The Relationship Between Nutrition Knowledge and Dietary Intake of University Students: A Scoping Review

Abstract

This study aimed to provide an overview of published studies that assess the relationship between nutrition knowledge and dietary intake among university students. A scoping review was undertaken and articles assessing the relationship between nutrition knowledge and dietary intake among university students were identified. EMBASE, PsycINFO and Scopus were searched for peer-reviewed articles reporting primary research. The initial search generated 805 potentially relevant articles. After reviewing titles and abstracts and applying the exclusion criteria, 22 articles were deemed eligible for inclusion. Nutrition knowledge was measured in all studies and information was predominantly collected using adapted General Nutrition Knowledge Questionnaires (GNKQs). Dietary intake measurement methods varied across the studies, with the Food Frequency Questionnaire (FFQ) being commonly used. Findings identified that in most studies a positive relationship was found between nutrition knowledge and dietary intake. The ability to draw strong conclusions about the relationship between nutrition knowledge and dietary intake in university students is limited by the heterogeneity of the study design, the subpopulations considered, and the tools used. Notwithstanding this, findings indicate that the majority of studies reported a positive relationship between nutrition knowledge and dietary intake. Future studies should consider the use of validated assessment tools for both nutrition knowledge and dietary intake and the inclusion of more male student participants.

1. Introduction

On transitioning from secondary school to college or university, many students go through significant life changes as they begin living away from their parents/guardians for the first time. As a result, they have more freedom to make their own choices in their day-to-day living [1]. With increased independence, students experience a life transition that can often result in the development and establishment of less healthy behaviours such as undesirable dietary intake and excessive weight gain [1,2,3,4,5]. It is a key period in the life cycle, as BMI rises the fastest during the transition from adolescence to adulthood [6]. In a 2014 study accessing 22 countries, Peltzer et al. found that 22% of university students from low, middle income and emerging economy countries were overweight or obese. More recent reports found that almost 40% of university students in Australia [7] and the USA [8] are overweight or obese. Unhealthy dietary habits developed while in university can persist into adulthood, contributing to health issues later in life [9,10,11]. With 47.4% of 25–34-year-olds possessing a tertiary level education, there is a need to prioritise research to explore the nutrition and diet in the growing student cohort as a large percentage of the population can be positively targeted within this setting [12].

Adhering to a healthy diet throughout the life cycle is crucial for overall health [13,14]. However, numerous studies have found that many university students fail to meet the recommendations of food-based dietary guidelines, alongside recommended energy and nutrient intakes [15,16,17,18]. They often have a poor dietary intake, which is widely accepted to contribute to immediate health risks such as weight gain [19] and non-communicable diseases such as obesity, diabetes mellitus, hypertension, CHD and particular types of cancer, contributing to premature mortality and physical morbidity [20,21,22]. Student’s dietary intake is characterised by a high consumption of fast food [23], snack foods with elevated contents of sugar and fat [24] and convenience meals [25]. Additionally, there is an insufficient consumption of fruit and vegetables [18,22,26,27,28], legumes, nuts and whole grains [24], and fish [29] in university students’ diets. Furthermore, dietary intake of calcium, fibre, folate, potassium and Vitamin A has been reported to be below recommendations [15,30,31,32,33] with an elevated intake of sodium [26,30,34].

There are a multitude of factors that may impact dietary intake in university students [27], which can be broadly categorised into individual, societal/interpersonal and physical/environmental factors [4]. Individual factors such as gender [7,35,36], age [7] sexuality [37] personal preference (taste) [38,39], childhood experience with food [40], cooking skills [41], personality [42], finances [36,43], time [44,45], convenience [23,46], academic activities [41] stress [17,36,47], mental health conditions such as anxiety, depression [48,49] and food addiction [50], and culture and religious beliefs [51] have been shown to have an impact on students’ dietary intake. Studies have shown that students’ dietary intake can also be impacted by their social environment, e.g., their social network [45] and their physical [52], e.g., living arrangements [53,54] and availability of healthy food [54,55]. Nutrition knowledge has also emerged as an influencing factor in promoting and maintaining a favourable dietary intake [56]. Nutrition knowledge is concerned with the comprehension of concepts and processes regarding nutrition and health [57]. This encompasses a broad range of topics such as nutritional processes, sources of food, the relationship between diet and health outcomes, and dietary recommendations [58,59]. It has been reported that the university student population is characterised by a low level of nutrition knowledge [60,61,62,63]. Understanding the level of nutrition knowledge and the relationship with dietary intake among university students is central to shaping effective interventions that enable students to make healthier food choices.

The introduction of the Ottawa Charter [64] and later the Okanagan Charter [65] saw an emphasis placed on the importance of the university setting as a supportive environment to empower students to be healthy through the health promotion campus concept. A key call to action for universities is the embedding of health promotion in all aspects of the university environment including the development of initiatives or programmes with the aim of enabling students to make healthier food choices by equipping them with appropriate knowledge. Despite the plethora of studies that have reported on the positive impact of dietary interventions on the dietary intake of university students [19], the evidence is heterogeneous in terms of the assessment of nutrition knowledge, methods of dietary intake assessment, and the possible relationship between these two variables. The lack of consistency may be ascribed to the innate complexities involved in measuring nutrition knowledge and dietary intake. In a previous review, ref. [66] examined studies that aimed to assess the relationship between nutrition knowledge and dietary intake of adults. In this study, the relationship between nutrition knowledge and dietary intake in the general population was reported to be either null or weak (r < 0.5). More recently, within the university population, several studies have reported that a higher level of nutrition knowledge has a positive impact on dietary intake [25,67] however, nutrition knowledge is not always positively related to improved dietary intake [68].

Published studies have reviewed the relationship between nutrition knowledge and dietary intake of the general population [66] and specific subpopulations [69]. However, few have specifically investigated the research available on the nutrition knowledge and dietary intake of university students, indicating a research gap. This research aims to identify and provide an up-to-date and comprehensive overview of the available studies that have investigated the relationship between the nutrition knowledge and dietary intake of university students.

The research objectives of this review are as follows:

(1)

Systematic identification and mapping of the breadth of research available on the relationship between the nutrition knowledge and dietary intake of university students;

(2)

Provide an overview of the study designs adopted and the methodological tools employed;

(3)

Uncover potential research gaps to inform future research inquiries.

A greater understanding of the relationship between nutrition knowledge and dietary intake may assist in advancing dietary interventions targeted at university students.

2. Methodology

2.1. Design

This scoping review was conducted in accordance with the methodological framework proposed by [70] incorporating the enhancements proposed by [71] and the Joanna Briggs Institute (JBI) [72]. The review utilised a five-stage framework as outlined in Figure 1.

