Enhancing Postsecondary Students' Dietary Behaviours: A Systematic Review of Mobile Health Interventions

Holly Schaafsma; Olivia Caruso; Smaranda Diaconita; Louise McEachern; Jamie Seabrook; Jason Gilliland

doi:10.1111/jhn.70130

. 2025 Oct 7;38(5):e70130. doi: 10.1111/jhn.70130

Enhancing Postsecondary Students' Dietary Behaviours: A Systematic Review of Mobile Health Interventions

Holly Schaafsma ^1,^2,³, Olivia Caruso ^1,^3,⁴, Smaranda Diaconita ¹, Louise McEachern ^1,^3,⁴, Jamie Seabrook ^1,^3,^5,^6,^7,⁸, Jason Gilliland ^1,^3,^4,^6,^7,^8,^9,^✉

PMCID: PMC12504629 PMID: 41058002

ABSTRACT

Inadequate dietary intake among postsecondary students can negatively impact academic achievement, mental health and the development of chronic disease later in life. Mobile health (mHealth) interventions offer a unique opportunity to improve dietary behaviours in this population, who report frequent use of mobile devices. This systematic review evaluated the impact of mHealth diet interventions on postsecondary students' dietary behaviours. A literature search was conducted in January 2025 using six electronic databases (Web of Science, Scopus, CINAHL, EMBASE, Medline and PsycINFO). Eligible intervention studies included randomized controlled trials (RCTs) and pre‐post studies, with or without a control group. The interventions had to include at least one dietary behaviour outcome variable and involve only enroled postsecondary students. Sample and intervention characteristics, intervention results, as well as equity and behaviour change theory use and reporting, were extracted. Findings were reported as a narrative synthesis. Eleven studies met the inclusion criteria and were included in this review. Of the studies reviewed, 10 reported a positive and significant impact on at least one dietary behaviour in the postsecondary student population. Notably, 5 of the 6 studies assessing fruit and/or vegetable intake found significant improvements following the mHealth interventions. However, the clinical significance of these should be noted: some reported dietary changes were relatively small. Overall, mHealth interventions show promise in improving postsecondary students' dietary behaviours. However, further research is necessary, and future interventions should ensure the use of validated dietary assessment tools and longer follow‐up periods to evaluate long‐term effectiveness.

Keywords: diet, mHealth, nutrition, postsecondary, student, university

Summary

mHealth nutrition interventions show promise to positively influence postsecondary students' dietary behaviours, particularly in improving fruit and vegetable intake. However, the clinical significance of reported findings should be considered when interpreting the results.
Future interventions should prioritize the use of validated dietary assessment tools and longer follow‐up periods to better assess the long‐term impact of these interventions on postsecondary students' dietary behaviours.

1. Introduction

Young people in postsecondary education, typically between 18 and 25 years old, represent a critical group for developing healthy dietary habits. The transition from secondary to postsecondary education often brings rapid increases in dietary autonomy, including the responsibility of obtaining and preparing their own food. Previous research has shown that higher diet quality is associated with better mental health outcomes (e.g., stress, depression and anxiety) [1, 2] and higher academic achievement in postsecondary students [3]. However, poor dietary behaviours in this population are prevalent, including a low intake of fruits and vegetables [4, 5] and high intake of fast‐food [6, 7]. Postsecondary students face unique developmental, social and structural challenges that may influence their diet differently than other young adults, such as academic stress, time constraints, and often limited financial resources, justifying their separate consideration [8, 9]. Additionally, poor food and nutrition literacy has been associated with inadequate dietary intake in postsecondary students [10, 11]. The dietary habits formed during this life stage may follow into later adulthood [12], impacting their risk for developing chronic diseases (e.g., diabetes, cardiovascular disease and cancer), making this a critical life‐stage at which to intervene.

Mobile health (mHealth) encompasses the use of mobile devices [e.g., smartphones, personal device assistants (PDAs), and other mobile devices] to support medical or public health services and interventions [13]. Mobile health interventions, most notably smartphone‐based interventions (e.g., text‐messaging, smartphone applications), are increasing in popularity due to their convenience of use and accessibility by diverse populations [13, 14]. Considering the high prevalence of smartphones amongst young adults and postsecondary students' frequent use of technology to obtain health information [15, 16], mHealth interventions may be a useful tool to improve postsecondary students' dietary behaviours. Previous mHealth interventions targeting mental health outcomes (e.g., depression, anxiety, well‐being), physical activity, and substance abuse (e.g., tobacco, alcohol and marijuana) have shown positive benefits on the postsecondary student population [17, 18, 19, 20]. Furthermore, systematic reviews on mHealth interventions targeting diet have shown promise in improving dietary intake in adolescent and adult populations, including an increase in fruit and vegetable intake, improvement in healthy snack intake, and a decrease in calorie intake [21, 22]. However, to our knowledge, there are currently no systematic reviews assessing the impact of these interventions in the postsecondary student population.

Given the significant role of dietary behaviour in both mental and physical health outcomes, and the increasing prevalence of smartphone use among young adults, this review aims to assess the impact of mHealth‐based interventions on dietary behaviours in postsecondary students.

2. Materials and Methods

2.1. Literature Search

This review was registered with PROSPERO (CRD42024577224) and was reported in line with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) [23]. A comprehensive literature search was conducted using six electronic databases: Web of Science, Scopus, CINAHL, EMBASE, Medline and PsycINFO, in January 2025. The search strategy aimed to identify peer‐reviewed studies on mHealth interventions targeting dietary behaviours of postsecondary students. The search terms included keywords related to smartphone‐based interventions, postsecondary students, and dietary behaviour (see Figure S1 for MEDLINE search). The reference lists of all studies included at the full‐text stage were also screened for further eligible studies by one author.

