Capturing Everyday Parental Feeding Practices and Eating Behaviors of 3- to 5-Year-Old Children With Avid Eating Behavior: Ecological Momentary Assessment Feasibility and Acceptability Study
Bibliographic record
Abstract
Background: The wide use of smartphones offers large-scale opportunities for real-time data collection methods such as ecological momentary assessment (EMA) to assess how fluctuations in contextual and psychosocial factors influence parents' feeding practices and feeding goals, particularly when feeding children with high food approaches. Objective: The main objectives of this study were to (1) assess parents/caregivers' compliance with EMA procedures administered through a smartphone app and (2) estimate the criterion validity of the EMA to capture children's eating occasions and parents' feeding practices. Participant adherence, technological challenges, and data quality were used to provide an overview of the real-time dynamics of parental mood, feeding goals, and contextual factors during eating occasions. Methods: Parents in the United Kingdom with a child aged 3 to 5 years who exhibit avid eating behavior were invited to participate in a 10-day EMA study using a smartphone app. Of the 312 invited participants, 122 (39%) parents initiated the EMA study, of which 118 (96.7%) completed the full EMA period and the follow-up feasibility and acceptability survey. Results: Of those parents who completed the EMA study, 104 (87.4%) parents provided at least 7 "full" days of data (2 signal surveys and 1 event survey), despite 51 parents (43.2%) experiencing technical difficulties. The parents received notifications for morning surveys (69.9% response rate), 3 daily mood surveys (78.7% response rate), and an end-of-day survey (84.6% response rate) on each of the 10 days. Over the EMA period, a total of 2524 child eating/food request surveys were self-initiated by the participants on their smartphones, an average of 2.1 times per day per parent (SD 0.18; min=1.7, max=2.3). The majority of parents felt that the surveys made them more aware of their feelings (105/118, 89%) and activities (93/118, 79%). The frequency of daily food requests estimated by parents at baseline was significantly correlated with the frequency of food requests reported daily during the EMA period (r=0.483, P<.001). However, the number of daily food requests per day estimated at baseline (mean 4.5, SD 1.5) was significantly higher than the number of food requests reported per day during the EMA period (mean 3.7, SD 1.1), (t116=18.8, P<.001). Conclusions: This paper demonstrates the feasibility of employing EMA to investigate the intricate interplay between parental mood, feeding goals, contextual factors, and feeding practices with children exhibiting an avid eating behavior profile. However, the use of EMA needs to be carefully developed and tested with parents' involvement to ensure successful data collection.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".