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DOI:10.1186/s12966-016-0451-1

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Background Meal skipping rates may be highest during young adulthood, a period of transition and development. Although these dietary behaviours may increase future risk of chronic disease, limited research has investigated correlates of meal skipping in young adults. MethodsA systematic literature search was conducted to identify studies that investigated correlates of meal skipping behaviours in young adults (aged 18–30 years). EBSCO host, MEDLINE Complete, Global Health, Scopus, EMBASE, Web of Science and Informit platforms were searched for eligible articles. Correlates were defined as any factor that was either associated with meal skipping or was self-reported by the participant to have an influence on meal skipping. Randomised controlled trials, prospective cohort studies, case-control studies, nested case-control studies, cross-sectional studies, and longitudinal studies were eligible for inclusion. ResultsThree-hundred and thirty-one articles were identified, 141 full-text articles assessed for eligibility, resulting in 35 included studies. Multiple methodological and reporting weaknesses were apparent in the reviewed studies with 28 of the 35 studies scoring a negative rating in the risk of bias assessment. Meal skipping (any meal), defined as the skipping of any meal throughout the day, was reported in 12 studies with prevalence ranging between 5 and 83%. The remaining 25 studies identified specific meals and their skipping rates, with breakfast the most frequently skipped meal 14–88% compared to lunch 8–57% and dinner 4–57%. Lack of time was consistently reported as an important correlate of meal skipping, compared with correlates such as cost and weight control, while sex was the most commonly reported associated correlate. Breakfast skipping was more common among men while lunch or dinner skipping being more common among women. Conclusions This review is the first to examine potential correlates of meal skipping in young adults. Future research would benefit from stronger design and reporting strategies, using a standardised approach for measuring and defining meal skipping.

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R E V I E W Open Access

Correlates of meal skipping in young

adults: a systematic review

Felicity J. Pendergast

*

, Katherine M. Livingstone, Anthony Worsley and Sarah A. McNaughton

Abstract

Background: Meal skipping rates may be highest during young adulthood, a period of transition and development.

Although these dietary behaviours may increase future risk of chronic disease, limited research has investigated correlates

of meal skipping in young adults.

Methods: A systematic literature search was conducted to identify studies that investigated correlates of meal skipping

behaviours in young adults (aged 18–30 years). EBSCO host, MEDLINE Complete, Global Health, Scopus, EMBASE, Web of

Science and Informit platforms were searched for eligible articles. Correlates were defined as any factor that was either

associated with meal skipping or was self-reported by the participant to have an influence on meal skipping. Randomised

controlled trials, prospective cohort studies, case-control studies, nested case-control studies, cross-sectional studies, and

longitudinal studies were eligible for inclusion.

Results: Three-hundred and thirty-one articles were identified, 141 full-text articles assessed for eligibility, resulting in 35

included studies. Multiple methodological and reporting weaknesses were apparent in the reviewed studies with 28 of

the 35 studies scoring a negative rating in the risk of bias assessment. Meal skipping (any meal), defined as the skipping

of any meal throughout the day, was reported in 12 studies with prevalence ranging between 5 and 83%. The remaining

25 studies identified specific meals and their skipping rates, with breakfast the most frequently skipped meal 14–88%

compared to lunch 8–57% and dinner 4–57%. Lack of time was consistently reported as an important correlate of meal

skipping, compared with correlates such as cost and weight control, while sex was the most commonly reported

associated correlate. Breakfast skipping was more common among men while lunch or dinner skipping being more

common among women.

Conclusions: This review is the first to examine potential correlates of meal skipping in young adults. Future research

would benefit from stronger design and reporting strategies, using a standardised approach for measuring and defining

meal skipping.

Keywords: Meal skipping, Young adults, Correlates, Systematic review, Eating behaviour

Background

Young adulthood, is a unique developmental phase

defined as a period of multiple transitions and the devel-

opment of independence [1, 2]. During this time, indi-

viduals develop the skills needed to engage and practice

behaviours, such as healthy eating, that track into later

life [3, 4]. Research suggests that young adults engage in

poor eating behaviours, such as low fruit and vegetable

consumption [4, 5], high consumption of energy-dense

snack foods [6], and frequently fail to consume regular

meals [7, 8].

Meal skipping is the omission or lack of consumption

of one or more of the traditional main meals (breakfast,

lunch or dinner) throughout the day [9]. The regular

omission of meals, particularly the breakfast meal, has

been associated with poorer diet quality [10], lower

intakes of total energy, vitamins and minerals [11–13],

increased risk of central adiposity [14, 15], markers of

insulin resistance [15, 16] and cardio metabolic risk fac-

tors [15, 17]. Estimated prevalence rates of meal skip-

ping in the young adult population vary between 24 and

87% [18, 19], with young adults consistently reporting

* Correspondence: fpenderg@deakin.edu.au

Institute for Physical Activity and Nutrition (IPAN), School of Exercise and

Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood,

Geelong, Victoria 3125, Australia

© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0

International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and

reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to

the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver

(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Pendergast et al. International Journal of Behavioral Nutrition

and Physical Activity (2016) 13:125

DOI 10.1186/s12966-016-0451-1

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

higher rates of meal skipping compared with other age

groups [5]. Recent data from the Australian Health

Survey 2011/12 showed that 39% of Australian young

adults (19–24 years) reported eating breakfast less than

5 days per week, compared with 10% children (8–11

years) and 33% of all adults (>18 years) [5]. Despite

the significant health implications of meal skipping

and its higher prevalence among young adults, limited

research has investigated correlates of this unhealthy

eating behaviour.

Conceptual models or frameworks are useful in under-

standing and explaining correlates of eating behaviours

such as meal skipping. While there are a number of pos-

sible conceptual frameworks in the literature, this review

will use the framework developed by Story et al. [20] to

categorise the correlates of meal skipping. This frame-

work combines ecological perspectives with social cogni-

tive theories (SCT) resulting in a framework which takes

into account the relationship between people and their

environments seen in ecological models [21], and socio-

environmental, personal and behavioural factors seen

within SCT [22]. This social-ecological framework (SEF)

is made up of the following four domains: 1) Individual

influences (intrapersonal); 2) Social environmental influ-

ences (interpersonal); 3) Physical environmental influ-

ences (community settings); 4) Macrosystem influences

(societal) [20], and has been used effectively in eating be-

haviour research [20, 23]. Previous research investigating

correlates of meal skipping in various populations have

identified correlates from each of these domains. Individual

influences such as smoking status [24] and infrequent phys-

ical activity [25]; social environmental influences of family

support [24]; and physical environmental influences such as

housing type have been associated with meal skipping

behaviours [26].

Given the importance of this life stage in the devel-

opment of long term health behaviours and the high

prevalence of poor eating behaviours in this popula-

tion group, understanding the correlates of meal

skipping is needed to better inform public health

strategies and dietary interventions. However, there

have been no systematic reviews to synthesise the evi-

dence on correlates of meal skipping. Therefore, the

aim of the present review was to systematically evalu-

ate the literature on correlates of meal skipping in

young adults (18–30 years) using a SEF.

Methods

Protocol

This study followed the procedures for systematic review

reporting as described by the Preferred Reporting Items

for Systematic Reviews and Meta-Analysis (PRISMA)

(Additional file 1) recommendations [27].