2.2. Eligibility Criteria

Studies were eligible for inclusion if the study participants were attending a postsecondary institution, encompassing both undergraduate and graduate level programmes. This review did not exclude studies based on the age range of participants, part‐time or full‐time student status, or programme of study. Studies that included young adults, but did not specify if they were postsecondary students, were initially included during the title/abstract screen and further investigated during the full‐text review. If studies targeted the young adult population generally or other populations (e.g., postsecondary staff), they were excluded. Studies focusing on populations with health conditions, such as individuals with diagnosed eating disorders, obesity, diabetes, cancer, or arthritis, were also excluded.

Studies published before January 2008 were excluded, as 2008 marks the advancement of smartphone technology (e.g., launch of the Apple App Store) and rise in smartphone uptake, which represents a meaningful shift in how mobile health interventions could be delivered and experienced. This exclusion criteria ensures comparability across intervention studies which had similar access to mobile advancements, as they were established in this modern era of smartphone technology. Eligible study types included intervention studies only, specifically randomized controlled trials (RCTs) and pre‐post studies with or without a control group. Cross‐sectional, case‐control, cohort, and qualitative studies, as well as protocol papers, review papers, abstracts, posters, and dissertations were excluded. Studies from all geographical regions were considered, but only those published in English were included. Further, only peer‐reviewed publications were included.

Eligible studies had to use smartphone/mHealth interventions, such as smartphone apps (custom‐designed or publicly available) or text messaging (SMS)‐based interventions that targeted dietary behaviour, using intervention components such as nutrition education, goal‐setting, or dietary tracking (i.e., self‐monitoring dietary intake). Studies were excluded if they relied solely on face‐to‐face telehealth interventions (e.g., FaceTime) or phone call‐based counselling. If mHealth interventions were combined with other methods (i.e., face‐to‐face counselling), outcomes specific to the mHealth intervention alone had to be reported and only results specific to the mHealth component were extracted for the current review. Studies had to measure at least one dietary behaviour outcome, such as dietary intake (e.g., fruit and vegetable consumption), eating behaviours (e.g., emotional eating), purchasing habits, or calorie and nutrient intake, but could also target additional health behaviours (e.g., mental health, physical activity).

2.3. Screening and Data Extraction

Studies obtained from the literature search were imported into a systematic review manager (Covidence) where duplicates were removed. Title and abstracts were reviewed for possible inclusion by two reviewers, a trained undergraduate student and one coauthor (S.D.) using pre‐determined eligibility criteria. The full texts of these studies were then reviewed by two independent reviewers (H.S., O.C.) for final consideration.

Data extraction was conducted by one author (S.D.) and verified by a second author (H.S.) to ensure accuracy and completeness. Extracted data included participant demographics, study objectives, smartphone/mHealth intervention characteristics, methods used to evaluate dietary behaviours (e.g., questionnaire, diet recall, food diary), and key findings related to dietary outcomes and their measurement (e.g., mean difference, confidence intervals, p‐values). Authors' use of behaviour change theory, which has been noted to improve intervention effectiveness [24], was also extracted. Further, as equity factors (e.g., sex/gender, socioeconomic status and ethnicity) can potentially influence healthy eating interventions [25, 26], reported analyses specific to equity were also extracted. The extracted data were systematically organized into a table to aid in comparison across studies.

2.4. Quality Assessment

To evaluate the quality of included studies, the NIH Quality Assessment Tool for Before‐After (Pre‐Post) Studies with No Control Group was used for pre‐post design interventions without a control group, and the NIH Quality Assessment Tool for Controlled Intervention Studies was used for studies with a control group [27]. Two reviewers (H.S., O.C.) independently scored the quality of each study. Studies were given a final quality rating of ‘good’, ‘fair’ or ‘poor’. After independently scoring each of the included studies, reviewers compared quality ratings and discrepancies were resolved via discussion between the two reviewers. Adjudication by a third reviewer (L.M.) was used if required. As this review included both randomized controlled trials and pre‐post intervention designs, differences in quality scores relating to randomization were considered and compared to the quality of other studies overall. For example, studies with a control group that did not conduct randomization and as a result received a ‘poor’ quality score, were still included in the final analysis if their overall quality was comparable to pre‐post design interventions where randomization was not applicable or evaluated.

3. Results

3.1. Study and Sample Characteristics

The initial search identified 5714 studies, with 2829 retained for title and abstract screening after removing duplicates. Of these, 254 full texts were reviewed, of which 11 studies met all inclusion criteria (see Figure 1 for PRISMA diagram). A reference scan of these 11 studies did not result in any additional relevant articles.

The majority of studies (n = 8) were RCTs, the other 3 studies used a pre‐post intervention design [28, 29], of which 1 included a control group [30] (see Table 1). Two of the included studies evaluated the same intervention but used different participant samples, 1 with 286 students from the Faculty of Medicine and Pharmacy [31], and the other with 385 students from the Faculties of Medicine, Pharmacy and Communication [31]. As they were reported as separate samples, both studies were included in the current review. Studies were published between 2014 and 2024. Studies were conducted in the United States (n = 4), Spain (n = 2), Australia, China, Iran, New Zealand and Japan.

Table 1.

Summary of findings from included studies on mhealth interventions aimed to improve dietary behaviours of postsecondary students (n = 11).

