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Personalizing digital pain management with adapted machine learning approach.

Yifat Fundoiano-Hershcovitz¹, Keren Pollak², Pavel Goldstein²

¹Dario Health, Caesarea, Israel.

|September 21, 2023

Summary

This summary is machine-generated.

This study introduces a new framework for personalized pain management using digital therapeutics (DTx). The model enhances treatment efficacy by analyzing user data and improving interpretability for better patient outcomes.

Keywords:

Back pain Decision tree Digital therapeutics Machine learning Mixed model Personalized digital therapeutics Posture biofeedback

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Area of Science:

Digital health
Computational medicine
Biostatistics

Background:

Digital therapeutics (DTx) show varied efficacy in pain management.
Machine learning (ML) offers personalization but often lacks clinical interpretability.
Classical ML models struggle with longitudinal DTx data and nonlinear patterns.

Purpose of the Study:

To present an analytical framework for personalized pain management using piecewise mixed-effects model trees.
To address data dependencies, nonlinear trajectories, and enhance model interpretability in DTx.
To personalize pain management by considering individual user characteristics.

Main Methods:

Implemented a piecewise mixed-effects model tree framework.
Analyzed 8-week posture biofeedback training data from 3610 users.
Developed personalized models for pain, posture quality, and training duration, incorporating age, gender, and BMI.

Main Results:

Significant improvements in pain and posture quality within the first 3 weeks, followed by sustained effects.
Age moderated pain fluctuations; age and gender interactively moderated posture quality trajectories.
Training duration initially increased for older users, then decreased for all users over time.

Conclusions:

The framework enables personalized DTx efficacy investigation for pain management.
It accounts for user characteristics, boosting interpretability.
Future research can benefit from incorporating additional user characteristics.