Search strategy

A systematic and comprehensive search of the literature

surrounding meal skipping was conducted in January

2016. The search was limited to human studies, pub-

lished in English post 1979. This time frame was chosen,

as the first studies describing eating patterns were pub-

lished in early 1980’s [28, 29]. Academic Search

complete, CINAHL Complete, PsycINFO, SocINDEX,

ERIC and Education Source were searched through

EBSCO Host. MEDLINE Complete, Global Health,

Scopus, EMBASE, Web of Science and Informit searches

were conducted independently. Bibliographies of in-

cluded articles were also reviewed (hand searched) for

additional articles. Search terms were tested prior to the

recorded search to ensure that appropriate articles were

identified. The following search terms were used during

the systematic searching of databases: (Meal skipping,

meal frequency, meal omission (skip* OR frequen* OR

omission*) N5 meal*) AND (Young adults, emerging

adult, college students (young OR emerge*) N5 adult*

(college OR university* OR undergraduate* OR “post-

secondary*”OR postgraduate*) N5 student*)) AND

(Eating habits, feeding habits, food habits, diet habits,

meal habits (diet* OR eat* OR meal* OR feed* OR

food*) N5 habit*).

Eligibility criteria

To be included in this review each article was required

to meet the following criteria: (1) original research art-

icle, published in a peer-reviewed journal, with full text

in English language; (2) the study participants were

young adults aged 18–30 years or with a mean age be-

tween 18 and 30 years, or aged 18–30 years at baseline

for longitudinal studies, for studies that did not report a

mean age, the participants needed to be referred to as

university or college students; (3) the study participants

were free from disease and were community-dwelling;

(4) there was a measure of meal skipping, meal omis-

sion, or meal frequency reported (assessed as any meal

skipped throughout the day or according to meal type

e.g. breakfast, lunch, dinner, and supper); (5) there was

at least one meal skipping correlate reported; (6) the

study design was one of the following: randomised con-

trolled trial, prospective cohort study, case-control

study, nested case-control study, cross-sectional study,

longitudinal study.

Articles were excluded if they met any of the following

criteria: (1) studies published as abstracts, conference

proceedings, posters or not in the English language; (2)

the article included specific populations of young adults

(e.g. athletes, institutionalised populations); (3) the par-

ticipants’mean age was outside the range of 18–30 years

or included children or older adults; (4) there was no

measure or report of meal skipping, meal omission, or

Pendergast et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:125 Page 2 of 15

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

meal frequency; (5) there was no correlate of meal skip-

ping reported; (6) the study design was one of the fol-

lowing; case reports, opinion articles, reviews, narrative

reviews, systematic reviews, meta-analyses.

Study selection

Two reviewers (FJP and KML) independently assessed

titles and abstracts for eligibility. Any articles that did

not meet eligibility criteria were excluded. Full texts of

the remaining articles were then obtained and screened

for inclusion. If consensus between reviewers was not

reached a third reviewer (SAM) was consulted and a

consensus approach was used.

Data extraction and synthesis

Data extraction was initially conducted by one reviewer

(FJP) using an electronic spreadsheet. Information ex-

tracted included author, year, country, design, sample

characteristics, participants’age, how meal skipping was

measured, definition of meal skipping; frequency of meal

skipping and correlates of meal skipping. Following the

initial data extraction the spreadsheet was verified for

accuracy and consistency by a second reviewer (KML)

with consensus reached with the help of the third

reviewer (SAM) in cases of disagreement.

Quality and risk of bias assessment

All included studies were assessed for quality and risk of

bias by two independent reviewers (FJP, KML) using the

Academy of Nutrition and Dietetics Quality Criteria

Checklist [30]. Any discrepancies between the reviewers

was resolved by discussion and consensus with a third

reviewer (SAM). Articles were assessed against 10 cri-

teria and were assigned a positive, negative or neutral

rating. As per guidelines, an article was deemed positive

if criteria 2, 3, 6, 7 and at least one additional criterion

was awarded a ‘yes’, neutral if criteria 2, 3, 6 and 7 did

not score a ‘yes’, or negative if more than six of the cri-

teria were awarded a ‘no’. Each criteria was assessed as

either ‘yes’or ‘no’. Criteria included: (1) the study clearly

stated the research question; (2) the selection of par-

ticipants was free from bias; (3) if study groups were

comparable; (4) participant withdrawal process was

documented; (5) the use of blinding was documented;

(6) participant compliance was measured; (7) the

measurements used were valid and reliable; (8) appro-

priate statistical analysis was used; (9) biases and limi-

tations were documented (10) funding or sponsorship

was reported.

Results

The study selection process, including reasons for ex-

cluding studies is summarised in Fig. 1. Of the 331 arti-

cles identified, 194 articles were screened based on their

title and abstract. Of these, 141 full-text articles were

assessed for eligibility and 35 studies were included in

the review. Study characteristics and risk of bias scores

are presented in Table 1 and Additional file 2 respect-

ively. Risk of bias assessment indicated four studies had

a positive rating (low risk of bias) [1, 31–33], 28 studies

had a negative rating (high risk of bias) [12, 34–60] and

three studies had a neutral rating [61–63].

Study characteristics

The included studies were conducted in 15 countries:

seven from the United States [1, 12, 31, 36, 44, 62, 63],

five from Japan [32, 33, 52, 53, 56] five from Turkey

[49, 51, 59–61] three from Nigeria [34, 50, 57] two

from Korea [37, 47], Ghana [35, 39] and Poland [54, 55]

and one from China [45], Croatia [38], Egypt [41], Greece

[43], India [40], Iraq [46], Saudi Arabia [42] and the

United Arab Emirates [48].

Of the 35 included studies, 17 consisted of primarily

female participants (>50%) [1, 12, 31, 35, 38–40, 43–45,

47, 50, 51, 58, 61–63], 10 included female participants

only [32, 37, 41, 42, 48, 52, 53, 55, 56, 59], six consisted

of primarily male participants (>50%) [33, 34, 36, 49, 57, 60].

One study had an even distribution of male and female par-

ticipants [54], and one study failed to report the sex distribu-

tion of its participants [46].

All but one of the included studies were cross-

sectional; the longitudinal study collected data at two

times points approximately 18 months apart [31]. The

included studies used a variety of different methods to

assess and define meal skipping. Dietary intake methods

were used by six studies; four used 24 h diet recalls

[12, 49, 53, 57], one used food records [44], and one

used a specially designed food frequency question-

naire (FFQ) [38]. This specially designed FFQ col-

lected information on the self-reported number of

meals and snacks consumed daily. Meal skipping was

defined as a non-reported meal within the respective

dietary assessment method. Binary response questions

were also used to measure meal skipping, with op-

tions “Yes/No”or “Regularly/Rarely”used by eight

studies [34, 35, 39, 41, 47, 50, 60, 62]. Question

wording influenced how meal skipping was defined in

these binary response questions. Meal skipping was

measured numerically by seven studies [1, 31, 32, 36,

46, 51, 54], and categorically by 14 studies [33, 37,

40, 42, 43, 45, 48, 52, 55, 56, 58, 59, 61, 63]. These

studies had varying response options and used varying

cut points to define meal skipping. Five of the 14

studies did not report how they defined meal skipping

from their response categories [43, 45, 48, 55, 61].

Meal skipping (any meal) was reported in 10 studies

[31, 34–36, 40, 45, 51, 54, 55, 62] and ranged from 4.8

to 83.3%, while the remaining 25 studies identified

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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Table 1 Characteristics of the included studies

Reference,

year

Country Design Sample characteristics,

N (% women)

Participants; age,

years (mean ± SD)

How was meal

skipping measured?

Definition of meal

skipping

Frequency of meal

skipping

Correlates of meal

skipping

1. Afolabi

et al. 2013 [34]

Nigeria Cross-

sectional

University students;

140 (40% F)

N/R Q: Do you skip meals?

(Yes/No)

Q: Why do you skip

meals?

“Yes”; Text 53.6% M skipped meals,

35% F skipped meals

Reasons for skipping

meals:

-Time 48%,

-Appetite 19%,

-Cannot cook 13%,

-Illness 3%,

-Money 6%

2. Akarslan et al.

2008 [61]

Turkey Cross-

sectional

Young adults;

416 (59% F)

18-25 years;

(23.2 ± 0.97 years)

Q: Frequency of B, L, D?

