2017 디지털 헬스케어 컨퍼런스 리뷰 (v1)

2017 디지털 헬스케어 컨퍼런스 리뷰
Health 2.0 Annual Conference DigiMed17 Rock Health Summit
최윤섭 디지털헬스케어 연구소

소장 최윤섭
Sponsored by

?

Disclaimer
스타트업 벤처캐피털
저는 위의 회사들과 지분 관계, 자문 계약 등의 이해 관계가 있음을 밝힙니다.

Sponsors
여러 회사 및 지인 분들께서 출장 및 이번 행사의 개최에 도움을 주셨습니다

다시 한 번 감사드립니다.
?

pelexus.symﬂow.com
질문은 Symflow를 통해서 받겠습니다.

아래의 주소로 접속하셔서, ‘업플로우’에 올려주시면 됩니다.

추천을 많이 받은 질문들을 위주로 답변하겠습니다.

Advanced Technology Inc

B-LAB

frontec

KB금융지주

LB인베스트먼트

LG경제연구원

Medigate

Siemens Healthineers

SKTelecom

SPONGY GROUP

SPORTRIUM

강북삼성병원

네오젠소프트

노바티스

닥터키친

더웰스인베스트먼트

동아에스티
라이나생명

마콜커뮤니케이션컨설팅

모델라인

베스티안 재단

블루포인트파트너스

삼성경제연구소

삼성서울병원

삼성화재

삼육대학교 약학대학

서울대학교

서울대학교병원

서울의료원

세브란스병원

엠트리케어

예비창업자

와이에스바이오

울산과학기술원UNIST
유비케어

이원다이애그노믹스

인제대학교 일산백병원

인천성모병원 심장내과

인천참사랑병원

인천참사랑병원 전략기획실

인하대병원

일산백병원 순환기내과

질병관리본부

창의사업개발원

카이스트

캐시워크

프론텍

한국 MSD

한국스트라이커

한독

한솔병원

휴레이포지티브
오늘 참석하신 분들

일정
•7:00 - 7:50 1부 발표

•7:50 - 8:00 휴식시간

•8:00 - 8:10 스폰서 세션 (UCB제약)

•8:10 - 9:00 2부 발표
앉으셨던 의자는 본인이 직접 카운터 뒤쪽의 저장 공간에 넣어주시기 바랍니다. 
 
 
(별도의 행사 진행 인력이 없습니다)

•시리즈 A 펀딩 단계 스타트업의 컴페티션

•8개의 결승 진출 스타트업이 전문 멘토들 (VC, 제약사 등등)의 멘토링 이후 발표

•심판과 청중이 함께 판정하여 2팀을 시상

Klara: Healthcare Communication Made Simple
https://www.youtube.com/watch?time_continue=2&v=SgfXgk0kLiw

•HIPPA compliant 의료 전문 메시징 플랫폼

•환자가 의료진 (의사, 약사 등등)과 문자 메시지로 커뮤니케이션할 수 있음

•하나의 플랫폼에서 진료 예약, 원격 진료/처방, 검사 결과 확인, 약품 배송 등을 이용할 수 있음

•EMR 연동은 되는지 모르겠음

•국내에서는…

•B2B로는 PCP (Primary Care Physician) 들을 주 고객으로 하고 있음

•피부과 전문의가 많은 한국에서는 과연 필요한 기기일까?

•B2C로 판매하여 가정에서 보유할 필요가 있는 기기일까?

Tell us what’s up.
With your own, plain words.
https://www.slideshare.net/ForumITESSS/mediktor

Answer a few questions
about how you feel.

Chat with the best
specialist for your
case within minutes.

•SNOO Smart Sleeper

•아기의 수면을 유도하여, 부모의 수면 퀄리티를 높여준다는 컨셉의 제품

•헬스케어 제품의 중요성: 구매 의사 결정권자와 실제 사용자는 다를 수 있다.  
•엄마 자궁과 비슷한 환경을 만들어 줘서 수면을 유도

•화이트 노이즈

•흔들어주기 (크게 울수록 더 많이 흔들고, 최대 3분 흔들어서 울음이 멈추지 않으면 stop)

•감싸기 등등

•한국에도 사용자 있으며, 비슷한 컨셉의 제품이 더러 있기는 한듯

•Cedars-Sinai Accelerator

•LA의 Health System인 Cedars-Sinai 와

•테크엑셀러레이터 Techstars가 함께 만든 의료 엑셀러레이팅 프로그램

•6-8개월마다 한 기수를 운영 (특이하게도 batch라고 하지 않고, cohort 라고 부름..)

•현재 세번째 코호트 운영

•한 코호트당 수백 개의 팀이 지원하여, 그 중 10개의 팀을 선발

•선발된 팀에게 2만불을 보통주 6%에 투자하고, 10만 불을 컨버터블 노트로 제공하는 조건

•마지막에 데모데이를 통해서 졸업시키는 방식

Health 2.0 2017 Annual Conference
VC’s Talk New Trends in Investing

•어디를 가나 VC들도 매크로 트렌드에 대해서 신경을 쓰는 것은 마찬가지

•이 세션에서도 오바마케어, FDA 규제 변화, 수가 등등에 대해서 한참 이야기했다.

•(얘들이 VC 이야기는 안 할건가? 하고 느낄 정도로..)

•웨어러블, VR, 인공지능, FDA Pre-Cert 등의 주요 주제

•우리가 가지고 있는 의견과 방향성에 대한 생각과 크게 다르지 않았음.

•우리도 방향을 제대로 잡고 있다는 이야기.

•관심 있고, 찾고 있는 스타트업이 매우 specific 하다는 느낌

•미국의 VC들은 아주 많은 pool 의 스타트업 중에서 정말 고르고 고를 수 있기 때문

•하지만 한국은 pool 자체가 한정되어 있어서 결정할 수 있는 범위가 제한적인듯

•GE와 Sanofi 의 CVC 처럼 한국에도 헬스케어 분야의 CVC나 LP가 많이 나왔으면.

•B2C로 컨슈머에게 직접 파는 서비스는 B2B에 비해서 덜 선호함

•지금까지 B2C 모델을 만들기 위해서 많은 투자가 있었으나, 성공적이었던 것은 별로 없었음

•이는 최근 Rock Health Report의 내용과도 일맥상통

•기존 이해관계자를 mimic하면서 완전히 새로운 모델을 제시하는 야심있는 스타트업

•‘a new-age payer’ or ‘a new-age PBM(Pharmacy Benefit Management)’

•기존의 헬스케어 산업의 범주를 뭉개버리는 스타트업

•Oscar는 기존의 payer 역할에서 provider 까지 진출하고 있음

•23andMe 는 유전정보 분석회사에서 제약회사도 되고 있음

•Ginger.io 는 B2B 헬스케어 스타트업에서 provider도 되었음
어떠한 스타트업을 찾고 있는가?

•‘어떠한 방식으로 돌아가는지 투명하지 않거나, 이해하기 어려운 분야’

•파괴적 혁신을 만들기 좋은 분야이다.

•예를 들어, PBM의 risk 계산과 drug formulary 가 어떻게 계산되는지는 아무도 모른다.

•VR의 경우에는 좋은 투자처를 찾기가 쉽지 않다.

•VR 하드웨어 시장은 이미 너무 establish 되어 있고,

•VR 소프트웨어 시장은 아직 충분히 ubiquitous 하지 않다.

•예전에는 이와 비슷한 문제를 가지고 있었지만, 이제는 충분히 무르익은 시장: Voice

•가정에 하나씩 Alexa, Google Home을 가지게 되면서,

•헬스케어에서도 아주 흥미로운 application 등이 가능해짐
어떠한 스타트업을 찾고 있는가?

•디지털 헬스케어도 전형적인 hype cycle을 따르게 될 것

•처음에는 사람들이 흥분하는 분야이지만, 기대하는 것보다 훨씬 더 오래 걸릴 수도 있다.

•하지만 결국 많은 사람들이 예상했던 것보다, 더 큰 분야가 될 것이다.

•지금이 헬스케어 시스템의 가치를 높이는 크리티컬 시점이다.

•앞으로 M&A와 투자가 더욱 활발해지는 싸이클이 올 것이다.

•과거에 클라우드나 SAS가 그랬던 것처럼 디지털 헬스케어 분야에서도 퍼펙트 스톰이 오고 있다.

•‘Dance among the Giants’ 를 어떻게 하는가가 중요

•실리콘 밸리에서는 많은 창업이 일어나고 있지만,

•단순히 기술적인 측면이 아니라, payer, provider 사이의 관계에서 ‘춤추는 것’ 이 중요하다.

Gartner Hype Cycle  
for Emerging Technologies 2017

•어떤 회사를 찾고 있는가?

•진료 과정 전체를 virtualize 하는 회사를 찾고 있다.

•현재의 원격의료 회사들은 전체 진료에서 ‘환자가 의사를 만난다’는 아주 일부분만 virtualize하고 있다.

•B2B vs B2C 모델: ‘디지털 헬스케어에 대해서 B2C 로 성공한 스타트업은 거의 없지 않나?’

•KPCB가 투자한 Kinsa의 경우에도 모바일 체온계라는 B2C 모델이었지만, 진짜 BM은 체온 데이터이다.

•Kinsa가 CDC보다 flu를 4주 일찍 파악할 수 있다는 것이 알려지자, 많은 회사들이 이 데이터를 구매하기로 결정함

•향후 대형 기업에 의한 더 많은 M&A가 있을 것이다.

https://rockhealth.com/reports/streamlining-enterprise-sales-in-digital-health
•It’s a B2B World!

•Rock Health의 2017년 9월 발표에 따르면,

•전체 조사기업의 85%가 B2B 혹은 B2B2C의 모델을 가지고 있음

•B2C의 모델을 유지하고 있는 곳은 전체 14% 밖에 되지 않음

•특히, 처음에 B2C로 시작했던 기업 중 61%가 B2B 혹은 B2B2C로 피보팅했음.  
•그만큼 헬스케어에서는 B2C 사업 모델을 만들기가 쉽지 않다는 뜻으로 해석됨

•그렇다면, 한국에서도 미국처럼 이러한 B2B 모델을 만들 수 있는가?

https://www.youtube.com/watch?v=N8g-Dk_EBjY
Suggestic Lens - Healthy eating powered by AR.

Suggestic Lens - Healthy eating powered by AR.

Summary: Health2.0
•Traction

•KLARA (messaging)

•DermaSensor (Melanoma)

•Mediktor (Triage)

•SleepTech Summit

•SNOO (Smart Sleeper for Babies)

•Cedars-Sinai Accelerator

•VCs Talk New Trends in Investing

•Investing in Health 2.0 Technologies

•Launch!

