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Home / Equine Research Bank / Equine Vet J / Accelerometer activity tracking in horses and the effect of ...
Equine veterinary journal2019; 51(6); 840-845; doi: 10.1111/evj.13130

Accelerometer activity tracking in horses and the effect of pasture management on time budget.

Abstract: Accelerometry is an accepted means of quantifying human physical activity. Quantitative physical activity tracking could be beneficial for studies into equine health and disease prevention, for example in relation to obesity management. Objective: Validate accelerometer use in grazing horses, determine between-day repeatability, and assess the effects of pasture size on time budget (i.e. duration in each activity category). Methods: Proof of concept. Methods: Accelerometers (ActiGraph) were positioned at the poll. Horses underwent 5 min of observed activity in three categories: standing, grazing and ambulating. Receiver-operating characteristic curve analysis, used on ten second data epochs, calculated cut points between the activities. A 20-day study was then undertaken on 6 horses at pasture. Time in each category (per day) was deduced; a Mann Whitney U test was performed to compare standard vs. small paddock and day vs. night turn out. Results: Cut-off values with the optimum sensitivity (94.7-97.7%) and specificity (94.7-96.8%) were found to be 702.7 counts for ambulating. Repeatability was analysed descriptively: Median (IQR) of the between-day difference in minutes standing, grazing and ambulating were 46.9 (21.3-87.9), 77.3 (40.2-124.5) and 15.6 (6.8-40.2) respectively. Median times standing and ambulating were significantly different between standard and small paddocks: standing: 8.7 vs. 10.3 h (P<0.001); ambulating: 55.7 vs. 39.6 min (P = 0.002). There was no significant difference in the median time spent grazing. There were significant differences between day and night: standing: 32.95% vs. 50.97% (P = 0.001), grazing: 60.81% vs. 46.77% (P<0.001) and ambulating: 4.57% vs. 2.40% (P<0.001). Conclusions: Small sample size and lack of cross-validation of cut-off points on independent, 'unseen' data. Conclusions: Accelerometry can differentiate standing, grazing and ambulating in horses. Our proof-of-concept study demonstrates modifying pasture size influences activity budgets; opening avenues into studying obesity management.
© 2019 EVJ Ltd.
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Publication Date: 2019-06-17 PubMed ID: 31009100DOI: 10.1111/evj.13130Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study validates the use of accelerometers in tracking physical activity in horses and explores how the size of their pasture affects their physical activity distribution. The research also hints at potential applications for managing horse obesity through modulation of physical activity.

Methodology

  • The researchers employed accelerometers, specifically ActiGraph, and stationed these at the poll—a horse’s highest point at the back of their heads. This allowed for accurate physical activity measurement as horses underwent different activities.
  • Each horse was observed for 5 minutes in three distinct activity states: standing, grazing, and ambulating (moving).
  • Receiver-operating characteristic curve analysis was used on ten-second data sets to define the thresholds between each type of activity.
  • Over a period of 20 days, the study observed the daily activity of six horses in their pasture to determine how much time they spent in each activity category. Researchers utilized a Mann Whitney U test to compare variations between a standard and small paddock, as well as day and night turnout.

Results

  • The researchers determined cut-off values for the accelerometer readings for each type of activity standing: 702.7 counts. These thresholds had optimal sensitivity (94.7%-97.7%) and specificity (94.7%-96.8%).
  • The results showed median times for standing and ambulating were significantly different between standard and small paddocks. Horses in smaller paddocks were found to be standing for 8.7 hours, compared to those in standard paddocks standing around 10.3 hours. Meanwhile, they were ambulating around 55.7 minutes in standard paddocks, compared to 39.6 minutes in small paddocks.
  • No significant difference was detected in time horses spent grazing in both paddock types.
  • The distribution of activities showed significant differences between daytime and nighttime. Horses stood 32.95% of the time during the day versus 50.97% at night and spent 60.81% of daytime grazing compared to 46.77% at night. The time spent on ambulation was 4.57% during the day versus 2.40% at night.

Conclusions

  • The study highlighted that accelerometer usage can differentiate standing, grazing, and ambulating in horses, but cautioned that a small sample size and lack of validation on independent data confine the scope of the conclusions.
  • The research suggests that modifications in pasture size significantly influence the physical activity of horses, which could lend itself to new strategies to manage horse obesity.

