An Information App (e-TOP) to Support Parents’ Information Needs During the First Year at Home After Preterm Birth: Development and Usability Study
Bibliographic record
Abstract
Background: Parents of preterm infants often face challenges in transitioning from hospital to home, requiring reliable and accessible information to support their caregiving. Mobile health interventions have the potential to supplement postdischarge education and empower parents by providing tailored, evidence-based information. Objective: The primary objectives of this study were to develop an information app (e-TOP) using a participatory design approach, incorporating input from parents and health care professionals, and evaluate its usability. Methods: A 2-phase, mixed methods design was used for this study. In Phase 1, the app was developed through iterative focus group discussions with parents of preterm infants, parents with limited health literacy, and TOP interventionists. In Phase 2, a usability study was conducted over 6 months with parents of preterm infants. Usability was assessed with a broad range of measurements: thinking aloud sessions, user engagement analytics, 2 questionnaires, the system usability scale (SUS), a customized satisfaction survey, open question sections, and semistructured interviews. Results: The collaborative approach with end users and experts for the development led to a fully functional e-TOP app. Expert review and content validation ensured that information was clinically accurate, accessible, and relevant to parental needs. For the usability testing, a total of 58 families (116 participants) were recruited and 69 participants actively used the app. The median cumulative e-TOP usage per participant in 6 months (26 weeks) was 39 minutes (IQR 8.8-53.0). The median number of actions was 64.0 (IQR 33.5-88.0). The e-TOP app received a median SUS score of 75 (IQR 67.5-80.0), indicating good usability. Participants rated their overall median satisfaction at 7.0 (IQR 7.0-8.0) out of 10. While the app was perceived as useful for finding information on prematurity-specific topics, engagement declined over time. Qualitative feedback highlighted a need for improved navigation (eg, a search function), expanded content (eg, motor development and sensory processing), and more interactive features (eg, chat support and parental community forums). Conclusions: The e-TOP app is a valuable digital resource that can supplement postdischarge care by providing tailored, evidence-based, and accessible information for parents of preterm infants. While usability scores were high, engagement trends and retrieved feedback suggest the need for enhanced retention strategies, such as push notifications, timeline-based navigation structure, interactive tools, and adding (practical) content. Future iterations to improve the inclusivity of the e-TOP app require strategies to engage fathers and parents with limited health literacy skills.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".