JMIR Publications

ABSTRACT

Background:

The treatment of non–small cell lung cancer (NSCLC) has evolved dramatically with the approval of immuno-oncology (IO) and targeted therapies (TT). Health-related social media has become an increasingly common resource utilized by patients and caregivers to share their journeys and experiences, representing a vast and novel source of real-world data.

Objective:

This study aimed to understand experiences of patients with NSCLC using lung cancer-specific social media by describing their disease symptoms and associated impacts.

Methods:

Publicly available posts (2010–2019) were extracted from selected lung cancer/NSCLC-specific websites. Social media users (patients and caregivers posting on these websites) were stratified by metastatic- and adjuvant-eligible subgroups and treatment received using natural language processing (NLP) and machine learning. Automated identification of symptoms was captured using NLP, and qualitative data analysis (QDA) was conducted on random samples of posts mentioning pain-related, fatigue-related, respiratory-related, or infection-related symptoms.

Results:

Among users in the metastatic group, pain/discomfort and fatigue were the most commonly mentioned symptoms (49.7% and 39.6%, respectively), and in the QDA (258 posts from 134 users), the most frequently reported impacts included physical impairments, sleep, and eating. Among users in the adjuvant group, pain/discomfort and respiratory symptoms were the most commonly mentioned (44.8% and 23.9%, respectively), and symptom impacts identified in the QDA (154 posts from 92 users) mostly related to physical functioning.

Conclusions:

Findings from this exploratory noninterventional retrospective analysis of social media among patients and caregivers informed on the lived experience of NSCLC in the era of novel therapies, including description of commonly mentioned symptoms. These findings can be used to inform future research in NSCLC.