FROM INDULGING THE QUANTIFIED SELVES TO IMPROVING THE LIVES OF THE FITBIT-FREE MASSESUsing data as a force for good

Stuart BowdenBehaviour change expertCEO MEC

• Founding member of the Institute of Practitioners in Advertising’s (IPA) Behavioural Economics Task Force

• A decade of consulting and working on government projects including the Department of Health, Electoral Commission, Transport for London and The British Armed Forces

STUART BOWDEN, BEHAVIOUR CHANGE EXPERT

• Champion for behavioural economics in practice and has spoken at The Cabinet Office and Whitehall.

• CEO of MEC, voted the UK’s media agency of the decade (2010).

It is time to move away from indulging the quantified selves to use passive data to transform the health of the fitbit-free masses.

What is the scale of the opportunity that passive data provides to improve the health of the masses?

Which data owners do governments need to engage in order to reap the benefits of mass passive data-mining?

At what point does using data to improve people’s health turn a nation into a big brother state?

Helping the disengaged masses

Data gathered from wearable tech users is only a small part of the whole story – what about the vast majority of people who can’t afford wearables or simply aren’t engaging with them?

Decisions are being made based on people who have the least need but are the most visible because they are tech enabled and engaging.

How can we use open, passive data to actively move those who are disengaged to make and sustain positive changes to their health?

MERGING GOOGLE SEARCH AND UK GOVERNMENT DATA TO MAP THE PREVALANCE OF UNHEALTHY BEHAVIOURS AND THE APPETITE TO CHANGE

WORKING WITH UCL* TO MINE THE SENTIMENT AND EMOTIONS OF SOCIAL DATA THROUGHOUT THE SMOKING QUIT JOURNEY

* University College London

PASSIVE DATA UNVEILS THE EMOTIONS EXPERIENCED THROUGHOUT THE QUITTING JOURNEY…

Symptom Week 1 Week 2 Week 3 Week 4 Week 5 post

Craving 1139 247 195 81 25 32

Stress 549 105 61 33 10 17

Constipation 416 125 61 69 22 50

Annoy 300 33 14 22 3 8

Anxious 253 89 25 31 7 11

Hungry 177 40 15 19 5 5

Sick 158 44 17 18 5 4

Depress 128 20 22 9 5 15

Headache 36 7 3 5 5 0

Concern 17 6 3 2 0 0

Diarrhoea 12 3 0 0 0 0

…AND DETERMINES EXACTLY THE SYMPTOMS WHICH CREATE THESE EMOTIONS

MESSAGES CAN TARGET QUITTERS ACCURATELY BASED ON THEIR EMOTIONS AND SENTIMENT EXPRESSED ON SOCIAL MEDIA

Emotional/sentiment analysis during Stoptober with University College London

CREATING REAL TIME RESPONSE IN SOCIAL SPACES TO ANSWER HEALTH QUERIES INSTANTLY

REAL TIME RESPONSE TO HEALTH ENQUIRIES ON SOCIAL SPACES IS WELCOMED BY THE VAST MAJORITY

A total of 137 Twitter responses attracted a

direct response from the recipient

Positive feedback far outweighed negative comments

Negative6%

Positive94%

WE NEED TO BE MORE FRONT-FOOTED IN THE WAY WE USE

DATA TO DRIVE POSITIVE OUTCOMES FOR THOSE WHO

AREN’T ENGAGING.