(Almost always/Sometimes/

Very seldom/Never in a one

year period)

N/R 70% had regular

main meals.

Regular B 69.2%,

Regular L 75.5%,

Regular D 76.0%

SEX: (0 TMS)

3. Aryee et al.

2013 [35]

Ghana Cross-

sectional

Nurses; 220;

(66% F)

20–60 years;

(67.3% 20–30 years)

Q: Do you meal skip?

(Yes/No)

“Yes”53.6% skipped meals BMI: (+TMS)

4. Bahl et al.

2013 [36]

USA Cross-

sectional

College students

(Business);

353 (43% F)

N/R Q: How many days

during the last week

(0–7) did you skip

meals?

Numerical Participants who had

mindfulness training

skipped meals on 1.25

days during the last

week compared to

1.94 days for the

non-training group

MINDFULLNESS: (- TMS)

SATISFIED: (quality of

life – TMS)

5. Beerman et al.

1990 [62]

USA Cross-

sectional

College students

(Nutrition);

152 (56% F)

74% ≤21 years Q: Do you skip meals?

(Regularly/Rarely)

“Regularly”66% of on or off

campus students

skipped meals.

36% of those living

in Greek housing

skipped meals.

LIVING SITUATION:

(Greek housing -TMS)

SEX: (0 TMS)

Reasons for meal

skipping:

-Time 61%

6. Chung et al.

2003 [37]

Korea Cross-

sectional

College students;

180 (100% F)

20.41 ± 1.82 years Q: What is your breakfast

status?

(Rarely eating, Frequently

eating or Daily eating)

“Rarely eating”or

“Frequently eating”

74.4% skipped B BMI: (0 TMS)

7. ColićBarićet al.

2003 [38]

Croatia Cross-

sectional

University students;

2075 (53% F)

21.7 ± 2.0 years Specially designed FFQ Numerical;

Regular B, defined as

having B 6 or 7 times

per week

B consumed on 3.4

days/week,

L 6 days/week,

D 4.7 days/week.

32.2% F and 25.7%

M consumed B

regularly

SEX: (F + L, +D)

BMI: (+BS)

EXERCISE: (3.5 h versus

2.6 h of exercise per

week -BS)

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Table 1 Characteristics of the included studies (Continued)

8. Danquah et al.

2010 [39]

Ghana Cross-

sectional

University students;

150 (75% F)

64.6% 21–30 years Q: Do you eat breakfast?

(Yes/No)

“No”25% skipped B,

8% skipped L,

5% skipped D

ETHNIC: (Caucasian -BS)

SEX: (F + BS)

AGE: (15–20 years -BS)

COURSE TYPE: (Science

students + BS)

Reasons for skipping B:

-No time (57%),

-Not hungry (22%),

-Eat late at night (5%),

-Busy schedule (3%),

-No reason (13%).

Reasons for skipping L:

-No time (50%),

-Not hungry (25%),

-No reason (3%),

Reasons for skipping D:

-No time (38%),

-Busy schedule (12%),

-Watching my weight

(50%)

9. Deepika 2015 India Cross-

sectional

College students;

120 (80% F)

18–23 years Q: Do you A) Take all

three meals, B) Skip

meals with substitute

or C) Skip meals

without substitute?

“B”or “C”83.3% skipped meals Reason for skipping

meals:

-Time 40%,

-Taste 30%,

-Social desirable 28.3%,

– Habit 1.7%

10. Eittah

2014 [41]

Egypt Cross-

sectional

University students

(Nursing); 300

(100% F)

17–22 years;

(20.05 ± 1.62 years)

Q: Do you always

neglect to eat – B, L, D?

(Yes/No)

“Yes”72.7% skipped B,

7.3% skipped D,

6% skipped B and D

MENSTRUAL REGULARITY:

(Menstrual regularity -BS)

11. Eldisoky,

2003 [42]

Saudi

Arabia

Cross-

sectional

University students;

61 (100% F)

19–24 years Q: Do you usually

have B?

(Yes/Sometimes/No)

“Sometimes”or “No”63% skipped B,

61% skipped L,

31% skipped D

MOTHERS EDUCATION

LEVEL: (0 TMS)

Reasons for skipping meals:

-Hunger 48%,

-Time 31%,

-Weight control 21%

12. Evagelou et al.

2014 [43]

Greece Cross-

sectional

University students

(Nursing); 435

(83.4% F)

N/R Q: N/R

(Rarely/At least one/

Two/Three/Four/Five/

Six/Seven times a week)

N/R 31% skipped B SEX: (0 BS)

13. Freedman

2010 [63]

USA Cross-

sectional

College freshman;

756 (61% F)

N/R Q: Frequency of meal

intake? (Never/1-3

times a week/4-6

times a week/7 days

a week)

“Never”24.7% skipped B,

7% skipped D

LIVING SITUATION:

(On campus + BS)

SEX: (F + DS)

ETHINICITY: (Caucasian

-BS, -DS)

14. Huang et al.,

1994 [44]

USA Cross-

sectional

College students

(Nutrition); 1912

(68% F)

M 20 years, F

19 years

1 day-food record

(weekday)

Meal not reported

in food record

22% skipped B,

8% skipped L,

5% skipped D

SEX: (0 TMS)

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Table 1 Characteristics of the included studies (Continued)

15. Kapinos &

Yakusheva,

2011 [31]

USA Longitudinal University students;

388 (63% F)

18.1 years Q: Over the past year,

how many meals per

day did you typically eat?

Numerical 2.88 meals/day

at baseline,

2.61 meals/day

one year later

ENROLLING IN UNI:

(Second year of uni + TMS)

LIVING SITUATION: (M living

in dormitories with a dining

hall -TMS)

16. Kim et al.,

2010 [45]

China Cross-

sectional

First year University

students; 2427

(63.4% F)

18.9 years Q: Have you in the past

month skipped meals?

(Often/Occasionally/Never)

N/R Skipped meals at least

monthly in past year

-16.2%, Skipped meals in

the past week – 4.8%

INTERNET USE: (4 + hours/

day + TMS)

17. Lamia Dhia &

Ban Faud 2014 [46]

Iraq Cross-

sectional

University students;

350 (Sex NR)

N/R Q: No of meal/day?

Q: If missed what is the most

missed meal?

Numerical; Text 51.1% consumed < 3

meals/day.

Of those who missed a

meal 88.5% skipped B,

11.5% skipped D

Reasons for meal skipping:

-Time

18. Laska et al.

2010 [1]

USA Cross-

sectional

Young adults;

1687 (56% F)

18–23 years;

(20.5 years)

Q: How often do you

eat B, L, D during the

past week?

(Never/1–2 d/3–4 d/5–

6 d/Every day)

Numerical B consumption ranged

from 2.7 to 3.5 days/

week,

L 5.3–5.8,

D 6.1–6.5.

LIVING SITUATION: (Living

with parents + BS, +DS)

19. Lee & Yoon

2014 [47]

Korea Cross-

sectional

University students

(Food and Nutrition

50.3%); 159 (62.3% F)

18–28 years; 56%

18–20 years

Q: Missed meal?

Q: Reason of skipping

meal?

Text; Text 83.6% skipped B,

6.9% skipped L,

8.2% skipped D

AGE: (18–20 years + TMS)

Reason for skipping B:

-Time 61%,

-Habit 17.6%,

-Appetite 11.9%

20. Musaiger &

Radwan 1995 [48]

United

Arab

Emirates

Cross-

sectional

University students;

215 (100% F)

18–30 years;

(19.7 ± 1.3 years)

Specially designed

questionnaire

on meal pattern

N/R 15.8% skipped B,

11.2% skipped L,

7% skipped D

BMI: (0 TMS)

21. Neslisah &

Emine 2011 [49]

Turkey Cross-

sectional

University students;

400 (42% F)

19–24 years;

(21.7 ± 1.8 years)

1 × 24 h diet record Meal not reported in

food record

47.7% skipped B,

25.2% skipped L

SEX: (M+ BS) (F + LS)

22. Nicklas et al.

1998 [12]

USA Cross-

sectional

Young adults;

504 (58% F)

19–28 years;

(23 years)

1 × 24 h diet recall B had to equal or

exceed macronutrient

value of 1 serving of

milk.

