Using Self-Collected Data to explore The Impact of Endurance Training on my Cholesterol


As you may already know, I often use "self-tracking" to learn new things about myself. If you are not familiar with what this entails, self-tracking (in the context of health and wellness) can be defined as a social movement in which individuals collect data about their own health, sometimes using tools equivalent to those of professional labs.

These individuals often have no formal training in medicine, research methods or statistics (like me!). However, they all believe in empowering people to take control of their health.

From helping prevent disease, to personalizing our diet and optimizing the way we train or rest, the possibilities are endless

The rise of sensors, direct-to-consumer tests and health tracking software tools, makes it possible for anyone to quantify different aspects of their lives.


I. What I did

I am usually not a big fan of endurance activities (see my self-tracking experiment "Training for strength or endurance"). However, I decided to run the NYC marathon, mostly because this was the last year I was able to defer without losing my (hardly-earned) entry :)

I've also heard many times that the race itself was very beautiful, since you get to see the streets of New York and how they change from one borough to another, not to mention the breathtaking views from a few NYC bridges that you cross as part of the race. (And all this was true!)

I trained for the race mostly between end of August 2017 and early November 2017, and I used this opportunity to try to explore the impact of this new type of physical activity.

I also gradually reduced my "regular training," which consists of a mix of power and power endurance (usually never more than 60-75 minutes). By end of September, I was already running all the time.


II. How I did it


1. I used NYRR Virtual Trainer by Runtrix to design a program

Fitnescity_NYRR Virtual Trainer.png


NYRR Virtual Runner is a software that helps you build dynamic training plans. It was recommended by NYRR (New York Road Runners), the organizer of the NYC Marathon.

The tool customizes your plan based on your running history (which was convenient since I've already done a lot of runs with NYRR over the past few years, even though none of them was longer than a half marathon. As a result, all they had to do was upload all my race history when I signed up).

The plan is dynamic; it can change anytime based on your performance. I chose the the 16-week "conservative" plan, but I also modified it based on other factors, such as work load, travel, how I felt, and (of course) the other data I was collecting (see below)!



2. I used my Strava account to track performance


I don't use Strava that much in general, since it's mostly for running, cycling and swimming. However, I use it by default every time I am not running on a treadmill.

As Strava addicts would say, "If it's not on Strava, it didn't happen." :-)

It's obviously great for tracking and analyzing runs. The interface is beautiful too!

In this case, I used it to keep track of my average pace. As I will explain later, this is relevant for measuring "intensity."

If it's not on Strava It Didn't Happen.png



3. I used the Freestyle Continuous Glucose Monitor (CGM)

Continuous Glucose Monitor

I used this device to track my glucose level. All I had to do was wear a sensor on my arm. If I want to get a reading at any specific time, I just scan the sensor.

The readings show the current glucose level, but the device can store data continuously. It also lets you download / export the data anytime.

I used the device mostly to manage my fueling strategy, especially for the long runs. Since I was new to distance running, this was very eye-opening. 

In the past, I used to run half-marathons without worrying much about my food intake.  I used to only eat before and much later after the run. 

So my first intuition was to run and not really worry much about food intake...until I started using my CGM device. I realized that I was running with (very) low glucose levels (see photo) most of the time.

This forced me to increase my carb intake (and reduce fat and protein intake). See below what this did to my cholesterol!




4. I used the CardioChek® Plus Analyzer to test my blood lipids

FullSizeRender (23).jpg

I used this whole blood analyzer to measure lipids (Cholesterol, HDL and triglycerides). 

Unlike my CGM device, this tool required me to take blood samples each time.

Most importantly, I did this as part of a participatory, high-frequency blood testing project.

A few months ago, a group of 21 Quantified Self enthusiasts (including myself) started a new collaborative project called “Blood Testers.” 


Our immediate goal with this experience was to learn more about ourselves through high-frequency self-testing of blood lipids (i.e., cholesterol and triglycerides). Our long-term goal was to help advance self-directed research by better understanding what makes these types of collaborative self-tracking projects succeed or fail.


