We would like to share the lessons we learned\h
as we designed for bringing\h data to the application
of implementation intentions\h with mental contrasting.
I would like to start by an example that may be familiar to many of us:
Consider Judy, a technology worker, who is spending most afternoons as well as weekends
working.
She really wants to change this working pattern\h
and have some space to do\h the many other activities
that are of interest to her.
She does not know what exactly goes wrong that she ends up working all the time.
She has tried multiple solutions but has ended up abandoning them for various reasons.
Judy is not alone.
A lot of people struggle with the very same challenges: They do not know where to start;
They can not stay on track with what they start; Or they fail to make necessary adjustments
to their efforts.
Setting goals and realizing them is hard
There are self-regulation strategies such as implementation intention and mental contrasting
that can help individuals like Judy overcome their challenges.
Implementation intentions are if-then plans that help individuals think once about what,
where, and how of achieving their goals and then act upon it.
For example, if someone\h wants to be more physically\h
active, they may come up with something like:
“whenever I use the restroom, I’ll do 10 squats.”
Mental contrasting asks people to think about their desired state juxtaposed with their
current reality and identify the obstacles that are in the way of reaching their desired
state.
Combining the two strategies has been shown to be particularly effective: mental contrasting
provides a direction for action, which is “addressing the obstacles”; implementation
intentions are then formed to take those actions.
So, if people can identify key obstacles as well as the doable actions for addressing
those obstacles, they have this proven technique for achieving their goals.
Well, that’s a big “if”.
It’s not easy to identify obstacles and it’s not easy to decide on doable actions.
What can we do about it?
Well, maybe reviewing personal data and reflecting on it can help with finding relevant obstacles
and actions.
So, we built a system to see what this might look like and I’m going to share what we
learned doing so.
Here is how our system looks like.
There are two major components: data collection on the left and reflection on the right.
Data collection component records information on activities, whereabouts, and how well one
follows their plans.
We opted for manual entry using the interface shown here but one can imagine this step to
happen automatically or semi-automatically.
We basically enabled users to select activities and locations they wanted to report from a
customized list and then mark those activities over 30-minute timeslots.
The other component of the\h system is the reflection\h
interface that presents personal data and
walks users through reflecting on it.
Let’s see how Judy might be able to use this tool.
She wonders “what am I doing that I end up working after hours?”
Using the interface she can look at the 7-day trend and see that she is spending a good
amount of time on development work but even more so on administrative work.
Using the filtering features she can further narrow down on these two activities and see
on the calendar view, as well as the hourly view, that most administrative work happens
in the morning while development work happens late afternoon and spills into evening and
after hours --- observe that\h green cells representing\h
administrative work are concentrated earlier,
while purple cells representing development work are concentrated later.
Well, Judy has discovered an important obstacle to finishing work in working hours right there
--- spending too much time too early on less important work.
Having identified this obstacle, the interface then walks her through goal setting.
For example, she may decide to front load days with more valued work, saying “I do
development work for two\h hours then do administrative\h
work at most 30 minutes before doing another
two hours of development work”.
She is next encouraged to further refine this plan by putting it into if-then format: “If
administrative work happens in the morning, I do not tend to it until after my development
block.”
Our interface provides a number of other ways to explore data to answer a variety of questions.
For example, it is possible to filter by time and look at what happens within a certain
window.
it is also possible to explore patterns in whereabouts, for example, in transitions across
locations.
We used this interface in a probing study with 16 information workers over the past
Summer.
Participants first described their desired state in the context of work-nonwork balance
and their goal for improving their state.
They then used our system between one and three weeks.
After at least one week of data collection, they used the reflection interface to revise
their plans.
We performed 30-minute semi-structured interviews after their final use and asked participants
to walk us through their use of the reflection tool and share their experience using it.
I’m next going to share what we learned from this study.
Some of these lessons are immediately relevant to designers and developers of data-driven
goal realization tools.
For example, we learned about the kinds of questions that people want to answer with
a system like ours.
These questions point to the tasks designers and developers should support in their solutions.
One task is selecting activities and getting various information about them, such as day
and time information.
Our basic tool did support this task.
Another task is comparing different activities for their timing and duration.
We provided basic functionality for this task, but there is certainly room for more.
Another task we learned people want to perform is defining, finding, and quantifying various
concepts, then using the\h concept to make comparisons.
For example, one might be interested in the concepts of multi-tasking or being productive,
and wonder about the times and contexts of each.
One other important task is expressing the criteria for timing, duration, and relevant
user-defined concepts for activities then using this criteria in making comparisons.
For example, one might want to know about development work at high energy times vs.
other times.
Sharing data and insights with managers and peers is also important to consider in systems
like ours as behavior change, esp. in overcoming obstacles, does not happen in a vacuum.
There are other considerations in building goal realization solutions.
There should be flexible exploration facilities that people can choose; one view or approach
which works well for some people fails others.
Also, it is much better to\h seek ways of integrating\h
goal realization in familiar tools such as
calendars and email applications.
We offer several possibilities in the paper.
Last but not least: while personal data collection can be fully automatic in theory, supporting
light-weight manual entry can be valuable.
Our probing study also led to lessons that matter in the study of data-driven implementation
intention and mental contrasting.
First, it takes some time before people get\h
a sense of data that helps\h them use this technique.
So they should have time to make adjustments to the data they collect and their use of
it.
We recommend at least a week for this tuning process before any study of implementation
intention and mental contrasting which typically lasts another week.
Our initial focus was on supporting short-term and day-to-day improvements for the individuals,
but we heard over and over that there is value in using data for longer term planning and
also within a group.
So formal studies of implementation intention and mental contrasting should consider and
measure both short and long-term improvements for the individuals and within groups.
There are also lessons to consider in future research on data-driven implementation intention
with mental contrasting.
I share two of them here.
Please refer to the paper for the rest.
In addition to drawing insights from their data using the reflection interface, participants
noted they reflected on their behaviors and took mental notes as they were recording their
activities and whereabouts.
This micro-reflection on limited and recent\h
data seemed to bring additional\h value to implementation
intention with mental contrasting technique beyond the macro-reflection that we designed
for.
So, it is important to not only investigate the use of data through macro-reflection but
also consider the unique benefits of data within micro-reflection.
We observed different breaking points in data exploration process.
For example, some people found it difficult to move from their goals to questions and
hypothesis that when answered, could help them achieve those goals.
Or, they could not map their questions to their data.
Future research should look into interface objects and interaction techniques that can
help overcome these challenges.
To summarize, we explored if and how\h data can support the application of\h\h
implementation intention with mental\h contrasting using a technology probe\h\h
and we shared lessons that we hope\h are of value to designers, developers,\h\h
and researchers hoping to support\h behavior change and goal realization.
Thank you!