How sleep quality affects impulse control and brain performance

Originally published on March 31, 2021

Sleep quality affects impulse control, next-day decision-making, and the amount of brain activity required to stay on task. In Episode 117 of the WHOOP Podcast, Bryan Johnson, founder and CEO of Kernel and the entrepreneur behind Braintree and Venmo before Braintree was sold to PayPal, explains how brain measurement and WHOOP sleep data were paired in a pilot study on willpower. The conversation also covers what Kernel can measure outside a lab, how WHOOP metrics such as deep sleep, total sleep, sleep latency, HRV, and resting heart rate shaped Johnson’s own routines, and why personal data can change performance decisions long before symptoms or habits feel obvious.

To listen to episode 117 in full, head to the WHOOP Podcast on Spotify.

What does it take to measure the brain outside a lab?

Measuring the brain outside a hospital or research center requires tools that can capture useful neural signals without forcing someone into a fixed, unnatural setting. That is the problem Johnson built Kernel to solve after deciding that most of human performance can already be measured, while routine brain measurement still lags behind.

Johnson described two systems. Kernel Flow measures oxygenation and deoxygenation linked to neural activity. It uses an optical approach, similar in spirit to the light-based sensing discussed in Episode 51 of the WHOOP Podcast on what WHOOP measures, where photoplethysmography, or PPG, helps translate light signals into physiological data. Kernel Flux, by contrast, is a magnetic system designed to observe cortical activity at the speed of neurons firing, while a person is moving in a more natural environment.

The practical goal is simple: make brain measurement regular enough that people can test how work, sleep, food, stress, and conversations change cognition across a day. Johnson’s argument is that once brain data becomes repeatable, people can run the same kind of n-of-1 experiments that WHOOP members already run with sleep, recovery, HRV, and resting heart rate.

Johnson framed the ambition this way:

“One is called Kernel Flow, and it measures the oxygenation and deoxygenation of brain activity. [...] It’s the first time in the world a technology could be reasonably imagined to be in every home by 2030.”

What you should take away

• Kernel was built to make brain measurement usable outside a traditional lab setting.
• Kernel Flow focuses on blood oxygen changes linked to neural activity, while Kernel Flux focuses on magnetic signals from neurons firing.
• Johnson sees brain measurement as the next layer of personal performance data, alongside sleep, HRV, and resting heart rate.

If you want to hear Johnson unpack why Kernel started with non-invasive brain measurement, listen to the full episode on Spotify.

How did WHOOP and Kernel study sleep and impulse control?

Once the discussion moves from hardware to outcomes, the first useful question is whether better sleep actually changes how the brain handles self-control. In the pilot Johnson described, the answer was yes.

The study paired nightly WHOOP sleep data with repeated brain-testing sessions over time. Johnson said the team completed up to 18 sessions across 13 weeks, measuring resting state, memory, and impulse control. The impulse control task was a go or no-go task, where participants had to withhold a response to a specific visual cue. In plain terms, it tested whether a person could stop an automatic action.

The sleep variables that stood out were deep sleep, total sleep, and sleep latency. Johnson said those WHOOP measures correlated with his performance on impulse control, which gave the team a practical example of how nightly sleep can shape next-day willpower. The published WHOOP and Kernel write-up on sleep and impulse control expands on the same theme: the more sleep participants got, the more actively the brain appeared to engage in willpower control.

Johnson summarized the design with unusually clear numbers:

“We did up to 18 sessions over 13 weeks measuring, we did resting state, impulse control, like whether I could stop myself from doing something, this is like willpower, memory task.”

What you should take away

• The pilot combined nightly WHOOP sleep data with repeated brain-testing sessions across 13 weeks.
• Deep sleep, total sleep, and sleep latency were the sleep measures Johnson said tracked most closely with impulse control.
• The task measured response inhibition, which makes the sleep and willpower connection concrete instead of abstract.

