Sleeping With Technology Christel De Maeyer Personal Informatics, a solution for behavior change in preventive healthcare?

Transcription

1 Sleeping With Technology Christel De Maeyer Personal Informatics, a solution for behavior change in preventive healthcare?

2 Overview Personal Informatics? Behavior Design? Overview of tools I research Research Question How? Fogg smat model and Behavior Grid Theory in real world practise Results

3 Personal Informatics A set of tools to map behavior Aim to change behavior Biggest market today lifestyle, wellbeing, health, mobile health apps in % US is tracking exercise, diets, weight 33 % tracks other aspects, sleep patterns, blood pressure, blood sugar 27 % Internet users track health data online 9 % Signed up for text messaging health alerts Swann, 2013

4 Connecting Anything Anyone Anytime Body Area Networks

5 Self-Tracking Apps Eatery App Lift App Runkeeper App

6 Self-Tracking Devices

7 Self-Tracking Devices Dashboards To much handling on the armband - discontinued To much detail, not able to go beyond the QS people

8 Fig.&23&Health&2050:&An&expanded&concept&of&health&and&health&care&(Swan,&,2012,&p95).&

9 Behavior Design Persuasive technology Behavior design Target audience is central starting from habits-behavior Triggers Hot and Cold Ability -Simplicity Three core motivators

10 Model for understanding persuasive side of devices and software application Simplicity factors depending on person and context to create more ability, the more ability the more motivation - Time (planning, time available) - Money - Braincycles(cognitive effort) - Physical effort (planning of physical activity) - Social acceptance/out of the ordinary - Non-Routine Fogg, 2008

11 3 Core Motivators each 2 facets Fogg, 2008

12 Measuring Behavior and Behavioral Change Fogg, 2010, Behavior Grid and Wizzard

13 None technical example on ability Stop Smoking Black Path Behavior In the home it is hard to do: - Smoking in his room or certain designated areas is forbidden, no ability. - It asks a physical effort to go to the smoking room for him. - The smoking room is like gas chamber, lots of smoke, not appealing, out of the ordinary for him. - But most of all, he feels much better now! More air to breath and no coughing anymore. And this in such a short time. This feel good feeling will make his stop smoking so easy! Simplicity factors depending on person and context to create more ability, the more ability the more motivation In this case we want to lower the ability - Physical effort - Social acceptance - Out of the ordinary Dad, 88, is smoking all his life! He is one month in a senior home, and stopped smoking!

14 Hot and Cold triggers Put HOT triggers in the path of motivated people

15 Is self-monitoring creatingenough awareness andpersuasiontoget a balancedlifestyle? Will itbringa generalwellbeingwith self-monitoredpeople? Will it create more engagement with people in need?

16 Behavior change through technology or Persuasive Technology Measuring: Calorie burning Food Intake Steps Physical Activity Sleep Body Media armband as a measuring device

17 Sampling Gender Age Culture Lifestyle

18 Mid-Long Period Research Phase I Fall tracking October - November Phase II Silent Period December -February Phase III Spring tracking March- April

19 Taxonomy of Self-Monitoring Data interpretation Goal setting Coaching/feedback loop Selfmonitoring process Adapted taxonomy Personal Coaching Ananthanarayan, Siek, 2012 Fogg, 2007

20 Findings Phase II Winter 8 out of 10 experimenters stop self-tracking Phase I Fall core motivators - Sensation: pleasure, new, curiosity - Anticipation: hope, trigger to change something - Social cohesion: being part of something cool, group feeling Device created dependencies: without device = Back to old routines, less awareness Phase III Spring core motivators turn into negative behavior with some participants: Sensation: new & curiosity gone Anticipation: confrontation with data Social cohesion: rejection, outside world reactions Phase III 2 women dropped out Data frustration- Problems with the device One only tracked herself for 20 % ignorable data Men more consistent focus on sleep and activity Women focus on weight loss, more difficult to do

21 Findings Dot and Span Behavior Ability factors/context factors: (Money, Time, Brain Cycles, Physical Effort, None-Routine, Out of the Ordinary) Device: Fall: feels comfortable, identifies with it, but first signs of Out of the ordinary Spring: doing good = takes time, less comfort in long time wearing, outsiders remarks Out of the ordinary

22 Findings Dot and Span Behavior Behavior: Fall: small changes, creating routines instructed, brain cycles (cognitive investment) made Spring: due to stop, routine not established, lack of maintenance in habit forming and cognitive effort

23 Findings Dot and Span Behavior Culture and season: Culture: SF vibrant city, nature aspects, attractive to be active (not representative for US!). BE city vsrural area, latter more attractive Season: SF weather is a trigger to go outside. BE seasonal effect, weather dependency high, less activity

24 Conclusion Facilitate (triggers and higher the ability, instead of motivate behavior change) Simplicity stimulates behavior change Help people what they already want to do or what they already do, and make it simple It has to be very easy! (LarkLife Body Media) Zeo Bedside Model versus Zeo Mobile Mainstream? Intuitive, discrete, second skin, less expensive, seamless synchronizing process, data presentation, there needs to be a need! For wearablesto succeed, they need to infer/deduce and predict, not dictate and prescribe. (Fogg, 2014)

25 Possible scenario? Jan Health behavior temperament Self-monitoring Self-reporting Long lasting lifestyle changes Therapy adherence Jan Care Path Call to action Information Call to action Information Peer support Community Care Virtual continuous Coaching Sharing Integration Tools Using Real-time Data to support Integrated Care Goals Analytics/data reasoning Web-based participatory Platform Data Filtering Data Storage e-health

26 Thanks Christel De Maeyer Quantified Self Co-organizer, member Persuasive Tech Lab, Stanford University, Research Lector Artevelde