Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance

Abstract

Background: Wearable devices offer innovative solutions for chronic pain (CP) management by enabling real-time monitoring and personalized pain control. Although they are increasingly used to monitor pain-related parameters, their potential for predicting CP progression remains underutilized. Current studies focus mainly on correlations between data and pain levels, but rarely use this information for accurate prediction.

Objective: This study aims to review recent advancements in wearable technology for CP management, emphasizing the integration of multimodal data, sensor quality, compliance with data security standards, and the effectiveness of predictive models in identifying CP episodes.

Methods: A systematic search across six major databases identified studies evaluating wearable devices designed to collect pain-related parameters and predict CP. Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.

Results: Wearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. Additionally, the integration of multimodal data, including physiological, psychological, and demographic factors, remains underexplored, presenting an opportunity to improve prediction accuracy.

Conclusions: Future research should prioritize developing robust predictive models, standardizing data protocols, and addressing security and privacy concerns to maximize wearable devices' potential in CP management. Enhancing real-time capabilities and fostering interdisciplinary collaborations will improve clinical applicability, enabling personalized and preventive pain management.

Keywords: chronic pain; predictive analytics; privacy; standardization; wearable device.

Figure 1

PRISMA flow chart for study selection.

Figure 2

Overview of wearable chronic pain management devices. For more information on these devices, please refer to the following ref. [21, 38, 40, 42, 43, 45, 64].

Figure 3

Predictive models, sensors, and variables used across literature for predicting chronic pain. CNN-LSTM, convolutional neural network-long short-term memory; MLM, multilevel model; RF, random forest; LMMs, linear mixed-effects models; ACC, accelerometer; BVP, blood volume pulse; EDA, electrodermal activity; Resp, respiration sensor; EMG, electromyography; ECG, electrocardiogram; HR, heart rate; HRV, heart rate variability; SC, skin conductance; STS: sit-to-stand.

Figure 4

Percentage of studies by parameter combination for chronic assessment purpose. HR, heart rate; HRV, heart rate variability; EDA, electrodermal activity.