⚡ Quick Summary
This scoping review highlights the potential of wearable devices in managing chronic pain (CP) through real-time monitoring and predictive modeling. Despite promising advancements, the integration of predictive algorithms remains limited, emphasizing the need for further research in this area.
🔍 Key Details
- 📊 Focus: Wearable devices for chronic pain management
- 🔍 Methodology: Systematic review of studies across six major databases
- ⚙️ Technologies: Random Forest, multilevel models, and Convolutional Neural Network-Long Short-Term Memory
- 🔒 Compliance: Adherence to General Data Protection Regulation and ISO standards
🔑 Key Takeaways
- 📈 Wearable devices can correlate physiological markers with chronic pain levels.
- 💡 Predictive models like Random Forest show consistent performance in pain prediction.
- 🤖 Advanced models face challenges related to data quality and computational demands.
- 🔒 Data security and privacy concerns remain significant issues in wearable technology.
- 🧩 Multimodal data integration is underexplored, presenting opportunities for improved prediction accuracy.
- 🌐 Future research should focus on developing robust predictive models and standardizing data protocols.
- 🤝 Interdisciplinary collaborations are essential for enhancing clinical applicability.
📚 Background
Chronic pain (CP) is a complex condition that affects millions of individuals worldwide, often leading to significant physical and emotional distress. Traditional methods of pain assessment can be subjective and inconsistent, highlighting the need for innovative solutions. Wearable devices have emerged as a promising technology, enabling real-time monitoring of pain-related parameters and offering the potential for personalized pain management strategies.
🗒️ Study
This study systematically reviewed recent advancements in wearable technology for chronic pain management. Researchers focused on the capabilities of various sensors, the quality of data collected, compliance with health standards, and the effectiveness of predictive models in identifying episodes of chronic pain. The review aimed to synthesize findings from multiple studies to provide a comprehensive understanding of the current landscape.
📈 Results
The findings indicate that while wearable devices show promise in correlating physiological markers with chronic pain, few studies have successfully integrated predictive models. Notably, Random Forest and multilevel models demonstrated consistent performance, while more advanced models like Convolutional Neural Network-Long Short-Term Memory encountered challenges related to data quality and computational demands. Despite adherence to regulations, concerns regarding data security and privacy persist, necessitating further attention.
🌍 Impact and Implications
The implications of this study are significant for the future of chronic pain management. By enhancing the capabilities of wearable devices and integrating predictive algorithms, healthcare professionals can potentially offer more personalized and preventive pain management strategies. This could lead to improved patient outcomes and a better quality of life for individuals suffering from chronic pain.
🔮 Conclusion
This scoping review underscores the untapped potential of wearable devices in predicting and managing chronic pain. Future research should prioritize the development of robust predictive models, standardization of data protocols, and addressing security and privacy concerns. By fostering interdisciplinary collaborations, we can enhance the clinical applicability of these technologies and pave the way for innovative solutions in chronic pain management.
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Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance.
Abstract
BACKGROUND: Wearable devices offer innovative solutions for chronic pain (CP) management by enabling real-time monitoring and personalized pain control. Although they are increasingly used to monitor pain-related parameters, their potential for predicting CP progression remains underutilized. Current studies focus mainly on correlations between data and pain levels, but rarely use this information for accurate prediction.
OBJECTIVE: This study aims to review recent advancements in wearable technology for CP management, emphasizing the integration of multimodal data, sensor quality, compliance with data security standards, and the effectiveness of predictive models in identifying CP episodes.
METHODS: A systematic search across six major databases identified studies evaluating wearable devices designed to collect pain-related parameters and predict CP. Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.
RESULTS: Wearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. Additionally, the integration of multimodal data, including physiological, psychological, and demographic factors, remains underexplored, presenting an opportunity to improve prediction accuracy.
CONCLUSIONS: Future research should prioritize developing robust predictive models, standardizing data protocols, and addressing security and privacy concerns to maximize wearable devices’ potential in CP management. Enhancing real-time capabilities and fostering interdisciplinary collaborations will improve clinical applicability, enabling personalized and preventive pain management.
Author: [‘Ayena JC’, ‘Bouayed A’, ‘Ben Arous M’, ‘Ouakrim Y’, ‘Loulou K’, ‘Ameyed D’, ‘Savard I’, ‘El Kamel L’, ‘Mezghani N’]
Journal: Front Digit Health
Citation: Ayena JC, et al. Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance. Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance. 2025; 7:1581285. doi: 10.3389/fdgth.2025.1581285