Author, Date	Sample	Design	Study objective and description	Intervention Length	Outcome (s)	Dietary behaviour findings
Béjar et al. 2022	n = 286	RCT	To evaluate the effectiveness of a smartphone application (e‐12HR) to improve adherence to the Mediterranean Diet in university students	28 days (follow‐up Week 2, 3 and 4)	Adherence to Mediterranean Diet (AMD) Index Score (out of 24)	AMD index score: Week 1–2, 0.54 IG increase^a
	74.1% F, 25.9% M		IG and CG: Completed the Electronic 12‐h Recall (e‐12HR) daily			Week 1–3, 1.43 IG increase^a
	Faculties of Medicine and Pharmacy		IG: Received personalized feedback report every 7 days			Week 1–4, 1.55 IG increase^a
						Week 2^b
						Week 3^b
						Week 4^b
						% with medium/high scores ( > 9.0)
						Week 2 (p = 0.015)^b
						Week 3 (p = 0.001)^b
						Week 4 (p = 0.000)^b
Béjar et al. 2023	n = 385	RCT	To evaluate the effectiveness of a smartphone application (e‐12HR) at improving adherence to the Mediterranean diet in a sample of Spanish university students.	28 days (follow‐up Week 2, 3, and 4)	Adherence to Mediterranean Diet (AMD) Index Score (out of 24)	AMD index score: Week 1–2, 1.13 IG increase^a
	76.9% F, 23.1% M		IG and CG: Completed the Electronic 12‐h Recall (e‐12HR) daily			Week 1–3, 1.63 IG increase^a
	Faculties of Medicine, Pharmacy and Communication		IG: Received personalized feedback report every 7 days			Week 1–4, 1.84 IG increase^a
						Week 2^b
						Week 3^b
						Week 4^b
						% with medium/high scores ( > 9.0)
						Week 2^b
						Week 3^b
						Week 4^b
Bhurosy et al. 2020	n = 165	RCT	To determine if counting and goal setting can increase red/orange vegetable intake.	3 Days (follow‐up Day 2 and Day 3)	Frequency of red/orange vegetable intake	Frequency of red/orange vegetable intake
	85.5% F, 14.5% M		IG and CG: Photographed daily meals on phone and uploaded onto online platform			Day 1–2: 0.7 times/d increase
	Programme of study NR		IG: Counted red/orange vegetables consumed and made goal to consume one more the next day through phone on online platform			Day 1–3: 0.4 times/d increase
						Day 2 × Intervention^a,^b
						Day 3 × Intervention^a,^b
Brookie et al. 2017	n = 171	RCT	To develop and test the effectiveness of a mobile phone‐based ecological momentary intervention (EMI) to increase FV consumption in low‐consuming young adults.	2‐weeks	Daily servings of: Fruit, Vegetables, ‘hot chips’ (potato fries, wedges, or kumara chips), and Sweets	FV in EMI: 1.20 serving increase^a
	67.3% F, 22.7% M		EMI group: 2 healthy eating text messages sent to users each day			FV in FVI group: 1.21 serving increase^a
						FV consumption^b
	Programme of study NR		FVI group: 1 serving of fruit and 1 serving of vegetables provided for each day			Fruit consumption^b
			CG: Chewing gum provided for each day			Vegetable consumption^b
Brown et al. 2014	n = 150	RCT	To Evaluate the acceptance and effectiveness of repetitive nutrition‐related text messages on college students' nutrition knowledge and FV consumption	7‐weeks	Skipping breakfast, > 4 restaurant meals, < 3 whole grain servings/day, < 2–3 fruit servings/day, < 3 vegetables/potatoes servings/day, < 2–3 milk/yogurt/cheese servings/day, use 2% milk instead of skim, use regular cheese	Fruit servings/day^b
	Sex NR		IG: 2 motivational text messages per week with MyPlate icon			Percentage or mean score NR.
	Non‐health major students		CG: Same messages in one mailed brochure
Hahn et al., 2021	n = 200	RCT	To determine if dietary self‐monitoring via a popular smartphone application (MyFitnessPal) to undergraduate women impacts eating disorder risk, other aspects of mental health, or health behaviours including dietary intake and physical activity.	1 month	Intake of FV, 100% fruit juice, soda, energy drink, sport drink, low‐calorie sports drink, sparkling water, and water consumption, and breakfast consumption times per week	No outcomes significant^a,^b
	F only		IG: Reminded daily to log food and drink in smartphone application MyFitnessPal immediately after consumption
	Programme of study NR		CG: Nothing
Jamshidnezhad et al., 2021	n = 48	Pre‐post	To develop and evaluate an integrated communication smartphone application on the nutritional behaviour of users and their opinion of the app usability.	60 days	Questionnaire validated by research team.	Nutrition behaviour score [mean, (SD)]^a
	Sex NR		IG: Calorie counting and diet recording, diet advice from nutrition experts, and weekly report through smartphone application		Questions NR.	Pre: 61.72
	Faculty of Medical Sciences				Scoring system NR.	Post: 92.10
Lyzwinski, et al., 2019	n = 90	RCT	To test the effectiveness, acceptability, and feasibility of a student‐tailored mindfulness smartphone application for weight, weight‐related behaviours, and stress.	11 weeks	Mindful Eating (score range NR), Emotional Eating (out of 4), Uncontrolled Eating (out of 4), Cognitive Restraint (out of 4)	Mindful eating: IG higher than CG by 0.295 points^b
	67.0% F, 33.0% M		IG: Tips, mealtime prompts, audio and video content on mindful eating through smartphone application			Emotional eating: CG higher than IG by 1.088 points^b
	Programme of study NR		CG: Received an electronic diet diary and WHO health guideline			Uncontrolled eating: CG higher than IG by 2.047 points^b
Ohtsuki M et al., 2024	n = 109	RCT	To determine whether a communication‐focused smartphone application (LINE) can be an effective tool to influence the health and dietary habits of young Japanese women.	6 weeks	Perception of intake: calcium, iron, vitamin c, dietary fibre, potassium, salt, fat, saturated fatty acids.	IG: Higher ‘no’ response to consuming enough fibre after intervention compared to baseline^a
	F only		IG: Nutrition education messages with quizzes and advice, individualized diet feedback at baseline and follow‐up, periodic check‐ins through LINE smartphone application		Recorded intake: calories, protein, fat, and carbohydrates (% of calories), calcium, iron, vitamin c, potassium (mg/1000 kcal), total dietary fibre, fat, fatty acids (g/1000 kcal), and salt equivalents (g/day)	IG: Iron: increase 0.4 mg/1000 kcal^a
	Programme of study NR, nutrition students excluded		CG: Nothing			Vitamin C: increase 10.8 mg/1000 kcal^a
						Total fibre: increase 0.6 g/1000 kcal^a
						Potassium: increase 98.2 mg/1000 kcal^a
						Saturated fat: decrease 0.8 g/1000 kcal^a
						Salt equivalents: increase 0.4 g/day^a
						CG: Saturated fat: decrease 0.8 g/1000 kcal^a
Rodgers et al., 2016	n = 43 F only	Pre‐post	To evaluate the capacity of a mobile technology–based intervention to support healthy eating among ethnic minority female students.	3 weeks (Follow‐up at 3 weeks and 10 weeks)	Vegetable servings per day, fruit servings per day, calories from SSB per day	Vegetable servings: increase 0.05 servings from time 1 to time 2^a
	Programme of study NR		IG: Goal setting, photographing meals via smartphone, motivational healthy eating text messages			Increase 0.07 servings from time 1 to time 3^a
Wang et al., 2019	n = 110	Pre‐post	To evolve traditional health interventions by using integrated methods based on social media and multiple mobile tools.	21 days	Daily and weekly consumption of fruit, vegetables, milk and dairy, grain, soybean and soybean products, aquatic products, black coloured food	Daily vegetable intake^a: % increase in participants reporting consuming 200–400 g, 400–500 g and > 500g^a
	F 59.0%, M 41.0%	with control group				Daily fruit intake^a: % increase 200–400 g, 400–500 g and > 500g^a
	Programme of study NR		CG and IG: Health education via broadcast and discussion regarding nutrition misconceptions × 4 times.			Daily milk and dairy products^a: % increase 200–400 g, 400‐500 g and > 500g^a
			IG: Meal logging, diet reports, diet advice, healthy habit reminders through WeChat smartphone application.			Weekly soybean/soybean product^a: % increase in less than once, twice/three times and almost every day

