Dataset Information

Longitudinal trajectories and prevalence of meeting dietary guidelines during the transition from adolescence to young adulthood.

ABSTRACT: BACKGROUND:Establishing healthy dietary habits during adolescence and young adulthood is critical for long-term health. OBJECTIVES:This study assessed the prevalence of meeting US Dietary Guidelines and trajectories in dietary intake for 4 MyPlate food groups during the transition from adolescence to young adulthood. METHODS:Three waves of surveys and food frequency questionnaires were collected as part of Project EAT (Eating and Activity in Teens and Young Adults), a 15-y longitudinal study. Adolescents (n = 1177, 57% female, mean ± SD age 15.0 ± 1.5 y) were recruited in 1998-1999 in Minneapolis-St Paul, Minnesota public schools and were resurveyed twice in young adulthood at mean ± SD ages 25.3 ± 1.5 and 31.1 ± 1.5 y. The prevalence of meeting guidelines for each MyPlate food group was calculated at each time point. Mean daily servings were compared over 5 y in young adulthood through the use of paired t tests. Adjusted least-squares means were calculated to compare dietary intake in young adulthood across quartiles of adolescent intake. RESULTS:Adolescents had the highest prevalence of meeting dietary guidelines for fruit (37% for females and 30% for males) and dairy (53% for females and 61% for males); young adults >30 y had the highest prevalence of meeting dietary guidelines for vegetables (19% for females and 8% for males) and whole grains (23% for females and 17% for males). From the mid-twenties to early thirties, vegetable intake increased, whereas dairy intake decreased. Dietary intake generally tracked over time with individuals in the lower quartiles of intake at adolescence generally continuing to have low intake in young adulthood. CONCLUSIONS:Although the prevalence of meeting dietary guidelines for whole grains and vegetables, and daily servings of vegetables increased with age, improving intake of whole fruit, whole grains, dairy, and vegetables remains key during the transition from adolescence to young adulthood.

SUBMITTER: Christoph MJ

PROVIDER: S-EPMC6408200 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Longitudinal trajectories and prevalence of meeting dietary guidelines during the transition from adolescence to young adulthood.

Christoph Mary J MJ Larson Nicole I NI Winkler Megan R MR Wall Melanie M MM Neumark-Sztainer Dianne D

The American journal of clinical nutrition 20190301 3

<h4>Background</h4>Establishing healthy dietary habits during adolescence and young adulthood is critical for long-term health.<h4>Objectives</h4>This study assessed the prevalence of meeting US Dietary Guidelines and trajectories in dietary intake for 4 MyPlate food groups during the transition from adolescence to young adulthood.<h4>Methods</h4>Three waves of surveys and food frequency questionnaires were collected as part of Project EAT (Eating and Activity in Teens and Young Adults), a 15-y ...[more]

PMID: 30831584

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Project description:Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant's alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes.

Project description:ImportanceLittle is known about cannabis vaping trajectories across adolescence and young adulthood or the co-occurrence with nicotine vaping.ObjectiveTo evaluate nicotine vaping and cannabis vaping trajectories from late adolescence to young adulthood (≥18 years of age) and the extent of polysubstance vaping.Design, setting, and participantsIn this prospective cohort study, 5 surveys (including information on substance vaped) were completed at 10 high schools in the Los Angeles, California, metro area. Students were surveyed at 6-month intervals from fall of 11th grade (October to December 2015; wave 5) through spring of 12th grade (March to June 2017; wave 8) and again approximately 1 to 2 years after high school (October 2018 to October 2019; wave 9).ExposuresPast 30-day nicotine and cannabis vaping frequency across 5 waves.Main outcomes and measuresSelf-reported frequency of nicotine vaping and cannabis vaping within the past 30 days across 5 time points from late adolescence to young adulthood. Trajectories were measured with these past 30-day use frequencies at each wave. Parallel growth mixture modeling estimated conditional probabilities of polysubstance vaping.ResultsThe analytic sample included 3322 participants with at least 1 time point of data (mean [SD] age, 16.50 [0.42] years at baseline; 1777 [53.5%] female; 1573 [47.4%] Hispanic or Latino). Growth mixture modeling identified the 5-trajectory model as optimal for both nicotine vaping and cannabis vaping. Trajectories for nicotine and cannabis vaping were similar (nonusers: 2246 [67.6%] nicotine, 2157 [64.9%] cannabis; infrequent users: 566 [17.0%] nicotine, 608 [18.3%] cannabis; moderate users: 167 [5.0%] nicotine, 233 [7.0%] cannabis; young adult-onset frequent users: 213 [6.4%] nicotine, 190 [5.7%] cannabis; adolescent-onset escalating frequent users: 131 [3.9%] nicotine, 134 [4.0%] cannabis). Males had greater odds of belonging to the adolescent-onset escalating frequent users nicotine (adjusted odds ratio, 2.88; 95% CI, 1.58-5.23; P < .01) and cannabis (adjusted odds ratio, 1.95; 95% CI,1.03-3.66; P < .05) vaping trajectories compared with nonusers. Polysubstance vaping was common, with those in trajectories reflecting more frequent nicotine vaping (adolescent-onset escalating frequent users and young adult-onset frequent users) having a high probability of membership (85% and 93%, respectively) in a cannabis-use trajectory.Conclusions and relevanceIn this cohort study, the prevalence and type of nicotine vaping and cannabis vaping developmental trajectories from late adolescence to young adulthood were similar. Polysubstance vaping was common from late adolescence to young adulthood, particularly among those reporting more frequent vaping use. The findings suggest that public health policy and clinical interventions should address polysubstance vaping in both adolescence and young adulthood.

