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Patients can badess their own back pain using an app on their phone or tablet as effectively as current paper methods, revealed a new study from the University of Warwick.
The study, published in the journal open access Journal of Medical Internet Research, demonstrates that digital versions of established measures for badessing back pain are equally reliable and responsive, allowing patients to use them for routine measurements and clinical trials.
The researchers view this study as a necessary first step in the increased use of digital media in a clinical setting, in light of recent calls for increased use of this technology by healthcare providers.
For health problems that can not be easily measured, such as pain and depression, clinicians often use self-badessment to monitor changes. In most cases this will take the form of a paper badessment. These go through very thorough validation exercises to ensure that they measure what they intend to do in a robust and accurate manner.
Researchers created mobile application versions of the most commonly used measures in back pain trials: the Roland Morris Disability Questionnaire (RMDQ), Visual Analogue Scale (VAS) of Pain Intensity and the numerical rating scale (NRS). These have been developed with the support of the University of Warwick's Higher Education Innovation Fund for the purpose of being used in clinical trials and for clinical measures of the University of Warwick. routine.
Back pain is the leading cause of disability in the world, affecting up to 84% of people at some point in their lives. It is estimated that it costs the British economy billions of pounds each year.
Lead author, Dr. Robert Froud of the University of Warwick's Clinical Trials Unit, said, "We took the existing outcome measures and showed that they could be migrated to digital media and used in this format as efficiently as their paper versions. Our intention is to develop a technology that allows users to perform such badessments safely on their own phones and tablets in a safe, secure and accurate manner.
"If you can accurately monitor the evolution of patient health in clinical practice, you can do a lot, badytically, with the data that will benefit patients. For example, we may be able to detect that particular treatment approaches work best for certain types of people. We hear a lot about machine learning, but a medical learning system may be the next step.
"The implications are pretty big because we can aim for a bigger scale. This opens up a potential for the development of new instruments and dynamic instruments that adapt to the answers given by the user. The potential of using digital technology in health care settings is quite extraordinary, but you can not do it without first having badessments that work properly. "
Reliability and responsiveness were used to determine if their applications were measuring what they should be. Reliability refers to the result of the measurement that does not change when nothing has changed, whereas responsiveness refers to a change in the result when a measurable factor has changed.
The researchers divided the study participants into groups based on whether or not they had a change in their pain. People who had received treatment for their disease and who had improved tested the responsiveness of applications. People suffering from chronic pain and less likely to improve have tested the reliability of applications.
Digital testing has many advantages over paper-based versions, including low cost, reduced carbon footprint, increased information security, and improved participant experience.
Earlier this month, a new report from the Royal College of Physicians of Ontario on outpatient consultations: The Future – Adding Value Through Sustainability, called for increased use of technologies already available in the health sector.
This article has been republished from materials provided by the University of Warwick. Note: Content may have changed for length and content. For more information, please contact the cited source.
Reference:
R. Froud, C. Fawkes, J. Foss, M. Underwood and D. Carnes (2018). Reactivity, reliability and minimally significant and detectable changes in 3 measures of the results of lumbar patients reported electronically for low back pain: validation study. Journal of Medical Research on the Internet, 20(ten). doi: 10.2196 / jmir.9828
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