Are Fitness Trackers Accurate? What Your Watch Really Knows

Fitness trackers are useful, but their numbers are not equally accurate. Step counts and resting or steady-state heart rate are generally among the more dependable consumer measurements, while calorie burn, sleep stages, stress scores, recovery scores and training-readiness scores depend much more heavily on algorithms and assumptions.

The safest way to use a fitness watch is to separate direct sensor measurements from calculated estimates, then judge trends over time rather than treating every displayed number as an exact physiological fact.

That distinction matters because modern watches can present all of these values with similar visual confidence – a heart rate of 72 bpm, 487 calories burned, a sleep score of 81 and a readiness score of 64 may all appear equally precise on the screen. They are not produced in the same way. A current Cleveland Clinic review of fitness-tracker accuracy similarly advises that some functions, including steps and heart-rate tracking, can be useful while estimates such as calorie expenditure and detailed sleep information require more caution.

Which Fitness Tracker Metrics Can You Trust?

The quickest way to understand fitness-watch accuracy is to ask what the device is actually sensing. An optical sensor can detect changes associated with blood flow, an accelerometer can detect movement, and GPS can estimate position and distance. Calories, sleep stages, stress and recovery scores require those signals to be interpreted through additional algorithms, so uncertainty usually increases as the watch moves farther from the raw measurement.

A large systematic review of commercial wearable devices found that accuracy varies considerably by both metric and device, with energy-expenditure estimates generally performing more poorly than basic movement and heart-rate measures. The practical lesson from the systematic review of wearable-device validity is therefore not that fitness trackers are either accurate or inaccurate as a category. You need to judge the particular measurement and the circumstances in which it was collected.

A Fitness Watch Measures Some Things and Estimates Others

Modern fitness watches combine several sensors, but the displayed result is often one or more steps removed from the physical signal the sensor detected. Understanding that chain is the easiest way to avoid overconfidence in the data.

A typical watch may contain:

• An accelerometer to detect movement and changes in direction.
• A gyroscope to help interpret orientation and motion.
• An optical heart-rate sensor to detect pulse-related changes in blood flow at the wrist.
• GPS or another satellite-positioning system to estimate outdoor location, distance and pace.
• An altimeter or barometer on some devices to estimate elevation changes.
• Temperature or electrical sensors on certain models for additional health or training features.

The watch then runs those raw signals through algorithms. Step counting is already an interpretation of movement. Sleep staging goes further by combining motion, heart-rate patterns and other signals to infer whether the wearer was awake, in light sleep, deep sleep or REM sleep. A readiness score may go another level further by combining sleep, HRV, recent training load, recovery time and stress into one simplified recommendation.

That creates a useful hierarchy:

Sensor signal -> measured metric -> estimated state -> composite score

The farther you move to the right, the more interpretation enters the result.

How Does Wrist Heart-Rate Tracking Actually Work?

Most fitness watches measure pulse using photoplethysmography, or PPG. Small LEDs shine light into the skin, and photodetectors measure changes in reflected light associated with changing blood volume as the heart beats. The watch converts those repeating optical changes into an estimated heart rate.

The method works particularly well when the watch has stable contact with the wrist and the wearer is relatively still or moving rhythmically. Problems become more likely when wrist motion produces signals that resemble the pulse signal, when the watch moves against the skin, or when circulation and optical contact are poor.

Heart Rate Is Usually Easier at Rest Than During Chaotic Movement

A watch may track resting heart rate closely enough to reveal a useful long-term baseline, yet struggle during a workout with fast changes in intensity. Rapid intervals create a moving target because heart rate is changing at the same time the wrist is moving aggressively. Resistance exercises can be difficult for another reason: gripping weights, flexing the wrist and changing forearm tension can interfere with optical contact and blood-flow signals.

That means the question “Is wrist heart rate accurate?” needs context. Consider:

• Resting or overnight readings: generally the easiest conditions for optical sensing.
• Walking and steady running: often suitable for trend tracking when the watch fits properly.
• Intervals: rapid changes can expose lag or temporary misreading.
• Cycling: wrist angle, road vibration and grip may affect some users.
• Strength training: wrist flexion and gripping can make optical readings less consistent.
• Sports with abrupt arm movement: motion artefacts become more challenging.

