Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults.
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ABSTRACT: Background
Wrist-worn accelerometers are emerging as the most common instrument for measuring physical activity in large-scale epidemiological studies, though little is known about the relationship between wrist acceleration and physical activity energy expenditure (PAEE).Methods
1695 UK adults wore two devices simultaneously for six days; a combined sensor and a wrist accelerometer. The combined sensor measured heart rate and trunk acceleration, which was combined with a treadmill test to yield a signal of individually-calibrated PAEE. Multi-level regression models were used to characterise the relationship between the two time-series, and their estimations were evaluated in an independent holdout sample. Finally, the relationship between PAEE and BMI was described separately for each source of PAEE estimate (wrist acceleration models and combined-sensing).Results
Wrist acceleration explained 44-47% between-individual variance in PAEE, with RMSE between 34-39 J•min-1•kg-1. Estimations agreed well with PAEE in cross-validation (mean bias [95% limits of agreement]: 0.07 [-70.6:70.7]) but overestimated in women by 3% and underestimated in men by 4%. Estimation error was inversely related to age (-2.3 J•min-1•kg-1 per 10y) and BMI (-0.3 J•min-1•kg-1 per kg/m2). Associations with BMI were similar for all PAEE estimates (approximately -0.08 kg/m2 per J•min-1•kg-1).Conclusions
A strong relationship exists between wrist acceleration and PAEE in free-living adults, such that irrespective of the objective method of PAEE assessment, a strong inverse association between PAEE and BMI was observed.
SUBMITTER: White T
PROVIDER: S-EPMC5147924 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PURPOSE:To evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data. METHODS:25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an "off the shelf" model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children. RESULTS:Root mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63-0.67 kcals/min. In the hold out sample, RMSE's for the hip LAB (0.62-0.71), retrained LAB (0.58-0.62) and FL models (0.61-0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE (0.73 ± 0.29 kcals/min) than the retrained LAB SVM (0.63 ± 0.30 kcals/min) and LAB SVM (0.64 ± 0.18 kcals/min). The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy. CONCLUSION:Machine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions.
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Project description:BackgroundAccurate assessment of energy expenditure (EE) is important for the study of energy balance and metabolic disorders. Combined heart rate (HR) and acceleration (ACC) sensing may increase precision of physical activity EE (PAEE) which is the most variable component of total EE (TEE).ObjectiveTo evaluate estimates of EE using ACC and HR data with or without individual calibration against doubly-labelled water (DLW) estimates of EE.Design23 women and 23 men (22-55 yrs, 48-104 kg, 8-46%body fat) underwent 45-min resting EE (REE) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. ACC and HR were monitored and TEE measured over 14 days using DLW. Diet-induced thermogenesis (DIT) was calculated from food-frequency questionnaire. PAEE (TEE ÷ REE ÷ DIT) and TEE were compared to estimates from ACC and HR using bias, root mean square error (RMSE), and correlation statistics.ResultsMean(SD) measured PAEE and TEE were 66(25) kJ·day(-1)·kg(-1), and 12(2.6) MJ·day(-1), respectively. Estimated PAEE from ACC was 54(15) kJ·day(-1)·kg(-1) (p<0.001), with RMSE 24 kJ·day(-1)·kg(-1) and correlation r = 0.52. PAEE estimated from HR and ACC+HR with treadmill calibration were 67(42) and 69(25) kJ·day(-1)·kg(-1) (bias non-significant), with RMSE 34 and 20 kJ·day(-1)·kg(-1) and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise (RMSE: 37 and 24 kJ·day(-1)·kg(-1), r = 0.40 and r = 0.55). TEE models also had high validity, with biases <5%, and correlations r = 0.71 (ACC), r = 0.66-0.76 (HR), and r = 0.76-0.83 (ACC+HR).ConclusionsBoth accelerometry and heart rate may be used to estimate EE in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.
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Project description:Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932&nbsp;±&nbsp;0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.
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Project description:BACKGROUND:Energy expenditure (EE) estimates for a broad age range of youth performing a variety of activities are needed. METHODS:106 participants (6-18 years) completed 6 free-living activities (seated rest, movie watching, coloring, stair climbing, basketball dribbling, jumping jacks) and up to 9 treadmill walking bouts (13.4 to 120.7 m/min; 13.4 m/min increments). Breath-by-breath oxygen uptake (VO2) was measured using the COSMED K4b2 and EE was quantified as youth metabolic equivalents (METy1:VO2/measured resting VO2, METy2:VO2/estimated resting VO2). Age trends were evaluated with ANOVA. RESULTS:Seated movie watching produced the lowest mean METy1 (6- to 9-year-olds: 0.94 ± 0.13) and METy2 values (13- to 15-year-olds: 1.10 ± 0.19), and jumping jacks produced the highest mean METy1 (13- to 15-year-olds: 6.89 ± 1.47) and METy2 values (16- to 18-year-olds: 8.61 ± 2.03). Significant age-related variability in METy1 and METy2 were noted for 8 and 2 of the 15 evaluated activities, respectively. CONCLUSIONS:Descriptive EE data presented herein will augment the Youth Compendium of Physical Activities.
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Project description:Wearable sensors enable long-term monitoring of health and wellbeing indicators. An objective evaluation of sensors' accuracy is important, especially for their use in health care.The aim of this study was to use a wrist-worn optical heart rate (OHR) device to estimate heart rate (HR), energy expenditure (EE), and maximal oxygen intake capacity (VO2Max) during running and to evaluate the accuracy of the estimated parameters (HR, EE, and VO2Max) against golden reference methods.A total of 24 healthy volunteers, of whom 11 were female, with a mean age of 36.2 years (SD 8.2 years) participated in a submaximal self-paced outdoor running test and maximal voluntary exercise test in a sports laboratory. OHR was monitored with a PulseOn wrist-worn photoplethysmographic device and the running speed with a phone GPS sensor. A physiological model based on HR, running speed, and personal characteristics (age, gender, weight, and height) was used to estimate EE during the maximal voluntary exercise test and VO2Max during the submaximal outdoor running test. ECG-based HR and respiratory gas analysis based estimates were used as golden references.OHR was able to measure HR during running with a 1.9% mean absolute percentage error (MAPE). VO2Max estimated during the submaximal outdoor running test was closely similar to the sports laboratory estimate (MAPE 5.2%). The energy expenditure estimate (n=23) was quite accurate when HR was above the aerobic threshold (MAPE 6.7%), but MAPE increased to 16.5% during a lighter intensity of exercise.The results suggest that wrist-worn OHR may accurately estimate HR during running up to maximal HR. When combined with physiological modeling, wrist-worn OHR may be used for an estimation of EE, especially during higher intensity running, and VO2Max, even during submaximal self-paced outdoor recreational running.
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Project description:INTRODUCTION:Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods. METHODS:Data were collected between April and October 2014 for the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes. RESULTS:ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ?60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%). CONCLUSIONS:Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.
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