Understanding the Use of Smartphone Apps for Health Information Among Pregnant Chinese Women: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Hospital-based health promotion resources to assist pregnant women in adopting a healthy lifestyle and optimizing gestational weight gain are important, but with limited effects. Increasingly, women are using mobile apps to access health information during the antenatal period. OBJECTIVE: The aims of the study were to investigate app-usage by Chinese women during pregnancy and to gain a better understanding of their views and attitudes toward apps containing health information. METHODS: A mixed methods study design was applied. Study participants were recruited from 2 maternity hospitals in Shanghai, China, between March and July 2018. A self-administered Web-based survey was conducted with 535 pregnant Chinese women on their sources of health information and reasons for using apps during pregnancy. A total of 4 semistructured focus groups were also conducted with the pregnant women (n=28). RESULTS: The use of pregnancy-related apps and the internet was common among the respondents. Almost half of the women had used pregnancy-related apps. Specifically, the use of apps for health information declined as pregnancy progressed from 70% (35/50) in the first trimester to 41.3% (143/346) in the third trimester. The main reason for using an app was to monitor fetal development (436/535, 81.5%), followed by learning about nutrition and recording diet in pregnancy (140/535, 26.2%). The women found that the apps were useful and convenient and can support lifestyle modifications during pregnancy. However, some apps also contained misinformation or incorrect information that could cause anxiety as reported by the participants. Many women expressed the need for developing an app containing evidence-based, well-informed, and tailored health information to support them during pregnancy. CONCLUSIONS: The study suggests that apps were widely used by many Chinese women during pregnancy to monitor fetal development, to obtain diet and physical activity information, and to track their body changes. The women highly appreciated the evidence-based information, expert opinions, and tailored advice available on apps. Smartphone apps have the potential to deliver health information for pregnant women.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".