For an athlete who needs high-confidence heart-rate data during intervals, a compatible chest strap can be a useful comparison because it measures cardiac electrical activity rather than inferring pulse optically at the wrist. That does not make wrist optical sensing useless; it means the best sensor depends on the question you need answered.

Why Watch Fit Can Change Heart-Rate Accuracy

A sophisticated sensor cannot compensate completely for poor contact with the skin. If the watch is loose enough to slide around during exercise, the optical sensor sees changing contact and additional motion. If it is positioned directly over the wrist bone, the shape of the wrist may also reduce consistent contact.

A better setup is usually simple:

• Wear the sensor flat against the skin.
• Position the watch slightly above the wrist bone rather than directly on it.
• During exercise, use a secure fit that limits movement without becoming uncomfortably tight.
• Keep the sensor surface reasonably clean.
• Compare unusual readings with how the workout actually feels before acting on one isolated value.

The goal is consistency. A fitness watch is much more valuable when it is worn in a similar position under similar conditions, because long-term trends then contain fewer changes caused simply by how the device was fitted.

Why Your Watch Can Show the Wrong Heart Rate

Unexpected values often come from signal problems rather than a sudden change in fitness. Optical sensing has to separate the pulse pattern from movement, ambient conditions and other noise, and sometimes the algorithm gets that separation wrong.

Common reasons include:

• Loose fit: the sensor shifts against the skin.
• Heavy wrist movement: motion can interfere with the optical pulse pattern.
• Cold conditions: reduced peripheral blood flow can make the wrist signal harder to detect.
• Sweat or moisture: changing sensor contact can affect some readings.
• Wrist flexion or gripping: common during strength training and cycling.
• Temporary signal lock: the algorithm may briefly follow rhythmic movement instead of pulse.
• Individual skin and anatomical factors: signal quality can vary between users and between devices.

One strange reading therefore does not automatically mean either the watch or your body has a serious problem. Look for repeatability. A number that appears once and immediately returns to your normal range deserves less confidence than an abnormal pattern that persists across repeated measurements under good sensor conditions.

Are Fitness Tracker Step Counts Accurate?

Step counting is one of the more useful functions of consumer fitness trackers because walking produces repetitive motion patterns that accelerometers can detect reasonably well. It is still an algorithmic classification rather than someone literally counting each footfall, so unusual movement can create both missed steps and false steps.

Activities that do not involve normal arm swing may be undercounted on some devices, while repetitive hand or arm movements can sometimes be mistaken for walking. Pushing a stroller, carrying bags, holding treadmill rails or performing household tasks may therefore produce results that differ from normal outdoor walking.

For most people, the strongest use of step data is consistent comparison against their own baseline. If your watch normally records roughly similar activity on comparable days, a sustained increase or decrease can be informative even if the absolute number is not perfect to the final step.

Step Accuracy Matters Less Than Many People Think

Suppose one tracker records 9,650 steps and another records 10,020 during the same day. The difference may look important because the interface gives an exact number, but for ordinary activity tracking the more valuable information may be that both devices describe a day with substantially more movement than a day recording 4,500 steps.

This is a recurring principle throughout wearable fitness data:

The number can be precise on the screen without being equally precise in the real world.

Fitness trackers become much more useful once you distinguish precision of display from accuracy of measurement.

How Accurate Is GPS Distance and Pace on a Fitness Watch?

GPS-based distance and pace are often useful outdoors, but accuracy depends heavily on the environment. A watch estimates your position repeatedly from satellite signals and then calculates how far you moved between those positions. When the watch has a clear view of the sky and enough satellite data, the resulting route and pace can be remarkably consistent. When the signal is obstructed or reflected, the watch may briefly place you somewhere you never actually went.

Tall buildings create one of the hardest environments because satellite signals can reflect between structures before reaching the watch. Dense tree cover, steep terrain, tunnels and narrow streets can create similar problems. A bad position point does not need to be wildly wrong to affect pace because the watch calculates speed from changes in position over relatively short periods.

This is why runners sometimes see a route line zigzag across a road, cut through a building or drift away from the real path. The watch may still produce a useful total distance, but instantaneous pace can become much less stable when position estimates jump.

