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Analgesia and Pain Management

Analgesia and Pain Management

Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...

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Updated: Apr 9, 2026

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot

Mun Yee Mimi Tse¹, Jiafan He^1,2, Tyrone Tai On Kwok¹

¹School of Nursing and Health Sciences, Hong Kong Metropolitan University, 1 Sheung Shing Street, Homantin, Kowloon, 999007, China (Hong Kong), 852 39708764.

JMIR Formative Research

|April 8, 2026

Summary

This study shows a gamified pain management program significantly reduced chronic pain and psychological distress in adults. Machine learning identified optimal intervention strategies for personalized pain treatment.

Keywords:

artificial intelligence chronic pain gamification machine learning pain management

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Last Updated: Apr 9, 2026

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Area of Science:

Digital Health
Chronic Pain Management
Machine Learning in Healthcare

Background:

Chronic pain (CP) affects global functioning, with engagement in interventions challenging in China.
Gamification enhances motivation, while machine learning (ML) optimizes pain management.
Biopsychosocial interventions are key for chronic pain, but adherence can be difficult.

Purpose of the Study:

Evaluate the effectiveness of a gamified pain management (GPM) program on CP and psychological outcomes.
Utilize ML to identify factors driving pain improvement for tailored interventions.
Assess the impact of GPM on pain intensity, interference, anxiety, depression, and quality of life.

Main Methods:

A 10-week web-based GPM intervention with educational, physical, and gamified components.
Single-arm, pre-post study with 16 participants with CP in mainland China.
Paired t tests for primary/secondary outcomes; ML models (e.g., gradient boosting, LASSO) for prediction and subgroup analysis.

Main Results:

Significant reductions in pain intensity (27.3%) and pain interference (27.3%).
Significant improvements in anxiety and depression symptoms.
ML models accurately predicted pain reduction; LASSO identified sessions 3 and 5 as key predictors.

Conclusions:

The GPM program shows preliminary efficacy in reducing pain and psychological distress.
ML successfully identified personalized intervention components and durations for different patient subgroups.
This data-driven approach offers adaptive, personalized digital health interventions beyond one-size-fits-all models.