Following a conversation with a research project on bipolar disorder, I explored how visualising data could empower non-technical users to better manage their mental health.
Type
Product / UX Design
Role
Sole Designer
Tools
Figma, Illustrator, Premier Pro
By collecting data on how we use digital devices, researchers have confirmed causality between certain patterns of usage and bipolar or depressive episodes.
An example of one of the metrics is:
When in a stress response, the brain shifts into survival mode, heavily filtering incoming information. In these situations, what usually makes sense can become overwhelming.
Similarly, during a bipolar episode, individuals often struggle to rationalise negative thoughts and feelings, and loose the ability to put them in perspective.
After depressive episodes, accurately recalling the number and intensity of past events can also be challenging.
Taking a moment to pause, breathe, and concentrate on the environment can be a key strategy for tackling the body's stress response and preventing a downward spiralling of mood.
Recognising that a downturn in mood could be momentary or situational might help users regain control more quickly.
Could additional touch points help users reflect on their mental health without needing to open an app?
Researchers have been correlating user interactions with bi-polar and depressive moods since 2015.
To create a visualisation that could reflect current and past moods, I propose mapping psychological data onto a two axis chart - with positive / negative feelings along an x-axis, and emotional stability plotted along a y-axis.
As a proof of concept, I placed a colour wheel over the graph. Although there are exceptions, red is widely associated with a state of alarm, anger, and possibly frustration - so I calibrated the colour space with red in the 'Negative Stable' quadrant.
Usability testing will be needed to discover how the visualisation is instinctively interpreted by users.
Because our moods naturally shift throughout the day, data points would appear scattered across the graph. By clustering the most frequent points into a heat map, prominent moods would form distinct ‘zones’ on the graph.
To generate a visualisation - the central value of each zone provides a colour, with the proportional space on the final screen determined by the sample size of each zone.
Two examples of how this might look are shown below:
The speed at which the colour gradients move around the screen would be calculated by the relative intensity and stability of the emotional state.
Opening the app would take users directly to a visualisation of their current mood.
From here users could change the date analysis window, improve the data via surveys or brain games, get information about the displayed visualisation and their data, and access settings and permissions.
As seen above, the user interface for the watch and widgets would correspond to those devices. For the watch, it could be possible to change the data range and to receive a short explanation of the data. A home screen widget would show the current mood and link to the app.
