Pulmonary Disease, Chronic ObstructiveNCT05655832
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 testing Vivalink wearable device in people with Pulmonary Disease, Chronic Obstructive, run by Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany at 11 sites in Germany.
Trial 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… Read more about this study
- Who is it for?
- Ages 40 to 80 years
- Conditions the study is for
- Pulmonary Disease, Chronic Obstructive
- What is being tested?
- Vivalink wearable device
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 77 participants
- Where
- 11 sitesGermany
- Study dates
- 5 Dec 2022to 31 Oct 2023
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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:
Show all 9Show fewer
- 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
Trial locations and contacts
11 sites in 1 country.
11 sites in 1 country
Germany11 sites
Praxis an der Oper
Berlin
Lungenzentrum Darmstadt GmbH
Darmstadt
Städtische Kliniken Darmstadt
Darmstadt
Lungenzentrum Frankfurt
Frankfurt
Thoraxklinik Heidelberg gGmbH
Heidelberg
ZERO Praxen
Mannheim
Pneumologisches Studienzentrum München-West
München
Pneumologische Gemeinschaftspraxis Saarbrücken
Saarbrücken
RespiRatio / Lungenpraxis
Schleswig
Pneumologische Praxis Wiesbaden
Wiesbaden
Lungenpraxis Dr. Franz / Dr. Weber
Witten
About this study
Official title: 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.
- Conditions
- Pulmonary Disease, Chronic Obstructive
- Being tested
- Vivalink wearable device
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Vivalink wearable deviceDevice
a CE marked device modified to add a temperature measurement algorithm in addition to ECG and respiratory rate measurements
| Study group | Role | What they receive |
|---|---|---|
| COPD cohort | Experimental | Not described Receives: Device: Vivalink wearable device |
| Calibration participants cohort | Experimental | Not described Receives: Device: Vivalink wearable device |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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)
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.).
Measured: 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.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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.
Measured: 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."
Measured: 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."
Measured: 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."
Measured: 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".
Measured: 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."
Measured: 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."
Measured: 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."
Measured: 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."
Measured: 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.
Measured: Up to 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2022
- Study start5 Dec 2022
- Primary completion31 Oct 2023
- Study completion31 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Other trials for Pulmonary Disease, Chronic ObstructiveStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 77 participants (actual)
Sponsor and investigators
- Lead sponsor
- Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Medical Responsible, Study director, Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05655832
- Sponsor's protocol ID
- MS202559_0001
- Status last verified
- Dec 2025
- First submitted
- 18 Nov 2022
- Last updated
- 15 Jan 2026
- Results posted
- Yes, on ClinicalTrials.gov
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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
Study at a glance
From the registry record, last updated by the sponsor on 15 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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)
- ClinicalTrials.gov ID
- NCT05655832
- Acronym / sponsor ID
- MS202559_0001
- Conditions
- Pulmonary Disease, Chronic Obstructive
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 77 participants (actual)
- Lead sponsor
- Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, GermanyIndustry (pharma, biotech, devices)
- Study start
- 5 Dec 2022 (actual)
- Primary completion
- 31 Oct 2023 (actual)
- Study completion
- 31 Oct 2023 (actual)
- First posted
- 19 Dec 2022 (actual)
- Results
- Posted 15 Jan 2026
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| COPD cohort | Experimental | Device: Vivalink wearable device |
| Calibration participants cohort | Experimental | Device: Vivalink wearable device |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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)
- Participants ≥40 and ≤80 years at baseline
Key exclusion criteria (9 listed)
- 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
- Participants with a cardiac pacemaker, defibrillators, or other implanted electronic devices
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Vivalink wearable devicea CE marked device modified to add a temperature measurement algorithm in addition to ECG and respiratory rate measurements | Device | COPD cohort, Calibration participants cohort |
Primary outcome
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Physical Activity
Time frame: Day 0(Baseline) and Day 8 to Day 14
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.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate
Time frame: Day 0(Baseline) and Day 8 to Day 14
Heart rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (SDRR, SDNN, SDNNI, RMSSD, ln(RMSSD))
Time frame: Day 0(Baseline) and Day 8 to Day 14
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)
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (pNN50)
Time frame: Day 0(Baseline) and Day 8 to Day 14
pNN50 is the percentage of adjacent NN intervals that differ from each other by more than 50 milliseconds.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (Stress Index)
Time frame: Day 0(Baseline) and Day 8 to Day 14
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.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF and HF)
Time frame: Day 0(Baseline) and Day 8 to Day 14
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).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Heart Rate Variability (LF/HF)
Time frame: Day 0(Baseline) and Day 8 to Day 14
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).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Temperature
Time frame: Day 0(Baseline) and Day 8 to Day 14
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.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Respiratory Rate
Time frame: Day 0(Baseline) and Day 8 to Day 14
Respiration rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Cough Frequency
Time frame: Day 0(Baseline) and Day 8 to Day 14
Cough Frequency was provided by vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Sleep Patterns
Time frame: Day 0(Baseline) and Day 8 to Day 14
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.).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Resting Heart Rate
Time frame: Day 0(Baseline) and Day 8 to Day 14
Resting Heart Rate is provided by Vivalink.
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Inspiration vs Expiration Time Ratio
Time frame: Day 0(Baseline) and Day 8 to Day 14
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).
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Frequency of Additional Medication
Time frame: Day 0(Basseline) and Day 8 to Day 14
Count of the number of times the use of additional medication as a log activity is reported per day.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Accuracy
Time frame: Up to 3 months
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.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Precision
Time frame: Up to 3 months
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.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Recall
Time frame: Up to 3 months
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.
Prediction of Moderate or Severe COPD Exacerbations by Building a Statistical Model Employing Sensor-Derived Data and Demographic and Medical Covariates - Specificity
Time frame: Up to 3 months
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.
19 secondary outcomes
Correlation of Sensor-Collected Data With COPD Assessment Test (CAT) Questionnaire: Participants Health Status and Symptoms at Baseline and Study End
Time frame: Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FEV1) at Baseline and Study End
Time frame: Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FVC) at Baseline and Study End
Time frame: Baseline (Day 0) and at 3 months
Correlation of Sensor-Collected Data With Lung Function (FEV1/FVC) at Baseline and Study End
Time frame: 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)
Time frame: 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)
Time frame: 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))
Time frame: 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))
Time frame: 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)
Time frame: 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
Time frame: Up to 3 months
Association Between Sensor Parameters (Heart Rate and Resting Heart Rate) and CAT Score
Time frame: 7 days before Severe/Moderate Excarbations(S/M E) (7-day window period)
Association Between Sensor Parameters (Respiration Rate) and CAT Score
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Physical Activity) and CAT Score
Time frame: 14 days before S/M E (1 day window period)
Association Between Sensor Parameters (Sleep Pattern) and CAT Score
Time frame: 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
Time frame: Up to 3 months
Results and safety
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Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jan 2026.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Trial Awareness and Transparency websiteLink provided by the sponsor.
- Individual participant dataSharing planned: No. 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
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Medical Responsible study director
Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany
11 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Vivalink wearable device in people with Pulmonary Disease, Chronic Obstructive, run by Merck Healthcare KGaA, Darmstadt, Germany, an affiliate of Merck KGaA, Darmstadt, Germany at 11 sites in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 11 sites in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 15 Jan 2026). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Chronic Obstructive Pulmonary Disease (COPD) Exacerbations of Sensor-collected Parameters During Observation Period - Physical Activity (time frame: Day 0(Baseline) and Day 8 to Day 14). The study lists 18 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jan 2026.