Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living

Montoye, Alexander H.K.; Mudd, Lanay M.; Biswas, Subir; Pfeiffer, Karin A.

doi:10.1249/mss.0000000000000597

Cited by 70 publications

(88 citation statements)

References 27 publications

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“…Though not widely used in physical activity research at present, these methods have demonstrated value for improving estimation of activity intensity over the more common cutpoint methods that rely primarily on less flexible models like linear regression (12,27,30,36).…”

Section: A C C E P T E Dmentioning

confidence: 99%

Validity of an Integrative Method for Processing Physical Activity Data

Ellingson

Schwabacher

Kim

et al. 2016

Medicine &Amp; Science in Sports &Amp; Exercise

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Section: A C C E P T E Dmentioning

confidence: 99%

Validity of an Integrative Method for Processing Physical Activity Data

Ellingson

Schwabacher

Kim

et al. 2016

Medicine &Amp; Science in Sports &Amp; Exercise

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“…Some have suggested that simple movement intensity approaches should be replaced by more sophisticated models that utilise a broader range of signal features 40,41 . Recent efforts to estimate energy expenditure have utilised a range of machine learning approaches, such as neural networks 42–44 and random forests 40 . While we are not aware of any such methodology with a performance that exceeds the simpler models validated in this paper, this is an interesting area of future work.…”

Section: Discussionmentioning

confidence: 99%

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

White

Westgate

Hollidge

et al. 2018

Preprint

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BackgroundMany large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer Activity Energy Expenditure (AEE) and consequently Total Energy Expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.MethodsMeasurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg·m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, Root Mean Squared Error (RMSE) and Pearson correlation.ResultsMean TEE and AEE derived from DLW was 11.6 (2.3) MJ·day-1 and 49.8 (16.3) kJ·day-1·kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r=0.93), but less so with thigh (r=0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ·day-1·kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ·day-1·kg-1, r ~0.71) with small mean biases at the population level (~6%). Only the thigh estimate bias was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ·day-1, r ~0.90). Conclusions: In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

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“…While there is robust evidence that physical activity assessment using accelerometry is a good proxy for inferring energy expenditure (e.g. Montoye et al ), there are limitations to these inferences. We did not utilize energy expenditure algorithms available for use with the Actical accelerometer because of ongoing assessment of their accuracy (e.g., Alhassan et al ; Brazeau et al ).…”

Section: Discussionmentioning

confidence: 99%

Quantitative physical activity assessment of children and adolescents in a rural population from Eastern Nepal

Williams

Subedi

Jha

et al. 2015

American J Hum Biol

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Energy Expenditure Prediction Using Raw Accelerometer Data in Simulated Free Living

Abstract: A single accelerometer placed on the thigh provided the highest accuracy for EE prediction, although monitors worn on the wrists or hip can also be used with high measurement accuracy.

Cited by 70 publications

References 27 publications

Validity of an Integrative Method for Processing Physical Activity Data

Validity of an Integrative Method for Processing Physical Activity Data

Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study

Quantitative physical activity assessment of children and adolescents in a rural population from Eastern Nepal

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