Infant crying predicts real-time fluctuations in maternal mental health in ecologically valid home settings.

Exposure to infant crying is a well-established predictor of mothers' mental health. However, this association may reflect many potential mechanisms. Capturing dynamic fluctuations in mothers' states simultaneously with caregiving experiences is necessary to identify the real-time processes influencing mental health.

INFANT CRYING DETECTION IN REAL-WORLD ENVIRONMENTS.

Most existing cry detection models have been tested with data collected in controlled settings. Thus, the extent to which they generalize to noisy and lived environments is unclear. In this paper, we evaluate several established machine learning approaches including a model leveraging both deep spectrum and acoustic features.

Validating a model to detect infant crying from naturalistic audio.

Human infant crying evolved as a signal to elicit parental care and actively influences caregiving behaviors as well as infant-caregiver interactions. Automated cry detection algorithms have become more popular in recent decades, and while some models exist, they have not been evaluated thoroughly on daylong naturalistic audio recordings. Here, we validate a novel deep learning cry detection model by testing it in assessment scenarios important to developmental researchers.

Sensing everyday activity: Parent perceptions and feasibility.

Mobile and wearable sensors provide a unique opportunity to capture the daily activities and interactions that shape developmental trajectories, with potential to revolutionize the study of development (de Barbaro, 2019). However, developmental research employing sensors is still in its infancy, and parents' comfort using these devices is uncertain. This exploratory report assesses parent willingness to participate in sensor studies via a nationally representative survey (N = 210) and live recruitment of a low-income, minority population for an ongoing study (N = 359).

Automated Detection of Infant Holding Using Wearable Sensing: Implications for Developmental Science And Intervention.

Physical contact is critical for children's physical and emotional growth and well-being. Previous studies of physical contact are limited to relatively short periods of direct observation and self-report methods. These methods limit researchers' understanding of the natural variation in physical contact across families, and its specific impacts on child development.

The impact of symptom severity on cognitive function in obsessive-compulsive disorder: A meta-analysis.

Research on cognitive functions in obsessive-compulsive disorder (OCD) is notoriously heterogeneous with no moderators identified that account for this variability. OCD severity is the primary potential moderator of interest given the longstanding trait versus state debate. Nevertheless, severity has been previously assessed exclusively as a moderator and was not directly and systematically investigated.