慢性病患者和精神疾病患者参与社交媒体模式的差异

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2024-04-14 DOI:10.3390/informatics11020018
Elizabeth Ayangunna, Gulzar Shah, Kingsley Kalu, Padmini Shankar, Bushra Shah
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引用次数: 0

摘要

在健康信息交流方面,互联网和支持的应用程序的使用达到了历史上前所未有的水平。越来越多地使用互联网和社交媒体平台会影响患者的健康行为。本研究旨在评估被诊断患有慢性疾病或精神疾病的患者参与社交媒体的模式差异。本研究使用了 2017 年至 2020 年期间四次迭代的全国健康信息趋势调查第 4 周期的数据,样本量(N)= 16,092 个。为了分析反映是否患有慢性疾病或精神健康状况的自变量与不同程度的社交媒体参与之间的关联,研究人员进行了描述性统计和逻辑回归。至少患有一种慢性疾病的受访者更有可能加入基于互联网的支持小组(调整比值比或 AOR = 1.5;置信区间,CI = 1.11-1.93)和在 YouTube 上观看与健康相关的视频(AOR = 1.2;CI = 1.01-1.36);患有精神疾病的受访者不太可能在社交媒体上访问和分享健康信息、加入基于互联网的支持小组和在 YouTube 上观看与健康相关的视频。种族、年龄和教育水平也会影响受访者是否选择在 YouTube 上观看与健康相关的视频。了解患者在社交媒体上参与健康相关内容的模式,以及他们的网络行为如何根据患者的病情而有所不同,有助于利用社交媒体平台制定更有效、更有针对性的公共卫生干预措施。
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Variations in Pattern of Social Media Engagement between Individuals with Chronic Conditions and Mental Health Conditions
The use of the internet and supported apps is at historically unprecedented levels for the exchange of health information. The increasing use of the internet and social media platforms can affect patients’ health behavior. This study aims to assess the variations in patterns of social media engagement between individuals diagnosed with either chronic diseases or mental health conditions. Data from four iterations of the Health Information National Trends Survey Cycle 4 from 2017 to 2020 were used for this study with a sample size (N) = 16,092. To analyze the association between the independent variables, reflecting the presence of chronic conditions or mental health conditions, and various levels of social media engagement, descriptive statistics and logistic regression were conducted. Respondents who had at least one chronic condition were more likely to join an internet-based support group (Adjusted Odds Ratio or AOR = 1.5; Confidence Interval, CI = 1.11–1.93) and watch a health-related video on YouTube (AOR = 1.2; CI = 1.01–1.36); respondents with a mental condition were less likely to visit and share health information on social media, join an internet-based support group, and watch a health-related video on YouTube. Race, age, and educational level also influence the choice to watch a health-related video on YouTube. Understanding the pattern of engagement with health-related content on social media and how their online behavior differs based on the patient’s medical conditions can lead to the development of more effective and tailored public health interventions that leverage social media platforms.
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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