Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study
September
Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study
Editor: François Criscuolo 0
CNRS 0
FRANCE 0
Søren Brage 0
Kate Westgate 0
Paul W. Franks 0
Oliver Stegle 0
Antony Wright 0
Ulf Ekelund 0
Nicholas J. Wareham 0
0 1 MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom, 2 Department of Clinical Sciences, Lund University , Malmö , Sweden , 3 Department of Physics, University of Cambridge, Cambridge, United Kingdom, 4 European Molecular Biology Laboratory, European Bioinformatics Institute , Hinxton, Cambridge , United Kingdom , 5 MRC Human Nutrition Research, Cambridge , United Kingdom
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Data Availability Statement: The authors can
confirm that all non-identifiable data are available on
request to the corresponding author. Their consent
prevents the authors from making these data
available publicly; third-party researchers would need
to sign a collaborative agreement. The authors' data
sharing policies and processes meet the
requirements and expectations of MRC policy on
sharing of data from population and patient cohorts:
http://www.mrc.ac.uk/research/research-policy-ethics/
data-sharing/policy/. These policies and processes
are in place to ensure that the use of data from this
study is within the bounds of consent given previously
Accurate 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).
To evaluate estimates of EE using ACC and HR data with or without individual calibration
against doubly-labelled water (DLW) estimates of EE.
23 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.
Mean(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
by study members, complies with MRC guidance on
ethics and research governance, and meets rigorous
MRC data security standards.
Funding: This study received funding from Medical
Research Council (MC_UU_12015/3), Wellcome
Trust, Unilever UK. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript
Competing Interests: This study was partly funded
by Unilever UK. There are no patents, products in
development or marketed products to declare. This
does not alter the authors' adherence to all the PLOS
ONE policies on sharing data and materials, as
detailed online in the guide for authors.
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).
Both 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.
Precise assessment of energy expenditure (EE) is essential for studies of energy balance and has
been used to identify underreporting of food intake [1]. Physical activity energy expenditure
(PAEE) is the most variable component of total energy expenditure (TEE) and the most
difficult to assess during free-living conditions. The most accurate method is doubly-labelled water
(DLW) assessment of TEE combined with measurement of the resting metabolic rate (RMR)
via indirect calorimetry [2,3]. These procedures require high levels of technical expertise, are
relatively expensive, and yield no information about underlying patterns of energy expenditure
over shorter periods of time, e.g. the intensity profile. Wearable sensors have potential to
provide estimates of PAEE which may be combined with estimates of REE; an approach which
holds promise for better quantifying dose-response relationships between EE and health
outcomes, as well as assessing intervention effects in trials, where self-report measures could be
biased [4].
For the assessment of PAEE, previous studies have demonstrated the advantage of
integrating physiological measures, li (...truncated)