Optimizing a Novel Smartphone App to Prevent Postpartum Depression Adapted From an Evidence-Based Cognitive Behavioral Therapy Program: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Postpartum depression (PPD) is more common among pregnant patients who have unmet social needs, such as financial stress or food insecurity, compared to those who do not. Mothers and Babies (MB) is a cognitive behavioral therapy (CBT)-based program that prevents up to 50% of de novo PPD when provided in-person to low-income Spanish- and English-speaking pregnant people without depression. MB's reach has been limited by the need for trained personnel to support the program. Transforming MB into a smartphone application (app) may mitigate this key barrier to scaling MB. OBJECTIVE: To utilize qualitative data from target end-users to create and optimize MBapp, a novel app centered on the MB program. METHODS: Draft wireframes of MBapp were created in English and Spanish with CBT-based modules adapted from MB. These wireframes included several features shown previously to sustain app engagement: 1) push notifications delivered at participant-preferred times; 2) text-, graphic-, and video-based content; and 3) gamification with digital rewards for app engagement. English- or Spanish-speaking individuals with public health insurance who were between 32 weeks' gestation and six months postpartum and owned smartphones were eligible to consent for individual in-depth interviews. Individuals with prior or current depression were excluded. Interviews were recorded, transcribed, and analyzed using deductive and inductive codes to characterize opinions about MBapp and perceptions of challenges and facilitators of use of MBapp or other perinatal or mental health apps. End-user feedback led to major modifications to the wireframes. Each of these changes was categorized according to the Framework for Modification and Adaptation (FRAME), an established method of systematically reporting adaptations and modifications to evidence-based interventions via end-user feedback. Recruitment ceased with content saturation, defined as three successive participants providing only positive feedback on MBapp's wireframe, without further suggestions for improvement. RESULTS: 25 interviews were completed. Participants were racially and ethnically diverse, generally representing our target end-user population, and 48% of interviews were conducted in Spanish. Participants' suggestions to improve MBapp were categorized within the FRAME as adaptations that improved either content or context to optimize reach, retention, engagement, and fit for end users. Specifically, the following features were added to MBapp secondary to end-user feedback: 1) audio narration; 2) "ask a clinician" non-urgent questions; 3) on-demand module summaries accessible upon module completion; and 4) choice to defer assessments and start the next module. Participants also provided insights into features of perinatal or mental health apps they found appealing or unappealing to understand preferences, challenges, and (non)negotiables for MBapp. CONCLUSIONS: Adapting MBapp to incorporate end users' perspectives optimized our digital PPD prevention intervention, ideally increasing its appeal to future users. Our team's next steps will confirm that MBapp is a feasible, acceptable intervention among English- and Spanish-speaking perinatal people at risk of PPD.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".