Terry, M. A., & Burke, L. E. (2016a). Association between self-weighing and percent weight change: Mediation effects of adherence to energy intake and expenditure goals. Journal of the Academy of Nutrition and Dietetics., 116, 660–666.

    Article  PubMed  Google Scholar 

  • Zheng, Y., Burke, L. E., Danford, C. A., Ewing, L. J., Terry, M. A., & Sereika, S. M. (2016b). Patterns of self-weighing behavior and weight change in a weight loss trial. International Journal of Obesity, 40, 1392–1396.

    Article  CAS  PubMed  Google Scholar 

  • Zheng, Y., Terry, M. A., Danford, C. A., et al. (2018). Experiences of daily weighing among successful weight loss individuals during a 12-month weight loss study. Western Journal of Nursing Research, 40, 462–480.

    Article  CAS  PubMed  Google Scholar 

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This study was funded by the National Institutes of Health [1U2CEB021881] and Salesforce Foundation.

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Correspondence to Yaguang Zheng.

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Yaguang Zheng, Susan M. Sereika, Lora E. Burke, Jeffrey E. Olgin, Gregory M. Marcus, Kirstin Aschbacher, Geoffrey H. Tison, Mark J. Pletcher declare that he/she has no conflict of interest.

Human and animal rights and Informed consent

All procedures performed in studies involving human participants were in accordance with the ethical standards of Institutional Review Board approval at UCSF and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This secondary analysis was also approved by the Institutional Review Board at Boston College. Informed consent was obtained from all individual participants included in the study. All participants provided remote, digital informed consent.

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Zheng, Y., Sereika, S.M., Burke, L.E. et al. Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study. J Behav Med 42, 873–882 (2019).

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  • Self-weighing
  • Weight change
  • Behavior changes
  • Temporal pattern