Mindfulness

Why Stress is Public Health Enemy Number One

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The following is an interview with Elissa Epel, a UCSF psychologist who has studied the health impacts of stress, from its effects on our DNA to its relationship to overeating, for two decades. Published on The Huffington Post, 4/3/2013

Some of your research has centered on the way that stress hormones contribute to increasing our drive to eat, particularly high-carbohydrate and high-fat “comfort foods.” To what degree is stress contributing to our national obesity crisis, in your opinion?

EE We can’t quantify exactly how big of a role stress plays. It could be huge. It’s invisible and it’s easy to ignore; it’s pervasive. Most of us have gotten so used to living in a matrix of stress – time pressure, demands, rushed social interactions, rushed eating – that we don’t even notice it. So we might not realize how stressed our body really is. But the effects of stress can still stimulate our appetite, and shift us to choosing more ‘white food’ – what we call “comfort food,” – high-calorie, high-fat food. This promotes metabolic disease because it causes us to store calories in the visceral area and liver. And that stored fat is at the core of many chronic diseases, not just diabetes.

I was surprised to see your study showing educational attainment is also related to telomere length. Why might that be?

EE That relationship is multi-layered and needs to be unpacked. One common theme in trying to understand health disparities is testing whether part of it stems from greater stress exposure or reactivity over a lifetime. For example, the effects of more years of education early in life can be seen decades later, in longer telomere length. Higher education, or maybe it’s the quality of education, can create an infrastructure in the brain for more adaptive coping – it can help with strengthening what we call ‘executive function’ -which helps us think clearly under stress.

Conversely, there are many active ingredients in the milieu of low socioeconomic status that cause wear and tear. Interestingly, though, perception can play a large role here. We have measured this by giving people a picture of a ladder and asking them to place themselves on a rung (the bottom rung being the lowest status). Rating oneself as low, regardless of actual income or education, relates to poor adaptation to stress. Specifically, when given the same task to do in the lab, people low on the ladder reacted hotly each time, as if it were new, instead of habituating to it. There is also the built environment of low socioeconomic status, which doesn’t leave opportunities for buying healthy food and places for exercise or safe walking. And the built environment can feed back and affect how people feel. For example, fewer parks or more liquor stores predict a decreased feeling of neighborhood trust and cooperation.

There seems to be a big disconnect between what people know is good for their health, and their actual behaviors. Is mindfulness – focusing on what we’re doing right now, in the present moment – the missing link, do you think?

EE I think that’s right on. We can’t possibly regulate our behavior and feelings, and suppress those pesky but strong impulses and other distractors, if we are not paying attention. In a high-stress environment, our brain activity shifts toward the limbic system and the emotional stress response, and away from the parts of the pre-frontal cortex that house executive control systems, the rational and analytical drivers of our behavior. So we react automatically and impulsively when we are under stress and not paying full attention.

And even if we are focusing a lot of effort on eating better or exercising, but in a really self-critical way, this can sabotage our efforts as well. Very few people meet their exercise, sleep, and nutrition goals each day. So mindful attention includes both an intention and a kind attitude, and these help clear our mind of unhelpful or intrusive thoughts, and improve our ability to carry out our intentions.

Eating is an interesting example of a behavior that is not under our full conscious control, although we have not admitted that yet. Eating is something that we can do without paying attention. Otherwise, if it took focus and effort, that wouldn’t be part of adaptive evolution. Overeating is related to stress but also altered neurobiology of the reward system, the source of our strongest motivational drives. This reward area responds to palatable food. This can drive compulsive behavior that feels out of control, an experience similar to being a drug addict for some people. We have to better understand how powerful certain types of foods can be, and that certain conditions, including stress, make people especially susceptible.

In some of our studies, we are trying to help low-income people who feel very little control over their life, with their weight. We are teaching mindfulness to pregnant women, and it looks like the training might be helping not only them but also their babies. We have to think of ‘stress reduction’ where it matters most – which includes the womb. Prenatal stress exposure can affect a child’s health for a long time, possibly a lifetime. For example, mothers who have experienced major stresses while pregnant have offspring with shorter telomeres.

