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.

Spontaneous infant crying modulates vagal activity in real time.

Porges' polyvagal theory (1991) proposes that the activity of the vagal nerve modulates moment-by-moment changes in adaptive behavior during stress. However, most work, including research with infants, has only examined vagal changes at low temporal resolutions, averaging 30+ s across phases of structured stressor paradigms. Thus, the true timescale of vagal regulation-and the extent to which it can be observed during unprompted crying-is unknown. The current study utilized a recently validated method to calculate respiratory sinus arrhythmia (RSA) dynamically at a high resolution of 5 Hz (updated every 200 ms) in a home-based infant study. Using an event-related analysis, we calculated the relative change in RSA around the onset of naturally occurring unprompted instances of n = 41 infants' 180 crying events. As predicted, RSA significantly decreased after the onset of crying compared to non-crying chance changes in RSA. Decreasing trends in RSA were driven by infants with higher pre-cry RSA values, infants rated lower in Negative Affectivity, and those rated both high and low in Orienting by their mothers. Our results display the timescale of RSA in spontaneous and naturalistic episodes of infant crying and that these dynamic RSA patterns are aligned with real-time levels of RSA and also caregiver-reported temperament.

Evaluating the Efficiency and Equity of Autism Diagnoses via Telehealth During COVID-19.

Given existing barriers to a timely autism diagnosis, this study compares the efficiency and equity of diagnoses conducted in-person vs. telehealth in a developmental behavioral pediatrics setting. The transition to telehealth was prompted by the COVID-19 pandemic. Eleven months of clinic data in electronic medical records were retrospectively analyzed for children diagnosed with autism in-person (N = 71) vs. telehealth (N = 45). Time to autism diagnosis, patient demographics, and deferred diagnoses did not significantly differ across visit types. However, privately insured patients and families living farther from the clinic had a longer time to diagnosis via telehealth vs. in-person. Results of this exploratory study highlight the feasibility of telehealth evaluations for autism and which families may benefit from additional support to ensure a timely diagnosis.

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.

Affording autism an early brain development re-definition.

The national priority to advance early detection and intervention for children with autism spectrum disorder (ASD) has not reduced the late age of ASD diagnosis in the US over several consecutive Centers for Disease Control and Prevention (CDC) surveillance cohorts, with traditionally under-served populations accessing diagnosis later still. In this review, we explore a potential perceptual barrier to this enterprise which views ASD in terms that are contradicted by current science, and which may have its origins in the current definition of the condition and in its historical associations. To address this perceptual barrier, we propose a re-definition of ASD in early brain development terms, with a view to revisit the world of opportunities afforded by current science to optimize children's outcomes despite the risks that they are born with. This view is presented here to counter outdated notions that potentially devastating disability is determined the moment a child is born, and that these burdens are inevitable, with opportunities for improvement being constrained to only alleviation of symptoms or limited improvements in adaptive skills. The impetus for this piece is the concern that such views of complex neurodevelopmental conditions, such as ASD, can become self-fulfilling science and policy, in ways that are diametrically opposed to what we currently know, and are learning every day, of how genetic risk becomes, or not, instantiated as lifetime disabilities.

Optimal sampling strategies for characterizing behavior and affect from ambulatory audio recordings.

Advances in mobile and wearable technologies mean it is now feasible to record hours to days of participant behavior in its naturalistic context, a great boon for psychologists interested in family processes and development. While automated activity recognition algorithms exist for a limited set of behaviors, time-consuming human annotations are still required to robustly characterize the vast majority of behavioral and affective markers of interest. This report is the first to date which systematically tests the efficacy of different sampling strategies for characterizing behavior from audio recordings to provide practical guidelines for researchers. Using continuous audio recordings of the daily lives of 11 preschool-aged children, we compared sampling techniques to determine the most accurate and efficient approach. Results suggest that sampling both low and high frequency verbal and overt behaviors is best if samples are short in duration, systematically rather than randomly selected, and sampled to cover at least 12.5% of recordings. Implications for assessment of real-world behavior are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Research Review: Outcomes of 24- to 36-month-old children with autism spectrum disorder vary by ascertainment strategy: a systematic review and meta-analysis.

BACKGROUND: Despite widespread recommendations for early surveillance of risk for autism spectrum disorder (ASD), no research to date has shown that early surveillance leads to better clinical outcomes. Preliminary research has suggested that children with ASD ascertained via prospective follow-up have better outcomes than those ascertained via community referral. Because prospective studies include early surveillance, by comparing outcomes of children with ASD across ascertainment strategies, we may gain insight into the effects of early surveillance relative to its absence. METHODS: A systematic review was conducted to identify studies reporting outcomes of 24- to 36-month-olds with ASD ascertained via prospective follow-up, community referral, or universal screening. A meta-analysis using a random effects model was used to calculate overall effect size estimates for developmental level and symptom severity across ascertainment cohorts. RESULTS: Eleven prospective, ten community referral, and eight universal screening studies were identified, reporting on 1,658 toddlers with ASD. We found no differences in outcomes between community referral and universal screening studies. Relative to both, prospective studies reported significantly higher developmental levels and lower symptom severities. CONCLUSIONS: Outcomes of young children with ASD ascertained via prospective follow-up are better than those of children with ASD recruited via community referral or universal screening. Although we discuss why sampling bias is not likely the driving force behind these findings, we cannot rule out the possibility that sampling bias contributes to the observed differences; future studies should probe the effects of sociodemographic variables on clinical outcomes as a function of ascertainment strategy. This limitation notwithstanding, our results raise the possibility that prospective follow-up may confer a 'surveillance effect' that contributes to improved developmental and diagnostic outcomes in children with ASD. Future research should test this hypothesis and determine the specific mechanism by which surveillance may improve outcomes.