Cite This Article

APA
Maisonpierre IN, Sutton MA, Harris P, Menzies-Gow N, Weller R, Pfau T. (2019). Accelerometer activity tracking in horses and the effect of pasture management on time budget. Equine Vet J, 51(6), 840-845. https://doi.org/10.1111/evj.13130

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 51
Issue: 6
Pages: 840-845

Researcher Affiliations

Maisonpierre, I N
  • Department of Clinical Science and Services, The Royal Veterinary College, Hatfield, UK.
Sutton, M A
  • Department of Clinical Science and Services, The Royal Veterinary College, Hatfield, UK.
Harris, P
  • Mars Horsecare UK Ltd, Equine Studies Group, Waltham Centre for Pet Nutrition, Bury St Edmunds, UK.
Menzies-Gow, N
  • Department of Clinical Science and Services, The Royal Veterinary College, Hatfield, UK.
Weller, R
  • Department of Clinical Science and Services, The Royal Veterinary College, Hatfield, UK.
Pfau, T
  • Department of Clinical Science and Services, The Royal Veterinary College, Hatfield, UK.

MeSH Terms

  • Accelerometry / veterinary
  • Animal Husbandry
  • Animals
  • Female
  • Horses / physiology
  • Male
  • Monitoring, Physiologic / instrumentation
  • Monitoring, Physiologic / veterinary
  • Motor Activity / physiology