In my case, I wanted to explore the impact of endurance training on my cholesterol. More precisely, I decided to look at two components: the long-term and the short-term impact of endurance training on cholesterol. 


4.1. Understanding the "long-term" effect of endurance training on cholesterol

Note: "Long-term" is defined here as two months. The goal in this section is to see how my cholesterol changed between the beginning and the end of the training period.


It is obvious and widely accepted that exercise helps improve cholesterol. However, I was surprised to learn that, as of today, there is very little consensus about the impact of exercise intensity, duration, frequency, type and mode on cholesterol.

Is it better to engage in moderate activity? Would strength training help improve your cholesterol better than cardio, or better than cardio and strength combined? What is the impact of distance running on cholesterol? Is it more about mileage (number of miles covered per week) or intensity (percentage of maximum heart rate, HRmax)?


Researching the scientific literature on exercise and cholesterol quickly became confusing (since there is very little consensus on the subject), so I decided to narrow down my research to running and cholesterol. I found that studies often looked at two variables when it comes to running and cholesterol: mileage and intensity.


As part of this experiment, I had to increase my mileage every week, until I reach the point where I can safely complete a 26 mile race. As a result, it was interesting to look at the impact of mileage on my cholesterol.

If we define intensity by percentage of HRmax, then the assumption here is that my intensity remained somewhat low, since I usually had to run at my marathon pace (i.e. a low, "boring" pace), even when I was covering much shorter distances. The only exception would be speedwork sessions, but I didn't do much of it anyway.


Here's what is supposed to happen, according to the research:

4.1.1. Impact of mileage:

This study demonstrates that, for men, HDL-cholesterol (i.e. the "good" one) increases with higher running mileage, and decreases within days of stopping exercise when caloric and alcohol intake remain elevated (1).  In fact, most of the exercise training studies identify a weekly mileage threshold of 7 to 10 miles/week for significant increases in HDL-C in men (2).

In women, the volume of exercise seems to be more important than the intensity of exercise for influencing HDL-C levels. Most studies suggest a large volume of exercise is necessary for significant HDL-C changes in women, however, the exercise volume threshold has not yet been defined (2).


4.1.2. Impact of intensity:

Exercise intensity studies on men show that men increase their HDL (i.e. "good" cholesterol) by increasing exercise intensity to 75% HRmax or above (2).

Unfortunately, training studies attempting to assess the role of exercise intensity on HDL-C in women are few and report conflicting results. Most of the research suggests that women (pre and postmenopausal) with low levels of HDL-C are more likely to respond positively to exercise training. Duncan et al. (1991) reported similar increases in HDL-C levels in women (29-40 years) following 24 weeks of walking (4.8 km/session), regardless of intensity. This finding suggests that moderate exercise will raise HDL-C levels as much as intense exercise. In addition, Spate-Douglas and Keyser (1999) reported that moderate-intensity training over a 12-week period was sufficient to improve the HDL-C profile, and high-intensity training appeared to be of no further advantage as long as training volume (total walking distance per week) was constant. Conversely, Santiago and others (1995 ) reported no changes in HDL-C levels in women following 40 weeks of endurance training similar to the program in Duncan’s study. However, the women in Santiago’s study had higher initial HDL-C levels than the women in Duncan’s study (65 vs. 55 mg/dl). These findings also support that women with lower levels of HDL-C are more likely to see increases in HDL-C with exercise training (2).


Last but not least, I didn't find much on distance running and triglycerides. (let me know if I missed anything?)

This is unfortunate, because that's where my results were the most surprising (!)


4.2. Understanding the immediate effect of endurance training on cholesterol

In the previous section, I looked at how my cholesterol changed throughout the entire September-November period.

This section focuses on how my cholesterol, HDL and triglycerides levels changed on a moderate activity day, as well as on a "long run" day (in addition to rest days). 

Here's what I did: On the days when I was running, I took a "before" and a "right after" measurement. Moreover, I tried to measure fasting cholesterol on most days. 

(The process was somewhat cumbersome, since I had to take a blood sample each time, so unfortunately I didn't do it every single day. However, there were days when I took 4-6 measurements).



5. I tried food logging (again)

I also tried to keep a manual food log, but I have to say that I wasn't very good with that.

