What Your Wearable Actually Knows

Your wearable says you slept for seven hours and fourteen minutes.

It says you spent forty-three minutes in deep sleep. Your readiness is 62. Your stress is elevated. Your Body Battery is low. Your recovery is yellow.

The numbers feel remarkably precise.

But precision on a screen is not the same as certainty about your body.

Your watch did not observe you sleeping. It did not measure your recovery. It does not know whether you are emotionally drained, excited to train, sore from skiing, fighting off an illness, or awake because your child needed you.

It detected a limited collection of signals. An algorithm translated those signals into estimates. Other algorithms combined those estimates into scores. Then the app assigned those scores meaning.

That does not make the information useless. It makes it important to understand.

Most wearables market the score. Movement IQ teaches you how to interpret the signal.

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The five layers beneath every score

When people look at a wearable dashboard, they often treat every number as though it came directly from the body. It did not.

There are at least five layers between your body and the recommendation on your screen.

The farther down the table you travel, the more interpretation has entered the picture.

A readiness score is not a sensor reading. There is no readiness detector touching your wrist. It is a conclusion produced from selected inputs, proprietary weighting, and a particular company’s definition of what being “ready” means.

That distinction is the beginning of wearable literacy.

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What the sensors actually sense

Most consumer wearables rely on some combination of a surprisingly small sensor toolkit:

• Optical sensors (PPG) shine light into the skin and detect changes in blood volume. From the resulting pulse waveform, software can derive heart rate and beat-to-beat timing and estimate variables such as blood-oxygen saturation.
• Electrical sensors (ECG) detect the electrical activity associated with each heartbeat. A chest strap can use this to track heart rate during exercise; some watches can record a brief, single-lead electrocardiogram when you intentionally take a reading.
• Accelerometers and gyroscopes detect movement, orientation, and changes in speed. They help recognize steps, activity, stillness, sleep windows, strokes, and repetitions.
• GPS and other satellite systems estimate location, distance, speed, route, and elevation change outdoors.
• Temperature sensors generally measure temperature at or near the skin—not core body temperature—and look for deviations from your personal baseline.
• Electrodermal sensors detect changes in skin conductance associated with sweat-gland activity, which may contribute to stress or arousal estimates.
• Barometric altimeters detect changes in air pressure to estimate elevation and floors climbed.
• Bioelectrical impedance analysis (BIA) sends a small electrical current through the body and detects opposition to that current. Smart scales and body-composition platforms combine impedance with body weight and personal information to estimate body water, fat-free mass, body fat, muscle mass, visceral-fat ratings, and other outputs.

The sophistication is real. So are the limits. Every number depends on the sensor, its placement, contact with the body, movement, environment, sampling rate, signal processing, and the algorithm layered on top.

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The metric decoder

What is measured, what is inferred, and what the number is good for

The broad evidence reflects the hierarchy above. Consumer wearables tend to perform better at heart rate and step counting than at energy-expenditure estimates. Sleep trackers are often better at recognizing sleep than correctly identifying wakefulness or assigning every sleep stage. Their greatest value is usually in repeated patterns—not in treating every displayed value as a laboratory result.

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A score is an opinion written in numbers

Consider a recovery score.

The device may use your overnight HRV, resting heart rate, respiratory rate, sleep duration, recent activity, and temperature trend. Those are meaningful inputs. But the company must still decide:

• Which inputs count?
• How much should each one count?
• What time window establishes your baseline?
• What qualifies as normal for you?
• How should yesterday’s training affect today’s score?
• What should happen when the signals disagree?
• What does “recovered” mean?

Different devices can observe the same person on the same night and produce different conclusions because their sensors, sampling, definitions, and priorities differ.

The score is not fraudulent. It is also not a fact in the same sense as your body mass on a calibrated scale or your time across a measured distance.

It is an interpretation.

Treat it accordingly.

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The major wearable ecosystems

Specific models change quickly. The more durable question is what each ecosystem is designed to notice—and what it tends to emphasize.

Garmin’s CIRQA makes an important distinction visible. Removing notifications and a glowing screen may reduce distraction, but it does not remove the interpretations. The dashboard has simply moved from your wrist to your phone.

Hume makes a different distinction visible. The Hume Band’s optical pulse, temperature, and movement signals—and the Hume Body Pod’s force and impedance signals—are not the same kind of information as heart rate, HRV, or body-fat percentage. And those derived measurements and estimates are not the same kind of information as Metabolic Capacity, Metabolic Momentum, Biological Age, or a visceral-fat rating. Each step adds models, definitions, and assumptions. That does not automatically make the outputs meaningless. It means their specificity should not be confused with direct observation.

This is not a ranking. The best device is the one that reliably captures information relevant to a decision you actually need to make—with the least unnecessary friction, anxiety, and distraction.

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What your wearable cannot know

A wearable can detect that your heart rate is elevated. It cannot know whether you are anxious, excited, overheated, dehydrated, digesting dinner, climbing stairs, or developing an illness.

It can estimate that you slept poorly. It cannot know whether staying awake to care for your child was more important than optimizing your sleep score.

It can identify that yesterday was physiologically demanding. It cannot know whether the demand came from an extraordinary day in the mountains that was worth every bit of fatigue.

It can see that your HRV is lower than usual. It cannot feel your joints, your motivation, your muscle soreness, your mood, or the difference between heaviness that improves once you warm up and the unmistakable feeling that you need a day off.

