Developing a Guided Web App for Postpartum Depression Symptoms: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Psychological internet-based interventions have shown promise in preventing and treating perinatal depression, but their effectiveness can be hindered by low user engagement. This challenge often arises from a misalignment between technology attributes, user needs, and context. A user-centered, iterative approach involving all stakeholders is recommended. OBJECTIVE: In this paper, we aimed to develop a user-friendly psychological internet-based intervention aimed at addressing the symptoms of perinatal depression through an iterative, user-centered approach. METHODS: The development process followed the Center for eHealth Research and Disease Management Roadmap phases of contextual inquiry, value specification, and design. It involved a comprehensive literature review, 2 surveys, 10 focus groups, 5 usability interviews, and 1 technical pilot. RESULTS: The contextual inquiry revealed a demand for accessible interventions for perinatal mental health, with internet-based solutions seen as viable options. Insights from the literature influenced intervention content and features. Stakeholders' openness to the intervention became evident during this phase, along with the integration of the first set of values. Initially, we assessed the broader perinatal context to identify the optimal period for the intervention. On the basis of the findings and practical considerations, we decided to specifically target postpartum depression symptoms. The value specification phase further defined the central values and translated them into requirements. In the design phase, feedback was obtained on the user experience of an early digital prototype and on the prototype's final version. The resulting intervention, named Mamá, te entiendo ("Mom, I get you"), is a guided web app based on cognitive behavioral therapy principles, integrating elements from attachment and mentalization theories. It aims to reduce depressive symptoms in women during the first months postpartum and consists of 6 core sequential modules, along with 3 additional modules, including 5 case examples illustrating depressive symptoms and therapeutic techniques. The intervention provides homework exercises and offers users the opportunity to receive feedback from an e-coach through the web app. CONCLUSIONS: This study emphasizes the importance of a user-centered and iterative development process for psychological internet-based interventions. This process helps clarify user needs and provides valuable feedback on service design and quality, ultimately having the potential to enhance the utility and, presumably, the effectiveness of the intervention. The Discussion section shares valuable insights from the project, such as the value of the requirement sessions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".