Research on the intelligent internet nursing model based on the child respiratory and asthma control test scale for asthma management of preschool children

BACKGROUND: Childhood asthma is a common respiratory ailment that significantly affects preschool children. Effective asthma management in this population is particularly challenging due to limited communication skills in children and the necessity for consistent involvement of a caregiver. With the rise of digital healthcare and the need for innovative interventions, Internet-based models can potentially offer relatively more efficient and patient-tailored care, especially in children. AIM: To explore the impact of an intelligent Internet care model based on the child respiratory and asthma control test (TRACK) on asthma management in preschool children. METHODS: The study group comprised preschoolers, aged 5 years or younger, that visited the hospital's pediatric outpatient and emergency departments between January 2021 and January 2022. Total of 200 children were evenly and randomly divided into the observation and control groups. The control group received standard treatment in accordance with the 2016 Guidelines for Pediatric Bronchial Asthma and the Global Initiative on Asthma. In addition to above treatment, the observation group was introduced to an intelligent internet nursing model, emphasizing the TRACK scale. Key measures monitored over a six-month period included the frequency of asthma attack, emergency visits, pulmonary function parameters (FEV1, FEV1/FVC, and PEF), monthly TRACK scores, and the SF-12 quality of life assessment. Post-intervention asthma control rates were assessed at six-month follow-up. RESULTS: The observation group had fewer asthma attacks and emergency room visits than the control group (P < 0.05). After six months of treatment, the children in both groups had higher FEV1, FEV1/FVC, and PEF (P < 0.05). Statistically significant differences were observed between the two groups (P < 0.05). For six months, children in the observation group had a higher monthly TRACK score than those in the control group (P < 0.05). The PCS and MCSSF-12 quality of life scores were relatively higher than those before the nursing period (P < 0.05). Furthermore, the groups showed statistically significant differences (P < 0.05). The asthma control rate was higher in the observation group than in the control group (P < 0.05). CONCLUSION: TRACK based Intelligent Internet nursing model may reduce asthma attacks and emergency visits in asthmatic children, improve lung function, quality of life, and the TRACK score and asthma control rate. The effect of nursing was significant, allowing for development of an asthma management model.