Enhancing Postsecondary Students' Dietary Behaviours: A Systematic Review of Mobile Health Interventions
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".