The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats

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Abstract

Introduction and objectives

Accelerometry is used as an objective measure of physical activity in humans and veterinary species. In cats, one important use of accelerometry is in the study of therapeutics designed to treat degenerative joint disease (DJD) associated pain, where it serves as the most widely applied objective outcome measure. These analyses have commonly used summary measures, calculating the mean activity per-minute over days and comparing between treatment periods. While this technique has been effective, information about the pattern of activity in cats is lost. In this study, functional data analysis was applied to activity data from client-owned cats with (n = 83) and without (n = 15) DJD. Functional data analysis retains information about the pattern of activity over the 24-hour day, providing insight into activity over time. We hypothesized that 1) cats without DJD would have higher activity counts and intensity of activity than cats with DJD; 2) that activity counts and intensity of activity in cats with DJD would be inversely correlated with total radiographic DJD burden and total orthopedic pain score; and 3) that activity counts and intensity would have a different pattern on weekends versus weekdays.

Results and conclusions

Results showed marked inter-cat variability in activity. Cats exhibited a bimodal pattern of activity with a sharp peak in the morning and broader peak in the evening. Results further showed that this pattern was different on weekends than weekdays, with the morning peak being shifted to the right (later). Cats with DJD showed different patterns of activity from cats without DJD, though activity and intensity were not always lower; instead both the peaks and troughs of activity were less extreme than those of the cats without DJD. Functional data analysis provides insight into the pattern of activity in cats, and an alternative method for analyzing accelerometry data that incorporates fluctuations in activity across the day.

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Most cited references 36

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Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables.

Louise C Mâsse, Bernard Fuemmeler, Cheryl B Anderson … (2005)

Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies.

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The technology of accelerometry-based activity monitors: current and future.

Kong Y Chen, David R Bassett (2005)

This paper reviews accelerometry-based activity monitors, including single-site first-generation devices, emerging technologies, and analytical approaches to predict energy expenditure, with suggestions for further research and development. The physics and measurement principles of the accelerometer are described, including the sensor properties, data collections, filtering, and integration analyses. The paper also compares these properties in several commonly used single-site accelerometers. The emerging accelerometry technologies introduced include the multisensor arrays and the combination of accelerometers with physiological sensors. The outputs of accelerometers are compared with criterion measures of energy expenditure (indirect calorimeters and double-labeled water) to develop mathematical models (linear, nonlinear, and variability approaches). The technologies of the sensor and data processing directly influence the results of the outcome measurement (activity counts and energy expenditure predictions). Multisite assessment and combining accelerometers with physiological measures may offer additional advantages. Nonlinear approaches to predict energy expenditure using accelerometer outputs from multiple sites and orientation can enhance accuracy. The development of portable accelerometers has made objective assessments of physical activity possible. Future technological improvements will include examining raw acceleration signals and developing advanced models for accurate energy expenditure predictions.

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Animal Models of Osteoarthritis: Comparisons and Key Considerations.

A McCoy (2015)

Osteoarthritis (OA) is unquestionably one of the most important chronic health issues in humans, affecting millions of individuals and costing billions of dollars annually. Despite widespread awareness of this disease and its devastating impact, the pathogenesis of early OA is not completely understood, hampering the development of effective tools for early diagnosis and disease-modifying therapeutics. Most human tissue available for study is obtained at the time of joint replacement, when OA lesions are end stage and little can be concluded about the factors that played a role in disease development. To overcome this limitation, over the past 50 years, numerous induced and spontaneous animal models have been utilized to study disease onset and progression, as well as to test novel therapeutic interventions. Reflecting the heterogeneity of OA itself, no single "gold standard" animal model for OA exists; thus, a challenge for researchers lies in selecting the most appropriate model to answer a particular scientific question of interest. This review provides general considerations for model selection, as well as important features of species such as mouse, rat, guinea pig, sheep, goat, and horse, which researchers should be mindful of when choosing the "best" animal model for their intended purpose. Special consideration is given to key variations in pathology among species as well as recommended guidelines for reporting the histologic features of each model.

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Author and article information

Contributors

Jaroslaw Harezlak: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 18 January 2017

Publication date Collection: 2017

Volume: 12

Issue: 1

Electronic Location Identifier: e0169576

Affiliations

[1 ]Comparative Pain Research Program, Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America

[2 ]Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina, United States of America

[3 ]Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America

[4 ]Center for Pain Research and Innovation, University of North Carolina School of Dentistry, Chapel Hill, North Carolina, United States of America

Indiana University, UNITED STATES

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceptualization: MG MC AMS BDXL.
Data curation: MG MC AMS AT AW BDXL.
Formal analysis: MC AMS.
Funding acquisition: MG AMS BDXL.
Investigation: MG AT AW BDXL.
Methodology: MG MC AMS BDXL.
Project administration: MG AT AW BDXL.
Resources: MG AT MC AMS BDXL.
Software: MC AMS.
Supervision: AMS BDXL.
Validation: AMS BDXL.
Visualization: MG MC AMS BDXL.
Writing – original draft: MG MC AMS BDXL.
Writing – review & editing: MG MC AT AW AMS BDXL.

* E-mail: astaicu@ 123456ncsu.edu

Article

Publisher ID: PONE-D-16-23113

DOI: 10.1371/journal.pone.0169576

PMC ID: 5242440

PubMed ID: 28099449

SO-VID: 34aa4b00-8ecc-4cc9-b4eb-52fe06e624c8

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 8 June 2016

Date accepted : 19 December 2016

Page count

Figures: 9, Tables: 3, Pages: 23

Funding

Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: T32OD011130

Award Recipient : Margaret E. Gruen

Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;

Award ID: DMS 0454942

Award Recipient : Ana-Maria Staicu

Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R01 NS085211

Award Recipient : Ana-Maria Staicu

MEG received funding from the Ruth L. Kirchstein T32 National Research Service Award (OD011130). AMS' research was supported partially by National Science Foundation DMS 1454942 and National Institutes of Health grant R01 NS085211. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Data Availability Data and code has been submitted to Dryad.com. The DOI is: doi: 10.5061/dryad.v1c16.

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The Use of Functional Data Analysis to Evaluate Activity in a Spontaneous Model of Degenerative Joint Disease Associated Pain in Cats

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Abstract

Introduction and objectives

Results and conclusions

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PLOS Climate

Most cited references 36

Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables.

The technology of accelerometry-based activity monitors: current and future.

Animal Models of Osteoarthritis: Comparisons and Key Considerations.

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