Advertisement
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1. McClintock MK, Dale W, Laumann EO, et al. Empirical redefinition of comprehensive health and well-being in the older adults of the United States. Proc Natl Acad Sci U S A 2016;113:E3071–E3080.
-
2. Menor-Campos DJ, Molleda-Carbonell JM, López-RodrÃguez R. Effects of exercise and human contact on animal welfare in a dog shelter. Vet Rec 2011;169:388.
-
3. Nuttall T, McEwan N. Objective measurement of pruritus in dogs: a preliminary study using activity monitors. Vet Dermatol 2006;17:348–351.
-
4. Culp WT, Mayhew PD, Brown DC. The effect of laparoscopic versus open ovariectomy on postsurgical activity in small dogs. Vet Surg 2009;38:811–817.
-
5. Mayhew PD, Brown DC. Prospective evaluation of two intracorporeally sutured prophylactic laparoscopic gastropexy techniques compared with laparoscopic-assisted gastropexy in dogs. Vet Surg 2009;38:738–746.
-
6. Jones S, Dowling-Guyer S, Patronek GJ, et al. Use of accelerometers to measure stress levels in shelter dogs. J Appl Anim Welf Sci 2014;17:18–28.
-
7. Lascelles BD, Hansen BD, Roe S, et al. Evaluation of client-specific outcome measures and activity monitoring to measure pain relief in cats with osteoarthritis. J Vet Intern Med 2007;21:410–416.
-
8. Brown DC, Boston RC, Farrar JT. Use of an activity monitor to detect response to treatment in dogs with osteoarthritis. J Am Vet Med Assoc 2010;237:66–70.
-
9. Wernham BG, Trumpatori B, Hash J, et al. Dose reduction of meloxicam in dogs with osteoarthritis-associated pain and impaired mobility. J Vet Intern Med 2011;25:1298–1305.
-
10. Guillot M, Moreau M, Heit M, et al. Characterization of osteoarthritis in cats and meloxicam efficacy using objective chronic pain evaluation tools. Vet J 2013;196:360–367.
-
11. Keefe FJ, Caldwell DS, Queen K, et al. Osteoarthritic knee pain: a behavioral analysis. Pain 1987;28:309–321.
-
12. Holden MA, Nicholls EE, Young J, et al. Exercise and physical activity in older adults with knee pain: a mixed methods study. Rheumatology (Oxford) 2015;54:413–423.
-
13. Lascelles BD. Feline degenerative joint disease. Vet Surg 2010;39:2–13.
-
14. Godfrey DR. Osteoarthritis in cats: a retrospective radiological study. J Small Anim Pract 2005;46:425–429.
-
15. Clarke SP, Mellor D, Clements DN, et al. Prevalence of radiographic signs of degenerative joint disease in a hospital population of cats. Vet Rec 2005;157:793–799.
-
16. Lascelles BD, Henderson AJ, Hackett IJ. Evaluation of the clinical efficacy of meloxicam in cats with painful locomotor disorders. J Small Anim Pract 2001;42:587–593.
-
17. Benito J, Hansen B, Depuy V, et al. Feline musculoskeletal pain index: responsiveness and testing of criterion validity. J Vet Intern Med 2013;27:474–482.
-
18. Gruen ME, Griffith EH, Thomson AE, et al. Criterion validation testing of clinical metrology instruments for measuring degenerative joint disease associated mobility impairment in cats. PLoS One 2015;10:e0131839.
-
19. Klinck MP, Gruen ME, del Castillo JRE, et al. Development and preliminary validity and reliability of the montreal instrument for cat arthritis testing, for use by caretaker/owner, MI-CAT(C), via a randomised clinical trial. Appl Anim Behav Sci 2018;200:96–105.
-
20. Bennett D, Morton C. A study of owner observed behavioural and lifestyle changes in cats with musculoskeletal disease before and after analgesic therapy. J Feline Med Surg 2009;11:997–1004.
-
21. Lascelles BD, Henry JB III, Brown J, et al. Cross-sectional study of the prevalence of radiographic degenerative joint disease in domesticated cats. Vet Surg 2010;39:535–544.
-
22. Guillot M, Moreau M, d'Anjou MA, et al. Evaluation of osteoarthritis in cats: novel information from a pilot study. Vet Surg 2012;41:328–335.
-
23. Gruen ME, Dorman DC, Lascelles BDX. Caregiver placebo effect in analgesic clinical trials for cats with naturally occurring degenerative joint disease-associated pain. Vet Rec 2017;180:473.
-
24. Inoue Y, Kimura T, Noro H, et al. Is laparoscopic colorectal surgery less invasive than classical open surgery? Quantitation of physical activity using an accelerometer to assess postoperative convalescence. Surg Endosc 2003;17:1269–1273.
-
25. Liszka-Hackzell JJ, Martin DP. An analysis of the relationship between activity and pain in chronic and acute low back pain. Anesth Analg 2004;99:477–481.
-
26. Brandes M, Schomaker R, Möllenhoff G, et al. Quantity versus quality of gait and quality of life in patients with osteoarthritis. Gait Posture 2008;28:74–79.
-
27. Semanik PA, Lee J, Song J, et al. Accelerometer-monitored sedentary behavior and observed physical function loss. Am J Public Health 2015;105:560–566.
-
28. Gruen ME, Alfaro-Córdoba M, Thomson AE, et al. The use of functional data analysis to evaluate activity in a spontaneous model of degenerative joint disease associated pain in cats. PLoS One 2017;12:e0169576.
-
29. Guedes AGP, Meadows JM, Pypendop BH, et al. Evaluation of tramadol for treatment of osteoarthritis in geriatric cats. J Am Vet Med Assoc 2018;252:565–571.
