Abstract: Since Muybridge's 'horse in motion', researchers in the equine movement analysis field continue to improve objective analysis and performance monitoring while ensuring representative data capture. However, subjective evaluation remains the primary method of equine gait analysis in the applied setting, despite evidence highlighting the unreliability of this approach. Objective: To map research trends, limitations and opportunities across the diverse equine gait analysis literature. Methods: Joanna Briggs Institute and Cochrane systematic scoping review. Methods: Search terms were chosen based on the 'PICO' framework and included keywords such as: Equine, Gait, Kinematics and Analysis. Studies were excluded based on predetermined criteria by two independent researchers. Data were extracted from 510 articles from 1978 to 2018. Results: Insights derived from movement analysis appear to be driven by tool availability. Observational research (42.9%) was the most popular study design. Use of wearable technology as a primary research tool is established within the field, accounting for 13.5% of studies. Analysis of limitations identified 17.8% of studies citing challenges to the transferability of research results. Restricted sample size appears to be an underlying contributor to many of the limitations identified. In terms of research opportunities, advances in intervention studies were called for (10.4% of studies) in the following three areas; clinical, rehabilitative exercise and performance/training. Conclusions: This review was confined to research in the English language. Conclusions: Standardised research reporting may alleviate sample size issues by facilitating data pooling, database creation and meta-analyses. Large holistic data collections and application frameworks based on wearable technologies are not reflected in the current equine gait analysis literature and thus represent an interesting opportunity for this field. Progress and lessons learned from the human field of movement analysis can be useful in supporting this potential development.
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The article is a comprehensive exploration of research trends in equine movement analysis. It emphasizes the need for more objective approaches to this type of analysis, pointing out potential opportunities and barriers to progress within the field in the digital age.
Research Methods
The authors used the Joanna Briggs Institute and Cochrane systematic scoping review to map research trends in equine gait analysis literature.
They selected search terms based on the ‘PICO’ framework and included keywords such as Equine, Gait, Kinematics, and Analysis.
The researchers excluded studies based on predetermined criteria, and the remaining data was extracted from 510 articles spanning from 1978 to 2018.
Findings
The insights derived from the analysis seem to be largely driven by the availability of tools.
They found that observational research was the most popular study design, followed by the use of wearable technology as the primary research tool, which featured in 13.5% of the studies.
The analysis of the limitations identified that 17.8% of studies citing challenges to the transferability of research results. The most common underlying contributor to these limitations was restricted sample size.
The review also identified future research opportunities, particularly in clinical and rehabilitative exercise studies, and performance/training studies.
Conclusion
Despite the research being limited to English language studies, the authors conclude that standardizing research reporting could alleviate sample size issues by enabling the pooling of data, creation of databases, and conducting meta-analyses.
Moreover, they point out that the current equine gait analysis literature does not reflect large holistic data collections and application frameworks based on wearable technologies, which represent interesting potential areas of development for the field.
The authors suggest that progress and lessons learned from the human field of movement analysis could be useful in supporting these potential developments in equine gait analysis.
Cite This Article
APA
Egan S, Brama P, McGrath D.
(2019).
Research trends in equine movement analysis, future opportunities and potential barriers in the digital age: A scoping review from 1978 to 2018.
Equine Vet J, 51(6), 813-824.
https://doi.org/10.1111/evj.13076
Schmidt M, Rheinländer CC, Wille S, Wehn N, Jaitner T. IMU-Based Determination of Fatigue During Long Sprint. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct 2016, pp 899-903.
Cummins C, Orr R, O'Connor H, West C. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med. 43, 1025-1042.
Moe-Nilssen R, Helbostad JL. Interstride trunk acceleration variability but not step width variability can differentiate between fit and frail older adults. Gait Posture 21, 164-170.
Bierbaum S, Peper A, Karamanidis K, Arampatzis A. Adaptational responses in dynamic stability during disturbed walking in the elderly. J. Biomech. 43, 2362-2368.
Lamoth CJ, van Deudekom FJ, van Campen JP, Appels BA, de Vries OJ, Pijnappels M. Gait stability and variability measures show effects of impaired cognition and dual tasking in frail people. J. Neuroeng. Rehabil. 8, 2.
Cunniffe B, Proctor W, Baker JS, Davies B. An evaluation of the physiological demands of elite rugby union using Global Positioning System tracking software. J. Strength Cond. Res. 23, 1195-1203.
