Why Calculators Are the Future of Medicine

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[this is a post I did for TheHealthcareBlog, crossposted here]

Want to know the future of medicine and healthcare in one sentence?

For my money, it goes like this: The real opportunity in healthcare is to combine our personal data with the huge amount of general biomedical and public health research, in order to create customized information that's specific to our person and our circumstance. We need relevance, and the right information at the right time will help us make better choices for prevention, helping us stay healthier longer, it'll help us navigate diagnosis, letting us select screening tests that are useful and not unnecessarily fearful, and it'll let us make better decisions on care and treatment - when we're trying to choose among various treatments to find our way back to health.

It's in the last category - care and treatment - that I wrote a recent post at the Huffington Post about one man's story with prostate cancer. Tom Neville got a diagnosis and then had to struggle to find information to help him make sense of what to do. Ultimately, he chose surgery, but the difficulty of the choice led him to create Soar Biodynamics, a company that offers decision-making support for men assessing their prostate health.

You can read his story here and learn more about his tool here, but for the purposes of this post I wanted to consider the kind of decision-making tool he created. It's called a nomogram, and it's one of my favorite discoveries in researching The Decision Tree.

A nomogram is basically a calculator - a way to assess our risk or outcome for a particular condition. A nomogram starts with an interface where a few telling datapoints can be entered, and then turns to an algorithm that crunch those numbers together with broader data about the condition. The result is a statistical prediction - the prediction can concern the outcome of the disease, or it can be a recommendation for particular treatment (a medical nomogram is not to be confused with mathematical nomograms, which are tools for calculating geometrical something or others).

The Framingham Risk Calculator, which calculates your risk of heart disease, is a kind of nomogram. Memorial Sloan-Kettering Cancer Center, the research institute and hospital in New York City, has developed almost a dozen nomograms for a range of cancer conditions. There are tools for predicting the spread of breast cancer, a tool for assessing lung cancer risk among smokers, a tool for predicting the prognosis after colon cancer surgery, and more. Dr. Pierre Karakiewicz at the University of Ottawa has developed nomogram.org, which offers prediction calculators on four different types of cancer. Nomograms are one of the best examples of Decision Tree thinking, the sorts of tools that are easy for patients and doctors alike to use and understand—particularly when they’re available online and free of charge. They're brilliant and auspicious because the turn research around so that it faces the patient: An individual can interrogate medical science for how it applies to his specific circumstances, rather than having to navigate through stacks of research papers and findings for some wisp of relevance.

Nomograms are especially powerful when they’re combined with a screening test, because they help people understand what to make of the test and point to what to do with the result. They immediately customize the clinical data, be they nanograms-per-milliliter figures or spots on mammograms. Nomograms let patients ignore the inscrutable repository of jargon that is medical research in favor of something personal, something real, and something to go on. They allow us to make sense of a screening test’s result, and allow us to take some measure of meaning from it.

The University of Texas at San Antonio, for instance, has developed a prostate risk calculator that lets a man enter his PSA level along with his age, race, family history, and a couple of other metrics and churns out his risk of developing prostate cancer. Importantly, the calculator also calculates the risk of a high-grade cancer, accounting for the fact that not all prostate cancers are lethal. The value of such a tool, says Ian M. Thompson, professor and chairman of the department of urology at the University of Texas Health Science Center at San Antonio, who developed the calculator, is that it turns the PSA figure from one isolated data point into one of many inputs. “We need to build in characteristics about the person, their age, their race, their family history,” says Dr. Thompson. “It’s not just what one test tells us.”

Nomograms, of course, are no substitute for a doctor's definitive assessment and treatment (or better yet, more than one doctor). And they are only as good as the data that goes into them; if they're not kept up to date on the latest information and research, they can lead people astray. But especially for conditions where we have some agency - where we can take actions today that can enhance our tomorrow - they are a terrific tool.

The catch with nomograms is that they must be developed one disease at a time, which means they don't scale up so well. Each one takes a great deal of work and expertise. But if I had millions of dollars for philanthropy, I'd spread it around to smart researchers across a lot of fields where nomograms could help people assess their risk for disease and potentially take actions today. It would be money well spent.

