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Mobile App Versions of Low Back Pain Questionnaires May be Just as Effective as Paper Versions

Alison Rodriguez
Low back pain is a common cause of disability globally and represents a significant economic burden for patients. According to a recent study, digital versions of pain assessments, such as mobile applications, may be as effective and reliable as paper methods that are currently used to assess pain.
 
Low back pain is a common cause of disability globally and represents a significant economic burden for patients. According to a recent study, digital versions of pain assessments, such as mobile applications, may be as effective and reliable as paper methods that are currently used to assess pain.

The study, published by the Journal of Medical Internet Research, evaluated the efficacy of mobile app versions of typically used measures of back pain, including the Roland Morris Disability Questionnaire (RMDQ), visual analogue scale (VAS) of pain intensity, and numerical rating scale (NRS). The researchers required adult patients with low back pain to complete the digital versions of the questionnaires at baseline, 1 week, and 6 weeks.

"We have taken existing outcome measures and shown that they can be migrated to digital media and used in that format just as effectively as their paper-based versions. Our intention is to develop technology that allows people to securely complete these kinds of assessments on their own phones and tablets in a way that is safe, secure, and accurate,” Robert Froud, PhD, MSc, lead author from the University of Warwick Clinical Trials Unit, said in a statement.

Patients who had received treatment for their condition and improved tested the responsiveness of the apps, while patients with chronic pain and less likely to improve tested the reliability of the apps.

"If you can accurately monitor in clinical practice what's happening to patients' health, then analytically there is a lot that could be done with the data that will benefit patients,” Froud noted. “For example, we may be able to detect that particular treatment approaches are working better for certain types of people. We hear a lot about machine learning, but a learning healthcare system is perhaps next.”

Overall the results demonstrated that the eRMDQ was reliable with borderline adequate responsiveness, the eNRS was responsive with borderline reliability, and the eVAS had adequate responsiveness and did not have attractive reliability. The researchers concluded that the electronic outcome measures’ metric properties were within the ranges of values reported in the literature for their paper counterparts and therefore are sufficient for measuring changes in low back pain.

"The implications are quite big because we can aim to scale up. It opens up potential for the development of new instruments and dynamic instruments that adapt to the answers that a user gives. The potential of using digital technology in healthcare settings is quite extraordinary but you can't do any of that without first having assessments that work robustly and well," Froud concluded.

 
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