A Mobile Phone App for Dietary Intake Assessment in Adolescents: An Evaluation Study
Bibliographic record
Abstract
BACKGROUND: There is a great need for dietary assessment methods that suit the adolescent lifestyle and give valid intake data. OBJECTIVE: To develop a mobile phone app and evaluate its ability to assess energy intake (EI) and total energy expenditure (TEE) compared with objectively measured TEE. Furthermore, to investigate the impact of factors on reporting accuracy of EI, and to compare dietary intake with a Web-based method. METHODS: Participants 14 to 16 years of age were recruited from year nine in schools in Gothenburg, Sweden. In total, 81 adolescents used the mobile phone app over 1 to 6 days. TEE was measured with the SenseWear Armband (SWA) during the same or proximate days. Individual factors were assessed with a questionnaire. A total of 15 participants also recorded dietary intake using a Web-based method. RESULTS: The mobile phone app underestimated EI by 29% on a group level (P<.001) compared to TEE measured with the SWA, and there was no significant correlation between EI and TEE. Accuracy of EI relative to TEE increased with a weekend day in the record (P=.007) and lower BMI z-score (P=.001). TEE assessed with the mobile phone app was 1.19 times the value of TEE measured by the SWA on a group level (P<.001), and the correlation between the methods was .75 (P<.001). Analysis of physical activity levels (PAL) from the mobile phone app stratified by gender showed that accuracy of the mobile phone app was higher among boys. EI, nutrients, and food groups assessed with the mobile phone app and Web-based method among 15 participants were not significantly different and several were significantly correlated, but strong conclusions cannot be drawn due to the low number of participants. CONCLUSIONS: By using a mobile phone dietary assessment app, on average 71% of adolescents' EI was captured. The accuracy of reported dietary intake was higher with lower BMI z-score and if a weekend day was included in the record. The daily question in the mobile phone app about physical activity could accurately rank the participants' TEE.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".