When GPS Data Is Most Useful

GPS data becomes more trustworthy when you compare like with like:

• Same route, similar conditions: useful for comparing repeated runs.
• Open roads or trails: generally easier for satellite positioning than dense urban streets.
• Longer steady efforts: small position errors matter less than they do during very short intervals.
• Consistent device settings: changing satellite modes between workouts can affect comparability.
• Enough time for signal acquisition: starting immediately before the watch has a stable position can degrade the beginning of the route.

If the watch repeatedly reports a known route differently, the problem may be environmental rather than a suddenly defective device. Comparing the recorded map with the actual route can reveal whether the distance error came from GPS drift.

Why Calorie-Burn Numbers Are Estimates

A fitness watch does not directly measure how many calories your body has burned. It estimates energy expenditure using information such as movement, heart rate, activity type, age, sex, body mass and proprietary algorithms. This makes calorie expenditure fundamentally different from a basic movement count or pulse measurement.

The limitation is important because two people performing the same workout can expend different amounts of energy, and heart rate alone does not reveal the complete metabolic cost of an activity. Strength training is an obvious example: a difficult set can create substantial muscular work without producing the same continuous wrist movement or cardiovascular pattern as steady running.

Systematic evaluations of consumer wearables have repeatedly found energy-expenditure estimates to be less reliable than several simpler tracker functions. The practical conclusion is that a watch can help show relative workload trends, but the displayed calorie value should not be treated like a laboratory measurement.

Should You Eat Back the Calories Your Watch Says You Burned?

Using every exercise-calorie estimate as permission to consume exactly the same amount can create false precision. If the wearable overestimates expenditure, automatically replacing all of those calories can undermine a weight-management plan. If it underestimates, the opposite problem can occur.

A safer approach is to use calorie estimates as one contextual signal alongside body-weight trends, hunger, training volume and nutritional goals. Someone with specific performance or weight-management requirements may benefit from guidance from a qualified dietitian rather than trying to balance intake against one day’s wearable estimate.

How Accurate Is Sleep Tracking?

Fitness watches can be useful for identifying sleep timing and broad sleep trends, but determining detailed sleep stages is a harder problem. A wrist device does not have access to the full set of measurements used in a clinical sleep study. Instead, it typically infers sleep from movement, heart-rate patterns and other available physiological signals.

That distinction matters because lying motionless in bed can sometimes look similar to sleep from the perspective of a wrist sensor. Conversely, restless sleep can contain enough movement to complicate classification. Algorithms try to resolve these situations, but they are estimating an internal state from indirect signals.

Detailed stages such as light, deep and REM sleep introduce another layer of interpretation. Laboratory polysomnography uses brain activity, eye movement, muscle activity and other signals to classify sleep stages. Consumer watches usually lack that complete measurement set, so a beautifully presented nightly sleep-stage graph should be viewed as an estimate rather than a clinical record.

What Sleep Data Is Most Useful?

For everyday fitness use, focus first on the parts of sleep tracking that can change your behavior:

• Bedtime consistency
• Wake-time consistency
• Approximate sleep duration
• Repeated nighttime disruption
• Long-term changes from your own baseline

Sleep-stage percentages can add context, but they should not dominate your judgment of whether you slept well. If the watch says you had poor deep sleep but you feel rested and the pattern does not persist, one night’s classification is not automatically meaningful.

The reverse also matters. If you repeatedly feel exhausted, unrefreshed or excessively sleepy, a reassuring sleep score does not rule out a genuine sleep problem. A consumer tracker is not a substitute for appropriate medical assessment when symptoms are significant.

What Does HRV on a Fitness Watch Actually Tell You?

Heart-rate variability, or HRV, describes variation in the time interval between successive heartbeats. A healthy heart does not normally beat like a perfectly regular metronome. Those beat-to-beat timing changes are influenced by autonomic nervous-system activity and can shift with training, sleep, stress, illness, alcohol, travel and many other factors.

The number becomes most useful when compared with your own baseline under similar conditions. Comparing your HRV directly with another person’s value is often much less informative because age, physiology, device, measurement method and timing can all differ.

A single low reading should therefore be interpreted cautiously. What matters more is whether it forms part of a repeated pattern and whether that pattern agrees with other information such as poor sleep, unusually difficult training, illness symptoms or elevated resting heart rate.

Why Measurement Timing Matters for HRV

HRV can change substantially throughout the day. Comparing a quiet overnight measurement with a reading taken after coffee, during work stress or immediately after exercise is not a clean comparison.

