Use and User Experience of a Preconception Lifestyle App for Couples Undergoing in Vitro Fertilization: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps are a promising way to improve healthy lifestyle behavior among people with infertility. However, sufficient engagement with mobile health apps is crucial to influence health outcomes, and identifying features to create more effective interventions is urgently needed. OBJECTIVE: This study conducted a process evaluation focusing on the use and user experience of the PreLiFe app, a mobile lifestyle app for couples undergoing in vitro fertilization (IVF). METHODS: A mixed methods approach was used among heterosexual couples with infertility undergoing IVF. An objective quantitative study using a tracking-based system assessed the actual use of the PreLiFe app over time in relation to partner use and in relation to the specific fertility treatment. A subjective quantitative study using online questionnaires assessed the acceptability (using the Mobile App Rating Scale) and partner support (based on the Social Support for Diet and Exercise Scale) experienced while using the PreLiFe app. A subjective qualitative study using semistructured interviews evaluated in-depth user experiences with the PreLiFe app. RESULTS: A total of 106 couples used the PreLiFe app for 2 to 365 days. Overall use was low; 18.9% (20/106) of the men and 49.1% (52/106) of the women used all the modules of the PreLiFe app. Mixed-model analyses revealed that higher app use was observed when a partner used the app as well and during fertility treatment. The average acceptability score was 6 (SD 1) of 10, and patients felt supported by their partners while using the app. Semistructured interviews with 10 patients indicated that the PreLiFe app was easy to use. CONCLUSIONS: Our findings showed good acceptability and user experiences but low actual objective use of a preconception lifestyle app for couples undergoing IVF. To increase use of and engagement with such apps, future studies should further focus on personalization and interaction with partners, health care providers, and other patient data systems.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".