10 Myths of Wearable Adoption

10 Wearable Adoption MythsA discussion on misconceptions in wearables

that are slowing the road to mass adoption.

One quarter innovator, one quarter provocateur,

and the remaining; iconoclast.

3 20 50 80/

50/50

80% 20%

A

KodakMoments

ZuneMoments

VS

80% 20%

B

BigPharma

MedTechnology

VS

50% 50%

C

DigitalTransformation

DigitalDisruption

VS

80% of

everything is crap

80% of

tipping is business

80% 20%

01

Input DevicesListening to the Body

Output DevicesMaking me Smarter

VS

80% of

sensors not invented

80% 20%

02

ImmersiveAnd Episodic

PassiveMonitoring

VS

80% of

activity is episodic

The pharmacy is poised to be

the largest device but the

smallest hospital

5K 60K .5MHOSPITALS PHARMACIES TELLER MACHINES

80% 20%

03

PreventativeHealth Model

DigitalHealth

VS

80% of

sickness is hedonism

20% 80%

04

MediaAnd Content

PersonalizationAnd Context

VS

80% of

clues are still missed

80% 20%

05

RemovingRetail Friction

PersonalizedOffers and Marketing

VS

80% of

marketing is wasted

50% 50%

06

SmarterFitness

ImprovedLifestyle

VS

Those who care about fitness

care, those who don’t, don’t.

80% 20%

07

InternetOf Things

WearableDevices

VS

20% 80%

08

TechnologyInnovation

Augmented Humanity

VS

80% of

humans are lazy

50% 50%

09

Fashion canSolve for Vanity

Design can SolveFor Most Laziness

VS

80% of

design is still UX, 20%

is function

80% 20%

10

CausingHypochondria

FixingHedonism

VS

80% of

sickness can be solved with

diet and exercise

80% of

everything is crap

Products do not tip

experiences and outcomes do

80% of

tipping is business

model innovation

A

KodakMoments

ZuneMoments

VS

20% of

tipping is from

product innovation

Streams of data

Single Point of Time (SPOT) Measure vs. Stream of Imformation

State of Sensor Innovation

On

colo

gy

Dig

esti

ve

Car

dio

logy

Res

pir

ato

ry

Hospital Doctor Lab-in-a-box Wearable

Stream x Year Month Month Day

Stream x x Day Year Month Month

Stream x Month Day Minute Second

Stream x x Year Year Month Year

Stream x x Day Day Day day

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