Grant Funding

  • SPrj023 / Horserace Betting Levy Board

References

This article includes 35 references
  1. Freedson PS, Melanson E, Sirard J. Calibration of the computer science and applications. Inc. accelerometer.. Med. Sci. Sports Exerc. 1998 30, 777-781.
  2. Mathie MJ, Coster AC, Lovell NH, Celler BG. Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.. Physiol. Meas. 2004 25, R1-R20.
  3. De Vries SI, Van Hirtum HW, Bakker I, Hopman-Rock M, Hirasing RA, Van Mechelen W. Validity and reproducibility of motion sensors in youth: a systematic update.. Med. Sci. Sports Exerc. 2009 41, 818-827.
  4. Morrison R, Sutton DGM, Ramsoy C, Hunter-Blair N, Carnwath J, Horsfield E, Yam PS. Validity and practical utility of accelerometry for the measurement of in-hand physical activity in horses.. BMC Vet. Res. 2015 11, 233.
  5. Chen KY, Bassett DR Jr. The technology of accelerometry-based activity monitors: current and future.. Med. Sci. Sports Exerc. 2005 37, S490-S500.
  6. Yang CC, Hsu YL. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring.. Sensors (Basel) 2010 pp 7772-7788.
  7. Ekelund U, Aman J, Yngve A, Renman C, Westerterp K, Sjostrom M. Physical activity but not energy expenditure is reduced in obese adolescents: a case-control study.. Am. J. Clin. Nutr. 2002 76, 935-941.
  8. Page A, Cooper AR, Stamatakis E, Foster LJ, Crowne EC, Sabin M, Shield JP. Physical activity patterns in nonobese and obese children assessed using minute-by-minute accelerometry.. Int. J. Obe. (Lond.) 2005 29, 1070-1076.
  9. Ness AR, Leary SD, Mattocks C, Blair SN, Reilly JJ, Wells J, Ingle S, Tilling K, Smith GD, Riddoch C. Objectively measured physical activity and fat mass in a large cohort of children.. PLoS Med. 2007 4, e97.
  10. Balkau B, Mhamdi L, Oppert JM, Nolan J, Golay A, Porcellati F, Laakso M, Ferrannini E. Physical activity and insulin sensitivity. RISC Study. 57 2008 2613-2618.
  11. Henson J, Yates T, Biddle SJ, Edwardson CL, Khunti K, Wilmot EG, Gray LJ, Gorely T, Nimmo MA, Davies MJ. Associations of objectively measured sedentary behaviour and physical activity with markers of cardiometabolic health.. Diabetologia 2013 56, 1012-1020.
  12. Buman MP, Winkler EA, Kurka JM, Hekler EB, Baldwin CM, Owen N, Ainsworth BE, Healy GN, Gardiner PA. Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005-2006.. Am. J. Epidemiol. 2014 179, 323-334.
  13. Carson V, Wong SL, Winkler E, Healy GN, Colley RC, Tremblay MS. Patterns of sedentary time and cardiometabolic risk among Canadian adults.. Prev. Med. 2014 65, 23-27.
  14. Brocklebank LA, Falconer CL, Page AS, Perry R, Cooper AR. Accelerometer-measured sedentary time and cardiometabolic biomarkers: a systematic review.. Prev. Med. 2015 76, 92-102.
  15. Yam PS, Penpraze V, Young D, Todd MS, Cloney AD, Houston-Callaghan KA, Reilly JJ. Validity, practical utility and reliability of Actigraph accelerometry for the measurement of habitual physical activity in dogs.. J. Small Anim. Pract. 2011 52, 86-91.
  16. Michel KE, Brown DC. Determination and application of cut points for accelerometer-based activity counts of activities with differing intensity in pet dogs.. Am. J. Vet. Res. 2011 72, 866-870.
  17. Yamada M, Tokuriki M. Spontaneous activities measured continuously by an accelerometer in beagle dogs housed in a cage.. J. Vet. Med. Sci. 2000 62, 443-447.
  18. Morrison R, Reilly JJ, Penpraze V, Pendlebury E, Yam PS. A 6-month observational study of changes in objectively measured physical activity during weight loss in dogs.. J. Small Anim. Pract. 2014 55, 566-570.
  19. Morrison R, Penpraze V, Greening R, Underwood T, Reilly JJ, Yam PS. Correlates of objectively measured physical activity in dogs.. Vet. J. 2014 199, 263-267.
  20. Wrigglesworth DJ, Mort ES, Upton SL, Miller AT. Accuracy of the use of triaxial accelerometry for measuring daily activity as a predictor of daily maintenance energy requirement in healthy adult Labrador Retrievers.. Am. J. Vet. Res. 2011 72, 1151-1155.
  21. Burla JB, Ostertag A, Westerath HS, Hillmann E. Gait determination and activity measurement in horses using an accelerometer.. Comput. Electron. Agric. 2014 102, 127-133.
  22. Hampson B, Morton J, Mills P, Trotter M, Lamb D, Pollitt C. Monitoring distances travelled by horses using GPS tracking collars.. Aust. Vet. J. 2010 88, 176-181.
  23. Fries M, Montavon S, Spadavecchia C, Levionnois OL. Evaluation of a wireless activity monitoring system to quantify locomotor activity in horses in experimental settings.. Equine Vet. J. 2017 49, 225-231.
  24. Geor RJ, Coenen M, Harris P. Equine Applied and Clinical Nutrition. Elsevier Ltd, Edinburgh 2013.
  25. . Support Centre.. ActiGraph 2017.
  26. Boyd LE, Carbonaro DA, Houpt KA. The 24-hour time budget of Przewalski horses.. Appl. Anim. Behav. Sci. 1988 21, 5-17.
  27. Boyd L, Bandi N. Reintroduction of takhi, Equus ferus przewalskii, to Hustai National Park, Mongolia: time budget and synchrony of activity pre- and post-release.. Appl. Anim. Behav. Sci. 2002 78, 87-102.
  28. Jørgensen GHM, Bøe KE. A note on the effect of daily exercise and paddock size on the behaviour of domestic horses (Equus caballus).. Appl. Anim. Behav. Sci. 2007 107, 166-173.
  29. Walker SL, Smith RF, Routly JE, Jones DN, Morris MJ, Dobson H. Lameness, activity time-budgets, and estrus expression in dairy cattle.. J. Dairy Sci. 2008 91, 4552-4559.
  30. Davidson EJ. Controlled exercise in equine rehabilitation.. Vet. Clin. N. Am.: Equine Pract. 2016 32, 159-165.
  31. van Weeren PR, Back W. Musculoskeletal disease in aged horses and its management.. Vet. Clin. N. Am.: Equine Pract. 2016 32, 229-247.
  32. Geor RJ, Harris P. Dietary management of obesity and insulin resistance: countering risk for laminitis.. Vet. Clin. N. Am.: Equine Pract. 2009 25, 51-65.
  33. Hansen BD, Lascelles BD, Keene BW, Adams AK, Thomson AE. Evaluation of an accelerometer for at-home monitoring of spontaneous activity in dogs.. Am. J. Vet. Res. 2007 68, 468-475.
  34. Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth.. J. Appl. Physiol. 2008 105, 977-987.
  35. Carroll CL, Huntington PJ. Body condition scoring and weight estimation of horses.. Equine Vet. J. 1988 20, 41-45.