37% skipped B ETHNICITY: (0 BS)

SEX: (0 BS)

23. Nzeagwu &

Akagu 2011 [50]

Nigeria Cross-

sectional

University students;

342 (63% F)

16–25 years; 81%

20–25 years

Q: What meal do you

usually skip?

Q: Why do you skip

meals?

Text; Text 27.8% skipped B,

16.9% skipped L,

5.6% skipped D,

4.4% skipped B and L,

2.3% skipped B and D,

.5% skipped L and D

Reasons for skipping

meals:

-Time 40.5%,

-Fasting/religion 6.7%,

-Weight control 10.4%,

-Money 9.9%

24. Ozilgen

2011 [51]

Turkey Cross-

sectional

University students;

408 (56% F)

18–24 years Q: How many times

a day do you eat B, L,

and D?

Numerical ~80% F skipped meals,

~72% M skipped meals,

~77% M skipped B,

~61% F skipped B

SEX: (M + BS)

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Table 1 Characteristics of the included studies (Continued)

25. Sakamaki

et al. 2005 [52]

Japan

and

Korea

Cross-

sectional

University students

(141 Korean), 124

Japan); 265 (100% F)

(20 ± 1.8 years) Q: Do you always take

breakfast?

(Daily/3-4 times per week/

Once or twice per week/

Rarely)

Everything except

“Daily”

21% Japanese

skipped B,

64% Korean skipped B

ETHNICITY: (Japanese –BS)

26. Sato-Mito

et al. 2011 [32]

Japan Cross-

sectional

University students

living at home

(Dietetics); 3304

(100% F)

18–20 years;

(18.1 ± 0.3 years)

Q: During the previous

month

how many meals have

you skipped?

Numerical B skipped 1.00 ± 1.74

times/week, L skipped

0.20 ± 0.73 times/week,

D skipped 0.32 ± 1.09

times/week

SLEEP: (Feel asleep later in

the night + TMS)

27. Shimbo

et al. 2004 [53]

Japan Cross-

sectional

University Students;

71 (100% F)

19–23 years; 1 × 24 h food duplicate

portion samples

Meal not reported in

food record

14% skipped B LIVING SITUATION: (Living

away from home + BS)

28. Suliburska

et al. 2012 [54]

Poland Cross-

sectional

Young adults; 600

(50% F)

18 years Q: How many meals

do you consumed

in a typical day of

the week?

Numerical 5% of overweight

ate >2 meals/day,

9% of normal

weight ate

>2 meals/day

BMI: (Overweight –TMS)

SEX: (F-TMS)

LIVING SITUATION:

(Rural + TMS)

29. Suliga et al.

2012 [55]

Poland Cross-

sectional

University students;

925 (100% F)

N/R Q: Frequency of main

meals (B, L, D, and S)?

(Daily/2-6 times a week/

Rarely)

“Rarely”11% rarely ate B,

8.2% rarely ate L,

12.6% rarely ate D

BODY WEIGHT SELF

PERCEPTION: (+ TMS)

30. Tanaka et al.

2008 [33]

Japan Cross-

sectional

University students

(Medicine); 127

(30.4% F)

(20.5 ± 0.8 years) Q: B consumption

(Every day/Not every

day/Completely skipping

everyday)

“Completely skipping

everyday”

15.7% skipped B FATIGUE: (+BS)

31. Tominaga et al.

2012 [56]

Japan,

Korea

and

Austria

Cross-

cultural

University students;

276 Japan, 103 Korea,

127 Austria, (100% F)

Japan; (19.9 ±

1.2 years), Korea

(21.5 ± 1.8 years),

Austria

(22.3 ± 5.2 years)

Q: Frequency of B, L, D

(Never/Occasionally/

Sometimes/Almost

every day)

“Never”or

“Occasionally”or

“Sometimes”

JAPAN – 50%

skipped B,

15% skipped L,

17% skipped D.

KOREA – 54%

skipped B,

51% skipped L,

46% skipped D.

AUSTRIA – 42%

skipped B,

29% skipped L,

47% skipped D

ETHNICITY: (Austrian –BS),

(Japanese –LD and DS)

32. Ukegbu et al.

2015 [57]

Nigeria Cross-

sectional

University students;

200 (47% F)

16–25 years 2 × 24 h recalls.

Consecutive days

including a weekend

day.

Meal not reported in

food record

41.5% skipped B,

21.5% skipped L,

7% skipped D

Reason for meal skipping:

-Time 42.5%,

-Weight control 23.5%,

-Fasting/religion 21.5%,

-Money 12.5%

33. Yahia et al.

2008 [58]

Lebanon Cross-

sectional

University students;

220 (56.4% F)

(20 ± 1.9 years) Q: Do you take breakfast?

(Daily/3-4 times per week/

1-2 per week/Rarely)

“Rarely”67% skipped B SEX: (M + BS)

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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Table 1 Characteristics of the included studies (Continued)

34. Yildiza et al.

2011 [59]

Turkey Cross-

sectional

University students

(Medical); 301

(100% F)

18–25 years;

(21.2 ± 1.7 years)

Q: Frequency of B, L, D?

(Never/Occasionally/Most of

the days/Everyday)

“Never”or

“Occasionally”or “Most

of the days”

74% skipped B,

57% skipped L,

37% skipped D,

69.7% were

skipping at

least one meal

per day.

Reason for meal skipping:

-Time 46.7%

35. Yilmaz et al.

2014 [60]

Turkey Cross-

sectional

University students

(Medical); 995 (48% F)

M (21.25 ± 1.97

years); F

(20.94 ± 1.77)

Q: I usually skip meals?

(Yes/No)

“Yes”35.8% skip B,

27.9% usually

skip meals

DEPRESSION: (+BS)

N/R not reported, Mmales, Ffemales, Bbreakfast, Llunch, Ddinner, BMI body mass index, FFQ food frequency questionnaire, BS Breakfast skipping, LS Lunch skipping, DS Dinner skipping, TMS Total meal skipping,

(0) = No association, (+) = Positive association, (-) = Negative association

Pendergast et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:125 Page 8 of 15

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

specific meals and their skipping rates. All 25 of these

studies reported breakfast skipping with prevalence rates

of ranging from 14 to 88.5%. Lunch skipping was re-

ported by 11 studies [39, 42, 44, 47–50, 56, 57, 59, 61]

with rates ranging from 8 to 57%. Dinner or supper skip-

ping was reported in 13 studies, [39, 41, 42, 44, 46–48,

50, 56, 57, 59, 61, 63] with rates ranging from 5 to 47%.

The majority of studies (28 of the 35 studies) exam-

ined correlates by examining associations between fac-

tors and meal skipping behaviours through Chi-square,

One-way ANOVA, Duncan’s’multiple range test and

regression (linear and logistic) statistical analysis [1, 12,

31–33, 36–39, 41–45, 47–49, 51–56, 58, 60–63].

Another approach used to examine correlates of meal

skipping (used in 10 studies), was the use of a ranking

methodology where participants were asked to rank po-

tential correlates against other meal skipping correlates

[34, 39, 40, 42, 46, 47, 50, 57, 59, 62]. From these ten

studies, ten ranked correlates were reported.

