Major theories of socioemotional development suggest that adaptive patterns of self-regulation reflect the accumulation of thousands of individual daily experiences of distress. In particular, parents? efforts to regulate their infants? distress are thought to provide critical inputs to developing self-regulation skills. Factors affecting caregiving, such as caregiver stress or mood disorders, are thought to limit parents? ability to provide this early support. Thus, early-emerging patterns of mother-infant interaction are theorized to be an important mechanism by which mental health risks are transmitted from caregivers to their children. However, empirical evidence for these theories are scarce, as it has not been possible to systematically capture episodes of distress as they occur in day-to-day mother and infant activity. Additionally, most studies cannot disentangle complex interactions between infant and maternal psychobiological factors. For example, highly fussy infants are more likely to stress and overwhelm their parents, thereby likely affecting parental regulation efforts and potentially exacerbating their own early biological predispositions. Datasets capturing the details of daily interactions between mothers and their infants are needed to access the basic bio-behavioral mechanisms of developing psychopathology risk. In the future, such rich datasets could also provide a foundation for emerging ?just in time? interventions that could provide mothers with real-time support and reassurance during day-to-day activities. To thus advance the field of developmental psychopathology, this proposal will leverage emerging ?wearable? or mobile-sensor technologies to capture episodes of infant distress and subsequent maternal regulation efforts as they occur in the typical day-to-day activities of infants and their mothers.
The research aims of this proposal are 1) to develop a mobile-sensing platform that will automatically detect detailed markers of mother-infant distress-related activity as participants go about their daily lives, captured via a synchronized suite of ?wearable? physiology, audio and proximity sensors, and once validated, to use this platform to 2) investigate the daily mechanisms of maladaptive mother-infant interaction dynamics The training goals of this proposal are to 1) develop my expertise with state-of-the art computational tools to study mother-infant activity in the ?wild?, allowing me unprecedented access to the dynamic processes of bio- behavioral development, and 2) to provide me the opportunity bridge my work to the domain of developmental psychopathology. The proposed K01 is thus a key step in my goal to develop a research program that can harness the rich dynamics of day-to-day activity to support theoretically-driven clinical innovations.

Public Health Relevance

Children of mothers with depression anxiety and stress are at increased risk for a range of maladaptive outcomes, including both internalizing and externalizing mental health disorders. The current training proposal will develop a state-of-the-art mobile sensor paradigm to capture high-density ecologically valid markers of mother-infant interactions?a key mechanism of the transmission of psychopathology from mothers to their children?and use this paradigm to examine how daily activity and exposures contribute to adaptive and maladaptive dynamics of mother-infant interaction.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Scientist Development Award - Research & Training (K01)
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Psychosocial Development, Risk and Prevention Study Section (PDRP)
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Bechtholt, Anita J
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University of Texas Austin
Schools of Arts and Sciences
United States
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