Digital Health Technologies in Rheumatoid Arthritis Monitoring - Medriva

Digital Health Technologies in Rheumatoid Arthritis Monitoring - Medriva unknown

In recent years, there has been a significant shift towards digital health technologies and their potential to revolutionize the way we understand, monitor, and treat various health conditions. One such promising field is the use of digital wearable devices in understanding the impact of rheumatoid arthritis (RA). This article delves into two significant studies that highlight the use of sensor-based measurements extracted from smartphone-based assessments and smartwatch-based activity monitoring to distinguish between RA status and severity levels.

The weaRAble PRO Study

Published in Nature (source: here), the weaRAble PRO study aimed to explore how digital health technologies such as smartphones and wearables can augment patient-reported outcomes (PRO) to determine rheumatoid arthritis (RA) status and severity. The study found that wearable sensor outcomes not only robustly distinguished RA from healthy controls but also confirmed that the combination of these modalities could reliably measure continuous RA severity. The primary objective of the study was to investigate how active and passive sensor-based measurements, through machine learning, can distinguish RA status from healthy controls and augment traditional patient self-reported outcome data. The findings also suggested that these measurements could estimate standard in-clinic assessments of RA severity.

RA Monitoring through Wearable Devices

The study analyzed sensor-based measurements extracted from smartphone-based assessments and smartwatch-based activity monitoring. The results showed that wearable sensor features could distinguish RA participants from healthy controls and accurately stratify RA symptom severity levels. The study also aimed to determine the minimal number of days of sensor data required to build a stable estimate of disease status in RA participants compared to healthy controls. The combination of PRO and sensor-based outcomes was found to accurately estimate RAPID-3 scores, a common measure of RA severity.

Mobile Health Systems and Rheumatic Disease

In another study (source: here), a semi-automatic mobile health system was designed with wearable devices to evaluate the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions like suspected rheumatic disease. The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.

Conclusion

The results from these studies provide valuable insights into the use of wearable sensor data to remotely monitor and assess RA symptoms. They underline the potential of digital health technologies in transforming the way we approach chronic diseases. The integration of these technologies can indeed augment traditional patient self-reported outcome data, offering a more comprehensive overview of a patient’s health status and the severity of their condition. It promises a future where continuous remote monitoring and assessment of chronic conditions like RA could lead to more personalized, effective treatment plans, ultimately resulting in improved patient outcomes.