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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2209.15553 |
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| _version_ | 1866910283856871424 |
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| author | Das, Rajenki Muldoon, Mark Lunt, Mark McBeth, John Yimer, Belay Birlie House, Thomas |
| author_facet | Das, Rajenki Muldoon, Mark Lunt, Mark McBeth, John Yimer, Belay Birlie House, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_15553 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study Das, Rajenki Muldoon, Mark Lunt, Mark McBeth, John Yimer, Belay Birlie House, Thomas Applications 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. |
| title | Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study |
| topic | Applications |
| url | https://arxiv.org/abs/2209.15553 |