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