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Abbreviations: CG, control group; CI, confidence interval; EMI, electronic momentary intervention; F, female; FV, fruit and vegetable; FVI, fruit and vegetable intervention; IG, intervention group; M, male; NR, not reported; SD, standard deviation; SE, standard error; SSB, sugar sweetened beverage; WHO, World Health Organization.

^{^a}

Significant (p ≤ 0.05) by time.

^{^b}

Significant (p ≤ 0.05) by group or intervention status.

The sample size of studies ranged from 43 [29] to 385 participants [32]. The age of participants in the included studies ranged from 18 to 25 years old, with the mean age falling between 18.4 [33] to 20.0 [31, 32, 34] for studies that reported the mean. One study did not report participant age [28]. Of the nine studies that reported participants' gender, all had more female than male participants, with the proportion of female participants ranging from 59% [30] up to 85.5% [35]. Three studies were conducted in females only [29, 33, 36]. Many studies did not specify participants' level of study (n = 6), but of those that did (n = 5), most were conducted among undergraduate students (n = 4). Only 1 study included both undergraduate and graduate students [28]. Of the studies that reported participants' programme of study (n = 4), 2 studies included students in health‐related programmes (e.g., medical sciences and pharmacy) [28, 31], 1 included students in both health and non‐health related programmes (e.g., communications) [32], 1 only included students in non‐health related programmes [37], and 1 study excluded students in a nutrition programme [33]. Length of time of the interventions ranged from 3 days [35] to 60 days [28]. A detailed description of each included study can be found in Table S1.

3.2. Quality Assessment Results

Of the 9 controlled intervention studies included in this review, 4 were rated good quality [31, 32, 34, 36], 4 were fair quality [33, 35, 37, 38], and 1 was poor quality [30] (see Table S2). Good quality studies satisfied at least 10 of the quality assessment criteria, while fair quality studies satisfied 5 to 9 criteria. Examples of quality concerns included inadequate randomization methods or lack of blinding [30, 31, 32, 33, 34, 36], inadequately reporting results [30, 31, 37], and/or not using valid or reliable outcome measures [32, 36]. Relative to the good quality studies, the flaws of fair quality studies were perceived to more meaningfully undermine the validity of their results. The poor‐quality study only satisfied 3 criteria [30].

Two studies used a before‐after (pre‐post) design with no control group [28, 29]. Of these, both satisfied at least 6 criteria of the quality assessment criteria and were rated fair [28, 29].

All studies were included in the final analysis of findings, even the study with a ‘poor’ rating, as the rating was due to inadequate reporting and lack of randomization. As pre‐post interventions without a control group were also included in this review, lack of randomization was not deemed severe enough to undermine the study's reliability in comparison to other included studies. Further, given the limited research available, retaining as many studies as possible was prioritized for this review.