Citations

This article has been cited 18 times.
  1. Liehrmann O, Cosnard C, Riihonen V, Viitanen A, Alander E, Jardat P, Koski SE, Lummaa V, Lansade L. What drives horse success at following human-given cues? An investigation of handler familiarity and living conditions. Anim Cogn 2023 Jul;26(4):1283-1294.
    doi: 10.1007/s10071-023-01775-0pubmed: 37072511google scholar: lookup
  2. Stachurska A, Kędzierski W, Kaczmarek B, Wiśniewska A, Żylińska B, Janczarek I. Variation of Physiological and Behavioural Parameters during the Oestrous Cycle in Mares. Animals (Basel) 2023 Jan 6;13(2).
    doi: 10.3390/ani13020211pubmed: 36670751google scholar: lookup
  3. Kelemen Z, Grimm H, Long M, Auer U, Jenner F. Recumbency as an Equine Welfare Indicator in Geriatric Horses and Horses with Chronic Orthopaedic Disease. Animals (Basel) 2021 Nov 8;11(11).
    doi: 10.3390/ani11113189pubmed: 34827921google scholar: lookup
  4. Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W. Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data. Animals (Basel) 2021 Oct 7;11(10).
    doi: 10.3390/ani11102904pubmed: 34679925google scholar: lookup
  5. Rumpel AS, Alievi MM, Jardim Filho JO, Rozo CAC, Schuster LAH, da Silva AV, Ferreira MP. Can the training regimen influence night time physical activity in racehorses?. Vet Anim Sci 2021 Dec;14:100208.
    doi: 10.1016/j.vas.2021.100208pubmed: 34622089google scholar: lookup
  6. Mao A, Huang E, Gan H, Parkes RSV, Xu W, Liu K. Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data. Sensors (Basel) 2021 Aug 29;21(17).
    doi: 10.3390/s21175818pubmed: 34502709google scholar: lookup
  7. Steinke SL, Montgomery JB, Barden JM. Accelerometry-Based Step Count Validation for Horse Movement Analysis During Stall Confinement. Front Vet Sci 2021;8:681213.
    doi: 10.3389/fvets.2021.681213pubmed: 34239913google scholar: lookup
  8. Kelemen Z, Grimm H, Vogl C, Long M, Cavalleri JMV, Auer U, Jenner F. Equine Activity Time Budgets: The Effect of Housing and Management Conditions on Geriatric Horses and Horses with Chronic Orthopaedic Disease. Animals (Basel) 2021 Jun 23;11(7).
    doi: 10.3390/ani11071867pubmed: 34201584google scholar: lookup
  9. Auer U, Kelemen Z, Engl V, Jenner F. Activity Time Budgets-A Potential Tool to Monitor Equine Welfare?. Animals (Basel) 2021 Mar 17;11(3).
    doi: 10.3390/ani11030850pubmed: 33802908google scholar: lookup
  10. Giles SL, Harris P, Rands SA, Nicol CJ. Foraging efficiency, social status and body condition in group-living horses and ponies. PeerJ 2020;8:e10305.
    doi: 10.7717/peerj.10305pubmed: 33240636google scholar: lookup
  11. Taylor DEF, Lancaster BE, Ellis AD. Time Budgets in Domesticated Male Icelandic Horses on Pasture Turnout in Winter and Spring. Animals (Basel) 2025 Nov 4;15(21).
    doi: 10.3390/ani15213206pubmed: 41227536google scholar: lookup
  12. Taylor DEF, Lancaster BE, Ellis AD. The Use of Sound Recorders to Remotely Measure Grass Intake Behaviour in Horses. Animals (Basel) 2025 Aug 4;15(15).
    doi: 10.3390/ani15152273pubmed: 40805063google scholar: lookup
  13. Kelemen Z, Vogl C, Torres Borda L, Auer U, Jenner F. Indicators of mortality risk in ageing horses. Geroscience 2025 Oct;47(5):6533-6547.
    doi: 10.1007/s11357-025-01738-ypubmed: 40555923google scholar: lookup
  14. Barnabé MA, Elliott J, Harris PA, Menzies-Gow NJ. Effects of pasture consumption and obesity on insulin dysregulation and adiponectin concentrations in UK native-breed ponies. Equine Vet J 2026 Jan;58(1):243-255.
    doi: 10.1111/evj.14507pubmed: 40257424google scholar: lookup
  15. Robertson T, Thomas E, Starbuck G, Yarnell K. Global distribution and gap analysis of equine housing research: The findings so far and where to go next. Anim Welf 2024;33:e58.
    doi: 10.1017/awf.2024.64pubmed: 39703212google scholar: lookup
  16. Matsubara T, Fukatsu R, Yamamoto M, Moriya M, Hano K, Nakamura K, Ohba Y, Takasu M. Assessment of horse behavior using an activity monitoring device used for cats and dogs. J Equine Sci 2024 Dec;35(4):47-55.
    doi: 10.1294/jes.35.47pubmed: 39670208google scholar: lookup
  17. Kirton R, Sandford I, Raffan E, Hallsworth S, Burman OHP, Morgan R. The impact of restricted grazing systems on the behaviour and welfare of ponies. Equine Vet J 2025 May;57(3):737-744.
    doi: 10.1111/evj.14411pubmed: 39275860google scholar: lookup
  18. Chiavaccini L, Gupta A, Chiavaccini G. From facial expressions to algorithms: a narrative review of animal pain recognition technologies. Front Vet Sci 2024;11:1436795.
    doi: 10.3389/fvets.2024.1436795pubmed: 39086767google scholar: lookup
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