Advancing Citizen Science Approach to He
Advancing Citizen Science Approach to Health Self-experimentation:
What I Learned from the Soylent Diet Geeks
Markéta Dolejšová (NUS)
Outline
•
1/ Digital Health: Personal Health & Digital
Culture
•
2/ Quantified Self-experimentation & Selftracking
•
3/ Online Communities of Health Selfexperimenters
•
4/ Self-experimentation as Extreme Citizen
Science
•
5/ Case Study: Soylent Diet Community
Digital Health: Personal Health & Digital Culture
Online Health Interest Communities
Personal Informatics
Ubiquitous Computing
Internet of Things
Social Media
Self-quantification
Citizen Science
Self-knowledge by Numbers
Ubiquitous (Bio) Data & Healthcare
Quantified Self-experimentation & Self-tracking
•
Self-experimentation: Researcher as a study subject (testing hypotheses on and by yourself)
•
Self-tracking: technology-aided self-experimentation (collecting, monitoring, recording, sharing and analyzing data about
the self through tech devices)
•
Self-tracking apps; social networking sites; online diaries, shared spreadsheets, cloud computing platforms
•
Data: body weight, food intake, physical and cognitive performance, sleep quality etc.
•
Self-trackers as “a new breed of self-experimenters” (Wolf, 2011) => shift from academia to hobbyism
Online Communities of Health Self-experimenters
•
•
•
•
•
•
Quantified Self - personal health and wellness improvement
23andMe; Ubiome - DTC “omics", low-cost kits for at-home collection of biological samples
(sweat, saliva, poop)
Longecity; Biohack.me - “self-biohackers”: supplements, nootropics, psychedelics
microdosing, subdermal sense-enhancing implants etc.
Open Humans - Health data crowdsourcing
Soylent Discourse - personalized powdered diets
Self-improvers, Self-quantifiers, Life Hackers, Health Enthusiasts and Self-experimenters,
Amateur Health Scientists, Diet Geeks, Food & Nutrition Hobbyists
Health Self-experimentation…Why?
•
Big data & information overload, food & health products
overload => “Overload confusion” (Mitchell et al., 2005)
•
Requirements on body image and changing beauty standards
•
Public health issues (rising stats of malnutrition, cancer,
lifestyle diseases)
•
Ambiguous policies: public distrust in expert healthcare
systems => "becoming an expert on and of oneself”
•
What does it mean to be healthy in the age of “ubiquitous
(bio) data”?
Who should have the right to define what is “healthy”?
•
Citizen Science
•
•
•
Citizen Science: any form of active, non-professional participation in science
Production of “less-systematized and contextual knowledges generated outside the formal scientific
institutions” (Irwin, 1995)
People powered research (Zooniverse, FoldIt, Citizen Ornithology, Kite Mapping, Air Quality Egg, etc.)
Health Self-experimentation as
“Extreme” Citizen Science
•
Citizens as amateur researchers; to *some
extent* guided by science professionals
•
“Genuine participation” vs. “Citizens as
sensors” (Haklay, 2013; Quarooni et al, 2016)
•
Genuine participation: Extreme Citizen Science
(ExCiteS - UCLA)
•
Health self-experimentation: n=1 data
collection (individual); n=we data evaluation
(online community)
•
Self-knowledge & peer-learning
Health Self-experimentation Research
•
•
•
•
•
•
•
•
•
•
•
•
•
Opportunities:
Democratization of expert science
Peer-learning, literacy advancement, selfawareness
Flexibility, time, price of n=1 studies => broader
scope of studied issues
New streams of data valuable for professional
research
Challenges:
Amateur Research & Data validity
Data reliability (n=1)
Personal health risks (amateur expertize)
Personal and collective responsibility (risk
scenarios)
Data privacy and security (personal biodata sharing)
Socio-economic accessibility (tech resources)
Techno-utopianism; neoliberal efficiency & “SelfTaylorization”; corporate surveillance
Case Study: Soylent
Quantified Self-experimentation with Personal Diets
Soylent is…
•
Full food replacement containing
all essential macro + micro
nutrients that human body
needs to stay “healthy”
•
Reverse-engineered food
powder introduced as a DIY selfexperiment
•
Continually developed by online
community of nutrition hobbyists
•
Premise: There is no universal
"healthy" diet and various
nutrients affect individual human
biochemistries differently
“I haven’t eaten a bite of
food in last 30 days and it
changed my life”
Robert Rhinehart
DIY Soylent Market
•
•
•
•
•
•
2014: Crowd-funding (US$3M)
2014: Rosa Labs at Silicon Valley
Open source recipe => customized DIY soylent
formulas
DIY soylent marketplace
Self-experimentation and self-tracking of
metabolic reactions to the diet
Findings discussed within the soylent user
community:
— discourse.soylent.me
— reddit.com/r/soylent
— diy.soylent.me
Soylent Discourse Forum
(n > 100 000 posts; 9 600 users)
Soylent Diet Self-Experimentation: Design
Challenges in Extreme Citizen Science Projects
•
•
Aim: Understand how the soylent dieters perform
nutrition literacy, risk awareness, and responsibility for
their self-experimental research
RQs:
(1) How do the data-sharing activities in the soylent
discourse forum impact users’ nutrition literacy and
understanding of risks related to the soylent diet?