I did, however, calculate my caloric intake (in my head) with the goal of keeping it somewhat consistent with the amount of training I was doing. This is what I usually do...

I managed to do it to a certain extent. This was especially tricky because I had to move to 60% carbs towards the end of my training to be able to cover the distances required without being constantly in the low (red) blood glucose zones.

The tricky part here is that it's very easy to get to 1700-1800 calories per day or more (while still being under the impression that you're barely at 1000 calories), if you start eating a lot of pasta consistently for the first time (!) 

Overall, my body weight didn't change much. However, I did gain body fat (and lose muscle).



III. What I Learned

QS High-Frequency Blood Testing Project     |      Laila Zemrani     |      @lailazem.png

Things to keep in mind:

Note (1): During the couple of weeks preceding and following my marathon training, I was mostly resting and/or recovering (i.e. I was not doing anything strenuous). 

I went back to my "regular training" towards mid-December. This consists of high-intensity power and power endurance activity for 60-75 minutes about five times a week, as well as cardio once a week and one rest day. This part is colored in gray in the chart above.

Note (2): The CardioChek® Plus Analyzer calculates LDL (LDL = Total cholesterol - HDL -.25*Triglycerides). This could make LDL results less accurate than the rest.


Insight #1

My total cholesterol went down as I increased my mileage, reaching all-time lows of 124 mg/dL right after my 20 miles prep race and 120 mg/dL after completing the marathon.

My fasting measurements also decreased to about 135 mg/dL around race day, down from about 160 mg/dL at the beginning of the training.


It's important to keep in mind that when I took my post-race cholesterol measurements, my blood glucose level was always in the normal range (including during the full marathon!). I was only able to do this because I was using the continuous glucose monitor; my intuition would have been to not eat, especially that didn't feel like I needed to (at all), and I really dislike running gels :)

In other words, my cholesterol was at these levels despite the fact that I was consistently in the normal range for blood glucose.


Insight #2

My total fasting cholesterol remained low a couple of weeks after the race, despite the fact that I was not training and was maintaining a relatively high daily calorie intake. However, it did end up increasing a lot later in November (the race was on November 5, 2017) to about the same level as it was before I started marathon training (!)


Insight #3

After I went back to my "regular training" towards mid-December (as described above), my total fasting cholesterol went down again in February 2018 to about the same level as it was on race day!

This could mean that exercise intensity also decreases my cholesterol, just as much as, if not actually more than, duration/mileage.


Insight #4

Both my HDL and triglycerides / LDL decreased as I increased mileage. The surprising part here is that my HDL levels decreased as well.

However, this was only the case until about the first week of October (four weeks before the race). See below.


Insight #5

My triglycerides and LDL started to increase significantly about four weeks prior to the race. I believe this was related to carb loading. (I have never had that much carbs!)

However, my HDL continued to drop (slightly). This level was surprisingly lower than when I started! I would have expected it to increase or remain steady overall. 

Points #4 and #5 are interesting because what I found earlier when researching the subject was that "in women, the volume of exercise (mileage) seems to be more important than the intensity of exercise for influencing HDL-C levels." 

According to this study, increasing my mileage should have increased my HDL levels.


Interestingly, my triglycerides and LDL continued to increase during the couple of weeks after the race, even though my total cholesterol was somewhat stable, as mentioned in point #2.


General Conclusion

Consistent, high-intensity exercise (described here as my "regular training") seems to be much better for me for controlling total cholesterol and triglycerides / LDL, than "event-based" endurance exercise, where I have to gradually increase mileage/duration in preparation for an event (i.e. a race).


As mentioned in point #3, my total cholesterol went down again in February 2018 (with my "regular training") to about the same level as it was on race day. 

This might be because exercise intensity decreases my total cholesterol just as much as, if not more than, duration, as mentioned above.

Most importantly, my triglycerides / LDL levels stayed consistently low during my "regular training," whereas they increased significantly about 4-5 weeks prior to the race.

The reason behind the lower triglycerides / LDL levels could be the relatively low-carb diet  that I usually follow as part of my "regular training," as opposed to the high-carb diet that I had to follow as I increased my mileage.




