Variability in Weight Change Early in Behavioral Weight Loss Treatment: Theoretical and Clinical Implications

Emily H Feig, Michael R Lowe, Emily H Feig, Michael R Lowe

Abstract

Objective: Response early in weight loss treatment predicts long-term weight change. Weight variability, independent of absolute early weight change, may also relate to long-term outcomes. This study examined whether weight variability early in treatment predicted later weight loss and maintenance.

Methods: Participants were 183 completers of a yearlong behavioral weight loss program (mean age = 51, 81% female, 69% white, mean BMI = 35 kg/m2 ). Weight variability was calculated using weights from the first 6 and 12 weekly treatment sessions. Multiple linear regressions examined whether weight variability predicted subsequent weight change 6, 12, and 24 months later.

Results: Weight variability over 6- and 12-week periods predicted less subsequent weight loss at 12 months (6-week: β = 0.18, P = 0.02; 12-week: β = 0.33, P < 0.01) and 24 months (6-week: β = 0.17, P = 0.03; 12-week: β = 0.15, P = 0.05). Relationships held when adjusting for covariates. Weight variability was more strongly associated with 6-month weight change in men than women (β = 0.27, P = 0.01).

Conclusions: Elevated weight variability early in a weight loss program predicted poor long-term outcomes, possibly reflecting inconsistent weight control behaviors. Tracking weight variability could prove useful for improving treatment outcomes.

Trial registration: ClinicalTrials.gov NCT01065974.

Conflict of interest statement

Disclosure: MRL receives compensation from funded users of the Power of Food Scale. EHF declared no conflict of interest.

© 2017 The Obesity Society.

Figures

Fig. 1
Fig. 1
Weekly weights for a participant with a low (left; root mean square error = 0.72) and high (right; root mean square error = 2.85) 12-week weight variability score. The x-axis represents weeks since the start of treatment and the y-axis represents weight in pounds. The weight variability score indicates the mean distance between each data point and the regression line for that individual, so higher weight variability score indicates a larger spread of individual data points around the best fitting line.
Fig. 2
Fig. 2
Scatterplots of weight variability (x-axis) and subsequent percent weight change (y-axis). Higher weight variability was associated with less weight loss. Top: 6-week weight variability; Bottom: 12-week weight variability; Left: percent weight change at 12 months; Right: percent weight change at 24 months.

