Hello everyone. I am Lakmal, a PhD student from EPFL
and a research assistant at Idiap Research Institute, Switzerland.
Today I am going to present our work on the topic Examining the
Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
The consumption of several drinks in a row,
or heavy drinking, can lead to many short-term adverse consequences
not only for the person drinking but also at the family and community levels.
On a larger time frame, heavy alcohol consumption can also lead to long-term consequences,
such as poor academic performance, diminished work capacity, and premature death.
Adolescence and early adulthood appear as particularly critical period of life for the
development of risky alcohol-related behaviors, and in order to limit excessive drinking among
young adults, it is essential to understand the etiology and antecedents of drinking occasions.
Prior work in alcohol research has emphasized the importance of the social context
in shaping people’s alcohol use and motives in the sense that,
the consumption of alcohol or not and the amounts consumed, vary depending on the
presence or absence of family members, friends or colleagues, spouse or partner, and other people.
In addition to the possibility of capturing in-situ self-reports,
mobile sensing research has used passive sensing data from wearables
and smartphones to infer aspects that could be useful to provide interventions or feedback.
Some such inferences include inferring drinking nights, inferring heavy drinking episodes,
identifying walking under influence, and detecting drunk driving.
However, to the best of our knowledge, mobile sensing has not been widely used
to automatically infer the social context of alcohol drinking events even though
identifying general social contexts has been explored before using mobile sensing.
So, while there are numerous definitions for the term social context in different disciplines,
in this paper, we borrow the concept commonly used in alcohol research
which refers to either one or both of the following aspects.
number one, it is the type of relationship. Then, number two, the number of people
belonging to each type of relationship. So, using self-reported information,
seven attributes were derived as shown in the table here.
Considering all these things, we ask three research questions.
Number one. What social contexts around drinking
events can be observed by analyzing self-reports and smartphone sensing data.
Number two. Can young adults’ social context of drinking be inferred using
smartphone sensing data?
Number three. Are social context inference models robust to different group sizes?
Can mobile sensing features infer the sex composition,
when drinking is done in a group of friends or colleagues?
So in order to test our hypothesis, we used a mobile sensing dataset from our previous work.
This dataset contains self-reports
about drinking events of a group of 241 young adults in Switzerland during weekend nights.
The dataset contains over 1250 alcohol drinking-related self-reports.
in addition to the self-reported data, passive smartphone sensing data related to drinking
context, physical activity levels, the phone usage were collected using eight
different sensing modalities to understand the drinking behavior in a holistic perspective.
Here we show the distribution of self-reports for different social contexts.
Participants consumed alcohol while being alone only on a small portion of occasions.
This result is comparable to prior alcohol research, which also shows that solitary
drinking episodes are less frequent as compared to other social contexts.
So if we move on to the inference, we defined and evaluated seven social context inferences
using passive smartphone sensing data.
In training, we used leave one user out strategy and to balance datasets,
we used synthetic minority oversampling technique.
If we consider results, all the two-class inference tasks achieved
accuracies over 80%. Further, all three-class inferences achieved accuracies over 75%.
When considering the five model types we used,
Random Forest classifiers performed the best across a majority of the inference tasks.
These results suggest that passive mobile sensing features could be used to infer both two-class and
three-class social contexts related to alcohol consumption, with reasonable performance.
Here, we plot a distribution of feature importance values
for all social context inferences, grouped by different sensing modalities.
Overall, the accelerometer produced the most informative feature,
for all seven social contexts and the distributions were sparse.
Even though location features were not among the highest for any inference,
mean feature importance for location modalities was even higher than accelerometer
because the location feature distributions are negatively skewed in many cases.
Then, for different groups sizes in three-class inferences, results show that,
regardless of the grouping threshold, family inference performed well.
In addition, for friends and people social contexts,
results dipped and later increased to the same level, for higher grouping thresholds.
Then, we defined and evaluated a three-class inference task for drinking episodes that
are done with friends or colleagues, with the classes: same-sex, opposite-sex and mixed-sex
According to the results, the random forest classifiers performed the best
with an accuracy of 75.86%. These results suggest that smartphone sensing features can
to some degree classify the sex composition of drinking groups,
but more in-depth work would be needed to understand this phenomenon further.
So, as a summary, our work emphasized the importance of understanding the social context of
drinking using smartphones, to obtain a holistic view regarding alcohol consumption behavior.
With multiple statistical analyses, we show that
features from modalities such as accelerometer, location, bluetooth,
and application usage could be informative about different social contexts of drinking alcohol.
Finally, we believe these findings could be useful for ubicomp and alcohol epidemiology researchers
towards implementing future mobile health systems with interventions and feedback mechanisms.
Thank you very much for listening, and please refer to our paper for more details.