David Shaywitz

Wearable devices have an ability to capture lots of data, in real-time and over long periods of time, that may reflect aspects of an individual person’s health.

But (and this is a common theme in the application of data science to healthcare), gathering volumes of data is one thing – deriving meaning from these data in a way that significantly improves a person’s health is another.

A recent paper in Nature Medicine paper highlights the delicate balance digital health researchers must maintain as they demonstrate the potential of emerging wearable device technology while taking care not to get ahead of the current state of the science, in terms of what the devices actually can tell us.

The research began in Mike Snyder’s lab at Stanford University, and was co-led by Jessilyn Dunn (a rising star in biomedical engineering now on faculty at Duke University) and Lukasz Kidzinski (now an AI researcher at Stanford and director of AI at Princeton, NJ-based Bioclinica).

Beyond Narciss-ome?

For a decade, Snyder has led the charge on wearables. He has famously used himself as a guinea pig.  So exhaustively has he monitored his own parameters, including genomics, proteomics, and every other -omic, that Baylor College of Medicine researcher Richard Gibbs, tongue-in-cheek, proposed a new term, the “narciss-ome”, to describe this comprehensive assessment.

Mike Snyder, chair, department of genetics, Stanford Medicine

As Snyder’s Stanford colleague Euan Ashley writes in Genome Odyssey (my recent WSJ review here),

“Mike Snyder was on a mission to measure everything about himself, all the time, using every technology possible. And I mean everything. Starting in 2010, shortly after he started at Stanford, Mike would show up to meetings sporting multiple different wearable devices. You would meet him, and there might be one smartwatch on one wrist and a different one on the other. Sometimes, he would wear an armband device the size of a pack of cards that detected airborne toxins in his environment. At one meeting, he showed up with a front-facing camera that took time-lapse pictures of everyone in the room. It freaked everyone out, so he stopped that soon after. Lloyd Minor, the dean of Stanford’s School of Medicine, refers to him as ‘the most studied organism in history.’”

Whether these exhaustive measurement efforts are truly useful has been less than clear; in some ways, like the dancing bear, they seem most remarkable not for quality of the clinical insight generated, but rather because they were conducted at all. Phrased differently, it’s not clear that the burden of such comprehensive data collection is (yet) justified, as I’ve recently discussed (here).

Nevertheless, the promise of rich data collection, particularly using wearables, remains as compelling as it was when Denny Ausiello and I articulated the ambition of digital health nearly a decade ago: we live our lives continuously, yet our medical needs tend to be evaluated episodically, and (hopefully) infrequently. 

Surely, there must be meaningful insight to be obtained from relatively dense, continuous, longitudinal measurements that can’t be gleaned from the occasional clinic visit. 

The challenge has been surfacing this hidden insight, and capturing the implicit value.

From Wearables To Insight?

Which is where the latest paper comes in. Utilizing data from 54 participants in the Stanford iPOP (integrative personal omics profiling) study, researchers examined data extracted from the smart watches the participants wore. The scientists first compared these values to two vital signs (temperature and resting heart rate) obtained in clinic visits using a validated instrument, and then utilized machine learning to see whether they could use either the wearable data or the clinical data to predict the values of routine clinical laboratory tests. 

The study utilized an Intel Basis watch, subsequently withdrawn from the market for safety concerns (the device could overheat, causing burns or blisters). The paper was originally submitted for publication in September 2018, but not published until May 2021, perhaps explaining why the Basis was used in this just-reported study.

The Basis could detect four parameters:

• Heart rate using PPG signals (the approach associated with the shiny green lights on the back of your Apple Watch);
• Skin temperature;
• Steps;
• Electrodermal activity (EDA), a measure of the electrical properties of the skin.

First, the researchers wanted to get a sense of how the measurement of resting heartrate obtained on the smart watch (using PPG) compared to clinic observations. Many devices use PPG to measure heart rate, including the Apple Watch, the Whoop strap, the Oura ring, and the Fitbit tracker, among others. The approach measures absorbance of the shined light, which is proportional to blood volume variation (each pulse transiently increases the volume). 

