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How Effective are Activity Sensors for Detecting Disease?
Aiden Juge | PhD Candidate
Courtney Daigle, PhD | Associate Professor, Animal Welfare
Texas A&M Department of Animal Science
There are many gadgets marketed to aid in the tracking of animal productivity and health. It can be challenging to know which ones will be worth the time and money. However, research shows that movement and rumination activity sensors can identify sick animals, which can promote early detection of disease and reduce the need to use antibiotics.
Visual clinical illness scoring correctly identifies 27 to 62% of cattle with bovine respiratory disease (BRD)1,2. Therefore, many sick cattle are not noticed until they are severely ill, decreasing productivity and increasing losses. On the other hand, the need for responsible use of antibiotics means that it is important to avoid treating cattle that aren’t sick. For this purpose, multiple researchers have tested activity sensors as a way to identify sick cattle early and accurately.
Accelerometers are activity sensors that can be attached to an animal’s leg, or on an ear tag or collar. These work similarly to the pedometers that people use for fitness tracking. The accelerometers can measure how many steps cattle take, how much time they spend standing up and lying down, and how many times they stand up and lie down. Research using leg-mounted accelerometers found that steers in a feedlot walked less, stood less, and switched between standing and lying less six days before they were diagnosed with BRD.3
Rumination collars are activity sensors that are frequently used in the dairy industry. Rumination collars contain sensors that measure both how much time cattle spend ruminating and their overall activity level. In a study of growing beef bulls, daily activity and rumination time decreased 3 to 6 days before BRD diagnosis.4 However, the changes identified by these collars did not only indicate BRD. A 9% decrease in rumination time was present in 81% of cattle with either lameness or BRD, and only 5% of healthy cattle. Lower rumination time was also linked with decreased average daily gain, making rumination collars a good option for monitoring both animal health and productivity.
Multiple sensors can identify sick cattle more accurately than one type of sensor alone. One study evaluated bull health with both leg-mounted accelerometers and feed bunk sensors that measured length and duration of bunk visits. The researchers combined information about lying behavior and feed bunk visits to correctly identify 92% of sick cattle nine days before BRD diagnosis. While this early detection method yielded a high rate of false positives, the authors of the study calculated that treatment of all animals identified as “sick” would decrease BRD treatment costs compared to treatment of all high-risk cattle.5
Current research in the Department of Animal Science used clinical illness scoring, temperature measurement, and accelerometers to measure signs of illness in steers undergoing a brief immune response after vaccination. 90% of post-vaccination (“sick”) cattle and 92% of pre-vaccination (“healthy”) were identified correctly using measures like step count, lying time, body temperature, rumen fill score, respiratory rate, head position, and excess saliva.6
Although locomotor sensors, rumination collars, and “smart” feed bunks all have value in enhancing early disease detection, the benefits must be weighed against the costs. Sensors are expensive, and setting up the antennae and software needed to download and interpret data can be challenging. On the other hand, the setup process only needs to be done once. Activity sensors designed for use with cattle are durable, and can be re-used for multiple animals. A quality activity tracking system is an investment that can yield increased cattle health and decreased sickness-related losses.
For more information, contact Aiden Juge at aidenjuge@tamu.edu
References
1. Timsit, E., N. Dendukuri, I. Schiller, and S. Buczinski. “Diagnostic Accuracy of Clinical Illness for Bovine Respiratory Disease (BRD) Diagnosis in Beef Cattle Placed in Feedlots: A Systematic Literature Review and Hierarchical Bayesian Latent-Class Meta-Analysis.” Preventive Veterinary Medicine 135 (December 2016): 67–73. https://doi.org/10.1016/j.prevetmed.2016.11.006.
2. White, Brad J., and David G. Renter. “Bayesian Estimation of the Performance of Using Clinical Observations and Harvest Lung Lesions for Diagnosing Bovine Respiratory Disease in Post-Weaned Beef Calves.” Journal of Veterinary Diagnostic Investigation 21, no. 4 (July 2009): 446–53. https://doi.org/10.1177/104063870902100405.
3. Pillen, Joelle L., Pablo J. Pinedo, Samuel E. Ives, Tanya L. Covey, Hemant K. Naikare, and John T. Richeson. “Alteration of Activity Variables Relative to Clinical Diagnosis of Bovine Respiratory Disease in Newly Received Feed Lot Cattle.” The Bovine Practitioner Vol. 50 (January 1, 2016): 1-8 Pages. https://doi.org/10.21423/BOVINE-VOL50NO1P1-8.
4. Marchesini, Giorgio, Davide Mottaran, Barbara Contiero, Eliana Schiavon, Severino Segato, Elisabetta Garbin, Sandro Tenti, and Igino Andrighetto. “Use of Rumination and Activity Data as Health Status and Performance Indicators in Beef Cattle during the Early Fattening Period.” The Veterinary Journal 231 (January 2018): 41–47. https://doi.org/10.1016/j.tvjl.2017.11.013.
5. Belaid, Mohammed Anouar, Maria Rodriguez-Prado, Eric Chevaux, and Sergio Calsamiglia. “The Use of an Activity Monitoring System for the Early Detection of Health Disorders in Young Bulls.” Animals 9, no. 11 (November 5, 2019): 924. https://doi.org/10.3390/ani9110924.
6. Juge, Aiden E., Reinaldo F. Cooke, Guadalupe Ceja, Morgan Matt, and Courtney L. Daigle. “Comparison of Physiological Markers, Behavior Monitoring, and Clinical Illness Scoring as Indicators of an Inflammatory Response in Beef Cattle.” Edited by Angel Abuelo. PLOS ONE 19, no. 4 (April 25, 2024): e0302172. https://doi.org/10.1371/journal.pone.0302172.