Descriptive epidemiology of cardiorespiratory fitness in UK adults: The Fenland Study

Gonzales, Tomas I.; Westgate, Kate; Hollidge, Stefanie; Lindsay, Tim; Wijndaele, Katrien; Forouhi, Nita G.; Griffin, Simon J.; Wareham, Nick; Brage, Sören

doi:10.1101/2022.03.01.22271683

Cited by 2 publications

(2 citation statements)

References 48 publications

(56 reference statements)

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“…The test was terminated if one of the following criteria were met: 1) the participant wanted to stop, 2) the participant reached 90% of age-predicted maximal heart rate (208-0.7*age) 15 , 3) the participants exercised at or above 80% of age-predicted maximal heart rate for 2 minutes. Details about the fitness characteristics of the cohort and the validation of the submaximal test are provided elsewhere 26 .…”

Section: Study Proceduresmentioning

confidence: 99%

“…To further validate the models trained on submaximal VO 2 max, we employ the external cohort UK Biobank Validation Study (BBVS) 26 . We recruited 105 female (mean age: 54.3y±7.3) and 86 male (mean age: 55.0y±6.5) validation study participants and VO 2 max was directly measured during an independent maximal exercise test, which was completed to exhaustion.…”

Section: Study Proceduresmentioning

confidence: 99%

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Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors

Spathis¹,

Perez-Pozuelo²,

Gonzales³

et al. 2022

Preprint

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Background Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO 2 max), or indirectly assessed using heart rate response to a standard exercise test. However, such testing is costly and burdensome, limiting its utility and scalability. Fitness can also be approximated using resting heart rate and self-reported exercise habits but with lower accuracy. Modern wearables capture dynamic heart rate data which, in combination with machine learning models that find latent patterns in high-dimensional sensor signals, could improve fitness prediction. Methods In this work, we analyze movement and heart rate signals from wearable sensors in free-living conditions from 11,059 participants who also underwent a standard exercise test, along with a longitudinal repeat cohort of 2,675 participants. We design algorithms and models that convert raw sensor data into cardio-respiratory fitness estimates, and validate these estimates' ability to capture fitness profiles in a longitudinal cohort over time while subjects engaged in real-world (non-exercise) behavior. Additionally, we validate our methods with a third external cohort of 181 participants who underwent maximal VO 2 max testing, which is considered the gold standard measurement because it requires reaching one's maximum heart rate and exhaustion level. Findings Our results show that the developed models yield high correlation (r = 0.82, 95CI 0.80-0.83), when compared to the ground truth in a holdout sample. These models outperform conventional non-exercise fitness models and traditional bio-markers using measurements of normal daily living without the need of a specific exercise test. Additionally, we show the adaptability and applicability of this approach for detecting fitness change over time in the longitudinal subsample who repeated measurements after 7 years. Interpretation These results demonstrate the value of wearables for the longitudinal assessment of fitness that today can be measured only with laboratory tests.

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Section: Study Proceduresmentioning

confidence: 99%

Section: Study Proceduresmentioning

confidence: 99%

Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors

Spathis¹,

Perez-Pozuelo²,

Gonzales³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates’ ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third external cohort using the UK Biobank Validation Study (N = 181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80–0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model’s latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests.

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Descriptive epidemiology of cardiorespiratory fitness in UK adults: The Fenland Study

Cited by 2 publications

References 48 publications

Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors

Longitudinal cardio-respiratory fitness prediction through free-living wearable sensors

Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

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