This document discusses using data science and digital technologies to better understand and influence human behavior change. It explores how collecting smartphone and user data can provide insights into behaviors like smoking cessation. Recommendation systems aim to tailor automated support to individuals, but challenges include a "cold start" period with little initial user data and ensuring interventions are grounded in behavioral theory rather than just predictive accuracy alone. Ongoing evaluation is also needed to test whether technologies truly enhance engagement and drive behavior change as intended.

1. In digital health
#wwdh @neal_Lathia
DATA
SCIENCE

2. SOCIAL
SCIENCE
DATA
SCIENCE
HUMAN
COMPUTER
INTERACTION
BEHAVIOUR
CHANGE?

3. UNDERSTAND AUTOMATE
DESIGN
BEHAVIOUR
CHANGE?

4. HOW DOES
BEHAVIOUR
CHANGE?
HOW COULD
TECHNLOGY
INTERACT WITH
PEOPLE?
HOW Do PEOPLE
INTERACT WITH
TECHNLOGY?
BEHAVIOUR
CHANGE?

5. DATA
SCIENCE
HUMAN
COMPUTER
INTERACTION
DIGITAL
BEHAVIOUR
CHANGE

6. MAKING
CHOICES
CASE 1: memory & choice (NOT HEALTH)

7. “Psychologists have recognized for many
years that humans have a limited capacity to
store current information in memory.”
- “Information Overload” on Wikipedia

8. SURROUNDED BY CHOICES

9. AUTOMATED BY RECOMMENDATION
- Neal's slides during his PhD

10. AUTOMATED BY RECOMMENDATION
- Neal's slides during his PhD
Navigating choice ~
Predicting missing data
Ranking on predictions

11. AUTOMATED BY RECOMMENDATION
- Neal's slides during his PhD
No “framework”
No “item” context
No theory/categorisation
Simplistic assumption
No uniformity
1000 outcomes for 1000 people

12. USES BEHAVIOURAL THEORY
Online Recommendations
EXPLAINS THE BEHAVIOUR
ALWAYS GETS IT RIGHT
AUTOMATED PROCESS
ENHANCES ENGAGEMENT
CHANGES BEHAVIOUR
NO
NO / BADLY
NO
YES
YES
YES

13. USES BEHAVIOURAL THEORY
EXPLAINS THE BEHAVIOUR
ALWAYS GETS IT RIGHT
AUTOMATED PROCESS
ENHANCES ENGAGEMENT
CHANGES BEHAVIOUR
NO
NO
NO
YES
YES
YES
DOMAIN
KNOWLEDGE
DATA
SCIENCE
BOTH
Online Recommendations

14. “Your decades of specialist knowledge are not
only useless, they're actually unhelpful; your
sophisticated techniques are worse than
generic methods; The algorithms tell you
what's important and what's not...”
- @jeremyphoward (Interview)

15. “...You might ask why those things are
important, but I think that's less interesting.
You end up with a predictive model that
works.”
- @jeremyphoward (Interview)

16. SOCIAL SCIENCE...?

17. WHAT SMARTPHONES CAN
SENSE THEMSELVES
What SMARTPHONES CAN
PROMPT YOU TO TELL
The Emotion Sense Platform:
Location, mobility, sociability, physical activity
Mood, symptoms, assessments

19. QUITTING
SMOKING
CASE 2: Automating support

20. YOUR SMOKING BEHAVIOUR
Smoking Cessation – Ideal
+ “ReCOMMENDED” SUPPORT
= BEHAVIOUR CHANGE

21. YOUR SMOKING BEHAVIOUR
Smoking Cessation – Ideal
+ “RECOMMENDED” SUPPORT
= BEHAVIOUR CHANGE
NO DATA ON THE “USER”
WHAT IS THE “ITEM?”
NOT POSSIBLE?

22. “Cold start is a potential problem in
computer-based information systems (...WHERE..)
the system cannot draw any inferences for
users (or items) about which it has not yet
gathered sufficient information.”
- “Cold Start” on Wikipedia

23. - “Cold Start” on Wikipedia
“Cold start is a potential problem in
computer-based information systems (...WHERE..)
the system cannot draw any inferences for
users (or items) about which it has not yet
gathered sufficient information.”
And beyond: in a given health
domain, what information
should we (can we) collect?

24. HEALTH
/SOCIAL
SCIENCE
DATA
SCIENCE
HUMAN
COMPUTER
INTERACTION
DIGITAL
BEHAVIOUR
CHANGE
Cold start

25. “cue-induced cravings: intense, episodic cravings
typically provoked by situational cues
associated with drug use (...) smokers exposed
to smoking-related cues demonstrate
increased craving (...).”
- Ferguson, Shiffman. The relevance and treatment
of cue-induced cravings in tobacco dependence. In J
Subst Abuse Treat. April 2009.

26. “cue-induced cravings: intense, episodic cravings
typically provoked by situational cues
associated with drug use (...) smokers exposed
to smoking-related cues demonstrate
increased craving (...).”
- Ferguson, Shiffman. The relevance and treatment
of cue-induced cravings in tobacco dependence. In J
Subst Abuse Treat. April 2009.
Situation: mood, craving,
location, social setting

28. Your location + your profile = tailored support
EXAMPLE

29. USES BEHAVIOURAL THEORY
EXPLAINS THE BEHAVIOUR
ALWAYS GETS IT RIGHT
AUTOMATED PROCESS
ENHANCES ENGAGEMENT
CHANGES BEHAVIOUR
YES
NO
NO
YES
YES?
YES?
Smoking Cessation
YES
(BUT what DATA!)

31. GOING
FORWARD
AND FINALLY:

32. UNDERSTAND IMPLEMENT EVALUATE
Design
Automate
HYPOTHESIS
Linear/hypothesis driven research: good for
publication, bad for software.

33. MONITOR LEARN
DELIVER
N. Lathia et. al. In IEEE Pervasive Computing. 2013.
SOFTWARE IS NEVER FINISHED...
... IT IS UPDATED.
HYPOTHESIS

34. UNDERSTAND AUTOMATE
DESIGN
BEHAVIOUR
CHANGE?

35. SCHIZOPHRENIA
ANXIETY
MOOD ADJUSTMENT
ANTI-SOCIAL PERSONALITY
ON/oFFLINE MOOD EXPRESSION
FREEMIUM
Code: http://emotionsense.github.io/

36. In digital health
#wwdh @neal_Lathia
DATA
SCIENCE