Tag Archives: science
“Personal experimentation is simply tracking, on a schedule.”
Ian Eslick is a scientist, researcher, and self-tracker. His unique history has led him down a path towards understanding what it means to understand yourself and your health in and outside the world of healthcare. Ian’s health history helped push him down this path. Since being diagnosed with psoriasis he’s been confronted with the difficult task of figuring out triggers, effects, and treatments as his symptoms changed over time. Ian, began to explore self-tracking by mentally noting what was going on in his life and his symptom severity. You would think that this “in my own head” tracking methodology would limit analytical capabilities, but it helped Ian create mental models that informed more consistent and rigorous tracking methods, as well as influenced his future research.
In this talk below Ian describes that research, both personal and community-based, that explored the concept of helping people learn how to create and engage with personal experimentation.
“What I came to in conclusion after all of this is that N of 1 is overkill for QS. It’s unnecessary level of rigor. Ninety-five percent confidence intervals are about scientific causal proof, but what I want to know is am I making a better decision. Is data improving my decision in some measurable way? Not is it a perfect decision or do I have proof. So we want to value personal significance over statistical significance. Statistical significance says that if I run this trial twenty more times I’m likely to get the same result, but what I want to know is should I keep doing this and in QS we’re never going to stop keep experimenting, in a way, because our life keeps going.”
Scanadu, a valued annual sponsor of the Quantified Self, invites you to donate your spit for science! Check out the announcement below to learn more.
Do you have a cough, fever, sore throat, achy muscles, and/or a runny nose?
If you do, you can help us better understand the biology of upper respiratory infections and/or the flu. Donating your spit may, down the line, help reduce unnecessary antibiotic use, help limit the spread of respiratory pathogens and contribute to the design of a new product.
Benefits of participating:
- An opportunity to participate in science and help Scanadu
- A $10 Amazon gift card
- Upon request, we will be happy to share your experimental results. It is understood that this is not an approved diagnostic test and results should not be used for medical diagnosis.
Who can participate?
- Children 6+ years and adults (parent/guardian consent required for children under the age of 18)
- Currently experiencing a common cold, sore throat or influenza
- Currently living in the United States
How do I participate?
Click the following link for more information and to sign-up: http://bit.ly/1dQNk8n
Need more information? For questions regarding participation in this study and the collection of saliva, please contact firstname.lastname@example.org or email@example.com
Today’s breakout session preview for the upcoming QS conference comes from Daniel Gartenberg, organizer of the Washington DC QS meetup group. Here is Daniel describing his session “Is QS Science? The Role of QS in Scientific Discovery:”
Do you believe in the power of using Quantified Self to solve some of science’s toughest questions, but have concerns about the validity of QS data? There is actually a long scientific tradition of N=1 studies (i.e. studies conducted with a single participant). Additionally, there are various advantages of N=1 studies, such as repeated, longitudinal, and naturalistic data. These advantages of N=1 studies enable the personalization of treatments because they can take into account individual differences.
Yet N=1 studies are atypical in current scientific research. We will be discussing why the scientific community frowns on N=1 studies, and how we can alleviate some of the scientific community’s concerns regarding QS. This involves understanding what makes something ‘Science.’ Additionally, this will involve identifying threats to validity when conducting studies and QS research. Threats to validity include, but are not limited to: Mortality, History, Maturation, Treatment Fidelity, Treatment Interaction, Compensatory Rivalry, Regression Towards the Mean, and Reactivity.
If done correctly, QS can be a new standard in scientific rigor, but this will require a concerted and collaborative effort by the QS community that will involve developing a system where QSers can post their data to the cloud and have the data aggregated and analyzed across individuals (e.g. curetogether). The potentials and challenges for creating a QS database will be discussed.
Come to this breakout session if you are trying to make sense of your QS data, are interested in the scientific method, are interested in data analysis techniques, or want to create systems and tools that make QS data meaningful to the general population.
Gustavo Glusman is a member of Leroy Hood’s group at the Institute for Systems Biology. At a recent Hood group retreat, the main topic of conversation was Quantified Self! In the video below, Gustavo gives a fascinating recap of the retreat, including how the researchers talked about QS, what experiments they did on themselves, and the main challenges they see with QS from a scientific perspective. (Filmed by the Seattle QS Show&Tell meetup group.)
This special guest post in our ongoing QS 101 series comes to us from Dan Gartenberg, our great QS-Washington DC meetup organizer, and his fellow graduate students in the Human Factors and Applied Cognition program at George Mason University.
Turning Scientific Concerns into Strengths for Quantified Self Experimentation
By Dan Gartenberg, Ewart de Visser, and Jonathan Strohl
Quantified Self and Science are not oil and water. They are intertwined with one another and have a long history together. Though some scientists may not hold QS in high regard, and have the following claims:
“these studies lack validity!”
“a study of a single individual will not generalize to the broader population”
“the possibility of experimenter bias makes your findings highly suspicious and inconclusive.”
“is your effect even real?”
While these are legitimate concerns, QS is science. And if we keep in mind the scientific method when conducting QS research, this strengthens the validity of our QS projects.
