THE PSYCHOLOGY OF FEEDBACK

INSPIRATION FOR ASS-KICKING WEARABLES

Action Design NYC

Brian Cugelman, PhD

@cugelman

New York City30 March 2015

BRIAN CUGELMAN, PHD

2

imcba.comalterspark.com

AGENDA

• Collecting data (input)

• Data science (processing)

• Interventions (Output)

• Cooking and digital psychology

• Domain 7. Audience Feedback

3

STARTING QUESTION

What feedback can you capture,

that’s useful to your users, that can

inspire them to take positive action?

How can you add feedback-based

persuasive design principles to

boost your impact?

Mind

Body

Relations

Ambience

Collecting data (input)

•Research-based feedback•Manual data capture •Interaction-based data capture•Automated data porting •Sensor-based data capture

Data science (processing)

•Algorithms•Statistics•Correlations

Interventions(Output)

Hardware•Mobile•Wearable devices•Desktop

Software•Applications

COLLECTING DATA (INPUT)

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DATA CAPTURE BY SENSORS

• A transducer that converts a signal in one form of energy to a signal in another

• Detects changes in quantities and converts them into an output, generally an electrical or optical signal

SENSORS WITH HUNDREDS OF SUB-CATEGORIES

AND THOUSANDS OF OPTIONS

• Thermal, heat, temperature

• Acoustic, sound, vibration

• Electric current, electric potential, magnetic, radio

• Environment, weather, moisture, humidity

• Flow, fluid velocity

• Ionizing radiation, subatomic particles

• Position, angle, displacement, distance, speed, acceleration

• Optical, light, imaging, photon

• Pressure

• Force, density, level

• Proximity, presence

• Chemical

E-HEALTH SENSOR PLATFORM V2.0 (ARDUINO AND RASPBERRY PI)

1. Pulse,

2. Oxygen in blood (SPO2)

3. Airflow (breathing)

4. Body temperature

5. Electrocardiogram (ECG)

6. Glucometer

7. Galvanic skin response (GSR- sweating)

8. Blood pressure (sphygmomanometer)

9. Patient position (accelerometer)

10. Muscle/eletromyographysensor (EMG)

10 different sensors in a dev kit for about $500

DATA SCIENCE (PROCESSING)

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PROCESSING DATA

Detecting

• Mind

• Body

• Ambience

• Relations

Challenge

• Making sense of complex

multi-device signals

• Lots of data: 400 MB/day from

a mobile with millions of

records on full throttle

• Accurately detecting events

that matter to people

• Building human centered

algorithms11

INTERVENTIONS (OUTPUT)

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COOKING AND DIGITAL PSYCHOLOGY

13

INFLUENCE CAPACITY COMES FROM

PERSUASIVE INGREDIENTS BLENDED INTO

OPTIMIZED RECIPES

Pilot 2011

OPTIMIZATION: THE MIX OF INGREDIENTS THAT

ACHIEVES THE MOST IMPACT WITH THE LEAST EFFORT

15

Number of persuasive ingredients

Infl

uen

ce p

ote

nti

al

How do you know when you have too few or too many?

Too

fewToo

manyJust right

Cugelman, B., Thelwall, M., & Dawes, P. (2011). Online Interventions for Social Marketing Health

Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors.

Journal of Medical Internet Research, 13(1), e17.

INTERACTIVE INFLUENCE MODEL

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WHAT INGREDIENTS MAKE MOTIVATING

WEARABLE TECH

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Feedback loop (self-regulation)

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Compare goal to performance

(reaction)

Behavioral outcome (variable)

Set a goal(reference value)

Barriers & friction

(disturbance)

Receive feedback on performance

(input)

Perform behavior(output)

Carver, C. and M. Scheier (2005). On the structure of behavioral self-regulation. Handbook of self-regulation. M. Boekaerts, P. Pintrich and M. Zeidner. San

Diego, USA, Guilford Press.

