Behavioral Change Intervention to Promote a Healthier Postpartum Lifestyle: Mixed Methods Pilot Study
Bibliographic record
Abstract
Background: Research has shown that many mothers lack tools needed to motivate and support themselves in a healthy lifestyle after giving birth. A mobile health app (mHealth) has the potential to become a tool to accommodate this need. Accordingly, Healthy Together-a module in a general mHealth app, My Hospital including Podcasts, weight tracking, and exercise videos-was developed. Objective: The aim was to assess mothers' use of the behavioral intervention, Healthy Together, which aims to support new mothers in a healthier lifestyle. Further, it evaluates mothers' experiences and attitudes when using the intervention. Methods: A mixed method pilot study was conducted, and 34 women were included. From 3 weeks to 6 months postpartum, the women were granted access to Healthy Together. App activity was registered during the intervention period. All of the women received a questionnaire at the end of the intervention period; of these, 28 responded. In addition, 18 women participated in an online, semi-structured interview. Results: On average, each invited participant accessed the module 37 times. Push notifications and podcasts were used by 65% (n=34), and thus the content used the most. One-third found push notifications motivating. Half used exercise videos, while slightly fewer utilized weight tracking. A total of 70% (n=28) of those who answered the questionnaire had used Healthy Together. About half of the users reported that the intervention had a positive influence on their health status, and 70% (n=20) of the users stated they would recommend Healthy Together to others. The mean age was 29.8 years for users and 32 years for non-users. Pre-pregnancy body mass index averaged 25 and 24.8, respectively, increasing to 25.6 and 25.7 at 6 months postpartum. The body mass index difference was 0.6 for users and 0.9 for non-users, corresponding to total increases of 2.4 and 3.6, respectively. By gradually introducing the mothers to new content, the mothers could more easily digest the information. Podcasts were, in general, the preferred information channel. Weight tracking reminders motivated some, while they had the opposite effect on others. The users seemed to be those who were more physically active and had a healthier diet prior to their pregnancy, compared to non-users. About one-third of the users experienced technical problems. Conclusions: This study demonstrates that Healthy Together potentially is a feasible tool to assist women in improving their postpartum lifestyle. Further research with a larger sample is needed.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".