Challenges of Using PPG Technology To Assess Clinical Endpoints

According to a 2018 review article in the International Journal of Biosensors and Bioelectronics:

“The popularity of the PPG technology as an alternative heart rate monitoring technique has recently increased, mainly due to the simplicity of its operation, the wearing comfort ability for its users, and its cost effectiveness.  However, one of the major difficulties in using PPG-based monitoring techniques is their inaccuracy in tracking the PPG signals during daily routine activities and light physical exercises. This limitation is due to the fact that the PPG signals are very susceptible to Motion Artifacts (MA) caused by hand movements.”

These concerns were further examined in a recent NPJ Digital Medicine paper from Dunn’s current lab at Duke, examining potential sources of PPG wearable variability, compared to an ECG gold standard. While skin tone turned out not to represent a significant source of variability, motion was; moreover, the wearables exhibited different degrees of accuracy, with the Apple Watch generally outperforming competitors.

Jessilynn Dunn, assistant professor of biomedical engineering, Duke University

Dunn’s data accord with my own experience using consumer wearables during exercise; I’ve found the Apple Watch works better than other wearables I’ve tested, but not nearly as well as measurement techniques detecting electrical activity directly, like the Polar chest strap I’ve now adopted. Consumer-facing ECG measurements, like Kardia, and like the Apple Watch measurement obtained when holding the crown for 30 seconds, also utilize electrical detection.

Notably, in Dunn’s recent paper, consumer wearables significantly outperformed several “research-grade” wearables that were also evaluated; research wearables allow investigators access to the underlying waveforms, while consumer wearables function like black boxes from a research perspective, dramatically limiting their use in clinical research and making it prohibitively difficult to utilize more than one wearable in a given clinical trial — a critical interoperability obstacle that Jordan Brayanov, Jen Goldsack, and Bill Byrom elegantly discussed last year in STAT. 

As the three authors explained,

“You’d think that monitoring heart rate remotely would be easy. But wearables from technology giants like Apple and Samsung measure it in different and proprietary ways. One device may record the number of beats over 10 seconds and multiply by six; another may communicate an ‘instant’ heart rate reported after every single heartbeat. This means the two platforms’ data aren’t consistent and so can’t easily be used simultaneously in clinical trials.”

Resting Heart Rate, Temperature: Wearables Data vs Clinical Data

Back to the original paper: Snyder’s team found that when they considered two weeks’ worth of resting heart rate measurements at the same time of day as the clinic visits, the values were similar, but the variability was significantly less in the wearables groups, compared to the clinical measurement group. 

In other words, you get more consistency measuring resting heart rate over weeks on a wearable than assessing it once in a while in the clinic.

Score one for the wearable!

However, temperature measurement was a different story; here, as the researchers report, “clinically measured oral temperature was a more consistent and stable physiological temperature metric than wearable-measured skin temperature….” 

Translation: compared to clinical measurement, assessment of temperature on wearables was somewhat scattered.

Wearable Data + Feature Engineering + ML = Clinical Lab Predictions?

With these foundational parameters of performance established, here’s where the paper gets interesting. The researchers examined the four basic categories of output from the watch – measurements of heart rate, temperature, electrodermal activity, and steps – and began the alchemy of data science known as “feature engineering.” 

Feature engineering involves selecting attributes from the raw data to use as an input for a machine learning model. It could include a statistical property of the data – average heartrate, say, or a property of the distribution of the heart rate, or it could be the implied activity state of the individual, based on number of steps. 

According to the authors, there were 5,736 possible features they could have considered, from which they selected 153 that seemed the most likely to be altered in a fashion that could conceivably be reflected in a clinical laboratory test.

These 153 features were then fed into several different types of models intended to predict the value of one of 44 different clinical labs that were also obtained from the study participants. The initial work suggested one modeling approach, called random forest, generated predictions that explained up to a fifth of the variability seen in measures of hematocrit, red blood cell count, hemoglobin levels, and platelet count. 

To be clear, the contention isn’t that wearable data predicted these clinical labs with exceptional accuracy, but rather that wearable-derived data seemed to very roughly correlate with these clinical labs, and others. 

When the researchers examined which features were driving the predictions, it turned out that various permutations of electrodermal activity played a critical role in predicting hematocrit, red blood cell count, and hemoglobin levels, while features driving platelet count predictions were all based on heart rate.

The authors then conducted what felt like a bit of a pedantic demonstration exercise, comparing predictions derived from the 153 wearable features with those derived from the two measurements from the clinical visit (heart rate, vital signs), and found that generally, more is better. The authors typically got better predictions when they had more data to consider, even if the source data was only consumer-grade, vs regulatory grade like the clinical measurements.