•Suggestic (AR for nutrition)

•DigiMed17

• 스크립스 중개과학연구소(STSI)의 디지털 헬스케어 학회

• 디지털 헬스케어의 슈퍼스타 에릭 토폴 박사, 스티브 스타인허블 박사의 주도(Nat Digital Med 에디터)

• Health 2.0, Connected Health 처럼 규모가 크지는 않지만, 내실있는 행사

•DigiMed17

• 상업적인 냄새는 덜하고, 주로 연구자들 위주의 발표와 토론이 이뤄짐

• 2015년 행사에 이어서, 대학 및 기업의 유명 연구자들의 발표

• (스티브 박사님과 이야기해보니) 올해가 아마도 마지막일듯…인력 부족, 스폰서 부족 등등의 이유

the manifestations of disease by providing a
more comprehensive and nuanced view of the
experience of illness. Through the lens of the
digital phenotype, an individual’s interaction
The digital phenotype
Sachin H Jain, Brian W Powers, Jared B Hawkins & John S Brownstein
In the coming years, patient phenotypes captured to enhance health and wellness will extend to human interactions with
digital technology.
In 1982, the evolutionary biologist Richard
Dawkins introduced the concept of the
“extended phenotype”1, the idea that pheno-
types should not be limited just to biological
processes, such as protein biosynthesis or tissue
growth, but extended to include all effects that
a gene has on its environment inside or outside
ofthebodyoftheindividualorganism.Dawkins
stressed that many delineations of phenotypes
are arbitrary. Animals and humans can modify
their environments, and these modifications
andassociatedbehaviorsareexpressionsofone’s
genome and, thus, part of their extended phe-
notype. In the animal kingdom, he cites damn
buildingbybeaversasanexampleofthebeaver’s
extended phenotype1.
Aspersonaltechnologybecomesincreasingly
embedded in human lives, we think there is an
important extension of Dawkins’s theory—the
notion of a ‘digital phenotype’. Can aspects of
ourinterfacewithtechnologybesomehowdiag-
nosticand/orprognosticforcertainconditions?
Can one’s clinical data be linked and analyzed
together with online activity and behavior data
to create a unified, nuanced view of human dis-
ease?Here,wedescribetheconceptofthedigital
phenotype. Although several disparate studies
have touched on this notion, the framework for
medicine has yet to be described. We attempt to
define digital phenotype and further describe
the opportunities and challenges in incorporat-
ing these data into healthcare.
Jan. 2013
0.000
0.002
0.004
Density
0.006
July 2013 Jan. 2014 July 2014
User 1
User 2
User 3
User 4
User 5
User 6
User 7
Date
Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions
(probability density functions) are shown for seven individual users over a two-year period. Density on
the y axis highlights periods of relative activity for each user. A representative tweet from each user is
shown as an example.
npg©2015NatureAmerica,Inc.Allrightsreserved.
http://www.nature.com/nbt/journal/v33/n5/full/nbt.3223.html

“Extended Phenotype”(확장된 표현형)

wers, Jared B Hawkins & John S Brownstein
phenotypes captured to enhance health and wellness will extend to human interactions with
gist Richard
cept of the
that pheno-
o biological
esis or tissue
l effects that
de or outside
sm.Dawkins
phenotypes
can modify
odifications
sionsofone’s
tended phe-
e cites damn
fthebeaver’s
increasingly
k there is an
theory—the
n aspects of
mehowdiag-
conditions?
Jan. 2013
0.000
0.002
0.004
Density
0.006
July 2013 Jan. 2014 July 2014
User 1
User 2
User 3
User 4
User 5
User 6
User 7
Date
Figure 1 Timeline of insomnia-related tweets from representative individuals. Density distributions
(probability density functions) are shown for seven individual users over a two-year period. Density on
the y axis highlights periods of relative activity for each user. A representative tweet from each user is
shown as an example.
Your twitter knows if you cannot sleep
Timeline of insomnia-related tweets from representative individuals.
Nat. Biotech. 2015

Ginger.io: your smartphone knows
if you are depressed

Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
The correlation analysis between the features and the PHQ-9 scores revealed that 6 of the 10 features were
signiﬁcantly correlated to the scores:
• strong correlation: circadian movement, normalized entropy, location variance
• correlation: phone usage features, usage duration and usage frequency

Digital Phenotype:
Your smartphone knows if you are depressed
J Med Internet Res. 2015 Jul 15;17(7):e175.
Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the
ones with (red). (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay;TT, transition time;TD,
total distance; CM, circadian movement; NC, number of clusters; UF, usage frequency; UD, usage duration).
Figure 4. Comparison of location and usage feature statistics between participants with no symptoms of depression (blue) and the ones with (red).
Feature values are scaled between 0 and 1 for easier comparison. Boxes extend between 25th and 75th percentiles, and whiskers show the range.
Horizontal solid lines inside the boxes are medians. One, two, and three asterisks show significant differences at P<.05, P<.01, and P<.001 levels,
respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance; CM, circadian
movement; NC, number of clusters; UF, usage frequency; UD, usage duration).
Figure 5. Coefficients of correlation between location features. One, two, and three asterisks indicate significant correlation levels at P<.05, P<.01,
and P<.001, respectively (ENT, entropy; ENTN, normalized entropy; LV, location variance; HS, home stay; TT, transition time; TD, total distance;
CM, circadian movement; NC, number of clusters).
Saeb et alJOURNAL OF MEDICAL INTERNET RESEARCH
the variability of the time
the participant spent at
the location clusters
what extent the participants’
sequence of locations followed a
circadian rhythm.
home stay

Submitted 23 June 2016
Accepted 7 September 2016
Published 29 September 2016
Corresponding author
David C. Mohr,
d-mohr@northwestern.edu
Academic editor
Anthony Jorm
Additional Information and
Declarations can be found on
page 12
DOI 10.7717/peerj.2537
Copyright
2016 Saeb et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
The relationship between mobile phone
location sensor data and depressive
symptom severity
Sohrab Saeb1,2
, Emily G. Lattie1
, Stephen M. Schueller1
,
Konrad P. Kording2
and David C. Mohr1
1
Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
2
Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation,
Northwestern University, Chicago, IL, United States
ABSTRACT
Background. Smartphones offer the hope that depression can be detected using
passively collected data from the phone sensors. The aim of this study was to replicate
andextendpreviousworkusinggeographiclocation(GPS)sensorstoidentifydepressive
symptom severity.
Methods. We used a dataset collected from 48 college students over a 10-week period,
which included GPS phone sensor data and the Patient Health Questionnaire 9-item
(PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS
featureswerecalculatedovertheentirestudy,forweekdaysandweekends,andin2-week
blocks.
Results. The results of this study replicated our previous findings that a number of
GPS features, including location variance, entropy, and circadian movement, were
significantly correlated with PHQ-9 scores (r’s ranging from 0.43 to 0.46, p-values
< .05). We also found that these relationships were stronger when GPS features were
calculatedfromweekend,comparedtoweekday,data.Althoughthecorrelationbetween
baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from
baseline, correlations with the end-of-study scores remained significant regardless of the
time point used to calculate the features.
Discussion. Our findings were consistent with past research demonstrating that GPS
features may be an important and reliable predictor of depressive symptom severity.
The varying strength of these relationships on weekends and weekdays suggests the role
of weekend/weekday as a moderating variable. The finding that GPS features predict
depressive symptom severity up to 10 weeks prior to assessment suggests that GPS
features may have the potential as early warning signals of depression.
Subjects Bioinformatics, Psychiatry and Psychology, Public Health, Computational Science
Keywords Mobile phone, Depression, Depressive symptoms, Geographic locations, Students
INTRODUCTION
Depression is common and debilitating, taking an enormous toll in terms of cost,
morbidity, and mortality (Ferrari et al., 2013; Greenberg et al., 2015). The 12-month
prevalence of major depressive disorder among adults in the US is 6.9% (Kessler et al.,
2005), and an additional 2–5% have subsyndromal symptoms that warrant treatment
Saeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537

The relationship between mobile phone location sensor
data and depressive symptom severity
Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and their 95% confidence
intervals. Features indicated with stars (∗) are replicated from our previous study (Saeb et al., 2015a.). Bold values indicate
significant correlations.
Table 2 Linear correlation coefficients (r) between individual 10-week features and PHQ-9 scores, and
their 95% confidence intervals. Features indicated with stars (⇤) are replicated from our previous study
(Saeb et al., 2015a.). Bold values indicate significant correlations.
Feature Baseline (n = 46) Follow-up (n = 38) Change (n = 38)
Location variance⇤
0.29 ± 0.008 0.43 ± 0.007 0.34 ± 0.008
Circadian movement⇤
0.34 ± 0.006 0.48 ± 0.006 0.33 ± 0.009
Speed mean 0.03 ± 0.007 0.06 ± 0.005 0.04 ± 0.008
Speed variance 0.07 ± 0.007 0.06 ± 0.005 0.06 ±0.007
Total distance⇤
0.23 ± 0.004 0.18 ± 0.006 0.03 ± 0.006
Number of clusters⇤
0.38 ± 0.005 0.44 ± 0.004 0.24 ± 0.007
Entropy⇤
0.31 ± 0.007 0.46 ± 0.005 0.28 ± 0.008
Normalized entropy⇤
0.26 ± 0.007 0.44 ± 0.005 0.30 ± 0.009
Raw entropy 0.17 ± 0.009 0.22 ± 0.008 0.15 ± 0.010
Home stay⇤
0.22 ± 0.008 0.43 ± 0.005 0.30 ± 0.009
Transition time⇤
0.30 ± 0.006 0.32 ± 0.005 0.12 ± 0.009
Data analysis
We evaluated the relationship between each set of features (10-week and 2-week, each for all
days, weekends, or weekdays) and depressive symptoms severity as measured by the PHQ-9.
We used linear correlation coefficient (r) and considered p < 0.05 as the significance level.
In order to reduce the possibility that results were generated by chance, we created 1,000
bootstrap subsamples (Efron & Tibshirani, 1993) to estimate these correlation coefficientsSaeb et al. (2016), PeerJ, DOI 10.7717/peerj.2537

Table 3 Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95% confidence in-
tervals. Bold values indicate significant correlations (see ‘Data Analysis’).
Feature Weekday Weekend
Baseline (n = 46) Follow-up (n = 38) Change (n = 38) Baseline (n = 46) Follow-up (n = 38) Change (n = 38)
Location variance 0.15 ± 0.008 0.20 ± 0.008 0.22 ± 0.009 0.31 ± 0.008 0.47 ±0.007 0.39 ± 0.008
Circadian movement 0.22 ± 0.007 0.28 ± 0.008 0.25 ± 0.009 0.35 ± 0.007 0.51 ±0.006 0.36 ± 0.008
Speed mean 0.00 ± 0.008 0.06 ± 0.005 0.03 ± 0.008 0.13 ± 0.005 0.06 ± 0.006 0.05 ± 0.009
Speed variance 0.05 ± 0.008 0.07 ± 0.005 0.02 ± 0.007 0.13 ± 0.004 0.05 ± 0.006 0.10 ± 0.008
Total distance 0.20 ± 0.004 0.15 ± 0.005 0.01 ± 0.006 0.25 ± 0.004 0.20 ± 0.005 0.03 ± 0.006
Number of clusters 0.19 ± 0.006 0.25 ± 0.005 0.14 ± 0.008 0.34 ± 0.006 0.46 ±0.004 0.32 ± 0.007
Entropy 0.21 ± 0.007 0.34 ± 0.006 0.20 ± 0.009 0.30 ± 0.008 0.55 ±0.004 0.38 ± 0.008
Normalized entropy 0.21 ± 0.008 0.39 ± 0.006 0.24 ± 0.009 0.28 ± 0.008 0.54 ± 0.004 0.41 ± 0.009
Raw entropy 0.05 ± 0.008 0.04 ± 0.008 0.01 ± 0.010 0.04 ± 0.008 0.01 ± 0.008 0.03 ± 0.009
Home stay 0.19 ± 0.008 0.37 ± 0.006 0.23 ± 0.009 0.23 ± 0.007 0.50 ± 0.004 0.35 ± 0.008
Transition time 0.27 ± 0.006 0.29 ± 0.006 0.14 ± 0.010 0.36 ± 0.006 0.32 ± 0.008 0.06 ± 0.009
only normalized entropy was significantly related to the scores as a weekday feature. The
magnitude of the relationship between weekend features and PHQ-9 scores was larger than
the magnitude of the relationship between 10-week features and PHQ-9 scores. However,
given the small sample size, we were not adequately powered to test if these differences were
significant.
2-week features
Finally, we examined how 2-week GPS features obtained at different times during the study
Linear correlation coefficients (r) between individual weekend and weekday features and PHQ-9 scores, and their 95%
confidence intervals. Bold values indicate significant correlations.All of those 10-week features that were significantly
related to PHQ-9 scores (seeTable 2) were also significant when calculated from weekends, whereas only normalized
entropy was significantly related to the scores as a weekday feature

Mean temporal correlations between 2-week location features, calculated at different time points during the study, and
baseline and follow-up PHQ-9 scores.

