A Text Messaging Intervention (Txt4HappyKids) to Promote Fruit and Vegetable Intake Among Families With Young Children: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Increasing fruit and vegetable intake among low-income populations, especially children, is a priority for United States federal food assistance programs. With over 49 million federal food assistance program recipients, cost-effective and efficient methods are needed to effectively deliver nutrition education to such a large population. OBJECTIVE: The objective of our study was to examine the preliminary efficacy and acceptability of a text messaging intervention, Txt4HappyKids, to promote fruit and vegetable intake among families with young children. METHODS: The intervention was evaluated using a pre-post study design. Parents (N=72) in Alaska were recruited from venues that serve a predominantly low-income population to participate in an 11-week intervention based on social cognitive theory. Parents received two texts per week promoting child fruit and vegetable intake. Behaviors, self-efficacy, and attitudes related to fruit and vegetable intake were measured at baseline and postintervention. Perceived changes in behaviors and open-ended feedback were also collected postintervention. RESULTS: Of all participants, 67.3% (72/107) completed the intervention. We found no changes in behavior (P=.26), self-efficacy (P=.43), or attitudes (P=.35) related to fruit and vegetable intake from pre- to postintervention. Completers reported that since their participation in Txt4HappyKids, 92% (66/72) served more fruits and vegetables to their child because they thought fruits and vegetables were beneficial, 86% (62/72) tried to follow a healthier diet, 85% (61/72) tried different ways of preparing fruits and vegetables, and 81% (58/72) were more aware of the foods their child consumes. Additionally, 79% (57/72) of completers thought that Txt4HappyKids was credible, 71% (51/72) found texts useful, and 82% (59/72) would recommend it to a friend. CONCLUSIONS: A text messaging intervention was not sufficient to increase fruit and vegetable intake among families with young children. However, parents felt positively impacted by Txt4HappyKids and were receptive to nutrition information, despite the absence of face-to-face contact. High satisfaction among completers indicates that text messaging may be an acceptable complement to budget-constrained nutrition programs. These findings are an important first step in developing larger multi-level interventions utilizing mobile technology; however, a more rigorous evaluation of the Txt4HappyKids intervention is warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".