Media Mention in PsyPost

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Our article on Black identity, depressive symptomology, and marital satisfaction recently was talked about on pop-science news website PsyPost.

My co-author August really comes across clearly in describing the findings.  I wish they’d take it the step further to point out how strongly this argues a need for more research along these lines.  As I alluded to in the earlier post, and as we talk about in the paper, a lot of the analyses we ran are really exploratory–they’re first takes at trying to capture the complexity of the intersections of different forces.  The models exist to understand this more completely, but finding the data is a much taller order.

You can find it online: “New study highlights important links between depressive symptoms, Black identity and marital satisfaction in African-American couples” by Eric Dolan.

New Article: Capturing temporal dynamics of fear behaviors on a moment‐to‐moment basis

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Detail of fear seqeunces from several children

One early indicator of psychopathology risk for small children shows up in patterns of fear behaviors. In our new paper Capturing temporal dynamics of fear behaviors on a moment‐to‐moment basis, published in Infancy, we apply discrete sequence methods to data about children’s responses to a fearful situation.

One of the very cool things about this paper is that we identify a few different clusters of child behavior. We were hoping to find a way to identify a high fear group at risk to show dysregulated fear, which has psychopathology implications. We found them to some extent, but also identified some other groups that are interesting: a group of external regulators, who use a parent to help regulate their emotions; one low reactive group, who just aren’t that scared by things; and a cool group called fearful explorers who show fear, but don’t let it stop them from checking things out. This work provides some new data points to help understand the emergence of emotion regulation across child development.

New Article: Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data

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Two timestreams, warped to common timing

As Wear-IT begins to take shape in more detail, we’ve been looking at new ways to understand and model physiological data on an individual level. Recent work with Ame Osotsi, Zita Oravecz, and Joshua Smyth examines individual differences in physiological response in a paper called Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data, just published in the Journal of Healthcare Informatics Research.
In it, we illustrate the importance of individual modeling for physiological states, showing that high-arousal states have physiological signatures that can be detected by machine learners, but that those signatures differ enormously from person to person, and individualized modeling is really important.

GSA 2019

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Dr. Brick and Ms. Dickens presented talks at the annual meeting of the Georntological Society of America (GSA 2019) in Austin, Texas this week. GSA is always an interesting meeting, looking at the science of aging–a vital science, especially as the global trends show our population living longer, and the proportion of older adults increasing as time goes on. Topics included methods to model and understand patterns in individual growth trajectories (like Dr. Brick’s sequence learning methods), and the relationships between sleep and well-being.

Six-state emotional day solution

Other topics focused on issues of cognitive aging, dementia and Alzheimers, and technological and methodological innovations to model and improve the well-being of older adults through improved cognitive function, better mobility, and increased independence.

Collaboration: Monash University, Australia

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A week-long visit last week kicked off the beginning of a collaboration between Penn State and Monash University in Australia.  The RTS lab is working with collaborators to examine social identity in recovery.  As a part of this project, we’ll be developing a computerized tool for understanding participants’ subjective views of their social identity–that is, how they relate to the groups around them–and examining how those social identities emerge from the momentary interactions that they engage in.  We’ll be asking them about their face-to-face, phone, text, and social media conversations with members of the groups they consider themselves to be a part of, and mapping out the way that those interactions lead to an identity as part of the group.  The longer-term goal is to be able to provide moment-to-moment advice on how to shape their relationships in order to promote the ones that lead to a sustained, healthy recovery that can help them to thrive.