•디지털 표현형의 대표주자, Ginger.io는 최근 사업 모델의 변화

• 병원 대상의 서비스에서 기업에 B2B2C로 서비스하는 모델로 피보팅 (BM 고민은 미국도 마찬가지)

• 페이스북, 스냅챗 등의 주요 기업의 직원에 ‘상담사가’ 정신건강 상담을 제공하는 모델

• 24시간, 수 초 이내에 상담사의 응답이 오며, 디지털 표현형은 내담자 분석의 기저에 깔리게 됨

•디지털 표현형의 대표주자, Ginger.io는 최근 사업 모델의 변화

• 이러한 B2B2C 모델은 고용주가 직원에게 건강보험을 제공하는 미국에서만 가능

• 특히 인상 깊었던 것은, 이 기업이 그 자체로 provider(의료기관)으로 발전했다는 것임

• 즉, 경쟁사들과 달리, 정신 상담 서비스를 전문적으로 원격 제공하는 병원을 설립

•디지털 표현형을 활용하여 정신 건강 서비스를 제공하는 Mindstrong

• 스마트폰의 센서, 키보드, 목소리/스피치 등을 feature 로 이용,

• 이를 기반으로 디지털 바이오마커를 추출하여, 머신러닝을 통해 질병 진단 및 모니터링 등에 활용 가능

•Mindstrong 에서 현재 진행 중인 연구들

• 스마트폰의 45가지의 키보드 및 스크롤 패턴을 기반 (문자와 스페이스 간의 지연도, 화면 내리는 패턴 등)

• 23가지의 signal processing transform을 통해, 총1,035가지의 잠재적 디지털 바이오마커 도출

• 가장 효과적인 바이오마커의 검증, 반복실험을 통해 오버피팅을 줄이기 위해 노력 중

•40명 규모의 moderate anxiety & depression 환자의 초기 연구 결과

• 디지털 바이오마커 중 많은 feature가 신경정신학적 수치와의 상관관계를 보임

• 구체적으로 275개의 feature가 어떤 것인지는 공개하지 않는듯

•인지와 관련한 gold standard test를 스마트폰의 디지털 바이오마커로 재현할 수 있는가?

• 기존의 cognition metric과 가장 상관 관계가 높은 디지털 바이오마커의 퍼포먼스

• P-value를 기준으로 유의미한 상관관계를 보이는 디지털 바이오마커가 있음

• (역시 바이오마커가 구체적으로 어떤 것인지는 공개하지 않음)

•인지능력 뿐만 아니라, 우울증의 측정에도 디지털 바이오마커가 효과적인가?

• Best biomarker와 best signature (the best set of biomarkers)가  
 
우울증 척도인 PHQ-9의 각 항목과 높은 상관관계를 보임

• 이를 기반으로 PHQ-9 점수를 예측할 수 있는지에 대해서 테스트 중
쾌감상실
무기력
정신운동성

Data-driven Healthcare에 대한 두 가지 전략
• top-down: 먼저 가설을 세우고, 그에 맞는 종류의 데이터를 모아서 검증해보자.

• bottom-up: 일단 ‘모든’ 데이터를 최대한 많이 모아 놓으면, 뭐라도 큰 게 나오겠지.

©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
NATURE BIOTECHNOLOGY ADVANCE ONLINE PUBLICATION 1
A RT I C L E S
In order to understand the basis of wellness and disease, we and
others have pursued a global and holistic approach termed ‘systems
medicine’1. The defining feature of systems medicine is the collec-
tion of diverse longitudinal data for each individual. These data sets
can be used to unravel the complexity of human biology and dis-
ease by assessing both genetic and environmental determinants of
health and their interactions. We refer to such data as personal, dense,
dynamic data clouds: personal, because each data cloud is unique to
an individual; dense, because of the high number of measurements;
and dynamic, because we monitor longitudinally. The convergence
of advances in systems medicine, big data analysis, individual meas-
urement devices, and consumer-activated social networks has led
to a vision of healthcare that is predictive, preventive, personalized,
and participatory (P4)2, also known as ‘precision medicine’. Personal,
dense, dynamic data clouds are indispensable to realizing this vision3.
The US healthcare system invests 97% of its resources on disease
care4, with little attention to wellness and disease prevention. Here
we investigate scientific wellness, which we define as a quantitative
data-informed approach to maintaining and improving health and
avoiding disease.
Several recent studies have illustrated the utility of multi-omic lon-
gitudinal data to look for signs of reversible early disease or disease
risk factors in single individuals. The dynamics of human gut and sali-
vary microbiota in response to travel abroad and enteric infection was
characterized in two individuals using daily stool and saliva samples5.
Daily multi-omic data collection from one individual over 14 months
identified signatures of respiratory infection and the onset of type 2
diabetes6. Crohn’s disease progression was tracked over many years
in one individual using regular blood and stool measurements7. Each
of these studies yielded insights into system dynamics even though
they had only one or two participants.
We report the generation and analysis of personal, dense, dynamic
data clouds for 108 individuals over the course of a 9-month study that
we call the Pioneer 100 Wellness Project (P100). Our study included
whole genome sequences; clinical tests, metabolomes, proteomes, and
microbiomes at 3-month intervals; and frequent activity measure-
ments (i.e., wearing a Fitbit). This study takes a different approach
from previous studies, in that a broad set of assays were carried out less
frequently in a (comparatively) large number of people. Furthermore,
we identified ‘actionable possibilities’ for each individual to enhance
her/his health. Risk factors that we observed in participants’ clinical
markers and genetics were used as a starting point to identify action-
able possibilities for behavioral coaching.
We report the correlations among different data types and identify
population-level changes in clinical markers. This project is the pilot
for the 100,000 (100K) person wellness project that we proposed
in 2014 (ref. 8). An increased scale of personal, dense, dynamic
data clouds in future holds the potential to improve our under-
standing of scientific wellness and delineate early warning signs for
human diseases.
RESULTS
The P100 study had four objectives. First, establish cost-efficient
procedures for generating, storing, and analyzing multiple sources
A wellness study of 108 individuals using personal,
dense, dynamic data clouds
Nathan D Price1,2,6,7, Andrew T Magis2,6, John C Earls2,6, Gustavo Glusman1 , Roie Levy1, Christopher Lausted1,
Daniel T McDonald1,5, Ulrike Kusebauch1, Christopher L Moss1, Yong Zhou1, Shizhen Qin1, Robert L Moritz1 ,
Kristin Brogaard2, Gilbert S Omenn1,3, Jennifer C Lovejoy1,2 & Leroy Hood1,4,7
Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests,
metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated
a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within
analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely
interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association
studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk
for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal
data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can
improve our understanding of health and disease, including early transitions to disease states.
1Institute for Systems Biology, Seattle, Washington, USA. 2Arivale, Seattle, Washington, USA. 3Department of Computational Medicine and Bioinformatics, University
of Michigan, Ann Arbor, Michigan, USA. 4Providence St. Joseph Health, Seattle, Washington, USA. 5Present address: University of California, San Diego, San Diego,
California, USA. 6These authors contributed equally to this work. 7These authors jointly supervised this work. Correspondence should be addressed to N.D.P.
(nathan.price@systemsbiology.org) or L.H. (lhood@systemsbiology.org).
Received 16 October 2016; accepted 11 April 2017; published online 17 July 2017; doi:10.1038/nbt.3870

NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
Intro
a
b
Round 1 Coaching sessions Round 2 Coaching sessions Round 3 Coaching sessions
Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9
Clinical labs
Cardiovascular
HDL/LDL cholesterol, triglycerides,
particle profiles, and other markers
Blood sample
Metabolomics
Xenobiotics and metabolism-related
small molecules
Blood sample
Diabetes risk
Fasting glucose, HbA1c, insulin,
and other markers
Blood sample
Inflammation
IL-6, IL-8, and other markers
Blood sample
Nutrition and toxins
Ferritin, vitamin D, glutathione, mercury,
lead, and other markers
Blood sample
Genetics
Whole genome sequence
Blood sample
Proteomics
Inflammation, cardiovascular, liver,
brain, and heart-related proteins
Blood sample
Gut microbiome
16S rRNA sequencing
Stool sample
Quantified self
Daily activity
Activity tracker
Stress
Four-point cortisol
Saliva

Pioneer 100 Wellness Project
• 108 individual
• for 9 months, at 3-month interval
• whole genome sequences
• clinical tests
• metabolites
• proteomes
• microbiomes
• frequent measurement
• activity (ﬁtbit)
actionable
possibilities
behavioural
coaching
(pilot of 100K person wellness project)

