Wearables can make behaviors and signals from our body more visible, but their value depends on data reliability, context, interpretation, and our ability to turn information into useful decisions.
From step counts and heart rate to sleep quality, HRV (heart rate variability), stress, and recovery: smartwatches, smart rings, and other wearables allow us to collect an amount of information about our bodies every day that would have been unimaginable just a few years ago. Having more data, however, does not automatically mean understanding our health better. Nor does it mean knowing what to do with that information.
The true potential of wearables emerges in the journey from data to action: how reliable the measurement is, how it is interpreted, what meaning it takes on in the context of the individual, and whether it can genuinely support useful decisions and behaviors.
- Not all wearable data is equally reliable
- A single number matters less than context
- From data to health: the wearable is only the beginning
- When data becomes truly useful
Not all wearable data is equally reliable
When we look at the screen of a smartwatch or smart ring, numbers and scores are often presented as though they were all on the same level. In reality, very different processes may lie behind that information.
Some data come from relatively direct measurements, some consists of estimates obtained by processing signals collected by sensors, while other data takes the form of scores created by combining multiple pieces of information through proprietary algorithms. This distinction is important if we want to understand how much we can trust what we see.
A living umbrella review published by Doherty and colleagues in 2024, which analyzed 24 systematic reviews comprising 249 non-duplicate validation studies, shows that accuracy varies considerably depending on the metric and the conditions under which it is measured. Heart rate and step count are generally among the best-validated measures. Estimates of calories burned and the classification of different sleep stages, on the other hand, have larger margins of error. Even a measurement that performs well can become less accurate under certain conditions, for example during intense movement.
A further level is represented by scores such as recovery or readiness, which aim to summarize, respectively, how well our body has recovered and how ready we are to take on new activities or workouts. They are created by combining information such as sleep, heart rate, and heart rate variability (HRV), meaning the variations in the time interval between one heartbeat and the next.
A 2025 review by Doherty and colleagues focusing specifically on these composite scores found substantial differences between manufacturers in how different metrics are combined, limited transparency around the algorithms, and still relatively little independent validation of these scores as a whole.
In other words, the fact that a device measures heart rate or HRV accurately does not automatically mean that a stress or recovery score built partly from that data is equally accurate.
There is one more limitation to keep in mind. A recent scoping review found that older adults, people with higher body weight, and people with different skin tones are still underrepresented in validation studies of the optical sensors used in wearables. This does not show that devices perform worse in these groups, but it does mean that we have less evidence to establish whether they maintain the same levels of accuracy across different population groups.
A single number matters less than context
One of the most interesting advantages of wearables is the ability to observe the same person repeatedly over time. This makes it possible to build a kind of individual baseline: knowing your own usual pattern can often be more informative than comparing every value with those of other people.
This is particularly evident with metrics such as HRV, or heart rate variability. The absolute value can differ considerably between individuals and may be influenced by age, training, sleep, stress, illness, alcohol, and many other factors.
Observing a variation from your usual pattern can therefore provide useful information. But detecting a change does not automatically mean knowing what caused it. If my HRV is lower than usual this morning, the measurement may be real without telling me, on its own, whether it is due to a difficult night, an intense workout, psychological stress, an infection developing, or simply normal physiological variability.
The same applies to sleep. A wearable can be useful for observing patterns over time, while placing too much importance on the classification of a single night — especially individual sleep stages — can give a number more significance than it actually has.
Data therefore becomes more valuable when it is interpreted together with your individual pattern, the context, and how you feel.
From data to health: the wearable is only the beginning
One of the most concrete strengths of wearables is their ability to make certain behaviors visible. Knowing how much we have moved, observing our daily movement profile over time, or comparing ourselves with a goal can increase awareness and encourage us to take action.
The evidence is particularly strong for physical activity: wearables can help increase step counts and overall movement. But monitoring, goals, feedback, reminders, education, and support can all contribute to behavior change, making it difficult to separate the contribution of the wearable itself from that of the overall process.
Moreover, information needs to translate into real action and be sustained over time. Moving more for a few days is not the same as making a lasting change in our habits, and even a genuine improvement in behavior does not automatically produce the same health effects in everyone. Outcomes depend on the extent and consistency of the actions taken, baseline conditions, the parameter being considered, and also on the individual’s motivation and attitude.
When data becomes truly useful
Having access to information does not automatically mean knowing how to use it. To become useful, data needs to be understood, placed in its proper context, and connected to a decision that makes sense in everyday life.
This requires certain skills: understanding, at least to some extent, what the device is measuring, recognizing its limitations, observing a pattern without reacting to every single fluctuation, and connecting what we see with what is actually happening in our lives.
A nationwide German study published in 2025 found an association between wearable use and higher digital health literacy, but we still do not know with certainty which way this relationship works. Monitoring may help some people develop greater awareness, but it is also possible that people who already have greater skills and a stronger interest in their health are better able to use these tools.
For many people, data can increase curiosity, motivation, and awareness of their own habits. In other cases, however, excessive attention to numbers can create confusion or concern, especially when a score built from one or more parameters is experienced as a definitive judgment about one’s condition.
SCIENTIFIC SOURCES – Selection for further reading
– Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement – Sports Medicine
– Readiness, recovery, and strain: an evaluation of composite health scores in consumer wearables – Translational Exercise Biomedicine
– Who is missing? A scoping review on the representation of different populations in studies assessing the validity of consumer wearable PPG-based measurements – JMIR mHealth and uHealth
– Resting Heart Rate Variability Measured by Consumer Wearables and Its Associations with Diverse Health Domains in Five Longitudinal Studies – Sensors
– An umbrella review of systematic reviews of the impact of wrist-worn wearables on health outcomes – Physiological Reviews
– Differential effects of wearable device-based interventions on weight and health outcomes in adults and youth with overweight or obesity: A systematic review and meta-analysis – DIGITAL HEALTH
– Sociodemographics and Digital Health Literacy in Using Wearables for Health Promotion and Disease Prevention – Journal of Prevention
– Sleep in the age of technology: the use of sleep apps and perceived impact on sleep and sleep habits – Frontiers in Psychology
– Systematic review exploring human, AI, and hybrid health coaching in digital health interventions: trends, engagement and lifestyle outcomes – Frontiers in Digital Health
