Why the study?
Subjective pain assessment can lead to inaccurate prescribing of pain medication, creating an urgent need for ambulatory devices that quantify pain in real time in ambulatory settings.
Does a smartphone application using wrist-worn EDA sensor data accurately detect acute pain compared to painless periods?
Does a smartphone application using wrist-worn EDA sensor data accurately detect acute pain compared to painless periods?
A smartphone application using wrist-worn EDA sensors can detect acute pain with over 80% accuracy, offering a potential objective measure for ambulatory pain assessment.
Answers are grounded in this paper and cite their sources.
May enable real-time home pain quantification to reduce opioid overprescribing; leaves open validation in larger cohorts before practice change.
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients' homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.
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Kong et al. (2021) studied this question.
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