Using Personal Mobile Phones to Assess Dietary Intake in Free-Living Adolescents: Comparison of Face-to-Face Versus Telephone Training
Bibliographic record
Abstract
BACKGROUND: Traditional paper-based methods to assess food intake can be cumbersome for adolescents; use of mobile phones to track and photograph what they eat may be a more convenient, reliable, and compelling way to collect data. OBJECTIVE: Our aims were to determine (1) the feasibility of using personal mobile phones to send food records with digital images (FRDIs) among free-living adolescents and (2) whether the quality of food records differed between a high-level intervention group (ie, face-to-face training plus real-time support) and a low-level intervention group (ie, telephone training plus next-day follow-up). METHODS: Adolescents (N=42, 11 males and 31 females) aged 12-18 years who had a mobile phone with camera enrolled in the study via consecutive sampling. The first group (n=21) received face-to-face training while the second group (n=21) was trained via telephone. Participants received a fiducial marker (FM) and completed a 1-day FRDI using their mobile phones. At every eating occasion, participants were to (1) take clear images of their meals/food with a correctly placed fiducial marker before eating, (2) send the image immediately to a designated email address, (3) right after completing a meal, send a text message listing the time and name of the meal, foods eaten, and amounts eaten, and (4) before sleep, send an "end" text message to indicate completion of food recording. Those who received face-to-face training received real-time support during reporting; those trained by telephone received next-day follow-up. Descriptive statistics and comparison tests were used to determine performance of the groups. RESULTS: All participants (N=42) who underwent training completed their 1-day FRDI. A significantly greater proportion of the low-level intervention group compared to the high-level intervention group placed their FM correctly in the image (95% vs 43%, P<.001), had complete information for each meal in their food record (95% vs 71%, P=.04), and had a higher overall score in meeting the criteria for food recording (4.3 vs 3.4 out of 5 points). Both groups had energy intake values that moderately correlated with their estimated energy requirements: low-intervention r=.55; high-intervention r=.51. CONCLUSIONS: Using personal mobile phones to report dietary intake via texting and digital images is feasible among free-living adolescents. Real-time support or high-level intervention does not guarantee better food recording quality among adolescents.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".