History speaks for itself. QS studies are actually in line with an age-old scientific tradition: The n=1 study. Back in the day, scientists did not have large labs and took to experimenting on themselves. For example, Hermann Ebbinghaus, one of the first cognitive psychologists, conducted experiments on himself to reveal the process of learning and forgetting. As a scientist he used a level of rigor that was expected of scientists at the time, and more importantly, Eddinghaus contemplated reasonable mechanisms that explained his results. Science gives us the tools to make precise measurements, and QS, with its emphasis on improvement of the self, provides a social framework for people to discuss novel phenomena. In this article we demonstrate how science lends itself to QS and how the scientific method provides us with useful tools for self-discovery. We first recommend a framework for conducting QS experiments and then discuss the scientific methods to keep in mind.
1) Achieve your goal: Unlike most experimental research, in QS our main objective is more often than not self improvement. A frequently used approach to improve yourself is by throwing the kitchen sink at the problem until you get the sought after effect. When we make this process social, we can then discuss with others what they are doing and how they think they are being affected by what they are doing. Based on this information, we get a better understanding of how to most effectively modify our behaviors for the desired outcome.
2) Use a simple design. When presenting your data the scientific establishment might criticize your conclusions because it was not the gold standard “double blind randomized control trial.” But running the right type of design isn’t the be-all-end-all of good science. If you see a difference and have a reasonable mechanism that explains the difference, with no viable alternative explanations – you are solid.
3) Stats don’t matter as much. Just graph it! One of the biggest sources of confusion is how to analyze QS data and knowing the right stats to run. But statistics are only really useful when predicting small effects or for more complex prediction models. In QS, any change is usually meaningful. For example, if you are tracking your mood and you see a small improvement it is likely meaningful to you. So don’t worry too much about statistics.
To discuss the roles of QS and science, we’ll use a dataset that we generated as a case study. After reading the 4 Hour Body by Tim Ferris, three friends all had the same goal of losing weight. None of us were extremely overweight at the time, but we could all stand to lose about 10-pounds. This inspired us to create the 10-Pound Challenge, where we competed to lose weight and either did a slow carb diet or a low carb diet. We then weighed ourselves every morning.
Quantified Friends: The 10-Pound Challenge:
Here are some issues and concepts that you should consider when making sense of your findings and how Science and QS can benefit one another:
|Threat to validity||Definition||10-pound application|
|Mortality||When your manipulation affects the likelihood of whether or not you respond to the measure of interest (i.e. you don’t respond to a survey out of embarrassment).||There are almost no skipped days over the course of our study. This demonstrates how QS can be used as a way to address the problem of mortality by making data collection more social. This makes people more accountable and motivated to input their data.|
|History||When external events from the environment impact the variables of interest.||The diet was made social when we shared our progress with one another. This socialization, where we competed to lose weight, may explain our progress. We knew that this resulted in alternative explanations, but it didn’t matter because we simply wanted to lose weight and were pulling out all the stops to help us reach our goal.|
|Maturation||When over the course of a study you have changed in other ways that have confounded the impact of the variables in your experiment.||In QS our goal is to actually change and mature. In our QS project the intention was to lose weight and the mechanism was only as important as it was necessary to understand and use in order to promote our increased weight loss.|
|Treatment Fidelity||In science we usually compare a treatment group to a control group, but what if the treatment is not much different from the control? (i.e. there is not a good counterfactual).||We administered relative treatments based on each of our unique situations (this is a common issue with QS). For example, one of us already had a relatively strict diet. He was able to make precise modifications to his diet in order to promote weight loss, whereas; the other two QSers made broader changes. This prevented us from making precise claims about how the diet affected weight loss. Though we still got the general idea that the diet worked.|
|Treatment Interaction||When the variable that you manipulate interacts with other variables that explain the outcome.||When undertaking the 10-pound challenge we frequently told people about the challenge. This in turn made us more accountable for what we consumed due to social pressure. In this example, the response to the treatment interacted with the social environment in a way that made us consume healthier meals and lose weight. Since in QS we are not as fixated on control, we can see how these interactions unfold in the environment and discuss them with others in order to confirm or deny our intuitions. This provides us with ways to explore new ideas and mechanisms.|
|Compensatory Rivalry||When the control group is aware that they are not getting the treatment and in turn seeks out other alternatives.||In the case of the 10-pound challenge, there was no control group. Control groups do not play a large role in QS because of the focus on self-improvement. This is an issue that can be addressed by the scientific method of an A-B-A design where the QSer acts as their own control group.|
|Regression Towards the Mean||This intuitive premise from science is the basic idea that at the extremes of a behavior you are increasingly likely to gravitate towards the mean.||QSers should be particularly sensitive to this because people frequently try to improve on a behavior when they are at an all time low or an all time high.|
|Reactivity||When your response is affected by external factors, for example, social desirability.||In QS, reactivity can actually be used to improve upon outcomes. In our example, there was a social desirability to discuss what was and was not working. At one point the team members independently agreed that eating too many slow-carbs (legumes) was hindering their progress. We then made the appropriate changes to our behaviors and found increased improvements.|
And these are just some of the threats to validity that QSers can consider to improve their projects. So look out for more to come!
We would like to thank Dr. Patrick McKnight and the MRES group (http://mres.gmu.edu) for providing helpful insights on our QS project.