Mothers speak less to infants during detected real-world phone use.

The current study is the first to document the real-time association between phone use and speech to infants in extended real-world interactions. N= 16 predominantly White (75%) mother-infant dyads (infants aged M = 4.1 months, SD = 2.3; 63% female) shared 16,673 min of synchronized real-world phone use and Language Environment Analysis audio data over the course of 1 week (collected 2017-2020) for our analyses. Maternal phone use was associated with a 16% decrease in infants' speech input, with shorter intervals of phone use (1-2 min) associated with a greater 26% decrease in speech input relative to longer periods. This work highlights the value of multimodal sensing to access dynamic, within-person, and context-specific predictors of speech to infants in real-world settings.

Maternal contingent responses to distress facilitate infant soothing but not in mothers with depression or infants high in negative affect.

Depression in mothers is consistently associated with reduced caregiving sensitivity and greater infant negative affect expression. The current article examined the real-time behavioral mechanisms underlying these associations using Granger causality time series analyses in a sample of mothers (N = 194; 86.60% White) at elevated risk for depression and their 3-month-old infants (46.40% female) living in a major metropolitan area in the United States. Overall, mothers contingently responded to infant distress, and mothers' responses to infant distress increased the likelihood of infant soothing in real time. However, there was no evidence for maternal contingent responding or facilitation of infant soothing in subsamples of mothers who were currently experiencing elevated depression symptoms or in mothers of highly negative infants. These findings suggest real-time behavioral mechanisms by which risks for maladaptive self-regulation may develop. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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).

Auditory chaos classification in real-world environments.

Background & motivation: Household chaos is an established risk factor for child development. However, current methods for measuring household chaos rely on parent surveys, meaning existing research efforts cannot disentangle potentially dynamic bidirectional relations between high chaos environments and child behavior problems. Proposed approach: We train and make publicly available a classifier to provide objective, high-resolution predictions of household chaos from real-world child-worn audio recordings. To do so, we collect and annotate a novel dataset of ground-truth auditory chaos labels compiled from over 411 h of daylong recordings collected via audio recorders worn by N=22 infants in their homes. We leverage an existing sound event classifier to identify candidate high chaos segments, increasing annotation efficiency 8.32× relative to random sampling. Result: Our best-performing model successfully classifies four levels of real-world household auditory chaos with a macro F1 score of 0.701 (Precision: 0.705, Recall: 0.702) and a weighted F1 score of 0.679 (Precision: 0.685, Recall: 0.680). Significance: In future work, high-resolution objective chaos predictions from our model can be leveraged for basic science and intervention, including testing theorized mechanisms by which chaos affects children's cognition and behavior. Additionally, to facilitate further model development we make publicly available the first and largest balanced annotated audio dataset of real-world household chaos.