this paper is about a collaborative project
between technical researchers who are shown
in black for the black aspects and nutrition researchers
shown in the purple
and the driver for our work was that every year
there are many health studies that evaluate the way they can use
ubicomp technology but they miss the opportunity
to learn about more than health and to gain
rich technical insights
so our context was a public health nutrition study where we wanted to
understand just what young adults eat in australia
we are funded by the cancer council which reflects the importance
of nutrition for health risks and our nutrition researchers really
needed very detailed records of what young adults ate over three days
that they volunteered to log their food intake.
The challenge is that detailed food logging is
very very tedious it's also error prone and so we set about tackling the
challenge of building an app that would really
make it easier for people to search to find their food in a large food
database and they had to do this on a very small
mobile phone screen. So we created this EaT app and you can
see it on the screen and one really important aspect of the
search is we started with the intuitive idea that
the top 100 foods that are most frequently eaten
would actually be really important not just because
they should be easy to log but also their words would be useful
for generating a way to help people search for other foods
and so our algorithm generates these search accelerators to help people
narrow down their search but then how could we evaluate it
and we wanted to do that by making use of our multi-disciplinary
collaboration. Our collaborators the nutritionists
recruited over a thousand participants and they created a database with over 6
000 foods and to give you a sense of the challenge of the search.
Here is a glass of milk and there are many entries that match the word milk
because there are different fat levels and some of it's plant and animal and so
on
and our participants over three days logged over 30 000 food items
What was our approach to using all of this to learn
about how to do a better job of building this technology?
We identified some key benchmarks and we then worked with our team to find
a way to measure them. So here's a taste of the benchmarks (full
details in the paper). The first one is timeliness. That's
really important because the mobile app was supposed to
enable people to log their food through the day as
they ate it. It's really important because it reduces
the risk they'll forget it. It's also important because the burden
of logging is then spread through the day when they have a few moments to
spare. So here's an example of one of the
benchmark visualizations. The top row is
breakfast and you can see there's a heat map here
where the more logging that's done the more likely it is to be a bright green
and you can see the top row of breakfast has a lot of green in the breakfast
period. We translated this into some measures
which would enable you to compare various apps for timeliness.
The second example is much more multidisciplinary
we wanted to understand search failures when people couldn't find a food
in the food database. EaT allows you to add a food
when you can't find it and we analyzed over a thousand of these.
I have to emphasize this was only useful because the nutritionists had cleaned up
all this data. They often had to make a phone call to
clarify things with the participants and then we analyzed all of these mapped
foods to understand what the cause of the failure was.
You can read the details in the paper. The highlight is our search did
pretty well. Not all the benchmarks came just from
the field trial. We also wanted to know how long it takes
people to log a typical day's food in terms of the time burden.
Our nutritionists designed a typical day of food
and then we ran a lab trial where we looked at how long people took to log it
and collected other qualitative data. This is a really important measure and
it should be reported for every app of this type.
We believe that all of our benchmarks are either directly useful for comparing
apps but also reflect an interesting approach
to doing similar analysis. Our contributions are
a novel food search algorithm method but also the evaluation benchmarks that
we developed. The way that these could be used in many
health studies by repurposing the work of the health
researchers and giving ubicomp insights. We've
provided foundations for systematic evaluation
and benchmarking of a mobile food logging app and
hopefully as an inspiration to others.