Shaping Adoption and Sustained Use Across the Maternal Journey: Qualitative Study on Perceived Usability and Credibility in Digital Health Tools
Bibliographic record
Abstract
BACKGROUND: Maternal and child health outcomes are positively influenced by early intervention, and digital health (DH) tools provide the potential for a low-cost and scalable solution such as informational platforms or digital tracking tools. Despite the wide availability of DH tools out there for women from before to after pregnancy, user engagement remains low. OBJECTIVE: This study aims to explore the factors that shape women's DH adoption and sustained use across the maternal journey from preconception to postbirth, to improve user engagement with DH tools. METHODS: One-hour semistructured qualitative interviews were conducted with 44 women from before to after pregnancy (age range 21-40 years) about their experiences with DH. This study is part of a larger study on women's maternal experiences with health care and DH and focuses on the factors that affected women's DH adoption and sustained use. Interviews were audio recorded, transcribed verbatim, and analyzed using inductive thematic analysis. RESULTS: Five main themes and 10 subthemes were identified that affected women's adoption and sustained use of DH tools. These included themes on their preexisting attitudes to DH, perceived ease of use, perceived usefulness, perceived credibility, and perceived value of the tool. CONCLUSIONS: The themes that emerged were fully or partially mapped according to the Unified Theory of Acceptance and Use of Technology 2 model. The applicability of the model and the need to consider specific cultural nuances in the Asian context (such as the importance of trust and social influence) are discussed. The interaction of the 5 themes with DH adoption and sustained use are explored with different themes being relevant at various points of the DH adoption journey. The insights gained serve to inform future DH design and implementation of tools for women to optimize their DH engagement and the benefits they derive from it. TRIAL REGISTRATION: ClinicalTrials.gov NCT05099900; https://clinicaltrials.gov/study/NCT05099900.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".