Warped Perceptions

To read some of the coverage describing this paper, you’d think we could forget about the need for future blood draws, and just rely on data extracted from smart watches. “Your smartwatch can predict blood study results,” one headlined declared. Another: “More than just a step-tracker, smartwatches can predict blood test results and infections, study finds.”

Some of this hyperbole likely stems from the actual title of the paper itself: “Wearable sensors enable personalized predictions of clinical laboratory measurements,” which seems, in the context of the reported data, a bit aspirational.

Co-author Dunn may have expressed the contribution of the paper best in a dialog on LinkedIn, commenting:

“I want to emphasize that this is more about directionality than about exact predictions of clinical labs. The current status of this work is certainly not to the point of replacing clinical labs with wearables, but rather it may indicate which labs are more likely to have changes, which can then be directly and specifically measured (think of it as a pre-screening tool for labs when you have limited time and resources).

This, in my opinion, falls under basic research. We need to establish the principle that these relationships exist before we can iterate over them to improve predictions toward more clinical utility. Agreed that there is much more to do, and I hope in this paper we succeed in making the case that this is a path worth following.”

In a larger sense, this assessment captures the current status of many digital and data technologies that are being brought to bear in biopharma and healthcare these days: neither ready for prime time nor quite living up to the hype, but nevertheless making real progress, which resolute cynics can choose to ignore only at their peril.

David Shaywitz

Sometimes the most relevant digital health companies have the least elaborate technology.

More than a decade ago, I discussed UpToDate, a fairly basic medicine e-textbook, served through a web-based app. It was then, and still remains, a go-to site for timely, high-quality medical information relevant to clinicians.

There’s nothing fancy about it, it just works.

Recently, I discovered a similar resource, PrecisionNutrition, that’s focused on coaching coaches to help their clients adopt consistent healthy behaviors. What’s interesting is how PN fosters a holistic, multidimensional view of health. The company stands out against a field of competitors that tend to have a myopic emphasis on a particular aspect of physical health like weight loss or physical training. 

Their stated goal is to teach the coaches they train to “transform” clients’ health, and help clients “thrive,” in the broadest sense. 

Physical health, PN tells fitness coaches, is just a small percentage of “what determines your clients’ success.”

Instead, PN emphasizes a focus on a multi-dimensional view of well-being, arguing that they are all interrelated, and to improve any one of them, coaches need to consider the many components of well-being. 

Not only will coaches attuned to the many components of well-being find themselves able to support their clients’ immediate goals (such as losing weight or gaining muscle), PN astutely argues, but an initial focus on fitness, say, may provide an opportunity, an on-ramp, to improve their clients’ well-being more generally, across multiple dimensions.

To me, this second part – fitness as an on-ramp to a broader opportunity to enhance well-being and to help people flourish more fully – is the profound health opportunity lurking in the consumer space.

It’s the opportunity that so many digital fitness companies, in their singular focus on driving athletic performance, may be missing.

Well-Being

At best, most of us probably think of “well-being” in an informal or casual sense; many probably share the perspective of one initial skeptic, who told Yale psychologist Laurie Santos he originally assumed, before taking her course on the subject, that it was “hippy California well-being crap.” (I can totally relate.)

Well-being, it turns out, has become a subject of serious scientific study, and represents a prominent area of focus for academic researchers and well as health organizations including the CDC and the WHO. There are treatises on the measurement of well-being, and academic centers focused on the cultivation of “human flourishing,” such as the Human Flourishing Program at Harvard, the Center for the Study of Human Flourishing at Kings’ College in New York, and the Stanford Flourishing Project. 

Flourishing itself turns out to be key concept within a burgeoning academic area called “positive psychology,” which focuses on what makes us happy or fulfilled. The term was said to have been coined by Abraham Maslow (of “hierarchy of needs” fame), and popularized by Martin Seligman of University of Pennsylvania (previously best known for studies of depression, and the concept of “learned helplessness”).

Some of today’s most popular academic psychologists are associated with this school of thought, including Daniel Gilbert at Harvard (though he apparently rejects the label), and Santos at Yale (who hosts the wonderful and highly recommended “Happiness Lab” podcast).

