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Abstract A substantial body of work has established that mothers’ internalizing distress can negatively affect children’s socioemotional development. Yet few studies have examined how distinct patterns of mothers’ distress over time differentially impact child behaviors across early childhood. To address this gap, the current study explored developmental trajectories of mothers’ internalizing distress and examined the associations of these patterns with child adjustment outcomes. Mexican immigrant, Dominican immigrant, and African American mothers (N= 272) were annually assessed for internalizing distress over the first 6 years following childbirth. Children’s psychological adjustment (internalizing, externalizing, and hyperactivity behaviors) was measured at the last yearly assessment in first grade. A growth mixture model revealed two distinct classes of distress where mothers were classified as having low stable distress (82.4%) or moderate distress that began as stable then declined when their children were 64 months old (17.6%). Children of mothers in themoderate, late declineclass showed greater internalizing, externalizing, and hyperactivity behaviors in the first grade compared to children of mothers in thelow stableclass. Findings highlight the necessity of supporting the mental health of ethnic minoritized mothers following childbirth and further expand our knowledge of family psychopathology to promote healthy psychological adjustment in children.
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Pilot study of a real-time early agitation capture technology (REACT) for children with intellectual and developmental disabilities
Objective: Children and adolescents with intellectual and developmental disabilities (IDD), particularly those with autism spectrum disorder, are at increased risk of challenging behaviors such as self-injury, aggression, elopement, and property destruction. To mitigate these challenges, it is crucial to focus on early signs of distress that may lead to these behaviors. These early signs might not be visible to the human eye but could be detected by predictive machine learning (ML) models that utilizes real-time sensing. Current behavioral assessment practices lack such proactive predictive models. This study developed and pilot-tested real-time early agitation capture technology (REACT), a real-time multimodal ML model to detect early signs of distress, termed “agitations.” Integrating multimodal sensing, ML, and human expertise could make behavioral assessments for people with IDD safer and more efficient. Methods: We leveraged wearable technology to collect behavioral and physiological data from three children with IDD aged 6 to 9 years. The effectiveness of the REACT system was measured using F1 score, assessing its usefulness at the time of agitation to 20s prior. Results: The REACT system was able to detect agitations with an average F1 score of 78.69% at the time of agitation and 68.20% 20s prior. Conclusion: The findings support the use of the REACT model for real-time, proactive detection of agitations in children with IDD. This approach not only improves the accuracy of detecting distress signals that are imperceptible to the human eye but also increases the window for timely intervention before behavioral escalation, thereby enhancing safety, well-being, and inclusion for this vulnerable population. We believe that such technological support system will enhance user autonomy, self-advocacy, and self-determination.
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- Award ID(s):
- 2124002
- PAR ID:
- 10598404
- Publisher / Repository:
- Sage Publisher
- Date Published:
- Journal Name:
- Digital health
- Volume:
- 10
- ISSN:
- 2055-2076
- Page Range / eLocation ID:
- 1-18
- Subject(s) / Keyword(s):
- Machine learning, wearable sensing, autism spectrum disorder, multimodal data, challenging behaviors
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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