Today, we’re happy to announce that Fitabase has supported over 200 studies that used Fitbit devices to incorporate activity, sleep, and heart rate data as part of their innovative research. 

Since our founding in 2012, we’ve been working hard to serve our research customers and support their groundbreaking endeavours. When we first launched, we knew that there was a better way to conduct research that depends on accurate and reliable measures of physical activity. Typical research devices were expensive, uncomfortable, and hindered by poor battery life. In 2012, we saw that Fitbit devices were solving these exact issues. 

“Fitbit’s consumer-friendly technology provides our customers with an accurate, meaningful way to capture 24/7, real-time data and design innovative study protocols in ways not possible before.”  - Aaron Coleman, Fitabase CEO 

Fitbit devices offer a unique ability to measure and collect longitudinal activity, sleep, and heart rate data for a variety of studies, and thanks to our long standing collaboration with Fitbit we have been able to provide our customers with an advanced platform to access this data at the highest resolution. From studies examining hospital readmission rates after spine surgery, to unique personalized intervention programs for cancer survivors, our clients are developing the next generation of health research. We’re proud to serve those looking to solve complex issues in health and healthcare, including researchers at Arizona State University, Northwest University, UCSF, and the many other institutions and organizations who have used Fitabase to support their work.

“Fitbit has always been focused on empowering people to lead healthier, more active lives through data and insights. Fitabase has helped make our mission a reality with researchers by allowing them to better engage study participants, collect more objective data, and ultimately, develop new interventions that may positively influence patient care.” -Amy McDonough, vice president and general manager of Fitbit Group Health

With the nationwide focus on precision medicine, we see that Fitbit and the entire wearable industry are providing opportunities to measure, track, and engage with research participants in ways that were never before possible. With our scalable web-based platform and these always-on connected devices worn by millions of people, we’re excited to be pushing the envelope of discovery. Today we’re happy to announce that the Fitabase platform has processed and delivered over 2 billion minutes of data, and we look forward to providing our innovative solutions for researchers looking to improve health around the world. 

If you’re interested in incorporating wearable devices in your work, we’d love to help. Get in touch today.

Read the full press release here


News Links

Quietly, startup Fitabase hits major Fitbit health research milestones (MedCityNews)

Scientists Are Really Using Fitbits To Study Health (Buzzfeed)

Fitbit data has been utilized for various clinical trials (Engadget)

Fitbit Makes a Play for mHealth Engagement (mHealth Intelligence)

Is Your Fitbit Helping Scientists Study Health? (Yahoo)

Fitbit is the fitness tracker of choice for clinical trials (Wareable)

Fitbit is the weareable of choice for researchers (Gadgets & Wearables)



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This past week we had the pleasure of taking part in the annual Fitbit Captivate Summit, where our founder and CEO, Aaron Coleman, was invited to share insights on using wearables sensors for health research. Aaron presented on the Fitabase platform and how it’s being used to understand participant activity, sleep, and overall health in over 150 research studies.

To date, we’ve helped researchers access and analyze over 1.9 billion minutes of Fitbit data and counting. --Aaron Coleman, Founder & CEO, Fitabase

During the event Fitbit also announced the creation of Fitbit Group Health, which is an effort to combine initiatives across their various wellness offerings including their work with clinical researchers. We’re proud to be a recognized partner and are thrilled to play a role in helping researchers and institutions use Fitbit devices spanning a broad range of use cases. We’re just getting started and we can’t wait to help create the next generation of health research studies.

For those of you who couldn’t make the event, we’re happy to share our slides here.

Are you interested in using Fitbit devices in your research projects or grant applications? We’re here to help you integrate the power of participant data. Get in touch today!



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Fitabase is proud to announce the publication of two new research studies that used Fitbit activity tracking devices to measure sleep. Since the launch of the Fitbit Classic in 2009, users have been able to track and better understand their sleep patterns. Recent advances to device firmware have made it even easier through the implementation of automatic sleep detection and tracking. Researchers have long been interested in how well Fitbit devices track sleep. In fact, one of the earliest published studies 1 using Fitbits focused on sleep, not steps!

