Development and Field Evaluation of the INTER-ACT App, a Pregnancy and Interpregnancy Coaching App to Reduce Maternal Overweight and Obesity: Mixed Methods Design
Bibliographic record
Abstract
BACKGROUND: The interpregnancy and pregnancy periods are important windows of opportunity to prevent excessive gestational weight retention. Despite an overwhelming number of existing health apps, validated apps to support a healthy lifestyle between and during pregnancies are lacking. OBJECTIVE: To describe the development and evaluation of the INTER-ACT app, which is part of an interpregnancy and pregnancy lifestyle coaching module, to prevent excessive weight gain in pregnancy and enhance optimal weight and a healthy lifestyle in the interpregnancy period. METHODS: A mixed methods design was used to identify the needs of health care providers and end users, according to 15 semistructured interviews, two focus groups, and two surveys. The user interface was evaluated in a pilot study (N=9). RESULTS: Health care providers indicated that a mobile app can enhance a healthy lifestyle in pregnant and postpartum women. Pregnant women preferred graphic displays in the app, weekly notifications, and support messages according to their own goals. Both mothers and health care providers reported increased awareness and valued the combination of the app with face-to-face coaching. CONCLUSIONS: The INTER-ACT app was valued by its end users because it was offered in combination with face-to-face contact with a caregiver.
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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 | 0.045 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".