Calculator image via Flickr by Ian Ruotsala

5 Ideas About Feedback & Behavior Change, Supported by Evidence

For several months I've been a regular at the Quantified Self meetups, where people of great passion for self-tracking present the results of their experiments in self-monitoring. These vary in everything from people who record themselves sleeping (in search of better sleep) to people who monitor the amount of coffee they drink to the mililiter - and then compare that to their productivity. One thing that I've noticed is that there's a general presumption, among the QS crowd, that self-monitoring - i.e., feedback - is an experiment with evidence behind it. That is, that self-monitoring works, insofar as it helps people reach a goal, whether it's to drink less coffee or get better sleep or lose weight. But there's not often any actual evidence presented, beyond these experiments (where N=1, often enough). So tonight, drawing on research from my forthcoming book, I'm presenting a few points of fact that support the notion that feedback can lead to improvements in health. This post will serve as a primer for those not in the audience, and for those in the audience a repository of links to the research cited.

Thus, here are five claims or presumptions about the benefits of feedback, with the evidence.

1) Observing our actions leads to better actions.

For this one I turn to the National Weight Control Registry, a nifty study out of Brown University that is tracking more than 5000 people who have successfully lost weight and kept it off. The registry basically studies what these people are doing right. And in addition to the common-sense things like exercise often and change to a healthier diet (less fat and sugar, more vegetables and whole grains), the registry has found a strong association between the simple act of stepping on a scale and sustained weight loss. In particular, the study found that 75% weighed themselves at least once a week, and 44% weighed themselves at least once a day. Tracking our behavior leads to better behavior.

2) Engaging with Our Health Leads to Better Health

The idea here is that self-tracking not only improves the direct behavior, but that it has broader health implications, and can be quite positive for overall health. The evidence comes from a wonderful study called ALIVE! (short for A Lifestyle Intervention Via Email), conducted by the research wing of Kaiser Permanente.

This study sent out weekly reminder emails to study members, each message tailored to specific health goals. So the email would nudge them to meet their goal of eating, say, 3 vegatables a week, or getting exercise twice a week, and so forth. The subject would respond whether or not they met these goals, thus tracking their progress and tailoring the results for next week's email. The results: study subjects reported more than 50% greater improvement in concentration and productivity than a control group, and were 50% more likely to successfully change their diet. More broadly, they reported significantly better physical and mental quality of life. A link to the research is here.

3) When patients participate, their outcomes improve.

This idea goes a step further than simple self-tracking - it argues that by giving people an opportunity not only to track their health but to use that data to make decisions regarding their health, there is an upside, in terms of better outcomes. This one speaks directly to physicians and care providers who are reluctant to open the door to their own decision making and involve the patient in the discussion of treatments and care choices. Well, the Centre for Studies in Family Medicine at the University of Western Ontario took up this question - Does involving patients in their care, giving them a participatory role in their healthcare, improve the outcomes?

Their research showed a clear YES - involved patients had better recovery, better emotional health, and had a surprisingly 50% fewer diagnostic tests and referrals. In other words, they were happier with their care and the care was more successful. Not bad.

4) When patients participate, healthcare can be more effecient (read: Cheaper).

This one is going one step further, claiming that not only do self-tracking patients have better health, but that they actually have cheaper healthcare, to boot. For the answer to this idea, I come to a Boston University study that enrolled nearly 30,000 employees of the EMC Corporation in an online tracking program called DASH for Health.

The patients tracked their weight, blood pressure, and other information, and received tailored recommendations for dietary choices that they may not be aware of (such as: adding cream to your coffee instead of milk amounts to nearly 8 sticks of butter a month). The study found that among patients with an increased risk for cardiovascular disease, employees using the DASH program had on average $814 less spent on their healthcare annually. For all patients, the costs were basically neutral.

5) Data means more when it's our data.

This one is a response to the idea that healthcare woes in the US like obesity are an information problem - that there's a lack of resources or research or information for people. That's pretty clearly not true - we don't lack for information or health advice. But what we do need is relevant information, information that's tailored to our specific circumstances and conditions and that spells out the choices that our own situation holds.