(2) What do the experiences of soylent users tell us about
the challenges and opportunities in extreme citizen
science projects?
Methods: Ethnography
— Online participant observation at soylent discourse forum
— Live interviews (n=43): EU / USA / Asia (04/2015-08/2016)
•
Findings
Demographics
•
Mostly males 25-35 yrs: “busy” entrepreneurs, tech enthusiasts, makers & tinkerers, self-improvers,
diabetics, all-around-sceptics etc.
•
21 soylent DIYers, 5 commercial soylent vendors, and 17 consumers of a ready-to-use soylent
•
Mostly 2 soylent meals daily; only 8 people maintain a 100% soylent diet
Findings
Motivations
•
Self-improvement
"long-term interest in longevity" (P43)
"escaping the vicious cycle of crappy foods
available all around” (P24)
•
Nutritional efficiency
“I now view every meal in comparison to soylent.
Every meal is more expensive, less nutritious and
more time-consuming to make.” (P12)
•
Information overload
“I feel overwhelmed by the volume of misleading
and nonscientific food information (...) soylent
means no variety, no need to make a choice.” (P33)
•
Scepticism with expert systems
“I'm just not a friend of a "click here, magic
happens, result” blackbox-thing.” (P8)
Findings
Nutrition literacy
•
Nutrition Knowledge
“I didn't know much before I've started
with soylent – but it has improved a lot.”
(P7)
•
I guess I'm now more aware of how
nutrients work (...) I trust myself now
much more in terms of food
choices” (P19)
•
Eating behavior
“I don’t need to go to supermarket
almost at all now…and if I do, I feel I'm
more aware of what I'm buying. I don’t
buy crap anymore.” (P10)
Findings
Community Support vs.
In-group Bias
•
Online Troubleshooting and peer-help:
”There is a lot of mutual feedback,
people are sharing ideas, but also
research studies, not just personal
opinions.” (P40)
•
In-group bias:
“I don't see any problem trusting the
people in the community. We all pursue
similar goals and are definitely much
more honest about what we say than
big food and health corporations.” (P29)
•
“You cannot fully understand it unless
you try it by yourself” (P30)
Findings
Risk Awareness & Responsibility
•
Literacy + scepticism => risk taking:
“There are no health studies or clinical
trials, so you cannot be totally sure if the
soylent system works. Still, tell me how
many people care about these studies for,
say, potato chips.” (P8)
•
Libertarian individualism & responsibilities:
“of course there are some risks, but as
long as I'm the one concerned and
affected, I can deal with those risks. It’s my
body, after all.” (P10)
•
Limited community reach:
“Well if I feel sick, I would go see a doctor,
of course.” (P22)
Findings
Data Validity and Privacy
•
Personal (bio)data sharing:
“I track my soylent routine every day, I also track my steps, my runs, my sleep cycle – the more info I get,
the better I feel about myself.” (P19)
•
Data validity of amateur science:
“The way people share their soylent records is rather random...there is no reasonable option to upload
more detailed charts and compare them with others.” (P19)
•
Data privacy and security:
“Open sharing of my personal biodata certainly makes me feel uncomfortable (...) we need a system that
would allow o submit such data anonymously and securely.” (P22)
Discussion
•
•
•
•
•
•
•
Soylent as citizen participation in food/health science: consumers as
active agents of knowledge production
Online community as environment of trust: peer-help, peer-learning
=> nutrition literacy advancement
New streams of potentially valuable data about human diet
In-group bias in the online forum that might lead to false beliefs in
safety of the soylent diet
Lack of personal and collective responsibilities for adverse outcomes
of soylent experiments
Lack of research protocols to support validity of soylent as research
practice
Ambiguous data security settings of the soylent forum
How to support health self-experimentation as an autonomous
knowledge production and a valid extreme citizen science practice?
