网上抑郁症社区作为一种辅助方法,可改善抑郁症患者的服药态度:横断面调查研究。

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Runnan Chen, Xiaorong Fu, Mochi Liu, Ke Liao, Lifei Bai
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引用次数: 0

摘要

背景:在中国,抑郁症患者不遵医嘱服药的现象十分普遍,这给医疗系统带来了严峻的挑战。研究发现,在线健康社区能有效提高患者的依从性。然而,在抑郁症治疗中,支持这一效果的实证证据并不存在,在线健康社区的内容对患者服药依从性态度的影响也未得到充分探讨:本研究旨在探讨在线抑郁症社区(ODC)是否有助于改善抑郁症患者服药不力的问题。借鉴刺激-组织-反应理论和感受-信息理论,我们建立了一个研究模型,以考察有用的机构生成内容(IGC)和积极的用户生成内容(UGC)在感知到的社会支持、感知到的抗抑郁药物价值以及无望感的调节作用的中介作用下对服药态度的影响:本研究采用横断面问卷调查法。研究方法:本研究采用横断面问卷调查法,从中国各开放数据中心招募参与者,为一项主要研究和两项稳健性检查提供数据。采用层次多元回归分析和引导分析作为检验假设的主要方法:我们共收到 1515 份有效回复,形成了 5 个不同的数据集:IGC 模型(n=353,23.3%)、UGC 模型(n=358,23.63%)、IGC+UGC 模型(n=270,17.82%)、IGC-B 模型(n=266,17.56%)和 UGC-B 模型(n=268,17.69%)。主要研究使用了 IGC 和 UGC 模型。通过感知社会支持和感知抗抑郁药物价值的中介作用,有用的 IGC 和积极的 UGC 被证明对抑郁症患者坚持服药的态度有积极影响。研究结果证实,无望感削弱甚至抵消了ODC内容对抑郁症患者坚持服药态度的积极影响:本研究首次通过实证研究证明了ODC内容与服药态度之间的关系,为解决中国抑郁症患者服药依从性差的问题提供了一种新的解决方案。我们的研究结果还为如何优化这一新方法提供了建议--医护人员应根据抑郁症患者的信息需求制作精准的在线内容,ODC服务提供者应努力调节社区氛围。尽管如此,我们还是要提醒大家,ODC 干预措施不能作为解决严重抑郁症状患者服药不良问题的唯一方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Depression Communities as a Complementary Approach to Improving the Attitudes of Patients With Depression Toward Medication Adherence: Cross-Sectional Survey Study.

Background: Lack of adherence to prescribed medication is common among patients with depression in China, posing serious challenges to the health care system. Online health communities have been found to be effective in enhancing patient compliance. However, empirical evidence supporting this effect in the context of depression treatment is absent, and the influence of online health community content on patients' attitudes toward medication adherence is also underexplored.

Objective: This study aims to explore whether online depression communities (ODCs) can help ameliorate the problem of poor medication taking among patients with depression. Drawing on the stimulus-organism-response and feelings-as-information theories, we established a research model to examine the influence of useful institution-generated content (IGC) and positive user-generated content (UGC) on attitudes toward medication adherence when combined with the mediating role of perceived social support, perceived value of antidepressants, and the moderating role of hopelessness.

Methods: A cross-sectional questionnaire survey method was used in this research. Participants were recruited from various Chinese ODCs, generating data for a main study and 2 robustness checks. Hierarchical multiple regression analyses and bootstrapping analyses were adopted as the primary methods to test the hypotheses.

Results: We received 1515 valid responses in total, contributing to 5 different datasets: model IGC (n=353, 23.3%), model UGC (n=358, 23.63%), model IGC+UGC (n=270, 17.82%), model IGC-B (n=266, 17.56%), and model UGC-B (n=268, 17.69%). Models IGC and UGC were used for the main study. Model IGC+UGC was used for robustness check A. Models IGC-B and UGC-B were used for robustness check B. Useful IGC and positive UGC were proven to have positive impact on the attitudes of patients with depression toward medication adherence through the mediations of perceived social support and perceived value of antidepressants. The findings corroborated the role of hopelessness in weakening or even negating the positive effects of ODC content on the attitudes of patients with depression toward medication adherence.

Conclusions: This study provides the first empirical evidence demonstrating the relationship between ODC content and attitudes toward medication adherence, through which we offer a novel solution to the problem of poor medication adherence among patients with depression in China. Our findings also provide suggestions about how to optimize this new approach-health care practitioners should generate online content that precisely matches the informational needs of patients with depression, and ODC service providers should endeavor to regulate the community atmosphere. Nonetheless, we warn that ODC interventions cannot be used as the only approach to addressing the problem of poor medication taking among patients with severe depressive symptoms.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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