Letter to the Editor: On the origin of lameness – do opinions differ less than it might appear at first glance?

Weeren, P.R. van

doi:10.1111/evj.13124

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…There has been considerable debate in recent years about what constitutes lameness (van Weeren et al 2018;Bathe et al 2018;Adair et al 2018Adair et al , 2019Dyson 2019a;van Weeren 2019). It is clear that there are some horses which exhibit measurable asymmetry in gait which may not influence performance (Greve and Dyson 2016).…”

Section: Introductionmentioning

confidence: 99%

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Application of a Ridden Horse Pain Ethogram to horses competing at 5‐star three‐day‐events: Comparison with performance

Dyson¹,

Ellis²

2020

Equine Veterinary Education

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Summary A Ridden Horse Pain Ethogram (RHpE) was previously developed to facilitate the detection of musculoskeletal pain. The objectives were to apply the RHpE during warm‐up for the dressage phase of two 5* three‐day events and to correlate the RHpE scores with subsequent performance. It was hypothesised that there would be a higher rate of failure to complete the cross‐country phase in horses which exhibited ≥7 behaviours compared with those showing <7 behaviours. The RHpE, comprising 24 behaviours, was applied for 10–12 min during warm‐up. Gait abnormalities in trot and canter were recorded. Dressage penalties, cross‐country performance, showjumping penalties and final placings were documented. Horses were categorised as those which completed cross‐country, or those which did not complete because of elimination or retirement. RHpE scores (n = 137) ranged from 0 to 9/24 (median 3 [range 0–9] for nonlame horses; median 5 [range 1–9] for horses with gait abnormalities in trot or canter). There was a moderate correlation between dressage penalty scores and the RHpE score (rho = 0.4, P<0.001, Spearman rank). Fifty‐nine per cent of horses (n = 10/17) with a RHpE score ≥7 failed to complete cross‐country, compared with 33% (n = 39/117) with a score <7. Horses that failed to complete the cross‐country phase had higher RHpE scores compared with those that completed (P = 0.04, W = 8.3, Kruskal–Wallis; pairwise comparison Bonferroni, P = 0.06). There was a significant (rho = 0.3) relationship between total RHpE score and final horse placings (n = 80, P<0.01, Spearman rank). Horses with lameness or gait abnormalities in canter had significantly higher RHpE scores (P<0.01, χ2 = 35, chi‐square test) compared with other horses. There was a strong correlation between the RHpE scores for horses which competed at both events (P<0.001, rho = 0.6, Spearman rank). The RHpE should facilitate earlier identification of horses which may benefit from diagnosis and treatment, resulting in improvement in both performance and equine welfare.

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Section: Introductionmentioning

confidence: 99%

“…2018; Adair et al . 2018, 2019; Dyson 2019a; van Weeren 2019). It is clear that there are some horses which exhibit measurable asymmetry in gait which may not influence performance (Greve and Dyson 2016).…”

Section: Introductionmentioning

confidence: 99%

Application of a Ridden Horse Pain Ethogram to horses competing at 5‐star three‐day‐events: Comparison with performance

Dyson¹,

Ellis²

2020

Equine Veterinary Education

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“…well above the postulated minimum Nyquist rate) to sample rates between 50 and 8 Hz, when calculating vertical displacement from a validated inertial measurement unit (MTw, Xsens, The Netherlands). This example is of specific relevance for quantifying movement symmetry during the equine lameness exam, a topic of recent discussions 12–15 . As a consequence, errors resulting from a reduction in sample rate will be assessed in relation to published repeatability values and movement asymmetry thresholds suggested for the lameness exam.…”

Section: Introductionmentioning

confidence: 99%

How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data

Pfau

Reilly

2020

Equine Veterinary Journal

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Background Low‐cost sensor devices are often limited in terms of sample rate. Based on signal periodicity, the Nyquist theorem allows determining the minimum theoretical sample rate required to adequately capture cyclical events, such as pelvic movement in trotting horses. Objectives To quantify the magnitude of errors arising with reduced sample rates when capturing biological signals using the example of pelvic time‐displacement series and derived minima and maxima used to quantify movement asymmetry in lame horses. Study design Data comparison. Methods Root mean square (RMS) errors between the ‘reference’ time‐displacement series, captured with a validated inertial sensor at 100 Hz sample rate, and down‐sampled time‐series (8 Hz to 50 Hz) are calculated. Accuracy and precision are determined for maxima and minima derived from the time‐displacement series. Results Average RMS errors are <2 mm at 50 Hz sample rate, <4 mm at 40 Hz, <7 mm between 25 and 35 Hz, and increase to up to 20 mm at 20 Hz and below. Accuracy for maxima and minima is generally below 1mm. Precision is 1 mm at 50 Hz sample rate, 3 mm at 40Hz and ≥9 mm at 20 Hz and below. Main limitations Only sample rate, no other sensor parameters were investigated. Conclusions Sample rate related errors for inertial sensor derived time‐displacement series of pelvic movement are <2mm at 50 Hz, a rate that many low‐cost loggers, smartphones or wireless sensors can sustain hence rendering these devices valid options for quantifying parameters relevant for lameness examinations in horses.

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Letter to the Editor: On the origin of lameness – do opinions differ less than it might appear at first glance?

Cited by 2 publications

References 5 publications

Application of a Ridden Horse Pain Ethogram to horses competing at 5‐star three‐day‐events: Comparison with performance

Application of a Ridden Horse Pain Ethogram to horses competing at 5‐star three‐day‐events: Comparison with performance

How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data

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