Comparing the standards of one metabolic equivalent of task in accurately estimating physical activity energy expenditure based on acceleration

Kim, Do Hyun; Lee, Jongshill; Park, Hoon Ki; Jang, Dong Pyo; Song, Soohwa; Cho, Baek Hwan; Jung, Yoo-Suk; Park, Rae-Woong; Joo, Nam-Seok; Kim, In Young

doi:10.1080/02640414.2016.1221520

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Next, to replicate predictions made by the present algorithms in free-living subjects, measured RMR may be required, which increases the researcher and participant burden. A suitable alternative in the absence of measured RMR would be prediction equations derived from BMI, age, height, and gender, rather than assuming a resting value of 3.5 ml O 2 /kg/min [ 55 , 56 ]. Finally, our use of the measured RMR to calculate METs may contribute to differences between the tested algorithms and the SenseWear manufacturer.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

O’Driscoll¹,

Turicchi²,

Hopkins³

et al. 2021

JMIR Mhealth Uhealth

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Background Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction of energy expenditure in several wearables (ie, Fitbit Charge 2, ActiGraph GT3-x, SenseWear Armband Mini, and Polar H7) using two laboratory data sets comprising different activities. Methods Two laboratory studies (study 1: n=59, age 44.4 years, weight 75.7 kg; study 2: n=30, age=31.9 years, weight=70.6 kg), in which adult participants performed a sequential lab-based activity protocol consisting of resting, household, ambulatory, and nonambulatory tasks, were combined in this study. In both studies, accelerometer and physiological data were collected from the wearables alongside energy expenditure using indirect calorimetry. Three regression algorithms were used to predict metabolic equivalents (METs; ie, random forest, gradient boosting, and neural networks), and five classification algorithms (ie, k-nearest neighbor, support vector machine, random forest, gradient boosting, and neural networks) were used for physical activity intensity classification as sedentary, light, or moderate to vigorous. Algorithms were evaluated using leave-one-subject-out cross-validations and out-of-sample validations. Results The root mean square error (RMSE) was lowest for gradient boosting applied to SenseWear and Polar H7 data (0.91 METs), and in the classification task, gradient boost applied to SenseWear and Polar H7 was the most accurate (85.5%). Fitbit models achieved an RMSE of 1.36 METs and 78.2% accuracy for classification. Errors tended to increase in out-of-sample validations with the SenseWear neural network achieving RMSE values of 1.22 METs in the regression tasks and the SenseWear gradient boost and random forest achieving an accuracy of 80% in classification tasks. Conclusions Algorithms trained on combined data sets demonstrated high predictive accuracy, with a tendency for superior performance of random forests and gradient boosting for most but not all wearable devices. Predictions were poorer in the between-study validations, which creates uncertainty regarding the generalizability of the tested algorithms.

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Section: Discussionmentioning

confidence: 99%

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

O’Driscoll¹,

Turicchi²,

Hopkins³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…To calculate the MET, the steady-state energy expenditure of each task was referenced to the resting metabolic rate (Kozey, Lyden, Staudenmayer, & Freedson, 2010). Most calibration studies used a standardized MET of 3.5 ml • kg −1 • min −1 VO 2 or an approximated MET based on personal characteristics (Kim et al, 2017;Kozey et al, 2010). However, both neglect individual variation and ambient factors, like temperature (Borges et al, 2016;Popp et al, 2016).…”

Section: Methodological Considerationsmentioning

confidence: 99%

Where to Place Which Sensor to Measure Sedentary Behavior? A Method Development and Comparison Among Various Sensor Placements and Signal Types

Kuster

Baumgartner

Hagströmer

et al. 2020

Journal for the Measurement of Physical Behaviour

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Background: Sedentary behavior (SB) is associated with several chronic diseases and office workers especially are at increased risk. SB is defined by a sitting or reclined body posture with an energy expenditure of ≤1.5 metabolic equivalents. However, current objective methods to measure SB are not consistent with its definition. There is no consensus on which sensor placement and type should be used. Aim: To compare the accuracy of newly developed artificial intelligence models for 15 sensor placements in combination with four signal types (accelerometer only/plus gyroscope and/or magnetometer) to detect posture and physical in-/activity during desk-based activities. Method: Signal features for the model development were extracted from sensor raw data of 30 office workers performing 10 desk-based tasks, each lasting 5 min. Direct observation (posture) and indirect calorimetry (in-/activity) served as the reference criteria. The best classification model for each sensor was identified and compared among the sensor placements, both using Friedman and post hoc Wilcoxon tests (p ≤ .05). Results: Posture was most accurately measured with a lower body sensor, while in-/activity was most accurately measured with an upper body or waist sensor. The inclusion of additional signal types improved the posture classification for some placements, while the acceleration signal already contained the relevant signal information for the in-/activity classification. Overall, the thigh accelerometer most accurately classified desk-based SB. Conclusion: This study favors, in line with previous work, the measurement of SB with a thigh-worn accelerometer and adds the information that this sensor is also accurate in measuring physical in-/activity while sitting and standing.

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“…cycling <16 km/h for a recreationally active man), and high intensity exercise is defined by MET scores of 6 and higher (e.g. high intensity running <16 km/h for a recreationally active man) [40]. Total exercise times at moderate and high intensities, as well as total time were summed across the logged days.…”

Section: Daily Diet and Activity Logmentioning

confidence: 99%

Dietary intake of tryptophan tied emotion-related impulsivity in humans

Javelle

Zimmer

et al. 2021

International Journal for Vitamin and Nutrition Research

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Abstract. Emotion-related impulsivity, defined as the tendency to say or do things that one later regret during periods of heightened emotion, has been tied to a broad range of psychopathologies. Previous work has suggested that emotion-related impulsivity is tied to an impaired function of the serotonergic system. Central serotonin synthesis relies on the intake of the essential amino acid, tryptophan and its ability to pass through the blood brain barrier. Objective: The aim of this study was to determine the association between emotion-related impulsivity and tryptophan intake. Methods: Undergraduate participants (N = 25, 16 women, 9 men) completed a self-rated measure of impulsivity (Three Factor Impulsivity Index, TFI) and daily logs of their food intake and exercise. These data were coded using the software NutriNote to evaluate intakes of tryptophan, large neutral amino acids, vitamins B6/B12, and exercise. Results: Correlational analyses indicated that higher tryptophan intake was associated with significantly lower scores on two out of three subscales of the TFI, Pervasive Influence of Feelings scores r = –.502, p < . 010, and (lack-of) Follow-Through scores, r = –.407, p < . 050. Conclusion: Findings provide further evidence that emotion-related impulsivity is correlated to serotonergic indices, even when considering only food habits. It also suggests the need for more research on whether tryptophan supplements might be beneficial for impulsive persons suffering from a psychological disorder.

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Comparing the standards of one metabolic equivalent of task in accurately estimating physical activity energy expenditure based on acceleration

Cited by 6 publications

References 20 publications

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Where to Place Which Sensor to Measure Sedentary Behavior? A Method Development and Comparison Among Various Sensor Placements and Signal Types

Dietary intake of tryptophan tied emotion-related impulsivity in humans

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