3.3. mHealth Intervention Components

The components of the smartphone interventions and the researchers' approach to dietary behaviour change varied across the included studies. Most interventions used a smartphone application as their platform (n = 7) [28, 30, 31, 32, 33, 34, 36], while others used text messaging (n = 3) [29, 37, 38] or required participants to use their smartphone to access an online platform (n = 1) [35]. The majority (n = 8) of interventions included some form of dietary recording (i.e., participants recorded what they ate in a day) [28, 29, 30, 31, 32, 33, 35, 36]. Of these interventions, some (n = 4) included a feedback component [30, 31, 32, 33]. Participants received feedback on their diet records through their smartphone, often as a diet report comparing their reported intake to relative healthy eating standards or intervention‐specific goals [30, 31, 32, 33]. For example, Bejar et al.'s two interventions had participants use a 12‐h electronic recall smartphone application (e‐12HR) to track their daily dietary intake [31, 32]. Participants received a personalized Mediterranean Diet Assessment report every 7 days throughout the 28‐day intervention, highlighting foods adequately consumed or missed to encourage adherence to the Mediterranean diet, with the aim to improve participants' adherence to the Mediterranean diet [31, 32]. The control group completed the e‐12HR recall but did not receive feedback [31, 32]. Participants in the intervention reported by Wang et al. [30], which intended to improve postsecondary students' healthy food intake and physical activity, took photos of their dietary intake each day and received daily auto‐generated diet reports for 21 days on the WeChat smartphone application, which dietitians on the research team could comment on. Comparatively, Ohtsuki et al.'s [33] intervention aimed to improve participants' diet and BMI and provided users individualized feedback on their diet, based on their reported dietary intake, at baseline and at follow‐up through the LINE smartphone application. Some interventions with dietary recording also included components like motivational messages, goal setting, education, or healthy eating advice [28, 29, 30, 33, 35]. For example, in addition to dietary feedback, Ohtsuki et al.'s [33] 6‐week intervention involved the intervention group receiving healthy eating messages through the smartphone application LINE. Messages were created by registered dietitians educated on balanced eating and included practical advice and quizzes to engage participants, with the option for participants to ask questions to the team through the app [33]. Similarly, Jamshidnezhad et al.'s [28] Fit‐mail App for Nutritional Self‐care (FANS) 60‐day intervention, which sought to improve participants' dietary behaviour, provided participants with dietary advice and the ability to ask questions virtually to nutrition experts through the smartphone application. The intervention described by Rodgers et al. [29] asked participants to choose 2 daily nutrition goals and sent participants encouraging text messages 3 times per day regarding healthy eating for 3 weeks, with the aim of improving participants' dietary intake. Likewise, the intervention reported by Bhurosy et al. [35] used goal setting to increase participants’ intake of red/orange vegetable. For 3 days, participants used their smartphone to take and upload photos and descriptions of their meals to an online platform, counted and reported the number of red/orange vegetables they consumed, and were given the goal to eat one more the next day [35]. The control group only uploaded meal photos [35]. Only 1 intervention, by Hahn et al. [36], included dietary recording (using the MyFitnessPal smartphone application) as their only intervention component and was 1 month long. Hahn et al.'s [36] intervention aimed to determine if daily dietary self‐monitoring would impact female postsecondary students' disordered eating risk, mental health, dietary intake, and physical activity.

The 3 studies that did not include dietary recording as part of their intervention focused on improving participants' knowledge and/or self‐efficacy around healthy eating behaviours [34, 37, 38]. For example, Lyzwinski et al.'s [34] Mindfulness App Weight Loss Trial used educational videos and audio content on a smartphone application to teach participants how to mindfully eat and sent application push notifications at mealtimes to motivate participants to eat mindfully for 11 weeks. The control group received an electronic food diary which included an area for self‐reflection and a World Health Organization guideline for eating [34]. The intervention reported by Brookie et al. aimed to improve participants' dietary intake by addressing nutrition knowledge and self‐efficacy through a 2‐week‐long text messaging intervention [38]. One group, the ecological momentary intervention (EMI) group, received two text messages daily about the benefits of consuming fruits and vegetables, another group received servings of fruits and vegetables to consume instead of the EMI, and another group acted as a control [38]. Similarly, Brown et al.'s [37] Mobile MyPlate intervention sent participants 2 motivational text messages per week for 7 weeks based on the MyPlate icon and dietary guidelines, with the aim of improving participants’ diet, specifically increasing intake of fruits and vegetables, while the control group received the same messages via a brochure [37].

3.4. Behaviour Change Theory Inclusion

Only 2 of the 11 included studies explicitly reported the use of behaviour change theory as part of their intervention [36, 38]. Brookie et al.'s [38] intervention curated smartphone messages were informed by the Health Action Process Approach (HAPA) theory, which considers both the motivational and volitional phase of behaviour change and attempts to understand discrepancies between intention and action regarding behaviour. Furthermore, Brookie et al. [38] applied theoretical concepts such as Abraham and Michie's taxonomy of behaviour change techniques (BCT), which include motivational messages and messages relaying information on the benefits and positive consequences of improving their diet. Comparatively, Bhurosy et al. [35] reported using goal‐setting theory, including monitoring, in their intervention. Participants were given set assigned goals for increasing intake of red/orange vegetables and monitored their own intake by counting their intake frequency and uploading photos. Although Rodgers et al. [29] did not explicitly report goal‐setting theory, their intervention included goal‐setting and monitoring as key intervention components as well.

3.5. Equity Factor Inclusion

Only 1 of the included studies specified any secondary analyses related to equity factors. Bhurosy et al. evaluated the impact of gender and age on the frequency of red/orange vegetable intake and on participants' engagement (uploading photographs) with the app intervention [35]. Participants who were 21–23 years old consumed significantly more red/orange vegetables (p < 0.05) compared to those 18–20 years old, and female participants consumed more red/orange vegetables than men (p < 0.05) [35]. Further, 65.1% of female participants completed uploading photos for all 3 days compared to only 39.3% of male participants (p = 0.002) [35].

