Use of accelerometry to investigate physical activity in dogs receiving chemotherapy

Helm, Jenny; McBrearty, Alix; Fontaine, Samantha; Morrison, R.; Yam, P.S.

doi:10.1111/jsap.12587

Cited by 10 publications

(10 citation statements)

References 39 publications

(69 reference statements)

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“…The Actigraph accelerometer (ActiGraph, Pensecola, FL, USA) was shown to be a valid, practical and reliable device for the measurement of habitual physical activity in dogs in 2011 [1]. The Actigraph monitor and other accelerometers have been used to assess activity in ideal weight, overweight and obese dogs [2], during weight loss in dogs [3], in dogs receiving chemotherapy [4], in shelter dogs [5], and to monitor the behavioral states in dogs [6]. ActiLife software (ActiGraph, Pensecola, FL, USA) allows researchers to process and score collected data via validated algorithms that include sedentary bout and activity bout analysis and physical activity intensity (cut-points).…”

Section: Introductionmentioning

confidence: 99%

Combining Actigraph Link and PetPace Collar Data to Measure Activity, Proximity, and Physiological Responses in Freely Moving Dogs in a Natural Environment

Ortmeyer

Robey

McDonald

2018

Animals

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Simple SummaryThe Actigraph accelerometry monitors, the most widely used and extensively validated devices for measuring physical activity in humans, have also been validated for use in dogs. The ActiGraph GT9X Link monitor has Bluetooth Smart technology and a proximity-tagging feature that potentially allows for the measurement of distance between subjects, e.g., between human caretakers and their dog(s). The PetPace Smart-collar is a non-invasive wireless collar that collects important health markers, including heart beats, variation in the intervals between heartbeats, breaths per minute, and position data (lying, sitting, standing), in addition to activity. The purpose of this study was to determine whether combining data from the Actigraph monitor and PetPace collar would provide reliable pulse, respiration, and heart rate variability results during various activity levels in dogs, and whether these variables were affected by the absence or presence of their caretakers.AbstractAlthough several studies have examined the effects of an owner’s absence and presence on a dog’s physiological responses under experimental conditions over short periods of time (minutes), little is known about the effects of proximity between humans and freely moving dogs under natural conditions over longer periods of time (days). The first aim of our study was to determine whether the combined data generated from the PetPace Collar and Actigraph Link accelerometer provide reliable pulse, respiration, and heart rate variability results during sedentary, light-moderate, and vigorous bouts in 11 freely moving dogs in a foster caretaker environment over 10–15 days. The second aim was to determine the effects of proximity (absence and presence of caretaker) and distance (caretaker and dog within 0–2 m) on the dogs’ physiological responses. Aim 1 results: Pulse and respiration were higher during light-moderate bouts compared to sedentary bouts, and higher at rest while the dogs were standing and sitting vs. lying. Heart rate variability (HRV) was not different between activity levels or position. Aim 2 results: During sedentary bouts, pulse and respiration were higher, and HRV lower, when there was a proximity signal (caretaker present) compared to no proximity signal (caretaker absent). Using multiple regression models, we found that activity, position, distance, and signal presence were predictors of physiological response in individual dogs during sedentary bouts. Our results suggest that combining data collected from Actigraph GT9X and PetPace monitors will provide useful information, both collectively and individually, on dogs’ physiological responses during activity, in various positions, and in proximity to their human caretaker.

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Section: Introductionmentioning

confidence: 99%

Combining Actigraph Link and PetPace Collar Data to Measure Activity, Proximity, and Physiological Responses in Freely Moving Dogs in a Natural Environment

Ortmeyer

Robey

McDonald

2018

Animals

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“…Therefore, objective methods for the assessment of activity are important as they can overcome the limitations of subjective evaluation (Conzemius & Evans 2018). Measurement of physical activity levels using accelerometers is a growing methodology in veterinary medicine (Yam et al 2011, Morrison et al 2013, Helm et al 2016. Accelerometry is considered a valid and objective method to quantify physical activity levels (Hansen et al 2007, Brown et al 2010a.…”

Section: Introductionmentioning

confidence: 99%

Physical activity measured with an accelerometer in dogs following extracapsular stabilisation to treat cranial cruciate ligament rupture

Schuster

Carvalho

Santos

et al. 2023

J of Small Animal Practice

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ObjectivesTo quantify the physical activity levels in dogs with cranial cruciate ligament rupture before and after lateral fabellar suture stabilisation surgery.Materials and MethodsSeventeen dogs (mean weight, 12.3±5.1 kg) with unilateral cranial cruciate ligament rupture were fitted with an accelerometer for seven consecutive days at four different time points: before surgery (T0), one (T1), three (T3) and six (T6) months after surgery. The total activity and times spent in sedentary activity, light to moderate activity and vigorous activity were recorded by the accelerometer, and preoperative and postoperative data were compared. At all time points, dogs underwent clinical evaluations (lameness score, stifle pain score and thigh circumference) and their owners were asked to respond to questionnaires to subjectively score the physical activity and quality of life of the dogs.ResultsAt the four time points, the dogs spent between 21.2 and 21.4 hours on sedentary behaviour, 2.3 and 2.5 hours performing light to moderate activity, and 13 to 15 minutes performing vigorous activity. There was no increase in physical activity variables or decrease in sedentary behaviour over time. Lameness scores, pain score and dogs' quality of life improved significantly during the postoperative period. At T6, 17 (100%) of 17 dogs presented no lameness, 16 (94%) of 17 dogs presented no stifle pain, 16 (94%) of 17 owners rated the quality of life as very good and excellent, and 16 (100%) of 16 owners reported a total return to normal activity levels.Clinical SignificanceThe clinical recovery after extracapsular stabilisation of the stifle joint was not associated with a spontaneous increase in physical activity or a decrease in sedentary behaviour.

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“…Accelerometry has recently been introduced in veterinary medicine as a new method to objectively assess the activity level of a dog [ 7 , 8 , 9 ]. The method is an indispensable tool often used in combination with other previously tested performance indicators such as ground reaction forces [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors

Muminov

Mukhiddinov

2022

Sensors

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The employment of machine learning algorithms to the data provided by wearable movement sensors is one of the most common methods to detect pets’ behaviors and monitor their well-being. However, defining features that lead to highly accurate behavior classification is quite challenging. To address this problem, in this study we aim to classify six main dog activities (standing, walking, running, sitting, lying down, and resting) using high-dimensional sensor raw data. Data were received from the accelerometer and gyroscope sensors that are designed to be attached to the dog’s smart costume. Once data are received, the module computes a quaternion value for each data point that provides handful features for classification. Next, to perform the classification, we used several supervised machine learning algorithms, such as the Gaussian naïve Bayes (GNB), Decision Tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM). In order to evaluate the performance, we finally compared the proposed approach’s F-score accuracies with the accuracy of classic approach performance, where sensors’ data are collected without computing the quaternion value and directly utilized by the model. Overall, 18 dogs equipped with harnesses participated in the experiment. The results of the experiment show a significantly enhanced classification with the proposed approach. Among all the classifiers, the GNB classification model achieved the highest accuracy for dog behavior. The behaviors are classified with F-score accuracies of 0.94, 0.86, 0.94, 0.89, 0.95, and 1, respectively. Moreover, it has been observed that the GNB classifier achieved 93% accuracy on average with the dataset consisting of quaternion values. In contrast, it was only 88% when the model used the dataset from sensors’ data.

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Use of accelerometry to investigate physical activity in dogs receiving chemotherapy

Cited by 10 publications

References 39 publications

Combining Actigraph Link and PetPace Collar Data to Measure Activity, Proximity, and Physiological Responses in Freely Moving Dogs in a Natural Environment

Combining Actigraph Link and PetPace Collar Data to Measure Activity, Proximity, and Physiological Responses in Freely Moving Dogs in a Natural Environment

Physical activity measured with an accelerometer in dogs following extracapsular stabilisation to treat cranial cruciate ligament rupture

Enhanced Classification of Dog Activities with Quaternion-Based Fusion Approach on High-Dimensional Raw Data from Wearable Sensors

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