(1) High-density lipoprotein-cholesterol in marathon runners during a 20-day road race.

Dressendorfer RHWade CEHornick CTimmis GC.

JAMA. 1982 Mar 26;247(12):1715-7.

(2) A Review of the Impact of Exercise on Cholesterol Levels
Chantal A. Vella, Len Kravitz, Ph.D., and Jeffrey M. Janot

Brownell K.D., P.S. Bachorik & R.S. Ayerle. 1982. “Changes in plasma lipid and lipoprotein levels in men and women after a program of moderate exercise”. Circulation. 65(3):477-84.
Drygas W. et al. 2000. “Long term effects of different physical activity levels on coronary heart disease risk factors in middle-aged men”. International Journal of Sports Medicine. 21(4):235-41.
Duncan J.J., N.F. Gordon, & C.B. Scott. 1991. “Women walking for health and fitness. How much is enough?” Journal of the American Medical Association. 266(23):3295-9.
Dunn A.L. et al. 1999. “Comparison of lifestyle and structured interventions to increase physical activity and cardiorespiratory fitness: a randomized trial”. Journal of the American Medical Association. 281(4):327-34.
Durstine J.L. & W.L. Haskell. 1994. “Effects of exercise training on plasma lipids and lipoproteins”. Exercise and Sports Science Reviews. 22:477-522.
Katzmarzyk P.T. et al. 2001. “Changes in blood lipids consequent to aerobic exercise training related to changes in body fatness and aerobic fitness”. Metabolism 50(7);841-8.
Kikkinos P.F. et al. 1995a. “Miles run per week and high density lipoprotein cholesterol levels in healthy, middle-aged men. A dose-response relationship”. Archives of Internal Medicine. 155(4):415-20.
Kikkinos P.F. et al. 1995b. “Cardiorespiratory fitness and coronary heart disease risk factor association in women”. Journal of the American College of Cardiology. 26(2):358-64.
Kikkinos P.F. & B. Fernhall. 1999. “Physical activity and high density lipoprotein cholesterol levels”. Sports Medicine. 28(5):307-14.
King A.C. et al. 1995. “Varying intensities and formats of physical activity on participation rates, fitness and lipoproteins in men and women aged 50-65 years”. American Heart Journal. 91(10)2596-04.
Lakka T.A. & J.T. Salonen. 1992. “Physical activity and serum lipids: a cross-sectional population study in eastern Finnish men”. American Journal of Epidemiology. 136(7):806-18.Seip R.L. et al. 1993. “Exercise training decreases plasma cholesteryl ester transfer protein. Arteriosclerosis”. 13(9):1359-67.
Leclerc S. et al. 1985. “High density lipoprotein cholesterol, habitual physical activity and physical fitness”. Atherosclerosis. 57(1):43-51.
Lindheim S.R. et al. 1994. “The independent effects of exercise and estrogen on lipids and lipoproteins in postmenopausal women”. Obstetrics and Gynecology. 83(2):167-72.
Moll M.E. et al. 1979. “Cholesterol metabolism in non-obese women. Failure of physical conditioning to alter levels of high density lipoprotein cholesterol”. Atherosclerosis. 34(2):159-66.
Neiman, D.C. 1998. The Exercise Health Connection. Champaign, IL: Human Kinetics.
Ready A.E. et al. 1996. “Influence of walking volume on health benefits in women post-menopause”. Medicine and Science in Sport and Exercise. 28(9):1097-105.
Santiago, M.C., A.S. Leon & R.C. Serfass. 1995. “Failure of 40 weeks of brisk walking to alter blood lipids in normolipidemic women”. Canadian Journal of Applied Physiology. 20(4):417-28.
Spate-Douglass T. & R.E. Keyser. “Exercise intensity: its effect on the high-density lipoprotein profile”. Arch Phys Med Rehabil. 80(6):691-5.
Stein R.A. et al. 1990. “Effects of different exercise training intensities on lipoprotein cholesterol fractions in healthy middle aged men”. American Heart Journal. 119:277-83.
Sunami Y. et al. 1999. “Effects of low-intensity aerobic training on the high-density lipoprotein cholesterol concentration in healthy elderly subjects”. Metabolism. 48(8):984-9.
Williams P.T. et al. 1982. “The effects of running mileage and duration on plasma lipoprotein levels”. Journal of the American Medical Association. 247(19):2674-9.
Williams P.T. 1996. “High-density lipoprotein cholesterol and other risk factors for coronary heart disease in female runners”.334(20):1298-303.
Williams P.T. 1998. “Relationships of heart disease risk factors to exercise quantity and intensity”. Archives of Internal Medicine. 158(3):237-45.
Wood P.D. et al. 1983. “Increased exercise level and plasma lipoprotein concentrations: a one year randomized, controlled study in sedentary middle-aged men”. Metabolism. 32(1):31-9.
Wood P.D. et al. 1991. “The effects on plasma lipoproteins of a prudent weight-reducing diet, with or without exercise, in overweight men and women”. New England Journal of Medicine. 325(7):461-6.





