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Predictors of fibromyalgia: a population-based twin cohort study

Overview of attention for article published in BMC Musculoskeletal Disorders, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

twitter
30 tweeters
facebook
1 Facebook page

Citations

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12 Dimensions

Readers on

mendeley
75 Mendeley
Title
Predictors of fibromyalgia: a population-based twin cohort study
Published in
BMC Musculoskeletal Disorders, January 2016
DOI 10.1186/s12891-016-0873-6
Pubmed ID
Authors

Ritva A. Markkula, Eija A. Kalso, Jaakko A. Kaprio

Abstract

Fibromyalgia (FM) is a pain syndrome, the mechanisms and predictors of which are still unclear. We have earlier validated a set of FM-symptom questions for detecting possible FM in an epidemiological survey and thereby identified a cluster with "possible FM". This study explores prospectively predictors for membership of that FM-symptom cluster. A population-based sample of 8343 subjects of the older Finnish Twin Cohort replied to health questionnaires in 1975, 1981, and 1990. Their answers to the set of FM-symptom questions in 1990 classified them in three latent classes (LC): LC1 with no or few symptoms, LC2 with some symptoms, and LC3 with many FM symptoms. We analysed putative predictors for these symptom classes using baseline (1975 and 1981) data on regional pain, headache, migraine, sleeping, body mass index (BMI), physical activity, smoking, and zygosity, adjusted for age, gender, and education. Those with a high likelihood of having fibromyalgia at baseline were excluded from the analysis. In the final multivariate regression model, regional pain, sleeping problems, and overweight were all predictors for membership in the class with many FM symptoms. The strongest non-genetic predictor was frequent headache (OR 8.6, CI 95 % 3.8-19.2), followed by persistent back pain (OR 4.7, CI 95 % 3.3-6.7) and persistent neck pain (OR 3.3, CI 95 % 1.8-6.0). Regional pain, frequent headache, and persistent back or neck pain, sleeping problems, and overweight are predictors for having a cluster of symptoms consistent with fibromyalgia.

Twitter Demographics

The data shown below were collected from the profiles of 30 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
United Kingdom 2 3%
Finland 1 1%
Australia 1 1%
Italy 1 1%
Canada 1 1%
Netherlands 1 1%
Unknown 66 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 19%
Student > Ph. D. Student 13 17%
Student > Master 11 15%
Student > Doctoral Student 8 11%
Student > Bachelor 5 7%
Other 16 21%
Unknown 8 11%
Readers by discipline Count As %
Medicine and Dentistry 25 33%
Psychology 14 19%
Nursing and Health Professions 12 16%
Agricultural and Biological Sciences 4 5%
Sports and Recreations 4 5%
Other 7 9%
Unknown 9 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2016.
All research outputs
#922,775
of 13,710,537 outputs
Outputs from BMC Musculoskeletal Disorders
#193
of 2,710 outputs
Outputs of similar age
#27,265
of 334,794 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#1
of 1 outputs
Altmetric has tracked 13,710,537 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,710 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,794 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them