Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions
Abstract Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health pri...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-87304-w |
| _version_ | 1819129572240654336 |
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| author | Zekun Xu Eric Laber Ana-Maria Staicu B. Duncan X. Lascelles |
| author_facet | Zekun Xu Eric Laber Ana-Maria Staicu B. Duncan X. Lascelles |
| author_sort | Zekun Xu |
| collection | DOAJ |
| description | Abstract Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model. |
| first_indexed | 2024-12-22T08:45:51Z |
| format | Article |
| id | doaj.art-c7da6acfde8c465aaafd5251c9eae10a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-22T08:45:51Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c7da6acfde8c465aaafd5251c9eae10a2022-12-21T18:32:07ZengNature PortfolioScientific Reports2045-23222021-04-011111910.1038/s41598-021-87304-wNovel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditionsZekun Xu0Eric Laber1Ana-Maria Staicu2B. Duncan X. Lascelles3Department of Statistics, North Carolina State UniversityDepartment of Statistics, North Carolina State UniversityDepartment of Statistics, North Carolina State UniversityComparative Pain Research and Education Center, College of Veterinary Medicine, North Carolina State UniversityAbstract Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.https://doi.org/10.1038/s41598-021-87304-w |
| spellingShingle | Zekun Xu Eric Laber Ana-Maria Staicu B. Duncan X. Lascelles Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions Scientific Reports |
| title | Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| title_full | Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| title_fullStr | Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| title_full_unstemmed | Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| title_short | Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| title_sort | novel approach to modeling high frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions |
| url | https://doi.org/10.1038/s41598-021-87304-w |
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