Research on the Early Warning Model of Children Asthma Acute Attack Based on Wearable Wrist Smart Device of Huami

NCT ID: NCT05243667

Last Updated: 2022-02-17

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-01
• Study Completion Date: 2024-12-30

Brief Summary

Childhood asthma is the most common chronic respiratory disease in childhood. The essence of asthma is chronic airway inflammation and airway hyperresponsiveness.The physiological characteristics of children and adults are very different, and the compensatory ability is very strong. There are often no obvious symptoms at the early stage of attack, or only intermittent or persistent cough of different degrees, without typical chest tightness and asthma.However, at this time, certain physiological indicators such as blood oxygen, heart rate, respiratory rate may have been significantly abnormal.If the disease continues to deteriorate and progresses to decompensation, it can quickly move from an asymptomatic state to a failure stage.Therefore, dynamic and accurate acquisition of real-time vital signs and assessment is of great significance for early warning and improvement of prognosis of asthma attacks in children.Intelligent wearable devices can be used to acquire real-time physiological index data of users, such as heart rate, blood oxygen, exercise and sleep dynamic data.An in-depth analysis of long-term and multi-scene dynamic data before and after asthma attacks can establish an early warning model for children with acute asthma attacks by wearable wrist smart devices, which may provide important help for severity assessment, follow-up tracking and out-of-hospital prevention and control of the disease.

Detailed Description

this project is selected 200 cases of children with asthma diagnosis definitely, collection and heart rate, blood oxygen, exercise and sleep dynamic data, followed up for 3 to 6 months (at least 3 months), records of clinical asthma attacks and clinical data, through the cloud data analysis and deep learning, analysis of children with asthma attacks and multiple physiological parameters (heart rate, blood oxygen, movement and the dynamic data of sleep, etc.), the connection between the building of asthma early warning and illness severity hierarchical evaluation model.Then choose 200 cases of diagnosis in clinical practice to determine follow-up, patients with asthma children to observe to verify the exactness of the model of asthma attack early warning, and according to the collected data to further improve, calibration model, designed to provide children with family members and medical personnel of an asthma attack warning and follow-up management wearable auxiliary equipment and management platform.

Conditions

Asthma in Children

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Interventions

Wearable wrist Smart Device of Huami

All enrolled patients underwent continuous monitoring by wearing huami blood oxygen testing equipment, and signed the informed consent for the clinical trial.Smart wrist wristbands will be issued, and patient information will be bound to the "Migang Health" platform, and clinical trial doctors will improve relevant personal information and clinical data.After binding to the APP "Migu Health", you can start collecting and recording dynamic data such as peripheral blood oxygen saturation, heart rate, exercise steps, sleep data, etc., and upload the data once a day.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

Clinical diagnosis of asthma.

Exclusion Criteria

Severe chronic diseases with organ dysfunction and dyspnea.

• Minimum Eligible Age: 3 Years
• Maximum Eligible Age: 14 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Guangzhou Institute of Respiratory Disease

OTHER

Sponsor Role: lead

Responsible Party

LI-HONG SUN

Clinical Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Qin C Pan, master

Role: PRINCIPAL_INVESTIGATOR

Guangzhou Institute of Respiratory Disease

Locations

Guangzhou institute of respiratory disease

Guangzhou, Guangdong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Li h Sun, master

Role: CONTACT

sunlihong9797@126.com

+86 13719240285

Sun K Huang, master

Role: CONTACT

amwkdga@163.com

+86 13512750833

Facility Contacts

Nanshan Zhong, master

Role: primary

nanshan@vip.163.com

+86-20-83062888

Lihong Sun, master

Role: backup

sunlihong9797@126.com

+86-13719240285

References

Kumar N, Akangire G, Sullivan B, Fairchild K, Sampath V. Continuous vital sign analysis for predicting and preventing neonatal diseases in the twenty-first century: big data to the forefront. Pediatr Res. 2020 Jan;87(2):210-220. doi: 10.1038/s41390-019-0527-0. Epub 2019 Aug 4.

Reference Type: RESULT

PMID: 31377752 (View on PubMed)

Jensen CS, Aagaard H, Olesen HV, Kirkegaard H. A multicentre, randomised intervention study of the Paediatric Early Warning Score: study protocol for a randomised controlled trial. Trials. 2017 Jun 8;18(1):267. doi: 10.1186/s13063-017-2011-7.

Reference Type: RESULT

PMID: 28595614 (View on PubMed)

Carew C, Cox DW. Laps or lengths? The effects of different exercise programs on asthma control in children. J Asthma. 2018 Aug;55(8):877-881. doi: 10.1080/02770903.2017.1373806. Epub 2017 Oct 16.

Reference Type: RESULT

PMID: 28872938 (View on PubMed)

Other Identifiers

GuangzhouIRD-LSUN2

Identifier Type: -

Identifier Source: org_study_id