# Adolescent Health in an Urban Environment

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $237,787

## Abstract

Project Summary
Co-location networks – two-mode networks that capture connections between individuals and locations in
geographic space – have broad relevance in the health sciences in areas ranging from the study of infectious
disease transmission to understanding the influence of social processes on health outcomes and behaviors.
Despite their broad relevance, however, statistical methods for understanding co-location networks are limited.
This methodologically oriented proposal focuses on the development of a statistical framework for the study of
co-location networks using a bilinear mixed-effects model with interacting latent activity pattern motifs and
profiles. Through latent interacting random effects, our model captures the dependence between individuals
based on their shared use of space and between locations based on the individuals who frequent them. Our
flexible modeling framework uses a mixed-membership structure to relax the assumption that activity profiles
are static and takes advantage of a data augmentation strategy to allow versions of the model with either direct
or indirect specification of the dependence between actor-location ties. Our novel statistical models will be
used in analyses of activity pattern data collected as part of the Adolescent Health and Development in
Context (AHDC) Study, an ongoing data collection effort in Franklin County, Ohio. Through GPS-based
smartphone tracking and space-time budget software, the AHDC Study provides rich detail on the co-location
networks of adolescents in the study area. In addition, a wealth of survey data, smartphone-administered
Ecological Momentary Assessments (capturing real-time measures of location, social network partner
presence, activities, risk behaviors, and mood), and biomeasure data on the study participants are available.
Recognizing that our proposed statistical model may not be able to capture the structure the co-location
network structure of AHDC adolescents based entirely on their observed activity patterns, we also propose to
embed relevant information derived from social media into our analyses through informative prior distributions
on model parameters. To do so, we propose novel data mining algorithms to retrieve potential activity pattern
motifs and coincident profiles from Twitter posts and network structure. In particular, we extend named entity
identification methods to the spatial setting to automatically retrieve information relevant to activity patterns and
develop novel methods for prioritizing activity pattern information based on its relevance to particular
subpopulations (here, adolescents) using scalable sentiment analysis. Using our new statistical and data
mining methodology, we will perform detailed statistical analyses to explore the relationship between spatial
and socio-spatial exposures derived from an inferred co-location network and physiological stress in
adolescents.

## Key facts

- **NIH application ID:** 10150049
- **Project number:** 5R01HD088545-05
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Catherine A Calder
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $237,787
- **Award type:** 5
- **Project period:** 2017-04-14 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10150049

## Citation

> US National Institutes of Health, RePORTER application 10150049, Adolescent Health in an Urban Environment (5R01HD088545-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10150049. Licensed CC0.

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