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Effect of yoga on chronic non-specific neck pain: An unconditional growth model

Overview of attention for article published in Complementary Therapies in Medicine, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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2 policy sources
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1 X user
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1 Facebook page

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128 Mendeley
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Article details
Title
Effect of yoga on chronic non-specific neck pain: An unconditional growth model
Published in
Complementary Therapies in Medicine, December 2017
DOI 10.1016/j.ctim.2017.11.018
Pubmed ID
Authors
Abstract

Chronic neck pain is a common problem that affects approximately half of the population. Conventional treatments such as medication and exercise have shown limited analgesic effects. This analysis is based on an original study that was conducted to investigate the physical and behavioral effects of a 9-week Iyengar yoga course on chronic non-specific neck pain. This secondary analysis uses linear mixed models to investigate the individual trajectories of pain intensity in participants before, during and after the Iyengar yoga course. Participants with chronic non-specific neck pain were selected for the study. The participants suffered from neck pain for at least 5 days per week for at least the preceding 3 months, with a mean neck pain intensity (NPI) of 40 mm or more on a Visual Analog Scale of 100 mm. The participants were randomized to either a yoga group (23) or to a self-directed exercise group (24). The mean age of the participants in the yoga group was 46, and ranged from 19 to 59. The participants in the yoga group participated in an Iyengar yoga program designed to treat chronic non-specific neck pain. Our current analysis only includes participants who were initially randomized into the yoga group. The average weekly neck pain intensity at baseline, during and post intervention, comprising 11 total time points, was used to construct the growth models. We performed a step-up linear mixed model analysis to investigate change in NPI during the yoga intervention. We fit nested models using restricted maximum-likelihood estimation (REML), tested fixed effects with Wald test p-values and random effects with the likelihood ratio test. We constructed 10 REML models. The model that fit the data best was an unconditional random quadratic growth model, with a first-order auto-regressive structure specified for the residual R matrix. Participants in the yoga group showed significant variation in NPI. They demonstrated variation in their intercepts, in their linear rates of change, and most tellingly, in their quadratic rates of change. While all participants benefitted from the yoga intervention, the degree to which they benefitted varied. Additionally, they did not experience a consistent rate of reduction in NPI - their NPI fluctuated, either increasing and then decreasing, or vice-versa. We comment on the clinical and research implications of our findings.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Bachelor 15 12%
Student > Master 15 12%
Student > Ph. D. Student 12 9%
Other 9 7%
Student > Postgraduate 7 5%
Other 15 12%
Unknown 55 43%
Readers by discipline
Readers by discipline Count As %
Nursing and Health Professions 22 17%
Medicine and Dentistry 16 13%
Psychology 6 5%
Sports and Recreations 5 4%
Social Sciences 4 3%
Other 7 5%
Unknown 68 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 06 April 2021.
All research outputs
#5,583,217
of 26,618,366 outputs
Outputs from Complementary Therapies in Medicine
#662
of 1,643 outputs
Outputs of similar age
#104,790
of 451,580 outputs
Outputs of similar age from Complementary Therapies in Medicine
#16
of 40 outputs
Altmetric has tracked 26,618,366 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,643 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.3. This one has gotten more attention than average, scoring higher than 59% 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 451,580 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.