A Smartphone-Assisted Brief Behavioral Intervention for Pregnant Women with Depression: a Study Protocol of a Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background: Pregnancy is strongly associated with increased risk for depression, but treating pregnant women is challenging. The use of psychiatric medications might result in developmental problems in the child, therefore must be used with caution. Psychosocial interventions require specialized professionals which are scarce, especially in low- and middle-income countries. In this context, smartphone-based interventions show immense potential. We created Motherly, a smartphone app designed to promote maternal mental health. The Motherly app delivers a package of interventions, including mental health, sleep, nutrition, physical activity, social support, prenatal/postnatal support, and psychoeducational content. With this study, we will test the effectiveness of the Motherly app in addition to brief cognitive-behavioral therapies (CBT) to treat maternal depression. Methods: We will conduct a 2-arm parallel randomized controlled clinical trial in which 70 pregnant women between 16-40 years with depression will be randomized to intervention or active control. The intervention group will have access to the Motherly app. The active control group will have access to a simplified version of the app consisting exclusively of psychoeducational content. Both groups will undergo four sessions of CBT in 8 weeks. Participants will be evaluated by assessors blind to randomization and allocation status at baseline (T0), midpoint (T1, week 4-5), posttreatment (T2, week 8), and follow-up (T3, when the child is two months-old). Maternal mental health (prenatal anxiety, psychological well-being, perceived stress, depression, depression severity, and sleep quality), quality of life, physical activity levels, and infant developmental milestones and social/emotional problems will be measured. Our primary outcome is the change in maternal prenatal depression from baseline to posttreatment (8 weeks). Discussion: There is a growing literature on interventions using smartphone applications to promote mental health, both with or without the intermediation of a mental health professional. Our study adds to the literature by testing whether an app providing an intervention package, including CBT, psychoeducation, nutrition, physical activity, and social support, can treat depression, a condition for which the use of digital technologies is still scarce. Smartphone applications designed to treat maternal depression have the potential to circumvent barriers that prevent pregnant women from accessing mental health care.Trial Registration: A Smartphone-Assisted Brief Behavioral Intervention for Pregnant Women With Depression (clinicaltrials.gov, registry number: NCT04495166, prospectively registered in 29/07/2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".