The optional sixth stage, consultation with consumers/stakeholders, was not conducted for this review [70]. The Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews (PRISMA-SCR) checklist was utilised to report results to ensure transparency [73].

2.2. Eligibility Criteria

The participants, concepts, context (PCC) framework for developing eligibility criteria was utilised in this review [72].

2.2.1. Participants

The participants included in this study were undergraduate and/or postgraduate students. All university study populations (i.e., years of study or programme-specific cohorts) and activities (i.e., university athletes) that met the criteria were included. Studies specifically focusing on pre-entry students or graduates were excluded as this review was interested in studies representing enrolled students. No restrictions were placed on sex or age.

2.2.2. Concept

Studies that provided a quantitative assessment of nutrition knowledge and dietary intake were included in this review. Each study was required to explicitly report the relationship between nutrition knowledge and dietary intake using statistical analysis. Qualitative studies were not considered, as the data extracted were not eligible for inclusion, given the nature of the aim and objectives of this review.

2.2.3. Context

This review included studies reporting on enrolled students from all higher education settings in all geographical locations. In studies with aggregated samples, that is, where the participants included two or more groups (e.g., students and university staff), students and coaches were excluded as the population was not solely students.

2.3. Types of Sources

While not required for a scoping review, peer-reviewed journal articles were specified to restrict the scope of the review to higher-quality research. Consequently, conference posters and abstracts were excluded. The review was restricted to primary research given the aim of the study was to investigate the relationship between nutritional knowledge and dietary intake; review articles were excluded. Due to time and financial constraints only, studies published in English were considered for this review. A list of inclusion and exclusion criteria is outlined in

Table 1. Eligibility criteria: Inclusion and Exclusion Criteria for the Review.

Parameter	Inclusion Criteria	Exclusion Criteria
Setting	Any country	None
Population group	Third-level/university/college students	Non-college/university students
Outcomes	Articles reporting research where the specific goal was to assess association between nutrition knowledge and dietary intake using statistical analysis.	Articles not reporting research where the specific goal was to assess association between nutrition knowledge and dietary intake using statistical analysis, e.g., collecting data on each phenomenon but with no explicit attempt to assess their association.
Study Type	Peer-reviewed articles reporting primary research.	Non-peer-reviewed articles not reporting primary research, e.g., conference posters or abstracts, literature review.
Study Methodology	Quantitative Instrument—assessment for nutrition knowledge and dietary intake	Qualitative Instrument
Publication Type	Peer-reviewed journal article	Grey Literature; conference abstract, study protocol, thesis, book, report and professional journal
Year	Earliest record to present	None
Language	Articles written in English	Articles written in all other languages.

.

2.4. Search Strategy

The literature was consulted to formulate key search terms in relation to the concepts. A search strategy was developed for each database. The combinations were adapted to use more general or specific terms, based on the limitations of each of the selected databases. A systematic approach was employed by combining every possible combination of keywords in each of the following three categories: students, nutritional knowledge and dietary intake. The search string was amended as necessary and applied to different online databases to ensure that all the relevant studies were captured. Three databases; EMBASE, PsychINFO and Scopus were searched using search terms in April 2021. The reference list on the [66] systematic review was consulted to ensure key relevant articles were captured with the search terms employed. An example of a search strategy for this study is outlined in

Table 2. Example of the overview of search terms employed for one database.

Search Number	Concept	Search String
#1	Population	“college student*” OR “university student*” OR “undergraduate” OR “freshm$n” OR “third level” OR “college* AND student*” OR “universit* AND student*”
#2	Nutrition Knowledge	(food OR diet* OR nutrition*) NEAR/3 knowledge OR food NEAR/3 literacy OR “food agency” OR “diet* guid*”
#3	Dietary Intake	(Diet* OR food OR nutri* OR calori* OR energy intake*) OR (diet* OR eating OR food pattern*) OR (energy or food consumption) OR (food OR feeding OR eating behavio?r*) OR (food OR eating habit*) OR ‘diet* quality’ OR ‘food choice*’
#4		#1 AND #2 AND #3

.

2.5. Study Selection

After the initial search was completed, results were exported to Endnote 20 where duplicates were excluded by one reviewer (MOL). The included studies were manually screened to select other relevant studies. The remaining search results were imported into Rayyan, a free online literature review software. Subsequently, the study selection was based on meeting the inclusion-exclusion criteria (Table 1). Articles were assessed, initially based on title and abstract content. Studies clearly unrelated to the review topic were removed by one reviewer (MOL) at this stage. Full-text manuscripts were then retrieved and assessed against the inclusion and exclusion criteria outlined in Table 1 by two reviewers (MOL and EM). In the case where conflicts surrounding article exclusion or inclusion could not be resolved via consensus, a third reviewer (AMC) was consulted, and a decision was made through discussion. In addition, the reference lists of all included studies were manually screened to identify potentially relevant additional studies that might have been missed in the search. Manual searches in Google Scholar, using key search terms, were undertaken to ensure no relevant articles were omitted. The selection and exclusion of studies for each stage of the screening process are outlined in Figure 2.

2.6. Charting the Data

The aim and objectives of the study guided the process of data extraction and synthesis. A predesigned data extraction form was created using Microsoft Excel, the form was piloted and amended where necessary prior to full extraction. The data extraction form was modified iteratively throughout the process to allow for additional items to be extracted that were not prespecified, e.g., duration of food frequency measurement. One reviewer (MOL), extracted data from the eligible full-text studies, including study details (name of first author, year of publication country of study, study design), participant demographic information (age (years), sex, sub-population), study characteristics (sample size, tool used to assess nutrition knowledge, alongside the method and duration of dietary intake assessment used) and the statement relating to the sub-population relationship between nutrition knowledge and dietary intake. Following extraction, the data extraction form was checked by another reviewer (EM) to ensure that all relevant information included was accurate and complete. Any queries over the data extracted were discussed with the third reviewer (AMC).

2.7. Collating, Summarising and Reporting the Results

The data extracted from each of the studies were categorised and mapped in line with the aim and objectives of this review [74]. To gain a detailed overview of the topic, a basic descriptive statistical analysis, including central tendency and measure of variability, was undertaken for data relating to the study characteristics. Study designs and populations were summarised to provide information on the number and types of studies undertaken in the various subpopulations within a university setting. The data obtained were grouped and presented in table format, frequency counts of data were used, and descriptive numerical summary analysis was provided for each category based on the objectives [70,71,74]. Quality assessment or advanced statistical analysis was not carried out for this review as these are not required elements of a scoping review [72]. When tabulating the data, a similar approach to that undertaken in a study by Spronk and colleagues was adopted [66].

