Danping Liu

Body size perception and weight control in youth: 9-year international trends from 24 countries.

Objectives:To examine 9-year trends and relationships regarding misperceptions of body size and dieting for weight loss among adolescents from 24 countries, and explore the influence of country-level overweight prevalence.Methods:Sociodemographic characteristics, body size perception and dieting for weight loss were assessed in the Health Behaviour in School-aged Children survey conducted in 24 countries cross-sectionally at three time points (2001/2002, 2005/2006 and 2009/2010). Logistic regression models examined change over time in overestimation of body size in non-overweight adolescents, underestimation of body size in overweight adolescents, dieting for weight loss in non-overweight and overweight adolescents and relationships between body size perception and dieting. Analyses were stratified by weight status and sex. Covariates included country-level overweight prevalence, family affluence and country level of development. Body mass index was only included in models examining dieting for weight loss.Results:Country-level overweight prevalence increased over time (11.6–14.7%). Compared with Time 1, overweight adolescents had greater odds of body size underestimation at Time 3 (odds ratio (OR)=1.68 for girls; OR=1.10 for boys), whereas non-overweight adolescents had lower odds of body size overestimation at Time 3 (OR=0.87 for girls; OR=0.89 for boys). Controlling for country-level overweight prevalence attenuated these relationships. Compared with Time 1, overweight and non-overweight boys were 10% more likely to diet at Time 3, whereas overweight and non-overweight girls were 19% and 16%, respectively, less likely to diet at Time 3. Controlling for country-level overweight prevalence did not impact trends in dieting for weight loss. Additionally, the association of self-perceived overweight with increased odds of dieting diminished over time.Conclusions:Body size perceptions among adolescents may have changed over time concurrent with shifts in country-level body weight. However, controlling for country-level overweight prevalence did not impact trends in dieting for weight loss, suggesting a potentially stronger impact of social comparison on weight-related perceptions than on behavior.

Trajectories of eating behaviors in a nationally representative cohort of U.S. adolescents during the transition to young adulthood

BackgroundDiets of U.S. adolescents and adults do not meet recommendations, increasing risk of chronic disease. This study examined trajectories and predictors of eating behaviors in U.S. youth from age 16–20 years, and evaluated longitudinal associations of eating behaviors with weight outcomes.MethodsData come from the first four waves (years) of the NEXT Generation Health Study, a nationally representative cohort of U.S. students in 10th grade during the 2009–2010 school year (nu2009=u20092785). Annual surveys queried frequency of food group intake (times/day of fruit and vegetables, whole grains, sugar-sweetened soda, sweet and salty snacks), and meal practices (days/week of breakfast, family meals, fast food, and television during meals). Body mass index (BMI, kg/m2) was calculated from self-reported height and weight. Adjusted generalized estimating equations and linear mixed models with multiple imputation for missing data estimated eating behavior trajectories overall and by baseline weight status (normal weightu2009=u20095u2009≤u2009BMI%ileu2009<u200985, overweightu2009=u200985u2009≤u2009BMI%ileu2009<u200995, obeseu2009=u2009BMI%ileu2009≥u200995), accounting for the complex sampling design. Separate GEE models estimated longitudinal associations of food group frequencies with meal practices and of BMI with eating behaviors.ResultsEating behaviors tracked strongly from wave 1–4 (residual intraclass correlationu2009=u200941xa0% - 51xa0%). Across all baseline weight categories, frequency of food group intake and meal practices decreased over time, except for fast food, which remained stable. Fruit/vegetable intake frequency was associated positively with family meals (βu2009±u2009SEu2009=u20090.33u2009±u20090.05) and breakfast (0.18u2009±u20090.03), and inversely with fast food (−0.31u2009±u20090.04), while whole grain intake frequency was associated positively with family meals (0.07u2009±u20090.02), television meals (0.02u2009±u20090.009) and breakfast (0.04u2009±u20090.01). Soda and snacks were positively associated with television meals (0.08u2009±u20090.008 and 0.07u2009±u20090.009, respectively) and fast food (0.24u2009±u20090.02 and 0.20u2009±u20090.03, respectively), while soda was inversely associated with breakfast frequency (−0.05u2009±u20090.01). Time-varying BMI was unrelated to eating behaviors other than an inverse association with time-varying snacks (−0.33u2009±u20090.12).ConclusionsStrong tracking over time supports the importance of early establishment of health-promoting eating behaviors in U.S. adolescents. Findings suggest meal practices may be important intervention targets. Lack of evidence for hypothesized associations of BMI and eating behaviors indicates the need for research confirming these findings using more precise measures of dietary intake.