©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved. Proteomics
Genetic
traits
Microbiome
Coriobacteriia
Allergic sensitization
GH
NEMO
CD40L
REN
T PA
HSP 27
LEP
SIRT2
IL 6
FABP4
IL 1RA
EGF
VEGF
A
CSTB
BETA
NGF
PPBP(2)
PPBP
NCF2
4E
BP1
STAM
PB
SIRT2
CSF
1IL
6
FGF
21
IL
10RA
IL
18R1IL8IL7
TNFSF14
CCL20
FLT3L
CXCL10CD5HGFAXIN1
VEGFAOPGDNEROSM
APCSINHBCCRP(2)CRPCFHR1HGFAC
MBL2
SERPINC1
GC
PTGDS
ACTA2
ACTA2(2)
PDGF SUBUNIT B
Deletion Cfhr1
Inflammatory Bowel Disease
Activated Partial Thromboplastin Time
Bladder Cancer
Bilirubin Levels
Gamma Linolenic Acid
Dihomo gamma Linolenic Acid
Arachidonic Acid
Linoleic Acid
Adrenic Acid
Deltaproteobacteria
Mollicutes
Verrucomicrobiae
Coriobacteriales
Verrucomicrobiales
Verrucomicrobia
Coriobacteriaceae
91otu13421
91otu4418
91otu1825
M
ogibacteriaceae
Unclassified
Desulfovibrionaceae
Pasteurellaceae
Peptostreptococcaceae
Christensenellaceae
Verrucom
icrobiaceae
Alanine
RatioOm6Om3
AlphaAminoN
ButyricAcid
Interleukinll6
SmallLdlParticle
RatioGlnGln
Threonine
3Methylhistidine
AverageinflammationScore
Mercury
DocosapentaenoicAcidDocosatetraenoicAcid
EicosadienoicAcidHomalrLeucineOmega3indexTyrosine
HdlCholesterolCPeptide
1Methylhistidine
3HydroxyisovalericAcid
IsovalerylglycineIsoleucine
Figlu
TotalCholesterolLinoleicDihomoYLinolejc
PalmitoleicAcid
ArachidonicAcid
LdlParticle
ArachidonicEicosapentaenoic
Pasteurellales
Diversity
Tenericutes
Clinical labs
Metabolomics
5Hydroxyhexanoate
Tl16:0(palmiticAcid)
Tl18:3n6(gLinolenicAcid)Tl15:0(pentadecanoicAcid)Tl14:1n5(myristoleicAcid)Tl20:2n6(eicosadienoicAcid)Tl20:5n3(eicosapentaenoicAcid)
Tl18:2n6(linoleicAcid)
Tldm16:0(plasmalogenPalmiticAcid)
Tl22:6n3(docosahexaenoicAcid)
Tl22:4n6(adrenicAcid)
Tl18:1n9(oleicAcid)
Tldm18:1n9(plasmalogenOleicAcid)
Tl20:4n6(arachidonicAcid)
Tl14:0(myristicAcid)
Arachidate(20:0)
StearoylArachidonoylGlycerophosphoethanolamine(1)*
1Linoleoylglycerophosphocholine(18:2n6)
StearoylLinoleoylGlycerophosphoethanolamine(1)*
1Palmitoleoylglycerophosphocholine(16:1)*
PalmitoylOleoylGlycerophosphoglycerol(2)*
PalmitoylLinoleoylGlycerophosphocholine(1)*
Tl20:3n6(diHomoGLinoleicAcid)
2Hydroxypalmitate
NervonoylSphingomyelin*
Titl(totalTotalLipid)
Cholesterol
D
ocosahexaenoate
(dha;22;6n3)
Eicosapentaenoate
(epa; 20:5n3)
3
Carboxy
4
M
ethyl 5
Propyl 2
Furanpropanoate
(cm
pf)
3
M
ethyladipate
Cholate
Phosphoethanolamine
1 Oleoylglycerol (1 Monoolein)
Tigloylglycine
Valine
sobutyrylglycine
soleucine
eucine
P Cresol Glucuronide*
Phenylacetylglutamine
P Cresol Sulfate
Tyrosine
S Methylcysteine
Cystine
3 Methylhistidine
1 Methylhistidine
N Acetyltryptophan
3 Indoxyl Sulfate
Serotonin (5ht)
Creatinine
Glutamate
Cysteine Glutathione Disulfide
Gamma Glutamylthreonine*Gamma Glutamylalanine
Gamma Glutamylglutamate
Gamma Glutamylglutamine
Bradykinin, Hydroxy Pro(3)
Bradykinin, Des Arg(9)
BradykininMannoseBilirubin (e,e)*
Biliverdin
Bilirubin (z,z)
L UrobilinNicotinamide
Alpha TocopherolHippurate
Cinnam
oylglycine
Ldl Particle
N
um
ber
Triglycerides
Bilirubin
Direct
Alkaline
Phosphatase
EgfrNon
AfrAm
erican
CholesterolTotal
LdlSm
all
LdlM
edium
BilirubinTotal
Ggt
EgfrAfricanAmerican
Cystine
MargaricAcid
ElaidicAcid
Proinsulin
Hba1c
Insulin
Triglycerides
Ldlcholesterol
DihomoGammaLinolenicAcid
HsCrp
GlutamicAcid
Height
Weight
Leptin
BodyMasIndex
PhenylaceticAcid
Valine
TotalOmega3
TotalOmega6
HsCrpRelativeRisk
DocosahexaenoicAcid
AlphaAminoadipicAcid
EicosapentaenoicAcid
GammaAminobutyricAcid
5
Acetylam
ino
6
Form
ylam
ino
3
M
ethyluracil
Adenosine 5
Monophosphate (amp)
Gamma Glutamyltyrosine
Gamma Glutamyl 2 Aminobutyrate
N Acetyl 3 Methylhistidine*
3 Phenylpropionate (hydrocinnamate)
Figure 2 Top 100 correlations per pair of data types. Subset of top statistically significant Spearman inter-omic cross-sectional correlations between
all data sets collected in our cohort. Each line represents one correlation that was significant after adjustment for multiple hypothesis testing using the
method of Benjamini and Hochberg10 at padj < 0.05. The mean of all three time points was used to compute the correlations between analytes. Up to
100 correlations per pair of data types are shown in this figure. See Supplementary Figure 1 and Supplementary Table 2 for the complete inter-omic
cross-sectional network.
Nature Biotechnology 2017
측정한 모든 종류의 데이터들 중에 가장 correlation이 높은 100개의 pair를 선정

©2017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
A RT I C L E S
edges. The majority of edges involved a metabolite (3,309) or a clini-
cal laboratory test (3,366), with an additional 20 edges involving the
130 genetic traits tested, 46 with microbiome taxa or diversity score,
and 207 with quantified proteins. The inter-omic delta correlation
network contained 822 nodes and 2,406 edges. 375 of the edges in
the delta correlation network were also present in the cross-sectional
network. The cross-sectional correlation network is provided in
Supplementary Table 2 (inter-omic only) and Supplementary Table
3 (full). The delta correlation network is provided in Supplementary
Table 4 (inter-omic only) and Supplementary Table 5 (full).
We identified clusters of related measurements from the cross-
sectional inter-omic correlation network using community
analysis, an unsupervised (i.e., using unlabeled data to find hidden
structure) approach that iteratively prunes the network (removing
the edges with the highest betweenness) to reveal densely inter-
connected subgraphs (communities)11. Seventy communities of
at least two vertices (mean of 10.9 V and 34.9 E) were identi-
fied in the cross-sectional inter-omic network at the cutoff with
maximum community modularity12 (Supplementary Fig. 2),
and are fully visualized as an interactive graph in Cytoscape13
(Supplementary Dataset 1). 70% of the edges in the cross-sec-
tional network remained after community edge pruning. The
communities often represented a cluster of physiologically related
analytes, as described below.
Guanidinosuccinate
Alanine
IsovalerylcarnitineValine
NAcetylleucineNAcetylisoleucine2Methylbutyrylcarnitine(c5)
IsoleucineLeucine
SAdenosylhomocysteine(sah)
CysteineCystineMethionineSulfone
NAcetyltryptophan
NAcetylkynurenine(2)
3IndoxylSulfate
Xanthurenate
Kynurenine
Kynurenate
Tryptophan
Phenylalanine
N
Acetylphenylalanine
4Hydroxyphenylpyruvate
Phenylpyruvate
Tyrosine
N
Acetyltyrosine
Phenylacetylcarnitine
G
lutam
ine
G
lutam
ate
N
Acetylglycine
G
lycine
Proline
N
Delta
Acetylornithine
N
Acetylcitrulline
Hom
oarginine
N2,n5
Diacetylornithine
Pro Hydroxy Pro
2 Aminoadipate
Lysine
Deoxycholate
Ursodeoxycholate
Arachidate (20:0)
Nonadecanoate (19:0)
Palmitate (16:0)
Erucate (22:1n9)
Tl16:0 (palmitic Acid)
Tl16:1n7 (palmitoleic Acid)
Tl18:1n7 (avaccenic Acid)
Tl14:1n5 (myristoleic Acid)
Tl24:1n9 (nervonic Acid)
Tldm18:1n7 (plasmalogen Vaccenic Acid)
Tldm18:0 (plasmalogen Stearic Acid)
Tl14:0 (myristic Acid)
Tl18:2n6 (linoleic Acid)
Tldm16:0 (plasmalogen Palmitic Acid)
Tl22:1n9 (erucic Acid)
Tl20:3n6 (di Homo G Linoleic Acid)
Tl20:4n3 (eicosatetranoic Acid)
Tl18:1n9 (oleic Acid)
Tl18:3n3 (a Linolenic Acid)
Tldm18:1n9 (plasmalogen Oleic Acid)
1 Linoleoylglycerophosphocholine (18:2n6)
1 Linolenoylglycerophosphocholine (18:3n3)*
2 Stearoylglycerophosphocholine*1 Palmitoleoylglycerophosphocholine (16:1)*1 Oleoylglycerophosphocholine (18:1)3 Hydroxylaurate2 Hydroxydecanoate3 Hydroxydecanoate
3 Hydroxyoctanoate
2 Hydroxystearate
3 Hydroxysebacate7 Alpha Hydroxy 3 Oxo 4 Cholestenoate (7 Hoca)
CholesterolCarnitinePregnanediol 3 Glucuronide
Epiandrosterone Sulfate
Stearoylcarnitine
Myristoleoylcarnitine*
Decanoylcarnitine
Laurylcarnitine
1 Linolenoylglycerol
1 Palmitoylglycerol (1 Monopalmitin)
1 Linoleoylglycerol (1 Monolinolein)
1 Dihomo Linolenylglycerol (alpha, Gamma)
Caprate
(10:0)
Laurate
(12:0)
Caprylate
(8:0)
5
Dodecenoate
(12:1n7)
Palm
itoyl Sphingom
yelin
StearoylSphingom
yelin
Sphinganine
NervonoylSphingom
yelin*
Sphingosine
OleoylSphingom
yelin
3
Hydroxybutyrate
(bhba)
Acetoacetate
Butyrylcarnitine
Propionylcarnitine
DihomoLinolenate(20:3n3OrN6)
Hexanoylglycine
Glycerophosphoethanolamine
Tltl(totalTotalLipid)
Eicosanodioate
Octadecanedioate
3Methyladipate
2MethylmalonylCarnitine
PalmitoylEthanolamide
NOleoyltaurine
N1Methyl2Pyridone5Carboxamide
Nicotinamide
AlphaTocopherol
GammaTocopherol
Threonate
Oxalate(ethanedioate)
Ergothioneine
NAcetylalliin
Erythritol
Cinnamoylglycine
SAllylcysteine
2Pyrrolidinone
2Hydroxyisobutyrate
Tartronate(hydroxymalonate)
1,3,7Trimethylurate
4Hydroxycoumarin
2AcetamidophenolSulfate
4AcetylphenolSulfate
Mannose
Erythronate*
Pyruvate
Lactate
Glucose
Glycerate
Xylitol
GammaGlutamylleucine
GammaGlutamylphenylalanine
Gam
m
a
Glutam
ylisoleucine*
Gam
m
a
Glutam
ylglutam
ine
Gam
m
a
Glutam
ylhistidine
G
am
m
a
G
lutam
ylglutam
ate
Bradykinin,Hydroxy
Pro(3)
G
lycylleucine
Succinylcarnitine
Succinate
Fum
arate
M
alate
Alpha Ketoglutarate
Citrate
Xanthine
LDL Particle
hs-CRP Relative Risk
ProinsulinHba1cInsulin
Gamma Linolenic Acid
Triglycerides
Manganese
Dihomo Gamma Linolenic Acid
Glutamic AcidLeptin
Body Mass Index
Total LC Omega9TryptophanLysineVitamin D
5 Hydroxyindoeacetic AcidWeightLactic Acid
Linoleic Dihomo Y LinoleioIsovalerylglycineQuinolinic Acid
C-PeptideHDL Cholesterol
Indoleacetic Acid
Adiponectin
Phenylalanine
Interleukin IL6
Small LDL Particle
Ratio Asn Asp
HOMA-IR
Lignoceric Acid
Succinic Acid
Homogentisic Acid
Homovanillic Acid
Average Inflammation Score
FIGLU
Ratio Gln Gln
Magnesium
Pyroglutamic Acid
Glucose
Gondoic Acid
Kynurenic Quinolinic Ratio
Alpha Amino N Butyric Acid
Tyrosine
Alanine
HDL Large
GGT
Triglycerides
Bilirubin Direct
LDL Medium
LDL Pattern
Alkaline Phosphatase
LDL Peak Size
Chloride
Glucose
LDL
Particle
Num
ber
LDL
Sm
all
Ferritin
CCL19
H
G
F
IL
10RAIL
6
CXCL10
TNFSF14
CCL20CD5
CD40
VEGF
A
IL18R1OSM
CRPF9(2)
APCSINHBCCRP(2)
MBL2(2)MBL2GC
F9
SERPINC1
TPALEPVEGFAVEGFD
IL6FABP4CSTBIL1RA
Pasteurellales
Pasteurellaceae
Omega6FattyAcidLevels(DGLA)
FG
F
21
hs-CRP
G
am
m
a
G
lutam
yltyrosine
Amino acid
metabolism
Olink
(CVD)
Olink
(inflammation)
Quest
diagnostics
Genova
diagnostics
Nucleotides
Energy
Peptides
Carbohydrates
Xenobiotics
Vitamins and
cofactors
Lipid
metabolism
SRM (liver)
Metabolites
Clinical labs
Microbiome
Genetic traits
Proteins
Figure 3 Cardiometabolic community. All vertices and edges of the cardiometabolic community, with lines indicating significant ( adj < 0.05)
correlations. Associations with FGF21 (red lines) and gamma-glutamyltyrosine (purple lines) are highlighted.
• inter-omics correlation network 의 분석을 통해서 환자들을 몇가지 cluster로 분류

