利用与健康相关的社交媒体了解免疫肿瘤学和靶向治疗时代成年肺癌患者的经历:观察性研究

IF 3.3 Q2 ONCOLOGY JMIR Cancer Pub Date : 2023-07-12 DOI:10.2196/45707
Alison Booth, Stephanie Manson, Sonia Halhol, Evie Merinopoulou, Mireia Raluy-Callado, Asha Hareendran, Stefanie Knoll
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引用次数: 0

摘要

背景:随着免疫肿瘤学(IO)和靶向治疗(tt)的批准,非小细胞肺癌(NSCLC)的治疗发生了巨大的变化。缺乏对这些疗法的患者体验及其影响的见解。患者越来越多地使用与健康相关的社交媒体来分享他们的疾病和治疗经验,从而成为了解患者声音和发现潜在未满足需求的宝贵现实数据来源。目的:本研究旨在描述在肺癌特异性社交媒体上发布的讨论中报告的非小细胞肺癌患者的疾病症状和相关影响。方法:从选定的肺癌或非小细胞肺癌特异性网站上提取公开可用的帖子(2010-2019)。社交媒体用户(在这些网站上发帖的患者和护理人员)按转移性和佐剂合格亚组进行分层,并使用自然语言处理(NLP)和机器学习方法进行治疗。使用NLP对症状进行自动识别。对提及疼痛相关、疲劳相关、呼吸相关或感染相关症状的帖子的随机样本进行定性数据分析(QDA),以捕捉患者对这些和相关影响的体验。结果:总体而言,转移组和辅助组分别包括1724名用户(50390个帖子)和574名用户(4531个帖子)。在转移性组的用户中,疼痛、不适和疲劳是最常提到的症状(分别为49.7%和39.6%),而在QDA(来自134名用户的258个帖子)中,最常见的影响与身体缺陷、睡眠和饮食习惯有关。在辅助组的用户中,疼痛、不适和呼吸症状是最常被提及的(分别为44.8%和23.9%),QDA中确定的影响(来自92名用户的154篇帖子)主要与身体功能有关。结论:这项对患者和护理人员的社交媒体的探索性观察分析的结果揭示了新疗法时代非小细胞肺癌的生活经历,揭示了大多数报告的症状及其影响。这些发现可用于为未来的NSCLC治疗发展和患者管理研究提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Using Health-Related Social Media to Understand the Experiences of Adults With Lung Cancer in the Era of Immuno-Oncology and Targeted Therapies: Observational Study.

Background: The treatment of non-small cell lung cancer (NSCLC) has evolved dramatically with the approval of immuno-oncology (IO) and targeted therapies (TTs). Insights on the patient experience with these therapies and their impacts are lacking. Health-related social media has been increasingly used by patients to share their disease and treatment experiences, thus representing a valuable source of real-world data to understand the patient's voice and uncover potential unmet needs.

Objective: This study aimed to describe the experiences of patients with NSCLC as reported in discussions posted on lung cancer-specific social media with respect to their disease symptoms and associated impacts.

Methods: Publicly available posts (2010-2019) were extracted from selected lung cancer- or 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 methods. Automated identification of symptoms was conducted using NLP. Qualitative data analysis (QDA) was conducted on random samples of posts mentioning pain-related, fatigue-related, respiratory-related, or infection-related symptoms to capture the patient experience with these and associated impacts.

Results: Overall, 1724 users (50,390 posts) and 574 users (4531 posts) were included in the metastatic group and adjuvant group, respectively. 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 frequent impacts related to physical impairments, sleep, and eating habits. Among users in the adjuvant group, pain, discomfort, and respiratory symptoms were the most commonly mentioned (44.8% and 23.9%, respectively), and impacts identified in the QDA (154 posts from 92 users) were mostly related to physical functioning.

Conclusions: Findings from this exploratory observational analysis of social media among patients and caregivers informed the lived experience of NSCLC in the era of novel therapies, shedding light on most reported symptoms and their impacts. These findings can be used to inform future research on NSCLC treatment development and patient management.

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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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