-
30. Guedes AGP, Meadows JM, Pypendop BH, et al. Assessment of the effects of gabapentin on activity levels and owner-perceived mobility impairment and quality of life in osteoarthritic geriatric cats. J Am Vet Med Assoc 2018;253:579–585.
-
31. Gruen ME, Thomson AE, Griffith EH, et al. A feline-specific anti-nerve growth factor antibody improves mobility in cats with degenerative joint disease-associated pain: a pilot proof of concept study. J Vet Intern Med 2016;30:1138–1148.
-
32. Clarke SP, Bennett D. Feline osteoarthritis: a prospective study of 28 cases. J Small Anim Pract 2006;47:439–445.
-
33. Merola I, Mills DS. Behavioural signs of pain in cats: an expert consensus. PLoS One 2016;11:e0150040.
-
34. Klinck MP, Frank D, Guillot M, et al. Owner-perceived signs and veterinary diagnosis in 50 cases of feline osteoarthritis. Can Vet J 2012;53:1181–1186.
-
35. de Boor C. A practical guide to splines. New York: Springer-Verlag New York Inc, 2001;69-77, 87–106.
-
36. Ramsay JO, Silverman BW. Functional data analysis. 2nd ed. New York: Springer Science and Business Media Inc, 2005;1-56, 85-109, 139–155.
-
37. Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer Science and Business Media LLC, 2002;331–338.
-
38. Johnson RA, Wichern DW. Applied multivariate statistical analysis. 6th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2007;576–621.
-
39. Efron B. The jackknife, the bootstrap, and other resampling plans. Montpelier, Vt: Society of Industrial Applied Mathematics, 1982;49–59.
-
40. Griffies JD, Zutty J, Sarzen M, et al. Wearable sensor shown to specifically quantify pruritic behaviors in dogs. BMC Vet Res 2018;14:124.
-
41. Pastell M, Tiusanen J, Hakojärvi M, et al. A wireless accelerometer system with wavelet analysis for assessing lameness in cattle. Biosyst Eng 2009;104:545–551.
-
42. Hokkanen A-H, Hänninen L, Tiusanen J, et al. Predicting sleep and lying time of calves with a support vector machine classifier using accelerometer data. Appl Anim Behav Sci 2011;134:10–15.
-
43. Cook DJ, Thompson JE, Prinsen SK, et al. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg 2013;96:1057–1061.
-
44. Aziz O, Lo B, King R, et al. Pervasive body sensor network: an approach to monitoring the post-operative surgical patient, in Proceedings. Int Workshop Wearable Implant Body Sens Netw 2006;4–18.
-
45. Wang Y, Nickel B, Rutishauser M, et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov Ecol 2015;3:2.
-
46. McClune DW, Marks NJ, Wilson RP, et al. Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): towards an automated interpretation of field data. Anim Biotelem 2014;2:5.
-
47. Fehlmann G, O'Riain MJ, Hopkins PW, et al. Identification of behaviours from accelerometer data in a wild social primate. Anim Biotelem 2017;5:6.
-
48. Graf PM, Wilson RP, Qasem L, et al. The use of acceleration to code for animal behaviours; a case study in free-ranging Eurasian beavers Castor fiber. PLoS One 2015;10:e0136751.
-
49. Yoda K, Naito Y, Sato K, et al. A new technique for monitoring the behaviour of free-ranging Adélie penguins. J Exp Biol 2001;204:685–690.
-
50. Straker L, Campbell A. Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts. BMC Med Res Methodol 2012;12:54.
-
51. Knazovicky D, Tomas A, Motsinger-Reif A, et al. Initial evaluation of nighttime restlessness in a naturally occurring canine model of osteoarthritis pain. PeerJ 2015;3:e772.
-
52. Piccione G, Marafioti S, Giannetto C, et al. Daily rhythm of total activity pattern in domestic cats (Felis silvestris catus) maintained in two different housing conditions. J Vet Behav 2013;8:189–194.
-
53. Dow C, Michel KE, Love M, et al. Evaluation of optimal sampling interval for activity monitoring in companion dogs. Am J Vet Res 2009;70:444–448.
Advertisement
Novel use of an activity monitor to model jumping behaviors in cats
Abstract
OBJECTIVE
To develop methods to identify and characterize activity monitor (AM) data signatures for jumps performed by cats.
ANIMALS
13 healthy, client-owned cats without evidence of osteoarthritis or degenerative joint disease.
PROCEDURES
Each cat was fitted with the same AM, individually placed in an observation room, then simultaneously recorded by 3 video cameras during the observation period (5 to 8 hours). Each cat was encouraged to jump up (JU), jump down (JD), and jump across (JA) during the observation period. Output from the AM was manually annotated for jumping events, each of which was characterized by functional data analysis yielding relevant coefficients. The coefficients were then used in linear discriminant analysis to differentiate recorded jumps as JUs, JDs, or JAs. To assess the model's ability to distinguish among the 3 jump types, a leave-one-out cross-validation method was used, and the misclassification error rate of the overall categorization of the model was calculated.
RESULTS
Of 731 jumping events, 29 were misclassified. Overall, the mean misclassification error rate per cat was 5.4% (range, 0% to 12.5%), conversely indicating a correct classification rate per cat of 94.6%.
CONCLUSIONS AND CLINICAL RELEVANCE
Results indicated that the model was successful in correctly identifying JUs, JDs, and JAs in healthy cats. With advancements in AM technology and data processing, there is potential for the model to be applied in clinical settings as a means to obtain objective outcome measures.
Contributor Notes
Powered by PubFactory