Developing Components of an Integrated mHealth Dietary Intervention for Mexican Immigrant Farmworkers: Feasibility Usability Study of a Food Photography Protocol for Dietary Assessment
Bibliographic record
Abstract
BACKGROUND: Rural-urban disparities in access to health services and the burden of diet-related noncommunicable diseases are exacerbated among Mexican immigrant farmworkers due to work demands, social and geographical isolation, literacy issues, and limited access to culturally and language-competent health services. Although mobile health (mHealth) tools have the potential to overcome structural barriers to health services access, efficacious mHealth interventions to promote healthy eating have not considered issues of low literacy and health literacy, and food preferences and norms in the Mexican immigrant farmworker population. To address this critical gap, we conducted a series of preliminary studies among Mexican immigrant farmworkers with the long-term goal of developing a culture- and literacy-specific smartphone app integrating dietary assessment through food photography, diet analyses, and a non-text-based dietary intervention. OBJECTIVE: This study aimed to report adherence and reactivity to a 14-day food photography dietary assessment protocol, in which Mexican immigrant farmworker women were instructed to take photos of all foods and beverages consumed. METHODS: We developed a secure mobile app with an intuitive graphical user interface to collect food images. Adult Mexican immigrant farmworker women were recruited and oriented to the photography protocol. Adherence and reactivity were examined by calculating the mean number of food photos per day over time, differences between the first and second week, and differences between weekdays and weekends. The type of foods and meals photographed were compared with reported intake in three 24-hour dietary recalls. RESULTS: In total, 16 Mexican farmworker women took a total of 1475 photos in 14 days, with a mean of 6.6 (SD 2.3) photos per day per participant. On average, participants took 1 fewer photo per day in week 2 compared with week 1 (mean 7.1, SD 2.5 in week 1 vs mean 6.1, SD 2.6 in week 2; P=.03), and there was a decrease of 0.6 photos on weekdays versus weekends (mean 6.4, SD 2.5 on weekdays vs mean 7, SD 2.7 on weekends; P=.50). Of individual food items, 71% (352/495) of foods in the photos matched foods in the recalls. Of all missing food items (n=138) and meals (n=36) in the photos, beverages (74/138, 54%), tortillas (15/138, 11%), snacks 16/36, 44%), and dinners (10/36, 28%) were the most frequently missed. Most of the meals not photographed (27/36, 75%) were in the second week of the protocol. CONCLUSIONS: Dietary assessment through food photography is feasible among Mexican immigrant farmworker women. For future protocols, substantive adjustments will be introduced to reduce the frequency of missing foods and meals. Our preliminary studies are a step in the right direction to extend the benefits of mHealth technologies to a hard-to-reach group and contribute to the prevention and control of diet-related noncommunicable diseases.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".