These are the slides for the Faculty Fellow short talk on October 27, 2016, at the Alan Turing Institute. In this talk, I summarise my approach to missing data analysis, and explain how my work fits into an interdisciplinary context. I will add a link to the YouTube recording once it has been uploaded.

1. partially joint work with Henry Potts and Katarzyna Stawarz (Turing Small Group), partially with the Help4Mood
Consortium
A Process View of
Missing Data
Maria Wolters
Reader in Design Informatics &
Faculty Fellow
University of Edinburgh
maria.wolters@ed.ac.uk
@mariawolters
http://www.slideshare.net/mariawolters

2. Overview
❖ Motivation: Help4Mood
❖ Example: Activity Trackers
❖ Collaborating across the Turing

3. Missing Data
❖ informally: observations that we would like to be there,
or that should be there, but that are not
❖ Statistical treatment differs according to whether
missing data are
❖ completely random (MCAR)
❖ predictable from existing data (MAR)
❖ not predictable from existing data (MNAR)

4. Goal
❖ To investigate why data goes missing (aka data
generation processes)
❖ qualitatively for deeper understanding
❖ quantitatively to feed into data analysis and
visualisation

5. How I Work
❖ Stage 1: map relevant aspects of data generation and
use, from multi disciplinary perspective
❖ Stage 2: drastically reduce complexity in collaboration
with statisticians and data scientists to create an
interesting model that converges

6. The Appropriation of Help4Mood,
or: Why Data Generation Processes?

7. depressioncomix.tumblr.com
Depression is a
change
relative to an
individual
baseline

8. Help4Mood: Supporting People with Depression
• daily monitoring
• of activity using
actigraph
• of mood, thought
patterns & psycho-
motor symptoms using
talking head GUI
• weekly one-page reports
to clinicians
Maria K. Wolters, Juan Martínez-Miranda, Soraya Estevez, Helen F. Hastie, Colin Matheson (2013). Managing Data in Help4Mood AMSYS ICST DOI: 10.4108/trans.amsys.
01-06.2013.e2

9. Pilot Randomised Controlled Trial
❖ Participants with Major Depressive Disorder (SCID
diagnosed)
❖ Use Help4Mood for 4 weeks every day
❖ Background measures include demographics and
attitudes to computers
❖ (Pre/Post measures to establish change)
❖ Qualitative interviews at intake and debriefing for those
randomized to Help4Mood

10. Usage Patterns during pilot RCT
❖ 18 in Romania, 7 in Scotland, 2 in Spain (EU Project)
❖ 14 treatment as usual (age 42 years +/- 10), 13 Help4Mood
(age 35 +/- 12)
❖ None formally tracked or measured their mood before, but
some used introspection
❖ Half used it regularly, but that was not daily; instead, it was
2-3 times per week. Why?
Appropriation: Users tweak technology to fit their needs,
departing from initial design
cf Dix, Alan (2007): Designing for Appropriation. In Proc. BCS HCI Group, (pp. 27-30)

11. Participants Used Help4Mood to Cope With
and Make Sense of Their Illness
The monitoring part helped me understand some things [. . .]
sometimes I did not realize how I felt that day, how happy I was
or how active I was. The system helped me observe these
things and also control them.
(RO14, female, 20–29)

12. The missing data (quantitative) alerted us to an
appropriation process
that we were able to describe and understand
through qualitative work
and that then changed how we would have
designed Help4Mood II
(had we gotten funding …)

13. Activity Tracking:
Example of Stage 1
From Summer Small Group work

14. TRACKER
when
who job (e.g. nurses)
allergies
wrist anatomy
forgetting
to wear to bringto charge
worried well
techy
motivated for change
not tracking
during lazy days
device
breaks
no longer holds charge
lost / stolen
what
swimming
weightlifting
team sports
no Internet
style / fashion
stigma
self-report
effort
not synching
properly
if there is a need
e.g.,Rooksby, J., Rost, M., Morrison, A., and Chalmers, M. C., (2014). Personal tracking as lived informatics. In Proc. CHI ’14 (pp. 1163–1172)
Lazar, A., Koehler, C., Tanenbaum, J., & Nguyen, D. H. (2015). Why we use and abandon smart devices. In Proc. UbiComp ’15 (pp. 635–646).

15. TRACKER
when
who
forgetting is a function of
- user characteristics
- illness
- external stress
fit with identity
device
what
detailed user modeldevice model
connectivity model
fit with activities
effort required
to track activity
fit with ulterior need
(why tracking?)
All except for ulterior need and identity amenable to formal
/ quantitative modelling from appropriate discipline.

16. http://thoughtstipsandtales.com/2014/11/06/fitbit-fun-ten-months-later/
http://thoughtstipsandtales.com/2015/03/05/fabulous-fitbit-accessory-to-keep-the-clasp-from-opening/

17. Working Across the Turing
Institute

18. Formal Collaborations
❖ Richard Dobson, Jacky Pallas, and the RADAR CNS
team with Mirco Musolesi, UCL Fellow (role: user
centred design). Intel funding
❖ Jon Crowcroft and his Turing PhD students, looking at
data generation processes in wearables. Revise and
resubmit, for use of HAT data

19. Paper And Grant Writing Groups
❖ Scott Hale, Faculty Fellow, OII, YouthNet summer
internship programme work
❖ Henry Potts, UCL (Farr; no Turing affiliation)
❖ Farr/Turing working group on health care data

20. Come talk to me!
❖ Turing Calendar: mariawolters.net/calendar/
availability
❖ maria.wolters@ed.ac.uk / @mariawolters
❖ Setting up Turing Azure RStudio server collaboration
space
❖ Preferred coffee: espresso macchiato / cortado