Evidence map›Paper›PMID 37254220›Full record

ArticleMovement ecology2023

Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable.

Pritish Chakravarty, Gabriele Cozzi, David Michael Scantlebury, Arpat Ozgul, Kamiar Aminian

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Movement ecology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.2field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. From electrocardiography to energetics: methods, constraints, and practical considerations for heart rate biologging in small animals.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2026
    Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 5 countries.

Pritish ChakravartyInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. chakravartypritish@gmail.com.
Gabriele CozziDepartment of Evolutionary Biology and Environmental Studies, Universität Zürich, Zurich, Switzerland.
David Michael ScantleburySchool of Biological Sciences, Queen's University Belfast, Belfast, Northern Ireland, UK.
Arpat OzgulDepartment of Evolutionary Biology and Environmental Studies, Universität Zürich, Zurich, Switzerland.
Kamiar AminianInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
University of Zurich · CHÉcole Polytechnique Fédérale de Lausanne · CHQueen's University Belfast · GB

Funding

Swiss National Science Foundation Interdisciplinary Grant CR32I3_159743
6 · The paper itself

Abstract

backgroundAll behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s

methodsWe used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.

resultsACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean).

conclusionsWe found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.

Indexed as

AccelerometerAllometryDaily energy expenditureDoubly labelled waterDynamic body acceleration (DBA)GyroscopeKalman filterSpeed estimation

Identifiers

PMID37254220
PMCPMC10228015
OpenAlexW4378782009

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.