3. Results

3.1. Selection of Articles

From the 805 potentially relevant abstracts that were identified through the initial search of the 3 databases, a total of 213 duplicate records were removed using EndNote 20. Of the remaining 592 potentially eligible articles, 286 were discarded on title review and 195 were deemed not fit after abstract screening as these articles did not satisfy the inclusion criteria. Of the 111 articles that were deemed suitable for full-text review, 5 could not be retrieved despite exhaustive efforts, including contacting the authors. In total, 106 full-text articles were retrieved for full-text review eligibility, most of the articles were deemed not relevant because they did not assess nutritional knowledge and dietary intake statistically. A total of 22 full-text articles were identified for inclusion in the review; the flow chart of articles through the study selection process is depicted in Figure 2.

3.2. Overview of Study Characteristics

3.2.1. Number of Studies by Year

The 22 studies included in this review were published between 2009 and 2021, for which the median year of publication was 2017. The highest number of articles were published in 2016 (n = 6), with 2020 (n = 4) having the second highest occurrence of articles.

3.2.2. Articles by Country

A total of 15 countries were represented within this review, with the United States having the highest frequency of studies (n = 4). The USA was followed by Croatia (n = 2), Poland (n = 2), Nigeria (n = 2) and Malaysia (n = 2). One study was multi-country, in this case, the first author’s origin was used to determine the primary location of the study. South and Central America had a low return (n = 1), possibly due to the language limitation placed on this review.

3.2.3. Overview of University Student Subpopulations

Table 3 summarises the study designs, methodological approaches and the NK assessment tool utilised. The included studies had a total sample size of 7976 participants, ranging from 12 to 1005 participants (media n = 332 participants; interquartile range (IQR) = 321.25). The age of the participants in the studies (n = 14) ranged from 17 to 70, with 18 of the studies (75%) reporting a mean age between 20 and 25 years. Where there were discrepancies between the reported number of participants within a study [67], a count was completed within tables to reach a final number of participants. Most of the studies were mixed-sex samples (n = 20, 91%), with two (9%) conducted with females only; there were no studies conducted with males only. In each of the 20 studies with a mixed-sex sample (n = 7381), there were more female participants (n = 4968, 67.3%) than male participants (n = 2413, 32.7%). Women represented the majority of participants assessed in all studies (n = 5563, 69.7%).

Of the 22 studies included in this study, 5 studies [75,76,77,78,79] broadly utilised university students and 2 studies [80,81] broadly utilised undergraduate students with no further restrictions on the populations. There were further limitations placed on the student population, for example, degree programme/area of study (n = 11) [67,82,83,84,85,86,87,88,89,90,91] year of study (n = 2) ([84,88], age (n = 4) [79,82,92,93], athlete populations (n = 10) [94], place of residence (n = 1) [93] and specific health concerns (n = 1) [93]. In the reviewed articles, one study targeted those who had not undertaken a college-level nutrition course [82]; two targeted students enrolled in nutrition, dietetics or culinary courses [67,85]; and six studies focused on students undertaking health-related programmes: pharmacy [87], medicine [83,84,88,91] and health science [90].

Table 3. Nutrition knowledge assessment of university students.

First Author	Year	n (Sex)	Mean	SD	Student Population	Sample	Country	NK Assessment	Design	Validation	No. of Items	Question Type
Almansour et al. [81]	2020	690 (87 M, 603 F)	21.7 (M) 20.7 (F)	3.1 (M) 2.5 (F)	>17 yo, UG, with or without children.	NST	Kuwait	Q (paper) GN	Adapted Established Questionnaire *	Yes	10	Multiple Choice
Cicognini et al. [92]	2016	110 (56 M, 54 F)	NST	NST	18–19 yo	NST	Italy	Q GN	NST	NST	15	Multiple Choice
Cooke et al. [76]	2014	500 (125 M, 375 F)	24.9	NST	US	CON	UK	GNKQ (online)	Established Questionnaire †	Yes	110	Multiple Choice, True/False, Other
Dissen et al. [82]	2011	279 (131 M, 148 F)	20.12	1.75	18–26 yo, UG, never taken a college-level nutrition course	CON	US	Q (online) GN	Author Designed Questionnaire	NST	22	Multiple Choice
Douglas et al. [93]	2021	12 F	22.5	2.71	18–26 yo US, with PCOS diagnosis, not pregnant or breastfeeding	NST	US	NKQ (paper)	Established Questionnaire ‡	Yes	60	NST
El Hajj et al. [79]	2021	303 (93 M, 210 F)	NST	NST	18–25 yo US	RAN	Lebanon	Q (online) (focus on healthy breakfast, healthy meal, components of the Mediterranean diet)	NST	NST	4	Multiple Choice
El-Ahmady et al. [87]	2017	423 (136 M, 287 F)	19	1.6	UG students enrolled in a pharmacy degree	NST	Egypt	Q GN	Author Designed Questionnaire	NST	7	True/False, Other
Folasire et al. [94]	2015	110 (63 M, 47 F)	22.06	2.39	UG Athletes	PUR	Nigeria	Q NK related to athletes	Author Designed Questionnaire	Yes	14	NST
Folasire et al. [80]	2016	367 (236 M, 131 F)	21.9	2	UG	RAN	Nigeria	Q NK related to cancer prevention	NST	NST	20	NST
Guiné et al. [78]	2020	670 (245 M, 425 F)	21.8	5.51	≥18 yo US	NON-PROB	Portugal	Q FK	Author Designed Questionnaire	NST	9	Multiple Choice, Other
Jovanovic et al. [83]	2011	390 (120 M, 270 F)	21.9 M 21.5 F	2.3 M 2.3 F	Medical US	CON	Croatia	GNKQ (part D—disease relationship)	Established Questionnaire †	Yes	30	Multiple Choice, True/False, Other
Kalkan [90]	2019	276 (130 M, 146 F)	20	1.6	Faculty of Health Science students	RAN	Turkey	ANLS (face to face)	Adapted Established Questionnaire §	Yes	22	Multiple Choice
Kresić et al. [75]	2009	1005 (264 M, 741 F)	21.7	2.3	US	CON	Croatia	GNKQ (adapted)	Adapted Established Questionnaire †	Yes	96	Multiple Choice, True/False, Other
Lwin et al. [88]	2018	101 (31 M, 70 F)	NST	NST	Medical US—Year 2	RAN	Malaysia	Q NK (related to food consumption)	Author Designed Questionnaire	NST	10	Multiple Choice
Rivera Medina et al. [67]	2020	83 (24 M, 59 F)	24.3	6.7	UG enrolled in: nutrition and dietetics, culinary nutrition, and culinary management.	CON	Puerto Rico	Q (paper) NK (4 Subsections: nutritional recommendations regarding daily food and water intake, food groups in the plate, food portions, and benefits of fiber consumption)	Adapted Established Questionnaire ¶	No	36
Ruhl et al. [77]	2016	583 F	20.89	NST	Female US -dieters and non-dieters	NST	USA	GNKQ (online)	Established Questionnaire †	Yes	35
Shaikh et al. [84]	2011	218 (158 M, 60 F)	NST	NST	Final year of medicine, interns and PG students in a Medical college	PUR	India	Q GN	Author Designed Questionnaire	NST	NST
Suhaimi et al. [95]	2018	400 (92 M, 308 F)	22	NST	US living in college residence	RAN	Malaysia	Q (face to face) GN	NST	NST	NST
Suliga et al. [91]	2020	394 (115 M, 279 F)	21.52	3.22	Students from the Medical College of Jan Kochanowski University in Kielce in Poland; the Faculty of Social Work, Health, and Music, The Brandenburg University of Technology Cottbus-Senftenberg in Germany; and the Faculty of Public Health of The Catholic University in Ružomberk in Slovakia.	CON	Poland, Germany and Slovakia	DHNBQ (Section 3; Nutrition beliefs)	Established Questionnaire #	Yes	25
Teschl et al. [89]	2018	365 (51 M, 314 F)	24.9 M 23.2 F	3.4 M, 4.0 F	Students in University of Education	NST	Germany	Q (online and paper) NK based on vegetable consumption	Author Designed Questionnaire	NST	1
Yahia et al. [85]	2016	231 (67 M, 164 F)	20.6	2	US enrolled in introductory nutrition classes	NST	US	GNKQ (online)	Established Questionnaire †	Yes	50
Zaborowicz et al. [86]	2016	456 (179 M, 277 F)	23.1 (23.3 M, 22.9 F)	NST	Students enrolled in humanities, life and engineering sciences programmes.	CON	Poland	Q NK	NST	NST	26