Cahill N, Lamb K, Worsfold P, Headey R, Murray S. The movement characteristics of English Premiership rugby union players. J. Sports Sci. 31, 229-237.
Hamill J, Knutzen KM. Biomechanical Basis of Human Movement. .
Armstrong R, Hall BJ, Doyle J, Waters E. Cochrane update. ‘Scoping the scope’ of a cochrane review. J. Public Health (Oxf) 33, 147-150.
Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement. Sci. 5, 69.
Barnett-Page E, Thomas J. Methods for the synthesis of qualitative research: a critical review. BMC Med. Res. Methodol. 9, 59.
Moher D, Liberati A. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151, 264-269.
Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int. J. Evid. Based Healthc. 13, 141-146.
Peters MDJ, Godfrey C, McInerney P, Baldini Soares C, Khalil H, Parker D. Joanna Briggs Institute Reviewer's Manual. .
Sampson M, McGowan J, Cogo E, Grimshaw J, Moher D, Lefebvre C. An evidence-based practice guideline for the peer review of electronic search strategies. J. Clin. Epidemiol. 62, 944-952.
Harris JD, Quatman CE, Manring MM, Siston RA, Flanigan DC. How to write a systematic review. Am. J. Sport. Med. 42, 2761-2768.
Back W, Schamhardt HC. A comparison between the trot of pony and horse foals to characterise equine locomotion at young age. Equine Vet. J. 31, 240-244.
Keegan KG, Dent EV, Wilson DA, Janicek J, Kramer J, Lacarrubba A, Walsh DM, Cassells MW, Esther TM, Schiltz P, Frees KE, Wilhite CL, Clark JM, Pollitt CC, Shaw R, Norris T. Repeatability of subjective evaluation of lameness in horses. Equine Vet. J. 42, 92-97.
Lord S, Galna B, Rochester L. Moving forward on gait measurement: toward a more refined approach. Mov. Disord. 28, 1534-1543.
Matyas JR, Gutmann A, Randev J, Hurtig M, Bertram JEA. Intra-articular anaesthesia mitigates established pain in experimental osteoarthritis: a preliminary study of gait impulse redistribution as a biomarker of analgesia pharmacodynamics. Osteoarthritis Cartilage 21, 1365-1373.
Pfau T, Fiske-Jackson A, Rhodin M. Quantitative assessment of gait parameters in horses: useful for aiding clinical decision making?. Equine Vet. Educ. 28, 209-215.
Kacker R, Jones A. On use of Bayesian statistics to make the guide to the expression of uncertainty in measurement consistent. Metrologia 40, 235.
Anderson JG. Social, ethical and legal barriers to e-health. Int. J. Med. Inform. 76, 480-483.
Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff. 23, 116-126.
Christodoulakis C, Asgarian A, Easterbrook S. Barriers to Adoption of Information Technology in Healthcare. Proc. 27th Ann. Inter. Conf. on Computer Science and Software Engineering 66-75.
Hersh W. Health care information technology: progress and barriers. JAMA 292, 2273-2274.
Heathfield H, Pitty D, Hanka R. Evaluating information technology in health care: barriers and challenges. BMJ 316, 1959-1961.
Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM. Advances in the application of information technology to sport performance. J. Sports Sci. 20, 755-769.
Dyer B. The controversy of sports technology: a systematic review. Springerplus 4, 524.
Okuda K, Abe N, Katayama Y, Senda M, Kuroda T, Inoue H. Effect of vision on postural sway in anterior cruciate ligament injured knees. J. Orthop. Sci. 10, 277-283.
Kerr G, Morrison S, Silburn P. Coupling between limb tremor and postural sway in Parkinson's disease. Mov. Disord. 23, 386-394.
Bialski D, Lanovaz JL. Effect of detomidine on postural sway in horses. Equine Comp. Exerc. Physiol. 1, 45-50.
Pentland A, Reid TG, Heidbeck T. Big data and Health. Revolutionizing Medicine and Public Health, Report of the Big Data and Health Working Group. .
Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J. Biomed. Health Inform. 19, 1193-1208.
Swan M. Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw. 1, 217-253.
Klonoff DC. Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care 28, 1231-1239.
Stergiou N, Harbourne R, Cavanaugh J. Optimal movement variability: a new theoretical perspective for neurologic physical therapy. J. Neurol. Phys. Ther. 30, 120-129.
Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J. Royal Soc. Med. 104, 510-520.
Citations
This article has been cited 14 times.
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