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Home / Equine Research Bank / Front Pain Res (Lausanne) / Time budgets and weight shifting as indicators of pain in ho...
Frontiers in pain research (Lausanne, Switzerland)2024; 5; 1410302; doi: 10.3389/fpain.2024.1410302

Time budgets and weight shifting as indicators of pain in hospitalized horses.

Abstract: Pain assessment in horses presents a significant challenge due to their nonverbal nature and their tendency to conceal signs of discomfort in the presence of potential threats, including humans. Therefore, this study aimed to identify pain-associated behaviors amenable to automated AI-based detection in video recordings. Additionally, it sought to determine correlations between pain intensity and behavioral and postural parameters by analyzing factors such as time budgets, weight shifting, and unstable resting. The ultimate goal is to facilitate the development of AI-based quantitative tools for pain assessment in horses. Unassigned: A cohort of 20 horses (mean age 15 ± 8) admitted to a university equine hospital underwent 24-h video recording. Behaviors were manually scored and retrospectively analyzed using Loopy® software. Three pain groups were established based on the Pain Score Vetmeduni Vienna : pain-free (P0), mild to moderate pain (P1), and severe pain (P2). Unassigned: Weight shifting emerged as a reliable indicator for discriminating between painful and pain-free horses, with significant differences observed between pain groups (p < 0.001) and before and after administration of analgesia. Additionally, severely painful horses (P2 group) exhibited lower frequencies of feeding and resting standing per hour compared to pain-free horses, while displaying a higher frequency of unstable resting per hour. Unassigned: The significant differences observed in these parameters between pain groups offer promising prospects for AI-based analysis and automated pain assessment in equine medicine. Further investigation is imperative to establish precise thresholds. Leveraging such technology has the potential to enable more effective pain detection and management in horses, ultimately enhancing welfare and informing clinical decision-making in equine medicine.
© 2024 Nowak, Martin-Cirera, Jenner and Auer.
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Publication Date: 2024-07-23 PubMed ID: 39109240PubMed Central: PMC11300370DOI: 10.3389/fpain.2024.1410302Google Scholar: Lookup
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  • Journal Article

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 research investigated behaviors associated with pain in horses, focusing on changes in weight shifting, time budgets, and unstable resting, to support the development of AI-based tools for automated pain detection. The study involved 20 horses under observation in a hospital setting, with findings indicating weight shifting as a reliable pain indicator and potential differences in behaviors across varying levels of pain.

Research Background and Purpose

  • The research is grounded in the need to accurately assess pain in horses, a task which presents inherent challenges due to their inability to verbally express discomfort and their instinct to conceal signs of pain from perceived threats, such as humans.
  • Thus, the primary aim of this study was to identify behaviors associated with pain in horses that could be detected through automated artificial intelligence (AI) systems using video recordings.
  • The ultimate objective is to further the creation of technology-based quantitative tools which can be used for more effective and reliable pain assessment in horses. Such tools can greatly enhance horse welfare, inform clinical decisions in equine medical practice, and ensure accurate pain management strategies.

Methods

  • The study worked with a cohort of 20 horses of varying ages that had been admitted to a university equine hospital. These subjects underwent 24-hour video recording for behavioral analysis.
  • The behaviors were scored manually and retrospectively analyzed using a software called Loopy®.
  • The horses were categorized into three pain groups (P0: pain-free, P1: mild to moderate pain, P2: severe pain) based on the Pain Score Vetmeduni Vienna, which is likely a standardized scoring system for pain intensity in horses.

Findings

  • Weight shifting was identified as a consistent and reliable indicator of pain in horses.
  • Significant differences were noted in the behavior of horses pertaining to weight shifting between the varying pain groups. The differences were also significant before and after the administration of pain relief medication (analgesia).
  • Severely painful horses (P2 group) showed a decrease in frequency of feeding and resting whilst standing per hour compared to pain-free horses. They also showed increased instances of unstable resting per hour.

Conclusions and Implications

  • The disparities in behavioral and postural parameters between pain groups indicate a strong potential for AI-based analysis and automated pain assessment in equine medicine.
  • However, more research is crucial to establish precise thresholds for quantifying these behaviors and accurately categorizing horses into the correct pain groups.
  • Despite the need for further investigation, the study’s findings are promising for the advancement of AI technologies in pain detection and management for horses. Leveraging such technology could greatly enhance the welfare of horses and facilitate better clinical decision-making in equine medicine.

Cite This Article

APA
Nowak M, Martin-Cirera A, Jenner F, Auer U. (2024). Time budgets and weight shifting as indicators of pain in hospitalized horses. Front Pain Res (Lausanne), 5, 1410302. https://doi.org/10.3389/fpain.2024.1410302

Publication

Frontiers in pain research (Lausanne, Switzerland)
ISSN: 2673-561X
NlmUniqueID: 9918227269806676
Country: Switzerland
Language: English
Volume: 5
Pages: 1410302

Researcher Affiliations

Nowak, Magdalena
  • Anesthesiology and Perioperative Intensive - Care Medicine Unit, Department of Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria.
Martin-Cirera, Albert
  • Precision Livestock Farming Hub and Institute of Animal Husbandry and Animal Welfare, University of Veterinary Medicine Vienna, Vienna, Austria.
Jenner, Florien
  • Equine Surgery Unit, Department of Companion Animals and Horses, University Equine Hospital, University of Veterinary Medicine Vienna, Vienna, Austria.
Auer, Ulrike
  • Anesthesiology and Perioperative Intensive - Care Medicine Unit, Department of Companion Animals and Horses, University of Veterinary Medicine Vienna, Vienna, Austria.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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