Abstract
Background/Objectives
Behavioral health interventions, including behavioral obesity treatment, typically target psychosocial qualities of the individual (e.g., knowledge, self-efficacy) that are largely treated as persistent, over momentary contextual factors (e.g., affect, environmental conditions). The variance in treatment outcomes that can be attributable to these two sources is rarely quantified but may help inform future research and treatment development efforts.
Subjects/Methods
The intraclass correlation coefficient (ICC) for weekly weight loss was calculated in three studies involving 10–12 weeks of behavioral obesity treatment delivered to adults via in-person group sessions, mobile application, or website. The ICC explains the proportion of variance between vs. within individuals, and was used to infer the contribution of individual vs. contextual factors to weekly weight loss. The analytic approach involved unconditional linear mixed effect models with a random effect for subject.
Results
The ICCs were very low, ranging from 0.01 to 0.06, suggesting that momentary contextual factors may influence obesity treatment outcomes to a substantial degree.
Conclusions
This study yielded preliminary evidence that the influence of contextual factors in behavioral obesity treatment may be underappreciated. Future research is needed to simultaneously identify and quantify sources of within- and between-subjects variance to optimize treatment approaches.
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References
Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13:623–49.
Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process. 1991;50:248–87.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.
Beck JS, Beck AT. Cognitive therapy: basics and beyond. New York: Guilford Press; 1995.
Prochaska JO, Johnson S, Lee P. The transtheoretical model of behavior change. In: Schron E, Ockene J, Schumaker S, Exum WM, editors. The handbook of behavior change. 2nd ed. New York, NY: Springer; 1998. p. 159–87.
Wadden TA, Tronieri JS, Butryn ML. Lifestyle modification approaches for the treatment of obesity in adults. Am Psychol. 2020;75:235.
Prestwich A, Sniehotta FF, Whittington C, Dombrowski SU, Rogers L, Michie S. Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Health Psychol. 2014;33:465.
Teixeira PJ, Going SB, Sardinha LB, Lohman T. A review of psychosocial pre‐treatment predictors of weight control. Obes Rev. 2005;6:43–65.
Carraça EV, Santos I, Mata J, Teixeira PJ. Psychosocial pretreatment predictors of weight control: a systematic review update. Obes Facts. 2018;11:67–82.
Lazzeretti L, Rotella F, Pala L, Rotella CM. Assessment of psychological predictors of weight loss: how and what for? World J Psychiatry. 2015;5:56.
Rights JD, Preacher KJ, Cole DA. The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects. Br J Math StatPsychol. 2019;73:194–211.
Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011;1:53–71.
Commenges D, Jacqmin H. The intraclass correlation coefficient: distribution-free definition and test. Biometrics. 1994;50:517–26.
Thomas JG, Bond DS, Raynor HA, Papandonatos GD, Wing RR. Comparison of smartphone‐based behavioral obesity treatment with gold standard group treatment and control: a randomized trial. Obesity. 2019;27:572–80.
Goldstein SP, Thomas JG, Foster GD, Turner-McGrievy G, Butryn ML, Herbert JD, et al. Refining an algorithm-powered just-in-time adaptive weight control intervention: a randomized controlled trial evaluating model performance and behavioral outcomes. Health Informatics J. 2020:2315–2331.
Thomas JG, Leahey TM, Wing RR. An automated internet behavioral weight-loss program by physician referral: a randomized controlled trial. Diabetes Care. 2015;38:9–15.
Goldstein SP, Zhang F, Thomas JG, Butryn ML, Herbert JD, Forman EM. Application of machine learning to predict dietary lapses during weight loss. J Diabetes Sci Technol. 2018;12:1045–52.
Engel SG, Crosby RD, Thomas G, Bond D, Lavender JM, Mason T, et al. Ecological momentary assessment in eating disorder and obesity research: a review of the recent literature. Curr Psychiatry Rep. 2016;18:37.
Kubiak T, Smyth JM. Connecting domains—ecological momentary assessment in a mobile sensing framework. In: Baumeister H, Montag C, editors. Digital phenotyping and mobile sensing. Cham, Switzerland: Springer; 2019. p. 201–7.
Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52:446–62.
Acknowledgements
The research team thanks the participants for their contribution to the research, without which the study would not have been possible.
Funding
National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute.
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JGT and SPG led data collection. All authors contributed to data analysis. JGT and SPG led the writing and LAB provided feedback on manuscript drafts.
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JGT has received compensation as a member of the scientific advisory board of Lummé Health and owns stock in the company. SPG and LAB declare no potential competing interests.
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Thomas, J.G., Goldstein, S.P. & Brick, L.A. Preliminary evidence of contextual factors’ influence on weight loss treatment outcomes: implications for future research. Int J Obes 46, 1244–1246 (2022). https://doi.org/10.1038/s41366-022-01070-x
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DOI: https://doi.org/10.1038/s41366-022-01070-x