Determining Predictors of Weight Loss in a Behavioral Intervention: A Case Study in the Use of Lasso Regression

Overview

Journal Front Psychiatry

Specialty Psychiatry

Date 2022 Feb 21

PMID 35185628

Authors

Carly Lupton-Smith

Elizabeth A Stuart

Emma E McGinty

Arlene T Dalcin

Gerald J Jerome

Nae-Yuh Wang

Gail L Daumit

Affiliations

Soon will be listed here.

Abstract

Objective: This study investigates predictors of weight loss among individuals with serious mental illness participating in an 18-month behavioral weight loss intervention, using Lasso regression to select the most powerful predictors.

Methods: Data were analyzed from the intervention group of the ACHIEVE trial, an 18-month behavioral weight loss intervention in adults with serious mental illness. Lasso regression was employed to identify predictors of at least five-pound weight loss across the intervention time span. Once predictors were identified, classification trees were created to show examples of how to classify participants into having likely outcomes based on characteristics at baseline and during the intervention.

Results: The analyzed sample contained 137 participants. Seventy-one (51.8%) individuals had a net weight loss of at least five pounds from baseline to 18 months. The Lasso regression selected weight loss from baseline to 6 months as a primary predictor of at least five pound 18-month weight loss, with a standardized coefficient of 0.51 (95% CI: -0.37, 1.40). Three other variables were also selected in the regression but added minimal predictive ability.

Conclusions: The analyses in this paper demonstrate the importance of tracking weight loss incrementally during an intervention as an indicator for overall weight loss, as well as the challenges in predicting long-term weight loss with other variables commonly available in clinical trials. The methods used in this paper also exemplify how to effectively analyze a clinical trial dataset containing many variables and identify factors related to desired outcomes.

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References

Mueser K, McGurk S . Schizophrenia. Lancet. 2004; 363(9426):2063-72. DOI: 10.1016/S0140-6736(04)16458-1. View

Casagrande S, Jerome G, Dalcin A, Dickerson F, Anderson C, Appel L . Randomized trial of achieving healthy lifestyles in psychiatric rehabilitation: the ACHIEVE trial. BMC Psychiatry. 2010; 10:108. PMC: 3016313. DOI: 10.1186/1471-244X-10-108. View

Chen X, Wang M, Zhang H . The use of classification trees for bioinformatics. Wiley Interdiscip Rev Data Min Knowl Discov. 2012; 1(1):55-63. PMC: 3329156. DOI: 10.1002/widm.14. View

Daumit G, Dickerson F, Wang N, Dalcin A, Jerome G, Anderson C . A behavioral weight-loss intervention in persons with serious mental illness. N Engl J Med. 2013; 368(17):1594-602. PMC: 3743095. DOI: 10.1056/NEJMoa1214530. View

Musoro J, Zwinderman A, Puhan M, Ter Riet G, Geskus R . Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. 2014; 14:116. PMC: 4209042. DOI: 10.1186/1471-2288-14-116. View

McGinty E, Baller J, Azrin S, Juliano-Bult D, Daumit G . Interventions to Address Medical Conditions and Health-Risk Behaviors Among Persons With Serious Mental Illness: A Comprehensive Review. Schizophr Bull. 2015; 42(1):96-124. PMC: 4681556. DOI: 10.1093/schbul/sbv101. View

McNeish D . Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences. Multivariate Behav Res. 2015; 50(5):471-84. DOI: 10.1080/00273171.2015.1036965. View

Dickerson F, Brown C, Kreyenbuhl J, Fang L, Goldberg R, Wohlheiter K . Obesity among individuals with serious mental illness. Acta Psychiatr Scand. 2006; 113(4):306-13. DOI: 10.1111/j.1600-0447.2005.00637.x. View

Unick J, Neiberg R, Hogan P, Cheskin L, Dutton G, Jeffery R . Weight change in the first 2 months of a lifestyle intervention predicts weight changes 8 years later. Obesity (Silver Spring). 2015; 23(7):1353-6. PMC: 4481874. DOI: 10.1002/oby.21112. View

10.

Jovanovic M, Radovanovic S, Vukicevic M, Van Poucke S, Delibasic B . Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression. Artif Intell Med. 2016; 72:12-21. DOI: 10.1016/j.artmed.2016.07.003. View

11.

Carney C, Jones L, Woolson R . Medical comorbidity in women and men with schizophrenia: a population-based controlled study. J Gen Intern Med. 2006; 21(11):1133-7. PMC: 1831667. DOI: 10.1111/j.1525-1497.2006.00563.x. View

12.

Dipasquale S, Pariante C, Dazzan P, Aguglia E, McGuire P, Mondelli V . The dietary pattern of patients with schizophrenia: a systematic review. J Psychiatr Res. 2012; 47(2):197-207. DOI: 10.1016/j.jpsychires.2012.10.005. View

13.

Bresee L, Majumdar S, Patten S, Johnson J . Prevalence of cardiovascular risk factors and disease in people with schizophrenia: a population-based study. Schizophr Res. 2010; 117(1):75-82. DOI: 10.1016/j.schres.2009.12.016. View

14.

Sallis J, Grossman R, Pinski R, Patterson T, Nader P . The development of scales to measure social support for diet and exercise behaviors. Prev Med. 1987; 16(6):825-36. DOI: 10.1016/0091-7435(87)90022-3. View

15.

Graham J . Missing data analysis: making it work in the real world. Annu Rev Psychol. 2008; 60:549-76. DOI: 10.1146/annurev.psych.58.110405.085530. View

16.

Daumit G, Goldberg R, Anthony C, Dickerson F, Brown C, Kreyenbuhl J . Physical activity patterns in adults with severe mental illness. J Nerv Ment Dis. 2005; 193(10):641-6. DOI: 10.1097/01.nmd.0000180737.85895.60. View

17.

Almirall D, Nahum-Shani I, Sherwood N, Murphy S . Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Transl Behav Med. 2014; 4(3):260-74. PMC: 4167891. DOI: 10.1007/s13142-014-0265-0. View

18.

Schmid N, Taylor K, Foldi N, Berres M, Monsch A . Neuropsychological signs of Alzheimer's disease 8 years prior to diagnosis. J Alzheimers Dis. 2012; 34(2):537-46. DOI: 10.3233/JAD-121234. View

19.

Mangurian C, Chaudhry S, Capitelli L, Amiel J, Rosario F, Jackson C . Implementation of a weight loss program for Latino outpatients with severe mental illness. Community Ment Health J. 2012; 49(2):150-6. DOI: 10.1007/s10597-012-9506-1. View

20.

Daumit G, Clark J, Steinwachs D, Graham C, Lehman A, Ford D . Prevalence and correlates of obesity in a community sample of individuals with severe and persistent mental illness. J Nerv Ment Dis. 2003; 191(12):799-805. DOI: 10.1097/01.nmd.0000100923.20188.2d. View