Association of signalment parameters with activity of pet dogs

Canine Geriatric Syndrome: A Framework for Advancing Research in Veterinary Geroscience

Biological aging is the single most important risk factor for disease, disability, and ultimately death in geriatric dogs. The effects of aging in companion dogs also impose significant financial and psychological burdens on their human caregivers. The underlying physiologic processes of canine aging may be occult, or early signs of aging may be ignored because of the misconception that biological aging is natural and therefore inevitable. The ability to detect, quantify, and mitigate the deleterious processes of canine aging would greatly enhance veterinary preventative medicine and animal welfare. In this paper we propose a new conceptual framework for aging in dogs, the Canine Geriatric Syndrome (CGS). CGS consists of the multiple, interrelated physical, functional, behavioral, and metabolic changes that characterize canine aging as well as the resulting clinical manifestations, including frailty, diminished quality of life, and age-associated disease. We also identify potential key components of a CGS assessment tool, a clinical instrument that would enable veterinarians to diagnose CGS and would facilitate the development and testing of interventions to prolong healthspan and lifespan in dogs by directly targeting the biological mechanisms of aging. There are many gaps in our knowledge of the mechanisms and phenotype of aging in dogs that must be bridged before a CGS assessment tool can be deployed. The conceptual framework of CGS should facilitate identifying these gaps and should stimulate research to better characterize the processes and effects of aging in dogs and to identify the most promising preventative strategies to target these.

Comparison of activity levels derived from two accelerometers in dogs with osteoarthritis: Implications for clinical trials

Accelerometer measurements are frequently reported as total weekly activity counts (AC). Methods of utilizing activity parameters to allow differentiation of activity intensities (i.e., manually derived cut-points) have been described. While this information may provide valuable data for researchers, only few investigators have utilized these methods. This may, in part, be due to the challenge associated with data processing. Some devices, however, generate activity intensity data automatically. This study was conducted to evaluate a novel accelerometer that allows for remote download of data via Wi-Fi (Heyrex), to compare automatically generated parameters quantifying activity levels to previously established cut-points (Actical) and to describe the variability of accelerometer data in dogs with osteoarthritis. Twelve client-owned dogs with osteoarthritis were fitted with a collar with two accelerometers (Heyrex and Actical). Accelerometer data were recorded for 28 days. Pearson bivariate correlations and coefficient of variation (CV%) were calculated for accelerometer data. There was a strong correlation between the AC reported by Heyrex and Actical devices. Several automatically generated parameters showed strong correlations to previously validated cut-points and displayed lower CV% than weekly AC. The activity intensity derived from the Heyrex was associated with the lowest CV% of all parameters from both accelerometers. Automatically generated activity intensity parameters should be considered as potential outcome measures in clinical trials for dogs with osteoarthritis. This novel technology may allow for convenient acquisition of activity intensity data in companion animals. However, technical difficulties (e.g., lack of Wi-Fi connectivity) may pose challenges when using this novel device.

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

BACKGROUND

Accelerometer-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.

RESULTS

Both 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%.

CONCLUSIONS

The 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.

Evaluation of an open source method for calculating physical activity in dogs from harness and collar based sensors

BACKGROUND

The ability to make objective measurements of physical activity in dogs has both clinical and research applications. Accelerometers offer a non-intrusive and convenient solution. Of the commercialy available sensors, measurements are commonly given in manufacturer bespoke units and calculated with closed source approaches. Furthermore, the validation studies that exist for such devices are mounting location dependant, not transferable between brands or not suitable for handling modern raw accelerometry type data.

METHODS

This paper describes a validation study of n = 5 where 4 sensors were placed on each dog; 2 on a harness and 2 on a collar. Each position held two sensors from different manufacturers; Actigraph (which has previously been validated for use on the collar) and VetSens (which provides un-filtered accelerometry data). The aims of the study was to firstly evaluate the performance of an open-design method of converting raw accelerometry data into units that have previously been validated. Secondly, comparison was made between sensors mounted at each location for determining physical activity state.

RESULTS

Once the raw actigraphy data had been processed with the open-design method, results from a 7 day measurement revealed no significant difference in physical activity estimates via a cutpoint approach between the sensor manufacturers. A second finding was a low inter-site variability between the ventral collar and dorsal harness locations (Pearsons r2 = 0.96).

CONCLUSIONS

Using the open-design methodology, raw, un-filtered data from the VetSens sensors can be compared or pooled with data gathered from Actigraph sensors. The results also provide strong evidence that ventral collar and dorsal harness sites may be used interchangeably. This enables studies to be designed with a larger inclusion criteria (encompassing dogs that are not well suited for wearing an instrumented collar) and ensures high levels of welfare while maintaining measurement validity.