JMIR Publications

ABSTRACT

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most reliable way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this article, we present a new mHealth platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale self-report pain datasets in single and multi-center studies by facilitating the acquisition, transfer and analysis of longitudinal, multi-dimensional self-report pain data. Data acquisition for SOMAScience is done via a user-friendly smartphone application SOMA that utilizes Experience Sampling Methodology (ESM) to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provides personal insights into daily pain dynamics. This article outlines the scientific, clinical, and technological considerations involved in the development of SOMAScience and how it can be used in clinical research studies. Our goal with SOMAScience is to provide a much-needed platform to lower the barrier for researchers and clinicians obtaining the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.