Clinical trial NCT05243667: Research on the Early Warning Model of Children Asthma Acute Attack Based on Wearable Wrist Smart Device of Huami
Official title: Research on the Early Warning Model of Children Asthma Acute Attack Based on Wearable Wrist Smart Device of Huami
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Asthma in Children
Keywords: Wearable wrist Smart Device
Lead sponsor: Guangzhou Institute of Respiratory Disease
Sponsor class: OTHER
First posted: 2022-02-17
Enrollment: 200
Sex: ALL
Minimum age (years): 3
Maximum age (years): 14
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Children with diagnosed asthma.

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.

Eligibility criteria: Inclusion Criteria:

Clinical diagnosis of asthma.

Exclusion Criteria:

Severe chronic diseases with organ dysfunction and dyspnea.

Primary outcomes:
measure: Heart rate
timeFrame: up to 24 weeks
measure: blood oxygen
timeFrame: up to 24 weeks
measure: exercise
timeFrame: up to 24 weeks
measure: sleep
timeFrame: up to 24 weeks
Secondary outcomes:
measure: weight
timeFrame: up to 24 weeks
measure: height
timeFrame: up to 24 weeks
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Guangzhou institute of respiratory disease
state: Guangdong
Study officials:
affiliation: Guangzhou Institute of Respiratory Disease
name: Qin C Pan, master
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.