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

NCT05655832 | Completed Results

• 1 other identifier: MS202559_0001
• Study Type: interventional
• Target: 77
• Locations: 1 country
• Sites: 11

Brief Summary

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.

Conditions

Pulmonary Disease, Chronic Obstructive

Outcome Measures

Primary Outcomes (18)

• 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 Outcomes (19)

• Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Participants Health Status and Symptoms at Baseline and Study End

  Baseline (Day 0) and at 3 months

• Correlation of Sensor-Collected Data With Lung Function (FEV1) at Baseline and Study End

  Baseline (Day 0) and at 3 months

• Correlation of Sensor-Collected Data With Lung Function (FVC) at Baseline and Study End

  Baseline (Day 0) and at 3 months

• Correlation of Sensor-Collected Data With Lung Function (FEV1/FVC) at Baseline and 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 (White Blood Cells Count)

  Baseline (Day 0) and at 3 months

Study Arms (2)

COPD cohort

EXPERIMENTAL

Device: Vivalink wearable device

Calibration participants cohort

EXPERIMENTAL

Device: Vivalink wearable device

Interventions

Vivalink wearable device DEVICE

a CE marked device modified to add a temperature measurement algorithm in addition to ECG and respiratory rate measurements

COPD cohort; Calibration participants cohort

Eligibility Criteria

• Age: 40 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• For participants with COPD:
• Participants ≥40 and ≤80 years at baseline
• Diagnosis of COPD stage II to IV
• For participants in the calibration cohort:
• Participants ≥40 and ≤80 years at baseline

You may not qualify if:

• 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

Sponsors & Collaborators

• Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany: lead

Study Sites (11)

Praxis an der Oper

Berlin, Germany

Location

Lungenzentrum Darmstadt GmbH

Darmstadt, Germany

Location

Städtische Kliniken Darmstadt

Darmstadt, Germany

Location

Lungenzentrum Frankfurt

Frankfurt, Germany

Location

Thoraxklinik Heidelberg gGmbH

Heidelberg, Germany

Location

ZERO Praxen

Mannheim, Germany

Location

Pneumologisches Studienzentrum München-West

München, Germany

Location

Pneumologische Gemeinschaftspraxis Saarbrücken

Saarbrücken, Germany

Location

RespiRatio / Lungenpraxis

Schleswig, Germany

Location

Pneumologische Praxis Wiesbaden

Wiesbaden, Germany

Location

Lungenpraxis Dr. Franz / Dr. Weber

Witten, Germany

Location

Related Links

• Trial Awareness and Transparency website

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Limitations and Caveats

The number of moderate or severe exacerbations documented during the observation period was very low.

Results Point of Contact

• Title: Communication Center
• Organization: Merck Healthcare KGaA, Darmstadt Germany, an affiliate of Merck KGaA, Darmstadt, Germany

service@emdgroup.com

+49-6151-72-5200

Study Officials

• Medical Responsible

  Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany

  STUDY DIRECTOR

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 18, 2022
• First Posted: December 19, 2022
• Study Start: December 5, 2022
• Primary Completion: October 31, 2023
• Study Completion: October 31, 2023
• Last Updated: January 15, 2026
• Results First Posted: January 15, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

DE (11)