Individual influences (Intrapersonal)

Of the 35 studies included in this review, 33 studies

assessed correlates from the SEF that could be consid-

ered intrapersonal correlates. These included sex, age,

ethnicity, body mass index (BMI), education, menstrual

regularity, physical activity, internet use, and a list of

cognitive influences.

Sex

Sex was reported as a correlate of meal skipping by 12 stud-

ies; three reported meal skipping (any meal) [44, 54, 61]

and nine reported on specific meal skipping [12, 38, 39, 43,

44, 49, 51, 58, 63]. Two studies identified no difference in

meal skipping (any meal) in relation to sex [44, 61], while

one study reported meal skipping (any meal) to be more

likely in males [54]. Two studies reported no significant dif-

ference in breakfast skipping between sexes [12, 43], three

reported breakfast skipping to be more likely in males

[49, 51, 58], while two reported breakfast skipping to

be more likely in females [39, 44]. However, Huang

et al. [44] reported that females were more likely to

skip breakfast in summer months, this associations

was not present in winter months. Two studies

reported lunch skipping [38, 49], and two dinner

skipping [38, 63], both studies found females to be

more likely to skip these meals (lunch [38, 49] and

dinner [38, 63]) compared to males.

Age

Two studies reported an association between age and

breakfast skipping [39, 47]. Danquah et al. [39], reported

breakfast skipping to be more likely in those aged 15–20

years when compared to those aged 21–30 years. While,

Lee and Yoon [47] reported meal skipping (any meal) to

be more likely in those aged 18–20 years compared to

those aged 24–28 years.

Ethnicity

Ethnicity was reported to be associated with breakfast

skipping in five studies [12, 39, 52, 56, 63]. Of the stud-

ies that included Caucasian participants [12, 39, 56, 63],

three found breakfast skipping to be more likely in those

Fig. 1 Flow chart summary of articles identified in search and included in review

Pendergast et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:125 Page 9 of 15

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

who were Caucasian compared with other ethnicities

(Japanese, Korean, African American) [39, 56, 63], and

one found no association [12]. Another study found

breakfast skipping and meal skipping (any meal) to be

more likely in Korean young adults compared with

Japanese young adults [52]. While, lunch and dinner

consumption was found to be more common in

Japanese young adults compared to Caucasian and

Korean young adults [56].

Body mass index (BMI)

Five studies reported that BMI was associated with meal

skipping [35, 37, 38, 48, 54]. Meal skipping (any meal)

was reported in four studies, two found no association

between BMI and meal skipping (any meal) [37, 48], one

reported meal skipping (any meal) to be more likely in

those with an increased BMI [35], while Suliburska [54]

found meal skipping (any meal) less likely in those with

an increased BMI. Breakfast skipping was reported by

one study and was more likely with those with an

increased BMI [38].

Education

Three studies examined education and its association

with meal skipping behaviours. Eldisoky [42], reported

maternal education status and its relationship with

breakfast skipping, although this was not significant.

Kapinos & Yakusheva reported those in second year uni-

versity were more likely to report meal skipping (any

meal) compared to those in first year university [31].

While, Danquah et al. [39], reported those in science

courses were more likely to report breakfast skipping

compared to students enrolled in humanities courses.

Menstrual regularity

Eittah [41] found breakfast skipping to be more likely in

those with an irregular menstrual cycle compared to

those with a regular menstrual cycle.

Physical activity

ColićBarićet al.[38], found breakfast consumption (6 or

7 times per week) was more likely in those who spent ≥

3.5 h exercising per week when compared to those who

did 2.6 h per week.

Internet use

One article reported meal skipping (any meal) to be

more common in those who used the internet heavily

(>4 h/day) [45].

Cognitive influences

Fatigue Two studies examined the association between

fatigue and meal skipping [32, 33]. Tanaka et al. [33]

found breakfast skipping to be more likely in those

experiencing fatigue, while Sato-Mito et al. [32] found

meal skipping (any meal) to be more likely in those

who’s mid-point in sleep was later (falling asleep after

1.30 AM and the mid-point of sleep falling at 5.31 ±

0.55 AM).

Psychological wellbeing Three studies documented as-

sociations between psychological factors and meal skip-

ping (any meal). Yilmaz et al. [60] found meal skipping

(any meal) to be more likely in those with depressive

symptoms; Suliga et al. [55], found meal skipping (any

meal) to be more likely in those with a self-perception of

being overweight; Bahl et al. [36] found meal skipping

(any meal) to be less likely in those who were mindful,

and meal skipping (any meal) to be less likely in those

who had increased body satisfaction.

Time Time or the lack of time was mentioned in 10

studies and when considered against other correlates,

time was ranked as the strongest perceived correlate of

meal skipping in nine of the 10 studies [34, 40, 42, 46,

47, 50, 57, 59, 62].

Hunger A lack of hunger was reported in four studies

and ranged in importance from being the strongest cor-

relate of meal skipping to the 3

rd

strongest perceived

correlate [34, 39, 42, 47].

Weight control Weight control was discussed in four

studies and ranged from being the strongest per-

ceived correlate of dinner consumption to the 3

rd

strongest perceived correlate of meal skipping (any

meal) [39, 42, 50, 57].

Money Money or the lack of money, was reported in

three studies and was ranked as either 3

rd

or 4

th

strongest perceived correlate of meal skipping (any

meal) [34, 50, 57].

Habit Dietary habit was reported in two studies, one

study ranked habit as the 2

nd

strongest perceived correl-

ate of breakfast skipping and the other ranked it as the

4

th

strongest perceived correlate of meal skipping (any

meal) [40, 47].

Religion Fasting/religion was reported in two studies,

where it was ranked as being either the 3

rd

or 4

th

strongest perceived correlate of meal skipping (any

meal) [50, 57].

Taste Taste was reported as being a correlate of meal

skipping (any meal), with one study ranking it as its 2

nd

strongest perceived correlate [40].

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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Cooking skills Lack of cooking skills was reported by

one study as 3

rd

strongest perceived correlate of meal

skipping (any meal) [34].

Social environmental influences (Interpersonal)

Of the 35 studies included in this review, only one study

assessed a correlate that could be considered to be part

of the social environmental domain. The variable exam-

ined was the notion of “being sociable”. It appears to as-

sess participants’preference for prioritising social

activities over eating, and participants ranked it as the

3

rd

strongest perceived correlate of meal skipping (any

meal) [40].

Physical environment influences

Of the 35 studies included in this review, six studies

assessed correlates that could be considered physical

environmental correlates. These included rural/urban

living environments and housing type.

Rural/urban living environment

Meal skipping (any meal) was more likely in those who

resided in a rural area compared to those who lived in

an urban area [54].

Housing type

Five studies focused on specific living environments such

as housing types. Kapinos & Yakusheva [31], reported

meal skipping (any meal) to be more likely in those liv-

ing in university/college dormitories. Similarly, Beerman

et al. [62], reported meal skipping (any meal) to be more

likely in those residing with parents or in university

dormitories when compared to those living in Greek

university housing (fraternity or sorority housing). Indi-

vidual meal skipping events were reported in three arti-

cles [1, 53, 63]. Two articles reported breakfast skipping

to be more likely in those who lived away from home

[53, 63], while one found breakfast skipping to be more

likely in those living by one’s self or with parents com-

pared with living on campus [1]. This same article

reported the same association for dinner skipping [1].

Overview

In conclusion, majority of included studies (n= 33)

examined correlates found within the intra-personal

domain of the SEF, one study examined a perceived

correlate from the interpersonal domain, with six

studies examining correlates from the physical envir-

onment domain.

Discussion

Main findings

To our knowledge, this is the first systematic review to

investigate correlates of meal skipping in young adults.

This review identified that the prevalence of meal skip-

ping among young adults ranged between 5 and 83%.

