Background
The postpartum period represents a major transition in the lives of many women, a time when women are at increased risk for the emergence of psychopathology including depression and PTSD. The current study aimed to better understand the unique contributions of clinically significant postpartum depression, PTSD, and comorbid PTSD/depression on mother–infant bonding and observed maternal parenting behaviors (i.e., behavioral sensitivity, negative affect, positive affect) at 6 months postpartum.
Methods
Mothers (n=164; oversampled for history of childhood maltreatment given parent study's focus on perinatal mental health in women with trauma histories) and infants participated in 6-month home visit during which dyads engaged in interactional tasks varying in level of difficulties. Mothers also reported on their childhood abuse histories, current depression/PTSD symptoms, and bonding with the infant using standardized and validated instruments.
Results
Mothers with clinically significant depression had the most parenting impairment (self-report and observed). Mothers with clinically significant PTSD alone (due to interpersonal trauma that occurred predominately in childhood) showed similar interactive behaviors to those who were healthy controls or trauma-exposed but resilient (i.e., no postpartum psychopathology). Childhood maltreatment in the absence of postpartum psychopathology did not infer parenting risk.
Limitations
Findings are limited by (1) small cell sizes per clinical group, limiting power, (2) sample size and sample demographics prohibited examination of third variables that might also impact parenting (e.g., income, education), (3) self-report of symptoms rather than use of psychiatric interviews.
Conclusions
Findings show that in the context of child abuse history and/or current PTSD, clinically significant maternal depression was the most salient factor during infancy that was associated with parenting impairment at this level of analysis.
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause children to go unassessed–suffering in silence because they never exhibiting the disruptive behaviors that would lead to a referral for diagnostic assessment. If left untreated these disorders are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying children with internalizing disorders using an instrumented 90-second mood induction task. Participant motion during the task is monitored using a commercially available wearable sensor. We show that machine learning can be used to differentiate children with an internalizing diagnosis from controls with 81% accuracy (67% sensitivity, 88% specificity). We provide a detailed description of the modeling methodology used to arrive at these results and explore further the predictive ability of each temporal phase of the mood induction task. Kinematical measures most discriminative of internalizing diagnosis are analyzed in detail, showing affected children exhibit significantly more avoidance of ambiguous threat. Performance of the proposed approach is compared to clinical thresholds on parent-reported child symptoms which differentiate children with an internalizing diagnosis from controls with slightly lower accuracy (.68-.75 vs. .81), slightly higher specificity (.88–1.00 vs. .88), and lower sensitivity (.00-.42 vs. .67) than the proposed, instrumented method. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.
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