M, male; Female, F; YO, Years old; Undergraduates, UG; NST, Not Stated; Q, Questionnaire; GN, General nutrition knowledge; US, University Students; CON, Convenience; GNKQ, General Nutrition Knowledge Questionnaire; PCOS, Polycystic ovary syndrome; RAN, Random; PUR, Purposeful; NKQ, Nutrition Knowledge Questionnaire; NK, Nutrition Knowledge; NON-PROB, Non-probability; FK, Food Knowledge; ANLS, Adolescent nutrition literacy scale; DHNBQ, Dietary Habits and Nutrition Beliefs Questionnaire. * Turconi et al. (2003) [96]; † Parmenter & Wardle (1999) [97]; ‡ Jones et al. (2015) [98]; § Bari (2012) [99], Türkmen et al. (2017) [100]; ¶ Tamayo et al. (2013) [101]; # Jezewska-Zychowicz et al. (2018) [102].

Table 3. Nutrition knowledge assessment of university students.

First Author	Year	n (Sex)	Mean	SD	Student Population	Sample	Country	NK Assessment	Design	Validation	No. of Items	Question Type
Almansour et al. [81]	2020	690 (87 M, 603 F)	21.7 (M) 20.7 (F)	3.1 (M) 2.5 (F)	>17 yo, UG, with or without children.	NST	Kuwait	Q (paper) GN	Adapted Established Questionnaire *	Yes	10	Multiple Choice
Cicognini et al. [92]	2016	110 (56 M, 54 F)	NST	NST	18–19 yo	NST	Italy	Q GN	NST	NST	15	Multiple Choice
Cooke et al. [76]	2014	500 (125 M, 375 F)	24.9	NST	US	CON	UK	GNKQ (online)	Established Questionnaire †	Yes	110	Multiple Choice, True/False, Other
Dissen et al. [82]	2011	279 (131 M, 148 F)	20.12	1.75	18–26 yo, UG, never taken a college-level nutrition course	CON	US	Q (online) GN	Author Designed Questionnaire	NST	22	Multiple Choice
Douglas et al. [93]	2021	12 F	22.5	2.71	18–26 yo US, with PCOS diagnosis, not pregnant or breastfeeding	NST	US	NKQ (paper)	Established Questionnaire ‡	Yes	60	NST
El Hajj et al. [79]	2021	303 (93 M, 210 F)	NST	NST	18–25 yo US	RAN	Lebanon	Q (online) (focus on healthy breakfast, healthy meal, components of the Mediterranean diet)	NST	NST	4	Multiple Choice
El-Ahmady et al. [87]	2017	423 (136 M, 287 F)	19	1.6	UG students enrolled in a pharmacy degree	NST	Egypt	Q GN	Author Designed Questionnaire	NST	7	True/False, Other
Folasire et al. [94]	2015	110 (63 M, 47 F)	22.06	2.39	UG Athletes	PUR	Nigeria	Q NK related to athletes	Author Designed Questionnaire	Yes	14	NST
Folasire et al. [80]	2016	367 (236 M, 131 F)	21.9	2	UG	RAN	Nigeria	Q NK related to cancer prevention	NST	NST	20	NST
Guiné et al. [78]	2020	670 (245 M, 425 F)	21.8	5.51	≥18 yo US	NON-PROB	Portugal	Q FK	Author Designed Questionnaire	NST	9	Multiple Choice, Other
Jovanovic et al. [83]	2011	390 (120 M, 270 F)	21.9 M 21.5 F	2.3 M 2.3 F	Medical US	CON	Croatia	GNKQ (part D—disease relationship)	Established Questionnaire †	Yes	30	Multiple Choice, True/False, Other
Kalkan [90]	2019	276 (130 M, 146 F)	20	1.6	Faculty of Health Science students	RAN	Turkey	ANLS (face to face)	Adapted Established Questionnaire §	Yes	22	Multiple Choice
Kresić et al. [75]	2009	1005 (264 M, 741 F)	21.7	2.3	US	CON	Croatia	GNKQ (adapted)	Adapted Established Questionnaire †	Yes	96	Multiple Choice, True/False, Other
Lwin et al. [88]	2018	101 (31 M, 70 F)	NST	NST	Medical US—Year 2	RAN	Malaysia	Q NK (related to food consumption)	Author Designed Questionnaire	NST	10	Multiple Choice
Rivera Medina et al. [67]	2020	83 (24 M, 59 F)	24.3	6.7	UG enrolled in: nutrition and dietetics, culinary nutrition, and culinary management.	CON	Puerto Rico	Q (paper) NK (4 Subsections: nutritional recommendations regarding daily food and water intake, food groups in the plate, food portions, and benefits of fiber consumption)	Adapted Established Questionnaire ¶	No	36
Ruhl et al. [77]	2016	583 F	20.89	NST	Female US -dieters and non-dieters	NST	USA	GNKQ (online)	Established Questionnaire †	Yes	35
Shaikh et al. [84]	2011	218 (158 M, 60 F)	NST	NST	Final year of medicine, interns and PG students in a Medical college	PUR	India	Q GN	Author Designed Questionnaire	NST	NST
Suhaimi et al. [95]	2018	400 (92 M, 308 F)	22	NST	US living in college residence	RAN	Malaysia	Q (face to face) GN	NST	NST	NST
Suliga et al. [91]	2020	394 (115 M, 279 F)	21.52	3.22	Students from the Medical College of Jan Kochanowski University in Kielce in Poland; the Faculty of Social Work, Health, and Music, The Brandenburg University of Technology Cottbus-Senftenberg in Germany; and the Faculty of Public Health of The Catholic University in Ružomberk in Slovakia.	CON	Poland, Germany and Slovakia	DHNBQ (Section 3; Nutrition beliefs)	Established Questionnaire #	Yes	25
Teschl et al. [89]	2018	365 (51 M, 314 F)	24.9 M 23.2 F	3.4 M, 4.0 F	Students in University of Education	NST	Germany	Q (online and paper) NK based on vegetable consumption	Author Designed Questionnaire	NST	1
Yahia et al. [85]	2016	231 (67 M, 164 F)	20.6	2	US enrolled in introductory nutrition classes	NST	US	GNKQ (online)	Established Questionnaire †	Yes	50
Zaborowicz et al. [86]	2016	456 (179 M, 277 F)	23.1 (23.3 M, 22.9 F)	NST	Students enrolled in humanities, life and engineering sciences programmes.	CON	Poland	Q NK	NST	NST	26