Fitness watches try to reduce this problem by collecting measurements under relatively standardized conditions, often during sleep or periods of low movement. Even then, different brands can process and summarize HRV differently, so it is generally better to follow one device’s long-term pattern than to compare absolute numbers between ecosystems.

Stress Scores Are Interpretations, Not Measurements of Emotion

A watch cannot directly detect that you are worried about a deadline, angry after an argument or relaxed because your afternoon is going well. What it can detect are physiological signals that may change during those states. Manufacturers can then combine signals such as HRV, heart rate and activity into a stress-related score.

That score may be useful when it identifies patterns you recognize. For example, you might notice consistently different readings after poor sleep or unusually demanding workdays. The problem begins when the score is treated as an objective measurement of psychological state.

Physical exercise, illness, dehydration, temperature and other physiological demands can produce changes that an algorithm may interpret as stress-related. A high stress score therefore means the device detected a pattern that fits its stress model, not that it has independently determined how you feel.

Can You Trust Training Readiness and Recovery Scores?

Training-readiness scores can be useful decision aids, but they are among the most algorithm-dependent numbers a fitness watch produces. Instead of measuring one variable, the watch may combine several signals such as sleep, recent training load, HRV, recovery time and stress history into a single score intended to answer a complicated question: How prepared are you to train today?

Garmin’s own Training Readiness documentation is a useful example. The score combines multiple underlying factors rather than measuring a distinct physiological substance called “readiness.” Other manufacturers use their own combinations and weighting systems.

That means a readiness score can be sensible while still being imperfect. If several underlying signals move in an unfavorable direction at once – poor sleep, unusually low HRV and heavy recent training – a low score may reasonably encourage caution. But a watch cannot know every piece of relevant context, including muscle soreness, joint pain, illness symptoms, psychological motivation, upcoming competition plans or unusual life stress.

Use the Score as a Question, Not an Order

A practical interpretation is:

• High score + you feel good: normal planned training may make sense.
• Low score + you feel unusually tired: the signals reinforce each other.
• High score + you feel ill or injured: trust the real-world warning signs, not the score.
• Low score + you feel normal: investigate why the score changed before automatically abandoning the workout.

Composite scores are most useful when they prompt a better decision rather than replace one.

Why Fitness Watch Readings Go Wrong

Most bad readings can be traced to one of four stages: the sensor could not obtain a clean signal, the device interpreted the signal incorrectly, the user conditions changed, or the final algorithm over-simplified a complicated situation.

How to Check Whether a Fitness Watch Reading Is Believable

When a number looks wrong, the best response is usually to verify the measurement conditions before assuming either the watch or your body has changed dramatically.

1. Check the Fit

For optical measurements, make sure the sensor is sitting flat against the skin and is not bouncing around. A watch that works perfectly during normal daily wear may need to be slightly more secure during exercise.

2. Repeat the Measurement Under Better Conditions

If possible, become still, allow the sensor to settle and see whether the reading returns to your normal range. One isolated value carries much less weight than a result that repeats under stable conditions.

3. Compare With an Appropriate Reference

The best reference depends on the metric. You might compare heart rate with a compatible chest strap, a known route with GPS distance, or actual bedtime with the watch’s sleep estimate. The goal is not to prove that the watch is perfect; it is to understand how it behaves in your typical use.

4. Look for Trends Instead of Hunting for Single-Number Perfection

A consistent device can still provide useful information even when it contains a small systematic error. If the same watch is worn the same way each day, changes from your normal baseline can often be more informative than whether every absolute number is exact.

5. Escalate When the Real-World Consequence Is Important

If a questionable reading would materially change a medical, nutritional or training decision, use a more appropriate reference method before acting on it. The higher the consequence, the less sensible it is to rely on one consumer-device estimate.

Do Fitness Trackers Actually Help You Get Fitter?

A fitness tracker can help some people become more active because it makes behavior visible. Steps, workout frequency and movement goals turn activity into something that can be monitored, compared and adjusted rather than vaguely remembered.

An umbrella review of wearable activity trackers found that tracker-based interventions can increase physical activity. The important word is interventions. A watch is most useful when its feedback changes behavior – prompting an extra walk, making weekly training consistency visible, or revealing that an ambitious plan is not actually being followed.

