Abstract

9 Background: Web-based patient reported outcome (PRO) improved quality of life (QoL), and overall survival (OS) in patients with advanced NSCLC who were treating with specific therapy. Lung Cancer Care application is a mobile application program that provides patients with individually tailored information on patient reported outcome. This study aims to invent a novel mobile application evaluating PRO for Thai NSCLC patients, and to evaluate the validity of mobile application. Methods: Our mobile application-based PRO was designed for monitoring quality of life. The validity of the application was tested following guidelines for translating, and validating a questionnaire. The quality of life score (FACT-L score). After the validated mobile application-based PRO, patients with advanced NSCLC were randomized to use mobile application-based PRO versus routine follow-up. The primary endpoint was quality of life (QoL). Secondary endpoint was OS. Results: Thirty-three patients with advanced NSCLC were enrolled. The mean of FACT-L score at baseline in mobile application-based PRO arm and routine follow up arm was similar (90.08 ± 5.66 vs 91.78 ± 5.26, p-value= 0.82). Patients with mobile application group had more FACT-L score at 3 months than patients with routine follow up arm (106 ± 5.97 vs 99.96 ± 5.74, p-value = 0.07). There was a trend towards increased in different mean of FACT-L score at baseline and 3 months in patients with mobile application compared to patients with routine follow up ( p-value = 0.05). The median follow-up time was 5.43 months, patients with mobile application had longer median OS than patients with routine follow up (4 months vs 2.9 months, p-value = 0.5). Conclusions: Lung Cancer Care application based on self-reported symptoms is a novel electronic device for real-time patient care monitoring. Our study results showed trend towards improved quality of life from using this novel mobile application. However, there was small samples for pilot testing, the relatively large sampling errors may reduce the statistical power needed to validate this tool.

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