Predicting Functional Improvement in Chronic Pain Using Machine Learning and Digital Health Data from the Manage My Pain App

Abstract

The effective management of chronic pain remains a significant challenge due to its complex nature. This study explores the utility of digital health tools, specifically the Manage My Pain app, to not only monitor symptoms but also collect valuable information that may be used to predict significant improvements in user outcomes through the application of machine learning techniques. In this study, a comprehensive set of features, including demographic details, pain descriptions, and app usage were extracted from one-month of self-reported data collected from 6,413 users of the Manage My Pain app. These features along with temporal sequences of pain and function scores were used to train and validate multiple models aiming to predict significant functional improvements the following month. We found that combining extracted and temporal features led to superior models, regardless of model architecture. On a held-out test set, a random forest achieved a balanced accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.85. A convolutional neural network with multilayer perceptron demonstrated a balanced accuracy of 0.79 and an AUC of 0.88. Finally, a time-series transformer combined with TabNet achieved a balanced accuracy of 0.77 and an AUC of 0.84. By integrating machine learning with digital health data from the Manage My Pain app, significant functional improvements in individuals with chronic pain can be predicted. This study highlights the potential of forecasting outcomes using regularly self-reported outcome information captured by patient-facing digital health tools. These forecasts could significantly alter treatment strategies and improve chronic pain management, underscoring the transformative impact of digital health technology in chronic pain care.

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