Psychological distress is the most common complication of pregnancy. High‐risk concerns can include severe emotion dysregulation, suicidality and self‐injury, and health risk behaviours, which bear substantial consequences for caregivers and families. Yet, effective, comprehensive interventions for high‐risk caregivers have received limited attention. Dialectical behaviour therapy (DBT) is a frontline treatment for such concerns. Accordingly, we conducted a scoping review on the implementation of DBT in the perinatal period. The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines were followed. Seven studies were identified; study designs included case studies and single‐arm pilot trials. Most studies used DBT‐informed protocols with significant adaptations, few included multiple components of DBT (i.e. skills group, individual therapy, phone coaching and consultation team), and none met criteria for adherent delivery of all four modes of DBT treatment. Findings suggest DBT‐informed interventions may be successfully implemented to treat a range of perinatal mental health symptoms, including borderline personality disorder, depression, anxiety, and post‐traumatic stress, and to promote emotion regulation and positive parenting behaviours. While results provide preliminary support for perinatal DBT, this literature is scant and empirical rigour considerably lacking. Clinical implications and future directions are outlined to aid researchers and providers in addressing the ongoing perinatal mental health crisis and developing sorely needed interventions to address the needs of high‐risk caregivers.
Detecting Clinically Relevant Emotional Distress and Functional Impairment in Children and Adolescents: Protocol for an Automated Speech Analysis Algorithm Development Study
Background Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. Objective The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. Methods Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. Results We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. Conclusions This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. International Registered Report Identifier (IRRID) DERR1-10.2196/46970
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- Award ID(s):
- 2126811
- NSF-PAR ID:
- 10437974
- Date Published:
- Journal Name:
- JMIR Research Protocols
- Volume:
- 12
- ISSN:
- 1929-0748
- Page Range / eLocation ID:
- e46970
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2196/46970