The device itself cannot create cardiovascular fitness, strength, mobility or recovery. Those outcomes still depend on training, nutrition, sleep, consistency and appropriate progression. A tracker is therefore best viewed as a feedback system rather than a digital personal trainer.

The Most Valuable Metric May Be the One That Changes Your Behavior

A less sophisticated metric that you consistently use can be more useful than an advanced score you barely understand. Someone trying to become less sedentary may gain more from daily steps and activity reminders than from detailed HRV analysis. A runner may care much more about pace, route distance and heart-rate trends. Someone managing training volume may find recovery context more useful.

That leads to a better buying question than “Which watch tracks the most things?”

Ask:

Which measurements will actually change what I do?

What Should You Look for Before Buying a Fitness Tracker?

The best fitness tracker is not the one with the longest specification list. It is the one that measures the things you actually use, remains comfortable enough to wear consistently, and provides data you can interpret without constantly second-guessing it. A device with excellent GPS may be ideal for a runner but unnecessary for someone mainly tracking daily movement and sleep, while a highly detailed recovery platform may add little value if the wearer only wants step counts and basic workout logging.

Start with the decision you want the device to support.

• Daily activity: prioritize comfort, battery life, step consistency and simple goal tracking.
• Running or cycling: prioritize GPS quality, pace, heart-rate tracking and route reliability.
• Strength training: prioritize comfort, workout logging and compatibility with an external heart-rate sensor if wrist readings become inconsistent.
• Sleep trends: prioritize overnight comfort, battery life and stable wear rather than choosing purely by the number of sleep metrics.
• Recovery and training load: prioritize consistent HRV collection, workout history and transparent recovery inputs.
• General smartwatch use: weigh fitness accuracy against apps, messaging, calls, payments and ecosystem compatibility.

A watch that is uncomfortable overnight will produce gaps in sleep and HRV data regardless of how advanced its algorithms are. Likewise, an athlete who constantly removes a bulky watch during training loses the very continuity needed for useful trends. Wearability is part of measurement quality.

Fitness Tracker vs Smartwatch: Which Is Better for Fitness?

A dedicated fitness tracker often emphasizes battery life, continuous activity tracking, smaller size and simpler fitness-focused interfaces. A smartwatch generally offers a broader combination of health sensors, apps, notifications, communication features, payments and third-party integrations, although modern categories overlap heavily.

The practical decision is therefore less about product labels and more about priorities.

A broader smartwatch is therefore not automatically a better fitness device, and a simpler tracker is not automatically less useful. The right choice depends on which signals you need and whether the device can capture them consistently in your normal routine.

Does More Sensor Technology Mean Better Accuracy?

Not necessarily. Adding more sensors gives a device more information, but the final result still depends on sensor quality, placement, signal processing, algorithm design and the conditions in which the measurement is taken. A sophisticated watch can still produce poor data if it moves on the wrist or loses GPS reception.

More sensors can be valuable when they provide genuinely different information. For example, combining movement and cardiovascular signals can help a device distinguish quiet wakefulness from sleep more effectively than movement alone. The same principle applies to recovery features: several inputs may provide richer context than one isolated metric.

The trade-off is that combining more signals can also make the final score harder for the user to audit. If a watch says your recovery is poor, you may not immediately know whether that conclusion came primarily from sleep, HRV, recent training load or another input. This is why transparent component scores are more useful than a single unexplained number.

Do You Need the Latest Fitness Watch to Get Better Data?

Usually not. New generations can improve sensors, satellite systems, algorithms, battery efficiency and processing, but a newer model does not automatically transform every metric into a more accurate one. Some limitations come from the measurement method itself rather than from old hardware.

Calorie expenditure is a good example. Even an advanced watch still has to estimate metabolic energy expenditure from indirect signals. A newer sensor may improve one part of the input chain, but it does not turn the watch into a metabolic laboratory.

The same is true for sleep staging. Better optical sensors and algorithms can improve classification, yet the watch still does not measure the full set of brain, eye and muscle signals used in polysomnography. Buying decisions should therefore focus on meaningful improvements in the metrics you use, not on the assumption that every new generation makes every number clinically accurate.

Can Two Fitness Watches Give Different Results and Both Be Useful?

Yes. Different devices may use different sensors, sampling intervals, filtering methods and proprietary algorithms. Two watches worn during the same activity can therefore report slightly different step totals, pace, calorie estimates or sleep stages without either device being completely unusable.

