Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study
Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study
It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the “Cloudy with a Chance of Pain” study, which collected longitudinal data from the residents of the UK with chronic pain conditions. Participants used an App to record self-reported measures of factors including mood, pain and sleep quality. The richness of these data allows us to perform model-based clustering of the data as a mixture of Markov processes. Through this analysis we discover four endotypes with distinct patterns of co-evolution of mood and pain over time. The differences between endotypes are sufficiently large to play a role in clinical hypothesis generation for personalised treatments of comorbid pain and low mood.
Das, Rajenki
028e3208-1145-40ef-96b2-88fe830023a9
Muldoon, Mark
87c08acf-2735-4952-b9d9-52d2d85ff86c
Lunt, Mark
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McBeth, John
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Yimer, Belay Birlie
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House, Thomas
672c78dd-c3dc-4708-834d-7017e882978e
30 March 2023
Das, Rajenki
028e3208-1145-40ef-96b2-88fe830023a9
Muldoon, Mark
87c08acf-2735-4952-b9d9-52d2d85ff86c
Lunt, Mark
c2b3288c-62f9-4a6c-aca5-ad0c1cc76ce5
McBeth, John
98012716-66ba-480b-9e43-ac53b51dce61
Yimer, Belay Birlie
58d2b62f-6d29-4e30-978a-f82569f57250
House, Thomas
672c78dd-c3dc-4708-834d-7017e882978e
Das, Rajenki, Muldoon, Mark, Lunt, Mark, McBeth, John, Yimer, Belay Birlie and House, Thomas
(2023)
Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study.
PLOS digital health, [e0000204].
(doi:10.1371/journal.pdig.0000204).
Abstract
It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the “Cloudy with a Chance of Pain” study, which collected longitudinal data from the residents of the UK with chronic pain conditions. Participants used an App to record self-reported measures of factors including mood, pain and sleep quality. The richness of these data allows us to perform model-based clustering of the data as a mixture of Markov processes. Through this analysis we discover four endotypes with distinct patterns of co-evolution of mood and pain over time. The differences between endotypes are sufficiently large to play a role in clinical hypothesis generation for personalised treatments of comorbid pain and low mood.
Text
journal.pdig.0000204
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Accepted/In Press date: 27 January 2023
Published date: 30 March 2023
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Local EPrints ID: 491491
URI: http://eprints.soton.ac.uk/id/eprint/491491
ISSN: 2767-3170
PURE UUID: 075e12a7-5906-4eef-abbc-c5fecc7da2ab
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Date deposited: 25 Jun 2024 16:41
Last modified: 26 Jun 2024 02:11
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Author:
Rajenki Das
Author:
Mark Muldoon
Author:
Mark Lunt
Author:
John McBeth
Author:
Belay Birlie Yimer
Author:
Thomas House
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