Data and code for: A standardised approach to quantifying activity in domestic dogs

Objective assessment of activity via accelerometry can provide valuable
insights into dog health and welfare. Common activity metrics involve
using acceleration cut-points to group data into intensity categories and
reporting the time spent in each category. Lack of consistency and
transparency in cut-point derivation makes it difficult to compare
findings between studies. We present an alternative metric for use in
dogs: the acceleration threshold (as a fraction of standard gravity,1g =
9.81m/s2) above which the animal’s X most active minutes are accumulated
(MXACC) over a 24-hour period. We report M2ACC, M30ACC and M60ACC data
from a colony of healthy beagles (n=6) aged 3-13 months. To ensure that
reference values are applicable across a wider dog population, we
incorporated labelled data from beagles and volunteer pet dogs (n=16) of a
variety of ages and breeds. The dogs’ normal activity patterns were
recorded at 200 Hz for 24-hours using collar-based Axivity-AX3
accelerometers. We calculated acceleration vector magnitude and MXACC
metrics. Using labelled data from both beagles and pet dogs, we
characterise the range of acceleration outputs exhibited for a variety of
behaviours, enabling meaningful interpretation of MXACC. These metrics
will help standardise measurement of canine activity, inform development
of exercise guidelines and adherence monitoring, and serve as outcome
measures for veterinary and translational research. This repository
contains two files: 1) `LMM_input_data.csv`, a spreadsheet file containing
the raw data used to explore the effect of modifying epoch length and
sample frequency on aggregate activity metrics using linear mixed effects
models as described in the manuscript. There is one row per dog per epoch
length, sample frequency and age combination, and 2)
`getMostActiveMinsThresh.m`, a MATLAB function used to compute the MX ACC
outcome metrics that are explored in the manuscript.