One of many intriguing facts you mentioned in your TEDMED 2011 talk was that technology can actually increase stress in various ways. At the same time, we’re seeing a slew of new apps aimed at helping us to calm down.

EE I think mobile apps for stress reduction are a fabulous potential use of technology, if they really work. For example, we could be using our mobile phones to remind us to rejoin with the moment, and to breathe fully, to notice our physical body and become embodied again. We live mired in our thoughts, above the neck, and this is made worse by multitasking.

But technology devices can become part of multitasking, thus adding to the strain on our limited attention, splitting it yet one more way. There are a lot of wellness apps out there, but I also think that we need data. Almost none of them are evaluated so although they seem promising, do people really benefit from them in a way that would lead to meaningful change? This is a powerful way to reach people, and I admit that even I am involved in an effort to test a stress reduction app!

There are so many answerable questions: Can we take people deeper into a meaningful life, or do these technology interventions contribute to fractured attention and more shallow social interactions? Do people stick with them? Do the apps make a dent in chronic stress arousal over time? As a society we desperately need stress reduction. Let’s hope we can use technology to get there.

If you had the power to enforce one public health measure based on your research, what would it be?

EE Public policy makers try to use their resources well to help people, but don’t always think about how to make policy motivating to an individual, nor take into account fundamental causes of societal and individual stress. Stress is caused by a perception of lack of control and unpredictability. Policymakers can promote empowerment, helping disadvantaged people gain a sense of control over their daily life. Social scientists understand which social and structural factors need to change to help individuals change.

A main message of research today, from epigenetics in basic models to epidemiology, is that adult health is shaped early in life, in important ways we can no longer ignore. So resources are best spent early in life, with the goal of promoting good health and habits, and preventing disease. Good quality education is critical, particularly for girls. It directly translates to better health behaviors and eventually health for the next generation. Resources are just much less effective when applied to diseases that are incurable and costly to manage. Our money is spent in an unbalanced and illogical way. We skimp on education — particularly in California — and spend a tremendous amount of money and time trying to cure incurable diseases such as obesity. Instead, we spend big money on bariatric surgery and costly band-aid procedures.

Has your research changed any of your own personal or work habits?

EE It has, but only in an incremental way over many years. I have been studying the field of stress for almost 20 years, so I know all too well what we should be doing, and how my behaviors such as curtailing sleep and having too many demands placed on me affects my daily physiology, and cellular stress. Does that mean I get enough sleep, exercise, meditate every day, keep work manageable, and prioritize the things that are most meaningful, versus the most urgent? No. I am closer to that than I used to be, and maybe in another stage of life… I still experience plenty of challenging situations, and have my reactions, but now in a more mindful way, and that is a qualitatively different experience. Like most people, I am a work in progress.

 

Can simple math help solve science’s big questions?

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As scientists struggle to publish, collaborate and keep up with current research, mathematicians and computer scientists are finding new ways to crunch data and answer big questions. Published on The Huffington Post, June 21, 2013

Following is an email interview with Max Little, Parkinson’s Voice Initiative founder, TEDMED speaker and TEDGlobal Fellow, who seeks to answer big questions using relatively simple algorithms.

You’ve been working to discover the practical value of abstract patterns in various fields, with surprising results in areas as varied as diagnosing Parkinson’s disease over the phone to predicting the weather. Can you explain your approach?

As an applied mathematician, my training shows me patterns everywhere. Electricity flows like water in pipes, and flocks of birds behave like turbulent fluids. In my projects, I collate mathematical models from across disciplines, ignoring the assumptions of that discipline. To a large extent, I put in overly simple models. I use artificial intelligence to throw out inaccurate models. And this approach of exploiting abstract patterns has been surprisingly successful.

For example, during my PhD I stumbled across the rather niche discipline of biomedical voice analysis, originating in 1940?s clinical work. With some new mathematical methods, and combining these with recent mathematics in artificial intelligence, I was able to makeaccurate medical predictions about voice problems. The clinician’s methods were not accurate. This sparked off research in detecting Parkinson’s disease from voice recordings – the basis of the Parkinson’s Voice Initiative.

