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Record W4404019481 · doi:10.2196/58460

Testing a Web-Based Interactive Comic Tool to Decrease Obesity Risk Among Racial and Ethnic Minority Preadolescents: Randomized Controlled Trial

2024· article· en· W4404019481 on OpenAlexvenueno aff
May May Leung, Katrina F. Mateo, Marlo Dublin, Lawrence E. Harrison, Sandra Verdaguer, Katarzyna Wyka

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsPreprintComicsRandomized controlled trialPsychologyMedicineComputer scienceWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood obesity prevalence remains high, especially in racial and ethnic minority populations with low incomes. This epidemic is attributed to various dietary behaviors, including increased consumption of energy-dense foods and sugary beverages and decreased intake of fruits and vegetables. Interactive, technology-based approaches are emerging as promising tools to support health behavior changes. OBJECTIVE: This study aimed to assess the feasibility and acceptability of Intervention INC (Interactive Nutrition Comics for Urban, Minority Preadolescents), a 6-chapter web-based interactive nutrition comic tool. Its preliminary effectiveness on diet-related psychosocial variables and behaviors was also explored. METHODS: A total of 89 Black or African American and Hispanic preadolescents with a mean age of 10.4 (SD 1.0) years from New York City participated in a pilot 2-group randomized study, comprising a 6-week intervention and a 3-month follow-up (T4) period. Of the 89 participants, 61% were female, 62% were Black, 42% were Hispanic, 53% were overweight or obese, and 34% had an annual household income of <US $20,000. Participants were randomly assigned to the experimental group (45/89, 50% received the web-based comic tool), or the comparison group (44/89, 50% received web-based nutrition newsletters). Primary measures included feasibility and usability at intervention midpoint (T2) and intervention end (T3). Semistructured interviews were conducted at the same time to assess acceptability and satisfaction. Secondary measures, collected at baseline (T1), T2, T3, and at T4, included attitudes, beliefs, and behaviors related to fruit, vegetable, water, sugar, and junk food intake. Descriptive analyses were conducted for use and usability data. Interviews were systematically analyzed to facilitate identification of patterns and themes. Secondary data were analyzed using descriptive statistics. Within- and between-group effect sizes were reported. RESULTS: In total, 72% (33/45) and 60% (27/44) of the experimental and comparison groups, respectively, accessed their tool weekly. The mean total usability score was high and moderately high for the experimental and comparison groups, respectively (mean 4.01, SD 0.37 and mean 3.81, SD 0.51; P=.048), based on a 5-point Likert scale). Children in both groups found the tool acceptable, and few reported difficulties logging in or accessing content. Between-group effect sizes for beliefs and attitudes related to dietary intake, while favoring the experimental group at T3, were in the small range. These improvements in both groups were largely diminished by T4. However, between-group effect sizes for behaviors related to fruit, vegetable, and water intake, favoring the experimental group, were medium to large and were maintained at T4. CONCLUSIONS: This pilot feasibility study suggests that an interactive comic tool may be an appealing and useful format to promote positive dietary behaviors in racial and ethnic minority preadolescents. However, further research, including a full-scale randomized controlled trial, is warranted to determine the effectiveness of Intervention INC. TRIAL REGISTRATION: ClinicalTrials.gov NCT03165474; https://clinicaltrials.gov/study/NCT03165474. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10682.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.366
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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