Data and code for: A standardised approach to quantifying activity in domestic dogs
<p>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 <em>(</em>as a fraction of standard gravity<em>,1g = 9.81m/s<sup>2</sup>)</em> above which the animal's X most active minutes are accumulated (MX<sub>ACC</sub>) over a 24-hour period. We report M2<sub>ACC,</sub> M30<sub>ACC</sub> and M60<sub>ACC</sub> 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 MX<sub>ACC</sub> 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 MX<sub>ACC</sub>. 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.</p>
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