WearIntell recently hosted Dr. Claudio Gil Soares de Araújo, a Brazilian physician and researcher whose work spans exercise medicine, exercise physiology, cardiorespiratory fitness, human functional assessment and healthy aging. During the visit, the discussion focused on how wearable devices could be used to study health over longer periods—not only by recording individual measurements, but by tracking changes in physical and physiological function over time.
That question is closely related to Dr. Araújo’s research. His work has examined links between exercise capacity, cardiovascular response, muscle function, balance and other aspects of physical performance with long-term health outcomes.
For WearIntell, the conversation provided a useful framework for thinking about where continuous health monitoring may go next.
Why Isolated Health Metrics Have Limits
Most wearable devices already collect familiar measurements such as heart rate, SpO₂, activity, sleep and, in some cases, blood pressure.
Those measurements are useful, but each one describes only part of a person’s condition.
Dr. Araújo’s research takes a broader view of physical function. His work has considered cardiorespiratory fitness, autonomic function, muscle strength and power, balance, flexibility, body composition and overall functional ability.
This matters because two people with similar resting heart rates or daily step counts may still differ considerably in aerobic capacity, balance, strength, recovery or mobility.
A single measurement also says little about direction. A heart-rate reading may be normal today, but it does not show whether resting heart rate has changed steadily over the previous six months. The same applies to activity, sleep, gait or recovery.
For health research, those changes can be more informative than one isolated result.
This is where the idea of Functional Health becomes relevant. Instead of treating each measurement separately, functional health considers how different systems contribute to what a person can actually do—move, recover, maintain balance, tolerate exercise and perform normal physical tasks.
The Sitting-Rising Test: A Practical Measure of Functional Capacity
One of the best-known examples of Dr. Araújo’s work is the Sitting-Rising Test (SRT), which he introduced in the late 1990s as a simple way to assess several components of non-aerobic physical fitness at the same time.
The test asks a person to move from standing to sitting on the floor and then return to standing while using as little support as possible. Sitting and rising are each scored from 0 to 5, for a maximum total score of 10. One point is deducted each time the person uses a hand, forearm, knee or the side of the leg for support, while 0.5 point is deducted for a noticeable loss of balance.
What makes the SRT relevant to functional health is that it does not isolate a single physical ability. Performance depends on a combination of muscle strength and power, flexibility, balance, coordination and body composition. In that sense, the test provides a practical example of the broader question discussed during the WearIntell visit: how different aspects of physical function can be considered together rather than interpreted as separate measurements.
What Continuous Monitoring Adds
Many clinical and exercise assessments are carried out at a particular point in time.
They can be detailed and highly controlled, but they still capture the person during a test session.
Wearables add another type of information: repeated observations during normal daily life.
Heart rate and HRV can be tracked across days and weeks. Activity patterns can show whether someone is moving less than before. Sleep data may reveal persistent changes. Motion sensors can record patterns related to walking, movement and stability.
The technical signals themselves are only the first layer.
The more useful question is whether changes in those signals can be interpreted in a consistent way over time.
The visit provided an opportunity to exchange perspectives on human function, healthy aging and long-term wearable health monitoring
This is also where Dr. Araújo’s experience with longitudinal research becomes relevant. CLINIMEX has maintained a long-term exercise medicine cohort since 1994, collecting repeated physiological and functional measurements and following later health and survival outcomes.
The research covers areas including cardiorespiratory fitness, exercise response, autonomic function, muscle strength, balance, flexibility and functional capacity, together with longer-term clinical outcomes.
Wearable monitoring cannot simply replace that type of clinical assessment. It can, however, add information from the time between formal tests.
An exercise test might be complemented by daily activity and heart-rate response. A balance assessment might be viewed alongside longer-term gait and movement patterns. Recovery testing may be considered together with repeated HRV, resting heart rate, sleep or temperature measurements.
Prediction Starts With Long-Term Data
“Health prediction” can easily become an overly broad term.
One sensor reading cannot tell us what will happen to someone’s health years later, and more data does not automatically produce a useful prediction.
A more practical starting point is to understand change.
If activity gradually falls, gait becomes less stable, recovery becomes slower or resting physiological patterns change, researchers can ask whether those trends are consistent, whether several changes occur together and whether they are associated with later functional outcomes.
That takes time and validation.
It also requires care when interpreting associations. Dr. Araújo has emphasized this distinction in his own research: an association between a functional measure and a later health outcome does not by itself establish cause and effect.
The same caution applies to wearable data.
A change in HRV, sleep or gait is not automatically evidence of disease. It may still be useful information, especially when it is tracked over a long period and examined together with other physiological and functional data.
That is the basis on which predictive models would need to be studied.
Functional Health and Healthy Aging
Several research directions discussed during the preparation for this exchange fit this longer-term approach.
One is Functional Age. Rather than relying only on chronological age, researchers could examine combinations of activity, cardiovascular response, recovery, balance, gait and other measures to study how physical function changes with aging.
Another is a broader Healthy Aging framework that considers several dimensions of function rather than producing separate scores for sleep, activity or recovery.
There is also interest in studying patterns associated with Functional Decline. In older adults, gradual changes in activity, walking, balance, sleep, resting heart rate or recovery may be worth examining together rather than as unrelated daily fluctuations.
Functional Age, Healthy Aging Index and functional-decline detection are research directions rather than established WearIntell product features or validated clinical tools.
Useful predictive health models will need more than wearable hardware. They depend on reliable raw data, consistent collection methods, appropriate clinical reference data and validation against outcomes that actually matter.
Where the Discussion Leaves Wearable Health Research
The visit brought together two different areas of expertise: long-term research into human function and the ability of wearable devices to collect physiological and movement data during everyday life.
Clinical testing can describe function in detail at selected points in time. Wearables can add continuous observations between those points. Combining the two raises useful research questions about which signals matter, how they change and whether those changes have consistent relationships with functional health.
For WearIntell, that is a more useful question than simply adding another metric to a device.
The value of long-term wearable data will depend on whether it can help researchers distinguish normal day-to-day variation from changes that persist, develop over time and deserve closer study.
That work comes before prediction.
If those patterns can eventually be validated against clinical and functional outcomes, they may provide a stronger basis for studying future health than any single measurement taken on its own.