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 c..., See detailed methods in accompanying paper, Karimjee et al. 2024, Royal Society Open Science.  The Methods description below are exerpts directly from the methods paper.  See the paper for more details including the equations, tables and figures. 
Data were collected via Axivity AX3 activity monitors (Axivity, U.K.). These have been previously validated for use in dogs and their parameters are easily modifiable by the user through open-source software, unlike many other commercial devices. Devices were mounted onto each dog’s neck collar using duct tape, and positioned ventrally to minimise likelihood of rotation of the logger over time. Additionally, the metrics selected were direction invariant to avoid any influence of device rotation. A sample frequency of 200Hz was used for recording. This was a higher sample frequency in comparison to most industry standard devices (30-100Hz). Considering that one of the main objectives of this work was to quantify the effect of sam..., , # Data and code for: A standardised approach to quantifying activity in domestic dogs

[Access this dataset on Dryad](10.5061/dryad.m0cfxppbs)

`LMM_input_data.csv` - this 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.

## Description of the data and file structure

As described above, the data included in `LMM_input_data.csv` was used to examine the effect of epoch length and sample frequency on aggregate activity metrics. There is one row per dog per epoch length, sample frequency and age combination. This data was used to create Figures 5-7 in the manuscript.

#### Columns:

* `dogCode` - ID of the dog recorded
* `AgeInMonths` - age of the dog at the time of recording
* `epochLength` - epoch length used to process the data
* `sampleFreq` - the data was re-sampled to this frequency for processing
* `M2_ACC` - the M2ACC value ...