3.6. Dietary Outcome Variables and Tools

Targeted dietary outcomes varied across studies. The consumption of fruits and/or vegetables was most common, being measured in 6 studies [29, 30, 35, 36, 37, 38], including one study that explored intake of red and orange vegetables only [35]. The consumption of sweets or sugar‐sweetened beverages was also common, measured in 3 studies [29, 36, 38]. The consumption of dairy products or replacement of low‐fat dairy products for regular was examined in 2 studies [30, 37]. Breakfast skipping [37] or breakfast consumption in the past 7 days [36] was each evaluated by one study. Bejar et al.'s studies [31, 32] assessed participants' adherence to the Mediterranean Diet, and Lyzwinski et al. [34] evaluated changes in mindful eating, emotional eating, uncontrolled eating, and cognitive restraint. Jamshidnezhed et al.'s [28] FANS intervention used a questionnaire and dietary behaviour score, assumed to reflect reduced intake of sausage, artificial sugar, and fast food, and increased fruit and vegetable intake, as these frequencies were also reported by authors. However, the calculation method and exact questions were unclear, and the authors of this current review were unable to receive clarification on this from the original study authors. Other dietary outcomes explored across studies included daily servings of hot chips (i.e., potato fries, wedges, kumara chips) [38], eating more than 4 meals at restaurants (duration of time not reported) [37] eating < 3 servings of whole grain products per day [37], 100% fruit juice, energy drink, sport drink, sparkling water, and water intake [36], total calories, protein (% of calories), fat (% of calories), carbohydrates (% of calories), calcium (mg/1000 kcal), iron (mg/1000 kcal), vitamin c (mg/1000 kcal), potassium (mg/1000 kcal), dietary fibre (g/1000 kcal), fat (g/1000 kcal), saturated fatty acid (g/1000 kcal), and salt equivalent (g/day) intake [33], soybean and soybean product, aquatic product, and black coloured food intake [30].

A promising finding of the current review is that most of the included studies (8/11) used a valid and reliable tool for assessing dietary intake [28, 29, 31, 32, 33, 34, 35, 37]. This included a food frequency questionnaire [29], a questionnaire reportedly tested for validity and reliability by the researchers [28], a 3‐factor eating behaviour and mindful eating questionnaire [34], an electronic‐12HR diet recall [31, 32], the Brief‐type Self‐administered Diet History Questionnaire (BDHQ) [33], a rapid eating and activity assessment [37], and food photographs taken by users and evaluated by trained independent coders who were tested for interrater reliability [35]. Other studies that did not use a validated method used food diaries built into the application [38] or used non‐validated questionnaires [30, 33, 36]. The intervention reported by Ohtsuki et al. [33] used a validated tool to assess direct dietary intake (BDHQ) and a non‐validated survey to obtain participants' perception of their dietary intake.

3.7. Dietary Behaviour Findings

Ten of the 11 studies reported statistically significant improvements in at least one dietary outcome due to the intervention (see Table 1). Five studies reported statistically significant changes in fruit and/or vegetable intake [29, 30, 35, 37, 38]. In Brookie et al.'s [38] 2‐week intervention, the EMI group, who received healthy eating text messages, and the group which received fruit and vegetables both had a significant 1.2 increase servings of FV/day from baseline to after the intervention, in comparison to the control group who did not significantly increase. Further, the group which received fruits and vegetables and the EMI group both had significantly higher FV intake, fruit intake, and vegetable intake than the control group at follow‐up [38]. Brown et al.'s [37] 7‐week intervention, which sent users MyPlate‐based healthy eating text messages 2 times per week, found that more intervention participants consumed at least 2–3 servings of fruit per day in comparison to the control group after the intervention (percentage not reported). Rodgers et al. [29] also reported a statistically significant increase over time in vegetable servings after the 3‐week text messaging based intervention which included photographing meals and goal setting; however, observed changes in daily vegetable intake were small [increase of 0.05 servings from time 1 (baseline) to time 2 (Week 3), increase of 0.07 servings from Week 1 (baseline) to time 3 (Week 10)]. Comparatively, Bhurosy et al.'s [35] 3‐day intervention, which also applied goal setting and required participants to photograph their meals using their smartphone, reported a mean frequency intake of red and orange vegetables from 0.9 times/day on Day 1, and then 1.6 on Day 2, and 1.3 on Day 3 after the self‐monitoring and goal‐setting was implemented, while the control group's mean frequency intake decreased on the 2 days following the intervention. Wang et al. [30] also reported a significantly higher percentage of intervention participants that reported consuming more fruits (p < 0.001) and vegetables (p = 0.045), as well as dairy products per day after the 21‐day WeChat smartphone application intervention, with no significant change in the control group. A higher percentage of intervention participants also reported consuming more soybean and soybean products weekly (p = 0.030) after the 21‐day intervention, with no change in the control group [30].

Bejar et al.'s studies [31, 32] both reported statistically significant increases in AMD scores in the intervention group, who received a personalized dietary feedback report every 7 days for the 4‐week intervention after using the e‐12HR smartphone application, but not the control group who did not receive dietary feedback. In Bejar et al.'s 2022 study [31], mean AMD scores increased by 1.55 points between Week 1 and Week 4 (maximum score 24.0 points), and the percentage of participants with med‐high scores (> 9.0) was higher in the intervention group compared to control group at Week 2, 3 and 4. Bejar et al.'s 2023 study [32] reported similar findings, as AMD scores increased in the intervention group by 1.84 points from Week 1 to Week 4, and the % of participants with med‐high scores were also significantly higher in the intervention group compared to the control group. Jamshidnezhed et al.'s [28] FANS smartphone application intervention also reported a mean increase in a dietary behaviour score by 30.3 points (maximum score and range not reported by authors) after the 60‐day intervention (p < 0.001).