• 가장 큰 cluster (246 Vertices, 1645 Edges): Cardiometaboic Health

• four most connected clinical analyses: C-peptide, insulin, MOMA-IR, triglycerides

• four most-connected proteins: leptin, C-reactive protein, FGF21, INHBC
gamma-glutamyltyrosine
FGF21

• inter-omics correlation network 의 분석을 통해서 환자들을 몇가지 cluster로 분류

• 가장 큰 cluster (246 Vertices, 1645 Edges): Cardiometaboic Health

• four most connected clinical analyses: C-peptide, insulin, MOMA-IR, triglycerides

• four most-connected proteins: leptin, C-reactive protein, FGF21, INHBC
atureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
A RT I C L E S
The largest community (246 V; 1,645 E) contains many clinical
analytes associated with cardiometabolic health, such as C-peptide,
triglycerides, insulin, homeostatic risk assessment–insulin resistance
(HOMA-IR), fasting glucose, high-density lipid (HDL) cholesterol,
and small low-density lipid (LDL) particle number (Fig. 3). The four
most-connected clinical analytes by degree (the number of edges
connecting a particular analyte) were C-peptide (degree 99), insulin
(88), HOMA-IR (88), and triglycerides (75). The four most-connected
proteins measured using targeted (i.e., selected reaction monitoring
analysis) mass spectrometry or Olink proximity extension assays
by degree are leptin (18), C-reactive protein (15), fibroblast growth
factor 21 (FGF21) (14), and inhibin beta C chain (INHBC) (10).
Leptin and C-reactive protein are indicators for cardiovascular
risk14,15. FGF21 is positively correlated with the clinical analytes
( = −0.41; padj = 2.1 × 10−3). Hypothyroidism has long been recog-
nized clinically as a cause of elevated cholesterol values19.
A community formed around plasma serotonin (18 V; 25 E) contain-
ing 12 proteins listed in Supplementary Table 6, for which the most
significant enrichment identified in a STRING ontology analysis20 was
platelet activation (padj = 1.7 × 10−3) (Fig. 4b). Serotonin is known to
induce platelet aggregation21; accordingly, selective serotonin reuptake
inhibitors (SSRIs) may protect against myocardial infarction22.
We identified several communities containing microbiome taxa,
suggesting that there are specific microbiome–analyte relationships.
Hydrocinnamate, l-urobilin, and 5-hydroxyhexanoate clustered with
the bacterial class Mollicutes and family Christensenellaceae (8 V;
8 E). Another community emerged around the Verrucomicrobiaceae
and Desulfovibrionaceae families and p-cresol-sulfate (7 V; 6 E). The
a
c
d
b
e
Figure 4 Cholesterol, serotonin, -diversity, IBD, and bladder cancer communities. (a) Cholesterol community. (b) Serotonin community. (c) -diversity
community. (d) The polygenic score for inflammatory bowel disease is negatively correlated with cystine. (e) The polygenic score for bladder cancer is
positively correlated with 5-acetylamino-6-formylamino-3-methyluracil (AFMU).
Cholesterol, serotonin, diversity, IBD, and bladder cancer communities. (a) Cholesterol community. (b) Serotonin community. (c)
-diversity community. (d) The polygenic score for inﬂammatory bowel disease is negatively correlated with cystine. (e) The
polygenic score for bladder cancer is positively correlated with 5-acetylamino-6-formylamino-3-methyluracil (AFMU).

017NatureAmerica,Inc.,partofSpringerNature.Allrightsreserved.
identified with elevated fasting glucose or HbA1c at baseline (pre-
diabetes), the coach made recommendations based on the Diabetes
Prevention Program36, customized for each person’s lifestyle. These
individual recommendations typically fell into one of several major
factors (fasting insulin and HOMA-IR), and inflammation (IL-8 and
TNF-alpha). Lipoprotein fractionation, performed by both labora-
tory companies, produced significant but discordant results for LDL
particle number. We observed significant improvements in fasting
Table 1 Longitudinal analysis of clinical changes by round
Clinical laboratory test Changes in labs in participants out-of-range at baseline
Health area Name N per round P-value
Nutrition Vitamin D 95 +7.2 ng/mL/round 7.1 × 10−25
Nutrition Mercury 81 −0.002 mcg/g/round 8.9 × 10−9
Diabetes HbA1c 52 −0.085%/round 9.2 × 10−6
Cardiovascular LDL particle number (Quest) 30 +130 nmol/L/round 9.3 × 10−5
Nutrition Methylmalonic acid (Genova) 3 −0.49 mmol/mol creatinine/round 2.1 × 10−4
Cardiovascular LDL pattern (A or B) 28 −0.16 /round 4.8 × 10−4
Inflammation Interleukin-8 10 −6.1 pg/mL/round 5.9 × 10−4
Cardiovascular Total cholesterol (Quest) 48 −6.4 mg/dL/round 7.2 × 10−4
Cardiovascular LDL cholesterol 57 −4.8 mg/dL/round 8.8 × 10−4
Cardiovascular LDL particle number (Genova) 70 −69 nmol/L/round 1.2 × 10−3
Cardiovascular Small LDL particle number (Genova) 73 −56 nmol/L/round 3.5 × 10−3
Diabetes Fasting glucose (Quest) 45 −1.9 mg/dL/round 8.2 × 10−3
Cardiovascular Total cholesterol (Genova) 43 −5.4 mg/dL/round 1.2 × 10−2
Diabetes Insulin 16 −2.3 IU/mL/round 1.5 × 10−2
Inflammation TNF-alpha 4 −6.6 pg/mL/round 1.8 × 10−2
Diabetes HOMA-IR 19 −0.56 /round 2.0 × 10−2
Cardiovascular HDL cholesterol 5 +4.5 mg/dL/round 2.2 × 10−2
Nutrition Methylmalonic acid (Quest) 7 −42 nmol/L/round 5.2 × 10−2
Cardiovascular Triglycerides (Genova) 14 −18 mg/dL/round 1.4 × 10−1
Diabetes Fasting glucose (Genova) 47 −0.98 mg/dL/round 1.5 × 10−1
Nutrition Arachidonic acid 35 +0.24 wt%/round 1.9 × 10−1
Inflammation hs-CRP 51 −0.47 mcg/mL/round 2.1 × 10−1
Cardiovascular Triglycerides (Quest) 17 −14 mg/dL/round 2.4 × 10−1
Nutrition Glutathione 6 +11 micromol/L/round 2.5 × 10−1
Nutrition Zinc 4 −0.82 mcg/g/round 3.0 × 10−1
Nutrition Ferritin 10 −14 ng/mL/round 3.1 × 10−1
Inflammation Interleukin-6 4 −1.1 pg/mL/round 3.8 × 10−1
Cardiovascular HDL large particle number 8 +210 nmol/L/round 4.9 × 10−1
Nutrition Copper 10 +0.006 mcg/g/round 6.0 × 10−1
Nutrition Selenium 6 +0.035 mcg/g/round 6.2 × 10−1
Cardiovascular Medium LDL particle number 20 +2.8 nmol/L/round 8.5 × 10−1
Cardiovascular Small LDL particle number (Quest) 14 −2.3 nmol/L/round 8.8 × 10−1
Nutrition Manganese 0 N/A N/A
Nutrition EPA 0 N/A N/A
Nutrition DHA 0 N/A N/A
Generalized estimating equations (GEE) were used to calculate average changes in clinical laboratory tests over time, for those analytes that were actively coached on. The ‘ per
round’ column is the average change in the population for that analyte by round adjusted for age, sex, and self-reported ancestry. ‘Out-of-range at baseline’ indicates the average
change using only those participants who were out-of-range for that analyte at the beginning of the study. Rows in boldface indicate statistically significant improvement, while
the italicized row indicates statistically significant worsening. N/A values are present where no participants were out-of-range at baseline. For example, the average improvement
in vitamin D for the 95 participants that began the study out-of-range was +7.2 ng/mL per round. Several analytes are measured by both Quest and Genova; with the exception of
LDL particle number, the direction of effect for significantly changed analytes was concordant across the two laboratories. An independence working correlation structure was used
in the GEE. See Supplementary Table 10 for the complete results.
• 수치가 정상 범위를 벗어나면 코치가 개입하여, 해당 수치를 개선할 수 있는 라이프스타일의 변화 유도

• 예를 들어, 공복혈당 혹은 HbA1c 의 증가: 코치가 당뇨 예방 프로그램(DPP)을 권고

• 몇개의 major category로 나눠짐

• diet, exercise, stress management, dietary supplements, physician referral

• 이를 통해서 가장 크게 개선 효과가 있었던 수치들

• vitamin D, mercury, HbA1c

• 전반적으로 콜레스테롤 관련 수치나, 당뇨 위험 관련 수치, 염증 수치 등에 개선이 있었음

개인적으로 재미있었던 사례 
: hemochromatosis (혈색소증) 관련
• 65살 환자가 등산 하다가 발목에 cartilage damage

• Baseline blood collection: ferritin levels of 399 ng/mL

• Homozygous for HFE C282Y (risk factor of hemochromatosis)

• 혈색소증 (hemochromatosis)

• 철에 대한 체내 대사에 이상이 생겨 음식을 통해 섭취한 철이 너무 많이 흡수되는 질환

• 간, 심장 및 췌장 등의 장기를 손상시키고, 간질환, 심장질환 및 악성종양을 유발

• 혈색소증 진단 & therapeutic phlebotomy 처방 (by hematologist)

• ferritin levels dropped to 175 ng/mL

Inherited Conditions
혈색소증은 유전적 원인으로 철에 대한 체내 대사에 이상이 생겨 음식을
통해 섭취한 철이 너무 많이 흡수되는 질환입니다. 너무 많이 흡수된 철
은 우리 몸의 여러 장기, 특히 간, 심장 및 췌장에 과다하게 축적되며 이
들 장기를 손상시킴으로써 간질환, 심장질환 및 악성종양을 유발합니다.

