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. In this study, we leveraged ecological momentary assessments (EMAs) and infant-worn audio recorders to capture variability in mothers' mental health symptoms and their exposure to infant crying over one week in a racially and socio-economically diverse urban North-American sample (N = 53). We use multilevel modeling to characterize within- and between-person effects of crying on maternal negative affect and symptoms of depression and anxiety. Within participants, when infants cried more than average in the 10 min, 1 hr, and 8 hr prior to an EMA report, mothers' negative affect subsequently increased, controlling for mean levels of infant crying. In contrast to findings from laboratory studies, in everyday settings crying exposure did not immediately increase feelings of depression. Only when crying was above average for 8 hr prior to EMA did mothers report increases in subsequent depression symptoms, suggesting that the effects of crying on maternal mental health take hours to unfold in ecologically valid home settings. Between participants, mothers of infants who cried more on average did not report higher negative affect or symptoms of depression or anxiety. Overall, our findings reveal that crying exposure dynamically influences maternal negative affect and depression but not anxiety in ecologically valid real-world settings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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. We also evaluate the deep learning model's performance relative to LENA's cry classifier, one of the most commonly used commercial software systems for quantifying child crying. Broadly, we found that both deep learning and LENA model outputs showed convergent validity with human annotations of infant crying. However, the deep learning model had substantially higher accuracy metrics (recall, F1, kappa) and stronger correlations with human annotations at all timescales tested (24 h, 1 h, and 5 min) relative to LENA. On average, LENA underestimated infant crying by 50 min every 24 h relative to human annotations and the deep learning model. Additionally, daily infant crying times detected by both automated models were lower than parent-report estimates in the literature. We provide recommendations and solutions for leveraging automated algorithms to detect infant crying in the home and make our training data and model code open source and publicly available.

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. This model was able to recognize crying events with F1 score 0.613 (Precision: 0.672, Recall: 0.552), showing improved external validity over existing methods at cry detection in everyday real-world settings. As part of our evaluation, we collect and annotate a novel dataset of infant crying compiled from over 780 hours of labeled real-world audio data, captured via recorders worn by infants in their homes, which we make publicly available. Our findings confirm that a cry detection model trained on in-lab data underperforms when presented with real-world data (in-lab test F1: 0.656, real-world test F1: 0.236), highlighting the value of our new dataset and model.

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). The survey allowed us to assess how protocol design influences acceptability, including various options for devices and datastream resolution, conditions of data sharing, and feedback. By contrast, our recruitment data provided insight into parents' true willingness to participate in a sensor study, with a protocol including 72 h of continuous audio, motion, and physiological data. Our results indicate that parents are relatively conservative when considering participation in sensing studies. However, nearly 41 % of surveyed parents reported that they would be at least somewhat willing to participate in studies with audio or video recordings, 26 % were willing or extremely willing, and 14 % reported being extremely willing. These results roughly paralleled our recruitment results, where 58 % of parents indicated interest, 29 % of parents scheduled to participate, and 10 % ultimately participated. Additionally, 70 % of caregivers stated their reason for not participating in the study was due to barriers unrelated to sensing while about 25 % noted barriers due to either privacy concerns or the physical sensors themselves. Parents' willingness to collect sensitive datastreams increased if data stayed within the household for individual use only, are shared anonymously with researchers, or if parents receive feedback from devices. Overall, our findings suggest that given the correct circumstances, mobile sensors are a feasible and promising tool for characterizing children's daily interactions and their role in development.

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. In this study we develop a mobile sensing platform that can provide objective, unobtrusive, and continuous measurements of physical contact in naturalistic home interactions. Using commercially available motion detectors, our model reaches an accuracy of 0.870 (std: 0.059) for a second-by-second binary classification of holding. In addition, we detail five assessment scenarios applicable to the development of activity recognition models for social science research, where required accuracy may vary as a function of the intended use. Finally, we propose a grand vision for leveraging mobile sensors to access high-density markers of multiple determinants of early parent-child interactions, with implications for basic science and intervention.

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. To address this gap in the literature, the aim of this study was to conduct a systematic meta-analytic review of correlations between cognitive function and symptom severity in OCD samples. Thirty-eight studies were included, allowing for analysis of 132 effects and meta-regression analyses for potential moderators. Small effects were found for the association between cognitive function and symptom severity on major neuropsychological domains, and some subdomains exhibited medium effects for this association. However, several significant methodological and conceptual problems were identified, including the use of the Yale-Brown Obsessive-Compulsive Scale that assesses severity in the past week and not at time of testing, a tendency to not report non-significant correlations, and problematic ecological validity of neuropsychological tests in OCD. In conclusion, we found a small-to-moderate degree of association between OCD symptom severity and cognitive function, but results should be interpreted cautiously given the limitations identified. We offer recommendations that will facilitate future research into this association and move the field beyond the largely stagnant debate about the state versus trait nature of cognitive functioning in OCD, and across disorders.