Literature DB >> 35032253

Individual differences in the effects of the ACTION-PAC intervention: an application of personalized medicine in the prevention and treatment of obesity.

Alena Kuhlemeier1, Thomas Jaki2,3, Elizabeth Y Jimenez4, Alberta S Kong4, Hope Gill5, Chi Chang6, Ken Resnicow7, Dawn K Wilson8, M Lee Van Horn9.   

Abstract

There is an increased interest in the use of personalized medicine approaches in the prevention or treatment of obesity, however, few studies have used these approaches to identify individual differences in treatment effects. The current study demonstrates the use of the predicted individual treatment effects framework to test for individual differences in the effects of the ACTION-PAC intervention, which targeted the treatment and prevention of obesity in a high school setting. We show how methods for personalized medicine can be used to test for significant individual differences in responses to an intervention and we discuss the potential and limitations of these methods. In our example, 25% of students in the preventive intervention, were predicted to have their BMI z-score reduced by 0.39 or greater, while at other end of the spectrum, 25% were predicted to have their BMI z-score increased by 0.09 or more. In this paper, we demonstrate and discuss the process of using methods for personalized medicine with interventions targeting adiposity and discuss the lessons learned from this application. Ultimately, these methods have the potential to be useful for clinicians and clients in choosing between treatment options, however they are limited in their ability to help researchers understand the mechanisms underlying these predictions.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Individual treatment effects; Obesity prevention; Personalized medicine

Mesh:

Year:  2022        PMID: 35032253     DOI: 10.1007/s10865-021-00274-2

Source DB:  PubMed          Journal:  J Behav Med        ISSN: 0160-7715


  24 in total

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Journal:  Eat Weight Disord       Date:  2001-03       Impact factor: 4.652

2.  A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results.

Authors:  Farideh Bagherzadeh-Khiabani; Azra Ramezankhani; Fereidoun Azizi; Farzad Hadaegh; Ewout W Steyerberg; Davood Khalili
Journal:  J Clin Epidemiol       Date:  2015-10-22       Impact factor: 6.437

3.  Validity and reliability of physical activity measures in greek high school age children.

Authors:  Eugenia C Argiropoulou; Maria Michalopoulou; Nikolaos Aggeloussis; Andreas Avgerinos
Journal:  J Sports Sci Med       Date:  2004-09-01       Impact factor: 2.988

Review 4.  Position of the Academy of Nutrition and Dietetics: interventions for the prevention and treatment of pediatric overweight and obesity.

Authors:  Deanna M Hoelscher; Shelley Kirk; Lorrene Ritchie; Leslie Cunningham-Sabo
Journal:  J Acad Nutr Diet       Date:  2013-10       Impact factor: 4.910

5.  Precision medicine: diagnosis and management of obesity.

Authors:  Gema Frühbeck; Dimitrios N Kiortsis; Victoria Catalán
Journal:  Lancet Diabetes Endocrinol       Date:  2017-09-14       Impact factor: 32.069

Review 6.  Precision Medicine in Obesity and Type 2 Diabetes: The Relevance of Early-Life Exposures.

Authors:  Angela C Estampador; Paul W Franks
Journal:  Clin Chem       Date:  2017-11-02       Impact factor: 8.327

7.  A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design.

Authors:  Xinyuan Chen; Joseph Chang; Donna Spiegelman; Fan Li
Journal:  Stat Methods Med Res       Date:  2021-11-29       Impact factor: 3.021

8.  Precision nutrition: hype or hope for public health interventions to reduce obesity?

Authors:  Angeline Chatelan; Murielle Bochud; Katherine L Frohlich
Journal:  Int J Epidemiol       Date:  2019-04-01       Impact factor: 7.196

9.  A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint.

Authors:  Jeroen Hoogland; Joanna IntHout; Michail Belias; Maroeska M Rovers; Richard D Riley; Frank E Harrell; Karel G M Moons; Thomas P A Debray; Johannes B Reitsma
Journal:  Stat Med       Date:  2021-08-16       Impact factor: 2.497

10.  Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models.

Authors:  Nicholas C Henderson; Thomas A Louis; Gary L Rosner; Ravi Varadhan
Journal:  Biostatistics       Date:  2020-01-01       Impact factor: 5.899

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