Design Recommendations for Extreme Citizen Science
•
I) Support the validity and reliability of findings through peer-defined data-collection and evaluation
protocols
•
2) Provide transparent feedback on the identity of who uses the data. Enable customizable privacy settings.
•
3) Support systematic data-sharing by providing a clearly defined space for the reporting different types of
data
•
4) Create a system to encourage users to share and discuss adverse experiences (e.g. health discomfort)
•
5) Promote a personal and collective responsibility; e.g. by setting up a crowdsourced fund to support
members who experience negative effects of self-experimentation.
If you have anything re:soylent, QS, self-biohacking etc. to share, let me know!
[email protected]
www.materie.me
Bibliography
Muki Haklay. 2013. Citizen science and volunteered geographic information: Overview
and typology of participation. In Crowdsourcing geographic knowledge Springer,
105-122.
Alan Irwin. 1995. Citizen Science: A Study of People, Expertise and Sustainable
Development . Psychology Press.
Vayne Mitchell, Walsh, G., & Yami, M. 2005. Towards a conceptual model of consumer
confusion. Advances in consumer Research 32, 1, 143-150
Danial Qaurooni, Ali Ghazinejad, Inna Kouper, and Hamid Ekbia. 2016. Citizens for
Science and Science for Citizens: The View from Participatory Design. In Proceedings
of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16).
ACM, New York, NY, USA, 1822-1826. DOI=http://dx.doi.org/
10.1145/2858036.2858575
UCLA ExCiteS group. 2013. Projects. Retrieved May 24, 2016 from https://
www.ucl.ac.uk/excites/projects
Gary Wolf. 2011. What is the quantified self? Quantified Self. Retrieved September 12,
2016 from http:// quantifiedself.com/2011/03/what-is-thequantified-self/
What I Learned from the Soylent Diet Geeks
Markéta Dolejšová (NUS)
Outline
•
1/ Digital Health: Personal Health & Digital
Culture
•
2/ Quantified Self-experimentation & Selftracking
•
3/ Online Communities of Health Selfexperimenters
•
4/ Self-experimentation as Extreme Citizen
Science
•
5/ Case Study: Soylent Diet Community
Digital Health: Personal Health & Digital Culture
Online Health Interest Communities
Personal Informatics
Ubiquitous Computing
Internet of Things
Social Media
Self-quantification
Citizen Science
Self-knowledge by Numbers
Ubiquitous (Bio) Data & Healthcare
Quantified Self-experimentation & Self-tracking
•
Self-experimentation: Researcher as a study subject (testing hypotheses on and by yourself)
•
Self-tracking: technology-aided self-experimentation (collecting, monitoring, recording, sharing and analyzing data about
the self through tech devices)
•
Self-tracking apps; social networking sites; online diaries, shared spreadsheets, cloud computing platforms
•
Data: body weight, food intake, physical and cognitive performance, sleep quality etc.
•
Self-trackers as “a new breed of self-experimenters” (Wolf, 2011) => shift from academia to hobbyism
Online Communities of Health Self-experimenters
•
•
•
•
•
•
Quantified Self - personal health and wellness improvement
23andMe; Ubiome - DTC “omics", low-cost kits for at-home collection of biological samples
(sweat, saliva, poop)
Longecity; Biohack.me - “self-biohackers”: supplements, nootropics, psychedelics
microdosing, subdermal sense-enhancing implants etc.
Open Humans - Health data crowdsourcing
Soylent Discourse - personalized powdered diets
Self-improvers, Self-quantifiers, Life Hackers, Health Enthusiasts and Self-experimenters,
Amateur Health Scientists, Diet Geeks, Food & Nutrition Hobbyists
Health Self-experimentation…Why?