The breakfast meal was the most frequently skipped

meal in comparison to the lunch or dinner meal, with

rates ranging from 14 to 88.5%. The perception of time

or lack of time was consistently reported as an import-

ant correlate of this behaviour, with nine of ten studies

rating time as the biggest correlate of meal skipping. Sex

was the most commonly reported associated correlate of

meal skipping: breakfast skipping was more common

among men and lunch or dinner skipping being more

common among women. However, the studies were dif-

ficult to compare because of inconsistencies in measure-

ment tools and definitions of meal skipping.

Breakfast skipping

This review identified that young adults skipped break-

fast more frequently than other main meals. These

results are consistent with studies of other age groups,

with the breakfast meal frequently reported as the most

commonly skipped main meal. A sample of American

elderly participants reported the prevalence of breakfast

skipping was highest (10.7%) when compared to lunch

skipping (8.6%) and dinner skipping (5.8%) [64], with

similar results seen in children and adolescent popula-

tions [65, 66]. Meal skipping was assessed in a sample of

college students with the breakfast meal never/rarely

consumed by nearly half of participants (44.2%), com-

pared with lunch (3.5%) and dinner (2.3%) [67]. This

highlights that different age groups experience meal

skipping at different rates and that different meals are

skipped at different proportions within each age bracket.

It is however important to note that within the literature

the breakfast meal is more frequently examined than

either the lunch or dinner meals.

Perception of time

The influence of time or the perceived lack of time was

reported within all ten of the studies that assessed

ranked correlates. Nine of the ten studies reported time

as the biggest perceived influence on meal skipping

when ranked against other important correlates of young

adult meal skipping. The young adult age period is char-

acterised by transition, including moving out of the fam-

ily home, commencement of further education and/or

starting a career [2]. These competing demands require

young adults to learn a range of skills, including priori-

tising tasks and coping with these new environments [3].

These findings are confirmed by previous literature, with

time scarcity being recognised as having a negative im-

pact on a range of eating behaviours [68]. Deliens et al.

[69] reported the impact of time scarcity on university

students, with results suggesting students preferred to

spend time on activities other than cooking, and

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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

highlighted the importance of short meal preparation

times. Similar results are seen within adolescent popula-

tions, with 52% indicating that a lack of time in the

morning was the main reason for skipping breakfast

[70]. Therefore, perceived lack of time, may be a result

of varied prioritisation with healthy eating behaviours

poorly prioritised [69].

The notion of time is a temporal structure, self-

reported by individuals and can have multiple interpreta-

tions and ramifications [71]. Psychologists’such as

Zimbardo have defined an individual’s time perspective

as one of the most powerful influences on human behav-

iour [72]. However, none of the included articles pro-

vided a definition of time, which limited the ability of

the present review to identify how time may have influ-

enced meal skipping. For example, “time”may have been

attributed to food shopping, preparation, cooking time

or eating time and may have been interpreted differently

by individuals. Given that the perception of time is

underpinned by person-specific psychological constructs,

the methodological approaches in the included studies

were not detailed enough to provide a conclusive associ-

ation with meal skipping. As the examination of time as

a major barrier to health behaviours is complex, it

requires stronger definitions and measurement tools be-

fore it is able to provide comparable and valid results.

Further research is needed to examine, and develop an

understanding of trade-offs and prioritisation of lifestyle

factors seen between individuals.

Sex and meal skipping

This review identified that meal skipping (any meal) and

breakfast skipping were more likely in males, while

lunch and dinner skipping were more likely in females.

These associations are unlike those seen in other age

groups, with adolescent studies reporting breakfast

[7, 25, 66, 70] and lunch [66] skipping more likely in

females. The results seen within this review however,

are consistent with other research in this age group,

with results in undergraduates student samples (not

eligible for this review) finding no association be-

tween sex and breakfast skipping [73], and females

more likely to skip dinner [74].

Results across included studies varied, with many stud-

ies samples dominated by a single sex. Direct compari-

sons between sexes become limited when sample sizes

are heavily skewed towards one particular sub group.

Previous literature looking at the difference between

sexes and eating behaviours reports significant differ-

ences in food choices between sexes; females generally

have higher intakes of fruit and vegetables, higher in-

takes of dietary fibre and lower intakes of fat [75].

Females are also however highly motivated by weight

control and are more likely to diet or restrain their

eating behaviour [75]. What is currently unknown how-

ever, is the driver behind the apparent differences in

meal skipping between sexes and certain meals during

young adulthood.

Strengths and limitations

The present review has several strengths. It is the first

attempt to bring together the literature on meal skipping

correlates and employed a rigorous search strategy

whilst adhering to the PRISMA protocol [27]. In

addition, the use the Academy of Nutrition and Dietetics

Quality Criteria Checklist by two independent reviewers

to assess risk of bias [30], and the use of an established

framework for reporting eating behaviour correlates

[20], are regarded as strengths of this review.

An important limitation of this review is the lack of

consistency in the terminology and definition of meal

skipping and the measurement of these behaviours. Defi-

nitions of meal skipping varied; consuming three meals

on <2 days/week, failing to report a meal in a food diary,

to answering yes to “Do you skip meals?”This limitation

is paralleled in the study of breakfast consumption [76],

and meal patterns in general [77]. In addition, meal skip-

ping was captured via differing methodologies, including

food diaries, 24-h recalls, surveys and FFQ’s (which in-

cluded a specially designed item to assess the daily con-

sumption of meals and snacks). Each of these methods

has its own strengths and weaknesses and are aimed at

capturing dietary intake rather than the omission of eat-

ing occasions [78]. Questions designed to evaluate meal

skipping were not consistent between studies, with con-

tinuous scales, binary and categorical responses utilised.

These inconsistencies in definition and measurement

limited our ability to compare the findings between

studies. Furthermore, multiple methodological and

reporting weaknesses were apparent in the reviewed arti-

cles. This was confirmed in our bias risk assessment,

where 28 of the 35 studies scored a negative ranking.

Results of the studies were poorly reported, with limited

use of appropriate statistical analyses.

Another limitation of this review is the classification

of young adults by age. Some included studies included

participants outside of the 18–30 year age range, while

the studies that reported university or college student

samples failed to report the percentage of mature age

students. Results therefore may not always be reflective

of young adult populations.

Moreover, given that the majority of studies were

cross-sectional, studies were not able to infer causation

and thus the direction of the relationship for influences

such as menstrual regulation and BMI was not clear. In

addition, the generalisability of our findings may be lim-

ited by predominantly female populations and wide

country-specific variations in religion, eating culture and

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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

socio-economic status. The subjectively ranked attri-

butes of meal skipping provided a valuable insight into

why young adults may skip meals. However, these results

should be interpreted with caution due to the lack of

clarity in the data collection methodology employed in

these studies. For example, studies did not report if

questions were open-ended or categorical, which may

have impacted the results. It was also unclear in many

cases if these questions were framed in terms of meal

skipping in general or if it was directed at a specific meal

(e.g. the breakfast meal), which is important given that

meal skipping correlates appear to vary between meals.

Implications for future research

This review highlighted several implications for future

research. Firstly, definitions used to identify meal skipping

are inconsistent. A standardised approach to defining meal

skipping would provide clarity and allow for more reprodu-

cible results across studies. Secondly, the measurement of

meal skipping in existing studies is inconsistent. With a high

number of methods used to quantify meal skipping identi-

fied by this review, there is a need to standardise measure-

ment so that more informative comparisons can be made.

Thirdly, many of the reported correlates were within the

intrapersonal domain of the SEF. This highlights the need

to assess associations between correlates outside this do-

main such as physical environmental influences, to further

examine why young adults are partaking in this unhealthy

eating behaviour. Fourthly, this review focused only on the

young adult population, future reviews should be conducted

to understand correlates of meal skipping in different popu-

lation groups e.g. elderly or child populations. Lastly, only

four of the 35 studies had a positive risk of bias assessment

score indicating that future nutrition research needs stron-

ger design and reporting strategies. The Strobe-NUT [79]

reporting guidelines are aimed at improving the reporting of

observational studies with a focus on diet and health and

should be employed in future research to increase transpar-

ency and consistency of nutritional epidemiology studies.

