Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| People's Hospital of Beijing Daxing District | OTHER |
| Beijing Miyun Hospital | UNKNOWN |
| Civil Aviation General Hospital | OTHER |
| Aerospace 731 Hospital |
Not provided
Not provided
Not provided
Not provided
Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases. Early detection and treatment are critical to prevent the deterioration of COPD. In this study, investigators aim to develop an algorithm that can detect and infer the severity level of COPD from physiological parameters and audio data which are collected by a wearable device. Investigators will complete the study in two stages: stage 1. A panel study to assess the ability to infer the severity of COPD by intelligent terminal devices; stage 2. Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices.
In this study, investigators aim to establish an algorithm that can detect and infer the severity level of COPD from physiological parameters, coughing sounds, and forceful blowing sounds data that are collected by wearable devices.
This study is divided into two stages. Stage one: A panel study to assess the ability to infer the severity of COPD by intelligent terminal devices. 30 patients with stable COPD will be enrolled and will undergo pulmonary function tests, electrocardiogram, echocardiography measurement, blood gas analysis, six-minutes walking test (6MWT), and polysomnography. And they are required to fill in the questionnaires related to COPD every day. Physiological parameters including oxygen saturation, heart rate, sleep, and physical activity will be collected by a wearable device for 7-14 consecutive days. Coughing and forceful blowing sounds will be collected twice daily. The association between the severity of COPD and physiological parameters from the wearable device will be analyzed.
Stage two: Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices. 200 patients with stable COPD and 200 non- COPD subjects will be enrolled. Questionnaires related to COPD will be collected, and subjects will undergo pulmonary function tests and electrocardiograms. Physiological parameters including oxygen saturation and heart rate will be continuously collected by a wearable device for about 3~7 days. Investigators will also collect coughing and forceful blowing sounds. A COPD diagnosis algorithm model based on physiological parameters and audio data of intelligent terminal devices will be established.
The study protocol has been approved by the Peking University First Hospital Institutional Review Board (IRB) (2022-083). Any protocol modifications will be submitted for IRB review and approval.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with stable COPD in Stage1 | no intervention | ||
| Patients with stable COPD in Stage2 | no intervention | ||
| Non-COPD subjects in Stage2 | no intervention |
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| Stage 1: Association between the severity of COPD airflow restriction and data collected by wearable devices | Association between the severity of COPD airflow restriction and data collected by wearable devices | 2 months |
| Stage 2:Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices | Establish an algorithm that can detect and infer the severity level of COPD by intelligent terminal devices | 5 months |
| Measure | Description | Time Frame |
|---|---|---|
| Stage 1: The compliance of subjects with wearable devices | The compliance of subjects with wearable devices is defined as the percentage of the actual completion time of data collection to the minimum required time (10 hours X 7 days=70 hours). | 2 months |
| Stage 1: Association between the severity of COPD airflow restriction, CAT score, mMRC score, echocardiography, blood gas analysis, six-minutes walking distance, polysomnography,and data collected by wearable devices |
Not provided
Stage 1:
Inclusion criteria:
Exclusion criteria:
Inclusion criteria:
Exclusion criteria: COPD and other serious chronic diseases (same exclusion criteria as COPD group).
Not provided
Not provided
Not provided
Stage I: patients with stable COPD Stage 2: patients with stable COPD and non-COPD subjects
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Guangfa Wang, MD | Peking University First Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Aerospace 731 Hospital | Beijing | Beijing Municipality | China | |||
| Beijing Jingmei Group General Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40093702 | Derived | Zhang C, Yu K, Jin Z, Bao Y, Zhang C, Liao J, Wang G. Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity. Digit Health. 2025 Mar 13;11:20552076251320730. doi: 10.1177/20552076251320730. eCollection 2025 Jan-Dec. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D029424 | Pulmonary Disease, Chronic Obstructive |
| ID | Term |
|---|---|
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
Not provided
Not provided
| OTHER |
| The Hospital of Shunyi District Beijing | UNKNOWN |
| Shichahai community health service center | UNKNOWN |
| Peking University Shougang Hospital | OTHER |
| Beijing Jingmei Group General Hospital | UNKNOWN |
| Beijing Luhe Hospital | OTHER |
| Beijing Jishuitan Hospital | OTHER |
Not provided
Not provided
Not provided
Association between the severity of COPD airflow restriction, CAT score, mMRC score, echocardiography, blood gas analysis, six-minutes walking distance, polysomnography,and data collected by wearable devices |
| 2 months |
| Stage 2: Association between the severity of COPD airflow restriction, CAT score, mMRC score,and data collected by wearable devices | Association between the severity of COPD airflow restriction, CAT score, mMRC score,and data collected by wearable devices | 5 months |
| Stage 2: number of adverse events | The number of adverse events | 5 months |
| Beijing |
| Beijing Municipality |
| China |
| Beijing Jishuitan Hospital | Beijing | Beijing Municipality | China |
| Beijing Luhe Hospital | Beijing | Beijing Municipality | China |
| Beijing Miyun Hospital | Beijing | Beijing Municipality | China |
| Civil Aviation General Hospital | Beijing | Beijing Municipality | China |
| Peking University Shougang Hospital | Beijing | Beijing Municipality | China |
| People's Hospital of Beijing Daxing District | Beijing | Beijing Municipality | China |
| Shichahai community health service center | Beijing | Beijing Municipality | China |
| The Hospital of Shunyi District Beijing | Beijing | Beijing Municipality | China |
| D020969 |
| Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |