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

Slower, shorter, sadder: a qualitative study exploring how dog walks change when the canine participant develops osteoarthritis

Background

Dog walking may have multiple physical and mental health advantages, but not all dog owners appear to benefit. Dog health is a described barrier to dog walking activity, but specific causes and impacts of reduced exercise in owners of dogs with health problems have not previously been reported. The current study used a qualitative methodology to explore the impact of canine osteoarthritis on dog walking activity.

Methods

Owners of dogs with osteoarthritis living in the United Kingdom (UK) were recruited through veterinary practices for semi-structured interview about life with an osteoarthritic dog. Participants were asked to reflect on walks that they had taken with their dog before he/she developed osteoarthritis, and to describe how those walks had changed. Interviews were transcribed verbatim and thematic analysis was used to construct key themes.

Results

Forty owners of 35 osteoarthritic dogs were interviewed. Prior to their dog’s development of osteoarthritis, dog walking distance, speed and location were usually decided by the owner to satisfy the needs and enjoyment of dog and walker. A diagnosis of canine osteoarthritis led to both dogs and their owners altering the walks undertaken. Walks were typically slower, shorter and limited to locations where physical infrastructure, underfoot surface and gradients were perceived by the owner to be navigable by their dog. Most owners did not go on additional walks without their dog due to feelings of guilt and because walking without a dog was less enjoyable. Many owners described negative effects on their own physical health and diminished enjoyment of walking as a result of their dog’s condition.

Conclusion

Our research suggests that osteoarthritic dogs may reduce the walking exercise their owners are able or willing to undertake. Since osteoarthritis is a common condition in older dogs, this is an important finding for those advocating dog ownership as a positive public health intervention. Strategies may be needed to ensure that owners of dogs that develop physical incapacities can continue to enjoy the health benefits they previously associated with dog walking. Future studies investigating dog walking activity should ensure that the health status of the dog has been considered.

A Canine Gait Analysis Protocol for Back Movement Assessment in German Shepherd Dogs

Objective—To design and test a motion analysis protocol for the gait analysis of adult German Shepherd (GS) dogs with a focus in the analyses of their back movements. Animals—Eight clinically healthy adult large-sized GS dogs (age, 4 ± 1.3 years; weight, 38.8 ± 4.2 kg). Procedures—A six-camera stereo-photogrammetric system and two force platforms were used for data acquisition. Experimental acquisition sessions consisted of static and gait trials. During gait trials, each dog walked along a 6 m long walkway at self-selected speed and a total of six gait cycles were recorded. Results—Grand mean and standard deviation of ground reaction forces of fore and hind limbs are reported. Spatial-temporal parameters averaged over gait cycles and subjects, their mean, standard deviation and coefficient of variance are analyzed. Joint kinematics for the hip, stifle and tarsal joints and their average range of motion (ROM) values, and their 95% Confidence Interval (CI) values of kinematics curves are reported. Conclusions and Clinical Relevance—This study provides normative data of healthy GS dogs to form a preliminary basis in the analysis of the spatial-temporal parameters, kinematics and kinetics during quadrupedal stance posture and gait. Also, a new back movement protocol enabling a multi-segment back model is provided. Results show that the proposed gait analysis protocol may become a useful and objective tool for the evaluation of canine treatment with special focus on the back movement.

A Combined Approach to Predicting Rest in Dogs Using Accelerometers

The ability to objectively measure episodes of rest has clear application for assessing health and well-being. Accelerometers afford a sensitive platform for doing so and have demonstrated their use in many human-based trials and interventions. Current state of the art methods for predicting sleep from accelerometer signals are either based on posture or low movement. While both have proven to be sensitive in humans, the methods do not directly transfer well to dogs, possibly because dogs are commonly alert but physically inactive when recumbent. In this paper, we combine a previously validated low-movement algorithm developed for humans and a posture-based algorithm developed for dogs. The hybrid approach was tested on 12 healthy dogs of varying breeds and sizes in their homes. The approach predicted state of rest with a mean accuracy of 0.86 (SD = 0.08). Furthermore, when a dog was in a resting state, the method was able to distinguish between head up and head down posture with a mean accuracy of 0.90 (SD = 0.08). This approach can be applied in a variety of contexts to assess how factors, such as changes in housing conditions or medication, may influence a dog's resting patterns.