Ohtsuki et al.'s [33] 6‐week long LINE smartphone application intervention significantly increased iron (0.4 mg/1000 kcal), vitamin C (10.8 mg/1000 kcal), dietary fibre (0.6 g/1000 kcal), and potassium (98.2 mg/1000 kcal) in the intervention group (all p < 0.05), with no change in the control group. However, the intervention group also saw a significant rise in salt equivalents (increase 0.4 g/day), and both groups had a significant decrease in saturated fatty acid intake (p < 0.05 for intervention [0.8 g/1000 kcal], p < 0.001 for control [0.8 g/1000 kcal]) [33].

Lyzwinski et al.'s [34] 11‐week‐long Mindfulness App Weight Loss Trial resulted in statistically significant differences in mindful eating, emotional eating, and uncontrolled eating levels between the intervention and control group at follow‐up. Intervention participants had significantly higher mindful eating scores [0.29 points (p < 0.05)] than the control group, and control participants had significantly higher emotional eating (1.09 points) and uncontrolled eating (2.05 points) scores (p < 0.05) [34].

Comparatively, Hahn et al.'s [36] 1‐month‐long dietary monitoring intervention, which used the MyFitnessPal smartphone application, did not result in a significant improvement in any evaluated dietary outcomes.

4. Discussion

4.1. Impact on Dietary Behaviours

Overall, of the 11 studies included in this systematic review, 10 reported a significant improvement in postsecondary students’ dietary behaviour because of their mHealth diet intervention. Across the numerous dietary outcomes evaluated, only fruit and vegetable intake (5 studies), Mediterranean diet adherence (2 studies), dietary behaviour score (1 study), dairy intake (1 study), soybean/soybean product intake (1 study), iron (1 study), vitamin C (study), potassium (1 study), dietary fibre (1 study), saturated fat (1 study), and mindful eating scores (1 study) appeared to significantly improve after the interventions. Specifically, 5 of the 6 studies exploring fruit and vegetable intake reported a statistically significant improvement in fruit and/or vegetable intake. There were no statistically significant improvements to sugary food or sugar‐sweetened beverage intake, or any other dietary outcomes evaluated. This suggests that mHealth dietary interventions may be effective at improving fruit and vegetable intake more so than other dietary intake outcomes and behaviours. However, this may also reflect the greater number of studies evaluating fruit and vegetable intake compared to other dietary behaviours.

Similar findings have been reported by systematic reviews on mHealth diet interventions among adult and adolescent populations [21, 22]. For example, Villinger and Wahl et al.'s [21] systematic review and meta‐analysis of app‐based interventions in adolescents and adults reported a statistically significant positive effect on fruit and vegetable intake, from their pooled analysis of 27 RCTs (Hedges' g = 0.32, Confidence Interval: 0.15–0.50). Comparatively, Schaafsma et al.'s [22] narrative systematic review of app‐based mHealth diet interventions in adolescents reported that most of the included studies (11/14) found a statistically significant improvement in at least one dietary intake outcome; however, only 1 of the 7 studies evaluating fruit and/or vegetable intake noted an improvement. Given these comparisons, mHealth interventions may be a promising approach to improve postsecondary students' fruit and vegetable intake. However, as the current review was limited to 11 studies, more research is needed in this population to determine the effectiveness of mHealth diet interventions on postsecondary students' dietary behaviours. Furthermore, the clinical significance (e.g., extent of change in relation to its impact on participants' quality of life and function) of the reported dietary changes in these studies should be considered, alongside the statistical significance [39], as the extent of some reported changes were practically small. For example, Rodgers et al.'s [29] study reported a statistically significant change in vegetable intake over time, but the change was less than 0.1 of a serving. Further, Brookie et al. [38] found a significant difference in fruit and vegetable servings between the EMI intervention and control group, but the difference was just under 1 serving per day. Additionally, although intervention length did not appear to predict intervention effectiveness, the lack of long‐term follow‐up across studies raises concerns about the persistence of the reported dietary improvements over time. Lack of sufficient follow‐up is likely due to the challenge of retaining participants over extended periods, particularly as postsecondary students often move during winter and summer breaks. The need for interventions with a longer follow‐up time (> 6 months) was also noted in a systematic review of mHealth interventions promoting healthy eating in adults [40].

4.2. Variation in Impact Based on Intervention Components

Almost all interventions (9/11) in the current review included some form of dietary education, either in the form of providing feedback on dietary intake and areas for improvement or through targeted healthy eating or healthy diet behaviour messaging. All 9 of these studies resulted in at least one statistically significant dietary improvement, suggesting a positive relationship between targeting nutrition knowledge and improvement of dietary behaviours in this population. A previous systematic review of general diet interventions in postsecondary students noted that nutrition education strategies may be useful for improving dietary intake [41]. Cross‐sectional research has also shown that higher food and nutrition literacy is associated with better healthy eating behaviour and lower consumption of fast food and ultra‐processed food in postsecondary students [42, 43]. The only study in the current review that showed no significant dietary improvements relied solely on dietary recording, without a nutrition education or goal‐setting component. Although dietary self‐monitoring is considered a useful behaviour change technique to increase self‐awareness of dietary choices [44, 45, 46], tracking food intake did not appear to be meaningful enough to drive dietary behaviour change in the postsecondary population. As such, integrating food and nutrition literacy into future mHealth diet interventions could improve their effectiveness in postsecondary students.

