Development of a Mobile App for Early Detection of Pediatric Respiratory Illnesses

Thesis Abstract

Pediatric respiratory illnesses remain a leading cause of morbidity and mortality globally, particularly in low-resource settings where early detection and timely intervention are often hindered by limited access to healthcare facilities and inadequate monitoring tools. This study aims to develop a mobile application capable of facilitating the early detection of respiratory distress and associated illnesses among children aged 0-5 years, thereby promoting prompt medical attention and reducing adverse health outcomes. The specific objectives include designing a user-friendly mobile app incorporating symptom input and sensor data, evaluating its usability and accuracy in detecting respiratory illnesses, and examining its potential impact on caregivers’ health-seeking behaviors. A mixed-methods research design was adopted, combining quantitative and qualitative approaches to ensure comprehensive evaluation. The quantitative component involved a cross-sectional study of 300 caregivers and their children across urban and rural clinics, using purposive sampling to capture diverse demographic and socioeconomic backgrounds. Data collection relied on structured questionnaires assessing caregivers’ awareness, mobile app usability, and symptom reporting accuracy, complemented by clinical assessments and sensor data recorded via the app during respiratory episodes. The qualitative component comprised semi-structured interviews with 30 healthcare providers and caregivers to explore contextual factors influencing app adoption and utilization. Data analysis employed descriptive statistics, chi-square tests, and logistic regression to evaluate the app’s diagnostic predictive capacity, while thematic analysis was conducted on interview transcripts to identify barriers and facilitators related to implementation. The core analytical framework integrated the Technology Acceptance Model (TAM) and the Health Belief Model (HBM) to understand factors influencing user adoption and adherence. Predictive modelling through logistic regression and machine learning algorithms such as Random Forests and Support Vector Machines aimed to assess the app’s accuracy in identifying respiratory illnesses, with sensitivity, specificity, and receiver operating characteristic (ROC) curves as primary evaluation metrics. It is anticipated that the mobile app will demonstrate high usability scores (>85%) and diagnostic accuracy with sensitivity and specificity exceeding 80%, significantly improving early recognition rates compared to traditional caregiver assessments. The findings are expected to show that the app effectively increases caregiver awareness and promotes timely health-seeking behavior, leading to early interventions and improved health outcomes. This research makes a substantive contribution to knowledge by integrating mobile health technology with pediatric respiratory disease detection, providing empirical evidence on the utility and acceptability of digital tools in resource-limited settings. It advances existing models of health behavior change by contextualizing the impact of technology-based interventions among caregivers and healthcare providers. The study also generates a validated prototype of a mobile app that can be adapted and scaled across similar contexts, facilitating cost-effective, community-based health management. Conclusively, the study recommends the widespread adoption of mobile health solutions for pediatric respiratory health monitoring, emphasizing the importance of stakeholder engagement, user-centered design, and capacity building for sustainable implementation. Further research is suggested to explore longitudinal impacts, integration with healthcare systems, and the app’s applicability to other pediatric conditions. Overall, this research underscores the potential of ICT-driven approaches to transform early diagnosis and management of respiratory illnesses among vulnerable pediatric populations, ultimately contributing to reduced healthcare burden and enhanced child health outcomes globally.