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. 2016 Jan 22;9:35.
doi: 10.1186/s13104-016-1856-2.

Secular Trends in Family Dinner Frequency Among Adolescents

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Free PMC article

Secular Trends in Family Dinner Frequency Among Adolescents

Kathryn Walton et al. BMC Res Notes. .
Free PMC article

Abstract

Background: Eating meals, particularly dinner, with family members has been found to be associated with improved dietary intake, lower prevalence of disordered eating behaviors, lower levels of substance abuse, and improved academic outcomes among adolescents. Limited research has examined how the frequency of family meals has changed over time. The objective of this study was to examine secular trends in family dinner frequency over a 12-year period using a large, nation-wide sample of adolescents.

Methods: Using data from two cohorts of the Growing up Today study (GUTS; n = 18,075 observations for 14,79,714 and 15 year olds), we compared family dinner frequency among 14-15-year-olds in 1996 (GUTS1) through 2008 (GUTS2) and rate of change in family dinner frequency from 1996 to 1998 (GUTS1) and 2004-2008 (GUTS2). We fit logistic models using generalized estimating equations with independence working correlation and empirical variance to account for correlation within individual and between siblings.

Results: From 1996 to 2008, the number of family dinners per week among males decreased from 5.3 to 4.6 (p = 0.04) and among females from 5.0 to 4.4 (p = 0.03). We found that the rate of decline in frequency of family meals was consistent in GUTS1 (1996-1998) and GUTS2 (2004-2008) among both males and females.

Conclusions: From 1996 to 2008, frequency of family dinners decreased among adolescents. Future research should explore reasons for this decline as well as strategies to increase family meals among adolescents.

Figures

Fig. 1
Fig. 1
Secular Trends in Family Dinner Frequency between 1996 and 2008 among 14- and 15-year-old adolescents. ap-values represent statistically significant decline in family dinner frequency over the study years by gender based on logistic regression models

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