Yoon Sup Choi
Hemochromatosis (HFE-related) • Inherited Conditions 1
© 2016 23andMe, Inc. All rights reserved.
Hemochromatosis (HFE-related)
This report was archived on 2016-08-03. Please note that this report is no longer updated and was developed using scientific criteria that
may have since been revised. The following results are based on Established Research for 3 reported markers, updated July 26th,
2012.
Introduction
Iron, an essential mineral, is absorbed via the intestines from food and is important for many bodily functions including red blood cell
formation and proper brain function. The iron absorption process must be tightly regulated or else iron can accumulate in the body, possibly
causing organ damage. Inherited forms of iron overload, known as hereditary hemochromatosis (HH), are caused by mutations in genes that
normally play important roles in regulating iron levels. This report includes three mutations in the HFE gene that are typically found in people
with European ancestry and are responsible for most cases of HH. HFE-related HH is inherited in a recessive manner, meaning that a
person must receive a mutated copy of the HFE gene from each parent to have the condition. In Europeans, roughly one in 300 individuals
has HFE-related HH and at least one in 10 carries a mutation for the condition. Rates are even higher in certain European populations
including Irish, Norwegian and Australian. HFE-related HH is much rarer in Asian and African populations.
Yoon Sup's Genetic Result
Who Genetic Result
Has two copies of the C282Y mutation in the HFE gene. A person with
two copies of this mutation is prone to higher levels of iron in the body,
which in a small percentage of cases leads to clinical symptoms of
hemochromatosis.
Has one copy of the C282Y mutation and one copy of either the H63D or
S65C mutation in the HFE gene. A person with this combination may be
prone to higher levels of iron in the body, which in a very small
percentage of cases leads to clinical symptoms of hemochromatosis.
Has two copies of either the H63D or S65C mutation, or one of each, in
the HFE gene. A person with this combination of mutations is not typically
prone to higher levels of iron in the body.
Yoon Sup Choi
Has one mutation in the HFE gene linked to hemochromatosis. A
person with one of these mutations is not typically prone to higher
levels of iron in the body, but can pass the mutation to offspring.
May have other mutations in the HFE gene (not reported here).
Does not have any of the three mutations in the HFE gene linked to
hereditary hemochromatosis. May still have other mutations in the HFE
gene (not reported here).

• 버릴리(구글)의 베이스라인 프로젝트

• 건강과 질병을 새롭게 정의하기 위한 프로젝트

• 4년 동안 만 명의 개인의 건강 상태를 면밀하게 추적하여 데이터를 축적

• 심박수와 수면패턴 및 유전 정보, 감정 상태, 진료기록, 가족력, 소변/타액/혈액 검사 등

• 버릴리의 ‘Study Watch’

• 2017년 4월 공개한 베이스라인 스터디 용 스마트워치

• 심전도, 심박수, EDA(Electrodermal Activity), 관성움직임(inertial movement) 등 측정

• 장기간 추적연구를 위한 특징들: 배터리 수명(일주일), 데이터 저장 공간, 동기화 (일주일 한 번)

• Linda Avey의 Precise.ly

• 23andMe의 공동창업자였던 Linda Avey가 2009년 회사를 떠난 이후, 2011년 창업

• ‘We Are Curious’ 라는 이름에서 최근에 Precise.ly로 회사 이름 변경

• Linda Avey의 Precise.ly

• Genotype + Phenotype + Microbiome + environment 모두 결합하여 의학적인 insight

• Genotype: Helix의 플랫폼에서 WES 을 통하여 분석

• Phenotype: 웨어러블, IoT 기기를 이용

• ‘Modern diseases’를 주로 타게팅 하겠다고 언급하고 있음

• 예를 들어, autism spectrum syndrome을 다차원적 데이터를 기반으로 분류할 수 있을까?

• Helix 플랫폼을 통해서 먼저 Chronic Fatigue 에 대한 앱을 먼저 출시하고,

• 향후 autism, PD 등에 대한 앱을 출시할 예정이라고 함.

iCarbonX

•중국 BGI의 대표였던 준왕이 창업

•'모든 데이터를 측정'하고 이를 정밀 의료에 활용할 계획

•데이터를 측정할 수 있는 역량을 가진 회사에 투자 및 인수

•SomaLogic, HealthTell, PatientsLikMe

•향후 5년 동안 100만명-1000만 명의 데이터 모을 계획

•이 데이터의 분석은 인공지능으로

• 충분한 수의 (1,000만 명) 데이터를 충분한 기간 (4-5년) 동안 모을 수 있을까?

• compliance, 고질적 문제: 5년 동안 모든 종류의 데이터를 측정할 사람이 얼마나

• 현재 단기적으로라도 증명된 방법은 돈을 주는 것 밖에는… (연구 > 사업)

• 이 방대한 종류의 데이터를 어떻게 분석할 것인가?

• 인공지능도 만능은 아니다.

• 일일이 가설을 세우고 분석할 수밖에 없지 않을까.

• So What?이 인사이트로 무엇을 할 수 있는가?

• 사용자의 건강/질병에 유의미할 정도의 예방/예측/관리/치료가 가능한가

• 발견한 insight에 맞는 바이오마커/치료법/예방법 등을 찾을 수 있나

• 그 결과로 나온 서비스에 대한 고객군의 지불 의사는 얼마나 될까?

• 근본적 문제 건강한 사람은 건강에 관심이 없다.

• Puretech Health

• ‘새로운 개념의 제약회사’를 추구하는 회사

• 기존의 신약 뿐만 아니라, 게임, 앱 등을 이용한 Digital Therapeutics 를 개발

• Digital Therapeutics는 최근 미국 FDA의 de novo 승인을 받기도 함

• Puretech Health

• 신약 파이프라인 중에는 일반적인 small molecule 등도 있지만,

• Akili: ADHD, 우울증, 알츠하이머 등을 위한 인지 능력 개선 목적의 게임 (Project EVO)

• Sonde: Voice biomarker 를 이용한 우울증 등 mental health의 진단 및 모니터링 목적

LETTER doi:10.1038/nature12486
Video game training enhances cognitive control in
older adults
J. A. Anguera1,2,3
, J. Boccanfuso1,3
, J. L. Rintoul1,3
, O. Al-Hashimi1,2,3
, F. Faraji1,3
, J. Janowich1,3
, E. Kong1,3
, Y. Larraburo1,3
,
C. Rolle1,3
, E. Johnston1
& A. Gazzaley1,2,3,4
Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto
interact with our complex environment in a goal-directed manner1
.
Humans regularly challenge these control processes when attempting
to simultaneously accomplish multiple goals (multitasking), generat-
ing interference as the result of fundamental information processing
limitations2
. It is clear that multitasking behaviour has become ubi-
quitous in today’s technologically dense world3
, and substantial evid-
ence has accrued regarding multitasking difficulties and cognitive
control deficits in our ageing population4
. Here we show that multi-
tasking performance, as assessed with a custom-designed three-
dimensional video game (NeuroRacer), exhibits a linear age-related
decline from 20 to 79 years of age. By playing an adaptive version of
NeuroRacer in multitasking training mode, older adults (60 to 85
years old) reduced multitasking costs compared to both an active
control group and a no-contact control group, attaining levels beyond
those achieved by untrained 20-year-old participants, with gains
persisting for 6 months. Furthermore, age-related deficits in neural
signatures of cognitive control, as measured with electroencephalo-
graphy,wereremediated by multitasking training (enhanced midline
frontal theta power and frontal–posterior theta coherence). Critically,
thistrainingresultedinperformancebenefitsthatextendedtountrained
cognitive control abilities (enhanced sustained attention and working
memory), with an increase in midline frontal theta power predicting
the training-induced boost in sustained attention and preservation
of multitasking improvement 6 months later. These findings high-
light the robust plasticity of the prefrontal cognitive control system
in the ageing brain, and provide the first evidence, to our knowledge,
ofhowacustom-designedvideogamecanbeusedtoassesscognitive
abilities across the lifespan, evaluate underlying neural mechanisms,
and serve as a powerful tool for cognitive enhancement.
In a first experiment, we evaluated multitasking performance across
the adult lifespan. A total of 174 participants spanning six decades of life
(ages 20–79; ,30 individuals per decade) played a diagnostic version of
NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’)
withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see
Supplementary Information for details of NeuroRacer). Performance
was evaluated using two distinct game conditions: ‘sign only’ (respond
as rapidly as possible to the appearance of a sign only when a green circle
was present); and ‘sign and drive’ (simultaneously perform the sign task
while maintaining a car in the centre of a winding road using a joystick
(that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was
evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’
index was used to assess multitasking performance by calculating the
percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that
greater cost (that is, a more negative percentage cost) indicates increased
interference when simultaneously engaging in the two tasks (see Methods
Summary).
Prior to the assessment of multitasking costs, an adaptive staircase
algorithm was used to determine the difficulty levels of the game at
which each participant performed the perceptual discrimination and
visuomotor tracking tasks in isolation at ,80% accuracy. These levels
were then used to set the parameters of the component tasks in the
multitasking condition, so that each individual played the game at a
customizedchallengelevel.Thisensuredthatcomparisonswouldinform
differences in the ability to multitask, and not merely reflect disparities in
component skills (see Methods, Supplementary Figs 1 and 2, and Sup-
plementary Information for more details).
Multitasking performance diminished significantly across the adult
lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup-
plementaryTable1),withtheonlysignificantdifferenceincostbetween
adjacent decades being the increase from the twenties (226.7% cost) to
the thirties (238.6% cost). This deterioration in multitasking perform-
ance is consistent with the pattern of performance decline across the
lifespan observed for fluid cognitive abilities, such as reasoning5
and
working memory6
. Thus, using NeuroRacer as a performance assess-
ment tool, we replicated previously evidenced age-related multitasking
deficits7,8
, and revealed that multitasking performance declines linearly
as we advance in age beyond our twenties.
In a second experiment, we explored whether older adults who trained
by playing NeuroRacer in multitasking mode would exhibit improve-
mentsintheirmultitaskingperformanceonthegame9,10
(thatis,diminished
NeuroRacer costs). Critically, we also assessed whether this training
1
Department of Neurology, University of California, San Francisco, California 94158, USA. 2
Department of Physiology, University of California, San Francisco, California 94158, USA. 3
Center for Integrative
Neuroscience, University of California, San Francisco, California 94158, USA. 4
Department of Psychiatry, University of California, San Francisco, California 94158, USA.
1
month
MultitaskingSingle taskNo-contact
control
Initial
visit
NeuroRacer
EEG and
cognitive
testing
Drive only Sign only Sign and drive
and
1 hour × 3 times per week × 1 month
or
Single task Multitask
6+
months
Training intervention
NeuroRacer
or
a
b
+ +
Figure 1 | NeuroRacer experimental conditions and training design.
a, Screen shot captured during each experimental condition. b, Visualization of
training design and measures collected at each time point.
5 S E P T E M B E R 2 0 1 3 | V O L 5 0 1 | N A T U R E | 9 7
Macmillan Publishers Limited. All rights reserved©2013