Since only 2 of the included studies explicitly reported using a behaviour change theory in their intervention design, we cannot draw conclusions on the effectiveness of theory integration in mHealth diet interventions for the postsecondary population. However, the lack of reporting behaviour change theory has been noted in other systematic reviews in the field of mHealth interventions in different populations [22, 47]. This is concerning, as using theory to develop behaviour change interventions has been shown to improve their effectiveness, by helping researchers develop and tailor their interventions to established behaviour change processes [24, 48]. Future interventions could benefit by applying behaviour change theory relevant to their population and context. However, it is important to note that the lack of explicitly reporting a theory does not mean authors did not apply theoretical concepts in their intervention design. For example, many interventions included a form of dietary education. Many of these may have had the intention of improving self‐efficacy, without directly referring to a behaviour change theory, such as Bandura's Social Cognitive Theory [49].

It is important to note the limitations of this review and the included studies. The dietary outcomes and evaluation methods of the included studies varied considerably, making it difficult to draw sound conclusions on the effectiveness of these mHealth interventions on postsecondary students’ dietary behaviours. Further, some of the included studies did not use validated dietary assessment tools, which may limit the reliability of the current review's findings, and the majority used self‐reported dietary data, which is prone to bias [50]. All the studies that reported gender distribution (9/11) had a higher percentage of female participants, with three studies focusing exclusively on women. As a result, the findings of this review may be biased toward the female postsecondary population. This review was also limited to only peer‐reviewed publications available in English, which may have narrowed the scope of the findings.

5. Conclusion

The use of mHealth diet interventions in postsecondary students appears to be promising, particularly at improving fruit and vegetable intake. However, despite reported statistically significant changes in dietary behaviours, the clinical significance and long‐term effects of these findings remain unclear, as follow‐up times were relatively short. Future interventions should include nutrition education, goal setting, or dietary feedback, as dietary recording alone appears insufficient to induce behaviour change. As only 11 studies were included in this review, there appears to be a paucity of mHealth diet intervention research in the postsecondary population. Future interventions are needed to determine the efficacy of these types of interventions at improving dietary outcomes in this population. Future mHealth diet interventions for the postsecondary population should use validated dietary assessments and include follow‐up of 6 months or more to assess long‐term effectiveness and clinical significance.

Author Contributions

All authors made contributions to this manuscript, including the design and implementation. H.S., L.M., O.C., J.S., and J.G. developed the search criteria. H.S. and O.C. conducted the database search. S.D. screened the title and abstracts and completed data extraction. H.S. and O.C. completed the full‐text review and quality assessment, and L.M. adjudicated when necessary. H.S. reviewed extracted data and draughted the original manuscript. All co‐authors critically reviewed and revised the manuscript for both intellectual content and writing quality. All authors approved the final version to be published.

Conflicts of Interest

The authors declare no conflicts of interest.

Peer Review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/jhn.70130.

Supporting information

Supporting Figure 1: MEDLINE Search.

JHN-38-e70130-s001.docx^{(14.7KB, docx)}

Supporting Table 1: Extended Summary of Included Studies (n = 11).

JHN-38-e70130-s002.docx^{(29.3KB, docx)}

Supporting Table 2: Quality Assessment of Included Studies (n = 11) using NIH Quality Assessment Tool for Controlled Intervention Studies and NIH Quality Assessment Tool for Before‐After (Pre‐Post) Studies with No Control Group.

JHN-38-e70130-s003.docx^{(20KB, docx)}

Acknowledgements

This study was supported by the Canadian Institutes of Health Research (CIHR) through a project grant (399384), and through trainee support from the Children's Health Foundation (CHF) provided through the Children's Health Research Institute (CHRI). The funders did not have any role in the conception, design, performance, or approval of this study, and the views expressed in this study are the authors and are not necessarily those of CIHR, CHF or CHRI.

Schaafsma H., Caruso O., Diaconita S., McEachern L., Seabrook J., and Gilliland J., “Enhancing Postsecondary Students' Dietary Behaviours: A Systematic Review of Mobile Health Interventions,” Journal of Human Nutrition and Dietetics 38 (2025): 1‐14. 10.1111/jhn.70130.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Figure 1: MEDLINE Search.

JHN-38-e70130-s001.docx^{(14.7KB, docx)}

Supporting Table 1: Extended Summary of Included Studies (n = 11).

JHN-38-e70130-s002.docx^{(29.3KB, docx)}

JHN-38-e70130-s003.docx^{(20KB, docx)}

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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PERMALINK

Enhancing Postsecondary Students' Dietary Behaviours: A Systematic Review of Mobile Health Interventions

Holly Schaafsma

Olivia Caruso

Smaranda Diaconita

Louise McEachern

Jamie Seabrook

Jason Gilliland

ABSTRACT

Summary

1. Introduction

2. Materials and Methods

2.1. Literature Search

2.2. Eligibility Criteria

2.3. Screening and Data Extraction

2.4. Quality Assessment

3. Results

3.1. Study and Sample Characteristics

Figure 1.

Table 1.

3.2. Quality Assessment Results

3.3. mHealth Intervention Components

3.4. Behaviour Change Theory Inclusion

3.5. Equity Factor Inclusion

3.6. Dietary Outcome Variables and Tools

3.7. Dietary Behaviour Findings

4. Discussion

4.1. Impact on Dietary Behaviours

4.2. Variation in Impact Based on Intervention Components

5. Conclusion

Author Contributions

Conflicts of Interest

Peer Review

Supporting information

Acknowledgements

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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