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From quantified selves to the Fitbit-free masses

Claire Billings
Claire Billings

Is it time we stopped indulging the quantified selves to focus on the bigger prize of improving the health of nations? Behaviour change expert Stuart Bowden highlights how to use passive data from open sources to guide health decisions. He will illustrate how this data can be used to actively move people who are disengaged with their health to make and sustain positive change. In 20 years experience, Bowden has worked with and consulted for numerous government bodies and private sector businesses using geographical search, emotional and social response data to trigger people into action. In the session, he will discuss how the scale of this opportunity far outweighs the quantified self.

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FROM INDULGING THE QUANTIFIED SELVES TO IMPROVING THE LIVES OF THE FITBIT-FREE MASSES
STUART BOWDEN, BEHAVIOUR CHANGE EXPERT
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 peoples 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 cant afford wearables or simply arent 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
From quantified selves to the Fitbit-free masses
WORKING WITH UCL* TO MINE THE SENTIMENT AND EMOTIONS OF SOCIAL DATA THROUGHOUT THE SMOKING QUIT JOURNEY
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
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 6% 94%
From quantified selves to the Fitbit-free masses

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From quantified selves to the Fitbit-free masses

  • 1. FROM INDULGING THE QUANTIFIED SELVES TO IMPROVING THE LIVES OF THE FITBIT-FREE MASSES
  • 3. 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 peoples health turn a nation into a big brother state?
  • 4. 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 cant afford wearables or simply arent 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?
  • 5. MERGING GOOGLE SEARCH AND UK GOVERNMENT DATA TO MAP THE PREVALANCE OF UNHEALTHY BEHAVIOURS AND THE APPETITE TO CHANGE
  • 7. WORKING WITH UCL* TO MINE THE SENTIMENT AND EMOTIONS OF SOCIAL DATA THROUGHOUT THE SMOKING QUIT JOURNEY
  • 8. PASSIVE DATA UNVEILS THE EMOTIONS EXPERIENCED THROUGHOUT THE QUITTING JOURNEY
  • 9. 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
  • 10. MESSAGES CAN TARGET QUITTERS ACCURATELY BASED ON THEIR EMOTIONS AND SENTIMENT EXPRESSED ON SOCIAL MEDIA
  • 11. CREATING REAL TIME RESPONSE IN SOCIAL SPACES TO ANSWER HEALTH QUERIES INSTANTLY
  • 12. REAL TIME RESPONSE TO HEALTH ENQUIRIES ON SOCIAL SPACES IS WELCOMED BY THE VAST MAJORITY 6% 94%