A network analysis of clinical variables in chronic pain: a study from the Swedish quality registry for pain rehabilitation (SQRP)
Åkerblom, Sophia LU ; Cervin, Matti LU ; Perrin, Sean LU
; Rivano Fischer, Marcelo
LU
; Gerdle, Björn
and McCracken, Lance
(2021)
In Pain Medicine
22(7).
p.1591-1602
- Abstract
- Background. Efforts to identify specific variables that impact most on outcomes from interdisciplinary pain rehabilitation are challenged by the complexity of chronic pain. Methods to manage this complexity are needed. The purpose of the study was to determine the network structure entailed in a set of self-reported variables, examine change, and look at potential predictors of outcome, from a network perspective. Methods. In this study we apply network analysis to a large sample of people seeking interdisciplinary pain treatment (N = 2,241). Variables analyzed include pain intensity, pain interference, extent of pain, depression, anxiety, insomnia, and psychological variables from cognitive behavioral models of chronic pain. Results. We... (More)
- Background. Efforts to identify specific variables that impact most on outcomes from interdisciplinary pain rehabilitation are challenged by the complexity of chronic pain. Methods to manage this complexity are needed. The purpose of the study was to determine the network structure entailed in a set of self-reported variables, examine change, and look at potential predictors of outcome, from a network perspective. Methods. In this study we apply network analysis to a large sample of people seeking interdisciplinary pain treatment (N = 2,241). Variables analyzed include pain intensity, pain interference, extent of pain, depression, anxiety, insomnia, and psychological variables from cognitive behavioral models of chronic pain. Results. We found that Acceptance, Pain Interference, and Depression were key, “central,” variables in the pretreatment network. Interestingly, there were few changes in the overall network configuration following treatment, specifically with respect to which variables appear most central relative to each other. On the other hand, Catastrophizing, Depression, Anxiety, and Pain Interference each became less central over time. Changes in Life Control, Acceptance, and Anxiety were most strongly related to changes in the remainder of the network as a whole. Finally, no network differences were found between treatment responders and non-responders. Conclusions. This study highlights potential future targets for pain treatment. Further application of a network approach to interdisciplinary pain rehabilitation data is recommended. Going forward, it may be better to next do this in a more comprehensive theoretically guided fashion, and ideographically, to detect unique individual differences in potential treatment processes. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6eee92a0-ee91-4e8c-a8e9-bde06bb57350
- author
- Åkerblom, Sophia
LU
; Cervin, Matti
LU
; Perrin, Sean
LU
; Rivano Fischer, Marcelo
LU
; Gerdle, Björn
and McCracken, Lance
- organization
- publishing date
- 2021-03-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Chronic Pain, Network Analysis, Registry data, Multidisciplinary treatment, Cognitive Behavioral Therapy, Psychological Flexibility, Fear Avoidance
- in
- Pain Medicine
- volume
- 22
- issue
- 7
- pages
- 1591 - 1602
- publisher
- Oxford University Press
- external identifiers
-
- pmid:33706371
- scopus:85108292258
- ISSN
- 1526-2375
- DOI
- 10.1093/pm/pnaa473
- language
- English
- LU publication?
- yes
- id
- 6eee92a0-ee91-4e8c-a8e9-bde06bb57350
- date added to LUP
- 2020-12-19 06:25:05
- date last changed
- 2022-04-26 22:42:12
@article{6eee92a0-ee91-4e8c-a8e9-bde06bb57350,
abstract = {{Background. Efforts to identify specific variables that impact most on outcomes from interdisciplinary pain rehabilitation are challenged by the complexity of chronic pain. Methods to manage this complexity are needed. The purpose of the study was to determine the network structure entailed in a set of self-reported variables, examine change, and look at potential predictors of outcome, from a network perspective. Methods. In this study we apply network analysis to a large sample of people seeking interdisciplinary pain treatment (N = 2,241). Variables analyzed include pain intensity, pain interference, extent of pain, depression, anxiety, insomnia, and psychological variables from cognitive behavioral models of chronic pain. Results. We found that Acceptance, Pain Interference, and Depression were key, “central,” variables in the pretreatment network. Interestingly, there were few changes in the overall network configuration following treatment, specifically with respect to which variables appear most central relative to each other. On the other hand, Catastrophizing, Depression, Anxiety, and Pain Interference each became less central over time. Changes in Life Control, Acceptance, and Anxiety were most strongly related to changes in the remainder of the network as a whole. Finally, no network differences were found between treatment responders and non-responders. Conclusions. This study highlights potential future targets for pain treatment. Further application of a network approach to interdisciplinary pain rehabilitation data is recommended. Going forward, it may be better to next do this in a more comprehensive theoretically guided fashion, and ideographically, to detect unique individual differences in potential treatment processes.}},
author = {{Åkerblom, Sophia and Cervin, Matti and Perrin, Sean and Rivano Fischer, Marcelo and Gerdle, Björn and McCracken, Lance}},
issn = {{1526-2375}},
keywords = {{Chronic Pain; Network Analysis; Registry data; Multidisciplinary treatment; Cognitive Behavioral Therapy; Psychological Flexibility; Fear Avoidance}},
language = {{eng}},
month = {{03}},
number = {{7}},
pages = {{1591--1602}},
publisher = {{Oxford University Press}},
series = {{Pain Medicine}},
title = {{A network analysis of clinical variables in chronic pain: a study from the Swedish quality registry for pain rehabilitation (SQRP)}},
url = {{http://dx.doi.org/10.1093/pm/pnaa473}},
doi = {{10.1093/pm/pnaa473}},
volume = {{22}},
year = {{2021}},
}