Associations between digital health intervention engagement and dietary intake: A Systematic Review (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> There has been a proliferation of digital health interventions (DHIs) targeting dietary intake. Despite their potential, the effectiveness of such interventions are thought to be dependent, in part, on user engagement. However, the relationship between engagement and the effectiveness of dietary DHIs is not well understood. </sec> <sec> <title>OBJECTIVE</title> As such, the aim of this systematic review is to describe the association between DHI engagement (both usage and subjective experience) and dietary intake. </sec> <sec> <title>METHODS</title> A comprehensive search for peer-reviewed literature was undertaken in four electronic databases (EMBASE, MEDLINE, PsychINFO, Scopus) from inception to December 2019. A hand search of targeted journals, grey literature searches and a search of relevant references of similar reviews was also conducted. Studies were eligible if they examined a quantitative association between objective measures of engagement with a DHI (subjective experience or usage) and measures of dietary intake in adults (aged ≥18 years). Authors single screened studies, with a pair of review authors assessing quality of studies and extracting relevant data. Narrative syntheses using vote counting was undertaken to explore to relationship between measures of engagement and dietary intake. </sec> <sec> <title>RESULTS</title> The search resulted in 10,653 citations, of which seven studies (from nine articles) were included in the review. The majority of studies (n=5) included usage measures of engagement rather than subjective experience (n=2). Logins were the most commonly reported usage measure (n=5 studies), and fruit and vegetable intake was the most common measure of dietary intake (n=4 studies). The heterogeneity of engagement and dietary intake measures limited the use of meta-analytic techniques, however narrative review (vote counting) found mixed evidence of an association with usage measures (5 of 12 associations indicating a positive relationship, 7 were inconclusive). No evidence regarding an association with subjective experience was found (0 of 2 associations were inconclusive). The majority of included studies (n=5) were rated poor quality according to the Newcastle Ottawa Scale. </sec> <sec> <title>CONCLUSIONS</title> The findings provide some evidence supporting an association between measures of usage and fruit and vegetable intake, however this was inconsistent. No evidence was found regarding an association with subjective experience. Given the limited number of studies included in the review and poor quality of available evidence further research examining the association between DHI engagement and dietary intake using consistent measures, with an additional focus on subjective experience is warranted. </sec> <sec> <title>CLINICALTRIAL</title> CRD42018112189 </sec>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".