Figure 1.

Framework for integration of wearable sensor data to guide personalized management of low back pain. (A) Continuous data from multiple domains are recorded using wearable sensors in the real world. User input data are recorded using apps to record factors that cannot be recorded with wearables (e.g., pain, psychological variables, etc.); (B) other patient data are collected to contribute to decision support. This might include multi-omics data (genomic, transcriptomic, proteomic, metabolomic, etc.), clinical features, imaging, clinically identified primary pain mechanism grouping, etc.; (C) data from wearable sensors are analysed using automated algorithm-based analysis supported by machine learning and combined with user input data; (D) all data inputs are interrogated to evaluation the complex interaction between multiple dimensions and low back pain experience using analytic methods including artificial intelligence to provide decision support for allocation of tailored interventions; (E) personalized management plan is provided that, depending on the individual patient, could include “in person” treatment and a suite of mobile Health (mHealth) solutions for self-management. mHealth solutions might include use of wearable data to support treatment (e.g., biofeedback, motivation, etc.); (F) wearable sensors and user input apps are used to monitor treatment adherence and evaluation of progress. These data would be fed back to a treating clinician; (G) all data from individual patients, the applied treatments and the treatment outcomes are uploaded to a central server to continue to build the accuracy of decision support. LBP, low back pain.