Misuse, Misdirection, and the Real Cost of Bio-data
There have been enough articles and posts about how we’re drowning in data from our wearables, and how the future depends on gaining “insights” from the data, beyond just numbers. Those miss bigger problems with biodata. Metrics can confuse more than they help, people often misunderstand what they mean, and chasing them usually leads nowhere useful. Is HRV telling you what you think it is? Is your sleep data misleading you into measuring or focusing on the wrong things? And what’s a better approach?
What does the data actually mean?
I have a few friends who love their Whoop, it tells them they’re an athlete, they’re elite, and helps them to manage their workouts. I personally think it’s a great device.
But when I’ve asked people explain to me what HRV is, they almost never understand what it actually is or what it really means. They know there is a number, it’s somehow related to their heart and cardiovascular fitness, which isn’t quite right, and they obsess over seeing the number go up or down on any given day.
We’ve given them a metric to track, and they feel like they are in control, but if you don’t know what is actually being measured, why do we care about the number?
Sleep is littered with these metrics. In the past we’ve talked about how sleep time doesn’t dictate sleep quality, and that the value in sleep is in the Neural Function of Sleep, not how many minutes are spent in any specific sleep stage.
As the sleep industry moves away from duration focused measures of sleep (which will be a long slow process), we’re seeing apps discuss the intricate details of Neural Function. Recently, with the recognition of the value of slow-waves during sleep, we’re seeing EEG devices try to differentiate themselves from other trackers by sharing details about the number of slow-waves in any night, or the length of a slow-wave train. Only EEG trackers, like our own Affectable Sleep Headband, can capture this kind of data, but just because it is recorded, does that automatically mean it is valuable to the user?
Specifically when we look at slow-wave activity, the numbers themselves are misleading. You might see more slow-waves than usual because each one has lower power. Your brain compensates by producing extra ones to get the same restoration done. That does not mean better sleep. It often means weaker, less efficient slow-waves.
Slow-wave trains can last longer too. But the waves usually get smaller as the train continues. Counting more slow-waves might just mean weaker ones adding up, not stronger activity.
Delta power alone misleads as well. It shows overall strength but ignores wave count and quality. High power with few waves differs from low power with many. Without the full context, these numbers can make your sleep look better or worse than it really is.
When many users don’t understand the more simple metrics like HRV, do we really expect them to dive into understanding the measures of slow-wave activity? Even if they did understand, how does knowing the numbers help them?
When hiding data provides a better experience and outcome
While people will tell you about their HRV scores, or resting heart-rate, you never hear of a person with a CGM telling you about their glucose readings. Why is that? They don’t really even need to know what the numbers mean. The devices give them a basic chart which shows the ups and downs, and likely danger levels. They understand what their diet is like and what is affecting their metabolism. The number itself doesn’t matter. The rate of change is all they need to know, and that is what the devices focus on.
The Shapa scale is a brilliant example of this. It’s a bathroom scale, but instead of showing you a number, it shows a color. The color isn’t defined by if your weight was up or down on that day, but rather how your weight is trending over time. It isn’t important to know if at this moment on this day you are 1/2 a pound heavier or lighter. Weight fluctuates, as does the time of day we take our measurements. Having a discrete number today isn’t helpful. It causes people to celebrate a win that may just be dehydration, or beat themselves up for a perceived negative result, which may also be a mirage. By showing only the trend over time by a color-code, the scale gives you the information to help you understand your health, without diving into the data.
I was struck by this idea, what data does a person actually need. Not what do they want, but what truly serves their needs. What puts them in control without overloading them with meaningless numbers or goals that don’t lead anywhere.
Have we gamified a metric that doesn’t move the needle?
I regularly hear people talking about what they are doing to spend more time in deep sleep, or to increase their deep sleep percentage. The percentage part is easy. Sleep less, and your percentage goes up. Misleading sure, but that’s the way it works. Want to increase time in deep sleep, take benzodiazepines. They will absolutely destroy the Neural Function of Sleep, leave you groggy and damage your cognition and health, but your deep sleep time will likely increase.
In the first Apple watch, they attempted to move away from sleep staging and instead focused on the more valuable and actionable consistency of wake and sleep time, which research shows is a better predictor of health than sleep duration (link to our post). Sadly, due to user expectations, Apple added sleep staging in a later addition to the Apple watch, which continues to gamify sleep duration.
Breaking free of the expected
At Affectable Sleep, we have a unique opportunity to do this differently and move away from overloaded and misleading data. Our headband accurately measures EEG and sleep activity through the night. We can share details about sleep regularity and consistency, which is within the control of the user. It’s something they can attempt to adapt or easily understand at least.
The core benefit is our UltraSleep™ stimulation, which enhances the Neural Function of Sleep, without altering sleep time or architecture. Because we measure the difference between stimulation and non-stimulation activity, we can describe what natural Neural Function looks like, versus the measured response to stimulation. This can be provided without overloading details or difficult to understand metrics which are also mostly impossible to influence directly.
We’re not asking you to take action, we’re providing the benefit for you while you sleep.
This is the future of wearables, going beyond tracking our biometrics and showing pretty graphs, to directly affecting our neurology, biology, and physiology to improve our health in real-time.
Our end goal is that people wake up feeling great and don’t have to be concerned about the metrics of their sleep.
Here at Affectable Sleep, we focus on enhancing the vital processes that happen during sleep that support your brain and body to function on the daily. We’ve spent the last 5 years developing neurotechnology to enhance Sleep’s Neural Function without altering sleep time. Affectable Sleep is pioneering a new type of wearable, that goes beyond harvesting our data and showing us pretty graphs, to directly affecting our biology, physiology, and neurophysiology to improve our health in real-time.
Learn more at our website and follow our thinking and research on Linkedin