LETTER doi:10.1038/nature12486
Video game training enhances cognitive control in
older adults
J. A. Anguera1,2,3
, J. Boccanfuso1,3
, J. L. Rintoul1,3
, O. Al-Hashimi1,2,3
, F. Faraji1,3
, J. Janowich1,3
, E. Kong1,3
, Y. Larraburo1,3
,
C. Rolle1,3
, E. Johnston1
& A. Gazzaley1,2,3,4
Cognitivecontrolisdefinedbyasetofneuralprocessesthatallowusto
interact with our complex environment in a goal-directed manner1
.
Humans regularly challenge these control processes when attempting
to simultaneously accomplish multiple goals (multitasking), generat-
ing interference as the result of fundamental information processing
limitations2
. It is clear that multitasking behaviour has become ubi-
quitous in today’s technologically dense world3
, and substantial evid-
ence has accrued regarding multitasking difficulties and cognitive
control deficits in our ageing population4
. Here we show that multi-
tasking performance, as assessed with a custom-designed three-
dimensional video game (NeuroRacer), exhibits a linear age-related
decline from 20 to 79 years of age. By playing an adaptive version of
NeuroRacer in multitasking training mode, older adults (60 to 85
years old) reduced multitasking costs compared to both an active
control group and a no-contact control group, attaining levels beyond
those achieved by untrained 20-year-old participants, with gains
persisting for 6 months. Furthermore, age-related deficits in neural
signatures of cognitive control, as measured with electroencephalo-
graphy,wereremediated by multitasking training (enhanced midline
frontal theta power and frontal–posterior theta coherence). Critically,
thistrainingresultedinperformancebenefitsthatextendedtountrained
cognitive control abilities (enhanced sustained attention and working
memory), with an increase in midline frontal theta power predicting
the training-induced boost in sustained attention and preservation
of multitasking improvement 6 months later. These findings high-
light the robust plasticity of the prefrontal cognitive control system
in the ageing brain, and provide the first evidence, to our knowledge,
ofhowacustom-designedvideogamecanbeusedtoassesscognitive
abilities across the lifespan, evaluate underlying neural mechanisms,
and serve as a powerful tool for cognitive enhancement.
the adult lifespan. A total of 174 participants spanning six decades of life
(ages 20–79; ,30 individuals per decade) played a diagnostic version of
NeuroRacertomeasuretheirperceptualdiscriminationability(‘signtask’)
withandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see
Supplementary Information for details of NeuroRacer). Performance
was evaluated using two distinct game conditions: ‘sign only’ (respond
as rapidly as possible to the appearance of a sign only when a green circle
was present); and ‘sign and drive’ (simultaneously perform the sign task
while maintaining a car in the centre of a winding road using a joystick
(that is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was
evaluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’
index was used to assess multitasking performance by calculating the
percentage change in d9 from ‘sign only’ to ‘sign and drive’, such that
greater cost (that is, a more negative percentage cost) indicates increased
interference when simultaneously engaging in the two tasks (see Methods
Summary).
algorithm was used to determine the difficulty levels of the game at
which each participant performed the perceptual discrimination and
visuomotor tracking tasks in isolation at ,80% accuracy. These levels
were then used to set the parameters of the component tasks in the
multitasking condition, so that each individual played the game at a
customizedchallengelevel.Thisensuredthatcomparisonswouldinform
differences in the ability to multitask, and not merely reflect disparities in
component skills (see Methods, Supplementary Figs 1 and 2, and Sup-
plementary Information for more details).
Multitasking performance diminished significantly across the adult
lifespan in a linear fashion (that is, increasing cost, see Fig. 2a and Sup-
and
working memory6
deficits7,8
(thatis,diminished
1
Department of Neurology, University of California, San Francisco, California 94158, USA. 2
Neuroscience, University of California, San Francisco, California 94158, USA. 4
1
month
control
Initial
visit
NeuroRacer
EEG and
cognitive
testing
and
or
6+
months
NeuroRacer
or
a
b
+ +
• 게임을 통해서 노년층의 인지 능력을 개선시킬 수 있음을 증명한 논문 (Nature 2013, UCSF)

• ‘뉴로레이서’라는 3차원 비디오 게임

• 왼손으로 꼬불꼬불한 길을 운전하면서

• 화면 상단에 특정 색깔/모양의 표시가 뜨면, 오른손으로 누르는 훈련
sking performance, as assessed with a custom-designed three-
mensional video game (NeuroRacer), exhibits a linear age-related
ecline from 20 to 79 years of age. By playing an adaptive version of
euroRacer in multitasking training mode, older adults (60 to 85
ars old) reduced multitasking costs compared to both an active
ontrol group and a no-contact control group, attaining levels beyond
ose achieved by untrained 20-year-old participants, with gains
ersisting for 6 months. Furthermore, age-related deficits in neural
gnatures of cognitive control, as measured with electroencephalo-
aphy,wereremediated by multitasking training (enhanced midline
ontal theta power and frontal–posterior theta coherence). Critically,
istrainingresultedinperformancebenefitsthatextendedtountrained
ognitive control abilities (enhanced sustained attention and working
emory), with an increase in midline frontal theta power predicting
e training-induced boost in sustained attention and preservation
multitasking improvement 6 months later. These findings high-
ght the robust plasticity of the prefrontal cognitive control system
the ageing brain, and provide the first evidence, toour knowledge,
howacustom-designedvideogamecanbeusedtoassesscognitive
bilities across the lifespan, evaluate underlying neural mechanisms,
nd serve as a powerful tool for cognitive enhancement.
e adult lifespan. A total of 174 participants spanning six decades of life
ges 20–79; ,30 individuals per decade) played a diagnostic version of
euroRacertomeasuretheirperceptualdiscriminationability(‘signtask’)
ithandwithoutaconcurrentvisuomotortrackingtask(‘drivingtask’;see
upplementary Information for details of NeuroRacer). Performance
as evaluated using two distinct game conditions: ‘sign only’ (respond
rapidly as possible to the appearance of a sign only when a green circle
as present); and ‘sign and drive’ (simultaneously perform the sign task
hile maintaining a car in the centre of a winding road using a joystick
hat is, ‘drive’; see Fig. 1a)). Perceptual discrimination performance was
aluatedusingthesignaldetectionmetricofdiscriminability(d9).A‘cost’
dex was used to assess multitasking performance by calculating the
ercentage change in d9 from ‘sign only’ to ‘sign and drive’, such that
eater cost (that is, a more negative percentage cost) indicates increased
terference when simultaneously engaging in the two tasks (see Methods
ummary).
gorithm was used to determine the difficulty levels of the game at
hich each participant performed the perceptual discrimination and
and
working memory6
deficits7,8
(thatis,diminished
epartment of Neurology, University of California, San Francisco, California 94158, USA. 2
uroscience, University of California, San Francisco, California 94158, USA. 4
1
month
control
Initial
visit
NeuroRacer
EEG and
cognitive
testing
and
or
6+
months
NeuroRacer
or
a
b
+ +

Video game training enhances cognitive control in older adults
https://www.youtube.com/watch?v=1xPX8F_wl0c

transferred to enhancements in their cognitive control abilities11
beyond
those attained by participants who trained on the component tasks in
isolation. In designing the multitasking training version of NeuroRacer,
during game play as a key mechanistic feature of the tr
In addition, although cost reduction was observed o
group, equivalent improvement in component task sk
byboth STTandMTT(seeSupplementary Figs 4 and
that enhancedmultitaskingabilitywas notsolelyther
component skills, but a function of learning to res
generated by the two tasks when performed concurr
the d9 cost improvement following training was not th
trade-off, as driving performance costs also diminish
group from pre- to post-training (see Supplementa
Notably in the MTT group, the multitasking pe
remained stable 6 months after training without boo
6 months, 221.9% cost). Interestingly, the MTT grou
cost improved significantly beyond the cost level attai
20 year olds who played a single session of NeuroRac
experiment 3; P , 0.001).
Next, we assessed if training with NeuroRacer le
enhancementsofcognitivecontrolabilitiesthatareknow
in ageing (for example, sustained attention, divided a
memory; see Supplementary Table 2)12
. We hypoth
immersed in a challenging, adaptive, high-interferen
for a prolonged period of time (that is, MTT) would
cognitive performance on untrained tasks that also dem
control. Consistent with our hypothesis, significant
interactions and subsequent follow-up analyses eviden
training improvements in both working memory (de
task with and without distraction7
; Fig. 3a, b) and su
†
–100%
–90%
–80%
–70%
–60%
–50%
–40%
–30%
–20%
–10%
Multitaskingcost(d′)
†
*
ba
1
month
later
6
months
later
Experiment 1: lifespan Experiment 2: training
Single task training
No-contact control
Multitasking training
0%
20s 30s 40s 50s 60s 70s Initial
Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan
(n 5 174) increased (that is, a more negative percentage) in a linear fashion
when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or
analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see
Supplementary Fig. 3), with significant increases in cost observed for all age
groups versus the 20-year-old group (P , 0.05 for each decade comparison).
b, Costs before training, 1 month post-training, and 6 months post-training
showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s
d 5 1.10), with follow-up analyses supporting a differential benefit for the
MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20).
{P , 0.05 within group improvement from pre to post, *P , 0.05 between
groups (n 5 46). Error bars represent s.e.m.
–100
0
100
200
Pre–post WM task with
distractions (RT)
RTdifference(ms)
†
*
a
–100
0
100
200
Pre–p
without d
RTdifference(ms)
†
b
RESEARCH LETTER
Nature 501, 97–101 (2013)
• 먼저 나이가 들면서 멀티태스킹 능력이 감소한다는 것을 해당 게임으로 증명