Before we dive into these studies, let’s stop here for a moment and learn about how sleep is measured in research settings. Typically, sleep researchers rely on a thorough combination of measurements called Polysomnography (PSG) to measure sleep. Often you’ll see PSG referred to as a sleep study. During overnight PSG monitoring, participants are  hooked up to a variety of different measurement devices in order to track heart rate, limb movement, eye movement, brain waves, breathing rate/effort, blood oxygen levels, and even snoring. As you can see in the image below, participating in a sleep study doesn’t look like a whole lot of fun.

Pediatric Polysomnogram. Photo by Robert Lawton (CC BY-SA 2.5)

Pediatric Polysomnogram. Photo by Robert Lawton (CC BY-SA 2.5)

In the two studies published last week, researchers wanted to see if different Fitbit devices were able to detect sleep in adolescents and children as well as the standard PSG recording.

In the first study2, published in the Journal of Otolaryngology Advances, researchers at UCLA Children’s Hospital and the University of Southern California recruited 14 children aged 3 to 11 years who were already scheduled for an overnight PSG (due to a prescribed evaluation for sleep disordered breathing). The children wore a Fitbit Flex on their non-dominant wrist and their parents helped to properly set the Flex for sleep mode. Data from the Fitbit was then compared to PSG readings for total time asleep, waking time, sleep efficiency, and movement.

Minute-level data from the Flex was also used to compare epochs of sleep, awake, and movement to PSG recordings. The researchers found the Flex to be very accurate for measuring total sleep time, only showing a difference of 0.2 minutes on average, but concluded this was due in large part to how the data was cleaned. While the results of the epoch (minute-level) comparison showed that the Flex had a hard time accurately distinguishing a waking period form a sleeping period, the researchers maintained that the Flex was beneficial for tracking movement in children with sleep disordered breathing,

“Our novel findings of a significant correlation between Fitbit and PSG related movements highlight the possibility that Fitbit measurements of movement might be used as a means to evaluate sleep disturbance in children with [sleep disordered breathing].”

In the second study3, accepted for publication in Physiology & Behavior, researchers from numerous institutions conducted research on the accuracy of the Fitbit ChargeHR for measuring sleep in 32 healthy adolescents (17 yrs old on average). After screening for sleep disorders, the adolescents wore a ChargeHR on their non-dominant wrist while also undergoing a standard PSG. Both aggregate and minute-level data was compared to PSG data for each participant.

Results of the comparison between the ChargeHR and the PSG data indicated that the ChargeHR accurately detected total time asleep, time awake, and sleep efficiency, with approximately 90% of the participants meeting the clinically set minimum satisfactory differences for these measures. As was found in the children’s study, the ChargeHR had a low level of accuracy for detecting waking minutes. The researchers also compared the heart rate data from the ChargeHR to the clinically ECG included in the PSG. They found that for all sleeping periods, the ChargeHR was as accurate as the ECG, finding with a less than one beat per minute difference on average.

Sleep is an integral part of our lives, and being able to understand how much sleep we get without having to step into a lab is important. As both studies highlight, there is a great deal of potential in using low-cost personal wearable devices to support large-scale research and evaluation studies.

Fitabase is proud to have supported the data collection efforts of both the above mentioned research teams. Our unique platform allows researchers to easily access, download, and explore sleep, activity, and weight data from all the Fitbit models.

Fitabase makes it very easy for us to get fine-grained data from Fitbits. Researchers like us want ever-better measurement capabilities, because ultimately, that it is what will allow us to more fully understand the factors that influence health. In our validation study, we were able to get minute-level data or better to examine how close the Fitbit Charge HR is to our gold-standard for measuring sleep. Our study would not have been as robust as it is if we did not use Fitabase. -- Job Godino, UCSD Center for Wireless and Population Systems

If you’re using Fitbits in your research, or would like to, we’d love to hear from you. Get in touch!

