A Comparison of Mobile and Fixed Device Access on User Engagement Associated With Women, Infants, and Children (WIC) Online Nutrition Education
Bibliographic record
Abstract
BACKGROUND: Online health education has expanded its reach due to cost-effective implementation and demonstrated effectiveness. However, a limitation exists with the evaluation of online health education implementations and how the impact of the system is attenuated by the extent to which a user engages with it. Moreover, the current online health education research does not consider how this engagement has been affected by the transition from fixed to mobile user access over the last decade. OBJECTIVE: This paper focuses on comparing the impact mobile versus fixed devices have on user engagement key performance indicators (KPI) associated with the wichealth website (.org), an Internet-based parent-child feeding intervention offered to clients associated with the US Department of Agriculture's Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). METHODS: Data were collected from 612,201 nutrition education lessons completed by 305,735 unique WIC participants in 21 states over a 1-year period. Data consisted of system-collected measures, profile items, and items from an exit survey administered at the conclusion of each lesson. User engagement was defined based on 3 KPIs associated with usage of the wichealth website: number of link views, link view time, and progression in stage of readiness to change. Independent samples t tests were used to compare KPIs between fixed only and mobile only device users and paired samples t tests were used to compare KPIs within users who completed at least one lesson each on both a fixed and mobile device. A logistic regression was performed to estimate the odds of KPI performance thresholds in the independent samples study group given access device type while controlling for confounding of user characteristics associated with these KPIs. RESULTS: Analysis of 8 user characteristics (lessons completed, race, ethnicity, language, state of residence, pregnancy status, beginning stage of change, and preferred nutrition education method) were significantly (P<.001) related to various KPI differences between mobile and fixed device access. Non-mobile users were significantly (P<.001) more likely to engage based on all 3 KPIs, even after logistic regression control for the potential confounding related to the strongly associated user characteristics identified. CONCLUSIONS: The findings of this study support the idea that online health education developers need to seriously consider access device when creating programs. Online health education developers need to take extra effort to truly understand access patterns of populations being served, and whether or not access device will influence user engagement performance indicators.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".