Tag Archives: precision medicine
On January 30th, President Obama announced the funding of a possibly groundbreaking research program — The Precision Medicine Initiative (PMI).
Launched with a $215 million investment in the President’s 2016 Budget, the Precision Medicine Initiative will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.
Since the announcement the National Institutes of Health (NIH) have been hard at work convening a working group to build a foundation of rules, standards, and principles upon which they hope will generate meaningful outcomes: improving the health for all Americans by moving towards a more nuanced and individual view of health and wellness. As part of this project, the largest portiont of funding is being dedicated to the “development of a voluntary national research cohort of a million or more volunteers to propel our understanding of health and disease and set the foundation for a new way of doing research through engaged participants and open, responsible data sharing.”
As part of engaging this cohort the NIH is considering the role of patient generated data from mobile phones and sensors. Of course this is where we at Quantified Self Labs become intrigued. We have a long history of supporting individual’s ingenuity, insight, and expertise when it comes to personal data they collect on their own. Since 2008, we’ve been bringing together people to share their stories of self-tracking using a variety of different methods, some of which are no doubt being examined by researchers and NIH leadership for use in the proposed Precision Medicine Cohort.
We are excited to hear that the NIH is taking the time to listen to the American public through the use of online feedback forms. They are currently seeking comments on the use of mHealth for the Precision Medicine Cohort. Specifically, they want to know how people think that data generated by current and future biometric and physiologic sensors (such as heart rate and physical activity tracking devices) could be useful. Furthermore, the NIH isinterested in reactions to using smartphones to collect data on volunteer participants in the cohort. In the short description of the feedback request they highlight five key considerations:
1. Willingness of participants to carry their smartphone and wear wireless sensor devices sufficiently throughout the day so researchers can assess their health and activities.
2. Willingness of participants without smartphones to upgrade to a smartphone at no expense.
3. How often people would be willing to let researchers collect data through devices without being an inconvenience.
4. The kind of information participants might like to receive back from researchers, and how often.
5. Other ways to conveniently collect information from participants apart from smart phones or wearable devices.
We spent a little time browsing through the current crop of comments, which didn’t take long as there are only 52 at the time of this writing, to understand how people are thinking about mHealth, their data, and what it means to contribute personal data for public health research.
Privacy & Confidentiality
A common theme was a concern over what type of privacy protections would be implemented to protect volunteers who contribute their data. Comments ranged from outright fear of the government “tapping into personal computers, phones or other devices for collecting health information” to thoughts on access, control and protection of data contributed as part of this project.
As long as citizens can remain in control of the collection, flow and use of their data — and the government can guarantee anonymity, much benefit can be had from this. If it is going to be a government initiative, then standardized collection methodologies and protections will be required and the data should not “also” be collected for commercial usage. Clarity surrounding use models, sharing permissions and general privacy and security are a must.
I would also like to make sure that it is clear who ‘owns’ the data. Many times health professionals collect data about patients and then use it–often without letting the person know the data is aggregated and studied.
Diversity and Representativeness
As with any research study, there is a call to make sure that the sample being studied is representative of the population. This is especially important given the expressed interest in using different types of technology to track, measure, and engage with research participants. Those who have offered feedback have clearly picked up on the need to make sure that even those who are not current users of wearable sensors and/or smartphones are considered.
This will leave out the severely ill and disabled who are bedridden, unable to move, and definitely unable to manage a smart phone (as well as anyone whose illness causes cognitive challenges). Yet these are the patients who really need to be studied. So while it’s not a bad idea, please factor in selection bias and please, please, accommodate the most ill if you decide to implement this.
Interestingly, there are a few comments on how dependence on a smartphone may limit the diversity of the cohort.
Smartphones, etc. will limit the diversity of the sample. For example, large areas of Appalachia will be excluded. Internet access is limited in rural areas; even if available, the technology is not adequate to support many applications.
While the diversity of the sample is something that must be considered, I’m left wondering about the true impact of using smartphones for data collection as recent data from Pew suggests that nearly two-thirds of Americans own a smartphone. It appears that economic diversity may be the limiting factor when it comes to using smartphones as part of the cohort. Clearly there is a gap in smartphone ownership across income and education levels, but also when we consider geographic location.