Combination of longitudinal biomarkers in predicting binary events

In disease screening, the combination of multiple biomarkers often substantially improves the diagnostic accuracy over a single marker. This is particularly true for longitudinal biomarkers where individual trajectory may improve the diagnosis. We propose a pattern mixture model (PMM) framework to predict a binary disease status from a longitudinal sequence of biomarkers. The marker distribution given the disease status is estimated from a linear mixed effects model. A likelihood ratio statistic is computed as the combination rule, which is optimal in the sense of the maximum receiver operating characteristic (ROC) curve under the correctly specified mixed effects model. The individual disease risk score is then estimated by Bayes theorem, and we derive the analytical form of the 95% confidence interval. We show that this PMM is an approximation to the shared random effects (SRE) model proposed by Albert (2012. A linear mixed model for predicting a binary event from longitudinal data under random effects mis-specification. Statistics in Medicine 31: (2), 143-154). Further, with extensive simulation studies, we found that the PMM is more robust than the SRE model under wide classes of models. This new PPM approach for combining biomarkers is motivated by and applied to a fetal growth study, where the interest is in predicting macrosomia using longitudinal ultrasound measurements.

Variability in measures of health and health behavior among emerging adults 1 year after high school according to college status

ABSTRACT Objective: To examine changes in health behaviors among US emerging adults 1 year after high school. Participants: The national sample of participants (N = 1,927), including those attending 4-year college/university (n = 884), 2-year colleges/technical schools (n = 588), and no college (n = 455), participated in annual spring surveys 2013–2014. Methods: Health behaviors were assessed the last year of high school and first year of college; differences by college status controlling for previous-year values were estimated using regression analyses. Results: Relative to 4-year college attendees, those attending technical school/community college were less likely to binge drink (odds ratio [OR] = 0.57, confidence interval [CI] = 0.38–0.86) but more likely to speed (OR = 1.26, CI = 1.0–2.84), consume sodas (OR = 1.57, CI = 1.0–2.47), and report lower family satisfaction (p < .01), with marginally more physical and depressive symptoms. College nonattendees reported more DWI (driving while intoxicated; OR = 1.60, CI = 1.05–2.47), soda drinking (OR = 2.51, CI = 1.76–3.59), oversleeping (OR = 4.78, CI = 3.65–8.63), and less family satisfaction (p < .04). Conclusions: Health risk behaviors among emerging adults varied by college status.

Do Young Drivers Become Safer After Being Involved in a Collision

As drivers age, their risk of being involved in a car collision decreases. The present study investigated if this trend is due, in part, to some risky drivers having a collision early in their driving lives and subsequently reducing their risky driving after that negative experience. Accelerometers and video cameras were installed in the vehicles of 16- to 17-year-old drivers (N = 254), allowing coders to measure the number of g-force events (i.e., events in which a threshold acceleration level was exceeded) per 1,000 miles and the number of collisions. Among the 41 participants who experienced a severe collision, the rate of g-force events dropped significantly in the 1st month after the collision, remained unchanged for the 2nd month, and increased significantly in the 3rd month. There were no changes in the rate of g-force events at comparable time points for the drivers not involved in a collision. Being involved in a collision led to a decrease in risky driving, but this may have been a temporary effect.

MIXED MODEL AND ESTIMATING EQUATION APPROACHES FOR ZERO INFLATION IN CLUSTERED BINARY RESPONSE DATA WITH APPLICATION TO A DATING VIOLENCE STUDY

The NEXT Generation Health study investigates the dating violence of adolescents using a survey questionnaire. Each student is asked to affirm or deny multiple instances of violence in his/her dating relationship. There is, however, evidence suggesting that students not in a relationship responded to the survey, resulting in excessive zeros in the responses. This paper proposes likelihood-based and estimating equation approaches to analyze the zero-inflated clustered binary response data. We adopt a mixed model method to account for the cluster effect, and the model parameters are estimated using a maximum-likelihood (ML) approach that requires a Gaussian-Hermite quadrature (GHQ) approximation for implementation. Since an incorrect assumption on the random effects distribution may bias the results, we construct generalized estimating equations (GEE) that do not require the correct specification of within-cluster correlation. In a series of simulation studies, we examine the performance of ML and GEE methods in terms of their bias, efficiency and robustness. We illustrate the importance of properly accounting for this zero inflation by reanalyzing the NEXT data where this issue has previously been ignored.