M, male; Female, F; YO, Years old; Undergraduates, UG; NST, Not Stated; Q, Questionnaire; GN, General nutrition knowledge; US, University Students; CON, Convenience; GNKQ, General Nutrition Knowledge Questionnaire; PCOS, Polycystic ovary syndrome; RAN, Random; PUR, Purposeful; NKQ, Nutrition Knowledge Questionnaire; NK, Nutrition Knowledge; NON-PROB, Non-probability; FK, Food Knowledge; ANLS, Adolescent nutrition literacy scale; DHNBQ, Dietary Habits and Nutrition Beliefs Questionnaire. * Turconi et al. (2003) [96]; † Parmenter & Wardle (1999) [97]; ‡ Jones et al. (2015) [98]; § Bari (2012) [99], Türkmen et al. (2017) [100]; ¶ Tamayo et al. (2013) [101]; # Jezewska-Zychowicz et al. (2018) [102].

3.2.4. Overview of Nutrition Knowledge Assessment Tools

The research method employed for each study was extracted, specifically, the tools adopted for measuring nutrition knowledge and dietary intake. Table 3 summarises the methodological approaches and nutrition knowledge tool format of the included studies. Of the 22 studies, 7 (31.8%) studies failed to report their sampling methods [77,81,85,87,89,92,93]. Of the 15 studies that reported the sampling method, only 5 (22.7%) studies used random sampling with 8 (36.4%) studies using convenience sampling, 2 (9%) using purposeful sampling and 1 (4.54%) using non-probability sampling. The studies used a variety of nutrition knowledge assessment tools. Seventeen studies explicitly reported on the design of the assessment tool, eight used author-designed nutrition knowledge questionnaires, six used established nutrition knowledge questionnaires and three used adapted established questionnaires. The number of reported items in the tools ranged from 1 to 110 (media n = 28.71), with studies (n = 16) adopting multiple choice (MC), True/False (TF) (n = 5), Check Mark (CM) (n = 1) and Fill in the Blank (FITB) (n = 1) methods of questioning. Where stated (n = 12), dissemination of the questionnaires was via online (n = 5), paper (n = 3), face-to-face (n = 3) and one study employed a dual-modality approach administering the questionnaire through both online and paper formats.

Most studies failed to report on any formal validation of the tool used for assessing nutrition knowledge (n = 13, 59%), eight (36.4%) were conducted using validated tools and one study (4.6%) stated that the tool used was not validated. Of the eight studies that used validated tools, the British-developed GNKQ designed by [97] (n = 5, 62.5%) was the most used directly or modified for regional variation [75,76,77,83,85]. The remaining studies used the Nutrition Knowledge Questionnaire (NKQ) [98] (n = 1), the Adolescent Nutrition Literacy Scale (ANLS) [99] (n = 1), the Dietary Habits and Nutrition Beliefs Questionnaire (DHNBQ) [102] (n = 1) and one study specifically designed and validated their own tool [94]. The questionnaires employed were based on general knowledge of nutrition, food, food groups, meals, portion sizes, nutrients, recommended dietary guidelines and diet-disease management.

3.2.5. Overview of Dietary Intake Assessment

Table 4. Relationship between Nutrition Knowledge and Dietary Intake of University Students.