Laila Zemrani

Twitter | Linkedin |


The 2018 Quantified Self Symposium: The Future of Self-Collected Data and Single-Subject Research


I recently participated in the 2018 Quantified Self Symposium on Cardiovascular Health at the University of California, San Diego. I hope this summary will capture the inspiring, exciting and pioneering nature of this event (I have rarely used these three adjectives combined to describe an event!)

As a quick reminder, the mission of the Quantified Self is to support people in making personal discoveries using everyday science; it has been shown over the last few years that individuals can make significant discoveries about their own health using self-collected, high temporal resolution data from open tools.

The goal of this annual event is to celebrate the community involved in these efforts, as well as explore how the work of these pioneers can eventually become mainstream.


How do we know that individuals can make consequential discoveries about their own cardiovascular health using self-collected data? 

Larry Smarr, a physicist, avid self-tracker and leader in scientific computing at the University of California San Diego, kicked off the event with a beautiful reminder of why it important for us to collect data about our own health:

It might seem obvious that the human body is a set of multi-component non-linear systems developing in time. However, what is still not that obvious (yet) to most people is that the most logical way to understand this system is to measure it over time with multiple variables. 

The good news is that the underlying technology that is enabling us to read our bodies is getting exponentially cheaper. This obviously goes far beyond devices like Fitbit, and includes practically everything from the microbiome to MRI data. 

As a result, more and more people are (and will be) able to collect data on their bodies, helping build a longitudinal, comprehensive view of their health. This shift will eventually help us move to a more preventative, pro-active healthcare system.

Susannah fox, formerly the CTO of the U.S. Department of Health and Human Services, reminded us that five years ago, a study showed that 70% of Americans engaged in some sort of self-tracking. However, "about half of them were actually doing it in their heads." It would be interesting to see how these numbers might have changed today, especially that data collection technology has (and will) become more and more available to everyone. 


Why has N-of-1 research never had any major impact on medical practice?

Randomized Control Trials, which aim at telling us whether a specific treatment works on average, are widely used in medical practice. The problem is that if a treatment is not working on a patient we have in front of us, it does not really matter that it works best on average, said Reza Mirza, a resident doctor at McMaster University.

Reza gave a couple of examples of how these conventional trials have been used since the sixteenth and seventeenth centuries (!) More recently though, a few pioneer physicians have started to rely on N-of-1 experiments to inform their decision making process. However, these remain isolated cases. Reza explained that one reason might be that it is a lot harder to organize an N-of-1 study than it is to do a conventional trial.


Hugo Campos: 10 Years with an Implantable Cardiac Device

Hugo Campos has been at the forefront of the battle to give patients with implanted cardiac devices access to their medical data. In July 2015, he was honored by the White House as a Champion of Change for Precision Medicine for his data liberation advocacy. 

Hugo explained how he correctly diagnosed his Atrial Fibrillation (AFib) through self-collected data (i.e. with his Alivecor device). He went to the emergency room and informed the ER doctor that he needed a cardioversion (i.e. an electroshock). He couldn't get the data/alert from his implanted defibrillator, but he eventually received the treatment.

