insight - Medical Technology - # Digital biomarkers for ME/CFS and Long COVID

Objective Measurement of Disease Severity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Long COVID Using a Wearable Sensor

Q: How could UpTime and other digital biomarkers be integrated with patient-reported outcomes and clinical assessments to provide a more comprehensive evaluation of ME/CFS and Long COVID?

In the context of ME/CFS and Long COVID, integrating UpTime and other digital biomarkers with patient-reported outcomes and clinical assessments can significantly enhance the evaluation process. By combining objective data from wearable sensors like UpTime with subjective reports from patients and clinical assessments, a more holistic view of the patient's condition can be obtained. Comprehensive Monitoring: Patient-reported outcomes, such as symptom severity, quality of life, and functional status, provide valuable insights into the subjective experience of the individual. By integrating these self-reported measures with objective data from digital biomarkers like UpTime, clinicians can track changes in symptoms and activity levels over time more accurately. Treatment Efficacy: Digital biomarkers like UpTime can serve as quantitative measures of disease severity and treatment response. By comparing changes in UpTime and other biomarkers with patient-reported outcomes, clinicians can assess the effectiveness of interventions and adjust treatment plans accordingly. Early Detection: The integration of digital biomarkers with patient-reported outcomes can aid in early detection of symptom exacerbations or disease progression. Changes in UpTime or other biomarkers may precede noticeable changes in symptoms, allowing for timely interventions. Personalized Medicine: By combining objective data from digital biomarkers with subjective reports and clinical assessments, healthcare providers can tailor treatment plans to individual patients. This personalized approach can lead to more effective management of ME/CFS and Long COVID.

Q: What are the potential limitations or confounding factors that may impact the reliability and generalizability of UpTime as a digital biomarker for these conditions?

While UpTime shows promise as a digital biomarker for ME/CFS and Long COVID, several limitations and confounding factors may impact its reliability and generalizability: Device Limitations: The accuracy and reliability of UpTime are dependent on the quality and calibration of the wearable sensors used. Variability in sensor performance or placement may introduce errors in data collection. User Compliance: The effectiveness of UpTime relies on consistent and accurate data collection by users. Non-compliance, improper sensor placement, or device removal can lead to incomplete or inaccurate data, affecting the reliability of the biomarker. Activity Variation: UpTime may not capture all aspects of disease severity, especially in individuals with fluctuating symptoms or varying activity patterns. Factors like cognitive fatigue or sedentary behaviors may not be fully reflected in UpTime measurements. Sample Diversity: The generalizability of UpTime as a biomarker may be limited by the homogeneity of the study population. Variations in age, gender, or disease subtypes could impact the applicability of UpTime across different patient groups.

Q: Could the insights gained from developing UpTime lead to the discovery of novel physiological mechanisms underlying the pathogenesis of ME/CFS and Long COVID?

The development and utilization of UpTime as a digital biomarker for ME/CFS and Long COVID have the potential to uncover novel physiological mechanisms underlying the pathogenesis of these conditions. Here's how: Orthostatic Intolerance: UpTime, as a measure of upright activity, provides valuable information on orthostatic intolerance in ME/CFS and Long COVID patients. By analyzing patterns of upright time and correlating them with symptoms, researchers can gain insights into the physiological mechanisms contributing to orthostatic intolerance. Activity Patterns: UpTime data can reveal unique activity patterns and fluctuations in disease severity over time. By studying these patterns, researchers may identify physiological mechanisms related to post-exertional malaise, fatigue, and other symptoms characteristic of ME/CFS and Long COVID. Treatment Response: Monitoring changes in UpTime before and after interventions can shed light on the physiological responses to treatments. By correlating improvements in UpTime with symptom relief, researchers can identify potential mechanisms of action for therapeutic interventions. Longitudinal Studies: Longitudinal analysis of UpTime data in conjunction with other biomarkers and clinical assessments can provide a comprehensive view of disease progression. This approach may uncover novel physiological pathways involved in the pathogenesis of ME/CFS and Long COVID. Overall, the insights gained from developing and utilizing UpTime as a digital biomarker have the potential to advance our understanding of the physiological mechanisms underlying ME/CFS and Long COVID, leading to novel discoveries in disease pathogenesis and treatment strategies.

Conceitos essenciais

UpTime, a parameter calculated from a single wearable sensor, can objectively measure disease severity in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or Long COVID.

Resumo

The content describes the development and validation of UpTime, a digital biomarker that can objectively measure disease severity in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or Long COVID.

Key highlights:

UpTime is calculated from raw data collected using a single inertial measurement unit (IMU) sensor worn on the ankle. It measures the percentage of time a person spends in an upright position during a 24-hour period.
The authors conducted a clinical study with 48 participants - 30 with ME/CFS, 15 with Long COVID, and 6 healthy controls. Participants wore the IMU sensor for 7 days.
UpTime effectively distinguished between healthy controls and subjects with ME/CFS (p=0.00004) and between healthy controls and subjects with Long COVID (p=0.01185).
Steps per day (Steps/Day) did distinguish between controls and subjects with ME/CFS (p=0.01) but not between controls and subjects with Long COVID (p=0.3).
UpTime was moderately correlated (r^2=0.68) with self-reported Hours of Upright Activity (HUA), suggesting some discrepancy between subjective reporting and objective measurement.
The authors conclude that UpTime is a promising objective measure of disease severity for both ME/CFS and Long COVID that could enable better treatment monitoring and development.

