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Analysis of the relationship between activity and pain in chronic and acute low back pain
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
Mayo Clinic, Department of Anesthesiology, Rochester, U.S.A..
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We studied the temporal relationship between pain and activity in patients with acute or chronic low back pain. Our hypothesis was that activity exacerbates acute pain, but not chronic pain. To test this hypothesis, we concurrently measured activity and pain using continual electronic recording, and analyzed the data using the cross-correlation function.

After obtaining IRB approval and patient consent, we studied fifteen patients with acute low back pain and fifteen patients with chronic low back pain over 3 weeks. The activity levels were collected automatically using a wrist accelerometer, and were sampled every 1-minute. The pain levels were recorded semi-automatically using a computerized pocket-sized diary, every 90 minutes. The patients were prompted to enter a number between 0 and 10, where 0 is no pain, and 10 is the worst possible pain. Patients were allowed to enter additional measurements as often as they wanted.

The first seven and the last seven of the daily pain and activity time series from each patient were then analyzed using the cross-correlation function at various time lags between -60 and +60 minutes. The null hypothesis was tested by R to Z transformation, followed by a one-sample t-test against an expected Z score of zero.

We found that during the first seven measurement periods of acute low back pain, there was a significant (p<0.01) degree of cross-correlation between activity and pain. As these patients improved and reported less pain, the relationship between activity and pain disappeared. There was no such relationship at any point in time among the patients with chronic low back pain. These results confirmed our hypothesis in this small sample of patients.

National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-81435OAI: diva2:552450
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2012-09-14Bibliographically approved
In thesis
1. Medical data time series analyses using AI techniques
Open this publication in new window or tab >>Medical data time series analyses using AI techniques
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many areas of medicine, we collect serial measurements of different variables as they change over time. Changes in a time series is often reflective of a change in a patient's status and may be helpful in guiding the appropriate therapy. As we tend to collect data more data as well as more frequently and for longer periods of time, the amount of information available to the clinician grows. Even though there is and has been a siguificant development in medical technology, the management of these growing anaounts of data still requires manual interpretation in many cases.

Typically, the information collected from serial measurements of data is used to establish a trend, which may be related to a patient's progress or deterioration. Isolated measurements of a variable also contiibute to a better understanding of a patient's status. The physician ahnost always manually interprets this type of infonnation. Time series may contain other types of information that may not be as easily be interpreted this way.

This thesis describes a number of cases where other types of information have been extracted from medical data time series. Time series may contain information that can be used for prediction as well as categorization (diagnosis). Except from the potential of improving patient care, this information is also helpful in expanding the understanding of the processes involved in each example.

The methods used in this thesis include principal component analysis, neural networks, cross-correlation analysis and the wavelet transform. By applying these methods to analyse the information in the various time series, adaptable and effective tools for an increased understanding of these problems may be created. This may contribute to better patient management by providing the clinician with improved decision support.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2002. 49 p.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 726
National Category
Medical and Health Sciences
urn:nbn:se:liu:diva-29437 (URN)14783 (Local ID)91-7373-164-1 (ISBN)14783 (Archive number)14783 (OAI)
Public defence
2002-04-15, Högdahlsalen, Universitetssjukhuset, Linköping, 13:00 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-09-14Bibliographically approved

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