Science has to “meet patients where they are.” - Hugo Campos.

Lessons from a participatory, high-frequency blood testing project


(a more detailed description of this project is available here).

A few months ago, a group of 21 Quantified Self enthusiasts (including myself) started a new collaborative project called “Blood Testers.” 

The reasoning behind this project was that, in most Quantified Self projects, one person does almost all of the work, perhaps with a bit of advice from friends and online feedback. But what if we could work in a group of people with varied skills to explore questions we developed through conversation and collaboration? 

Our immediate goal with this experience was to learn more about ourselves through high-frequency self-testing of blood lipids (i.e., cholesterol and triglycerides). Our long-term goal was to help advance self-directed research by better understanding what makes these types of projects succeed or fail.

A detailed summary of my experience with the project is available here.


A few participants have presented their findings at the Symposium. Perhaps one of the most interesting lessons was that a single measurement, taken at the doctor's office at a single time of the day, month or year might not be telling the whole story.

We all learned that our cholesterol (including fasting measurements) varied a lot based on different factors that include the food we've had before (Jana beck presented her experience with low-carb and high-carb diets), the physical activity we've done, not to mention our circadian rhythm and (if applicable) menstrual cycle.

Whitney Boesel, a writer, researcher, sociologist and DIY medicine enthusiast, presented her experience with high-frequency measurements of morning fasting cholesterol throughout the month, including nine, 10 and 14 months postpartum. 


What's Next for Self-Collected Data and Single-Subject Research

The rest of the event focused a lot on the barriers that stand in the way of using personal and public data for understanding and improving individual cardiovascular health. One of the points that came up a lot is the lack of consensus about the "legitimacy" of self-initiated research and self-collected data.

As a person who collects data as a hobby (and spends the rest of the time helping people collect their data), I believe that the rise of self-collected data among consumers and patients will help overcome this perception. As Marcel van der Kuil, Sports & Health Tech entrepreneur, pointed out, as more and more people become aware of the importance of self-collected data, their combined force will drive change.



Laila Zemrani

Twitter | Linkedin |


Dinner with Fitnescity Founder/CEO and Elysium Health Founder/CEO @ Alley NYC

Dinner with Fitnescity Founder/CEO and Elysium Health Founder/CEO @ Alley NYC

If you've been following our journey, you probably know that we were invited to Brunchwork last month. Brunchwork is a new way of learning, meeting and dining!

The event is based on a pretty unique concept; members get to attend private dinners with the leadership of some of the hottest companies in NYC (yes, it's called "brunchwork," even though a lot of the events are actually dinners :))

Fitnescity had to honor to lead one of the dinners, along with fellow health-tech (and MIT!) company, Elysium Health. For those of you who might not be familiar with it, Elysium is the company behind Basis, "the anti-aging pill." Basis is clinically proven to increase NAD+ levels, which decline with age. NAD+ is required for energy creation, regulating circadian rhythms, and maintaining healthy DNA.

Dinner with Fitnescity Founder/CEO and Elysium Health Founder/CEO @ Alley NYC



Question 1:  Tell us more about Fitnescity and what inspired you to create it

At the core, unlike any other fitness experience, what we do is we quantify the entire journey.

We help you collect lot of data (physiology, lifestyle, history) so you can learn more about your wellness. 

We truly believe that wellness starts with understanding, and we aim at enabling people of all ages and fitness levels to fully take control of their health.

We offer wellness tests that enable anyone to understand their current level of fitness and risks, as well as track progress. The assessments use clinical-grade equipment and range from body composition analysis to metabolic testingperformance analysis as well as injury prevention.

 Fitnescity also offers its clients the option of working with a personal trainer, and/or combining their test results with data from wearables and other sensors.

The vision behind our work at Fitnescity is to enable anyone to gradually build a longitudinal and comprehensive view of their wellness. We think that prevention is the future of medicine, and that a data-driven approach to wellness can help optimize someone’s wellbeing, as well as prevent lifestyle-related disease before it actually happens.


Question 2: What are some advancements in health tech/fitness that excite you?

