Using Technology to Track Pain and Pain-related Outcomes

Chronic pain affects over 100 million Americans, costs the US over $630 billion annually, and reduces quality of life. It is among the most clinically challenging and financially burdensome conditions facing clinicians and healthcare organizations. Sleep disturbance is common in chronic pain conditions with some studies reporting a prevalence as high as 70%-88%. Psychiatric disorders, including substance abuse and mood disorders are prevalent in chronic pain and are associated with impairment and decreased quality of life. Sleep is increasingly recognized as a critical regulator of mental health. Taken together, epidemiological, cross-sectional, and prospective studies support the hypothesis that insomnia, chronic pain, and depression are mutually interacting, each increasing the risk for the emergence and/or exacerbation of the other. The gold standard of chronic pain management is multidisciplinary pain treatment (MPT), but patients rarely receive MPT secondary to limited access and a severe shortage of pain management specialists. Thus, there is an urgent need for empirically supported, cost-effective multidisciplinary pain self-management options that are accessible to patients and trusted by primary and tertiary care providers. To address this problem, the investigators' group in collaboration with the Johns Hopkins Technology Innovation Center (TIC) has developed a mobile chronic pain, medication and symptom tracking digital technology platform designed to eventually support multidisciplinary pain treatment by enhancing patient-provider communication and delivering comprehensive, personalized, interactive evidence-based pain management strategies. The investigators' App (version 1.3) is currently able to collect self-report data (i.e., pain; sleep; mood; catastrophizing; stress; pain flares) and continuous, passively collected wearable biosensor data (i.e., heart rate; breathing; sleep; heart rate variability/stress). The investigators propose a prospective, observational proof of concept study to demonstrate feasibility and adherence while establishing the psychometric properties of a mobile pain App and to compare these data with passively collected physiological data and laboratory indices of pain in patients with chronic low back pain (CLBP).