A new study published today in the Nature Journal (scientific reports) examined results from smartphone-recorded physical activity for estimating cardiorespiratory fitness and observed that the results were fairly accurate when compared with laboratory-based VO2 max testing.
Fitness estimates can also be used to identify those at risk for diabetes, obesity, and falls to target with preventive measures such as exercise programs, nutrition programs, and fall prevention.
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Monitoring of cardiorespiratory fitness should not require expensive lab equipment or wearables but can be accurately estimated using movement data that is already available on our smartphones.
Researchers from Beth Israel Deaconess Medical Center, Boston, used smartphone derived physical activity data to estimate fitness among 50 older adults.
The team exclusively focussed on recruiting iPhone owners who were undergoing cardiac stress testing and collected their recent iPhone physical activity data.
Cardiorespiratory fitness was measured as peak metabolic equivalents of task( METs). VO2 max is often expressed in metabolic equivalents of task, which are multiples of normal baseline oxygen uptake at rest.
Testing of functional aerobic capacity is classically measured in a lab by functional exercise testing, such as with a treadmill, where oxygen uptake is measured as the workload is incrementally increased.
Smartwatches such as the Apple Watch have made it easier to measure and monitor cardio-fitness levels (VO2 max) in recent years but many people do not have the latest smartwatches.
Since smartphones are ubiquitous these days, the researchers wanted to develop a model to estimate a person cardio fitness level using smartphone data.
The advent of smartphones has introduced physical activity measurement as a common staple as most smartphones integrate accelerometers and gyroscopes which can track owner motion. These measurements are passive and do not require the smartphone owner’s intervention.
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Your Apple iPhone Health app keeps track of various mobility metrics including gait distance, speed and walking asymmetry.
The team used a predictive model to estimate cardiorespiratory fitness. Such models may input anthropometric data (i.e. age, sex, and body-mass index) and day-to-day physical activity, which itself is associated with physical fitness.
The researchers exported the data out of Apple Health app and used specialized multi-variate regression models to estimate cardiorespiratory fitness.
Cardiorespiratory fitness as peak metabolic equivalents of task (METs) was estimated by maximal treadmill stress testing using the extensively validated Bruce protocol
The team found the results to be very accurate.
“Our model using smartphone physical activity estimated cardiorespiratory fitness with high performance. Our results suggest larger, independent samples might yield estimates accurate and precise for risk stratification and disease prognostication.”
Accuracy was higher when compared to results from HRV4Training app.
Accurate estimates of cardiorespiratory fitness available at the point of care have enormous potential value. Estimated fitness could be used to estimate mortality and guide end-of-life planning.
Smartphone estimated fitness could also guide decisions for risky medical interventions such as chemotherapy and surgery. Individuals with low fitness and high baseline mortality may be less inclined to take on additional risk.
The promising results from this new study demonstrate that the incremental predictive utility of smartphone data, combined with its ready accessibility, open new exciting opportunities for clinical and research estimation of cardiorespiratory fitness.