Hello everyone. I am Lakmal, a PhD student at EPFL
and a research assistant at Idiap Research Institute, Switzerland.
Today I am going to present our work on the topic one more bite. inferring
food consumption level of college students using smartphone sensing and self-reports.
Many young adults show a tendency to adopt unhealthy eating practices
during college years, when they undergo significant lifestyle changes.
Unhealthy eating habits at this age could lead to adverse health outcomes in the long term.
Due to these reasons, researchers in nutrition, behavioral science, and psychology
are extensively studying causes and contexts of food consumption, specially among young adults.
prior research in those domains have linked factors such as the social context,
eating location , availability and types of food, psychological aspects
and concurrent activities to the food consumption level.
So, if we talk about mobile health applications, after fitness tracking,
food and nutrition has become the second most popular type of mHealth applications.
Many widely used commercial apps allow users to keep their food intake as a mobile food diary,
providing basic statistical insights so that people can adhere to healthy eating patterns.
However, in their current state, mobile food diary related studies and research are primarily based
on self-reports and do not yet make full use of smartphone sensing capabilities. So, another under
explored area is how can we combine mobile food diaries and passive smartphone sensing.
If we talk about food consumption level, there are subjective as well as objective interpretations.
The objective food consumption level attempts to capture the exact calorie
consumption during eating episodes from a purely nutritional science standpoint. Many
currently available mobile food diary-based studies attempt to capture this attribute
using self-reports by requesting the user to enter each food type and the amount they eat.
However, these techniques might not be accurate
due to a range of individual and measurement factors.
Further, nutrition researchers have also widely used
subjective measures to capture food consumption levels of people
by considering the psychology of eating, also known as self-perceived food consumption level.
This view is primarily based on the idea that, when you ask people whether they overate or not,
more often than not, the answer would be based on an eating episode level,
and the self-perceived amount of food they have eaten. A lot of prior work in nutrition science
support this view regarding food consumption level.
So, in this study, we also use the same subjective food consumption.
So participants self-reported their food consumption as overeating,
eating more than usual, undereating, eating less than usual, and eating as usual.
considering all these aspects, we ask two research questions.
number one. What behaviors and contextual patterns around food consumption levels
can be observed by analyzing everyday eating episodes
of a group of college students obtained via passive smartphone sensing and self-reports?
then number two. Can self-perceived food consumption level be inferred
using contextual data obtained through a mobile application?
So the diagram here shows our study objective. That is to leverage findings in nutrition science
and mobile sensing research in order to do the food consumption level inference
using smartphone sensing features.
So, to test our hypothesis, we conducted a new mobile data collection campaign in Mexico with
84 college students to capture their eating behavior including food consumption levels.
During our study, we collected 3278 self-reports. in addition to self-reported data,
we collected passive sensing data related to eating context, physical activity levels,
and phone usage to characterize eating episodes in a holistic perspective.
So as a summary, with self-reports that are similar to a mobile food diary,
we captured eating episode level data about food categories, mood and stress when eating,
and also contextual information about activity, social context, and semantic location.
Then we also manually crafted features using passive smartphone sensing data
by considering a time window of one hour around eating episodes.
So this is a result from a descriptive analysis. Results show that participants
reported more overeating as compared to undereating when they ate fats and oils,
meat and sausages, sugar juices, and prepared dishes from restaurants.
This results shows that when participants were in more social setting, there were more
overeating as opposed to undereating and when they were commuting there were more undereating
episodes as compared to both overeating and as usual eating. Further, participants
reported overeating more often as opposed to undereating when they were in positive moods.
In a statistical analysis, we calculated t-statistic, p-value, and cohen’s-d for
all the features. Results show that features from food diaries such as the social context,
some food categories such as fats or oils, and also mood and stress while eating,
can help in discriminating between different food consumption levels
with a high statistical significance. In addition, passive smartphone
sensing features from accelerometer, app usage, and screen usage too showed high
statistical significances when discriminating between different food consumption levels.
Finally, We defined and evaluated a novel ubicomp task by inferring
eating more than usual ("overeating"), less than usual ("undereating"), and "as usual"
episodes using passive smartphone sensing and self-report data, with an accuracy of
87.81 percent. We evaluated more than 6 different models and different feature group combinations
and got best results with random forest classifiers with leave k users out strategy.
Further, we show that the same inference task can be done with an accuracy of 83.5 percent
by only using passive smartphone sensing features and time of eating.
Results from our study illustrates the potential of using passive smartphone sensing
alone or together with mobile food journals
towards building context-aware food-related mobile systems.
Thank you very much.