Continuous monitoring. I think this might even change a lot of things that we know about medicine and about the human body. I've personally tried continuous glucose monitoring and I also did some experiments with high frequency cholesterol (HDL/LDL/Triglycerides) testing. The information you get from is so eye-opening. For instance, you can see in real-time whether you actually need a snack, or whether your pre-exercise meal was a good choice. If you track your data for a few weeks and months, you can potentially start to understand the impact of your diet and exercise on your body. Ideally, you can find an optimal path for maintaining a good blood glucose or cholesterol level.

The human body is very complex. It changes a lot during the day, and from one next day. So having this much insight on it (as opposed to getting a blood test once every year or once every three years) will certainly help us make better decisions.


Question 3: What advice can you give entrepreneurs who are interested in health and wellness?

Stay real and practical. Don't get caught in hype, buzz words and "fake" entrepreneurship. Running a company is not about having a "CEO" title. In fact, most of the time, it's not glamorous at all (especially at the beginning). Make sure you actually enjoy what you're doing. The process is fun, in my opinion, but it might not be fun if what you're looking for is an ego-boost.



Laila Zemrani

Twitter | Linkedin |




I Used 3D Fitness Tracking for a Year. Here's What Happened.

3D Body Shape.jpeg


Why 3D Body Scanning

We weigh ourselves, assess our body composition, and measure our waist and limbs.

But would seeing how the shape of our entire body changed over time be a stronger motivator than numerical data?

Psychological motivations aside, 3D body scanning may replace BMI as a go-to screening tool [1]

A couple of years ago, I heard someone say that in the future, everyone will have a 3D body model as part of their digital identity. The 3D model would have many applications, including ordering clothes online, and of course, tracking fitness progress. 

As a Quantified Self enthusiast, I decided to try 3D body scanning. I was curious about what I would learn about myself, both from a physiological perspective and a behavioral one.


Tracking the Human Body (Some Context)

Anthropometry (i.e. the measure of the human body) has long been used to tracked human body size and shape; changes in lifestyles, nutrition, and ethnic composition of populations lead to changes in the distribution of body dimensions (e.g. the rise in obesity). 

While scientists have have mostly relied on instruments like tapes, calipers and stadiometers in the past, it is now increasingly easy to use three-dimensional body scanners as a tracking tool. Rather than collect hand-measurements, anyone can now use a body scanner to take a 3D snapshot of the body in just a few seconds.


How 3D Body Scanning Works

3D Body Scanner_Fitnescity.png

Once a scan is taken, the scan data is used to generate measurements, along with a three-dimensional view of the body. 

The output of whole body scanners is a cloud of points, which are typically converted into a triangulated mesh. This step is used to support the 3D visualization of the surface and the extraction of meaningful anthropometric landmarks and measurements.


I did my first scan in July 2016. At that time, I was not familiar with the technology, so I used a scanner that was available at my gym. Here are the results!



Why Body Shape Matters

Body Shape_Apple_Pear_HourGlass_Rectangle_FITNESCITY.jpeg


3D body scanning is a fast and reliable tool for collecting body measurements. A scale might indicate that the subject has gained or lost weight, but it does not show weight distribution (i.e. where these changes have happened). This is especially important if you're looking to reduce abdominal fat (belly fat).

It also does not indicate whether the subject has gained body fat or lean mass in various body parts.



Limitations of Weight / BMI as a Health Indicator:

3D Body Scanning as an Alternative to BMI

BMI Adolphe.jpg

Adolphe Quetelet created the BMI for measuring human body shape. It has prevailed for over 160 years

The BMI is an attempt to quantify the amount of tissue mass (muscle, fat, and bone) in an individual, and then categorize that person. However, the BMI does not indicate weight distribution. It also does not differentiate between body fat and lean mass. 



Here's the experiment in a 7.5 minute video presentation

Podcast Interview: Designing Smarter Senior Living Facilities

Luxury Gym

This is an interview conducted by Chris Carruthers, Vice President of Health Services Marketing, Love and Company. In this podcast, I discuss how data-driven personal wellness can impact the lives of seniors.

