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Record W4393062936 · doi:10.2196/53890

A Mindfulness-Based App Intervention for Pregnant Women: Protocol for a Pilot Feasibility Study

2024· article· en· W4393062936 on OpenAlexvenueno aff
Silvia Rizzi, Stefania Poggianella, Maria Chiara Pavesi, Lorenzo Gios, Giorgia Bincoletto, Isabella Scolari, Claudia Paoli, Debora Marroni, Irene Tassinari, Barbara Baietti, Anna Gianatti, Veronica Albertini, Barbara Burlon, Vanda Chiodega, Barbara Endrizzi, Elena Benini, Chiara Guella, Erik Gadotti, Stefano Forti, Fabrizio Taddei

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessAnxietyPsychologyPregnancyIntervention (counseling)Clinical psychologyPsychological interventionMedicinePsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Pregnancy is a complex time characterized by major transformations in a woman, which impact her physical, mental, and social well-being. How a woman adapts to these changes can affect her quality of life and psychological well-being. The literature indicates that pregnant women commonly experience psychological symptoms, with anxiety, stress, and depression being among the most frequent. Hence, promoting a healthy lifestyle focused on women's psychological well-being is crucial. Recently developed digital solutions have assumed a crucial role in supporting psychological well-being in physiologically pregnant women. Therefore, the need becomes evident for the development and implementation of digital solutions, such as a virtual coach implemented in a smartphone, as a support for the psychological well-being of pregnant women who do not present psychological and psychiatric disorders. OBJECTIVE: This study aims to assess the feasibility, acceptability, and utility of a mindfulness-based mobile app. The primary objective is to explore the feasibility of using a virtual coach, Maia, developed within the TreC Mamma app to promote women's psychological well-being during pregnancy through a psychoeducational module based on mindfulness. Finally, through the delivery of this module, the level of psychological well-being will be explored as a secondary objective. METHODS: This is a proof-of-concept study in which a small sample (N=50) is sufficient to achieve the intended purposes. Recruitment will occur within the group of pregnant women belonging to the pregnancy care services of the Trento Azienda Provinciale per i Servizi Sanitari di Trento. The convenience sampling method will be used. Maia will interact with the participating women for 8 weeks, starting from weeks 24 and 26 of pregnancy. Specifically, there will be 2 sessions per week, which the woman can choose, to allow more flexibility toward her needs. RESULTS: The psychoeducational pathway is expected to lead to significant results in terms of usability and engagement in women's interactions with Maia. Furthermore, it is anticipated that there will be improvements in psychological well-being and overall quality of life. The analysis of the data collected in this study will be mainly descriptive, orientated toward assessing the achievement of the study objectives. CONCLUSIONS: Literature has shown that women preferred web-based support during the perinatal period, suggesting that implementing digital interventions can overcome barriers to social stigma and asking for help. Maia can be a valuable resource for regular psychoeducational support for women during pregnancy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/53890.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0500.008

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.

Opus teacher head0.354
GPT teacher head0.599
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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