The key question is whether the difference is systematic and understandable.

If one watch consistently reports slightly longer running distances than another, but remains internally consistent on repeated routes, it may still be useful for tracking your own training. If the same device produces erratic results from day to day under similar conditions, confidence should be lower.

Switching devices can also make trend interpretation difficult. A change in resting heart rate, HRV or sleep score immediately after moving to a new platform may partly reflect the new sensor and algorithm rather than a sudden physiological change. When possible, establish a fresh baseline after changing devices instead of treating the two data histories as perfectly interchangeable.

How Long Should You Track Before Trusting a Trend?

There is no universal number of days that makes every wearable metric reliable. The amount of data needed depends on how variable the metric is, how consistently the watch is worn and whether the user’s routine has changed.

For highly variable measurements such as HRV, stress and sleep, a pattern across repeated measurements is generally more useful than one isolated day. Resting heart rate can also fluctuate because of exercise, illness, hydration, sleep and other factors, so several days of context can help distinguish a real shift from ordinary noise.

The stronger principle is baseline before interpretation. Give the device enough consistent wear time to learn what is normal for you, then pay attention to changes that are sustained and supported by other signals.

A meaningful change often becomes more convincing when several things move together. For example, a falling HRV trend accompanied by elevated resting heart rate, poor sleep and unusual fatigue tells a more coherent story than any one of those measurements alone.

When Should You Ignore a Fitness Watch Reading?

Some numbers deserve very little attention because the measurement conditions were clearly poor. Ignoring bad data can be more rational than trying to explain every anomaly.

Be cautious about a reading when:

• the watch was loose or shifted position;
• the sensor was partly off the skin;
• GPS reception was obviously poor;
• the workout involved unusually intense wrist motion;
• the battery was critically low and the device changed tracking behavior;
• the watch was removed for part of the activity;
• the reading appears only once and immediately returns to normal;
• the result conflicts strongly with several better-quality measurements.

This does not mean automatically dismissing every unusual value. The useful distinction is between an isolated reading collected under questionable conditions and a persistent abnormal pattern collected repeatedly under good conditions.

When a Fitness Watch Should Not Make the Decision for You

Fitness trackers are consumer devices, and even advanced health features should not be treated as universal diagnostic systems. A watch can help identify patterns worth noticing, but symptoms and clinically important decisions should not be reduced to a score on the wrist.

Seek appropriate medical advice rather than relying on the watch alone if you experience symptoms such as chest pain, fainting, severe shortness of breath, persistent palpitations or another concerning change in your health. An apparently normal wearable reading does not rule out a medical problem, just as one abnormal watch reading does not by itself establish a diagnosis.

The same principle applies to exercise decisions after injury or illness. A high readiness score cannot assess every structural injury, infection or medical condition. If your body is giving you a clear warning, the wearable should provide context rather than override it.

Can Fitness Watch Data Cause More Stress Than It Solves?

Yes, especially when someone starts treating every normal fluctuation as a problem that must be corrected. Sleep scores are a common example: a person may feel reasonably rested, see a low score, and then become anxious about having slept “badly.” That anxiety can make the metric less useful than simply paying attention to sleep consistency and daytime function.

The same thing can happen with HRV or recovery data. Normal physiological variation becomes visually dramatic when an app plots it with alerts, colors and daily rankings. A lower-than-usual number can be useful information, but it is rarely a complete explanation by itself.

If tracking starts making you more preoccupied than informed, simplify what you monitor. You do not need to optimize every metric the device can display. Focus on the few measurements that have a clear connection to an action you can reasonably take.

How to Use Fitness Tracker Data Without Overreacting

A simple hierarchy keeps wearable data useful:

1. Start with the rawer signals

Steps, heart rate, GPS distance and other relatively direct measurements are easier to interpret than composite scores.

2. Check the conditions

Ask whether the watch fit, exercise type, GPS environment and timing were suitable for that measurement.

3. Compare with your baseline

Your own repeated pattern is often more informative than comparing a score with another person’s.

4. Look for agreement between signals

A single unusual metric deserves less confidence than several independent signals moving in the same direction.

5. Match the evidence to the decision

Use rough estimates for rough decisions. Use stronger reference methods when the consequence of being wrong is important.

6. Keep symptoms and real-world performance in the picture

A watch does not know everything happening in your body, training schedule or life.