Days Needed to Characterize the Healthfulness of a Typical Dinner Meal in Direct Observational Research: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Prior research around the home meal environment has demonstrated that family meals are associated with positive health outcomes for children and adolescents. Researchers have begun using direct observational methods to understand key aspects of family meals such as meal healthfulness and family meal frequency to explain the protective nature of family meals. Direct observational research, however, can be resource intensive and also burdensome for participants. Information about the number of days needed to sufficiently characterize typical meal healthfulness using direct observational research methods is needed. OBJECTIVE: The current study aimed to produce guidance about the number of meals necessary to approximate typical meal healthfulness at the family dinner meal occasion in a direct observational, mixed methods study of the home food environment. METHODS: Families were recruited between 2012-2013 from primary care clinics in the Minneapolis-St Paul metropolitan area (N=120). A total of 800 meals were collected as part of the Family Meals LIVE! mixed methods study. The Healthfulness of Meal Index was used to evaluate meal dietary healthfulness of foods served at 8 family meal occasions. Participating families were provided an iPad (Apple Inc) and asked to video-record 8 consecutive days of family dinner meals with a minimum of two weekend meals. After the meal, families completed a meal screener, which is a self-reported, open-ended measure of the foods served at the meal. RESULTS: Weekend and weekday meals differed in their measurement of meal healthfulness, indicating that at least one weekday and one weekend day are necessary to approximate meal healthfulness. Single-day measurement mischaracterized the strength of the relationship between the quality of what was served and intake by almost 50%, and 3 to 4 observation days were sufficient to characterize typical weekly meal healthfulness (r=0.94; P<.001). CONCLUSIONS: Relatively few direct observational days of family meals data appear to be needed to approximate the healthfulness of meals across 1 week. Specifically, 1 weekday and 1 weekend observation are needed, including a total of 3 to 4 days of direct observational meal data. These findings may inform future direct observational study designs to reduce both research costs and participant burden in assessing features of the meal environment.
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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 | 0.063 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".