Predicting Functional Improvement in Chronic Pain Using Machine Learning and Digital Health Data From the Manage My Pain App
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".