Hi everyone My name is Subigya
Today I am going to introduce our work on COVID student study.
In this work, we compare and contrast behavior of students pre-COVID and during COVID using
Mobile Phone Sensing.
This work is from a collaboration among Computer scientists and behavioral psychologists from
Dartmouth College.
The work we are presenting today is based on an extension of our previous StudentLife
study.The StudentLife study tracked 48 undergrad students at Dartmouth College throughout a
term.
In this next iteration of the study, we follow 220 undergraduate students throughout their
4 years at Dartmouth College.
The overarching goal of the study is to track experiences of undergrad students throughout
their academic tenure, with a particular focus on mental health.
As such, we ask students to self-report their mental health status such as anxiety, depression,
self-esteem, social-level and stress every week.
In this work, we focus on first year of COVID and a year prior to it.
When COVID happened we already had our study running for almost 2 years.
So we added some questions specific to COVID to ask to participants.
These were optional questions and asked every week in the form of ecological momentary assessments.
The analysis that follows includes 180 of the original 220 participants who answered
the COVID questions.
Specific to mobile sensing, our sensing app collected a wide range of behavioral passive
sensing data from the phone — both iOS and Android.
We compute Features on a daily basis.
Specifically, we compute the features including physical activities, mobility, sleep patterns
and semantic location.
We assign semantics to places where participants visit based on a third party API from HERE.
We also calculate smartphone usage and smartphone-based audio.
We further break down different activities and behaviors based on whether they were performed
at the participants most significant place of stay, meaning their home.
This allows us to model people’s behaviors during COVID as they spend more time at their
home.
We label a significant location as home where a participant spends most of the time between
2 am to 6 am.
We also generate several regularity features.
As mentioned earlier, in this work, we used data from a year prior to COVID i.e. data
from February 2019 to March 2020 and compared that with the first year of COVID i.e. data
from March 2020 to February 2021.
Here is an example of what the behavior change looks like.
The vertical red line is March 13, 2020, when COVID was declared a national emergency in
the US.
The x-axis is the time-period for both the figures.
In the first figure, we plot the time spent at study places within the campus.
We can see that immediately following the national emergency announcement, there’s
a sudden decrease in time spent at study places.
On the other hand, we can see that in the second figure, the duration of unlocks of
the phone increases following March 13.
We perform similar pre-post analysis with all the features that we collect — we compare
the median score of pre-COVID-19 year with the first COVID-19 year.
The x-axis is the percentage change, whereas the y-axis contains the name of the features
or behaviors, if you will.
Some of the notable changes include an increase in number of audio plays, regularity of unlock
duration at home and increase in sleep duration by a small amount.
As expected, we find a decrease in visits to unique location as well as walking duration.
Note that all these results are statistically significant.
Next, we look at whether all the participants react to the pandemic in the same manner.
For this, we cluster participants first by their self-reported response to our surveys.
We perform PCA to reduce the dimensions of the feature sets we have, and then perform
clustering on the principal components.
The first figure is the result of performing clustering on the self-reports.
Next, we run clustering on the behavioral change data.
So we subtract the pre-COVID-year data from the first COVID-year data and then cluster
the resulting information.
We use Silhouette score to identify optimum numbers of clusters, which turns out to be
2.
The tables we see are the result of clustering, first on self-reports and then on sensing
data only.
From self-reports, we see that students in cluster C2 are more concerning since they
score high in mental health metrics — they have higher depression, anxiety, stress, lower
self-esteem and higher covid concern.
Note that COVID-1 is an additional question we asked during the pandemic which simply
asks students to report how concerned they are about COVID.
In the second table, we can see that students have different changes in behavior during
the pandemic.
Since we subtract the pre-COVID year data from the first-COVID year data, the negative
symbol in the front means that that specific behavior reduced during the pandemic.
A positive number indicates that the associated behavior increased during the pandemic.
We can see that while some students had an increase in a certain behavior, others had
a decrease.
For instance, the sleep start time decreased for cluster C1 students whereas it increased
for cluster C2 students.
This analysis shows that individuals handle pandemic differently.
Even within a homogenous population of undergraduate students belonging to a single institution,
there are subgroups of students with differing changes in mental health and behaviors.
So far, we looked at behavioral changes because of COVID, and how we have sub-groups of students
who reacted to pandemic differently.
Our focus moving forward will be the COVID-1 question i.e.
How concerned are you about COVID-19?
Given the significance of COVID, we are interested in learning if COVID concern is associated
with mental health and whether we can use passive assessment to monitor and predict
COVID concern of participants.
This is a plot of the COVID concern responses we received from our participants since March
2020.
The second plot is taken off of New York Times and shows the national averages in number
of COVID cases.
We can see similarities in both the plots, especially around Feb and March 2021.
Both the COVID concern among our students, as well as the national number of COVID cases
peak around the same time and then there’s a downward trend for a while until both the
graphs increase following July 2021.
When we perform correlation analysis, we find that COVID concern of students is moderately
correlated with their stress, anxiety and depression.
This begs the question…Can we predict COVID concern of students through passive behavioral
sensing derived from their smartphones?
We experiment with different methods to find the best predictive model for COVID concern.
The COVID concern was assessed every week, although not everyone answered it all the
time since it was optional.
We median splitted the participants responses in order to create two classes — high COVID
concern and low COVID concern.
Note that we treated the problem as a time series classification task.
We evaluate the prediction performance of different deep learning models by applying
60-20-20 split.
We train the predictive model with the best hyper-parameter using all the training data
and test on the testing set.
Hyper-parameters are tuned manually.
Overall, the CNN outperforms all the other models.
We obtain an AUC of 0.70 and F1 score of 0.71.
We use permutative feature importance approach in order to identify top features.
Unique locations visited, duration spent at home, running duration are the top 3 important
features for our model.
We believe our findings pave the way for further research to investigate how pandemics impact
the mental health of young adults at colleges.
Importantly, we are now in the second year of the COVID-19 pandemic and while many students
are hopeful that their lives will eventually return to normal in the near future, it looks
like we may be living with the pandemic and its variants for sometime to come.
In fact, we have little visibility of the end of the pandemic with any certainty.
As a result, we believe that the insights from our work on finding connections between
phones and human behaviors associated with the pandemic remains very relevant going forward.
For example, by profiling participants we are able to address gaps in our understanding
of how subgroups of specific populations may respond to traumatic events differently.
Also, we envision our predictive model as an early detection system that could provide
individualized suggestions to help people navigate difficult times.
Thank you for listening!