Cardiorespiratory fitness estimation from heart rate and body movement in daily life

Abstract

Low cardiorespiratory fitness (CRF) increases risk of all-cause mortality and cardiovascular events. Periodic CRF assessment can have an important preventive function. The objective of this study was to develop a protocol-free method to estimate CRF in daily life based on heart rate (HR) and body acceleration measurements. Acceleration and HR data were collected from 37 subjects (men = 49%) while they performed a standardized laboratory activity protocol (sitting, walking, running, cycling) and during a 5-day free-living monitoring period. CRF was determined by oxygen uptake (V̇o2max) during maximal exercise testing. A doubly labeled water-validated equation was used to predict total energy expenditure (TEE) from acceleration data. A fitness index was defined as the ratio between TEE and HR (TEE-pulse). Activity recognition techniques were used to process acceleration features and classify sedentary, ambulatory, and other activity types. Regression equations based on TEE-pulse data from each activity type were developed to predict V̇o2max. TEE-pulse measured within each activity type of the laboratory protocol was highly correlated with V̇o2max ( r from 0.74–0.91). Averaging the outcome of each activity-type specific equation based on TEE-pulse from the laboratory data led to accurate estimates of V̇o2max [root mean square error (RMSE): 300 mL O2/min, or 10%]. The difference between laboratory and free-living determined TEE-pulse was 3.7 ± 11% ( r = 0.85). The prediction method preserved the prediction accuracy when applied to free-living data (RMSE: 367 mL O2/min, or 12%). Measurements of body acceleration and HR can be used to predict V̇o2max in daily life. Activity-specific prediction equations are needed to achieve highly accurate estimates of CRF. NEW & NOTEWORTHY This is among the very few studies validating, in free-living conditions, a method to estimate cardiorespiratory fitness using heart rate and body acceleration data. A novel parameter called TEE-pulse, which was defined as the ratio between accelerometer-determined energy expenditure and heart rate, was highly correlated with maximal oxygen uptake (V̇o2max). Activity classification and the use of activity-selective prediction equations outperformed previously published methods for estimating V̇o2max from heart rate and acceleration data.

An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity

“…Further, the effects of exercise have traditionally been measured via oxygen uptake [ 14 ]; however, this monitoring method is not suitable for non-experimental settings. Alongside the rise in the use of wearables, concurrent evaluation of physical and physiological intensity, in which acceleration-derived indices and heart-rate-derived indices have become the preferred monitoring, enables non-invasive, day-to-day observations [ 15 , 16 , 17 ].…”

Smartphone-recorded physical activity for estimating cardiorespiratory fitness

“…Based upon day-to-day physical activity, Kwon et al built a model predicting cardiorespiratory fitness with an R 2 of 0.66 using activity data from Fitbit activity trackers 38 . Bonomi et al attained an R 2 of approximately 0.77 using both a heart rate monitor and activity tracker 37 . However, all these models relied upon specialized fitness tracking equipment and minute-to-minute heart rate measurements.…”

“…age, sex, and body-mass index) and day-to-day physical activity, which itself is associated with physical fitness. Measurements of physical activity have included questionnaires 25 – 30 , walking tests 31 – 34 , accelerometry 35 – 37 , and fitness tracking devices 38 – 40 . The advent of smartphones has introduced near ubiquitous physical activity measurement as most smartphones integrate accelerometers and gyroscopes which can track owner motion.…”

An experimental feasibility study evaluating the adequacy of sportswear-type wearables for recording physical exercise intensity

“…Further, the effects of exercise have traditionally been measured via oxygen uptake [13]; however, this monitoring method is not suitable for non-experimental settings. As a result, alongside the rise in the use of wearables, heart rate indices -which allow non-invasive, day-to-day observations -have become the preferred monitoring method over oxygen uptake [14][15][16]. Therefore, in recent years, the heart rate indices have been widely used in daily life as a non-invasive method in the field of wearable sensing [14][15][16].…”

“…As a result, alongside the rise in the use of wearables, heart rate indices -which allow non-invasive, day-to-day observations -have become the preferred monitoring method over oxygen uptake [14][15][16]. Therefore, in recent years, the heart rate indices have been widely used in daily life as a non-invasive method in the field of wearable sensing [14][15][16].…”