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Objective. Maternal stress is a psychological response to the demands of motherhood. A high level of maternal stress is a risk factor for maternal mental health problems, including depression and anxiety, as well as adverse infant socioemotional and cognitive outcomes. Yet, levels of maternal stress (i.e., levels of stress related to parenting) among low-risk samples are rarely studied longitudinally, particularly in the first year after birth. Design. We measured maternal stress in an ethnically diverse sample of low-risk, healthy U.S. mothers of healthy infants (N = 143) living in South Florida across six time points between 2 weeks and 14 months postpartum using the Parenting Stress Index-Short Form, capturing stress related to the mother, mother-infant interactions, and the infant. Results. Maternal distress increased as infants aged for mothers with more than one child, but not for first-time mothers whose distress levels remained low and stable across this period. Stress related to mother-infant dysfunctional interactions lessened over the first 8 months. Mothers’ stress about their infants’ difficulties decreased from 2 weeks to 6 months, and subsequently increased from 6 to 14 months. Conclusions. Our findings suggest that maternal stress is dynamic across the first year after birth. The current study adds to our understanding of typical developmental patterns in early motherhood and identifies potential domains and time points as targets for future interventions.
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Toward Predicting Infant Developmental Outcomes from Day-Long Inertial Motion Recordings
As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.
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- Award ID(s):
- 1706964
- PAR ID:
- 10162125
- Date Published:
- Journal Name:
- IEEE transactions on neural systems and rehabilitation engineering
- ISSN:
- 1558-0210
- 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/10.1109/TNSRE.2020.3016916