If you want to hear Johnson go deeper on the sleep and impulse control study design, listen to the full episode on Spotify.

What can brain data show beyond a right-or-wrong task score?

That pilot result leads to a deeper point. Brain data can show how much effort the brain needed to produce a behavior, even when the outward behavior looks the same.

Johnson said the neural data carried information that behavior alone could not capture. Two people may both avoid the cookie, hold back the button press, or complete the task correctly. Brain measurement may still reveal that one person needed much more neural effort to get there. That is useful because performance is rarely just about whether you got the answer right. It is also about the cost of getting there.

He used a practical example: declining a late-night cookie when you know it will hurt deep sleep. A simple yes or no outcome says whether the person resisted. Neural data may show whether the brain was well prepared for that moment or whether it had to work much harder because sleep was worse the night before.

Johnson put that possible next step this way:

“We might be able to start saying things such as, when I get this kind of deep sleep, this kind of total sleep, and this kind of sleep latency, and I’m presented with a willpower task, maybe my brain only needs to spend 20% of the energy that otherwise need to in order to accomplish the same thing.”

The same logic appears in earlier research Johnson cited from Emory University on neural predictors of music popularity. In that study, brain activity from teenagers listening to songs predicted future popularity better than the listeners’ stated opinions. For Johnson, that is an example of how brain signals can add information that conscious reporting misses.

What you should take away

• Brain data may show the cost of a decision, not just the result of a decision.
• Better sleep could change how efficiently the brain handles self-control, even when behavior looks identical.
• Johnson sees this as the start of a broader category of performance measurement that includes effort, readiness, and neural efficiency.

If you want to hear Johnson unpack what neural effort could mean for daily decision-making, listen to the full episode on Spotify.

What has Bryan Johnson learned from WHOOP about meal timing, resting heart rate, and HRV?

After the study findings, Johnson turns the same testing mindset on himself. WHOOP gave him three signals that changed his routine: when to stop eating, what resting heart rate he wants before bed, and which unexpected habits may lift HRV.

The strongest pattern, according to Johnson, was meal timing. He said the last meal of the day had the biggest effect on his sleep quality, and that self-testing pushed his final meal earlier and earlier. He eventually settled on a last meal around 9:00 a.m., which left him about a 13-hour fast before bedtime. That is a personal result, not a universal prescription, though it shows how far individualized testing can go once the pattern is clear.

Johnson also said that a resting heart rate of about 46 beats per minute right before bed usually signals that he is set up for very high sleep quality. That gave him a forward-looking target for the whole day. Instead of waiting until the next morning to judge the result, he works backward from the pre-sleep number.

His most surprising HRV finding involved singing. Johnson said 30 minutes of singing with friends improved his HRV by 17% on one night. That fits with a broader WHOOP pattern discussed in Episode 74 of the WHOOP Podcast on sleep, HRV, and recovery, where behaviors that affect breathing, relaxation, and stress response can change next-day recovery signals.

Johnson gave the clearest example in one sentence:

“I tested all the way from about 2 hours before bed and now I have my final meal by about 9 am or so. Before I go to bed, I have about a 13-hour fast going on.”

What you should take away

• Johnson said last meal timing was the single biggest driver of his sleep quality.
• A pre-sleep resting heart rate target can help frame decisions earlier in the day.
• HRV can respond to unexpected behaviors, which is why repeated self-testing matters more than assumptions.
• Personal physiology varies, so the useful lesson is to test patterns against your own WHOOP data.

If you want to hear Johnson go deeper on meal timing, resting heart rate, and HRV experiments, listen to the full episode on Spotify.

Where could brain measurement be useful next?

Those self-experiments point to the bigger picture. Johnson thinks the first wave of value will come from repeated personal testing and research programs in areas where cognition, mood, attention, or recovery are hard to track with behavior alone.

