The relationship between personalised health, physical activity and wearables
What do wearables or smart devices have to do with personalised health and physical activity? Stick around — this will interest you.
Personalised health through multimodal data – which is based on personalised medical care and self-care through the study of molecular data, such as genetics and biochemistry, as well as the study of the exposome, including lifestyle habits and environment – is becoming increasingly embedded in our daily lives and is a key part of 21st-century healthcare.
Wearables: empowering self-care and quality of life
In addition, thanks to the rapid development we are witnessing in science and technology, personalised health continues to be enriched by other types of data, such as those provided by wearables or smart mobile devices, such as smartwatches or digital watches.
These smart devices not only help and empower us to take responsibility for our health, adopt healthier lifestyle habits and therefore improve quality of life, but they also have the ability to provide us with a constant flow of highly valuable healthcare data for the diagnosis, monitoring and treatment of diseases through the recording of physiological parameters.
When we talk about these mobile devices, the first thing that usually comes to mind is smartwatches, as they are widespread, affordable and easy to access. However, there are other less well-known devices such as continuous glucose monitors, smart rings, GPS or tracking devices, smart shoes and smart glasses, among others.
Diversity of wearables: beyond smartwatches
Clearly, these devices have the potential to become a pillar of healthcare, but what about sport and training? Do they really help both elite athletes and the physically active general population?
In high-performance sport, such as swimming — where correct technique is directly linked to performance — certain wearables such as inertial sensors are widely used. These devices are able to provide the athlete’s exact position in space during movement, allowing technique analysis and optimisation.
However, for amateur athletes or physically active individuals, there are more affordable devices such as smartwatches. These allow the monitoring of outcomes such as energy expenditure, training load, maximal oxygen uptake (VO2max), step count and heart rate, among other parameters.
To date, studies investigating the validity and reliability of these devices are limited, with considerable variability in findings, probably due to differences in the devices tested and the study populations, among other factors. For this reason, a literature review was published last year analysing the reliability of these smartwatch-derived parameters, which we summarise below.
VO2max: how accurate are wearables really?
This parameter, defined as the maximum amount of oxygen an individual can utilise per unit of time, provides a comprehensive view of cardiorespiratory capacity and overall fitness.
VO2max is traditionally measured in a laboratory using techniques that require trained personnel and expensive equipment, which is one of the main drivers behind the push to develop wearable alternatives.
VO2max measurements obtained from smartwatches have shown an inaccuracy of greater than 10% compared with laboratory conditions. Some studies report overestimation, while others show underestimation. INTERLIVE (International Network for the Evaluation of Physical Activity towards Health and Intelligent Wellbeing) concluded that wearable-based VO2max estimates show a certain degree of accuracy, but with a large margin of error at the individual level, which could potentially be reduced by incorporating contextual environmental data. The same applies to other parameters such as oxygen saturation (SaO2 or SpO2).
Energy expenditure: limitations and considerations when using wearables
Energy expenditure refers to the amount of energy consumed at rest or during physical activity, and is widely used to manage body weight, making it directly relevant to health. For example, knowing your daily energy expenditure allows the creation of nutritional plans by adjusting calorie intake to achieve goals such as weight gain or weight loss.
This parameter is currently estimated in these devices using METs (metabolic equivalents), where 1 MET = 3.5 VO2/kg/min. These units of physical activity are defined by the Compendium of Physical Activities and allow us to assess activity level. However, one of the known limitations is that factors such as sex, age and adiposity are not taken into account, meaning the data may be subject to deviation.
When reviewing the information and data used by these devices to calculate metrics — which are not always transparent — it becomes clear that many individual factors influence these calculations but are not currently considered. These include age, sex, height, body mass, physical activity level and heart rate, all of which are relatively easy to measure.
But what about other influencing factors such as an individual’s genetic information?
Scientific evidence indicates that genetic variations in genes such as ADIPOQ, GNAS, UCP2 and UCP3 may favour a slower metabolism, leading to reduced caloric expenditure. Another key factor is body composition, which differs from body size and has a major impact on energy expenditure.
In short, this parameter can serve as a general guide, but it should not be used rigidly to design nutritional plans.
Step count: measuring sedentary behaviour and cardiometabolic profile
This parameter reflects the degree of sedentary behaviour, which is directly related to quality of life and cardiometabolic risk.
A systematic review published in 2020 examined 158 publications across nine different wearable brands and concluded that wearable devices are accurate for step counting in laboratory settings, but their accuracy may be overestimated or underestimated in free-living conditions.
Smartwatches appear useful for tracking physical activity and step count, although accuracy may be affected by factors such as placement — for example, on the wrist rather than the leg.
Need a final conclusion? Here it is.
Technological advances in health sciences are extremely powerful, as they promote self-care; however, despite their potential, they still have limitations today.
We have seen that some parameters may present variability and deviations in accuracy, but this does not mean the data are useless. One of their greatest strengths is their ability to detect trends and changes over time: even if a parameter is not perfectly accurate, its evolution can still be highly informative.
References
Lu L, Zhang J, Xie Y, et al. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth. 2020;8(11):e18907. doi:10.2196/18907
Jeong IC, Bychkov D, Searson PC. Wearable Devices for Precision Medicine and Health State Monitoring. IEEE Trans Biomed Eng. 2019;66(5):1242–1258.
Kang HS, Exworthy M. Wearing the Future—Wearables to Empower Users to Take Greater Responsibility for Their Health and Care: Scoping Review. JMIR Mhealth Uhealth. 2022;10(7):e35684.
Morais JE, Oliveira JP, Sampaio T, Barbosa TM. Wearables in Swimming for Real-Time Feedback: A Systematic Review. Sensors (Basel). 2022;22(10):3677.
Shei RJ, Holder IG, Oumsang AS, Paris BA, Paris HL. Wearable activity trackers—advanced technology or advanced marketing? Eur J Appl Physiol. 2022;122(9):1975–1990.
Albinali F, Intille SS, Haskell W, Rosenberger M. Using Wearable Activity Type Detection to Improve Physical Activity Energy Expenditure Estimation. Proc ACM Int Conf Ubiquitous Comput. 2010;2010:311–320.
Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498–S504.
