Wear-IT: Real Time Science in Real Life

The Wear-IT framework, developed here at the RTS Lab, combines the latest in research on well-being, psychopathology, and addiction with cutting-edge approaches to data collection to help measure and model a person’s well-being in real time. The ultimate goal of the project is simple:

Help people thrive.

The app is available from the Google Play and Apple app stores–click “I don’t have one” when asked for a study ID to see a demo app.

Note to Researchers: Wear-IT is now available to outside researchers as well through the Penn State Survey Research Center.  If you are a researcher interested in using Wear-IT in a research study, please contact us at wearitpsu@psu.edu.

Measurement.

Modern smartphones provide a wealth of tools for passive data collection to measure context (e.g., GPS) and behavior (e.g. screen time, app usage). Wearable technology adds novel ways to measure both behavior (e.g. actigraphy) and physiology (e.g. heart rate variability). Wear-IT unites these disparate data streams into smooth, easily interpretable data sets and visualizations.The Wear-IT framework combines passive ambulatory assessment via smartphone and wearables, complemented by friendly, low-burden surveys that can be answered on a participants’ own smartphone–no need to mail a custom smartphone or pay for extra cellular plans.

Wear-IT provides a variety of different item types and methods for responding.
Screenshots of Wear-IT scientific item types

Our survey-delivery software makes it possible to collect self-report data from participants following modern paradigms like daily-diary collection, ecological momentary assessment, or experience sampling methodology, and seamlessly harmonizes data from supported wearables and passive smartphone sensors into a unified data view.

Ambulatory Cognitive Testing.

The Mobile Monitoring of Cognitive Change (M2C2) project has partnered with Wear-IT to integrate M2C2 tests into our mobile research platform. The focus of M2C2 is to develop mobile cognitive tests suitable for high-frequency assessments (e.g., EMA, daily “e-diaries”, bursts).  M2C2 provides the research community with flexible, usable tools to enable scientific progress that depends on sensitive and accurate measurement of cognitive change.

Grid memory tasks are a validated cognitive test of visual working memory from the M2C2 toolset.
Screenshots of an M2C2 working memory task

If you have questions specifically about M2C2 Cognitive tasks, please fill out this form; if you have more general inquiries about Wear-IT and M2C2 projects, please contact us at wearitpsu@psu.edu.

Science.

Physiological measures offer one new approach to understanding processes like stress, but the signal they provide varies widely from person to person.
An example slice of physiological measurement showing the beginning and end of a stressful event.

The scientific goal of the Wear-IT project is focused on creating theoretical models, statistical methods, and technological tools to make it possible to understand the complicated systems at work in human emotion and well-being.  Specifically, we want to build models that allow us to understand and intervene into the processes of everyday life as they happen and at the speed they happen.  That is, to do Science in Real Time.

The most interesting parts of these process are when they are under strain.  That is, when people are faced with complex situations like interpersonal interactions, team projects, or educational contexts; when they are confronted with difficulties like stress, and addiction; and in cases where social responding is difficult or atypical, such as autism and PTSD.

Infrastructure.

The Wear-IT framework is a computational framework including smartphone applications, web tools, wearable physiological monitors, and a cloud-based back-end that allow us to collect and model processes at work in real time.  Our innovative edge of the cloud computational model allows us to monitor, model, and respond to events in real time, enabling responsive assessment and customized intervention in the real world.

The modular design of the Wear-IT architecture makes it easy to rapidly develop and deploy new measurement and analysis elements as new sensors, measurement approaches, and analytical tools become available.

Our initial evaluation of Wear-IT is published at the Journal of Medical Internet Research: Formative Research.

Latest Wear-IT News

  • New Article: Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data January 25, 2020
    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…
  • Society for Ambulatory Assessment (SAA2019) June 25, 2019
    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,…
  • CCSA Conference 2019 May 29, 2019
    Penn State's Consortium to Combat Substance Abuse (CCSA) had its first annual conference today.  Members of the RTS lab presented a poster there focused on trying to define and understand the process of recovery at a basic level. One primary focus of the poster was aimed at understanding something called recovery capital.  The idea is…