MamaConecta: Digital Tool for Maternal Mental Health

BACKGROUND:

Mental disorders are becoming one of the major health problems in today's society, exacerbated in women who are and will become mothers. Global studies indicate that 80% of women who are going to become mothers experience mood disorders such as stress, fear, loneliness, or guilt during the perinatal period. In 1 out of every 5 cases, these mental disorders worsen and lead to severe mental health issues such as post-traumatic stress disorder, psychosis, suicidal ideation, or postpartum depression. However, 75% of women with perinatal mental disorders are underdiagnosed and do not receive adequate treatment, either due to a lack of detection or support within healthcare systems. This has a consequent impact on the health and well-being of the mother, her family, the child, and ultimately, society. In this context, the company A Thousand Colibris S.L (ATC) has developed digital health software to improve well-being during this period, as well as to detect, assist in the diagnosis, and monitor mental health disorders in mothers from pregnancy up to two years after childbirth.

Consequences of Perinatal Mental Health Disorders:

The consequences of perinatal mental health problems extend beyond the mother, affecting the child, the family, and society as a whole.

1. Maternal health disorders: Perinatal mental health disorders have been identified in women of all cultures, ages, social levels, and races. Although the term postpartum depression is often used to describe disorders that develop after childbirth, a range of disorders can appear during pregnancy and the postpartum period, including anxiety, depression, stress disorders, panic, or postpartum psychosis. If left untreated, these illnesses can have a devastating impact on women and their families, being one of the leading causes of maternal death during pregnancy and the two years following childbirth.
2. Impact on the future health of the child: Without appropriate intervention, these disorders can persist and have negative consequences for both the mother and the baby. The impact of the mother's health on the future health of the baby, on mother-newborn interactions, breastfeeding difficulties, and the mother-child bond is well documented. The failure to detect certain aspects of maternal health can manifest in complications during childbirth or later in the child's development.
3. Impact on family well-being: The mother's mood and symptoms of anxiety and other disorders also have a direct impact on the partner, who may not understand what is happening, feel overwhelmed, and not know how to help.

Digital psychological interventions:

Given the growing ubiquity of the Internet and the widespread use of smartphones, mindfulness-based cognitive-behavioral interventions can also be offered digitally. In this regard, researchers have been successfully adapting decades-old techniques commonly used in cognitive-behavioral therapies to the context of smartphone applications, which have demonstrated efficacy in reducing symptoms of depression, anxiety, stress, insomnia, and even suicidal thoughts. However, the potential of smartphone applications in promoting well-being, and preventing and treating mental health problems in pregnant women, has yet to be fully exploited. Despite the limited number of studies and controlled trials involving health apps for pregnant women, online app stores are flooded with pregnancy apps that have not been tested using rigorous scientific methods. This has sparked an increasing debate among researchers and policymakers regarding the evaluation of quality and certification of health apps that do not meet the needs of patients and doctors, particularly concerning safety and efficacy. Health applications should be evaluated with the same rigor as other types of interventions, such as pharmacotherapy and psychotherapy.

HYPOTHESES:

Hypothesis 1: Dana is a digital tool that improves the well-being of women transitioning into motherhood and increases the early detection of mental health disorders.

Hypothesis 2 (Class IIa Certification): The tool provides monitoring of maternal health and assists in diagnosis for healthcare professionals.

OBJECTIVES:

General:

To validate a digital tool as a solution to (a) improve maternal well-being, mental and physical health during the perinatal period, (b) increase early detection of maternal mental health disorders, and (c) assist in the diagnosis and monitoring of perinatal mental health problems.

Specific:

• Validate the effectiveness of the digital tool in improving well-being at the end of pregnancy (34 weeks gestation), initial postpartum (6 weeks postpartum), and long-term (6, 12, and 24 months postpartum).
• Verify the effectiveness of the digital tool in preventing symptoms of anxiety and depression during the perinatal period (pregnancy and postpartum up to 24 months postpartum).
• In women with symptoms detected in the baseline assessment, analyze the effectiveness of the tool in preventing symptom escalation.
• In women with stabilized mental pathology, analyze the effectiveness of the tool in preventing symptom escalation.
• Verify the effectiveness of the digital tool in improving secondary outcomes related to childbirth: childbirth experience and postpartum-related post-traumatic stress.
• Verify the effectiveness of the digital tool in improving obstetric and neonatal health secondary outcomes.
• Analyze the use of healthcare services and the cost of perinatal mental health.
• Verify the effectiveness of the intervention developed to increase prenatal bonding and postpartum attachment.
• Verify the effectiveness of the intervention developed to improve postpartum secondary outcomes: satisfaction with the maternal role, self-efficacy, stress with the maternal role, and proportion of breastfeeding.
• Analyze the secondary relationship between childbirth experience, post-traumatic stress, and postpartum depression.
• Analyze the data collected by the digital tool and wearables to identify patterns and correlations that may be useful for the prediction and early detection of disorders in mothers and babies.
• Analyze infant neurodevelopment and psychopathology during the first two years of life.

METHODOLOGY:

1. Design: A randomized clinical trial, blinded to the investigator, with two parallel groups (1 experimental group using the digital tool and 1 control group). The protocol has been designed following the CONsolidated Standards Of Reporting Trials (CONSORT: http://www.consortstatement.org) and the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT).
2. Participants: The study will be conducted at Vall d'Hebrón Hospital, the Sexual and Reproductive Health Care Services (ASSIR) in Tarragona, ASSIR del Litoral (Barcelona), and Hospital del Mar. Participants will be pregnant women between 12-14 weeks of gestation who are following their pregnancy at these centers, and they and their babies will be followed up until 24 months postpartum.
3. Sample Size: To estimate the necessary sample size to detect significant differences between groups, an alpha risk error of 0.05 and a beta risk error of 0.2 for one-tailed tests have been accepted, considering a dropout rate of 40%. According to data from Viskovich et al. (2020) and Rash et al. (2011) related to the Satisfaction With Life Scale, 16 subjects per group are needed to determine minimum differences of 1.86 points, assuming a standard deviation of 2.07 points. According to data from Tovote et al. (2014) related to the World Health Organization-5 Well-Being Index, 11 subjects per group are needed to determine minimum differences of 20.6 points, assuming a standard deviation of 19.0 points. According to data from Ahlqvist-Björkroth et al. (2019) related to the Edinburgh Postnatal Depression Scale, 26 subjects per group are needed to determine minimum differences of 2.13 points, assuming a standard deviation of 2.46 points. Finally, according to data from Moix et al. (2021) related to the State-Trait Anxiety Inventory, 11 subjects per group are needed to determine minimum differences of 5.54 points, assuming a standard deviation of 4.5 points. Thus, 64 subjects per arm are needed, so a total sample of 300 pregnant women per center should be recruited.
4. Procedures:

4.1. Recruitment: Participants will be recruited through midwives from the Primary Care Centers and Sexual and Reproductive Health Care Services (ASSIR) of Tarragona, and ASSIR of Barcelona Litoral - Hospital del Mar. Recruitment will be through obstetricians and midwives from the same center. Women who visit before 15 weeks of gestation and meet the inclusion and exclusion criteria will be eligible to participate in the study. The healthcare professional conducting the visit will be responsible for validating the inclusion and exclusion criteria.

4.2. Randomization: Balanced randomization by center will be carried out through the digital tool itself, so the researchers will be blind to the intervention. Participants will need to download the mobile application, where during the registration process and center selection, they will be randomized into the control or intervention group. Women in the intervention group will be offered access to the full digital tool, while those in the control group will have an app with no content.

4.3. Blinding: Researchers analyzing the primary data and conducting infant evaluations will not know which group the women have been assigned to. Midwives and obstetricians who conduct recruitment will also be unaware, unless the participant discloses it after being randomized. Neither the researchers nor the midwives will have access to group assignments. Researchers conducting primary data analysis or neonatal evaluations will not know the participants' group assignments until the end of the study. Due to the nature of the intervention, participants cannot be blinded to their group assignment.

4.4. Pseudonymization: Data pseudonymization and protection will be ensured using the REDCap system. Each participating health center and hospital (as independent data controllers) will have access only to their own center's data, not to data from other centers. Data from the entire study recorded in REDCap and app data will be accessible only by the co-sponsors and co-controllers of the data mentioned in the protocol.