Standardizing Measurements of Sleep, Discrepancies Among Digital Tools

Importance of Sleep

The impact of sleep and sleep quality is pervasive, affecting nearly every aspect of health and well-being. The NIH has linked poor sleep to many conditions, including heart disease, kidney disease, hypertension, diabetes, stroke, obesity, and mental illnesses.  While the mechanisms are not fully understood, it is clear that sleep is a critical component of health.

“People often think sleep is a pillar of health. But really, it can be thought of as a foundation of health that all the pillars stand on because research has shown that if people don't get enough sleep, it can lead to later disorders, such as obesity, diabetes, heart disease, stroke, and dementia. It's also been linked to cancer,” noted Griffith. “But it's a two-way street because although lack of sleep or people's poor sleeping habits can lead to those diseases, sleep can also be an early sign of those diseases manifesting.”

She explained that some studies have found that changes in sleeping patterns or poor-quality sleep can be a relatively good predictor of diseases such as dementia.

Sleep Measurements

Griffith also explained how complex sleep could be to quantify, noting that its unconscious nature makes it difficult for some patients to describe sleep quality accurately. A patient could say they sleep well and later discover that the actual quality of their sleep is not ideal. While sleep disturbances, wakeup, and time to sleep can be self-reported fairly accurately, other things like movement during sleep, duration of rapid eye movement (REM) sleep, and more may not be as accurately reported.

“I'm not saying one's more important than the other, but there are these unconscious elements, like total sleep time, REM sleep, and deep sleep stages, that can be measured unconsciously and linked to different diseases,” she said. “But there are also things that can relate to how the patient feels and functions.”

Measuring the unconscious elements of sleep that patients cannot accurately self-report is essential for digital home measures. These tools, if optimized properly, may provide clinicians with actionable clinical sleep data.

Current Standards for Sleep Measurement

One of the most common ways to measure sleep is a polysomnogram (PSG), commonly referred to as a sleep study. This diagnostic tool records multiple aspects of sleep, including leg and eye movement, heart rate, oxygen levels, brain waves, and more. It is the standard for diagnosing most sleep disorders, such as sleep apnea, periodic limb movement disorder, and insomnia. This diagnostic test is an overnight test done in a hospital or facility with the patient attached to multiple machines.

“One of the reasons we're doing this project is because this idea of PSG or polysomnography is the gold standard of measuring sleep — that unconscious sleep. It is used to diagnose sleep apnea and other disorders, including nighttime seizures and things. It's beneficial,” said Griffith.

But, despite being the gold standard, PSG does not consistently deliver good data to physicians. Even with the use of artificial intelligence, the clinical assessment is done by an expert, leaving room for error. Beyond the error margin —an inevitable fact of data collection — PSG can also leave clinicians with artificial data.

One additional limitation of PSG is its cost and availability. Because the patient is monitored overnight in a facility, GoodRx estimates that the average cost for a sleep study can range from $1,250 to $6,700, depending on location, duration, and the types of machines used. Even on the lower end of the cost spectrum, without insurance assistance, PSGs are inaccessible to lower-income communities.

“It's not representative of a typical night's sleep. It's costly, and the waiting lists are very long. Some areas of the world don't even have access to this regularly. If there's anything that can be done to address those issues, it's worth looking into,” Griffith said. “There are no arguments here about trying to replace polysomnography. Maybe there are ways we can supplement it or try and help people measure sleep before they must have more of a firm clinical assessment.”

Standardizing Sleep Data

The natural solution to PSG’s cost and other difficulties is at-home digital data collection using tools such as wearable devices. However, without a standard for measurement, these tools provide data that cannot be confidently used in clinical assessments. Griffith explained that the goal of the new project launched by DiME is to figure out what data is critical to sleep assessments, then standardize that data.

“DiME is working as part of a pre-competitive collaboration. It's multi-stakeholder. We have people from the pharma industry and people who develop technology devices like wearables and sleep mats. We've got groups of academia, sleep societies, and foundations involved, as well,” she commented. “All these people are coming together because they've all got an interest in sleep in one way or another, but no one can do this alone. Because if one person or team was going to do this alone, as we've seen before, they come up with their definition of sleep. Then it's not comparable to other people's definition of sleep. That's bad for research purposes but also general interpretation.”

Griffith explained that the primary goal of this collaboration is to look at existing data and determine the critical aspects of sleep and the best measurement strategies to become — what she and her team call — omni-therapeutic.

“We need to find out what's important and come together to put those into terminology and anthology so that device developers are all working from the same page when making these devices. They're all interpretable similarly. There are standards when they talk to their clinicians, but also for research, so everyone's playing from the same hymn sheet,” she explained.

Secondary Research

With this big goal in mind, Griffith and her team plan to conduct secondary research.

By diving into the literature and assessing existing devices, the team hopes to determine the essential sleep measurements first. Then, DiME plans to collect data on measurement tools and devices using the Human First Atlas, a catalog of the measurement tools available on the market. Additionally, the team intends to supplement data from the atlas with secondary research, ensuring the broadest reach possible.

“The idea is to try and link those concepts of interest that matter to patients to existing technology,” Griffith noted. “But then there are loads of technologies. Some are great, and some technologies are quite rubbish. DiME is going to come up with a set of evidentiary standards and make this online resource where the general population go on and look at the device that they want to use or want to buy and say, ‘Hey, what evidence has it got to support it?’”

She also explains that this anthology of data will help researchers and developers identify care gaps where there is a need for new or improved devices.

Anticipated Difficulties

Although Griffith and the team at DiME have a clear idea of what they want to accomplish and a plan of how to get there, they are anticipating the potential bumps in the road and making plans to combat them.

First, she noted, “We've got loads of people at the table, and they all might have their own vested interest to some extent because they may already have an opinion on how they should be measured.”

DiME is set to collaborate with many partners, including Lilly, Boston University, Duke University, Google, Sleep Number, and more.  The benefit of many stakeholders is multiple perspectives; however, it is essential to look at data objectively despite any existing bias.

Diverse Data Sets

The second potential obstacle to collecting accurate and widely applicable data is a lack of diversity in existing research. Since DiME is not conducting primary research, if diverse secondary research does not exist, the researchers may struggle to create widely applicable standards.

According to the Sleep Foundation, insufficient sleep affects specific communities at higher rates. Black individuals report sleeping too little twice as often as White individuals. Additionally, single parents, shift workers, active-duty military, women, and other marginalized groups are more likely to report insufficient sleep, sleep disturbances, or sleep disorders.

“Trying to do this with secondary research relies on people having done this across various cultures and populations in representative samples. That might not always be the case. What we try and do is when we do our systematic literature review — we did this for our previous project as well — we make sure we are not just picking the selection of journals from the highest rated impact factors,” explained Griffith.

She and her team have developed a plan to collect as much data as possible across different communities. Beyond that, Griffith noted that DiME intends to be transparent once a data set has been created. She said that if, despite best efforts, the data set is not representative of the global population, DiME will make that clear to those accessing and utilizing the data.

“We're going to collate all this and put our own stamp on it to come up with the specific digital parameters that would be needed for measuring each of the concepts of interest — each of the parts of sleep and sleep disturbance that is going to be important for patients,” Griffith concluded.