Project description:ImportanceCharacterizing patterns of handgun carrying among adolescents and young adults can inform programs to reduce firearm-related harm. Longitudinal patterns of handgun carrying among rural adolescents have not been identified.ObjectivesTo assess specific points of intervention by characterizing patterns of handgun carrying by youths in rural communities from early adolescence to young adulthood and to quantify how age at initiation, duration, and frequency of carrying differ across identified patterns.Design, setting, and participantsThis cohort study uses the control group of the community-randomized trial of the Communities That Care prevention system, conducted among public school students in 12 rural communities across 7 states. Participants self-reported their handgun carrying at 10 data collection points from 12 to 26 years of age (2005-2019). Data were analyzed from January to July 2021.Main outcomes and measuresHandgun carrying in the past 12 months. Latent class growth analysis was used to estimate handgun carrying trajectories.ResultsIn this longitudinal rural sample of 2002 students, 1040 (51.9%) were male; 532 (26.6%) were Hispanic, Latino, Latina, or Latinx; 1310 (65.4%) were White; and the highest level of educational attainment of either parent was a high school degree or less for 649 students (32.4%). The prevalence of handgun carrying in the last 12 months ranged from 5.3% (95 of 1795) to 7.4% (146 of 1969) in adolescence and increased during the mid-20s (range, 8.9% [154 of 1722] to 10.9% [185 of 1704] from 23 to 26 years of age). Among the participants who reported handgun carrying at least once between 12 and 26 years of age (n = 601 [30.0%]), 320 (53.2%) reported carrying a handgun in only 1 wave. Latent class growth analysis indicated 6 longitudinal trajectories: never or low probability of carrying (1590 [79.4%]), emerging adulthood carrying (166 [8.3%]), steadily increasing carrying (163 [8.1%]), adolescent carrying (53 [2.6%]), declining carrying (24 [1.2%]), and high probability and persistent carrying (6 [0.3%]). The earliest mean (SD) age at initiation of handgun carrying occurred in both the adolescent and declining carrying groups at the ages of 12.6 (0.9) and 12.5 (0.7) years, respectively. More than 20% of some groups (emerging adulthood [age 26 years: 49 of 154 (31.8%)], steadily increasing [age 26 years: 37 of 131 (28.2%)], declining [age 13 years: 7 of 23 (30.4%)], and high probability and persistent carrying [age 15 years: 3 of 6 (50.0%)]) reported carrying 40 times or more in the past year by the age of 26 years.Conclusions and relevanceThis study found distinct patterns of handgun carrying from adolescence to young adulthood in rural settings. Findings suggest that promoting handgun safety in rural areas should start early. Potential high-risk trajectories, including carrying at high frequencies, should be the focus of future work to explore the antecedents and consequences of handgun carrying in rural areas.

Dataset Information

Longitudinal trajectories and prevalence of meeting dietary guidelines during the transition from adolescence to young adulthood.

Publications

Longitudinal trajectories and prevalence of meeting dietary guidelines during the transition from adolescence to young adulthood.

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