However, one of the considerations clearly states that an “upgrade to a smartphone at no expense” may be part of this research initiative. What is the true cost of this free upgrade? It appears to be unknown, but it’s going to be important to think about the recurring costs of data plans associated with these devices, especially when data transfer is part of participating in the research.
The issue probably depends on the participant and what is being provided. I’m assuming providing the smartphone would include providing the data plan, which can be expensive.
Barriers to Using mHealth
While using wearable sensors, apps, and smartphones to collect personal health data is growing trend, there are still concerns regarding how long individuals will actively use the tools. While the Fitbits of the world are selling millions of devices, we don’t really know how long people are willing to use them, and if they’re willing to contribute that data to research (although preliminary studies are promising).
There is concern that adding devices, measurement, and data collection to the everyday lives of individuals may present a burden and that the data will suffer from inconsistent engagement. The “life gets in the way” of participating in research is a common, and justified, refrain.
In our experience, conducting a number of trials using devices, many people do not carry or use devices for very long that do not fit into their existing habits. People may use new devices provided by researchers for short periods of time, if provided research support, monitoring, and prompting, but for large scale trials and longer assessment periods, adherence will fall off considerably.
Benefits of mHealth
It’s not all doom and gloom and negativity though, there is an overwhelmingly positive outlook on the use of wearables and mobile-based data collection for informing personal and public health. From researchers to individuals already using these devices to understand their own health, the current comments are full of support for exploring new technologies, sensing capabilities, and personal data collection methods to deliver personal precision medicine.
Integration of data from personal sensors and mobile devices has the potential to change the role of the patient in their health and care, to improve the accuracy and value of behavioral data in healthcare, and to provide unparalleled insights into the how and why of behavior change in health.
I am 60 but use a Withing Blood Pressure Cuff, Basis Peak and Alive EKG. I am a cancer survivor of Hodgkin’s Lymphoma and Breast Cancer. Due to my treatment I am left with complications that need to be monitored. I feel more secure using these devices. Knowledge is power.
In the name of transparency, below you’ll find the full text of our comments submitted to the NIH.
The world is changing, more information is flowing across what were once impassable borders. That information is changing the way we see the world, and how we understand ourselves. Health and healthcare is a big part of this evolution in information flows. At Quantified Self, we’ve seen hundreds of examples from individuals around the world who have used personal data collected through a variety of means to impact and understand their health. It’s with these examples in mind that I’d like to share my thoughts on using mHealth for the Precision Medicine Cohort.
The considerations mentioned above are clear, but vague enough to make the only appropriate answer, “it depends.”
Will participants carry their smartphones and wear sensors? It depends. It depends on how participants are recruited and what they are being asked to contribute to. Do they have a say in what questions are being asked? Is constant data collection a requirement for participation, or can participants engage inconistently, contributing data sparsely? What activities would researchers want to understand? Some individuals may be perfectly okay with contributing physical activity data, but not geolocation data supplied by a smartphone GPS.
Will participants be willing to upgrade to a smartphone? It depends. Will they also be compensated for an increase in costs associated with data plans so that their smartphone can send data to a researcher? Will they be able to use their smartphone for personal use, downloading apps and services freely? Is the smartphone upgrade dependent on participation in research for a given amount of time, and if so, how long? Is this form of compensation coercive for those who have never been able to afford a smartphone? Including a socially and economically diverse population in the cohort while not introducing increased costs and burden will be important to consider.
Will participants allow researchers to collect data, and how often? It depends. Will participants play an active role in the data collection, or will it be passively collected through sensors and background data transfer? Will participants be engaged in the full research process, helping develop the questions, data collection methods, and even the analysis? Will participants be able to choose the frequency of data transfer that makes sense to them and their lifestyle? It’s possible to envision participants contributing infrquently over a long timescale. Will these type of contributions help push precision medicine forward?
What kind of information would participants like to receive, and how often? It depends. Are participants able to access and control the data they create? Do they get to choose what researchers or studies are able to use their data? Will they receive the same information and data that researchers have access to? All participants may not actually want their data, but I believe that the research community has an ethical obligation to be open, honest, and transparent when it comes to what we collect. This includes giving data back to participants, especially with regards to health data. Participants should also receive timely access to any and all research findings. Published work that results from Cohort data should be published in open access and freely available outlets (and open data sets should be published simultaneously).
