Understanding Eating Behavior during the Transition from Adolescence to Young Adulthood: A Literature Review and Perspective on Future Research Directions
<p>PRISMA flow diagram of the identification, screening and selection of articles for the review.</p> "> Figure 2
<p>Example models of static and dynamic research designs. Note: T = time point; SD = standard deviation. This figure provides a visualization of static (panels <b>A</b>–<b>C</b>) and dynamic (panel <b>D</b>) research designs. Each design investigates a relation between a shaping factor and an outcome—for the current purposes, this would be an outcome related to eating behavior. The designs become progressively more complex: (Panel <b>A</b>) describes a cross-sectional design where a factor at time 1 is associated with an outcome at the same time point. (Panel <b>B</b>) describes a simple longitudinal design where a factor at time 1 predicts an outcome at a later time, time 2. (Panel <b>C</b>) describes an adjusted longitudinal design wherein a factor at time 1 still predicts an outcome at a later time, but now taking into account the baseline (time 1) level of that outcome. (Panel <b>D</b>) describes a dynamic research design including two measurement moments for both factor and outcome, such that it can be determined whether changes in the factor predict changes in the outcome. This figure is adapted from Renner et al., 2008 [<a href="#B41-nutrients-10-00667" class="html-bibr">41</a>] and is reproduced with the first author’s permission.</p> ">
Abstract
:1. Introduction
A Focus on Shaping Factors
2. Materials and Methods
3. Results
4. Discussion
4.1. Static Versus Dynamic Research Designs
4.2. Modifiability of Factors
4.3. A Focus on US College Freshmen
5. Strengths and Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Search Terms | |
---|---|
1. | (student* or freshman or freshmen or college* or universit* or “higher education” or “late adolesc*” or “young adult*” or “emerging adult*” or “18-2*” or “17-2*” or “16-2*” or “new adult*”).ti. |
2. | (nutrit* or diet* or eat* or food* or fruit* or vegetable* or sugar* or fat* or soda* or “soft drink*” or “sugar sweetened beverage*” or intake or snack*).ti. |
3. | (transition* or change or “school to work” or “school to college” or period* or critical or phase* or stage*).ti. |
4. | (determinant* or correlat* or associat* or mediat* or moderat* or predict*).ti. |
5. | 1 and 2 and 3 (first search performed in each database) |
6. | 1 and 2 and 4 (second search performed in each database) |
Ref # | Article | Country | Study Population | Description of Study | Study Design |
---|---|---|---|---|---|
[23] | Barr-Anderson et al., 2009 | US | from middle school and high school to 17–20-year olds (mean age at follow-up = 17.2 ± 0.6 years and 20.5 ± 0.8 years for younger and older cohorts, respectively) | television viewing as a predictor of FV, whole grain, calcium, trans fat, fried food, fast food, snacks, and SSB intake | simple longitudinal |
[24] | Brunstrom et al., 2008 | UK | first-year undergraduate students (mean age = 18.7 ± 0.8 years) | determinants of portion size (of snacks, side dishes, and main meals) | cross-sectional |
[25] | Cluskey & Grobe, 2009 | US | college students (mean age = 19.0 years) | determinants of eating behavior | qualitative |
[26] | Deliens et al., 2014 | Belgium | university students (mean age = 20.6 ± 1.7 years) | determinants of eating behavior | qualitative |
[27] | Guagliardo et al., 201 | France | first-year students (mean age = 19.5 years; range = 18–24 years) | eating at university canteen as predictor of FV, meat, fish, salt, fat, and fiber intake | cross-sectional |
[28] | Kwok et al., 2016 | Hong Kong | first-year students (age range = 18–24 years) | determinants of food choice | qualitative |
[29] | LaCaille et al., 2011 | US | college students (mean age = 19.3 ± 1.2 years) | determinants of eating behavior | qualitative |
[30] | Larson et al., 2007b | US | adolescence to young adulthood (mean age at follow-up = 20.4 years) | family meal frequency as a predictor of main meal frequency and FV, whole grain, calcium and SD intake | adjusted longitudinal |
[31] | Larson et al., 2008a | US | adolescence to young adulthood (mean age at follow-up = 20.4 ± 0.8 years) | correlates of FV intake | adjusted longitudinal |
[32] | Larson et al., 2008b | US | adolescence to young adulthood (mean age at follow-up = 20.5 ± 0.9 years) | correlates of fast food intake | adjusted longitudinal |
[33] | Larson et al., 2009 | US | adolescence to young adulthood (mean age at follow-up = 20.5 ± 0.8 years) | correlates of calcium and dairy intake | adjusted longitudinal |
[34] | Lipsky et al., 2015 | US | adolescence to young adulthood (mean age at baseline = 16.3 years) | determinants of whole grain, SSB, snacks, and FV intake | dynamic longitudinal |
[35] | Lloyd-Richardson et al., 2008 | US | college freshmen (mean age = 18.6 ± 0.04 years) | alcohol consumption as a predictor of overeating and unhealthy eating | cross-sectional |
[36] | Nelson et al., 2009 | US | freshmen and sophomore college students (mean age = 19.4 years; range = 18–21 years) | determinants of dietary intake | qualitative |
[37] | Poulos & Pasch, 2015 | US | college freshmen (mean age = 18.7 years) | energy drink consumption as a predictor of (diet) soda, milk, snacks, frozen food, FV, and fast food intake and breakfast and restaurant frequency | cross-sectional |
[38] | Strong et al., 2008 | US | first and second year college students (mean age = 18.3 ± 0.1 years) | determinants of eating behavior | qualitative |
[39] | Tomasone et al., 2015 | Canada | first-year undergraduate students (mean age =17.8 ± 0.5 years) | trait self-control, attitudes, subjective norms, perceived behavioral control and intentions as predictors of FV intake | simple longitudinal |
[40] | Wengreen & Moncur, 2009 | US | first-year college students (aged 18–19 years) | changes in weight, dietary intake, and other health-related behaviors, and correlations between these | adjusted longitudinal * |
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Stok, F.M.; Renner, B.; Clarys, P.; Lien, N.; Lakerveld, J.; Deliens, T. Understanding Eating Behavior during the Transition from Adolescence to Young Adulthood: A Literature Review and Perspective on Future Research Directions. Nutrients 2018, 10, 667. https://doi.org/10.3390/nu10060667
Stok FM, Renner B, Clarys P, Lien N, Lakerveld J, Deliens T. Understanding Eating Behavior during the Transition from Adolescence to Young Adulthood: A Literature Review and Perspective on Future Research Directions. Nutrients. 2018; 10(6):667. https://doi.org/10.3390/nu10060667
Chicago/Turabian StyleStok, F. Marijn, Britta Renner, Peter Clarys, Nanna Lien, Jeroen Lakerveld, and Tom Deliens. 2018. "Understanding Eating Behavior during the Transition from Adolescence to Young Adulthood: A Literature Review and Perspective on Future Research Directions" Nutrients 10, no. 6: 667. https://doi.org/10.3390/nu10060667