Topics covered in this podcast:

  • The wellness industry is at an inflection point. It is now so much easier to collect data on the human body — Consumers can now use sensor technology to monitor key aspects of their wellness, such as heart rate, blood pressure, blood glucose, body composition, bone density, metabolism, sleep and stress levels. Consumers can also easily order at-home lab tests and explore their microbiome, genetics.


  • There's a common misconception that wellness technology is mostly for the young, healthy and tech-savvy consumer.  The data shows that seniors can not only benefit a lot more from these technologies, but they're also willing to adopt it, provided that it is easy to use and that the "use case" and the value are clearly defined. It cannot be just about counting steps. It has to be about creating a smart environment that can help alert, prevent and optimize someone's life. 


  • Other topics covered in this podcast: "Digital Aging," data visualization, interactive smart living spaces, ROI of data-driven and tech-enabled wellness services.

Data-Driven Health: Live from the 2017 Health Technology Forum, Stanford School of Medicine

Earlier this week, the Health Technology Forum (HTF) held its sixth annual Innovation Conference at the Stanford School Of Medicine

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The event focused on health and wellness enhancements made possible through technology. It gathered speakers such as Vinod Khosla, prominent entrepreneur, investor and technologist, and Michael Snyder, Stanford W. Ascherman Professor, Chair of the Department of Genetics, and Director of the Center for Genomics and Personalized Medicine. 

Our Co-founder and CEO Laila Zemrani spoke on a "Data-Driven Health" panel. The session discussed the opportunities and challenges offered by new methods and sources for incorporating more personal, longitudinal, and real-time data. 

 From Left to Right: Ernesto Ramirez, Director, R&D, Fitabase; Laila Zemrani, Co-Founder, CEO, Fitnescity; Aenor Sawyer, Associate Director, Center for Digital Health Innovation, UCSF; Mary Vincent, Advisor, EIR SK Telecom; Marco Altini, Head of Data Science, Bloomlife

From Left to Right: Ernesto Ramirez, Director, R&D, Fitabase; Laila Zemrani, Co-Founder, CEO, Fitnescity; Aenor Sawyer, Associate Director, Center for Digital Health Innovation, UCSF; Mary Vincent, Advisor, EIR SK Telecom; Marco Altini, Head of Data Science, Bloomlife


Data-Driven Health Starts with Data-Driven Prevention

The overwhelming majority (99%) of the healthcare system's resources are spent on illness. "We spend about $1 to keep someone well, and about $99 to treat them when they're sick," said Laila Zemrani. This is happening at a time when 80% of some of the most common and costly health conditions that we're seeing are are preventable. Wellness is poised to play a prominent role in health over the years to come, according to Zemrani.

So why aren't we seeing major players, such Google or Uber, in the health or wellness tech industry, asked Ernesto Ramirez, Director, R&D at Fitabase and moderator of the Data-Driven Health panel.


Data-Driven Health is a Team Sport

Aenor Sawyer, Associate Director, Center for Digital Health Innovation at UCSF, stressed the importance of "team work" in digital health and wellness. The panel agreed that applications of data-driven health need to be at the intersection of different disciplines, such as data science, design, business and behavioral science, in addition to healthcare itself.

Laila Zemrani pointed out that the biggest challenge facing data-driven wellness is understanding what consumers want and how to appeal to them. 


Are We Only Solving the Problems of the Wealthy?

It is often argued that digital health products and services are either unaffordable or targeted mostly towards those who are already healthy (i.e. the "worried well.")

The panel offered different perspectives on the issue. For instance, Laila Zemrani argued that many technological innovations have historically been offered in the wealthiest neighborhoods and countries first. As companies achieve economies of scale and as R&D costs decrease, the innovation starts to reach a larger population. For instance, mobile phones weren't always used by women in rural villages of Kenya to conduct business and make a living. 

A member of the audience argued that healthcare, unlike the mobile industry, has certain unique design challenges. Everyone agreed, however, that behavioral design will be a key component of any successful application of data-driven heath. In the words of Sawyer: "Behavioral scientists are the new heroes in healthcare."

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Learn more about Fitnescity's upcoming events: Stanford MedX 2017, Quantified Self Annual Conference and much more.



Laila Zemrani

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