In digital health#wwdh @neal_Lathia

DATASCIENCE

SOCIAL

SCIENCE

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

BEHAVIOUR

CHANGE?

UNDERSTAND AUTOMATE

DESIGN

BEHAVIOUR

CHANGE?

HOW DOES

BEHAVIOUR

CHANGE?

HOW COULD

TECHNLOGY

INTERACT WITH

PEOPLE?

HOW Do PEOPLE

INTERACT WITH

TECHNLOGY?

BEHAVIOUR

CHANGE?

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

DIGITAL

BEHAVIOUR

CHANGE

MAKING

CHOICES

CASE 1: memory & choice (NOT HEALTH)

“Psychologists have recognized for many

years that humans have a limited capacity to

store current information in memory.”- “Information Overload” on Wikipedia

SURROUNDED BY CHOICES

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

Navigating choice ~Predicting missing dataRanking on predictions

AUTOMATED BY RECOMMENDATION

- Neal's slides during his PhD

No “framework”No “item” context

No theory/categorisationSimplistic assumption

No uniformity1000 outcomes for 1000 people

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

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

“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)

“...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)

SOCIAL SCIENCE...?

WHAT SMARTPHONES CAN

SENSE THEMSELVES

What SMARTPHONES CAN

PROMPT YOU TO TELL

The Emotion Sense Platform:

Location, mobility, sociability, physical activity

Mood, symptoms, assessments

QUITTING

SMOKING

CASE 2: Automating support

YOUR SMOKING BEHAVIOUR

Smoking Cessation – Ideal

+ “ReCOMMENDED” SUPPORT

= BEHAVIOUR CHANGE

YOUR SMOKING BEHAVIOUR

Smoking Cessation – Ideal

+ “RECOMMENDED” SUPPORT

= BEHAVIOUR CHANGE

NO DATA ON THE “USER”

WHAT IS THE “ITEM?”

NOT POSSIBLE?

“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

- “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?

HEALTH

/SOCIAL

SCIENCE

DATA

SCIENCE

HUMAN

COMPUTER

INTERACTION

DIGITAL

BEHAVIOUR

CHANGE

Cold start

“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 treatmentof cue-induced cravings in tobacco dependence. In JSubst Abuse Treat. April 2009.

“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 treatmentof cue-induced cravings in tobacco dependence. In JSubst Abuse Treat. April 2009.

Situation: mood, craving,location, social setting

Your location + your profile = tailored support

EXAMPLE

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!)

GOING

FORWARD

AND FINALLY:

UNDERSTAND IMPLEMENT EVALUATEDesignAutomate

HYPOTHESIS

Linear/hypothesis driven research: good forpublication, bad for software.

MONITOR LEARN

DELIVER

N. Lathia et. al. In IEEE Pervasive Computing. 2013.

SOFTWARE IS NEVER FINISHED...

... IT IS UPDATED.

HYPOTHESIS

UNDERSTAND AUTOMATE

DESIGN

BEHAVIOUR

CHANGE?

SCHIZOPHRENIA

ANXIETY

MOOD ADJUSTMENT

ANTI-SOCIAL PERSONALITY

ON/oFFLINE MOOD EXPRESSION

FREEMIUM

Code: http://emotionsense.github.io/

In digital health#wwdh @neal_Lathia

DATASCIENCE