This requires matching up our data with the right research data at the right time - when it's more specific and most useful. This is relevance. Annette O'Connor at the Ottawa Hospital Research Institute has done some extraordinary work in this area, looking at the growing body of decision aids available to patients to help them understand their options. Increasingly, these decision aids are turning up online in the form of nomograms - decision tools that allow us to input our personal information - data gleaned from our physicians or our own tracking - and then use that information to guide us to a decision that's right for us.

A couple years ago, O'Connor conducted a meta-analysis - a study of studies - examinging several hundred decision aids, with the goal of determining what effect they had, if any, on outcomes. It turns out that decision aids can actually be quite effective in improving our decisions and our outcomes. Patients using decision aids, O'Connor found, had significantly better understanding of benefits and harms, they perceived they were receiving better care, and they chose significantly less surgery (about 25% less). When they understood the pros and cons, it seemed, patients chose to avoid the knife more often, and were happier for it.

This research shows that self-tracking isn't just for a geeky fringe - it's actually a sensible strategy that anybody can take advantage of to gain some control of their health. Yes, often it requires a computer - but that's a good thing, insofar as it's easier to track and monitor stuff that way.

A Microbial Census

One morning, a little over a year ago, I woke up with a very sore, and slightly swollen elbow. I remembered that I had cut my arm on a neighborhood bar table while watching a football game with some friends a few days prior, and I wondered if the cut was infected. I made an appointment with my primary care physician, who quickly diagnosed me with bursitis, an inflammation of the fluid-filled sac that pads the elbow. Since I had broken skin, the doctor wisely prescribed clindamycin, an antibiotic, to treat any tissue infection that may have seeped in. As the hours crept by, the pain in my elbow worsened, until I woke up in the middle of the night with extreme arm pain. I immediately checked the elbow that had been swollen the previous day. The swelling had doubled in size, and the skin was an angry-red color. The following morning, I was back in the clinic, and my doctor started to suspect that this was no ordinary infection on my elbow, and may in fact be a drug-resistant staph infection. Gulp. Nonetheless, he felt confident that the clindamycin should clear it up.

Under the doctor's orders, I spent the next day meticulously tracing the swollen area on my elbow with a Sharpie marker, carefully noting how much it spread. By the end of the day, my entire forearm was puffy and discolored, and my doctor said it was time for me to be admitted to the hospital. I spent 3 days there, getting intravenous treatments of vein-burning, gastrointestinal-rearranging Vancomycin pumped into my system. Not fun.

Afterward, I talked to a number of physician friends about my experience. They said my doctor's treatment plan was textbook. He had done everything right. When docs suspect drug-resistant staph, the first line of defense is typically a hearty dose of clindamycin. The problem in my case was that the staph I contracted was actually resistant to clindamycin. That explains why the infection continued to spread even though I was taking the antibiotics.

Since this little microbial foray, I've had a growing interest in infectious disease. Specifically, I like seeing smart, new ways to keep tabs on how bacterium move from place to place. I wonder, if my doctor had known that clindamycin-resistant staph was infiltrating San Francisco, would I have initially received a different antibiotic? In my opinion, this was a clear case where having more data would have aided the diagnosis, and hastened a healthy outcome.

As Thomas pointed out at The Huffington Post, the true promise of personalized medicine is more about data than specialty drugs. Data can be our personal metrics, such as blood pressure, glucose levels, or cholesterol values. But keeping medical data to ourselves would be somewhat shortsighted. The internet has taught us the power of sharing data. We share our photos on Flickr. We share our status messages on Facebook. We share links on Twitter. Likewise, we can share our health and medical data, enabling pooled statistics from large populations. In the case of infectious disease, the best preventive strategy is to know exactly what strains you're up against, and how the microbes are moving into different geographic regions over time.

Researchers recently confirmed the power of sharing microbial data in a new report, published this month in PLoS Medicine. Roughly 25% of us walk around with staph on our skin, yet not all of us get sick. That's because there's relatively few strains that cause serious symptoms. These so-called virulent strains are the ones docs want to track.