  1. Montgomery-Downs, H. E., Insana, S. P., & Bond, J. a. (2012). Movement toward a novel activity monitoring device. Sleep and Breathing, 16(3), 913–987. doi:10.1007/s11325–011–0585-y ↩
  2. de Zambotti, M., Baker, F. C., Willoughby, A. R., Godino, J. G., Wing, D., Patrick, K., & Colrain, I. M. (2016). Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiology & Behavior. doi:10.1016/j.physbeh.2016.03.006 ↩
  3. Osterbauer, B., Koempel, J. A., Davidson Ward, S. L., Fisher, L. M., & Don, D. M. (2016). A Comparison Study Of The Fitbit Activity Monitor And PSG For Assessing Sleep Patterns And Movement In Children. Journal of Otolaryngology Advances, 1(3), 24–35. doi:10.14302/issn.2379–8572.joa–15–891 ↩

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Today we're releasing an experiment called Prox. It's a simple, serverless native app that can be useful to pilot the functionality of iBeacons as they're currently implemented on iOS. We built prox to help researchers create small experiments, whereby they could set up individual smartphones to observe any number of Estimote beacons, collect that data, and decide for themselves how beacons might be meaningful for their research.

There are two modes for prox, but most of the functionality is hidden behind an admin password after setup.

Prox Screenshot

It's open source, and free. We'll soon be adding an app-store preconfigured version but for anyone interested today please get in touch.


We developed this iOS app, and ran a small pilot study along with the Designing Health lab at ASU, headed by Dr. Eric Hekler. Lots of credit as well to Sayali Phatak and Elizabeth Korinek for their work putting the protocols together and the experiment in to action.

Prox is a small agile experiment created by Fitabase and Arizona State University's Designing Health Lab. It was funded by the Health Data Exploration Project (, with generous funding by the Robert Wood Johnson Foundation (

Want to learn more? Have ideas for proximity sensors? Please get in touch!



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Can BuzzFeed Be Trusted To Write About Science?

A response to Can Fitbits be Trusted In Science

by Aaron Coleman Founder & CEO of Fitabase, a web platform for data collection, analysis, and export using Fitbit wearable devices. Fitabase has assisted with data collection and analysing Fitbit data for over 100 research studies.

It only took a few retweets today to catch my attention. BuzzFeed is talking about Fitbits in research? This should be good. I wanted to give them the benefit of the doubt, that they looked through the literature, contacted the right people, crunched some data, maybe even did testing of devices themselves.


Author Stephanie M. Lee starts out touting some of the reasons why wearables are great in research: price, acceptability, data streaming, but drops it on us in the next sentence: “The only problem? The results may not be very accurate.” (hey BuzzFeed, here’s a good spot to add in a little thing we call a “citation”; science, you know?).

Later on we do get a little more of a clue as to where BuzzFeed is getting their data. They quote Hawley Montgomery-Downs who says “I have a heebie-jeebie factor about somebody using them in their science”. It’s true, in 2011 she co-authored this publication showing innacuracies in sleep:

The problem? The case they’re making broadly for Fitbits being unreliable in science relies on research published in 2011, so it has to have been done on a Fitbit Classic or Fitbit Ultra -- neither of which have been sold for years. This is the equivalent of comparing the original iPhone to the latest Android and declaring the iPhone inferior.

In fact, the rest of the article continues to rely exclusively on research done using just the Fitbit Classic or Ultra:

What’s worse? They brush over in one sentence that even in the outdated model, which predates many hardware and algorithm improvements, the Ultra actually did pretty well outside of sleep measurements:

“In the case of Fitbits, the consumer activity tracker most used in the studies reviewed for this article, step-counting seems to be highly accurate, independent reviews have found, supporting Fitbit’s claims that they are 95% to 97% accurate when worn correctly.”

AND, there’s another giant elephant in the room. The author is claiming this based on studies that tested it for sleep measurement, holding these (old gen) Fitbits to the standard polysomnography. You know what that looks like? It’s more wires, electrodes, and tape than anyone could reasonably be expected to wear and get a good night’s sleep ( ) This is like comparing a fighter jet to a Jetta.

Which brings me to the biggest miss for BuzzFeed -- but they almost got there: 

“Another benefit is cost. Actigraphs, medical-grade sleep-tracking devices, can strain budgets at prices that range between $300 and $1,000, and don’t always include associated software. In contrast, a sleep-tracking Fitbit starts at $100 — software included.”

