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Preliminary evidence of contextual factors’ influence on weight loss treatment outcomes: implications for future research

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

  1. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13:623–49.

    Article  Google Scholar 

  2. Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process. 1991;50:248–87.

    Article  Google Scholar 

  3. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50:179–211.

    Article  Google Scholar 

  4. Beck JS, Beck AT. Cognitive therapy: basics and beyond. New York: Guilford Press; 1995.

  5. 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.

  6. Wadden TA, Tronieri JS, Butryn ML. Lifestyle modification approaches for the treatment of obesity in adults. Am Psychol. 2020;75:235.

    Article  Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Teixeira PJ, Going SB, Sardinha LB, Lohman T. A review of psychosocial pre‐treatment predictors of weight control. Obes Rev. 2005;6:43–65.

    Article  CAS  Google Scholar 

  9. Carraça EV, Santos I, Mata J, Teixeira PJ. Psychosocial pretreatment predictors of weight control: a systematic review update. Obes Facts. 2018;11:67–82.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Commenges D, Jacqmin H. The intraclass correlation coefficient: distribution-free definition and test. Biometrics. 1994;50:517–26.

    Article  CAS  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

  16. 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.

    Article  Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

  20. 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.

    Article  Google Scholar 

Download references

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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Authors and Affiliations

Authors

Contributions

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.

Corresponding author

Correspondence to J. Graham Thomas.

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Competing interests

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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