Forget the science of attrition a moment. How

long could you realistically stay on this never ending

self-regulation loop, without falling off the

bandwagon.

PSYCHOLOGICAL ARCHITECTURE OF

HEALTH BEHAVIOR CHANGE TECHNOLOGIES

CUGELMAN, B., THELWALL, M., & DAWES, P. (2011) Online interventions for social marketing health behavior change

campaigns: A meta-analysis of psychological architectures and adherence factors. Journal of Medical Internet Research, 13(1),

e17. http://www.jmir.org/2011/1/e17/19

Eff

ect

Siz

e (

d)

Inte

rve

nti

on

s (

%)

Let’s take a realistic

approach to habit

formation with 66 avg

days and most people

abandoning your

technology after just

a few sessions.

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DOMAIN 7. AUDIENCE FEEDBACK

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SEQUENTIAL REQUESTS

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Can you sign this petition?

Can you donate $100?

Would you donate $100?

Ok, how about $10?

[P-702]

SHOPPING CART ABANDONMENT:

DOOR IN THE FACE

23Wang, D. (2014). 13 Amazing Abandoned Cart Emails (And What You Can Learn From Them).

REMINDERS AND PROMPTS

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[P-703]

Engaging at the right time (kairos)

TARGETED, PERSONALIZED, TAILOREDADAPTED FROM KREUTER ET AL. (2000)

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1.

Inp

ut

2. Output

Personalized

Targeted

Tailored

No

feedback

Lots of

feedback

Generic

Interpersonal

Generic Individualized

[P-705]

[P-706]

[P-707]

PERSONALIZATION AND RECOMMENDATION ENGINES

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Targeting [P-705]Tailoring [P-707]Personalization [P-706]

TARGETING IS LIKE TAILORING FOR A GROUP

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Group A

Group B

Group C

Group D

WHY DOES TAILORING WORK

1. Unnecessary information is eliminated

2. The information is personally relevant to the individual

3. People pay more attention to information that is personally relevant

4. People are more likely to act on information that they have pondered in-depth

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SETTING GOALS AND MAKING COMMITMENTS

(INTENTIONS)

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[P-708]

PERSONAL ACTION PLAN

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[P-709]

PERSONAL BARRIERS AND FRICTION

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Goal

Cugelman, B., M. Thelwall, et al. (2011). "Online Interventions for Social Marketing Health Behavior

Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors." Journal

of Medical Internet Research 13(1): e17.

Drivers

Friction

[P-710]

FEEDBACK ON PERFORMANCE

Cugelman, B., M. Thelwall, et al. (2011). "Online Interventions for Social Marketing Health

Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and

Adherence Factors." Journal of Medical Internet Research 13(1): e17.

[P-711]

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REINFORCEMENT

A reinforcer is anything that occurs with an act

that increases the odds of it happening again.

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Negative reinforcer Positive reinforcer

[P-712]

Something someone wantsSomething someone wants

to avoid

PUNISHING AND REWARDING

WITH DIALOGUE BOXES

Nasty Mac

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FAILURE

You have NOT reached your goal.LOSER!

You’re not yet finished. Try harder. Lazy bugger.

Never degrade, insult, or offend.

For negative feedback, focuses on positive encouragement or loss aversion.

Punishment can undermine users’ motivation, confidence, and boost the odds

that they avoid your technology like a nasty person.

[P-712 b] Nice Mac

SUCCESS

You’ve reached your goal. CONGRATULATION!!!

You’ve finished. Way to go!

[P-712 a]

DON’T CONFUSE

MOTIVATION WITH REINFORCEMENT

Before acting While acting After

Motivation(Incentives / Loss aversion)

Reinforcement(Rewards / Punishments)

Don’t confuse threats of loss aversion with punishment.

Be careful with punishment, and only use it with care.

ENDING QUESTION

What feedback can you capture,

that’s useful to your users, that can

inspire them to take positive action?

How can you add feedback-based

persuasive design principles to

boost your impact?

CLOSING

38

Thanks so much.

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@cugelman

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