Without the wearer having to exercise, Cambridge researchers have created a way for wearable devices to detect general fitness properly—aand more robustly than current consumer smartwatches and fitness monitors.
VO2max is a crucial indicator of general fitness and a critical predictor of mortality and heart disease risk. Normally, examinations to effectively measure VO2max require expensive laboratory equipment and are often reserved for elite athletes. Without the use of contextual data like GPS measurements, the new method uses machine learning to predict VO2max, the body’s ability to perform aerobic labor, throughout daily activities.
The Fenland Study included over 11,000 participants, and in what is by far the largest study of its kind, the researchers used wearable devices sensors to collect activity data from these participants. A subgroup of subjects was then evaluated again seven years later. The results were utilized by the researchers to create a VO2max prediction model, which was then tested against a third group that completed a routine lab-based exercise test. The model outperforms previous methods and has shown a high level of accuracy when compared to lab-based tests.
Some smartwatches and fitness trackers currently on the market make the claim to provide an estimate of VO2max, but it’s unclear whether the predictions are accurate or whether an exercise regimen is having any long-term impact on an individual’s VO2max because the algorithms underlying these predictions aren’t publicly available and are subject to change at any time.
The Cambridge-developed wearable devices model is reliable, transparent, and solely uses accelerometer and heart rate data to make precise predictions. The model’s ability to track fitness changes over time makes it valuable for measuring population-wide fitness levels and determining how changing lifestyles affect them. The journal npj Digital Medicine publishes the findings.
VO2max testing is regarded as the “gold standard” of fitness evaluations. Professional athletes, for instance, measure their oxygen intake when exercising to the point of exhaustion in order to determine their VO2max. There are more methods for evaluating fitness in a lab setting, such as heart rate response to exercise tests, but these call for a treadmill or exercise cycle. Strenuous exercise can also pose a risk to some people.
Co-author Dr. Soren Brage from Cambridge’s MRC Epidemiology Unit stated, “VO2max isn’t the only assessment of fitness, but it’s an important one for endurance and is a powerful predictor of diabetes, heart disease, and other mortality concerns.” However, because the majority of VO2max tests are performed on moderately healthy individuals, it might be challenging to obtain results from less fit individuals who may be at risk for cardiovascular disease.
Dr. Dimitris Spathis from Cambridge’s Department of Computer Science and Technology is a co-lead author on the study. “We wanted to discover whether it was possible to accurately forecast VO2max using data from a wearable device, so that there would be no need for an exercise test,” he stated. “Our main concern was whether fitness can be assessed using wearable technology in natural settings.” The majority of wearables include measures like heart rate, steps, or sleep duration, which are indicators of health but not necessarily indicators of outcomes.
The study was a collaboration between the two departments: the team from the Department of Computer Science and Technology contributed expertise in machine learning and artificial intelligence for mobile and wearable data, while the team from the MRC Epidemiology Unit contributed knowledge of population health, cardiorespiratory fitness, and data from the Fenland Study, a long-running public health study in the East of England.
For six days, study participants wore wearable technology nonstop. The sensors collected 60 values every second, producing a massive amount of data that needed to be processed. Spathis explained that in order to compress this enormous amount of data and use it to provide an accurate prediction, the right models and algorithm pipelines have to be created. This prediction is difficult because we are attempting to predict a high-level outcome (fitness) from noisy low-level data (wearable sensors), which is a free-living characteristic of the data.
To filter and extract useful information from the unprocessed sensor data and forecast VO2max from it, the researchers employed an AI model known as a deep neural network. Beyond making predictions, the trained models can be utilized to identify subpopulations that especially require fitness-related intervention.
The Fenland Study’s baseline data from 11,059 participants was compared to follow-up data collected from a subset of 2,675 of the original participants seven years later. To verify the correctness of the algorithm, a third group of 181 UK Biobank Validation Study participants undertook lab-based VO2max testing. Both at the baseline (82% agreement) and follow-up testing (72% agreement), the machine learning model and the measured VO2max scores had excellent agreement.
Dr. Ignacio Perez-Pozuelo, the study’s co-lead author, said, “This study is a fantastic instance of how we can draw on expertise spanning epidemiology, public health, machine learning, and signal processing.”
According to the researchers, their findings show how wearable devices may precisely assess fitness, but greater transparency is required if results from commercially accessible wearables are to be believed.
Although many fitness trackers and smartwatches do, in theory, measure VO2max, Brage noted that it is exceedingly challenging to determine whether or not these claims are real. People may find it challenging to tell whether their fitness has genuinely increased or whether it has simply been calculated by a new algorithm because the models aren’t frequently disclosed and the algorithms are subject to frequent change.
Every health and fitness-related statistic on your smartwatch is an estimate, according to Spathis. “We performed it at scale and are open about our modeling.” We demonstrate that using noisy data along with conventional biomarkers allows for better outcomes. Additionally, everyone can utilize all of our methods and models because they are open-sourced.
The wearables we use every day can be just as effective if they have the right algorithm behind them, according to senior author Professor Cecilia Mascolo from the Department of Computer Science and Technology. “We’ve shown that you don’t need an expensive test in a lab to get a real measurement of fitness,” she said. “Cardio-fitness is a crucial health indicator, but there has never been a way to quantify it.” “Weak health proxies like the Body Mass Index (BMI) can be replaced with these findings, which may have important consequences for public health strategies.”
Jesus College in Cambridge and the Engineering and Physical Sciences Research Council (EPSRC), a division of UK Research and Innovation, both contributed to the research’s funding (UKRI). A fellow at Cambridge’s Jesus College is Cecilia Mascolo.
