两性色午夜

两性色午夜 Professors Use Mobile Devices To Study Behaviors During Pandemic

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Before leaving the house, you most likely check to ensure you have your ID, your shoes and most importantly your smartphone. In the past decade, American smartphone usage has grown more than 50% according to a . Smartphones have become as commonplace as a wallet or car keys and 两性色午夜 University researchers are taking advantage of this new commodity by using cell phone data to study individuals鈥 behavioral patterns during the COVID-19 pandemic and link cell phone use behaviors to mental health. 

Ruoming Jin, Ph.D., partnered with Deric Kenne, Ph.D., in an exploratory research effort to develop a computer learning framework that collects mobile sensor data and tracks participating smartphone users鈥 movements while keeping personal information private. 

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Professor Deric Kenne

鈥淲e have an interest in understanding college students鈥 behavior and how they behaved during the pandemic as a representation of the overall population,鈥 said Jin.

The pilot-study is funded by a $150,000 grant from the National Science Foundation as well as funding from the University Research Council.

Jin, a professor in the Department of Computer Sciences in 两性色午夜鈥檚 College of Arts and Sciences, explained that study participants will download an app allowing sensor-based metadata to be pulled and analyzed in the first stage, and in a second stage, the participants will help test the app  which can predict their behavior and mental wellness through  federated learning machine process, a process emphasized in privacy protection.

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Professor Ruoming Jin

鈥淚n the last few years there鈥檚 been a lot of interest in building a federated learning framework,鈥 Jin said, 鈥渨hich essentially allows every person鈥檚 personalized data to be used in the learning framework without sharing all data to the cloud.鈥

Jin explained that by using a federated learning framework, mobile data can be collected and interpreted without including personalized information. Study participants鈥 personal details will be protected while the metadata, things like location, screen time and sensor data, will contribute to the overall machine learning process.

鈥淲e cannot see the content of what you really do, only the profile,鈥 Jin said. 

Sensor data will be used to give researchers a sense of physical behaviors, whether an individual is sitting, standing or riding a bicycle. In terms of pandemic responses, it can be used to see how often the person is at home or how much time is spent on their phones. 

The app will also prompt participants to fill out short surveys and complete self evaluations to gauge anxiety and mental health effects. 

鈥淭he app will periodically ask questions about what you are doing, and send surveys to learn the person鈥檚 mental state,鈥 Jin said. 鈥淭hose data points will help us to potentially link the person鈥檚 behaviors to their mental health.鈥 

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Student sitting in a window on their cellphone

Jin explained that beyond the COVID-19 framework, the app could be developed as a potential mental health resource for students that would be specified to that individuals鈥 physical behaviors and mental health responses. 

Kenne, an associate professor in the College of Public Health, said the mental health component has the potential to act as an early intervention resource for students.

鈥淚f we鈥檝e got students walking around with cell phones and we can detect certain levels of depression or anxiety, we can give the student feedback that there might be issues of depression creeping up,鈥 Kenne said. 鈥淒epression and anxiety is different for everyone, it can ebb and flow and goes in waves. If this works it鈥檚 an opportunity to pick up on those things very early and be able to intervene if necessary.鈥

Kenne explained that intervention from the app could look like a message sent from the app or possibly a peer-led care team that could reach out to students to prevent a mental health issue from becoming more severe. 

鈥淭his pilot study will help us work out kinks with the app; maybe students don鈥檛 respond to messaging through the app, so we can tweak things going forward,鈥 Kenne said. 鈥淚 see years and years of research evolving from this initial study.鈥

Kenne said the popularity of smartphone use among several generations opens a large demographic range for future studies. 

鈥淭here is such broad applicability with this technology. We are starting with the student population because it鈥檚 convenient for us and it鈥檚 important, but we potentially could be reaching populations from 10-years-old all the way to senior citizens,鈥 Kenne said. 鈥淓verybody could be part of this at some point.鈥

This study is a collaboration with New Jersey Institute of Technology. Jin explains there will be students from both campuses contributing, and the study will involve three months of tracking sensor data.

 

For more information on 两性色午夜鈥檚 Department of Computer Science visit: /cs


For more information on the Center for Public Policy & Health in 两性色午夜's College of Public Health visit: /mhsu

POSTED: Monday, September 28, 2020 09:39 AM
Updated: Friday, December 9, 2022 12:49 PM
WRITTEN BY:
Colleen Carroll