The key premise around positive psychology, and flourishing, is the idea that thriving isn’t just about reducing misery, as critical as this obviously is. Rather, the goal of positive psychology, as Seligman puts it, is to “supplement its venerable goal” of misery mitigation with a new aim: “exploring what makes life worth living and building the enabling conditions of a life worth living.” 

The question of what it means to be well, and to live a good life, is both ancient and elemental.  Researchers tend to consider two formulations: a hedonic approach, focused on the idea that we’re driven primarily to make life pleasant and pleasurable, and a eudaimonic approach, which recognizes a greater range of motives, such as self-actualizing and pursuit of meaning.  

From these approaches, frameworks have emerged that emphasize different aspects.

Notes for figure:

• Ryff – Chapter 4 from Measuring Well Being, 2021
• Deep Health from PrecisionNutrition.com, link.
• WHO (WHOQOL) from WHO.org, link.
• WBT (Well-Being Theory) from Seligman, 2011, which describes PERMA; subsequent iterations of this framework (PERMA+, PERMA-V) include vitality.

While well-being – definitionally – represents a valued end in itself, there are also data linking attributes often associated with well-being to traditional endpoints such as mortality. 

A 2019 review by Trudel-Fitzgerald and colleagues at the Harvard T. H. Chan School of Public Health found that several dimensions of well-being “are associated with a reduced risk of premature all-cause mortality among the general population, with small to medium effects,” associations that, critically, held up even after adjusting for confounders. 

The attributes with the strongest evidence, according to this review?

“Purpose in life, optimism, and ikigai (a Japanese term that translates into happiness, worth, and benefit of being alive, and apparently capturing both eudaimonic features like purpose and hedonic features like pleasure). 

According to the authors, there’s also somewhat less robust evidence linking other dimensions such as life satisfaction, positive affect, mastery, and sense of coherence to reduction in mortality. Studies examining the relationship between happiness, personal growth, and autonomy with mortality reductions “suggested no effect or were too limited to draw firm conclusions.”

Vicious vs Virtuous Cycles

In medicine, we’re all too familiar with the vicious cycles leading to the rapid downward spiral of health: an injury might impede exercise, leading to weight gain and making exercise even more difficult. The fragility of life, so easily taken for granted, can be tragically revealed by even the slightest setback, and the cascade of challenges it can produce.

How encouraging, then, to encounter a publication (thoughtfully discussed by Gretchen Reynolds in the New York Times, here) describing a rare virtuous cycle – the reinforcing findings from a longitudinal study of aging American adults that people who have a greater sense of purpose are subsequently more likely to exercise, and people who are more likely to exercise are subsequently likely to describe having a greater sense of purpose.

The study utilized an approach called a “cross-lagged panel model” in effort to reduce confounding. The bidirectional effects observed were small but statistically robust, according to the authors.

Fitness as an on-ramp to a broader opportunity to enhance well-being and to help people flourish more fully is the profound health opportunity lurking in the consumer space

The very appeal of the conclusion should remind us to keep our enthusiasm in check, and as always, seek replication and additional data.

But this study, and others like it, speak to a broader point: we need to look at physical activity from a perspective less reductive than either athletic performance – the focus of digital fitness companies (“improve your position on the leaderboard!”) – or mortality reduction – the focus of many clinicians (“exercise more if you don’t want to die sooner”). 

Exercise offers a range of benefits, in many domains, perhaps including an enhancement of our sense of purpose. Similarly, attention to these other domains, as PN advises coaches, can help motivate greater physical activity. 

Both physicians and connected fitness companies need to reframe how they conceptualize exercise; the opportunity is to see it not only as a path to improved cardiovascular health and performance, but as tangible starting point for greater well-being. 

Entrepreneurs leading the digital exercise movement may be missing an opportunity if they don’t profoundly enlarge their ambitions, and take, as their job-to-be-done, not the need to get people fit, but rather the opportunity to help people flourish.

David Shaywitz

A frequent – and frequently correct – critique of entrepreneurs bearing technology is “your solution is not my problem.” 

Healthcare – among many other domains, perhaps all domains – has been beset by “solutionism,” the idea that my clever technology will solve your hideously complex problem. 

But perhaps it makes no more sense to instinctively reject this mindset as it does reflexively embrace it. After all, technological advances have enabled profound scientific advances as well as contributed to a range of benefits and comforts we take for granted. 