Researchers have, for well over a decade, come to rely on actigraphy to measure physical activity, energy expenditure, and sleep in free living conditions. These are the devices that have been the basis for an enormous body of free living measurement, so perhaps these are the devices that we should be comparing Fitbits to?  Guess what!  When comparing the Fitbit to these devices, they do very, very well -- sometimes even better than the “gold standard” Actigraph:

Bottom Line - Fitbits are Excellent and Reliable for Science

Founding Fitabase, I’ve had the privilege of seeing what many very smart research institutions are doing with wearables. Much of this work is ongoing or awaiting publication, but keep a look out for impressive new metrics and benchmarks utilizing heart rate models that are influenced by both tri-axis accelerometer and continuous heart rate without a chest strap.

Fitbit and other types of wearables mark a huge shift in what a researcher can do:

  • Compliance: A device people will reliably wear and feel comfortable with. We’ve seen astounding compliance numbers from some of our customers.
  • Data availability: No more costly requirements for participants to come in every couple weeks, or worse, mail in devices. Our customers view all their participants data in just one dashboard.
  • The opportunity for feedback: Scientists are testing what kinds of behavioral interventions can be delivered with this new data. This has population-level health implications.
  • Battery Life: On the order of one week or 3 months for the Fitbit Zip.
  • Cost (this point was already mentioned but it bears mentioning again): Research budgets continue to be cut and the power of the science is strengthened by increasing the number of participants.
  • Precision Medicine: A few months ago the President outlined a new health delivery directive -- make care precise and tailored to the individual. Personal data capture and wearables have a big part to play in that future, and in the coming months the NIH will announce a slew of new funding opportunities for this.

I wish BuzzFeed well but when it comes to their science reporting maybe they should leave that to someone else and save the sensational headlines for celebrity gossip.

For anyone interested in running a research study using Fitbits, please shoot us an email at, we’d love to hear what you’re planning.



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by Aaron Coleman, Founder & CEO of Small Steps Labs, maker of Fitabase

The Health Data Exploration Project has selected 5 recipient projects for their first wave of agile science grants. I’m excited to announce that we’ll be working with Dr. Eric Hekler, PhD in the department of the School of Nutrition and Health Promotion to explore new research possibilities using contextual proximity sensors. The project expands on Dr. Hekler’s ongoing work on Just In Time Adaptive Interventions (JITAI), an innovative and dynamic approach in system design to better tailor and understand the individual and the context of their behavior to provide more relevant in-moment experiences.

For this project we will contribute extensive experience in software development and signal processing from remote sensors, as well as years of experience working closely with the research community and a thorough understanding of the challenges of mHealth research. We believe that the use of proximity sensors in research offers a unique opportunity to capture an important layer of data that may otherwise be overlooked. With Fitabase today, we can see minute-by-minute metrics in activity, sleep, and heart rate from a wearable sensor like the Fitbit, but we lack the understanding of where a person is in the context of their routine. This project will prototype an application for mobile phones that captures data and provides the opportunity to create a behavior-changing intervention that prompts actions based on proximity sensors by Estimote. The concept should be generalizable to any beacons that implement the iBeacon standard, developed by Apple and now widely supported on other platforms.

Previous research has attempted to use GPS to understand context. But this method has proven to have its challenges: privacy, and resolution while indoors to name two. Since iOS 8 introduced native support for iBeacons and is the way Apple is tackling these same challenges, we thought it only natural to see how these advancements might translate to behavioral science research.

As rich as a GPS dataset may be, the question that needs to be answered is whether or not it’s necessary for an adaptive intervention to know where your home is, when a signal that indicates that you’re inside your house, close to your kitchen, etc, is entirely sufficient without including sensitive location information We might see the need for study participants to take home several beacons, placed strategically in their daily routine. For example, one might be by the charging location of their phone or night stand, another in their kitchen, car, or office. The fact that these beacons are so inexpensive and offer a battery life of up to 3 years makes the possibilities almost endless.

We are thankful to have our interest in this area boosted by the Health Data Exploration Project. The Agile Science award allows us to work with Hekler’s lab at ASU and share the resulting technology with the broader research community. We intend to prototype a working mobile app, open source it, and present it to the community with some recommendations and guidance. In the spirit of this Agile Sciences award (and the Agile methodology), we are presenting this idea early in order to have an opportunity to see how it works, gauge what needs refining, and keep an ear open for suggestions and feedback from other interested groups.