Dietary Assessment
Study	Instrument	Design	Validation	Knowledge Score Relationship
First author (year)
Almansour et al. (2020) [81]	FFQ (19 items) EHQ (13 items)	Established Questionnaire Author Designed Questionnaire	VAL NST	+ Association between nutrition knowledge and better dietary habits (r = 0.229, p < 0.05)
Cicognini et al. (2016) [92]	Questionnaire—alcohol consumption	Author Designed Questionnaire	NST	NS between NK in females and alcohol 1 and alcohol 2 scores (r = 0.162, r = 0.048, respectively) NS between NK in males and alcohol 1 and alcohol 2 scores (r = −0.004, r = −0.047, respectively) (p values not reported for NS associations as not significant)
Cooke et al. (2014) [76]	Five Factor Screener  All-Day Fruit and Vegetable Screener Fast Food Consumption Screener  Percentage Energy from Fat Screener (total of 63 items)	Established Questionnaire Established Questionnaire Established Questionnaire Established Questionnaire	VAL VAL VAL VAL	NK is a significant predictor of diet quality (R2 = 0.029, p = 0.001)
Dissen et al. (2011) [82]	Fruit—vegetable-fibre—dietary fat screener	Established Questionnaire	VAL	+ Association with fruit/vegetable intake in males (r = 0.313, p = 0.0003). Relationship in females was not reported.
Douglas et al. (2021) [93]	3 d DR—2 weekdays and 1 weekend day	NA	NA	NK inversely related to fruit consumption for ECI and (r(9) = −0.689, p < 0.05) and MyPlate participants (r(9) = −0.639, p < 0.05).  + Relationship NK was inversely related to total sugars intake (r(9) = 0.634, p < 0.05). No significance between NK and energy, micronutrient, or macronutrient intake.
El Hajj et al. (2021) [79]	KIDMED (16 items)	Established Questionnaire	VAL	+ Significant differences found between correct and false response in NK, with correct scores having a higher score in: healthy breakfast options (p = 0.014; p < 0.05), components of a healthy meal (p < 0.0001; p < 0.05) and characteristics of a Mediterranean diet (p = 0.006; p < 0.05). No significant difference between NK and reasons to consume breakfast (p = 0.405).
El-Ahmady et al. (2017) [87]	Nutrition Habits Questionnaire (16 items)	NST	NST	+ Association between NK and Nutrition habits (r = 0.56, p < 0.0001).
Folasire et al. (2015) [94]	3 × 24 h recall FFQ	NA NST	NA VAL	+ Relationship between the NK and the NP scores (r = 0.396, p < 0.05). Inverse relationship between NK and Calcium intake (r = 0.231, p < 0.05). No association between NK and Energy intake (r = 0.180) and Iron intake (r = 0.136).
Folasire et al. (2016) [80]	FFQ (36 items)	Adapted Established Questionnaire	NST	+ Associations between knowledge of cancer prevention and consumption pattern of processed cereals/grains (polished rice, white bread, noodles and spaghetti etc.) (χ2 = 13.724, p < 0.0001), legumes/nut (beans, groundnut, melon) (χ2 =17.268, p < 0.0001), meat (beef ) (χ2 = 22.972, p < 0.0001), fish (χ2 = 23.017, p < 0.0001), alcohol (χ2 = 19.534, p < 0.0001), sugary drinks (χ2 = 6.067, p = 0.014) and snacks (χ2 = 36.159, p < 0.0001).  NS between NK on cancer prevention and vegetable (χ2 = 0.075, p = 0.785) and fruit (χ2 = 0.316, p = 0.574) consumption.
Guiné et al. (2020) [78]	Eating Habits Questionnaire (4 items)	Author Designed Questionnaire	NST	+ Association between NK and eating habits (p = 0.016, (V = 0.096)
Jovanovic et al. (2011) [83]	FFQ (items NST)	Established Questionnaire	VAL	+ Association between diet  – disease knowledge and higher intake of fish (p = 0.027, p = 0.001) and vegetables (p = 0.019, p = 0.001) in both high–fibre groups (p = 0.038, p = 0.007).  – Correlations between overall examined nutrition knowledge and daily energy intake (p = 0.019, p < 0.001), energy density of the diet ( p = 0.038, p = 0.001), SFA intake (p = 0.036, p < 0.001), and consumption of legumes (p = 0.027, p = 0.001) and soft drinks (p = 0.001, p < 0.001) for both sexes, both high-fibre groups.
Kalkan (2019) [90]	Adolescent Food Habits Checklist (AFHC) (19 items)	Established Questionnaire	VAL	+ Association between nutrition literacy and AFHC scores (r = 0.307, p < 0.05).
Kresić et al. (2009) [75]	FFQ (ninety-seven food and beverage items)	Established Questionnaire	VAL	+ NK significant predictor of adherence to dietary recommendations (p < 0.001).  + NK significant predictor of intake of grains (p < 0.001), meat and beans (p < 0.001), fruits (p = 0.002), vegetables (p < 0.001) and oils (p < 0.001).
Lwin et al. (2018) [88]	24 h recall	NA	NA	+ Association with total calorie intake (r = 0.052, p < 0.05)
Rivera Medina et al. (2020) [67]	Youth Risk Behavior Surveillance System of the Center for Disease Control Food Consumption Frequency Questionnaire FFQ 1 month (34 items)	Adapted Established Questionnaire Adapted Established Questionnaire Adapted Established Questionnaire	NST NST NST	+ Association with eating habits (p = 0.002, p < 0.05)
Ruhl et al. (2016) [77]	FFQ 2 months (195 items)	Established Questionnaire	VAL	+ Association with consumption (r = 0.18, p < 0.001)
Shaikh et al. (2011) [84]	Eating Habits Questionnaire	Author Designed Questionnaire	NST	NS with healthy eating (p > 0.340)
Suhaimi et al. (2018) [95]	FFQ	NST	NST	+ Association with higher NK and food variety (or = 5.4, p = 0.05)
Suliga et al. (2020) [91]	Dietary Habits and Nutrition Beliefs Questionnaire devised in Poland for people aged 15–65 years old (KomPAN) (24 items)	Established Questionnaire	VAL	NS with DI (β = 0.14; p = 0.411)
Teschl et al. (2018) [89]	FFQ 2–3 weeks	Established Questionnaire	NST	NS between knowledge of recommended vegetable servings and vegetable intake for females: F(2, 78.243) = 2.460, p = 0.092; and males: F(2, 18.365) = 2.556, p = 0.105.
Yahia et al. (2016) [85]	Block Dietary Fat Screener (17 items)	Established Questionnaire	VAL	– Association with saturated fat intake (−0.15, p < 0.0001) and cholesterol intake (–1.38, p < 0.0001)
Zaborowicz et al. (2016) [86]	Questionnaire of Eating Behaviour (QEB)	Established Questionnaire	NST	+ Association between poorer NK and less frequency of snacking on fruit (p < 0.05) and vegetables p < 0.05), higher frequency snacking on salty snacks (p < 0.01), addition of salt to served dishes (p < 0.01) and addition of sugar to hot beverages (p < 0.01). NS association between NK and regularity of meals, snacking, snack frequency, snacking on yoghurts/cheese, sweets/biscuits and nuts/seeds. (omission of p value in study)

VAL—Validated; EHQ—Eating Habits Questionnaire; NST—Not Stated; NS—No significant association; NA—Not applicable.

summarises the dietary intake assessment tools and the relationship between nutrition knowledge and dietary intake. A variety of tools was used to assess dietary intake within the studies. Most studies used one type of tool for assessing dietary intake (n = 21, 87.5%). The FFQ was the most frequently used tool (n = 10, 41.6%), three studies used Dietary Recall (12.5%), three studies used Dietary Screeners (12.5%) and eleven studies used other types of questionnaires (i.e., questionnaires with specific questions relating to the nutrient, food group or food pattern being investigated, e.g., healthy eating, nutrition habits, alcohol consumption, Mediterranean diet, etc.) (41.6%) to assess dietary intake. Of the ten studies that utilised the FFQ method of dietary assessment, three studies used a validated FFQ, and seven studies failed to report on validation or did not use validated tools. Of the three studies that used dietary screeners, all used validated instruments. All three studies used a fat screener [76,82,85], two studies also used a fruit and vegetable screener [76,82] and one study used a fast-food consumption screener and a five-factor screener alongside the fat and fruit and vegetable screeners [76]. The length of recording for dietary intake varied between 24 h and 2 months.