Following both methycilin-resistant (MRSA) and methycilin-susceptible (MSSA) staph strains through Europe, the authors coordinated the participation of 450 hospitals in 26 European countries, a logistic feat in its own right. When a case of staphylococcus aureus was found, the bacterium was genotyped (i.e. its DNA was analyzed to identify which strain it came from), and its location recorded. After collecting all the data, researchers could see how a particular strain of staph localized in different geographic regions. For instance, did the virulent strains stay in one hospital, or had they spread throughout the community?

The authors found that most virulent MRSA strains were contained in a health care clinic, meaning that drug-resistant staph was simply hopping from person-to-person within the hospital walls. Occasionally, that MRSA strain would show up at a different, nearby hospital, and rapidly spread in admitted patients. This implies that the carriers of the virulent MRSA strains are patients who are repeatedly admitted to different regional hospitals.

I'll leave you with a final thought: tracking microbes isn't just a task for researchers. In fact, DIYBio types should check out a cool new project called BioWeatherMap, which asks volunteers to swab commonly used public surfaces, such as door knobs or crosswalk buttons, to track pending microbial storm fronts.

Spreading the Word About Health at Huffington Post

Just a quick update that I've begun posting now and again at the Huffington Post, for their living section. Some of the material will be familiar to readers of TheDecisionTree.com, where Brian & I wade deeper into the science of personalized medicine, but the more-populist forum of HuffPost is a good way to test the principles and larger messages that are sometimes taken for granted here. My first post a few weeks back, Welcome to the Era of Personalized Medicine, argued that the idea of tailoring healthcare to individuals has arrived - but that it may take some work on our part to take advantage of it. The basic premise is that personalized medicine is about data, more than drugs (specifically the notion of pharmacogenomics where drugs are matched to specific genetic traits).

My second post just went up today: in the tired & true format of service journalism it's a list: 3 Ways to Take Control of Your Health Today. In this one I confront the disconnect between the surfeit of health information and the failure most people make to actually turn that information into action (what Aristotle called akrasia). In an attempt to bridge disconnect, I propose that there are clear health benefits just by choosing to engage in our health - just by asserting control over our health. And that's where I offer three ways to get started: Choosing to Care about our health, Deciding What to Care About, and finally Knowing That There's No Such Thing As Perfection (either in our behavior or, ultimately, in our health).

Anyway, take a look. As always, feedback is welcome, here or there.

Finding the FDA's Drug Safety Information Online

A smart post by The Sunlight Foundation's Nancy Watzman has me thinking about what it really means to have access to all of our personal health data. In the past, I've myopically viewed personal health data as anything that my body produced, in one way or another, and now sits in my shadowy file at the doctor's office. Things like X-rays, MRIs, and blood test results. No doubt, I should have access to all of this information. What about prescription medication? Sure, I can easily make a list of the meds I'm currently taking, or get my doctor to hand this list over if memory fails me. But how much do I really know about these drugs? Most people, myself included, take our doctor's word when he or she decides to put us on a commonly prescribed medications. For example, let's say a patient has blood-work that shows elevated LDL cholesterol on two consecutive screenings, comes from a family where cardiovascular disease runs rampant, and was previously unable to regulate cholesterol levels with strict diet and exercise regimes. If the doctor prescribed Lipitor to treat the problem, a patient may not even think twice about taking it. After all, we see commercials for such drugs on our TV, and we flip past their ads in our magazines. Direct-to-consumer marketing by pharmaceutical companies makes drugs familiar and, presumably, safe.

But regardless of what advertisements say, the FDA is ultimately responsible for giving drugs the safety stamp of approval. The decision to approve a drug is based on substantial amounts of preclinical (testing in animals) and clinical (testing in humans) data submitted to the FDA by the drug manufacturer.

Let's say someone – a doctor, a patient, a concerned citizen – wants to review the data that the FDA uses to approve a drug. If the drug you're taking was approved after 1998, you can find the FDA's review documents online. If you're prescribed an older medicine, you may strike out when trying to find what the FDA has to say about it. The government's information on drugs approved through 1997 may be released if someone makes a request through the Freedom of Information Act, but the FDA reserves the right to not publish reports if the agency deems the preserved documents are of “poor quality”. In fact, Watzman found that online safety information is missing for 9 of the 25 most commonly prescribed drugs.

