Development and Pilot Testing of the Eating4two Mobile Phone App to Monitor Gestational Weight Gain
Bibliographic record
Abstract
BACKGROUND: The number of pregnant women with a body mass index (BMI) of 30kg/m(2) or more is increasing, which has important implications for antenatal care. Various resource-intensive interventions have attempted to assist women in managing their weight gain during pregnancy with limited success. A mobile phone app has been proposed as a convenient and cost-effective alternative to face-to-face interventions. OBJECTIVE: This paper describes the process of developing and pilot testing the Eating4Two app, which aims to provide women with a simple gestational weight gain (GWG) calculator, general dietary information, and the motivation to achieve a healthy weight gain during pregnancy. METHODS: The project involved the development of app components, including a graphing function that allows the user to record their weight throughout the pregnancy and to receive real-time feedback on weight gain progress and general information on antenatal nutrition. Stakeholder consultation was used to inform development. The app was pilot tested with 10 pregnant women using a mixed method approach via an online survey, 2 focus groups, and 1 individual interview. RESULTS: The Eating4Two app took 7 months to develop and evaluate. It involved several disciplines--including nutrition and dietetics, midwifery, public health, and information technology--at the University of Canberra. Participants found the Eating4Two app to be a motivational tool but would have liked scales or other markers on the graph that demonstrated exact weight gain. They also liked the nutrition information; however, many felt it should be formatted in a more user friendly way. CONCLUSIONS: The Eating4Two app was viewed by participants in our study as an innovative support system to help motivate healthy behaviors during pregnancy and as a credible resource for accessing nutrition-focused information. The feedback provided by participants will assist with refining the current prototype for use in a clinical intervention trial.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".