Digital Health Needs and Preferences During Pregnancy and the Postpartum Period: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Digital health is increasingly used to meet the needs of perinatal people, with estimates of pregnancy-related internet use ranging from 90% to 97% of pregnant people. As digital health takes on greater importance during the perinatal period, it is essential that providers and developers of digital health content understand why perinatal people use these resources and the features that enhance their experience. However, gaps remain in understanding the content that is most helpful and how the platforms are navigated. Learning directly from perinatal people about their needs will help ensure alignment between perinatal needs and available content. OBJECTIVE: This formative study aims to identify the reasons why perinatal people use digital health resources; the features of the digital health platforms that are of greatest importance to them; and how these differ by perinatal stage (pregnancy vs post partum), mental health conditions, parity, and demographics (race and ethnicity). METHODS: This mixed methods study used interviews; surveys; and secondary data on demographic, health, and pregnancy characteristics to identify the digital health needs and preferences of pregnant and postpartum people who used the Maven digital health platform in the United States during their pregnancy or postpartum period. The interviews informed the content of the surveys and provided additional insights and examples for interpreting the survey results. The surveys were used to collect data from a sample of Maven users, and the results were linked to the secondary data set. The interviews were thematically analyzed, and survey data were analyzed using descriptive statistics and stratified by parity, race, and mental health status. RESULTS: Overall, 13 people were interviewed (including n=4, 31% pilot interviews), and 147 pregnancy and 110 postpartum survey respondents completed the surveys and had linkable secondary data. Top reasons for using digital health resources during pregnancy were to (1) know what is normal or typical during pregnancy, (2) have access to a health care provider when needed, and (3) know how the baby is developing. Top reasons for postpartum use were to (1) help with breastfeeding, (2) know what normal baby development is, and (3) help with the baby's health issues. Top platform features during pregnancy and the postpartum period were (1) credible and trustworthy information and providers, (2) nonjudgmental information and support, and (3) no cost to the user. In general, more reasons for using digital resources were identified as extremely important during pregnancy compared with post partum. The results showed minor variations across strata. CONCLUSIONS: This formative research found minor differences in digital resource needs and preferences across user characteristics among perinatal people in the United States. Future work should examine whether there are variations in interests within topics by user characteristics, which may provide additional opportunities to better meet user needs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".