Mobile Phone App Aimed at Improving Iron Intake and Bioavailability in Premenopausal Women: A Qualitative Evaluation
Bibliographic record
Abstract
BACKGROUND: Low iron intake can lead to iron deficiency, which can result in impaired health and iron-deficiency anemia. A mobile phone app, combining successful dietary strategies to increase bioavailable iron with strategies for behavior change, such as goal setting, monitoring, feedback, and resources for knowledge acquisition, was developed with the aim to increase bioavailable iron intake in premenopausal women. OBJECTIVE: To evaluate the content, usability, and acceptability of a mobile phone app designed to improve intake of bioavailable dietary iron. METHODS: Women aged 18-50 years with an Android mobile phone were invited to participate. Over a 2-week period women were asked to interact with the app. Following this period, semistructured focus groups with participants were conducted. Focus groups were audio recorded and analyzed via an inductive open-coding method using the qualitative analysis software NVivo 10. Themes were identified and frequency of code occurrence was calculated. RESULTS: Four focus groups (n=26) were conducted (age range 19-36 years, mean 24.7, SD 5.2). Two themes about the app's functionality were identified (frequency of occurrence in brackets): interface and design (134) and usability (86). Four themes about the app's components were identified: goal tracker (121), facts (78), photo diary (40), and games (46). A number of suggestions to improve the interface and design of the app were provided and will inform the ongoing development of the app. CONCLUSIONS: This research indicates that participants are interested in iron and their health and are willing to use an app utilizing behavior change strategies to increase intake of bioavailable iron. The inclusion of information about the link between diet and health, monitoring and tracking of the achievement of dietary goals, and weekly reviews of goals were also seen as valuable components of the app and should be considered in mobile health apps aimed at adult women.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".