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

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.