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...

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Autores principales:	Zekun Xu, Eric Laber, Ana-Maria Staicu, B. Duncan X. Lascelles
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c7da6acfde8c465aaafd5251c9eae10a
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spelling	oai:doaj.org-article:c7da6acfde8c465aaafd5251c9eae10a2021-12-02T14:15:53ZNovel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions10.1038/s41598-021-87304-w2045-2322https://doaj.org/article/c7da6acfde8c465aaafd5251c9eae10a2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87304-whttps://doaj.org/toc/2045-2322Abstract 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.Zekun XuEric LaberAna-Maria StaicuB. Duncan X. LascellesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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
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.
format	article
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
title	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_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_sort	novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c7da6acfde8c465aaafd5251c9eae10a
work_keys_str_mv	AT zekunxu novelapproachtomodelinghighfrequencyactivitydatatoassesstherapeuticeffectsofanalgesicsinchronicpainconditions AT ericlaber novelapproachtomodelinghighfrequencyactivitydatatoassesstherapeuticeffectsofanalgesicsinchronicpainconditions AT anamariastaicu novelapproachtomodelinghighfrequencyactivitydatatoassesstherapeuticeffectsofanalgesicsinchronicpainconditions AT bduncanxlascelles novelapproachtomodelinghighfrequencyactivitydatatoassesstherapeuticeffectsofanalgesicsinchronicpainconditions
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Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

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