For older and newer drugs alike, when the FDA publishes a review online, it's never in a text-searchable format. Rather, the agency prints the original paperwork, edits with white-out to cover “propriety information”, scans the newly edited document, and finally, posts the altered PDFs online.

Are these edited documents, with words covered with white-out and entire sections omitted, really providing us with useful information? Somewhere along the drug approval process, there has to be a succinct memo that circulates around the FDA headquarters describing the agency's major findings on a particular drug. Why can't the FDA publish a simple summary of their findings?

Skeptics may argue that detailed information about a drug already published by pharmaceutical companies in medical journals, such as the New England Journal of Medicine (NEJM) or the Journal of the American Medical Association (JAMA), should suffice. However, there are two problems with the information reported in medical journals. First, most of these journals are not open access, meaning the average consumer cannot access them without paying for the article. Second, many drug companies only publish positive findings, and bury negative results that show less than desirable efficacy or safety.

Watzman's report is an eye-opening look at what's wrong with the flow of drug information as it goes from pharmaceutical company to the FDA to the consumer. If pharmaceutical companies are allowed to market directly to consumers, we should demand the right to know what the FDA has to say about the safety of these drugs.

Why Self-Tracking Isn't Just for Geeks

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One of the themes of The Decision Tree - both the blog and the book - has been the idea of self-tracking: the notion that when people monitor their health, they are more likely to improve their health. In the aggregate, that's true - research shows that when people start to track or even care about their health (when the start to feel vested in it, and in control of it) they tend to have better outcomes. That's a powerful and important message, one that I believe hasn't really gotten widespread recognition - and I hope one benefit of the book is that this message will spread and help change people's lives (as well as the approach of care providers, who may have heretofore been reluctant to engage their patients in their own care).

But as I've been talking about these ideas in recent weeks, one frequent and important question has come up repeatedly. I'll paraphrase it like so: Does self-tracking scale? Or, as a non-geek would say, Is self-tracking just for geeks? Do you have to be a nerd to do it? In which case, is it a realistic strategy for the rest of us, those of us who aren't comfortable with data and navel-gazing and tricked out gadgets? In other words: Is this a transformative strategy for mainstream society, or a trick for the marginal few?

Well, good question.

My answer comes in two parts. First, I completely acknowledge that right now, most of this self-tracking stuff is for the geek crowd. It's got all the hallmarks of a early-adopter phenomenon: the tools aren't always easy to use, they don't always work right, and the whole idea is a bit complicated. In other words, there's lots of friction to the notion. But early-adopters, as the term implies, tend to pave the way for the rest of us. They iron out the kinks and spot the bugs that make the next generation of tools and technologies easier and friendlier to use for all of us. I see every indication that the same thing is happening here. And in this case, the upside isn't just a better way to watch television (the way Tivo early adopters paved the way for DVR ubiquity), but a better way to be healthy. That's a big upside.

Second, what's happening with self-tracking today is rather remarkable. It's not just the idea that tracking tends to help people make better health decisions (though that's true, and that's huge in itself). It's the idea that the principles of self-tracking tend to synch up, rather remarkably and serendipitously, with the principles of effective behavior change.

This is no small thing: there have been literally billions of dollars spent in recent decades researching how to get people to behave better (i.e., less self-destructively). The result has been a great set of basic understanding of 1) what we should do for better health and 2) how we can do it. Unfortunately, these principles have tended not to scale beyond the resources of any one research project. In other words, a research project will spend a lot of money figuring out how people should behave, but just when they prove their point, the money runs out and the project is over. The insights may be published, but there's almost never that necessary second step that puts those insights into action.

Until now. Consumer technologies that let people track their own health results synch up - to a remarkable extent - with the insights of research. Part of it's coincidence, and part of it is planning, but whatever the reason, the fact is that technology has finally progressed to the point that these insights - which boil down to giving people access to their data and then letting them share and compare their data with others - are the mainstay not just of research, but also of the nascent self-tracking industry.