•
Big data & information overload, food & health products
overload => “Overload confusion” (Mitchell et al., 2005)
•
Requirements on body image and changing beauty standards
•
Public health issues (rising stats of malnutrition, cancer,
lifestyle diseases)
•
Ambiguous policies: public distrust in expert healthcare
systems => "becoming an expert on and of oneself”
•
What does it mean to be healthy in the age of “ubiquitous
(bio) data”?
Who should have the right to define what is “healthy”?
•
Citizen Science
•
•
•
Citizen Science: any form of active, non-professional participation in science
Production of “less-systematized and contextual knowledges generated outside the formal scientific
institutions” (Irwin, 1995)
People powered research (Zooniverse, FoldIt, Citizen Ornithology, Kite Mapping, Air Quality Egg, etc.)
Health Self-experimentation as
“Extreme” Citizen Science
•
Citizens as amateur researchers; to *some
extent* guided by science professionals
•
“Genuine participation” vs. “Citizens as
sensors” (Haklay, 2013; Quarooni et al, 2016)
•
Genuine participation: Extreme Citizen Science
(ExCiteS - UCLA)
•
Health self-experimentation: n=1 data
collection (individual); n=we data evaluation
(online community)
•
Self-knowledge & peer-learning
Health Self-experimentation Research
•
•
•
•
•
•
•
•
•
•
•
•
•
Opportunities:
Democratization of expert science
Peer-learning, literacy advancement, selfawareness
Flexibility, time, price of n=1 studies => broader
scope of studied issues
New streams of data valuable for professional
research
Challenges:
Amateur Research & Data validity
Data reliability (n=1)
Personal health risks (amateur expertize)
Personal and collective responsibility (risk
scenarios)
Data privacy and security (personal biodata sharing)
Socio-economic accessibility (tech resources)
Techno-utopianism; neoliberal efficiency & “SelfTaylorization”; corporate surveillance
Case Study: Soylent
Quantified Self-experimentation with Personal Diets
Soylent is…
•
Full food replacement containing
all essential macro + micro
nutrients that human body
needs to stay “healthy”
•
Reverse-engineered food
powder introduced as a DIY selfexperiment
•
Continually developed by online
community of nutrition hobbyists
•
Premise: There is no universal
"healthy" diet and various
nutrients affect individual human
biochemistries differently
“I haven’t eaten a bite of
food in last 30 days and it
changed my life”
Robert Rhinehart
DIY Soylent Market
•
•
•
•
•
•
2014: Crowd-funding (US$3M)
2014: Rosa Labs at Silicon Valley
Open source recipe => customized DIY soylent
formulas
DIY soylent marketplace
Self-experimentation and self-tracking of
metabolic reactions to the diet
Findings discussed within the soylent user
community:
— discourse.soylent.me
— reddit.com/r/soylent
— diy.soylent.me
Soylent Discourse Forum
(n > 100 000 posts; 9 600 users)
Soylent Diet Self-Experimentation: Design
Challenges in Extreme Citizen Science Projects
•
•
Aim: Understand how the soylent dieters perform
nutrition literacy, risk awareness, and responsibility for
their self-experimental research
RQs:
(1) How do the data-sharing activities in the soylent
discourse forum impact users’ nutrition literacy and
understanding of risks related to the soylent diet?
(2) What do the experiences of soylent users tell us about
the challenges and opportunities in extreme citizen
science projects?
Methods: Ethnography
— Online participant observation at soylent discourse forum
— Live interviews (n=43): EU / USA / Asia (04/2015-08/2016)
•
Findings
Demographics
•
Mostly males 25-35 yrs: “busy” entrepreneurs, tech enthusiasts, makers & tinkerers, self-improvers,
diabetics, all-around-sceptics etc.
•
21 soylent DIYers, 5 commercial soylent vendors, and 17 consumers of a ready-to-use soylent
•
Mostly 2 soylent meals daily; only 8 people maintain a 100% soylent diet
Findings
Motivations
•
Self-improvement
"long-term interest in longevity" (P43)
"escaping the vicious cycle of crappy foods
available all around” (P24)
•
Nutritional efficiency
“I now view every meal in comparison to soylent.
Every meal is more expensive, less nutritious and
more time-consuming to make.” (P12)
•
Information overload
“I feel overwhelmed by the volume of misleading
and nonscientific food information (...) soylent
means no variety, no need to make a choice.” (P33)
•
Scepticism with expert systems
“I'm just not a friend of a "click here, magic
happens, result” blackbox-thing.” (P8)
Findings
Nutrition literacy
•
Nutrition Knowledge
“I didn't know much before I've started
with soylent – but it has improved a lot.”