In the conversation, he said academic teams using Kernel were already exploring traumatic brain injury, concussion, stroke, aging, lucid dreaming, meditation, meditation assistance, and psychedelic interventions. That list matters because it shows how broad the early use cases could be. Some applications sit close to clinical care, while others sit closer to performance or self-observation.

Johnson also suggested that future systems could help people retrain responses to fears or stressful cues by pairing brain data with machine learning. Even before that future arrives, the short-term implication is clear: if people can measure how sleep, stress, work, media, or routines shape the brain, they can make those inputs easier to manage. Anyone who wants more on Johnson’s later self-experimentation can compare this episode with Episode 209 of the WHOOP Podcast with Bryan Johnson.

Johnson described the early research map like this:

“They’re looking at things like concussion, stroke, aging of the brain, lucid dreaming, meditation, meditation assistance, psychedelics, about 20 or so different areas.”

What you should take away

• Johnson expects early brain-measurement value to come from both personal experiments and targeted research programs.
• The first visible use cases include concussion, stroke, aging, meditation, lucid dreaming, and psychedelic research.
• Sleep and cognition are likely to stay central, because repeated nightly data is easier to collect than occasional lab snapshots.

The bottom line

• Sleep quality can affect impulse control the next day, and Johnson said deep sleep, total sleep, and sleep latency were the WHOOP measures most closely tied to that effect.
• The WHOOP and Kernel pilot used up to 18 brain-testing sessions across 13 weeks to connect nightly sleep data with willpower and cognitive performance.
• Brain measurement can add information beyond task accuracy by showing how much neural effort was required to produce the same behavior.
• Johnson said last meal timing was the strongest driver of his sleep quality, which led him to stop eating about 13 hours before bed.
• A pre-sleep resting heart rate target can become a practical signal for shaping exercise, food, and stress decisions earlier in the day.
• HRV can respond to unexpected habits, and Johnson said one 30-minute singing session increased his HRV by 17%.
• Johnson sees the next phase of brain measurement in repeated personal experiments and research areas such as concussion, stroke, aging, meditation, and lucid dreaming.

Frequently asked questions about things discussed in this episode

How does WHOOP measure sleep quality in a study like this?

WHOOP measures sleep through nightly data on sleep duration, sleep stages, sleep latency, resting heart rate, HRV, and related recovery signals, which gives researchers a consistent way to compare sleep against next-day cognitive or brain outcomes.

What does WHOOP track that can relate to impulse control?

WHOOP tracks sleep variables that can relate to impulse control, including total sleep, deep sleep, and sleep latency, which Johnson said were the measures most closely tied to his willpower task performance.

How does WHOOP help you test meal timing against sleep?

WHOOP helps you test meal timing against sleep by showing whether earlier or later eating patterns change sleep quality, resting heart rate, HRV, and next-day recovery over repeated nights.

What does WHOOP do for people running n-of-1 experiments on recovery?

WHOOP gives people repeatable personal data for n-of-1 experiments, so changes in routines such as exercise timing, fasting, mindfulness, or evening habits can be compared against sleep and recovery trends instead of guesswork.

How does WHOOP help you understand HRV changes from habits like singing or breathing?

WHOOP helps you understand HRV changes by showing whether a specific behavior is followed by a higher or lower HRV relative to your own baseline, which makes unexpected patterns easier to spot and retest.

What does WHOOP measure before bed that can help explain next-day performance?

WHOOP measures pre-sleep physiological signals such as resting heart rate and then connects those signals to overnight sleep and next-day recovery, which can help people see whether they were physiologically ready for high-quality sleep.

How does WHOOP fit into research on the brain and cognition?

WHOOP fits into brain and cognition research by supplying nightly sleep and physiology data that can be lined up with brain imaging, memory tasks, or impulse control tests to study how recovery affects mental performance.

When sleep data and brain data point in the same direction, WHOOP becomes a practical way to see whether tomorrow’s decision-making started the night before.