A Study to Investigate the Association of Real-world Sensor-derived Biometric Data With Clinical Parameters and Patient-reported Outcomes for Monitoring Disease Activity in Patients With COPD

A Study to Investigate the Association of Real-world Sensor-derived Biometric Data With Clinical Parameters and Patient-reported Outcomes for Monitoring Disease Activity in Patients With Chronic Obstructive Pulmonary Disease (COPD)

The purpose of this multicenter, prospective cohort study is to investigate the correlation of real-world sensor-derived biometric data obtained via a wearable device with clinical parameters and patient-reported outcomes (PROs) for monitoring disease activity and predicting exacerbations for participants with Chronic Obstructive Pulmonary Disease (COPD). The cohort of participants with COPD will be followed for 3 months. A calibration cohort with non-COPD participants will be included and followed for 2 weeks.

Study Overview

• Status: Completed
• Conditions: Pulmonary Disease, Chronic Obstructive
• Intervention / Treatment: Device: Vivalink wearable device

• Study Type: Interventional
• Enrollment (Actual): 77
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Germany
  • Berlin, Germany
    • Praxis an der Oper
  • Darmstadt, Germany
    • Lungenzentrum Darmstadt GmbH
  • Darmstadt, Germany
    • Städtische Kliniken Darmstadt
  • Frankfurt, Germany
    • Lungenzentrum Frankfurt
  • Heidelberg, Germany
    • Thoraxklinik Heidelberg gGmbH
  • Mannheim, Germany
    • ZERO Praxen
  • München, Germany
    • Pneumologisches Studienzentrum München-West
  • Saarbrücken, Germany
    • Pneumologische Gemeinschaftspraxis Saarbrücken
  • Schleswig, Germany
    • RespiRatio / Lungenpraxis
  • Wiesbaden, Germany
    • Pneumologische Praxis Wiesbaden
  • Witten, Germany
    • Lungenpraxis Dr. Franz / Dr. Weber

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 36 years to 76 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

For participants with COPD:

• Participants ≥40 and ≤80 years at baseline
• Diagnosis of COPD stage II to IV
• History of moderate or severe exacerbations (≥2 moderate exacerbations or ≥1 severe exacerbations in any 12-month time window during last 3 years prior to inclusion and ≥1 moderate or severe exacerbations in the last 12 months prior to inclusion, considering that the last 12 months may reflect lower exacerbation rate due to Covid-19 measures)

For participants in the calibration cohort:

• Participants ≥40 and ≤80 years at baseline

Exclusion Criteria:

For participants with COPD:

• Clinically relevant and/or serious concurrent medical conditions including, but not limited to visual problems, severe mental illness or cognitive impairment, musculoskeletal or movement disorders, cardiac disease (e.g., heart failure, arrythmia [esp. atrial fibrillation and conduction blocks]), lung cancer (currently treated) that in the opinion of the Investigator, would interfere with participant's ability to participate in the study or draw meaningful conclusions from the study
• Participants with a cardiac pacemaker, defibrillators, or other implanted electronic devices
• Participants with known allergies or sensitivity to silicon or hydrogel
• Less than 6 weeks since previous moderate/severe exacerbation

For participants in the calibration cohort:

• Participants with a cardiac pacemaker, defibrillators, or other implanted electronic devices
• Participants with known allergies or sensitivity to silicon or hydrogel
• Diagnosis of pulmonary disease including, but not limited to COPD, asthma, pulmonary fibrosis, with impact on the lung function and exercise capacity

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: COPD cohort	Device: Vivalink wearable device; a CE marked device modified to add a temperature measurement algorithm in addition to ECG and respiratory rate measurements
Experimental: Calibration participants cohort	Device: Vivalink wearable device; a CE marked device modified to add a temperature measurement algorithm in addition to ECG and respiratory rate measurements