It can calculate your training load. It cannot fully see technical learning, impact forces, isometric effort, emotional stress, the novelty of a movement, or the cost of protecting an injury.

It can observe signals from your body.

It cannot observe your life.

That missing context is not noise. It is part of the data.

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The risk of outsourcing body awareness

Wearables can strengthen self-awareness. They can also replace it.

You may wake up feeling rested, see a poor recovery score, and begin to feel tired. Or wake up exhausted, receive a green light, and override what your body is clearly communicating.

Sleep data can be especially seductive. Some people become so preoccupied with achieving perfect sleep numbers that the pursuit itself creates anxiety and worse sleep—a pattern researchers have called orthosomnia.

The problem is not measurement. The problem is confusing measurement with authority.

Your wearable should help you ask better questions:

• Did alcohol change my overnight heart rate and HRV?
• Does a late meal consistently affect my sleep?
• Am I accumulating more training load than I realized?
• Has my resting heart rate been elevated for several days?
• Do I feel the same way the dashboard says I should feel?
• When the data and my experience disagree, what context might explain it?

If the device makes you more curious, more consistent, or more aware of patterns, it is working for you.

If it makes you anxious, compulsive, afraid to move, or unable to trust your own experience, it may be working against you.

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How to read any wearable dashboard

Before acting on a number, ask five questions.

1. What sensor produced the original signal?

Was this built from optical pulse data, electrical cardiac activity, motion, location, temperature, or something else?

2. Is this a measurement, an estimate, or an interpretation?

Heart rate, sleep stage, calories, and readiness do not occupy the same rung on the ladder.

3. How trustworthy is this metric under these conditions?

Wrist heart rate during a quiet night is a different measurement problem from wrist heart rate during kettlebell swings. GPS beneath open sky is different from GPS between tall buildings. A stable personal baseline is different from three nights with a new device.

4. Is the value useful as a trend, or does the exact number matter?

Many wearable metrics are more useful directionally than literally. Consistently rising resting heart rate may matter even if no single reading is perfect. A VO₂ max estimate may reveal improvement without being your true laboratory value.

5. What decision will this information change?

Data without a decision is often decoration.

If the number will not change what you do—or deepen your understanding of why you are doing it—you may not need to monitor it.

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Combine the dashboard with the human

The most useful daily check-in has two columns.

Neither column is complete by itself.

If both suggest you are ready, proceed confidently.

If both suggest you need recovery, listen.

If they disagree, investigate. You do not need to blindly obey either one. You need context.

Sometimes the right adjustment is to rest. Sometimes it is to replace intervals with an easy aerobic session, trade heavy lifting for skill practice, shorten the workout, take a longer warm-up, go for a walk, or simply begin and reassess after ten minutes.

Readiness is not always a yes-or-no question. Often it is a programming question.

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Your body is the vehicle. You are still the driver.

A dashboard is useful because it helps the driver understand what is happening inside a complex machine.

But the dashboard does not know the destination. It does not see the weather ahead. It does not know why the trip matters. And it does not drive the vehicle.

Your wearable is an instrument panel—not an autopilot.

Use it to notice patterns you might otherwise miss. Let it challenge your assumptions. Let it reveal the effects of your habits. Let it teach you about your physiology over time.

But do not surrender your body to the score.

Your wearable knows a handful of signals. You know the experience in which those signals occurred. The best decisions require both.

The goal is not to ignore the data or obey it.

The goal is to become better at reading it.

That is Movement IQ.

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Evidence and further reading

• A 2024 living umbrella review of systematic reviews found that consumer wearables generally performed better for heart rate and step count than for energy expenditure, while results vary by device, activity, and population.
• A systematic review of commercial devices likewise found energy-expenditure estimates were consistently less accurate than heart-rate and step measurements.
• A 2025 laboratory comparison of six commercial sleep wearables found high sensitivity for detecting sleep but lower specificity for wake and only fair-to-moderate agreement for multistage sleep classification when compared with polysomnography: performance validation study.
• The American Academy of Sleep Medicine states that consumer sleep technology should not replace validated clinical evaluation or testing: position statement.
• Consumer VO₂ max estimates can be useful, but their validity varies across devices, algorithms, protocols, and populations: systematic review and expert statement.
• The FDA distinguishes low-risk general-wellness functions from medical-device functions: General Wellness guidance.
• The FDA has warned against unauthorized wearable blood-pressure claims and watches or rings that claim to measure glucose without piercing the skin: blood-pressure safety communication and noninvasive glucose safety communication.
• Continuous glucose monitors are different: they use a small sensor beneath the skin. The FDA first cleared an over-the-counter system for adults in 2024: FDA announcement.
• Smart scales can measure weight accurately while producing less accurate body-composition estimates; repeated measurements are most useful when taken under consistent conditions: consumer smart-scale validation study and longitudinal BIA reliability study.
• Hume Health’s own product materials describe the raw signals and interpreted longevity metrics produced by the Hume Band 2.0 and the multi-frequency, eight-electrode BIA used by the Hume Body Pod. Hume’s Band FAQ states that blood-pressure functionality is pending FDA approval.
• Garmin’s official announcement and product guidance describe the CIRQA Smart Band, including its screenless design, wrist or upper-arm wear, connected GPS, training and recovery metrics, up-to-10-day battery life, and lack of a required subscription.
• For examples of how composite scores are constructed, see official explanations of Google/Fitbit Daily Readiness, WHOOP Recovery and Strain, Oura HRV and Readiness, and Apple cardio-fitness estimates.