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arxiv.org

Estatísticas

"UpTime effectively distinguishes between healthy controls and subjects diagnosed with ME/CFS (p = 0.00004) and between healthy controls and subjects diagnosed with Long COVID (p = 0.01185)."
"Steps/Day did distinguish between controls and subjects with ME/CFS (p = 0.01) but did not distinguish between controls and subjects with Long COVID (p = 0.3)."

Citações

"UpTime is an objective measure of ME/CFS and Long COVID severity. UpTime can be used as an objective outcome measure in clinical research and treatment trials."
"Objective assessment of ME/CFS and Long COVID disease severity using UpTime could spur development of treatments by enabling the effect of those treatments to be easily measured."

Principais Insights Extraídos De

System and Method to Determine ME/CFS and Long COVID Disease Severity Using a Wearable Sensor

by Yifei Sun,Su... às arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04345.pdf

System and Method to Determine ME/CFS and Long COVID Disease Severity Using a Wearable Sensor

Perguntas Mais Profundas

How could UpTime and other digital biomarkers be integrated with patient-reported outcomes and clinical assessments to provide a more comprehensive evaluation of ME/CFS and Long COVID?

In the context of ME/CFS and Long COVID, integrating UpTime and other digital biomarkers with patient-reported outcomes and clinical assessments can significantly enhance the evaluation process. By combining objective data from wearable sensors like UpTime with subjective reports from patients and clinical assessments, a more holistic view of the patient's condition can be obtained.

Comprehensive Monitoring: Patient-reported outcomes, such as symptom severity, quality of life, and functional status, provide valuable insights into the subjective experience of the individual. By integrating these self-reported measures with objective data from digital biomarkers like UpTime, clinicians can track changes in symptoms and activity levels over time more accurately.

Treatment Efficacy: Digital biomarkers like UpTime can serve as quantitative measures of disease severity and treatment response. By comparing changes in UpTime and other biomarkers with patient-reported outcomes, clinicians can assess the effectiveness of interventions and adjust treatment plans accordingly.

Early Detection: The integration of digital biomarkers with patient-reported outcomes can aid in early detection of symptom exacerbations or disease progression. Changes in UpTime or other biomarkers may precede noticeable changes in symptoms, allowing for timely interventions.

Personalized Medicine: By combining objective data from digital biomarkers with subjective reports and clinical assessments, healthcare providers can tailor treatment plans to individual patients. This personalized approach can lead to more effective management of ME/CFS and Long COVID.

What are the potential limitations or confounding factors that may impact the reliability and generalizability of UpTime as a digital biomarker for these conditions?

While UpTime shows promise as a digital biomarker for ME/CFS and Long COVID, several limitations and confounding factors may impact its reliability and generalizability:

Device Limitations: The accuracy and reliability of UpTime are dependent on the quality and calibration of the wearable sensors used. Variability in sensor performance or placement may introduce errors in data collection.

User Compliance: The effectiveness of UpTime relies on consistent and accurate data collection by users. Non-compliance, improper sensor placement, or device removal can lead to incomplete or inaccurate data, affecting the reliability of the biomarker.

Activity Variation: UpTime may not capture all aspects of disease severity, especially in individuals with fluctuating symptoms or varying activity patterns. Factors like cognitive fatigue or sedentary behaviors may not be fully reflected in UpTime measurements.

Sample Diversity: The generalizability of UpTime as a biomarker may be limited by the homogeneity of the study population. Variations in age, gender, or disease subtypes could impact the applicability of UpTime across different patient groups.

Could the insights gained from developing UpTime lead to the discovery of novel physiological mechanisms underlying the pathogenesis of ME/CFS and Long COVID?

The development and utilization of UpTime as a digital biomarker for ME/CFS and Long COVID have the potential to uncover novel physiological mechanisms underlying the pathogenesis of these conditions. Here's how:

Orthostatic Intolerance: UpTime, as a measure of upright activity, provides valuable information on orthostatic intolerance in ME/CFS and Long COVID patients. By analyzing patterns of upright time and correlating them with symptoms, researchers can gain insights into the physiological mechanisms contributing to orthostatic intolerance.

Activity Patterns: UpTime data can reveal unique activity patterns and fluctuations in disease severity over time. By studying these patterns, researchers may identify physiological mechanisms related to post-exertional malaise, fatigue, and other symptoms characteristic of ME/CFS and Long COVID.

Treatment Response: Monitoring changes in UpTime before and after interventions can shed light on the physiological responses to treatments. By correlating improvements in UpTime with symptom relief, researchers can identify potential mechanisms of action for therapeutic interventions.

Longitudinal Studies: Longitudinal analysis of UpTime data in conjunction with other biomarkers and clinical assessments can provide a comprehensive view of disease progression. This approach may uncover novel physiological pathways involved in the pathogenesis of ME/CFS and Long COVID.

Overall, the insights gained from developing and utilizing UpTime as a digital biomarker have the potential to advance our understanding of the physiological mechanisms underlying ME/CFS and Long COVID, leading to novel discoveries in disease pathogenesis and treatment strategies.