(P7)
•
I guess I'm now more aware of how
nutrients work (...) I trust myself now
much more in terms of food
choices” (P19)
•
Eating behavior
“I don’t need to go to supermarket
almost at all now…and if I do, I feel I'm
more aware of what I'm buying. I don’t
buy crap anymore.” (P10)
Findings
Community Support vs.
In-group Bias
•
Online Troubleshooting and peer-help:
”There is a lot of mutual feedback,
people are sharing ideas, but also
research studies, not just personal
opinions.” (P40)
•
In-group bias:
“I don't see any problem trusting the
people in the community. We all pursue
similar goals and are definitely much
more honest about what we say than
big food and health corporations.” (P29)
•
“You cannot fully understand it unless
you try it by yourself” (P30)
Findings
Risk Awareness & Responsibility
•
Literacy + scepticism => risk taking:
“There are no health studies or clinical
trials, so you cannot be totally sure if the
soylent system works. Still, tell me how
many people care about these studies for,
say, potato chips.” (P8)
•
Libertarian individualism & responsibilities:
“of course there are some risks, but as
long as I'm the one concerned and
affected, I can deal with those risks. It’s my
body, after all.” (P10)
•
Limited community reach:
“Well if I feel sick, I would go see a doctor,
of course.” (P22)
Findings
Data Validity and Privacy
•
Personal (bio)data sharing:
“I track my soylent routine every day, I also track my steps, my runs, my sleep cycle – the more info I get,
the better I feel about myself.” (P19)
•
Data validity of amateur science:
“The way people share their soylent records is rather random...there is no reasonable option to upload
more detailed charts and compare them with others.” (P19)
•
Data privacy and security:
“Open sharing of my personal biodata certainly makes me feel uncomfortable (...) we need a system that
would allow o submit such data anonymously and securely.” (P22)
Discussion
•
•
•
•
•
•
•
Soylent as citizen participation in food/health science: consumers as
active agents of knowledge production
Online community as environment of trust: peer-help, peer-learning
=> nutrition literacy advancement
New streams of potentially valuable data about human diet
In-group bias in the online forum that might lead to false beliefs in
safety of the soylent diet
Lack of personal and collective responsibilities for adverse outcomes
of soylent experiments
Lack of research protocols to support validity of soylent as research
practice
Ambiguous data security settings of the soylent forum
How to support health self-experimentation as an autonomous
knowledge production and a valid extreme citizen science practice?
Design Recommendations for Extreme Citizen Science
•
I) Support the validity and reliability of findings through peer-defined data-collection and evaluation
protocols
•
2) Provide transparent feedback on the identity of who uses the data. Enable customizable privacy settings.
•
3) Support systematic data-sharing by providing a clearly defined space for the reporting different types of
data
•
4) Create a system to encourage users to share and discuss adverse experiences (e.g. health discomfort)
•
5) Promote a personal and collective responsibility; e.g. by setting up a crowdsourced fund to support
members who experience negative effects of self-experimentation.
If you have anything re:soylent, QS, self-biohacking etc. to share, let me know!
[email protected]
www.materie.me
Bibliography
Muki Haklay. 2013. Citizen science and volunteered geographic information: Overview
and typology of participation. In Crowdsourcing geographic knowledge Springer,
105-122.
Alan Irwin. 1995. Citizen Science: A Study of People, Expertise and Sustainable
Development . Psychology Press.
Vayne Mitchell, Walsh, G., & Yami, M. 2005. Towards a conceptual model of consumer
confusion. Advances in consumer Research 32, 1, 143-150
Danial Qaurooni, Ali Ghazinejad, Inna Kouper, and Hamid Ekbia. 2016. Citizens for
Science and Science for Citizens: The View from Participatory Design. In Proceedings
of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16).
ACM, New York, NY, USA, 1822-1826. DOI=http://dx.doi.org/
10.1145/2858036.2858575
UCLA ExCiteS group. 2013. Projects. Retrieved May 24, 2016 from https://
www.ucl.ac.uk/excites/projects
Gary Wolf. 2011. What is the quantified self? Quantified Self. Retrieved September 12,
2016 from http:// quantifiedself.com/2011/03/what-is-thequantified-self/