Hello! My name is Jaime Snyder and I am faculty at the University of Washington's Information
School. Over the next ten minutes, I will provide a brief overview of my paper entitled
Visualizing Personal Rhythms: A Critical Visual Analysis of Mental Health in Flux.
A warning: This presentation will include some moderately graphic descriptions of experiences
with serious mental illness. Also, if not noted here, all image credits and citations
can be found in the paper.
This paper focuses the ways in which personal data is visually represented for the purpose
of managing serious mental illness.
For the past few years, my team and I have been working on a personal informatics system
to support therapeutic tracking practices for people managing bipolar disorder, a serious
mental illness characterized by abrupt and hard-to-predict mood changes. This work focuses
on the importance of supporting collective engagement with personal data across different
stakeholders, including clinicians, family members, other caregivers, and peer supporters.
One of the most important aspects of that work has been to surface the importance of
empathy and communication among these stakeholders.
Within our group, I lead our efforts to understand how visualizations of personal data can better
reflect lived experiences with bipolar disorder and more fully support social engagement across
stakeholders.
One of the ways I have done this is by adopting participatory and co-design methods. Our 2019
CHI paper provides details about the visual elicitation methodology I have developed for
this work. My presentation today focuses on a critical visual analysis of the image making
practices and visual artifacts generated during co-design sessions with participants diagnosed
with bipolar disorder.
Bipolar disorder is a chronic mental health condition characterized by shifts in mood,
from highly elevated manic states to depressive states. Therapeutic self-tracking is often
recommended, along with medication, as a means to manage bipolar. In addition to tracking
mood states, people tend to track things like sleep, activity levels, social interactions,
and spending using everything from consumer activity trackers, to clinician-provided worksheets,
to DIY spreadsheets.
So, what do I mean by lived experiences? On this slide I have included a few quotes from
participants in an earlier study. We asked people with bipolar disorder how they describe
their experiences to other people. Many provided really rich statements like this:
In thinking about ways to visually encode these experiences, we were really struck by
the ways that conventional approaches to representing time series data typically used in tracking
apps don�t seem to reflect the complexities we heard in these narratives.
As Kennedy and colleagues note, ``...histories of visualization conventions matter: rooted
in Enlightenment ideas about objectivity and rational knowledge, they play a role in making
visualizations appear neutral''.
Evidence of biological rhythms has typically been shared through line graphs depicting
change over time --- these graphs are created by adapting a Cartesian coordinate system
to use the horizontal x-axis as a timeline and the vertical y-axis to indicate change
within a given variable, with points plotted on a grid to indicate measurements at regular
intervals. A solid connector is often drawn between adjacent plot points, creating an
impression of seamless transition and ordered continuity.
This graphic system reflects a normative geometry and a normative temporality that visually
privileges a structured, regular periodicity to biological rhythms. The connecting line
refers to a causal and logical relationship between the readings taken at two adjacent
points in time. In this way, a visualization of change over time supports the argument
that even when variations in the periodicity, frequency, or signal strength of rhythms are
noisy, the regular ``beat'' of an organism's internal clock is visible.
In the process of representing change over time, outliers are minimized, and differences
are smoothed, reinforcing notions of temporal predictability and the possibility of knowing
what will happen next. Data visualizations in many self-tracking tools draw on these
normative (i.e., regular, healthy, desirable, attainable, true) standards as a basis for
displaying health and wellness data in familiar mountains and troughs.
The critical visual analysis that I conducted compared these standard visualizations with
drawings representing personal experiences with biological rhythms created by individuals
diagnosed with bipolar disorder.
The full visual elicitation methodology deployed to collect these data is described in detail
in our 2019 CHI paper.
So, now I will review the two drawing activities that produced the data for the visual analysis�.
All drawing activities used an iPad pro and apple stylus. Audio and screen recordings
were captured. To protect the identity of participants, all names are pseudonyms and
drawing annotations have been de-identified in any report of findings.
For the first activity, participants drew a single line to represent their experiences
with BD. They were then asked to add a second line to represent someone close to them during
that time. Last, participants added a third line that represented what they would have
considered an ideal state during that period.
For the second activity, participants created a more detailed timeline, or a diagram showing
a sequence of events in their life and how they relate to each other. Participants were
encouraged to freely interpret this instruction and were reminded that there were no wrong
responses.
For analysis of 14 design session transcripts and approximately 35 hand-drawn annotated
images. I followed Rose's framework for critical visual analysis, focusing on the four sites
at which the meanings of an image are made: sites of Production, Image, Circulation, and
Audience
The first phase of analysis surfaced insights regarding sites of
Production, referring yo the means by which participants identified their current state,
Circulation, how these self assessments were externalized or made accessible and verifiable,
and Audiences--with whom these representations
were shared and why. Full details of findings related to these
sites can be found in the paper.
