Category: EMA/Live Surveys

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.

Society for Ambulatory Assessment (SAA2019)

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Dr. Brick presented at the Society for Ambulatory Assessment’s 2019 meeting in Syracuse, New York.  The SAA is dedicated to examining the applications and uses of tools like Wear-IT to clinical and research settings.  As always, a phenomenal set of scientists were there.  Penn State’s College of Health and Human Development was there in force, with a number of great presentations.

Dr. Brick presented in-process work using new statistical and data mining methods focused on real-time feature selection.  The idea is that smartphone surveys are annoying, and the longer they are the more annoying they are.  Annoyance leads to people ignoring the surveys, which means less data and more participant costs (e.g. money, energy, burnout).  The goal of this new work is to make in-the-moment decisions about which questions to ask and how to ask them, in order to minimize the burden on the participants and maximize the amount of data gathered.

Quantitative Methods Series at USC

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Dr. Brick took a trip this week to the University of Southern California to visit Dr. Chris Beam in the Psychology department, and Dr. John Prindle in the School of social work.

He also gave a presentation to kick off USC’s Quantitative Methods Series, discussing new models of day-to-day affect and emotion, and their expected relationships to emotion regulation.  Lots of interesting discussion about exciting data sets and interesting new approaches to measurement!

Recovery Science Research Collaborative

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The Recovery Science Research Collaborative had its annual meeting at Penn State this week.  Great scientific discussion around the way to understand recovery.  The group combines qualitative researchers, clinicians, recovery specialists, and a few quantitative folks.  Lots of great discussion around frameworks and metatheories of recovery, the emergence of recovery science as an interdisciplinary junction of biology, psychology, sociology and medicine–combining the health, behavioral, and social sciences all at once.  Dr. Brick spoke about Wear-IT and EMA+Wearables approaches to modeling and intervening in the process of recovery to improve well being.

Emotion Over Time

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Core affect is a way of thinking about emotional mood state in terms of two measures: valence (from positive to negative) and arousal (from highly active to placid). So if you’re super ticked off about something, that’s negative valence and high arousal, where if you’re just blissfully chill, you’re low arousal and positive valence.   But people aren’t just in one state forever–much more interesting is the way that they change.

Zita Oravecz and I look at the dynamics of core affect change within an individual in a recent paper: Associations Between Slow- and Fast-Timescale Indicators of Emotional Functioning in the journal Social Psychological and Personality Science.  There, we characterize the changes in core affect within a person in terms of a person’s home base in each dimension, the state they drop back to when there’s no real input, how much they fluctuate around that home base, how strongly their system regulates itself back to base, and the correlations between the ways those characteristics show up in valence and arousal.  Importantly, these characteristics turn out to be predictive of other “trait-level” characteristics, like the strategies you use when you have to deal with negative emotion.