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Physical Activity	An activity flag is extracted from the accelerometer by Vivalink, by using a predefined threshold for adult movement. For stair climbing, first periodic movement was determined, by using frequency analysis on specific time windows, and generating a ratio to the total spectrum indicating periodic activity over a certain threshold.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate	Heart rate is provided by Vivalink.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (SDRR, SDNN, SDNNI, RMSSD, ln(RMSSD))	Heart rate variability reflecting differences in time intervals between 2 R-waves in the ECG (milliseconds) SDRR (Standard Deviation of Intervals between Heartbeats), SDNN (Standard Deviation of Intervals between Heartbeats, after removing abnormal Beats), SDNNI (Mean of the Standard Deviations of all the NN intervals for each 5 min Segment of a 24-h HRV Recording), and RMSSD (Mean of the Standard Deviations of all the NN intervals for each 5 min Segment of a 24-hour HRV Recording) and In(RMSDD)	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (pNN50)	pNN50 is the percentage of adjacent NN intervals that differ from each other by more than 50 milliseconds.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (Stress Index)	Baevsky's Stress Index is a heart rate variability (HRV) measure used to assess autonomic nervous system activity and physiological stress, especially in monitoring chronic obstructive pulmonary disease (COPD) exacerbations. It is calculated as: amplitude of the mode (AMo) divided by two times the mode (Mo) multiplied by the difference between the maximum and minimum RR intervals (MxDMn). AMo is the percentage of RR intervals at the most frequent value, Mo is the most common RR interval, and MxDMn is the range of RR intervals. The index typically ranges from 50 to over 900. Lower values (50-150) indicate low stress and better autonomic balance, while higher values (above 500) reflect increased stress and sympathetic activity. Values above 900 are considered very high stress. This is a single composite score with no subscales; higher scores represent worse outcomes.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF and HF)	Applying a Fast Fourier Transformation (FFT) or autoregressive (AR) modeling one can separate Heart rate variability (HRV) into its component ultra-low-frequency (ULF), very low frequency (VLF), Low-Frequency power (LF), and High-Frequency power (HF) rhythms that operate within different frequency ranges. Given in absolute values of power (milliseconds squared). LF power, low frequency power (0.04-0.15 Hz). HF power, high frequency power (0.15-0.40 Hz). LF/HF Ratio, spectral HRV index computed as (LF/HF).	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF/HF)	Applying a Fast Fourier Transformation (FFT) or autoregressive (AR) modeling one can separate Heart rate variability (HRV) into its component ultra-low-frequency (ULF), very low frequency (VLF), Low-Frequency power (LF), and High-Frequency power (HF) rhythms that operate within different frequency ranges. Given in absolute values of power (milliseconds squared). LF power, low frequency power (0.04-0.15 Hz). HF power, high frequency power (0.15-0.40 Hz). LF/HF Ratio, spectral HRV index computed as (LF/HF).	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Temperature	Temperature is provided by Vivalink. The value for temperature is derived by Vivalink from the display temperature and then calibrated using initial calibration values, in an IP protected process. The sensor temperature is considered only as a relative value to evaluate changes in the temperature, and not as an objective human body temperature value, meaning no thresholds relative to normal human body temperature are considered, and it will not be used as a marker for fever or hypothermia.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Respiratory Rate	Respiration rate is provided by Vivalink.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Cough Frequency	Cough Frequency was provided by vivalink.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Sleep Patterns	The basis of the sleep pattern calculations is the self-reported bedtimes. With the same technique as the cough frequency prediction, inactivity signals can be predicted from the labeled data to improve the bedtime accuracy, and the changes in accelerometer (step detection algorithms) can be used to quantify the number of clear breaks in the sleep (standing up, strong cough, etc.).	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Resting Heart Rate	Resting Heart Rate is provided by Vivalink.	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Inspiration vs Expiration Time Ratio	Using the breathing signal one can determine the inspiration and expiration peaks. The difference between said peaks in milliseconds can be used to determine the ratio of inspiration (distance from lower point to next peak) vs expiration (distance from peak to next lower point).	Day 0(Baseline) and Day 8 to Day 14
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Frequency of Additional Medication	Count of the number of times the use of additional medication as a log activity is reported per day.	Day 0(Basseline) and Day 8 to Day 14
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Accuracy	Accuracy was calculated as (True Positives + True Negatives) / Total Population. True Positives (TP) are events correctly predicted as exacerbations. True Negatives (TN) are events correctly predicted as non-exacerbations. Total Population refers to the total number of events evaluated. Accuracy scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.	Up to 3 months
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Precision	Precision was calculated as True Positives / (True Positives + False Positives). True Positives (TP) are events correctly predicted as exacerbations. False Positives (FP) are events incorrectly predicted as exacerbations. Total Population refers to the total number of events evaluated. Precision scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.	Up to 3 months
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Recall	Recall was calculated as True Positives / (True Positives + False Negatives). True Positives (TP) are events correctly predicted as exacerbations. False Negatives (FN) are events incorrectly predicted as non-exacerbations. Total Population refers to the total number of events evaluated. Recall scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.	Up to 3 months
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Specificity	Specificity was calculated as True Negatives / (True Negatives + False Positives). True Negatives (TN) are events correctly predicted as non-exacerbations. False Positives (FP) are events incorrectly predicted as exacerbations. Total Population refers to the total number of events evaluated. Specificity scores reflect XGBoost algorithm performance using random and time-based 70/30 data splits. The values were calculated in form of percentage where 100% is the ideal scenario for perfect predictability.	Up to 3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Participants Health Status and Symptoms at Baseline and Study End	Participants health status and symptoms at baseline (Day 0) and study end will be measured as the summary score across items of the CAT questionnaire that consists of 8-items in which participants can choose a score from 0 to 5, for each visit.	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FEV1) at Baseline and Study End	Lung function was assessed using plethysmography.	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FVC) at Baseline and Study End	Lung function was assessed using plethysmography.	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FEV1/FVC) at Baseline and Study End	Lung function was assessed using plethysmography.	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (White Blood Cells Count)	Lung function was assessed using plethysmography and lab values including Complete Blood Count with differential, Blood Gas Analysis, procalcitonin and CRP will be assessed as per standard practice at baseline (Day 0) and at study end	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Erythrocytes Count)	Lung function was assessed using plethysmography and lab values including Complete Blood Count with differential, Blood Gas Analysis, procalcitonin and CRP will be assessed as per standard practice at baseline (Day 0) and at study end	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Partial Pressure of Oxygen (pO2))	Lung function was assessed using plethysmography and lab values including Complete Blood Count with differential, Blood Gas Analysis, procalcitonin and CRP will be assessed as per standard practice at baseline (Day 0) and at study end	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (Partial Pressure of Carbon Dioxide (pCO2))	Lung function was assessed using plethysmography and lab values including Complete Blood Count with differential, Blood Gas Analysis, procalcitonin and CRP will be assessed as per standard practice at baseline (Day 0) and at study end	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Lung Function and Lab Values at Baseline and Study End (O2 Saturation)	Lung function was assessed using plethysmography and lab values including Complete Blood Count with differential, Blood Gas Analysis, procalcitonin and CRP will be assessed as per standard practice at baseline (Day 0) and at study end	Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Number, Date of Onset, and Duration of Mild, Moderate, and Severe Exacerbations	Exacerbations are classified as mild if they are treated with short-acting bronchodilators only, moderate if they are treated additionally with antibiotics or oral corticosteroids, or severe if the patient visits the emergency room or requires hospitalization because of an exacerbation.	Up to 3 months
Association Between Sensor Parameters (Heart Rate and Resting Heart Rate) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (Heart Rate and Resting Heart Rate). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	7 days before Severe/Moderate Excarbations(S/M E) (7-day window period)
Association Between Sensor Parameters (Respiration Rate) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (Respiration Rate). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (SDRR, SDNN, SDNNI, RMSSD, In(RMSSD)) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (SDRR, SDNN, SDNNI, RMSSD, In(RMSSD)). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Stress Index, LF/HF) and CAT Score	During the observation period, participants completed the CAT questionnaire daily via a digital app. Stress Index, based on heart rate variability (HRV), assessed autonomic activity and physiological stress. It was calculated as AMo/(2 * Mo * MxDMn), where AMo is the % of RR intervals at the most frequent value, Mo is the most common RR interval, and MxDMn is the RR interval range. Stress Index values range from 50-900; lower values (50-150) indicate low stress and better autonomic balance, while higher values (>500) reflect increased stress and sympathetic activity. LF power reflects sympathetic activity; HF power reflects parasympathetic activity. Linear mixed models assessed associations between CAT score and each sensor parameters. Fixed effect estimates represent change in CAT score per unit change in each parameter. Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI".	7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (pNN50) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (pNN50). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	7 days before Severe/Moderate Excarbations(S/M E) (7-day window period) and 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Temperature) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (Temperature). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Physical Activity) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (Physical activity). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Sleep Pattern) and CAT Score	During the observation period the CAT score was obtained via a digital application daily. The daily CAT questionnaire summary score was computed. The fixed effect estimate represents the change in CAT score per unit change in the corresponding parameter. Linear mixed models were performed to assess the association between the CAT score and each sensor parameter (Sleep pattern). Data was calculated through linear mixed model; reported as "fixed effect estimate" with measure type as "number" and measure dispersion as "95% CI."	14 days before S/M E (1 day window period)
Predicting the CAT Score by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates	Patients' health status and symptoms at baseline (Day 0) were measured using the CAT questionnaire, an 8-item tool with scores ranging from 0 to 5 per item. CAT scores were collected daily via a digital application during the observation period. Various machine learning algorithms were evaluated for predictive performance using metrics including accuracy, specificity, sensitivity, precision, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve. R² (coefficient of determination) was computed for CAT score prediction models, defined as R² = 1 - (SS_res / SS_tot), where SS_res is the residual sum of squares and SS_tot is the total sum of squares. R² values range from 0 to 1, with higher values indicating better model fit.	Up to 3 months