What other ways might their be to collect information from participants? It depends. What type of information is important to researchers? Many different research groups, and even commercial entities, have discovered the power of using text messaging for data collection. Text messaging should be examined as a possible large-scale and low-cost (financial and time) method for understanding many different aspects of personal health.
Smartphones, wearable devices, and personal data applications and services represent an unprecedented look into our daily lives. The Precision Medicine Initiative should continue to explore how best to incorporate the public in the process of crafting the protocols and methods. The public will be the participants, and we should remember that this research is being conducting with them, not on them. A spirit of openness and transparency should guide this important work.
The public comment period for input on using mHealth for the Precision Medicine Cohort closes on Friday, July 24. We invite you to add your thoughts and ideas.
I’m filling in for Ernesto. I hope you enjoy this week’s list of articles and visualizations!
Don’t Relax: Uncomfortability Is The New Convenience by Adele Peters. This article looks at some products where a tolerable level of inconvenience is built into the design that prompts healthy actions or occasions for reflection.
Using Biometric Data to Make Simple Objects Come to Life by Liz Stinson. A whimsical project on display at Dublin Science Gallery’s Life Logging exhibition uses household objects to reflect and amplify the signals from your body.
The High Price of Precision Healthcare by Joseph Guinto. This is a fairly in-depth article on the relationship between drug and insurance companies and what happens when drug companies are given incentives for developing medicine for smaller populations. Not a breezy read by any means, but important for understanding the unintended consequences of changes made to the American healthcare system.
If Algorithms Know All, How Much Should Humans Help? by Steve Lohr. An exploration of a quandary that arises from machine learning methods. At what point do the automatic, self-learning processes mature to the point where any human intervention for correction is seen as injecting sullying “human bias.”
Networking the Coffee Maker by David Taylor. A fun, little project using an ElectricImp micro-controller to track when the office coffee pot was brewing. The author helpfully includes his code.
Using 750words.com and self-quantification by Morris Villarroel. Morris has been using 750words.com for the past three months and reflects on his previous attempts to use the service consistently and how he uses it now.
My brain on electricity: a 130 day tDCS experiment. This is a fascinating self-experiment where the author tries different tDCS montages while doing thirty minutes of dual n-back training.
My Path to Sobriety by ERAU. From Reddit, the poster shares the data from an effort to reduce one’s alcohol consumption.
From the Forum
This morning President Barack Obama announced a new Precision Medicine Initiative, a key $215 million piece of the proposed 2016 budget. Much has been written since last week’s State of the Union, when this initiative was first mentioned by President Obama. In brief, the initiative is an investment in new programs and funding initiatives at major government bodies that influence the current and future health of all Americans, including the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC). These programs will focus on developing “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.”
There is a lot of information being circulated about this new initiative, and we’ve collected some links below, but we’d like to highlight something directly related to our interests in self-tracking data, personal data access, and new models of participatory research. In this morning’s announcement President Obama mentioned a long-term goal of creating a participatory research cohort comprised of 1 million volunteers who will be called upon to share personal medical record data, genetic samples, biological samples, and diet and lifestyle information. This is truly an ambitious goal and we are happy to see the President take care to mention the importance of including patients and the individuals who collect this data in the decision making and research process. For example, here is the description of this specific program from the NIH Precision Medicine Infographic
Here at QS Labs, we’re dedicated to helping create and grow a culture that enables everyone to generate personal meaning from their personal data. Sharing, participation, and exploring new models of discovery are a core themes we’re exploring as part of our QS Access work. We’ll be following this initiative as it moves from today’s announcement to tomorrow’s reality. Be sure to stay tuned to our QS Access Channel for more updates as we learn more.
Learn more about the Precision Medicine Initiative
NIH mini site describing the initiative
White House Blog: The Precision Medicine Initiative: Data-Driven Treatments as Unique as Your Own Body
FACT SHEET: President Obama’s Precision Medicine Initiative
A New Initiative on Precision Medicine by Francis Collins and Harold Varmus (New England Journal of Medicine).