Evidence map›Paper›PMID 29558919›Full record

ArticleBMC veterinary research2018

A step in the right direction: an open-design pedometer algorithm for dogs.

C Ladha, Z Belshaw, J O'Sullivan, L Asher

Open access · goldAbstract read
In one paragraph

Article in BMC veterinary research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.5field-weighted citation impact, top 12% 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, 10 citations in OpenAlex.

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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

4 authors at 3 institutions in 2 countries.

C LadhaCentre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Newcastle, NE2 4HH, UK. cas@vetsens.co.uk.ORCID http://orcid.org/0000-0001-8617-886X
Z BelshawSchool of Veterinary Medicine and Science, University of Nottingham, Sutton, Bonington Campus, Leicestershire, LE12 5RD, UK.
J O'SullivanCentre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Newcastle, NE2 4HH, UK.
L AsherCentre for Behaviour and Evolution, Henry Wellcome Building, Newcastle University, Newcastle, NE2 4HH, UK.
Newcastle University · GBUniversity of Nottingham · GBWestminster Theological Seminary · US

Funding

Biotechnology and Biological Sciences Research Council BB/J014508/1
6 · The paper itself

Abstract

backgroundAccelerometer-based technologies could be useful in providing objective measures of canine ambulation, but most are either not tailored to the idiosyncrasies of canine gait, or, use un-validated or closed source approaches. The aim of this paper was to validate algorithms which could be applied to accelerometer data for i) counting the number of steps and ii) distance travelled by a dog. To count steps, an approach based on partitioning acceleration was used. This was applied to accelerometer data from 13 dogs which were walked a set distance and filmed. Each footfall captured on video was annotated. In a second experiment, an approach based on signal features was used to estimate distance travelled. This was applied to accelerometer data from 10 dogs with osteoarthritis during normal walks with their owners where GPS (Global Positioning System) was also captured. Pearson's correlations and Bland Altman statistics were used to compare i) the number of steps measured on video footage and predicted by the algorithm and ii) the distance travelled estimated by GPS and predicted by the algorithm.

resultsBoth step count and distance travelled could be estimated accurately by the algorithms presented in this paper: 4695 steps were annotated from the video and the pedometer was able to detect 91%. GPS logged a total of 20,184 m meters across all dogs; the mean difference between the predicted and GPS estimated walk length was 211 m and the mean similarity was 79%.

conclusionsThe algorithms described show promise in detecting number of steps and distance travelled from an accelerometer. The approach for detecting steps might be advantageous to methods which estimate gross activity because these include energy output from stationary activities. The approach for estimating distance might be suited to replacing GPS in indoor environments or others with limited satellite signal. The algorithms also allow for temporal and spatial components of ambulation to be calculated. Temporal and spatial aspects of dog ambulation are clinical indicators which could be used for diagnosis or monitoring of certain diseases, or used to provide information in support of canine weight-loss programmes.

Indexed as

AccelerometryAlgorithmsAnimalsDogsFemaleMaleWalkingAccelerometerActivity levelDogGPSMotion analysisStep counting

Identifiers

PMID29558919
PMCPMC5861607
OpenAlexW2801894284

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.