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

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.

Heart rate is provided by Vivalink.

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)

pNN50 is the percentage of adjacent NN intervals that differ from each other by more than 50 milliseconds.

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.

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).

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).

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.

Respiration rate is provided by Vivalink.

Cough Frequency was provided by vivalink.

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.).

Resting Heart Rate is provided by Vivalink.

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).

Count of the number of times the use of additional medication as a log activity is reported per day.

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.

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.

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.

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.

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.

Lung function was assessed using plethysmography.

Lung function was assessed using plethysmography.

Lung function was assessed using plethysmography.

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

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

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

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

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

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.

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."

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."

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."

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".

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."

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."

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."

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."

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.