Conclusions

This systematic review addressed a gap in the literature on

the correlates of meal skipping in young adults. Results

are consistent with previous research reporting that the

breakfast meal is the most commonly skipped meal for

this age group. This review highlights the perceived lack

of time to be an important correlate of meal skipping. The

sex of an individual was also reported to be an important

correlate of meal skipping, with males more likely to skip

breakfast and females more likely to skip lunch or dinner.

Therefore, sex and meal specific components, and im-

provements in time management skills, may warrant fur-

ther investigation as effective strategies for interventions

targeting meal skipping in young adults.

Additional files

Additional file 1: PRISMA 2009 Checklist. (DOC 68 kb)

Additional file 2: Table S2. Risk of bias assessment of included articles

according to the Academy of Nutrition and Dietetics Quality Criteria

Checklist [30]. (DOCX 57 kb)

Abbreviations

BMI: Body mass index; FFQ: Food frequency questionnaire; PRISMA: Preferred

Reporting Items for Systematic Reviews and Meta-Analysis; SCT: Social Cognitive

Theory; SEF: Social-ecological framework

Acknowledgements

Not applicable.

Funding

FJP is supported by an Australian Postgraduate Award Stipend, KML is

supported by an Alfred Deakin Postdoctoral Fellowship, AW –None, SAM is

supported by an NHMRC Career Development Fellowship Level 2, ID1104636.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or

analysed during the current study.

Authors’contributions

FJP conducted searches, analysed results and wrote paper, KLM was the

second reviewer of abstracts and full text and helped with the writing of the

paper, AW provided conceptual and structural advice and support, SAM was

the third reviewer of included studies, provided conceptual advice and was

responsible for the final wording of the article. All authors read and approved

the final manuscript.

Authors’information

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethics approval was not required for systematic review. No identifying data

was obtained or analysed.

Received: 2 August 2016 Accepted: 21 November 2016

References

1. Laska MN, et al. Dietary patterns and home food availability during

emerging adulthood: Do they differ by living situation. Public Health Nutr.

2010;13(2):222–8.

2. Mulye TP, et al. Trends in adolescent and young adult health in the United

States. J Adolesc Health. 2009;45(1):8–24.

3. Betts NM, et al. Ways young adults view foods. J Nutr Educ. 1997;29(2):73–9.

4. Haberman S, Luffey D. Weighing in college students’ diet and exercise

behaviors. J Am Coll Health. 1998;46(4):189–91.

5. Australian Bureau of Statistics. Australian Health Survey: Nutrition First Results –

Foods and Nutrients, 2011-12. Canberra: Australian Government; 2014.

6. Piernas C, Popkin BM. Snacking increased among US adults between 1977

and 2006. J Nutr. 2010;140(2):325–32.

7. Australian bureau of Statistics. National Nutrition Survey: Selected Highlights,

Australia, 1995. Canberra: Australian Government; 1997.

8. McNaughton SA, et al. An index of diet and eating patterns is a valid measure

of diet quality in an Australian population. J Nutr. 2008;138(1):86–93.

9. Dubois L, et al. Breakfast skipping is associated with differences in meal

patterns, macronutrient intakes and overweight among pre-school children.

Public Health Nutr. 2009;12(01):19–28.

10. Timlin MT, Pereira MA. Breakfast frequency and quality in the etiology of

adult obesity and chronic diseases. Nutr Rev. 2007;65(6):268–81.

Pendergast et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:125 Page 13 of 15

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11. Deshmukh-Taskar PR, et al. The relationship of breakfast skipping and type

of breakfast consumption with nutrient intake and weight status in children

and adolescents: the National Health and Nutrition Examination Survey

1999-2006. J Am Diet Assoc. 2010;110(6):869–78.

12. Nicklas TA, et al. Impact of breakfast consumption on nutritional adequacy

of the diets of young adults in Bogalusa, Louisiana: ethnic and gender

contrasts. J Am Diet Assoc. 1998;98(12):1432–8.

13. Kerver JM, et al. Meal and snack patterns are associated with dietary intake

of energy and nutrients in US adults. J Am Diet Assoc. 2006;106(1):46–53.

14. Ma Y, et al. Association between eating patterns and obesity in a free-living

US adult population. Am J Epidemiol. 2003;158(1):85–92.

15. Smith KJ, et al. Skipping breakfast: longitudinal associations with

cardiometabolic risk factors in the Childhood Determinants of Adult Health

Study. Am J Clin Nutr. 2010;92(6):1316–25.

16. Pereira MA, et al. Breakfast frequency and quality may affect glycemia and

appetite in adults and children. J Nutr. 2011;141(1):163–8.

17. Farshchi HR, Taylor MA, Macdonald IA. Deleterious effects of omitting

breakfast on insulin sensitivity and fasting lipid profiles in healthy lean

women. Am J Clin Nutr. 2005;81(2):388–96.

18. Silliman K, Rodas-Fortier K, Neyman M. A survey of dietary and exercise

habits and perceived barriers to following a healthy lifestyle in a college

population. Cal J Health Promot. 2004;18:281.

19. Sakamaki R, et al. Nutritional knowledge, food habits and health attitude of

Chinese university students–a cross sectional study–. Nutr J. 2005;4(1):1.

20. Story M, Neumark-Sztainer D, French S. Individual and environmental influences

on adolescent eating behaviors. J Am Diet Assoc. 2002;102(3):S40–51.

21. Sallis JF, Owen N, Fisher EB. Ecological models of health behavior. Health

behavior and health education: Theory, research, and practice. 2008;4:465–86.

22. Bandura A. Social Foundations of Thought and Action. Englewood Cliffs:

Prentice-Hall; 1986.

23. Robinson T. Applying the socio-ecological model to improving fruit and

vegetable intake among low-income African Americans. J Community Health.

2008;33(6):395–406.

24. SmithKJ,McNaughtonSA,ClelandVJ,CrawfordD,BallK.Health,behavioral,

cognitive, and social correlates of breakfast skipping among women living in

socioeconomically disadvantaged neighborhoods. J Nutr. 2013;143(11):1774–84.

25. Keski-Rahkonen A, et al. Breakfast skipping and health-compromising

behaviors in adolescents and adults. Eur J Clin Nutr. 2003;57(7):842–53.

26. Ene‐Obong H, Akosa U. Adolescents living in boarding houses in Nsukka,

Enugu State, Nigeria I: Meal patterns, nutrition knowledge and nutrient

intake. Ecol Food Nutr. 1993;30(3-4):179–93.

27. Moher D, et al. Preferred reporting items for systematic reviews and meta-

analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

28. Khan M, Lipke L. Snacking and its contribution to food and nutrient intake

of college students. J Am Diet Assoc. 1982;81(5):583.

29. de Castro JM. Macronutrient relationships with meal patterns and mood in the

spontaneous feeding behavior of humans. Physiol Behav. 1987;39(5):561–9.

30. Dietetics, A.o.N.a. Evidence Analysis Manual: Steps in the Academy Evidence

Analysis Process. 2012. Available from: https://www.andeal.org/vault/2440/

web/files/2012_Aug_EA_Manual.pdf. Cited 20 April 2016.

31. Kapinos KA, Yakusheva O. Environmental influences on young adult weight

gain: evidence from a natural experiment. J Adolesc Health. 2011;48(1):52–8.

32. Sato-Mito N, et al. The midpoint of sleep is associated with dietary intake and

dietary behavior among young Japanese women. Sleep Med. 2011;12(3):289–94.