But in each instance, someone had to figure out how to apply an emerging new technology to a relevant task. 

A prismatic example may be found in the invention of the Post-It note. It was developed in the 1970s by a 3M research chemist named Spencer Silver and a 3M chemical engineer, Art Fry. Silver passed away this week; the fascinating story behind the Post-It was eloquently captured in his New York Times obituary by Richard Sandomir.   

In the early 1970s, Silver had been trying to make a super strong adhesive that could be used in aircraft construction. Instead, he developed something that was comparatively weak, but which stuck to surfaces, peeled easily, and was reusable. The glue was patented in 1972, but despite Silver’s efforts to highlight its potential within 3M, it didn’t get much… traction. But he reached many colleagues, including Fry, who was trying to develop new products.

One day, at choir practice, Fry found himself wishing he had had a way to mark the songs in his hymnal – the slips of paper he had been using kept falling out. But if only there was some way to make the slips stick to the pages…

The Post-It note was born.

The Post-It Note (original name: Press ‘n Peel) was introduced in test markets in 1977, and nationally in 1980. According to the Times, “There are currently more than 3,000 Post-it Brand products globally.”

When you think about how this invention happened, it’s clear that the technology – the novel adhesive – was developed first, and its properties and capabilities studied and understood. Then, the originator of the technology – Silver – went looking for something useful to do with it. It was unarguably a solution in search of a problem. And Fry eventually provided the problem.

Silver turns out to be in good company. Many accomplished entrepreneurs begin with solutions, and cast about for the right problem.

As serial entrepreneur Max Levchin, co-founder of the billion-dollar startups PayPal and Affirm, explains in Ali Tamaseb’s Super Founders (discussed here),

“Most companies that I’ve started have been these really half-baked ideas that were initially about technology. I don’t often look at the world as ‘There’s a big problem; what can I bring to bear to solve it?’ Instead, I sort of say, ‘I can do this cool thing. That’s a nice hammer. What’s a nail?’  Sometimes you have hammers looking for nails and there’s no value to be built. But a lot of times, you can actually look at something new, and say, ‘Oh, cool. Artificial intelligence – it will help us to X. Or virtually reality – this will be useful for Y.’”

In some sense, you can also view the “pivots,” so central to startup evolution, as an expression of technology’s search for a problem. 

“That’s a nice hammer. What’s a nail?” – Max Levchin, co-founder, PayPal and Affirm

Tamaseb cited a number of familiar examples of technology applications that took a while to find their groove. These include:

• YouTube: envisioned originally as a dating site, with the slogan “Tune In, Hook Up”
• Shopify: founded as an e-commerce site for snowboarding equipment, pivoted to software that facilitates e-commerce
• Instagram: initially a site like FourSquare where users could check-in – eventually founders focused on photos
• Slack: initially an online game company that later focused on the internal communication tool they developed

From the perspective of healthcare, the point is that figuring out how to use raw new technology in a notoriously complex domain like healthcare is inordinately difficult, but also vitally important. 

Healthcare incumbents may have been right to reject many of the arrogant and ignorant technologists who showed up 10 years ago, certain their app would “solve” healthcare. Incumbents may also be right to look critically on the digital transformation imperatives consultancies are urging organizations to adopt post-haste.

But there needs to be a thoughtful middle ground between the false comforts of tech worship, on the one hand, and tech cynicism, on the other. 

Like Art Fry, we need to thoughtfully engage with technology developers like Spencer Silver, ideally in an environment that cultivates such engagement and exploration, like 3M in the 1970s.

As Safi Bahcall writes in Loonshots (my Wall Street Journal review here), when 3M brought in a new CEO whose single-minded focus on efficiency squeezed out such chance encounters, innovation plummeted. It didn’t recover until another CEO restored the old system.

This lesson is especially relevant to healthcare. 

To discover new medicines, it’s critical to provide “the intellectual space for tinkering and capitalizing on the chance observations and unexpected directions so important in medical research,” Nassim Taleb and I wrote in 2008.

An inquisitive culture is equally vital for the practice of medicine itself – see this PLOS Medicine commentary, and this Atlantic piece.

The point: innovation blossoms in an environment and culture that affords adequate oxygen — time, space, and (critically, I’d argue) receptivity to novelty — for innovators to match promising new, and perhaps still somewhat raw technology with pressing, worthy, and suitable problems.