↓ Skip to main content

PLOS

Barcoding Human Physical Activity to Assess Chronic Pain Conditions

Overview of attention for article published in PLOS ONE, February 2012
Altmetric Badge

Mentioned by

policy
1 policy source
twitter
7 X users
reddit
1 Redditor

Citations

dimensions_citation
56 Dimensions

Readers on

mendeley
158 Mendeley
Title
Barcoding Human Physical Activity to Assess Chronic Pain Conditions
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0032239
Pubmed ID
Authors

Anisoara Paraschiv-Ionescu, Christophe Perruchoud, Eric Buchser, Kamiar Aminian

Abstract

Modern theories define chronic pain as a multidimensional experience - the result of complex interplay between physiological and psychological factors with significant impact on patients' physical, emotional and social functioning. The development of reliable assessment tools capable of capturing the multidimensional impact of chronic pain has challenged the medical community for decades. A number of validated tools are currently used in clinical practice however they all rely on self-reporting and are therefore inherently subjective. In this study we show that a comprehensive analysis of physical activity (PA) under real life conditions may capture behavioral aspects that may reflect physical and emotional functioning.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 3 2%
Germany 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
United States 1 <1%
Unknown 150 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 20%
Student > Ph. D. Student 25 16%
Student > Master 23 15%
Student > Bachelor 16 10%
Professor 11 7%
Other 29 18%
Unknown 23 15%
Readers by discipline Count As %
Medicine and Dentistry 28 18%
Engineering 22 14%
Neuroscience 13 8%
Psychology 13 8%
Nursing and Health Professions 12 8%
Other 32 20%
Unknown 38 24%