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

Area of Science:

• Obesity research
• Behavioral science
• Data science

Background:

• Obesity treatments often lack long-term efficacy.
• Understanding predictors of weight change is crucial for effective obesity management.

Purpose of the Study:

• To test if weight variability during early weight loss predicts long-term weight management success.
• To investigate weight variability as an independent predictor of weight change.

Main Methods:

• Utilized data from 24,009 American users of Withings smart scales with over a year of weight data.
• Calculated weight variability using multilevel modeling of weekly average weights from the first 12 weeks of weight loss.
• Employed linear regressions to assess the predictive power of weight variability on weight change at 48, 72, and 96 weeks.

Main Results:

• Higher weight variability in the initial 12 weeks predicted less weight loss and more weight regain at 48, 72, and 96 weeks.
• These predictions held true even after controlling for baseline BMI and initial weight change.
• An interaction effect indicated that weight variability's impact on later weight change was more pronounced in individuals with lower baseline BMI.

Conclusions:

• Early weight variability is a significant predictor of long-term weight loss outcomes in a large population.
• Weight variability should be considered a critical factor in predicting future weight change.
• Further research is necessary to elucidate the underlying mechanisms driving this association.