If you’re doing research in this area and you’d like to learn more or tell us how you’d use this technology, drop us a line at



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After two years of customer-driven features and enhancements, Fitabase is officially coming out of beta with immediate support for heart rate monitoring and auto sleep detection from the just announced Fitbit Charge HR and Surge. These new devices announced by Fitbit offer continuous heart rate monitoring, marking a turning point in energy expenditure objective measurement tools available to researchers. “We’re excited about the potential for researchers to run long observance studies using an even better data set for energy expenditure and MVPA level,” says Small Steps Labs founder Aaron Coleman. “We are pleased to announce Day 0 support for heart rate and automatic sleep time detection in Fitabase.” Starting today Fitabase data reports, alerts, and export tools support the just announced heart rate and auto sleep detection in addition to all the current physical activity and sleep data that have been available during the beta period.

“Fitabase facilitates a new kind of connected research model,” says Coleman, a former employee at the UC San Diego Center for Wireless Population & Population Health Systems. “Researchers are increasingly looking to gather data in real time, and are adapting and personalizing their interventions. Fitabase makes working with hundreds or thousands of participants manageable and has all the data ready to export at any moment for our customers to do their analyses.” Heart rate data tools are being announced today in connection with the release of Fitbit models Charge HR and Surge. “We're very grateful to our friends at Fitbit who have continuously supported Fitabase. The coordination helps us provide the best service to our customers and today it means we were able to build tooling for the new heart rate enabled models."

Coleman started Small Steps Labs to create Fitabase after noticing the limitations of collecting measures using traditional research devices where data is only available once the devices have been returned by participants. Fitabase was recently featured in a report by the California Institute for Telecommunications and Information Technology(Calit2) supported by the Robert Wood Johnson Foundation highlighting Fitabase as an example of new technology catering to the needs of the research community. The same report indicated that 89% of researchers surveyed agreed or strongly agreed that self-tracking data would be useful in their own research, and 95% of respondents stated that this kind of data could answer questions that other data could not.

In comparison to the traditional offline actigraphy tools that have long dominated physical activity research, Fitbits have proven to be surprisingly accurate and a recent validation study by researcher Jung Min Lee at Iowa State showed that both models tested, the Fitbit Zip and Fitbit One, had fewer error rates of energy classification than even the current “gold standard” device made by Actigraph.

“Increased accuracy, lower cost, and a friendly form factor is leading more researchers to explore consumer devices as an alternative,” says Coleman. Researchers from a multitude of disciplines are already using Fitabase including studies in aging, pediatrics, physical activity intervention, obesity prevention, telehealth, and post-operative recovery.  Fitabase customers include many academic institutions, clinics, hospitals and private research firms such as Columbia University Medical School, Brown Alpert Medical School, University of Michigan Health System, Cincinnati Children’s Hospital and Johns Hopkins Medicine.

Today Fitabase is also announcing support for the Fitbit Aria scale. As for the future, Coleman says, “We plan to introduce support for additional consumer devices and some mobile tooling allowing researchers to design adaptive studies which tailor to participants. We think dynamic, connected, and contextually aware research modeling is the future.”



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Johns Hopkins affiliated researchers launched a teenage obesity study using Fitbit activity monitors at a Florida high school. The study was highlighted this week in this article by Reuters. The study, funded by a $100,000 grant from a philanthropic arm of the insurer Florida Blue, is a school-based program that will be recruiting 50 overweight students and will use Fitbit devices to track physical activity and sleep data.

"It’s cool. You can wear it and it measures your activity" Dr. Raquel Hernandez, lead researcher and assistant professor at Johns Hopkins, said to Reuters regarding the Fitbit. "It can also help the student know what they really are doing."

The study combines the use of Fitbit devices and the MyFitnessPal app as a means of monitoring fitness and engaging participants in their nutrition. This digital intervention is then paired with bi-weekly behavioral change sessions with a psychologist. Using Fitabase researchers are able to view participant data in near real time. Using this data they then intend to send tips on healthy eating and exercise via text message or via twitter.