Here, I highlight the compositional analysis of the fourth site: the images themselves
LINES Participants noted as they worked that the
relatively straightforward geometry of the line chart made it difficult to be satisfied
with their drawing. As they clarified specific aspects of what the line signified (i.e.,
Did it represent a specific period of time, or was it intended to show a general trend
or pattern? ) they adapted their lines to more explicitly reflect their experiences.
For example, Andrew arrived at the solution of adding a series of circular bubbles hanging
off his line from below, like ripe fruit growing from a branch, to convey passages of time
that just seemed to take up more space than others. Stephanie looped a line around a horizontal
axis to show that daily life without episodes was not without bumps and turns, but was different
from the more acute and irregular peaks and troughs of BD.
Sarah used a patchwork of red and green to indicate a series of mixed state episodes
during a particularly complicated period of time.
Similarly, for Eleanor red indicated a lack of self-awareness, while green represented
not only intellectual understanding but a synthesis and transformative understanding
of what having bipolar disorder means for her. Major events (represented by circles)
were characterized by a mix of confusion and insight, gradually over time giving way to
greater understanding and relative stability (decreased red and increased green).
Slope, apex, and nadir Typically, a rising line indicates increased
values while a falling line represents decreased values. For many participants, interpretations
of slope (i.e., rate of change), apex (i.e., highest point experienced), and nadir (i.e.,
lowest point experienced) did not precisely map to these conventions. Although manic episodes
were initially drawn as peaks, some individuals struggled to explain the exact state at the
top of a curve because that period was simply irreconcilable with ``normal'' life. In these
cases, the continuity of the line contrasted sharply with a lived experience. Jack
suggested that perhaps it would be more accurate to make an annotation, ``Thar be dragons,''
referring to the tradition of marking unknown and possibly dangerous territory on maps.
Doug also commented on the difficulty of pinpointing a ``starting point'' from which to orient
a slope because, in hindsight, the appearance of symptoms often predated explicit crisis
or maladaptive behavior.
Thomas also noted that transitions, marked in many of the drawings by slope, often mapped
to periods of great uncertainty and shifting points of reference, depicted as microfluctations
along an otherwise smooth slope.
Both Doug and Emily also depicted a feeling of discontinuity caused by rapidly shifting
states of orientation. They both drew disconnected plateaus and however, they each expressed
dissatisfaction about their ability to represent what they were trying to communicate.
Nonlinear alternatives While some participants adapted and modified
conventional line charts, others abandoned linear approaches for representing time altogether.
In creating and explaining her drawing, Emily identified with the ideas of spread and growth,
and with certain edges or parts of an organism hardening over time, but still having the
possibility of expansion in any direction.
Mae similarly explained her mandala-like timeline ``I think of my life as kind of a circle as
opposed to a timeline because I feel like a lot of things come back and my moods, they
cycle and everything kind of just feels like it's going around and around.''
DISCUSSION
The ways that participants adapted visualization conventions in order to express and depict
their experiences surfaced opportunities for thinking more inclusively about visual encodings
for personal data visualizations. Participatory design methods, like those used in this study,
are a start. However, it is also important to challenge formal data and design literacies.
Viegas and Wattenberg used the term vernacular visualization to draw attention to differences
between the activities of expert visualization researchers who design systems to support
specialized analytic tasks, and non-experts who create visualizations using tools at hand
to accomplish highly situated and personal tasks. A vernacular designer is not an apprentice
who is aspiring to attain expertise in a professional domain; the vernacular designer is already
an expert in his or her own local context and situated needs. In vernacular contexts,
individuals sometimes choose to make design choices that differ in form or format from
those vetted by experts. In this sense, the person doing vernacular visualization is not
striving to be a data scientist or graphic designer and falling short or failing. This
individual is responding to needs within their own environment and using visualization in
order to address these needs.
In this sense, the drawings discussed here are also examples of everyday adaptive design,
an important area of HCI research that acknowledges and values the ways in which people alter,
tailor, appropriate, and manipulate materials and practices in order to meet localized needs.
Everyday adaptions highlight the ways that design practices exist beyond the boundaries
of a lab or a studio. In this study, we show that even those untrained in quantitative
analysis and unfamiliar with data science are able to engage with subtle aspects of
data representation based on their personal experiences. These drawings contribute to
emerging insights about what it means to be data literate in
the world today.