33. Tanaka M, et al. Relationships between dietary habits and the prevalence of

fatigue in medical students. Nutrition. 2008;24(10):985–9. 5p.

34. Afolabi W, et al. Pattern of Fast Food Consumption and Contribution to

Nutrient intakes of Nigerian University Students. Int J Educ Res. 2013;1(5):1–10.

35. Aryee PA, et al. Prevalence and risk factors for overweight and obesity

among nurses in the tamale metropolis of Ghana. J Medical Biomedical Sci.

2013;2(4):13–23.

36. Bahl S, et al. Mindfulness: A long-term solution for mindless eating by

college students. J Publ Policy Market. 2013;32(2):173–84.

37. Chung H, Song M, Park M. A study of the anthropometric indices and

eating habits of female college students. J Community Nutr. 2003;5(1):21–8.

38. ColićBarićI, SatalićZ, LukesićZ. Nutritive value of meals, dietary habits and

nutritive status in Croatian university students according to gender. Int J

Food Sci Nutr. 2003;54(6):473–84.

39. Danquah AO, et al. A pilot study of the dietary and physical activity

behaviours of international students: implications for health promotion. Afr

J Food Sci. 2010;4(3):86–92.

40. Deepika D, Vinti D. Food preference and dietary intake of college students

in Haryana College. Rev Res J. 2015;4(8):ROR–1543.

41. Eittah HFA. Effect of breakfast skipping on young females’menstruation.

Health Sci J. 2014;8(4):469–84. 16p.

42. Eldisoky ST. Interactive relation between dietary habits and spread of

malnutrition diseases. Ann Agric Sci (Cairo). 2003;48(2):663–75.

43. Evagelou E, et al. Exploration of Nursing students’ dietary habits. Health Sci

J. 2014;8(4):452–68. 17p.

44. Huang YL, et al. What do college students eat? Food selection and meal

pattern. Nutr Res. 1994;14(8):1143–53.

45. Kim JH, et al. Brief report: Predictors of heavy Internet use and associations

with health-promoting and health risk behaviors among Hong Kong

university students. J Adolesc. 2010;33(1):215–20.

46. Lamia Dhia A, Ban Faud I. Knowledge and practice of dietary habits and

healthy lifestyle in a sample of medical and non medical college students

in Baghdad. Middle East J Fam Med. 2014;12(3):37–47.

47. Lee JE, Yoon WY. A study of dietary habits and eating-out behavior of

college students in Cheongju area. Technol Health Care. 2014;22(3):435–42.

48. Musaiger AO, Radwan HM. Social and dietary factors associated with obesity

in university female students in United Arab Emirates. J R Soc Health.

1995;115(2):96–9.

49. Neslişah R, Emine AY. Energy and nutrient intake and food patterns among

Turkish university students. Nutr Res Pract. 2011;5(2):117–23.

50. Nzeagwu OC, Akagu JC. Food habits and nutritional status of Nigerian

undergraduates. Food. 2011;5(Special Issue 1):89–95.

51. Ozilgen S. Gender is correlated with body mass index, eating habits and

exercise frequency in students attending a private university in western

Turkey. Acta Aliment (Budapest). 2011;40(3):400–6.

52. Sakamaki R, et al. A comparative study of food habits and body shape

perception of university students in Japan and Korea. Nutr J. 2005;4:31.

53. Shimbo S, et al. Effects of life away from home and physical exercise on

nutrient intake and blood/serum parameters among girl students in Japan.

Tohoku J Exp Med. 2004;203(4):275–86.

54. Suliburska J, et al. Analysis of lifestyle of young adults in the rural and urban

areas. Ann Agric Environ Med. 2012;19(1):135–9.

55. Suliga E, Wronka I, Pawlinska-Chmara R. Nutritional Habits of Female

University Students in Relation to Self-Perception of Body. Biomedical Hum

Kinet. 2012;4:98–102.

56. Tominaga M, et al. Differences in self-assessment regarding eating

behaviors among female university students living in Japan, Korea, and

Austria. Food Nutr Sci. 2012;3(12):1673–81.

57. Ukegbu PO, Uwaegbute AC, Usonka VA. Contribution of snacks to energy

and nutrient intake of undergraduates in a Nigerian university. Malaysian J

Nutr. 2015;21(1):15–23.

58. Yahia N, et al. Eating habits and obesity among Lebanese university

students. Nutr J. 2008;7:32.

59. Yildiza EA, et al. Evaluation of the dietary habits, body images and BMI of

Turkish University students who live in dormitory. Pakistan J Med Sci.

2011;27(1):85–9.

60. Yilmaz Y, et al. The relationship between medical education and eating

habits along with mental condition in medical students. Erciyes Tip Dergisi.

2014;36(2):75–81.

61. Akarslan ZZ, et al. Dietary habits and oral health related behaviors in

relation to DMFT indexes of a group of young adult patients attending a

dental school. Med Oral Patol Oral Cir Bucal. 2008;13(12):E800–7.

62. Beerman KA, Jennings G, Crawford S. The effect of student residence on

food choice. J Am Coll Health. 1990;38:215–20.

63. Freedman MR. Gender, residence and ethnicity affect freshman BMI and

dietary habits. Am J Health Behav. 2010;34(5):513–24.

64 Lee CJ, Templeton S, Wang C. Meal skipping patterns and nutrient intakes

of rural southern elderly. J Nutr Elder. 1996;15(2):1–14.

65 Gross SM, et al. Breakfast and lunch meal skipping patterns among fourth-

grade children from selected public schools in urban, suburban, and rural

Maryland. J Am Diet Assoc. 2004;104(3):420–3.

66 Savige G, et al. Snacking behaviours of adolescents and their association

with skipping meals. Int J Behav Nutr Phys Act. 2007;4(1):36.

67 DeBate RD, Topping M, Sargent RG. Racial and gender differences in weight

status and dietary practices among college students. Adolescence.

2001;36(144):819.

68 Jabs J, Devine CM. Time scarcity and food choices: an overview. Appetite.

2006;47(2):196–204.

Pendergast et al. International Journal of Behavioral Nutrition and Physical Activity (2016) 13:125 Page 14 of 15

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

69 Deliens T, et al. Determinants of eating behaviour in university students: a

qualitative study using focus group discussions. BMC Public Health. 2014;14(1):53.

70 Shaw ME. Adolescent breakfast skipping: an Australian study. Adolescence.

1998;33(132):851–61.

71 Graham RJ. The role of perception of time in consumer research. J Consum

Res. 1981;7(4):335–42.

72 Boniwell I, Zimbardo PG. Time to find the right balance. Psychologist. 2003;

16:129–31.

73 Moy FM, Johari S, Ismail Y, Mahad R, Tie FH, Wan Ismail WMA. Breakfast

skipping and its associated factors among undergraduates in a public

university in Kuala Lumpur. Mal J Nutr. 2009;15(2):165–74.

74 Lee MS, Kwak CS. The comparison in daily intake of nutrients, quality of

diets and dietary habits between male and female college students in

Daejeon. Korean J Community Nutr. 2006;11(1):39–51.

75 Westenhoefer J. Age and gender dependent profile of food choice. Forum

Nutr. 2005;57:44–51.

76 Rampersaud GC, et al. Breakfast habits, nutritional status, body weight, and

academic performance in children and adolescents. J Am Diet Assoc.

2005;105(5):743–60.

77 Leech RM, et al. Understanding meal patterns: definitions, methodology and

impact on nutrient intake and diet quality. Nutr Res Rev. 2015;28(01):1–21.

78 Block G. A review of validations of dietary assessment methods. Am J

Epidemiol. 1982;115(4):492–505.

79 Lachat C, et al. Strengthening the Reporting of Observational Studies in

Epidemiology—Nutritional Epidemiology (STROBE-nut): An Extension of the

STROBE Statement. PLoS Med. 2016;13(6):e1002036.

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