3.2.6. Overview of the Relationship Between Nutrition Knowledge and Dietary Intake

The relationship between nutrition knowledge and dietary intake was noted in all studies (Table 4). Most of the studies reported a positive relationship between nutrition knowledge and dietary intake whether as a whole or individual food group or specific nutrients (n = 13). Overall, higher knowledge was significantly associated with a healthier dietary intake more in line with the recommended dietary guidelines. Of the studies reporting a positive relationship between nutrition knowledge and dietary intake, an increased intake of fruit (n = 3) and vegetables (n = 3) was most noted. Significant associations were reported between nutrition knowledge and an intake of processed cereals/grains, legumes, nuts, beans, meat, oil, protein and fat. Very few studies (n = 2) reported a negative association between nutrition knowledge and dietary attributes. Of the eight studies that reported non-significant associations, three studies reported a non-significant association between nutrition knowledge and general dietary intake. Two studies reported an inverse relationship between NK and alcohol (n = 1) and sugary drinks (n = 1).

4. Discussion

This scoping review examined the relationship between the nutrition knowledge and dietary intake of university students. It appears to be the first review to provide a summary of the subpopulations, study location, study design, methodological tools and the relationship between the nutrition knowledge and dietary intake of the university student population. A total of 22 primary studies were included in the review. Synthesis of these articles indicated that most studies used convenience sampling (n = 8), had predominantly female participants (n = 5898, 70.5%) and were carried out in the USA (n = 4). Findings highlight that the included studies are heterogeneous in terms of subpopulations, study design, and tools used to assess nutrition knowledge and dietary intake. Most of the studies reported a positive correlation between nutrition knowledge and some aspect of dietary intake, most often between nutrition knowledge and the intake of fruit and vegetables. While the quality of the studies was not assessed in this review, it is important to acknowledge that many studies failed to use validated tools for the assessment of nutrition knowledge and dietary intake. Of the 24 studies, 7 studies used both nutrition knowledge and dietary assessment tools that were validated [75,76,77,83,85,90,91]. To gain an in-depth understanding of the relationship between nutrition knowledge and dietary intake, it is crucial that stakeholders are guided by high-quality research.

Among the 22 studies, most reported on mixed-sex samples; however, male and female participants were not represented equally. In the 20 mixed-sex studies in this review, females consistently surpassed their male counterparts in terms of representation. The greater female participation could reflect the heightened concern for diet and nutrition of females compared to males [103,104,105]. This increased interest in nutrition and diet among females has also been reported in the university setting [85]. Overall, this review found an underreporting of the male student population in the included studies. Thus, it is important to be sensitive to the unbalanced gender representation when drawing conclusions. Studies have shown that females [106,107] and, specifically, female university students [75,85,86,87,108,109,110] possess a greater nutrition knowledge than males. Consequently, this review may include studies that overestimate the nutrition knowledge of the entire student population due to the higher female participation rate. Furthermore, a disparity between sexes in the student population has been noted concerning dietary intake. For example, males exhibited poorer diet quality than females [35] and females displayed a greater adherence to recommended dietary guidelines than males [111,112].

As females were more heavily represented in the studies in this review, it could potentially impact the observed relationship between nutrition knowledge and dietary intake. Further studies are warranted to assess the relationship between the nutrition knowledge and dietary intake of male university students.

The most common limiting factor on the student population within the studies was the area of study/enrolled degree programme (n = 11), with the programmes of study varying greatly. Students undertaking health-related programmes (n = 6) such as medicine were the most targeted. It was noted [113] that nutrition is insufficiently incorporated into medical education and that medical students reported inadequate nutrition knowledge. Furthermore, medical students face high stress levels [114] and a high prevalence of mental health issues [115] such as anxiety [116] and depression [117], burnout [118], sleep problems [119] and feeding and eating disorders [120] which may put them at risk of experiencing poor dietary intake [121]. However, ref. [122] found that Norwegian medical students’ diets reflected current health recommendations. It could be argued that this may be in part due to Norwegian medical students having a relatively high level of subjective well-being [123], which is related to healthy diets [124]. Gaining a better understanding of medical students’ nutrition knowledge is crucial as graduates from this degree programme are tasked with providing evidence-based, up-to-date and effective nutritional advice to their future patients [125].

Of the studies reviewed, two specifically investigated university students undertaking nutrition/diet-related modules [67,85]. Both studies reported a positive correlation between nutrition knowledge and dietary intake. Malinowska et al. (2023) While [126] found that dietetics students consume vegetables, whole grains and fatty fish more frequently than those in non-diet-related programmes. Nutrition knowledge may be a driver of healthy dietary intake of students who study nutrition-related programmes as they are exposed to a greater degree of nutrition education. However, while students enrolled in diet-related programs have higher levels of nutrition knowledge [127,128] and a better dietary intake than other students, they still present with inadequate eating habits, revealing that other factors may have a greater influence on the diet of students [67,126]. Interestingly, only two studies [76] reported on the entire university student population with no limiting factors or restrictions. Globally, there appears to be a need for further research to gain a more accurate understanding of the relationship between the nutrition knowledge and dietary intake of specific student populations.

The studies included in the review used a variety of tools to assess the nutrition knowledge and dietary intake of university students. While a formal evaluation of the study quality and validation is beyond the remit of a scoping review, it is important to acknowledge the heterogeneity of the study design and instruments adopted. Of the studies that used a validated tool to assess nutrition knowledge (n = 9), over half (n = 5) used the GNKQ or an adapted version [75,76,77,83,85]. The GNKQ has been validated and adapted for different populations [97,106,127,129,130,131,132,133,134,135]. and is widely used to assess nutrition knowledge. In a review assessing nutrition knowledge assessment among university students [136] reported that 6 of the 20 studies included in the review used a standard or modified GNKQ. The GNKQ consists of four sections: (1) sources of nutrients, (2) dietary recommendations, (3) choosing everyday foods and (4) diet-disease [97]. While using the validated GNKQ to assess nutrition knowledge gives a higher quality to the study, it is quite lengthy when assessing a large population [56]. To gain a more accurate understanding of the nutrition knowledge of university students, it is worthwhile considering the efficacy of nutrition knowledge assessment tools by examining the types of knowledge they aim to capture. Different assessment tools are designed to measure various aspects of nutrition knowledge, and understanding the type of knowledge being assessed, be it declarative or procedural, can inform the knowledge captured. Declarative knowledge is factual knowledge, ‘knowing that’, while procedural knowledge is the knowledge of how a specific skill is performed, ‘knowing how’ [137]. Furthermore, ref. [138] found that declarative nutrition knowledge was not a good indicator of healthy dietary behaviour. It has been argued that declarative knowledge may have little to no relationship with the practical knowledge required for maintaining a healthy diet [66,138]. It was asserted by [138] that procedural knowledge, knowing how to have a healthy diet, is more closely related to dietary habits than declarative knowledge. Within this review, the GNKQ was the most used validated tool to assess nutrition knowledge. The GNKQ primarily focuses on assessing declarative knowledge, with some questions indirectly capturing procedural knowledge by requiring participants to apply their understanding of nutritional concepts to practical scenarios. Two main question types where participants apply their declarative knowledge include interpreting food labels to determine nutrient content and evaluating dietary patterns or recommending modifications based on specific health conditions. However, there is a lack of balance between the assessment of declarative and procedural knowledge, resulting in a somewhat limited assessment of procedural knowledge. Consequently, the GNKQ potentially overlooks the importance of practical competencies required for developing the nutrition knowledge necessary for maintaining a healthy diet. Future studies should consider utilising validated nutrition knowledge assessment tools that encompass a more balanced approach to assessing both declarative and procedural knowledge [139]. However, instruments that assess the various types of nutrition knowledge in one tool are scarce [66]. A broader assessment of knowledge could better inform the development of suitable nutrition education programmes aimed at eliciting dietary change. Future studies should consider the use of validated tools that assess the knowledge and understanding of dietary guidelines alongside the procedural knowledge such as the cooking food skills required to adhere to dietary guidelines [66].