But success like this raises suspicions. So, with collaborators, I tried to make this approach fail. We assembled 30,000 data sets across a wide range of disciplines: exploration geophysics, finance, seismology, hydrology, astrophysics, space science, acoustics, biomedicine, molecular biology, meteorology and others. We wrote software for 9,000 mathematical models from a deep dive into the literature. We exhaustively applied each model to each data set.

When finished, a very revealing, big picture emerged. We found that many problems across the sciences could be accurately solved in this way. In many cases, the best models were not the ones that would be suggested by prevailing, disciplinary wisdom.

Are you doing other research that might have implications for clinical diagnosis?

Here is another example: There is a decades-old problem in biomedical engineering: automatically identifying epileptic seizures from EEG recordings. But we found over 150 models, some exceedingly simple, each of which, alone, could detect seizures with high accuracy.

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Max Little at TEDMED 2013. Photo: Jerod Harris/TEDMED
This challenges quite a few assumptions — but it is not as if we are the first to find this. It happens often when new approaches to address old problems are attempted: for example, in obesity, a new, simple mathematical model revealed some surprising relationships about weight and diet.

You’ve also used fairly simple algorithms to successfully predict weather.

After my PhD, I teamed up with a hydrologist and an economist. We wanted to try weather forecasting using some fairly simple mathematics applied to rainfall data. Now, weather forecasting throws $10m-supercomputers and ranks of atmospheric scientists together, and they crunch the equations of the atmosphere to make predictions. So, competing against this Goliath with only historical data and a laptop would seem foolhardy.

But after two years of hard work, I came up with mathematics that, when fed with rainfall data, could make predictions often as accurate as weather supercomputers. We even discovered that models as simple as calculating the historical average rainfall, and using this as a forecast, were sometimes more accurate than supercomputers. We were all surprised. but this finding seems to line up with results that others have found in climate science: it is actually possible to make forecasts of future global temperatures using simple statistical models that are as accurate as far more complex, general circulation models relied upon by the Intergovernmental Panel on Climate Change.

Is this a new way of doing science?

If we divide science into three branches: experiment, theory and computer simulation, then what I am describing here doesn’t quite fit. These are not just simulations: the results are entirely reproducible with just the data and the mathematics. This approach mixes and matches models and data across disciplines, using recent advances in artificial intelligence.

I don’t know what to call this approach, but I’m not the only one doing it. The most enthusiastic proponents are computer scientists, who do something like this regularly in mass-scale video analysis competitions or one-off prizes financed by big pharma for molecular drug discovery as do statisticians working in forecasting.

In your TEDMED 2013 talk, you expressed concern that advances in science have stagnated. Can you explain?

Like many scientists, I’m concerned that science is becoming too fragmented. So many scientific papers are published each year that it is impossible to keep track of most new findings. Since most articles are never read, much new research has never been independently tested.

And, unfortunately, scientists are encouraged to ‘hyper-specialize,’ working only in their narrow disciplines. It is alien to we applied mathematicians that a scientist who studies animal behavior might never read a scientific paper on fluid mechanics! In isolation from each other, could they just be duplicating each other’s mistakes?

What can we do to create a more unified approach?

First of all, open up the data. There is far too much politics, bureaucracy and lack of vision in sharing data among researchers and the public. Sharing data is the key to eliminating the lack of reproducibility that is becoming a serious issue. Second, don’t pre-judge. We need to have a renewed commitment to radical impartiality. Too often, favoured theories, models, or data persist (sometimes for decades), putting whole disciplines at risk of missing the forest for the trees.

Collaboration would greatly speed advances. Is first-to-publish attribution of scientific findings really that productive? I think of science as a collaborative journey of discovery, not a competition sport of lone geniuses and their teams.

Scientific theories that can withstand this “challenge” from other disciplines will have passed a very rigorous test. Not only will they be good explanatory theories, they will have practical, predictive power. And this is important because without this mixing of disciplinary knowledge, we will never know if science is really making progress, or merely rediscovering the same findings, time and again.