Utilizing Fitabase, the researchers are able to see and interact with all of data sets we provide including minutes spent in various intensity classifications, METs, calories, and minutes awake, asleep, or restless allowing them to monitor participant behavior in near real time. “Fitabase will be a critical tool to helping us follow our teens throughout the program” said Dr. Hernandez. "It’s a fantastic opportunity to capture activity, sleep and nutritional data on a population of kids who are at risk."

The study is also using a recently launched, yet unannounced feature of Fitabase: Food Log exports. Data shared to the Fitbit profile of a particiapnt from MyFitnessPal food logging is now available for viewing and export in participant report screens. This new feature gives researchers exportable data on logged food items including description, portion size, and numerous nutritional values aggregated from one of the largest digital food sets online.

Dr. Hernandez hopes the data captured by this study using Fitabase will be foundational as they continue to build more effective health programs for teens.

We are are very pleased to support this study and are always interested in supporting similar research. Is your research group running a similar study and interested in more information about Fitabase? Please visit our website and get in touch for more details.



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We're excited to announce a new experiment we're launching we call Tappy Fit. It's a difficult game that gets a little easier if you get more daily steps and is paired with Fitbit data.

We built this after thinking about what the casual gamer does in his/her life. Whether it be on the bus, in the checkout line, or in carved out time during a lunch break there are micro moments of attention ripe for health intervention. Then we looked at the App Store and saw a flood of simple games on the top charts. However, the existing flying character genre reportedly produced some addictive and negative impacts on lives. The idea to integrate daily steps came into place after thinking about how we might reframe the addictive nature positively for health. Best yet, it took just a few days to develop as started with an open source project and adapted it to connect to the Fitbit API.

There is plenty of literature that suggests keeping people mindful of their daily physical activity level makes them more likely to increase it. There are also lots of “health games” meant to be played at the gym, during a run, or in front of a screen. The Wii Fit and XBox Kinect for example engage you in the moment. The goal with Tappy Fit was that the sum total of movement from your entire day, week, or month is what influences the gameplay and I can't recall any other examples of that.

We're launching this as an experiment. We're not sure what to expect but since we also happen to maintain a great analytic platform for working with Fitbit data called Fitabase we think we can measure how game play translates to real world movement trends.

We're hoping to see that letting Fitbit data bleed into casual gaming shows an increase in daily steps for those who play the game often. Either way we'll learn something and happily publish what we've observed. We're not exclusively a game company, but interested generally in what kinds of experiences can be built upon self-gathered data. Regardless of if we see any impact with Tappy Fit there are lots of other experiments we and organizations we might partner with intend to run in the future.

If you'd like to partner with us on something you're interested in working please get in touch.

Related links:


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A new report from the California Institute for Telecommunications and Information Technology (Calit2), supported by the Robert Wood Johnson Foundation, highlights Fitabase by Small Steps Labs as an example of new technology catering to the needs of the research community now utilizing personal data collection devices like the Fitbit.

From the report:

Additional approaches that can address this issue appear to be emerging. One is signaled in what we found with one company, Small Steps Labs, whose business model is to serve as an intermediary between a data rich company, in this case Fitbit, and academic researchers via a “preferred status” API held by the company. Researchers pay Small Steps Labs for this access as well as other enhancements that they might want.


Of note the report, which interviewed 134 researchers showed an overwhelming interest by this community for self-tracked data:

Researchers in our survey were generally enthusiastic about the potential for using self tracking data in their research, with 89% agreeing or strongly agreeing that self-tracking data will be useful in their own research, and 95% saying that this kind of data could answer questions that other data couldn’t.

When asked about the potential impact on research by Tiffany Fox from Calit2, lead author Dr. Kevin Patrick responded:

The amount of data captured by these devices and apps dwarfs anything that we have ever had before. New computational and analytical strategies will need to be applied that have not commonly been used in health-related research.

The report serves as both a validation of the vision for Fitabase and also highlights the importance and attention to detail around privacy and handling of this kind of data. For the full report see: 

For additional info about Fitabase for your current or future research email us at


Disclosure: Small Steps Labs Founder Aaron Coleman is a former alumnus of UC San Diego and former employee of Calit2 and the Center for Wireless Population & Population Health Systems. 



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