Diet quality of US adolescents during the transition to adulthood: changes and predictors

Background: Influences on diet quality during the transition from adolescence to adulthood are understudied.Objective: This study examined association of 3 diet-quality indicators-Healthy Eating Index-2010 (HEI), Whole Plant Foods Density (WPF), and Empty Calories (EC; the percentage of calories from discretionary solid fat, added sugar and alcohol)-with lifestyle behaviors, baseline weight status, and sociodemographic characteristics in US emerging adults.Design: Data come from the first 4 waves (annual assessments) of the NEXT Plus Study, a population-based cohort of 10th graders enrolled in 2010 (n = 566). At each assessment, participants completed 3 nonconsecutive 24-h diet recalls, wore accelerometers for 7 d, and self-reported meal practices and sedentary behaviors. Self-reported sociodemographic characteristics were ascertained at baseline. Generalized estimating equations examined associations of time-varying diet quality with baseline weight status and sociodemographic characteristics and time-varying lifestyle behaviors.Results: Diet quality improved modestly from baseline (mean ± SE: HEI, 44.07 ± 0.53; WPF, 1.24 ± 0.04; and EC, 35.66 ± 0.55) to wave 4 for WPF (1.44 ± 0.05, P < 0.001) and EC (33.47 ± 0.52, P < 0.001), but not HEI (45.22 ± 0.60). In longitudinal analyses, higher HEI and lower EC scores were observed in Hispanic compared with white participants. Better diet quality was associated with greater moderate-to-vigorous physical activity, more frequent breakfast and family meals, less frequent fast food and meals during television viewing, and shorter durations of television viewing, gaming, and online social networking. Diet-quality indicators were not consistently associated with time-varying physical inactivity, baseline weight status, or sociodemographic characteristics.Conclusions: Diet quality of emerging adults in the US remained suboptimal, but some aspects improved marginally over the 4-y study period. Meal contexts and sedentary behaviors may represent important intervention targets. There is substantial room for improvement in diet quality in all sociodemographic subgroups. This trial was registered at clinicaltrials.gov as NCT01031160.

Greater Food Reward Sensitivity Is Associated with More Frequent Intake of Discretionary Foods in a Nationally Representative Sample of Young Adults

Food reward sensitivity may influence individual susceptibility to an environment replete with highly palatable foods of minimal nutritional value. These foods contain combinations of added sugar, fat, and/or salt that may enhance their motivational salience. This study examined associations of food reward sensitivity with eating behaviors in the NEXT Generation Health Study, a nationally representative sample of U.S. young adults. Participants (nu2009=u20092202) completed self-report measures including the Power of Food Scale, assessing food reward sensitivity, and intake frequency of 14 food groups. Multiple linear regressions estimated associations of food reward sensitivity with each of the eating behaviors adjusting for covariates. Higher food reward sensitivity was associated with more frequent intake of fast food (bu2009±u2009linearized SEu2009=u20090.24u2009±u20090.05, pu2009<u20090.001), sweet and salty snacks (0.21u2009±u20090.05, pu2009<u20090.001), foods made with cheese (0.14u2009±u20090.06, pu2009=u20090.03), soda (0.12u2009±u20090.04, pu2009=u20090.009), processed meats (0.12u2009±u20090.05, pu2009=u20090.045), and fish (0.08u2009±u20090.03, pu2009=u20090.03) but was not associated with intake frequency of fruit or juice, green or orange vegetables, beans, whole grains, nuts/seeds, or dairy products. Food reward sensitivity was associated with greater intake of discretionary foods but was not associated with intake of most health-promoting foods, suggesting food reward sensitivity may lead to preferential intake of unhealthful foods.

Post-High School Changes in Tobacco and Cannabis Use in the United States

ABSTRACT Background: The transition from high school into young adulthood is a critical developmental period with many young people going to college, moving residence, and entering the work force for the first time. The NEXT Generation Health Study (NEXT) is a nationally representative longitudinal study of adolescent health behaviors. Previous NEXT research has found that the post-high school environment is associated with changes in alcohol use. Objectives: The current study investigated the impact of school status, residential status, and work status on cannabis and cigarette use among post-high school participants. Results: Living in a dorm/fraternity/sorority was associated with an increased prevalence in cannabis use while attending a 4-year college was associated with a decreased prevalence in cigarette use. Conclusions: Some aspects of the post-high school environment are related to cannabis and cigarette use. Differences in the social circumstances of cigarette and cannabis use and recent campaigns in colleges to reduce smoking may explain some of these trends.

Beyond Sleep Duration: Bidirectional Associations Among Chronotype, Social Jetlag, and Drinking Behaviors in a Longitudinal Sample of US High School Students

Clinical Trial RegistrationnHealth Behavior in School-Aged Children: NEXT Longitudinal Study 2009-2013, https://clinicaltrials.gov/ct2/show/NCT01031160?term=Simons-Morton&rank=3, NCT01031160.