Collaborators and Investigators

• Sponsor: Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany
• Investigators: Study Director: Medical Responsible, Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany

Publications and helpful links

Helpful Links

• Trial Awareness and Transparency website

Study record dates

Study Major Dates

• Study Start (Actual): December 5, 2022
• Primary Completion (Actual): October 31, 2023
• Study Completion (Actual): October 31, 2023

Study Registration Dates

• First Submitted: November 18, 2022
• First Submitted That Met QC Criteria: December 9, 2022
• First Posted (Actual): December 19, 2022

Study Record Updates

• Last Update Posted (Actual): January 15, 2026
• Last Update Submitted That Met QC Criteria: December 24, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Chronic Disease; Disease Attributes; Respiratory Tract Diseases; Lung Diseases; Lung Diseases, Obstructive; Pathological Conditions, Signs and Symptoms; Pulmonary Disease, Chronic Obstructive
• Other Study ID Numbers: MS202559_0001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: We are committed to enhancing public health through responsible sharing of clinical trial data. Following approval of a new product or a new indication for an approved product in both the US and the European Union, the study sponsor and/or its affiliated companies will share study protocols, anonymized patient data and study level data, and redacted clinical study reports with qualified scientific and medical researchers, upon request, as necessary for conducting legitimate research. Further information on how to request data can be found on our website http://bit.ly/IPD21

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No