Denver Broncos and Olympians Go Digital

The “measured life” of tracking health vital signs, calorie intakes, feces analysis and more is moving past the domain of scientists and onto the professional fields of NFL football and the 2012 Olympics. Data capture, tracking and analysis is taking precedence over “old school” ways of preparing for excellence. Will these new data driven approaches create a whole new breed of champions?

Image courtesy of Denver Broncos. All rights reserved.

For the 2012 NFL football season, the Denver Broncos released their former strength and conditioning coach and hired Luke Richesson.  Mr. Richesson comes to the Broncos from the Jacksonville Jaguars and has a different mentality for getting the best performance from his athletes.

According to an article in the Denver Post, gone are most of the dumbbells and free weights in the Denver Broncos training facility. Now Richesson focuses on resistance training, mixed martial arts, agility drills and heavy conditioning. But Richesson knows that a new approach to strength and conditioning can only take his athletes so far. Data collection and analysis has to be part of the mix.

“These guys are like Ferrari’s”, Richesson says. And Richesson knows that while it’s ultimately the player’s responsibility to be in the best shape possible, he can provide input and guidance along the way towards ensuring maximum output on the football field.

It starts with data collection. Inputs are taken verbally and documented with iPads. Richesson wants to know what athletes eat for breakfast, lunch and dinner. He documents their sleep quality and quantity. And he has outfitted all players with heart monitors during workouts.  Richesson’s staff then uses this data to monitor a player’s workout on a real time basis. And of course, data aren’t discarded afterwards, but stored on a server for future analysis.

“The numbers don’t lie,” Richesson says. He’s constantly looking for peaks, valleys and outliers in the data. When his staff discovers the slightest variance from baseline data, Richesson and his staff start questioning athletes as to changes in diet, sleep schedules or possibly injuries.   In effect, everything is monitored, recorded and analyzed, with the ultimate goal of creating a high performance team.

2012 Olympians have a similar idea. Some are accepting use of high tech gadgets on a trial basis in exchange for providing their personal performance data.  For example, the Daily Mail notes the US track cycling team is “testing a sleep monitor, glucose monitor…and (is receiving) genetic reports on their nutritional needs and muscular capacity.”  While these are “trials” in 2012, it’s highly likely that such data collection and reporting will be commonplace in the near future.

Consistent performance and finishing strong matters for both athletes—and corporate decision makers. It’s good to see that both groups are starting to take analytics seriously in an effort to get a competitive edge.

Data, Feces and the Future of Healthcare

University of California computer scientist Dr. Larry Smarr is a man on a mission—to measure everything his body consumes, performs, and yes, discharges. For Dr. Smarr, this data collection has a goal –to fine tune his ecosystem in order to beat a potentially incurable disease. Is this kind of rigorous information collection and analysis the future of healthcare?

Talk to a few friends and you’ll probably find those who count calories, steps, or even chart exercise and/or eating regiments.  But it’s not very likely that your friends are quantifying their personal lives like Larry Smarr.

Atlantic Magazine’s June/July 2012 issue describes efforts of Dr. Larry Smarr in capturing his personal data – but not necessarily those of financial or internet viewing habits. Dr. Smarr is capturing health data, and lots of it. He uses armbands to record skin temperature, headbands to monitor sleep patterns, has blood drawn eight times a year, MRIs and ultrasounds when needed, and regular colonoscopies. And of course, he writes down every bite of food and also collects his own stool samples and then ships them to a laboratory.

Monitoring calories makes sense, but stools are also “information rich” says Smarr. “There are about 100 billion bacteria per gram. Each bacterium has DNA whose length is typically one to ten megabases—call it one million bytes of information,” Smarr exclaims. “This means human stool has a data capacity of 100,000 terabytes of information (~97 petabytes) stores per gram.” And all kinds of interesting information on the digestive tract, liver and pancreas can be culled from feces including infection, nutrient absorption and even cancer.

Armed with all this health data, Dr. Smarr is attempting to “model” his ecosystem. This means producing a working model that when fed inputs, can help report, analyze and eventually predict potential health issues. Just as sensor and diagnostic data are useful for auto manufacturers to perform warranty and quality analysis, Dr. Smarr is collecting and analyzing data to fine tune how his human body performs its functions.

