Feasibility and Usability of the Job Adjustment Mobile App for Pregnant Women: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Working pregnant women often need to adjust their physically demanding jobs for a healthy pregnancy. However, uncertainty about the extent of these adjustments can hinder their effectiveness. To address this, we developed the Job Adjustment mobile app, which allows users to input job and health details to generate a variety of personalized action plans. As this is the first version of the app, assessing its feasibility and usability is crucial. OBJECTIVE: This study aims to verify the feasibility and usability of the Job Adjustment mobile app. METHODS: A longitudinal observational study was conducted on pregnant Japanese women who were allowed to use the app anytime from 12 to 34 weeks of gestation; they received reminder emails every 2 weeks encouraging app use. A questionnaire was administered before app use and at 20 and 32 weeks of gestation. Feasibility was evaluated across 4 domains: implementation, demand, acceptability, and adverse events. Implementation was evaluated based on 3 parameters: dropout rate, initial reminder email receipt rate, and adherence rate (measured as pregnant women who used the app at intervals of 2.5 weeks or less). Demand was measured by intervals between use and intervals between log-in, and participants answered 15 questions to assess acceptability. Adverse events were assessed by analyzing the degree of anxiety related to work. Demographic data were analyzed to determine any statistically significant differences in intervals between uses. Usability was evaluated using the System Usability Scale. RESULTS: The analysis included 66 pregnant women, and 61% (n=40) of them were multipara. The dropout rate, adherence rate, and initial reminder email receipt rate were 18% (13/71), 44% (29/66), and 79% (52/66) respectively. The median intervals between use and intervals between log-in were 2.94 (IQR 2.00-5.13) weeks and 2.28 (IQR 1.81-4.00) weeks, respectively. Overall, 60% (35/58) to 90% (52/58) of the participants responded positively to all 15 questions assessing acceptability, and no anxiety regarding work was recorded. The mean System Usability Scale score was 66.1 points. Multipara women had significantly longer intervals between app use compared to primipara women (P=.01). CONCLUSIONS: The results demonstrated acceptable levels of feasibility and usability of the app. However, the low adherence rates, especially among multipara women, suggest the need for modifications to reduce the time burden of the app. Further research should explore more effective and acceptable intervals between use and timing, involving a larger sample and accounting for diverse characteristics of pregnant women. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000042943; https://tinyurl.com/ydrchfas.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".