Case in point: the iTunes apps store. A year or so ago, I did a post on this blog about the new health apps that touched on self-tracking. There were about 10 apps. Today, there are more than 5,000 apps that touch on self-tracking in the Apps store. Yeah, some of them are crap, and many of them are rip-offs. But surprisingly, many of them - including many free ones - are quite well thought out, easy to use, and intuitive.

In other words, they're not just for geeks. They're for all of us. This is a big idea. And it's going to get bigger.

How "The Science of Success" Redefines Psychology

I just finished reading Dave Dobbs' new article in the the December issue of The Atlantic, "The Science of Success".  Dobbs turns the classic question of Nature vs. Nurture, whether our genes or our environment are the deterministic drivers of our fate, on its head.  Traditionally, those who support "nature" say that our genes are most influential in defining us.  On the other hand, those that support the "nurture" side say that our environment plays a more important role. Based on new research, Dobbs introduces the idea of two types of people, "dandelions" and "orchids".  Dandelions can thrive anywhere, despite their environment or upbringing.  Orchids, however, are more temperamental, and require a stable environment to survive.  At first glance, the orchids may seem like a liability, and in fact, they often carry genes that make them susceptible to mood disorders and psychological disease.  The astounding part of Dobbs' report is that he shows that given the right care, or environment, the orchids don't just do OK, but far surpass the dandelions in perfomance.  In other words, given the right training, orchids may in fact be destined for greatness.

This finding redefines conditions we typically may have classified as undesirable.  ADHD, depression, and generalized anxiety disorder, are no longer conditions to dread, because given the right training, people with these predispositions may in fact be the true "movers and shakers" in the world.

Please read the full article for yourself.  And, as always, I'd welcome a discussion here...

Why Behavior Change Is (Still) Better Medicine Than Drugs

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While attending the Institute for the Future's Health Horizons Fall Conference on Monday, one thing became eminently clear. The 21st century will be the era of brain, the last great scientific frontier. Due to societal shifts, environmental changes, and the fact that we are just living longer, we are poised to see a sharp rise in cases of diseases such as Alzheimer's, Parkinson's, autism, and post-traumatic stress disorder. The only thing worse than the increasing prevalence of brain disease is the sobering fact that few viable treatments currently exist.

For years, we've heard the mantra of behavior change and health. Exercise more and you'll cut your risk for heart disease and stroke. Eat more fruits and vegetables and you can decrease your risk for colon cancer (or possibly prostate cancer, as discussed in a previous Decision Tree post, "Why Behavior Change is Better Medicine than Drugs"). Could behavior change serve our brain health as well as it did other organs of the body?

On Monday, the neurotechnology community drew a definitive line in the sand with regard to treating the brain.  On one side were panelists that believed that society is not being medicated enough for mental disorders, including ADHD in children. On the other side, proponents of behavioral training argued that brain plasticity, the innate ability of the brain to rewire itself continuously throughout life, is our best bet to combat brain disease.

Consider the use of ADHD drugs in children, or cognitive-enhancing drugs, such as modafinil, by professionals (including a large group of scientists) in the workplace. Proponents of medication say that the cognitive enhancers are not doing anything unnatural. Rather, they are taking someone who's a mediocre performer in terms of concentration, and simply moving them to the upper 90th percentile. Then, according to the same logic, I guess these panelists would also support legalizing steroids in major league baseball. After all, the steroids are not making the athletes super-human. Rather, they're taking the middle-of-the-road performers and nudging them to the upper echelon of the sport. Hmmm....

My former postdoc advisor, Dr. Michael Merzenich of the University of California San Francisco, led the charge for behavioral training as a better alternative to drugs for diseases of the brain. Mike's lifelong work focused on the neuroscience of learning, and how brain plasticity occurs at various stages of development. He believes that many ailments of the brain, including ADHD, occur because we are using our brains "incorrectly", but specific behavioral training can reverse and improve these deficits.

