Evidence map›Paper›PMID 41928302›Full record

ReviewMovement ecology2026

Six decades of animal accelerometry: trends, applications, and future directions.

Jessica L Rudd, Martin A Collins, David Righton, Holly L White, Matthew J Witt, Serena Wright, Lucy A Hawkes

Abstract readReview
In one paragraph

Review in Movement ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jessica L RuddHatherly Laboratories, Faculty of Health and Life Sciences, University of Exeter, Prince of Wales Road, Exeter, EX4 4PS, UK. jr646@exeter.ac.uk.
Martin A CollinsBritish Antarctic Survey, NERC, High Cross, Cambridge, CB3 0ET, UK.
David RightonCentre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, NR33 0HT, UK.
Holly L WhiteHatherly Laboratories, Faculty of Health and Life Sciences, University of Exeter, Prince of Wales Road, Exeter, EX4 4PS, UK.
Matthew J WittHatherly Laboratories, Faculty of Health and Life Sciences, University of Exeter, Prince of Wales Road, Exeter, EX4 4PS, UK.
Serena WrightCentre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, NR33 0HT, UK.
Lucy A HawkesHatherly Laboratories, Faculty of Health and Life Sciences, University of Exeter, Prince of Wales Road, Exeter, EX4 4PS, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of animal-borne devices to measure acceleration has yielded insights into animal locomotion, behaviour and energy expenditure. We present the first systematic review of accelerometry on animals, spanning 60 years, across ecology, biomechanics, agriculture, neurosciences, medical and veterinary sciences from over 400 species and 1520 studies. Most work to date is on terrestrial mammals, particularly domesticated species, while aerial and aquatic animals are relatively under-represented owing to device size and attachment constraints, and just one study has been conducted on amphibians. Despite this, accelerometry research has expanded globally across nearly 100 countries, across all groups of animals including birds, fish, invertebrates, mammals, and reptiles, and across wild and captive animals, including domesticated and non-domesticated species. A variety of software has been used, with open-source software and, more recently, machine learning tools being employed. On-board processing of accelerometry data, including real-time behavioural classification with deep learning to improve accuracy, can reduce power consumption and memory use several-fold, increasing deployment duration. Transfer of device types, sensors and analytical routines between fields in accelerometry offers considerable opportunities for advancing the field. For example, precision livestock farming uses real-time welfare monitoring, which could be adapted for aquaculture or even wild animal monitoring in areas with good data connectivity, while medical sector health monitoring could be used for wild animal disease tracking. The rapid growth in accelerometry requires urgent collaborative approaches to address big data challenges. This includes establishing standardised data repositories, developing open-electronics for low-cost device development and bespoke sensors, fostering capacity building and technical collaboration for device and algorithm advancements. This review also highlights geographic inequities in research accessibility and authorship, with less than 6% of accelerometry studies originating from authors from Africa, South America and Asia (excluding Japan, which contributed the second most studies globally) combined. The field must prioritise translational and inclusive research practices and cross-disciplinary collaboration to maximise scientific value and foster innovation while maximising animal welfare.

Indexed as

AccelerometerBehavioural classificationBiologgingEnergy expenditureMachine learningPrecision livestock farmingWelfareWildlife

Identifiers

PMID41928302
PMCPMC13045147

What Socratic 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 Socratic graph.