References

    1. Wadden TA, West DS, Neiberg RH, et al. One-year weight losses in the Look AHEAD study: factors associated with success. Obesity (Silver Spring) 2009;17(4):713–722. doi: 10.1038/oby.2008.637.
    1. Barte JCM, ter Bogt NCW, Bogers RP, et al. Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review. Obes Rev. 2010;11(12):899–906. doi: 10.1111/j.1467-789X.2010.00740.x.
    1. Unick JL, Hogan PE, Neiberg RH, et al. Evaluation of early weight loss thresholds for identifying nonresponders to an intensive lifestyle intervention. Obesity. 2014;22(7):1608–1616. doi: 10.1002/oby.20777.
    1. Carels Ra, Cacciapaglia HM, Douglass OM, Rydin S, O’Brien WH. The early identification of poor treatment outcome in a women’s weight loss program. Eat Behav. 2003;4(3):265–282. doi: 10.1016/S1471-0153(03)00029-1.
    1. Tanaka M, Itoh K, Abe S, et al. Irregular Patterns in the Daily Weight Chart at Night Predict Body Weight Regain. Exp Biol Med. 2004;229(9):940–945.
    1. Orsama AL, Mattila E, Ermes M, Van Gils M, Wansink B, Korhonen I. Weight rhythms: Weight increases during weekends and decreases during weekdays. Obes Facts. 2014;7(1):36–47. doi: 10.1159/000356147.
    1. Gorin aa, Phelan S, Wing RR, Hill JO. Promoting long-term weight control: does dieting consistency matter? Int J Obes Relat Metab Disord. 2004;28(2):278–281. doi: 10.1038/sj.ijo.0802550.
    1. Brownell KD. LEARN Program for Weight Management 2000. American Health; 2000.
    1. 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(5):995–999.
    1. Johnson F, Pratt M, Wardle J. Dietary restraint and self-regulation in eating behavior. Int J Obes (Lond) 2012;36(5):665–674. doi: 10.1038/ijo.2011.156.
    1. Klein S, Sheard NF, Pi-Sunyer X, et al. Weight Management Through Lifestyle Modification for the Prevention and Management of Type 2 Diabetes: Rationale and Strategies. Diabetes Care. 2004;27(8):2067–2073. doi: 10.2337/diacare.27.8.2067.
    1. Nijs IMT, Franken IHA, Muris P. The modified Trait and State Food-Cravings Questionnaires: Development and validation of a general index of food craving. Appetite. 2007;49(1):38–46. doi: 10.1016/j.appet.2006.11.001.
    1. Lowe MR, Butryn ML, Didie ER, et al. The Power of Food Scale. A new measure of the psychological influence of the food environment. Appetite. 2009;53(1):114–118. doi: 10.1016/j.appet.2009.05.016.
    1. Gormally J, Black S, Daston S, Rardin D. The assessment of binge eating severity among obese persons. Addict Behav. 1982;7(1):47–55. doi: 10.1016/0306-4603(82)90024-7.
    1. Timmerman GM. Binge eating scale: further assessment of validity and reliability. J Appl Biobehav Res. 1999;4(1):1–12.
    1. Gearhardt AN, Corbin WR, Brownell KD. Preliminary validation of the Yale food addiction scale. Appetite. 2009;52(2):430–436.
    1. Clark MM, Abrams DB, Niaura RS, Eaton CA, Rossi JS. Self-efficacy in weight management. J Consult Clin Psychol. 1991;59(5):739–744. doi: 10.1037/0022-006X.59.5.739.
    1. Navidan A, Abedi MR, Baghban I, Fatehizade MS, Poursharifi H. Reliability and validity of the weight efficacy lifestyle questionnaire in overweight and obese individuals. Int J Behav Sci. 2009;3(3):217–222.
    1. Karlsson J, Persson LO, Sjöström L, Sullivan M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int J Obes Relat Metab Disord. 2000;24(12):1715–1725. doi: 10.1038/sj.ijo.0801442.
    1. Herman CP, Polivy J. A boundary model for the regulation of eating. Psychiatr Ann. 1983;13(12):918–927.
    1. Westenhoefer J. Dietary restraint and disinhibition: Is restraint a homogeneous construct? Appetite. 1991;16(1):45–55. doi: 10.1016/0195-6663(91)90110-E.
    1. Byrne SM, Cooper Z, Fairburn CG. Psychological predictors of weight regain in obesity. Behav Res Ther. 2004;42(11):1341–1356. doi: 10.1016/j.brat.2003.09.004.
    1. Foster GD, Wadden Ta, Swain RM, Stunkard aJ, Platte P, Vogt Ra. The Eating Inventory in obese women: clinical correlates and relationship to weight loss. Int J Obes Relat Metab Disord. 1998;22:778–785. doi: 10.1038/sj.ijo.0800659.
    1. Savage JS, Hoffman L, Birch LL. Dieting, restraint, and disinhibition predict women’s weight change over 6 y. Am J Clin Nutr. 2009;90(1):33–40. doi: 10.3945/ajcn.2008.26558.
    1. Schumacher L, Gaspar M, Remmert J, Zhang F, Forman E, Butryn M. Small weight gains during obesity treatment: normative or cause for concern? Obes Sci Pract. 2016;2(4):366–375.
    1. Varady KA. Intermittent versus daily calorie restriction: Which diet regimen is more effective for weight loss? Obes Rev. 2011;12(7) doi: 10.1111/j.1467-789X.2011.00873.x.
    1. Lewis KH, Edwards-Hampton SA, Ard JD. Disparities in Treatment Uptake and Outcomes of Patients with Obesity in the USA. Curr Obes Rep. 2016:1–9. doi: 10.1007/s13679-016-0211-1.
    1. Butryn M, Forman E, Lowe M, Gorin A, Zhang F, Schaumberg K. Efficacy of Environmental and Acceptance-Based Enhancements to Behavioral Weight Loss Treatment: The ENACT Trial. Obesity. 2017
    1. RDC, Weisberg S. Residuals and influence in regression. Biometrical J. 1982

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner