Infant sleep is an important aspect of infancy. Its influence extends into later years of child development and across various domains, including cognitive, emotional, and behavioral development. High-quality sleep in the first few years of life also lays the foundation for healthy sleep habits later on. Recent advances in technology have provided new opportunities and tools for collecting intensive longitudinal measurements of sleep quality in ways that are not intrusive to daily life. We are using dynamic system modeling techniques to analyze nighttime actigraphy (movement) data and e
One common problem in longitudinal studies is the presence of missing data. Intensive longitudinal data involving repeated assessments of constructs such as emotions are particularly prone to nonignorable missingness, namely, missingness where the missing data mechanism depends on unobserved information. For example, in measuring day-to-day changes in negative emotions, it is possible that the participants may opt not to report their feelings on the days with heightened negative emotions, thereby leading to nonignorable missingness when modeling emotion processes.
The Intraindividual Study of Affect, Health and Interpersonal Behavior is an intensive longitudinal study that collected rich repeated measures of its namesake variables at multiple time-scales.
With survival analysis, the most widely-used model is Cox's (1972) proportional hazard model. However, cancer control organizations and researchers using cancer registry data most often fit relative survival models. These models estimate survival in a hypothetical world where cancer is the only cause of mortality. This language arouses curiosity. We wondered whether it was used in the original 1961 cancer monograph and whether it is used now as simple shorthand because at no time in the past or in the future will we experience a world where cancer is the only cause of mortality.
The availability of mobile real-time data streams (e.g., laptops, smart phones, etc.) make this an exciting time. These new data are allowing us to re-conceptualize how we live, learn, communicate, and organize our daily lives to achieve personal goals. This project is about the interdependence of behavior across multiple domains (school, health, leisure), multiple time-scales (days, months), and multiple spatial/virtual locations (classroom, gym, home, games, media).
The BeingWellProject is a research project in which we re-imagine well-being as a complex dynamical system. By combining cognitive psychometric techniques and dynamical systems modeling, the goal is to unpack the mechanisms, pathways, and synchronicity dynamics of emotional and cognitive elements of well-being. We conduct ecological momentarily assessment studies in which participants report on their well-being while living their everyday life.
This project is focusing on the merging of intraindividual variability (IIV) methods and network methods and applying this fusion method on intensive longitudinal studies. We used a two-stage approach to accomplish this. The first stage is to construct a high-dimensional person-specific network structure using IIV method and the second stage is to use network methods to examine the characteristic and measure change of this person-specific network. So far, we have found empirical evidence that impact of major life events (e.g.
Computational modeling is central to a rigorous understanding of the development of the child’s first social relationships. The project addresses this challenge by modeling longitudinal change in the dynamics of early social interactions. Our proposed models integrate objective (automated) measurements of emotion and attention and common genetic variants relevant to those constructs. This project is supported by funding from the National Institute of General Medical Sciences.
The past several decades have seen the rise of intensive longitudinal data (e.g. via ecological momentary assessments) and the resulting dynamic modeling methods in social and behavioral sciences. To make the estimation of these models more accessible to researchers, we have created an R package that is based on a novel and efficient state-space estimation algorithm in C.
Irregularly spaced longitudinal data have become increasingly prevalent in empirical studies. In the study of human emotions, researchers often adopt ecological momentary assessment (EMA) procedures to obtain responses at random or event-contingent time intervals.
This research study aims to improve the ability of individuals to use patient-supported online forums to obtain useful information for their personalized concerns. To do so, we are:
Conversational rapport refers to a close and harmonious relationship in which the people or groups involved understand each other's feelings and ideas and engage in effective communication, which leads to better quality relationships in the classroom or the workplace. This project is interested in using computer vision and computational linguistics technologies to understand how synchrony, symmetry, nonverbal behavior, and a variety of inter- and intra-personal measures interact to create (or fail to create) rapport in a dyad.
Co-robots are robots that work side-by-side with humans, assisting them and adapting to their needs rather than operating as isolated entities.
Social interaction is an important part of everyday life, but it is difficult to study in an ecologically valid environment. The goals of this project are to integrate scientific know-how about social interaction, physiology, and overall well-being into a unified platform for social and behavioral wellness.
Intensive longitudinal measurement designs prominent in econometrics, engineering, biophysics and brain imaging are also quickly coming to the forefront in psychology. Vector autoregressive models (VARs), often in combination with Granger causality testing, are often used in statistical analysis of time series data. Granger causality testing is used to establish the network of effective dynamic connections underlying the data.
Self-regulation is a core construct in health research due to its relation to a broad array of child and adult health problems, such as obesity, mental disorders, and heart disease. The breadth of evidence implicating self-regulation in health emerged from varied approaches to its definition and examination. Our work addresses the basic scientific need for greater consistency and integration in how self-regulation is conceptualized, modeled, and measured.
Asthma requires daily monitoring of lung function, which can be affected by variations in medication usage, in addition to a number of different behaviors and triggers. Problems associated with an inappropriate type or dosage level of medication are especially important. Despite our recognition of the variability in patients’ responses to various types of medications and dosage levels, little has been put into place that would monitor a patient’s responses to medication in a continuing way so that proper adjustments can be made for the duration of the use of that drug therapy. The determ
Family and dynamic systems theories have emerged from basic principles of general systems theory (von Bertalanffy, 1968). In this project we are exploring how the modeling frameworks being used in ecology (nonlinear dynamic models) can be used to study family systems. First, we review some of the theoretical principles at the core of dynamic systems theory that can be applied to the study of families.