Using Mobile Health Technology and Real-Time Assessments to Address Multilevel Influences on Lumbar Spine Surgery Outcomes

PROJECT SUMMARY/ABSTRACT Lumbar spine surgery for degenerative disease is one of the most common and most expensive surgeries performed in the United States. However, there is substantial variation in lumbar surgery rates, approaches, and patient-reported outcomes at the surgeon, hospital, and regional levels. One major cause of these inconsistencies is the lack of evidence-based tools to both predict outcome and personalize treatment recommendations. For example, while there has been growing recognition that pain symptoms reflect a complex interplay of biochemical, psychological, and social factors that influence surgical outcomes, these factors are not typically incorporated into surgical treatment plans. One important factor preventing the expansion of evidence-based treatments, particularly related to behavioral and cognitive interventions, is a lack of precision tools to measure dynamic symptom profiles for pain and related psychosocial comorbidities. In particular, traditional patient assessments use cross-sectional (i.e., one-time) questionnaires that are subject to recall bias and fail to capture longitudinal symptom dynamics. Mobile health (mHealth) technology has enabled a fundamentally new approach to collect intensive longitudinal patient-reported and biometric data to support individualized decision-making. In particular, ecological momentary assessment (EMA) is an emerging tool that leverages brief mobile surveys to obtain momentary, longitudinal assessments of core disease constructs. Complementing EMA, mobile fitness trackers, such as Fitbit, passively collect biometric data (e.g., activity, heart-rate, and sleep) that reflect the physiologic manifestations of lumbar spine-related disability and impaired psychosocial health. These innovative tools may provide a newfound ability to capture important biopsychosocial features that impact surgical outcome. Recognizing these evidence gaps and the value of emerging mHealth technology, this study’s overall objective is to establish the utility of using real-time patient-reported and biometric data to prognosticate and stratify lumbar spine patients, and to establish the value proposition for implementing these methods. This objective will be accomplished through the following Aims. In Aim 1 I will investigate the ability of mHealth assessments to identify novel disease features with prognostic importance for degenerative lumbar spine surgery patients. In Aim 2, I will use real-time mHealth assessments to identify novel phenotypes of lumbar disease. In Aim 3, I will define the value proposition and implementation context for using mHealth assessments to address functional and psychosocial influences on lumbar surgery outcomes. These research activities will be combined with a rigorous mentored training program incorporating chronic pain research, machine learning, behavioral intervention development, and implementation science. At the completion of this award, I will be prepared to submit an R01 application integrating data analytics, implementation science, and the biopsychosocial model of pain to test interventions to improve lumbar spine surgery outcomes.