• 20-70대 별로 각각 30명을 대상으로 실험

transferred to enhancements in their cognitive control abilities11
beyond
those attained by participants who trained on the component tasks in
isolation. In designing the multitasking training version of NeuroRacer,
during game play as a key mechanistic feature of the tr
In addition, although cost reduction was observed o
group, equivalent improvement in component task sk
byboth STTandMTT(seeSupplementary Figs 4 and
that enhancedmultitaskingabilitywas notsolelyther
component skills, but a function of learning to res
generated by the two tasks when performed concurr
the d9 cost improvement following training was not th
trade-off, as driving performance costs also diminish
group from pre- to post-training (see Supplementa
Notably in the MTT group, the multitasking pe
remained stable 6 months after training without boo
6 months, 221.9% cost). Interestingly, the MTT grou
cost improved significantly beyond the cost level attai
20 year olds who played a single session of NeuroRac
experiment 3; P , 0.001).
Next, we assessed if training with NeuroRacer le
enhancementsofcognitivecontrolabilitiesthatareknow
in ageing (for example, sustained attention, divided a
memory; see Supplementary Table 2)12
. We hypoth
immersed in a challenging, adaptive, high-interferen
for a prolonged period of time (that is, MTT) would
cognitive performance on untrained tasks that also dem
control. Consistent with our hypothesis, significant
interactions and subsequent follow-up analyses eviden
training improvements in both working memory (de
task with and without distraction7
; Fig. 3a, b) and su
†
–100%
–90%
–80%
–70%
–60%
–50%
–40%
–30%
–20%
–10%
Multitaskingcost(d′)
†
*
ba
1
month
later
6
months
later
Experiment 1: lifespan Experiment 2: training
Single task training
No-contact control
Multitasking training
0%
20s 30s 40s 50s 60s 70s Initial
Figure 2 | NeuroRacer multitasking costs. a, Costs across the lifespan
(n 5 174) increased (that is, a more negative percentage) in a linear fashion
when participants were grouped by decade (F(1,5) 5 135.7, P , 0.00001) or
analysed individually (F(1,173) 5 42.8, r 5 0.45, P , 0.00001; see
Supplementary Fig. 3), with significant increases in cost observed for all age
groups versus the 20-year-old group (P , 0.05 for each decade comparison).
b, Costs before training, 1 month post-training, and 6 months post-training
showed a session X group interaction (F(4,72) 5 7.17, P , 0.0001, Cohen’s
d 5 1.10), with follow-up analyses supporting a differential benefit for the
MTT group (Cohen’s d for MTT vs STT 5 1.02; MTT vs NCC5 1.20).
{P , 0.05 within group improvement from pre to post, *P , 0.05 between
groups (n 5 46). Error bars represent s.e.m.
–100
0
100
200
Pre–post WM task with
distractions (RT)
RTdifference(ms)
†
*
a
–100
0
100
200
Pre–p
without d
RTdifference(ms)
†
b
RESEARCH LETTER
z
• 게임을 통한 고령층의 인지 능력 (멀티태스킹 능력) 개선 효과가 있음을 증명

• 60-85세 참가자 46명을 4주간 뉴로레이서를 통해서 훈련

• 그 결과 훈련 받지 않은 20대보다 더 잘 하게 되었으며,

• 연습을 하지 않고 6개월이 지나도, 능력은 그대로 남아 있었다.
Nature 501, 97–101 (2013)

(vigilance; test of variables of attention (T
group (Fig. 3c; see Supplementary Table
several statistical trendssuggestive of impro
ance on other cognitive controltasks (dual-
and changedetectiontask;see analysisofco
in Supplementary Table 2). Note that alth
and sustained attention improvements w
rapid responses to test probes, neither im
alternative version of the TOVA) nor accu
cant group differences, revealing that traini
of a speed/accuracy trade-off. Importantl
ments were specific to working memory a
cesses, and not theresult ofgeneralized incr
as no group X session interactions were fou
tasks (a stimulus detection task and the dig
see Supplementary Table 2). Finally, only
significant correlation between multitaski
withNeuroRacer)andimprovementsonan
task (delayed-recognition with distraction
(Fig. 3d).
These important ‘transfer of benefits’ sug
lying mechanism of cognitive control was c
MTT with NeuroRacer. To assess this furth
basis of training effects by quantifying even
tions (ERSP) and long-range phase coheren
of each sign presented during NeuroRacer
Wespecificallyassessedmidlinefrontalthe
EEG measure of cognitive control (for exam
tained attention15
and interference resolutio
prefrontal cortex. In addition, we analysed
between frontal and posterior brain region
measure also associated with cognitive con
memory14
and sustained attention15
). Se
power and coherence each revealed signifi
b Long-range theta coherence
Older adult post-training
PLV
(% coherence)
1 5 10
*
)
Initial
Older adults Younger adults
†
Midline frontal theta
Power(dB)
Initial
*
a
Older adults Younger adults
Older adult post-training
Single task
training
Multitasking
training
No-contact
control
3.40
3.05
2.70
2.35
1.65
1.30
0.95
0.60
0.25
–0.10
–0.45
–0.80
–1.15
–1.50
2.00
Nature 501, 97–101 (2013)
• 인지 능력의 개선은 brain activity 로도 동일하게 관찰되었다.

• 노년층 실험군에서 기술이 향상될수록 cognitive control을 관장하는  
 
prefrontal cortex 의 activity가 높아지는 것이 관찰되었다.

OPEN
ORIGINAL ARTICLE
Characterizing cognitive control abilities in children with
16p11.2 deletion using adaptive ‘video game’ technology: a
pilot study
JA Anguera1,2
, AN Brandes-Aitken1
, CE Rolle1
, SN Skinner1
, SS Desai1
, JD Bower3
, WE Martucci3
, WK Chung4
, EH Sherr1,5
and
EJ Marco1,2,5
Assessing cognitive abilities in children is challenging for two primary reasons: lack of testing engagement can lead to low testing
sensitivity and inherent performance variability. Here we sought to explore whether an engaging, adaptive digital cognitive
platform built to look and feel like a video game would reliably measure attention-based abilities in children with and without
neurodevelopmental disabilities related to a known genetic condition, 16p11.2 deletion. We assessed 20 children with 16p11.2
deletion, a genetic variation implicated in attention deficit/hyperactivity disorder and autism, as well as 16 siblings without the
deletion and 75 neurotypical age-matched children. Deletion carriers showed significantly slower response times and greater
response variability when compared with all non-carriers; by comparison, traditional non-adaptive selective attention assessments
were unable to discriminate group differences. This phenotypic characterization highlights the potential power of administering
tools that integrate adaptive psychophysical mechanics into video-game-style mechanics to achieve robust, reliable measurements.
Translational Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178; published online 20 September 2016
INTRODUCTION
Cognition is typically associated with measures of intelligence
(for example, intellectual quotient (IQ)1
), and is a reflection of
one’s ability to perform higher-level processes by engaging
specific mechanisms associated with learning, memory and
reasoning. Such acts require the engagement of a specific subset
of cognitive resources called cognitive control abilities,2–5
which
engage the underlying neural mechanisms associated with atten-
tion, working memory and goal-management faculties.6
These
abilities are often assessed with validated pencil-and-paper
approaches or, now more commonly with these same paradigms
deployed on either desktop or laptop computers. These
approaches are often less than ideal when assessing pediatric
populations, as children have highly varied degree of testing
engagement, leading to low test sensitivity.7–9
This is especially
concerning when characterizing clinical populations, as increased
performance variability in these groups often exceeds the range of
testing sensitivity,7–9
limiting the ability to characterize cognitive
deficits in certain populations. A proper assessment of cognitive
control abilities in children is especially important, as these
abilities allow children to interact with their complex environment
in a goal-directed manner,10
are predictive of academic
performance11
and are correlated with overall quality of life.12
For pediatric clinical populations, this characterization is especially
critical as they are often assessed in an indirect fashion through
intelligence quotients, parent report questionnaires13
and/or
behavioral challenges,14
each of which fail to properly characterize
these abilities in a direct manner.
One approach to make testing more robust and user-friendly is
to present material in an optimally engaging manner, a strategy
particularly beneficial when assessing children. The rise of digital
health technologies facilitates the ability to administer these types
of tests on tablet-based technologies (that is, iPad) in a game-like
manner.15
For instance, Dundar and Akcayir16
assessed tablet-
based reading compared with book reading in school-aged
children, and discovered that students preferred tablet-based
reading, reporting it to be more enjoyable. Another approach
used to optimize the testing experience involves the integration of
adaptive staircase algorithms, as the incorporation of such appro-
aches lead to more reliable assessments that can be completed in
a timely manner. This approach, rooted in psychophysical
research,17
has been a powerful way to ensure that individuals
perform at their ability level on a given task, mitigating the possi-
bility of floor/ceiling effects. With respect to assessing individual
abilities, the incorporation of adaptive mechanics acts as a
normalizing agent for each individual in accordance with their
underlying cognitive abilities,18
facilitating fair comparisons between
groups (for example, neurotypical and study populations).
Adaptive mechanics in a consumer-style video game experi-
ence could potentially assist in the challenge of interrogating
cognitive abilities in a pediatric patient population. This synergistic
approach would seemingly raise one’s level of engagement by
making the testing experience more enjoyable and with greater
sensitivity to individual differences, a key aspect typically missing
in both clinical and research settings when testing these
populations. Video game approaches have previously been
utilized in clinical adult populations (for example, stroke,19,20
1
Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; 2
Department of Psychiatry, University of California, San Francisco, San Francisco, CA,
USA; 3
Akili Interactive Labs, Boston, MA, USA; 4
Department of Pediatrics, Columbia University Medical Center, New York, NY, USA and 5
Department of Pediatrics, University of
California, San Francisco, San Francisco, CA, USA. Correspondence: JA Anguera or EJ Marco, University of California, San Francisco, Mission Bay – Sandler Neurosciences Center,
UCSF MC 0444, 675 Nelson Rising Lane, Room 502, San Francisco, CA 94158, USA.
E-mail: joaquin.anguera@ucsf.edu or elysa.marco@ucsf.edu
Received 6 March 2016; revised 13 July 2016; accepted 18 July 2016
Citation: Transl Psychiatry (2016) 6, e893; doi:10.1038/tp.2016.178
www.nature.com/tp
Figure 2. Project: EVO selective attention performance. (a) EVO single- and multi-tasking response time performance f
non-affected siblings and non-affected control groups). (b) EVO multi-tasking RT. (c) Visual search task performance
Characterizing cognitive control abilities in child
JA Anguera et al
•Project EVO (게임)을 통해서,

•아동 집중력 장애(attention disorder) 관련 특정 유전형 carrier 를 골라낼 수 있음

•게임에서의 Response Time을 기준으로 carrier vs. non-carrier 간 유의미한 차이

2017 디지털 헬스케어 컨퍼런스 리뷰 (v1)

2017 디지털 헬스케어 컨퍼런스 리뷰 (v1)

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à 2017 디지털 헬스케어 컨퍼런스 리뷰 (v1)

Similaire à 2017 디지털 헬스케어 컨퍼런스 리뷰 (v1) (20)

Plus de Yoon Sup Choi

Plus de Yoon Sup Choi (15)

Dernier

Dernier (20)

2017 디지털 헬스케어 컨퍼런스 리뷰 (v1)