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Randomized Controlled Trial
. 2019 Mar;38(3):248-258.
doi: 10.1037/hea0000702.

Predictive utility of subtyping women smokers on depression, eating, and weight-related symptoms

Rachel H Salk  1 , Lisa J Germeroth  1 , Rebecca L Emery  1 , Rachel P Kolko Conlon  1 , Zheng Wang  2 , Yu Cheng  2 , Marsha D Marcus  1 , Kenneth A Perkins  1 , Michele D Levine  1
Affiliations
Randomized Controlled Trial

Predictive utility of subtyping women smokers on depression, eating, and weight-related symptoms

Rachel H Salk et al. Health Psychol. 2019 Mar.

Abstract

Objective: Smoking and overweight or obesity are preventable causes of disease and death. Women are reluctant to quit smoking because of concerns about postcessation weight gain, underscoring the need to elucidate patterns of weight concerns and associated psychosocial factors that may affect smoking cessation outcomes. The present study aimed to subtype women smokers based on psychosocial and behavioral factors associated with smoking and weight, and examine the utility of these subtypes to predict abstinence and postcessation weight gain.

Method: Weight-concerned women (N = 343) were randomized to 1 of 2 smoking cessation counseling adjuncts and 1 of 2 cessation medication conditions. At baseline, women were weighed and completed measures of depression, weight or appearance concerns, and eating behaviors. At 3-, 6-, and 12-months after the target quit date, women were weighed and completed self-report and biochemical smoking assessments.

Results: Latent profile (LP) analyses supported a 3-profile model. The groups had typical (53%, LP1), minimal (33%, LP2), and high (14%, LP3) levels of depressive symptoms and weight concerns. At 12-months posttarget quit date, women in LP3 were more likely to relapse than women in LP1 (odds ratio, OR = 2.93). Among abstinent women, those in LP2 and LP3 gained more postcessation weight than those in LP1.

Conclusions: Heterogeneity in symptoms of depression, weight or appearance concerns, and eating behaviors was captured by three groups of women smokers, with unique risks for relapse and postcessation weight gain. The distinct profiles identified may help personalize the delivery of care for smoking cessation and, ultimately, reduce disease risk. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Figures

Figure 1.
Figure 1.
Group differences among three-profile solution using latent profile analysis. LP = latent profile. Error bars indicate +/− 1 standard error of the mean. Rating scale ranges: Depressive symptoms (0–66), Appearance concerns (1–5), Weight concerns (1–5), Restraint (0–21), and Disinhibition (0–19).
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