The wonders of behavioral training and brain plasticity are not limited to sparse findings in a dark lab. In fact, Mike's most promising research has been translated into several commercial computer software applications, which have enhanced the reading capabilities of dyslexic children, as well as improved the speech processing and memory of senior citizens.

Whether you are sold on behavioral training as a feasible alternative to drug therapy in brain illness or not, one point remains solid: the cost of conducting clinical trials for behavioral training regiments is a mere fraction of the cost of drug trials. Given that it's terribly expensive to run drug trials, and that only a small fraction of drugs in a pharma company's pipeline succeeds in the clinic, we clearly can't afford to ignore behavioral training as a new way to treat the brain.

White-Noise and the Developing Brain

Usually, we think of preventive medicine as a first-person experience, e.g. what we can do to keep ourselves healthy. But preventive medicine includes steps to keep our families healthy as well, as in the case of an elderly relative, or a newborn baby. My first postdoc stint was in a developmental neuroscience lab at UCSF, where many talented researchers spent years answering questions like, "How do different types of environmental noise affect the development of the auditory system?". So when a friend of mine sent me a message the other day, asking about using a white-noise generator to stop her crying, colicky baby, some red flags immediately went off in my head. Because I've been asked this question several times over the past few months, I decided to post my take here.

There's been a lot of anecdotal evidence that white-noise calms a crying baby. In fact, some parents swear by the method. But this is a clear case where the science disproves the hype. In 2003, our lab at UCSF published a study in Science Magazine with a striking finding. The auditory system of newborn rat pups, which normally progresses like clockwork, was under-developed after the pups were exposed to white noise compared to animals raised in normal conditions. But why would white noise cause a problem with the development of the brain?

First, let's look at what happens to the auditory system during normal development. When rats are born, the area of the brain responsible for making sense of sounds, the auditory cortex, undergoes constant changes. Scientists refer to this phenomenon as brain plasticity. Newborns are unique because brain plasticity occurs just by passive exposure to sounds during a very well-defined time in development called the "critical period", which lasts through several postnatal days in rats. A correlate in humans might be the fact that children learn new languages just by being exposed to sounds, while adults have to spend hours studying, memorizing, and practicing. This developmental period is a crucial time for a newborn, where the brain "sets" itself to efficiently process its native language.

When white-noise was played for the newborn rats, the lab found that the "critical period" remained open indefinitely, which means there was a delay in normal brain development. For this reason, members of the lab were against using white-noise generators on newborn babies. Theories suggest that the white-noise might interfere with a newborn's ability to grasp its native language, leading to progressive developmental problems.

Even if the results found in rats did not directly carry over to humans, I really feel that you just shouldn't screw with Mother Nature when it comes to brain development. I'm not a parent, and I can only imagine the empathy, or even frustration, that ensue when a baby is crying hours on end. But using white-noise generators just doesn't seem like the best answer. Our brains evolved to process biologically- and socially-relevant sounds, and exposing newborns to extremely unnatural sounds seems like an needless gamble.

How Much Personal Data is Needed to Stay Healthy?

A few months ago, a story ran in Wired Magazine that described a noticeable shift in the scientific method, and attributed the change to our ability to produce and store large amounts of data.Historically, the scientific method was built around a testable theory.  But in the 21st century, theories were becoming obsolete; the data simply spoke for itself.

Data from our bodies is no exception -- physiologic data can now be accessed as a real-time data stream thanks to personal health monitors. But does the vast amount of data we get from our bodies make us any healthier? Do we need to collect data 24-hours a day in order to learn something interesting about our health? Is it even feasible to wear these sensors all day, every day?

I am embarking on a new self-tracking experiment to answer these questions (and possibly a few others). For 30 days, I will be using devices such as the Zeo personal sleep coach, the Philips DirectLife activity monitor, the Mio Motiva wristband on-demand heart rate monitor, and the Nike+ sportband. The goal of this study is not to pit one device against another; rather, I want to focus on what the data tells me, and how I can best use it to stay healthy.

I'll get a blog post up here at least once a week, all the while working on a longer story about the journey that will be released at the end of the month.

Stay tuned. It should be a fun ride...