This review identified heterogeneity in the dietary assessment tools and reporting approaches employed for dietary intake. Some studies commented on overall dietary intake [67,76,77,78,80,81,84,87,90,91,94], others broke it down into specific food [75,82,83,86,89,92,93], some concentrated on meal components [79], one study specified energy intake [88] and one focused on single nutrients [85]. The most common type of dietary assessment tool used for assessing dietary intake in this review was the FFQ (n = 10, 41.6%), rather than a measure of diet quality. The FFQ is designed to assess habitual dietary intake over a specified period and can incorporate specific food items as well as portion sizes [140]. FFQs are a commonly used dietary assessment tool [141] due to their relative cost-effectiveness, ease of administration and processing, and low respondent burden compared to other assessment methods such as diet records or recalls [140,142]. However, incomplete listings of food items, reliance on respondents’ memory, recall bias, under/over-reporting and respondents’ ability to evaluate portion size of food may impact the accuracy resulting in measurement errors [140,143]. Furthermore, using a FFQ to assess dietary intake often prevents measuring the extent to which respondents conform to dietary guidelines [124]. Furthermore, ref. [66] argue that assessing adherence to dietary guidelines is crucial in exploring the links between nutrition knowledge and dietary intake.

A key strength of the studies included in this review is the use of validated tools. Seven studies used validated tools for assessing both nutrition knowledge and dietary assessment tools. Ref. [66] found that only 3 studies out of 29 used validated tools for assessing both nutrition knowledge and dietary intake of adults. There appears to be a move towards employing validated tools for assessing both nutrition knowledge and dietary intake; however, this may not be feasible for all studies given the additional cost associated with using validated tools [141]. Despite the use of validated tools across these studies, the disparate nature of the tools posed a challenge in comparing the relationship between nutrition knowledge and dietary intake across the diverse studies and populations [144]. Notwithstanding this, previous studies have found a positive and significant correlation between nutrition knowledge and healthy dietary intake in the adult population when adjusted for demographic characteristics [66,145]. Similar to [66], fruit and vegetables emerged as the food groups most commonly correlated with nutrition knowledge. Furthermore, it was noted [145] that individuals with higher nutrition knowledge were almost twenty-five times more likely than those with limited knowledge to meet fruit and vegetable intake recommendations. This is mirrored in the university setting, with a positive and significant correlation between students’ nutrition knowledge and dietary intake of vegetables noted [75]. The inverse has also been observed with university students with the lowest level of nutrition knowledge consuming the least amount of fruit and vegetables [109]. The level and type of nutrition knowledge and the relationship this has with the dietary intake of university students is central in developing interventions that empower the student population to improve dietary habits and promote healthier eating patterns [146,147,148] while in university and into their adult lives.

Strengths and Limitations

There are multiple strengths to this review. Based on current information, this is the first study to solely explore the relationship between the nutrition knowledge and dietary intake of university students. Strengths of this review included the rigorous adherence to the Arksey and O’Malley [70] framework for conducting scoping reviews alongside following the PRISMA-SCR checklist [73]. An experienced research librarian assisted with the selection of suitable databases and designing and developing a search strategy to encompass all relevant studies.

There are several limitations that need to be addressed. While a scoping review aims to be inclusive and capture the breadth of available evidence, it provides a less in-depth evaluation of the literature than that of a systematic review. The quality of the studies included was not assessed for this review. It was also necessary to establish exclusion criteria within this study. Limitations of this review largely relate to the scope of the search. Studies may not have been captured because they did not match keywords in the search terms utilised. The review excluded studies in languages other than English, several non-English language studies could have been included based on the abstracts. This study was limited to peer-reviewed articles, resulting in the absence of grey literature, research that was conducted by professional bodies or organisations, and theses that were not published on an academic database. Furthermore, narrowing the scoping review to include quantitative studies that aimed to assess the relationship between nutrition knowledge and dietary intake through statistical methods and exclude qualitative methodologies highlights a fundamental knowledge gap in the literature that remains. Lastly, data extraction was solely based on information available in the studies, details relating to the tools utilised for the assessment of nutrition knowledge and dietary intake may have been omitted as the information could not be identified in the study.

5. Conclusions

This review summarises the current research published that examines the relationship between the nutrition knowledge and dietary intake of university students. To date, limited evidence synthesis research has focused on summarising the nutrition knowledge and dietary intake of university students. It is important to understand the role nutrition knowledge plays in dietary intake to inform dietary interventions at the university level. The findings indicate that the majority of studies reported a positive relationship between increased nutrition knowledge and a healthier dietary intake in the university student population. However, the strength of the evidence is constrained due to the limitations placed on the populations considered and the heterogeneity of the study design and measurement tools employed to assess nutrition knowledge and dietary intake. Notwithstanding this, due to the number of young adults that can be reached in the university setting, gaining a more complete and accurate understanding of the nutrition knowledge and dietary intake nexus may be useful in informing targeted interventions. Such interventions hold the potential to positively influence students’ dietary choices during university, and also, as they progress into their professional lives, to improve the health and overall life quality of the current and future generations. Subsequent research may consider a more comprehensive investigation of the general male student populace, with a greater emphasis on the use of validated nutrition knowledge tools that assess both declarative and procedural knowledge. Additionally, employing validated dietary intake tools that assess the conformity to dietary guidelines would contribute to a more accurate understanding of the relationship between food knowledge and dietary intake.