Blog
About

11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 41

          • Record: found
          • Abstract: found
          • Article: not found

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

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

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

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              ActiGraph and Actical physical activity monitors: a peek under the hood.

              Since the 1980s, accelerometer-based activity monitors have been used by researchers to quantify physical activity. The technology of these monitors has continuously evolved. For example, changes have been made to monitor hardware (type of sensor (e.g., piezoelectric, piezoresistive, capacitive)) and output format (counts vs raw signal). Commonly used activity monitors belong to the ActiGraph and the Actical families. This article presents information on several electromechanical aspects of these commonly used activity monitors. The majority of the article focuses on the evolution of the ActiGraph activity monitor by describing the differences among the 7164, the GT1M, and the GT3X models. This is followed by brief descriptions of the influences of device firmware and monitor calibration status. We also describe the Actical, but the discussion is short because this device has not undergone any major changes since it was first introduced. This article may help researchers gain a better understanding of the functioning of activity monitors. For example, a common misconception among physical activity researchers is that the ActiGraph GT1M and GT3X are piezoelectric sensor-based monitors. Thus, this information may also help researchers to describe these monitors more accurately in scientific publications.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 January 2017
                2017
                : 12
                : 1
                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.

                Article
                PONE-D-16-23113
                10.1371/journal.pone.0169576
                5242440
                28099449
                © 2017 Gruen et al

                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.

                Page count
                Figures: 9, Tables: 3, Pages: 23
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32OD011130
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DMS 0454942
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 NS085211
                Award Recipient :
                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.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Cats
                Engineering and Technology
                Electronics
                Accelerometers
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Medicine and Health Sciences
                Pharmacology
                Drugs
                Analgesics
                Medicine and Health Sciences
                Pain Management
                Analgesics
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Biology and Life Sciences
                Veterinary Science
                Veterinary Diseases
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Joints (Anatomy)
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Joints (Anatomy)
                Biology and Life Sciences
                Veterinary Science
                Veterinary Medicine
                Custom metadata
                Data and code has been submitted to Dryad.com. The DOI is: doi: 10.5061/dryad.v1c16.

                Uncategorized

                Comments

                Comment on this article