But there’s more to the story. In his charting process, Dr. Smarr noticed his C-reactive protein (CRP) count was high—which rises in response to inflammation.  “Troubled, I showed my graphs to my doctors and suggested that something bad was about to happen,” he says.  Believing his higher CRP count was acting as an early warning system, Carr was dismissed by doctors as too caught up in finding a problem where there was none.

Two weeks later Dr. Smarr felt a severe pain in the side of his abdomen.  This time, the doctors diagnosed him with an acute bout of diverticulitis (bowel inflammation) and told him to take antibiotics. But Dr. Smarr wasn’t convinced. He tested his stools and came up with additional alarming numbers that suggested his diverticulitis was perhaps something more—early Crohn’s disease which is an incurable and uncomfortable GI tract condition.  The diagnosis of Crohn’s was subsequently confirmed by doctors.

Critics of “measuring everything” in terms of healthcare suggest that by focusing on massive personal data collection and analysis we’ll all turn into hypochondriacs, looking for ghosts in the machine when there are none. Or, as Nassim Taleb argues; the more variables we test, the disproportionately higher the number of spurious results that appear (to be)”statistically significant”.  And there is also the argument is that predictive analytics may do more harm than good in suggesting potential for illness where a patient may never end up developing a given disease. Correlation is not a cause in other words.

That said, you’d have a hard time convincing Dr. Smarr that patients, healthcare providers and even society at large couldn’t benefit more by quantifying and analyzing inputs, outputs thus gaining a better understanding of our own “system health”.  And fortunately, due to Moore’s Law and today’s software applications, our ability to apply brute force computation to our data-rich problems is now not only possible, it’s available now.

However, what sometimes makes sense conceptually is often much more of a difficult implementation in the real world. A sluggish healthcare system, data privacy issues, and lack of data scientists to perform big data analysis are potential roadblocks in seeing the “quantified life”—for everyone—become a reality any time soon.

Questions:

  • Does data collection and analysis methods as described in this article portend a revolution in healthcare?
  • If everyone rigorously collects and analyzes their personal health data, could this end up raising or reducing overall healthcare costs?

What’s Next – Predictive “Scores” for Health?

In the United States health information privacy is protected by the Health Information Portability and Accountability (HIPAA) act.  However, new gene sequencing technologies are now available making it feasible to read an individual’s DNA for as little as $1,000 USD.  If there is predictive value in reading a person’s gene sequence, what are implications of this advancement? And will healthcare data privacy laws be enough to protect employees from discrimination?

The Financial Times reports a breakthrough in technology for gene sequencing, where a person’s chemical building blocks can be catalogued—according to one website—for scientific purposes such as exploration of human biology and other complex phenomena. And whereas DNA sequencing was formerly a costly endeavor, the price has dropped from $100 million to just under $1,000 per genome.

These advances are built on the back of Moore’s Law where computation power doubles every 12-18 months paired with plummeting data storage costs and very sophisticated software for data analysis.  And from a predictive analytics perspective, there is quite a bit of power in discovering which medications might work best for a certain patient’s condition based on their genetic profile.

However, as Stan Lee’s Spiderman reminds us, with great power comes great responsibility.

The Financial Times article mentions; “Some fear scientific enthusiasm for mass coding of personal genomes could lead to an ethical minefield, raising problems such as access to DNA data by insurers.”  After all, if indeed there is predictive value via analyzing a patient’s genome, it might be possible to either offer or deny that patient health insurance—or employment—based  on potential risks of developing a debilitating disease.

In fact, it may become possible in the near future to assign a certain patient or group of patients something akin to a credit score based on their propensity to develop a particular disease.

And something like a predictive “score” for diseases isn’t too outlandish a thought, especially when futurists such as Aaron Saenz forecast; “One day soon we should have an understanding of our genomes such that getting everyone sequenced will make medical sense.”

Perhaps in the near future, getting everyone sequenced may make medical sense (for both patient and societal benefit) but there will likely need to be newer and more stringent laws—and associated penalties for misuse) to ensure such information is protected and not used for unethical purposes.

Question:

  • With costs for DNA sequencing now around $1000 per patient, it’s conceivable universities, research firms and other companies will pursue genetic information and analysis. Are we opening Pandora’s Box in terms of harvesting this data?