Privacy Concerns Versus Personalized Health Content-Pregnant Individuals' Willingness to Share Personal Health Information on Social Media: Survey Study.

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES JMIR Formative Research Pub Date : 2025-02-10 DOI:10.2196/60862
Haijing Hao, Yang W Lee, Marianne Sharko, Qilu Li, Yiye Zhang
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Abstract

Background: Often lacking immediate access to care providers, pregnant individuals frequently turn to web-based sources for information to address their evolving physical and mental health needs. Social media has gained increasing prominence as a source of news and information despite privacy concerns and unique risks posed to the pregnant population.

Objectives: This study investigated the extent to which patients may be willing to disclose personal health information to social media companies in exchange for more personalized health content.

Methods: We designed and deployed an electronic survey to pregnant individuals worldwide electronically in 2023. We used the classical Internet Users' Information Privacy Concerns (IUIPC) model to examine how privacy concerns modulate pregnant individuals' behaviors and beliefs regarding risk and trust when using social media for health purposes. Results were analyzed using partial least squares structural equation modeling.

Results: Among 317 respondents who initiated the survey, 84% (265/317) of the respondents remained in the study, providing complete responses. Among them, 54.7% (145/265) indicated willingness to provide their personalized health information for receiving personalized health content via social media, while 26% (69/265) were uncertain and 19.3% (51/265) were opposed. Our estimated IUIPC model results are statistically significant and qualitatively align with the classic IUIPC model for the general population, which was previously found in an e-commerce context. The structural model revealed that privacy concerns (IUIPC) negatively affected trusting beliefs (β=-0.408; P<.001) and positively influenced risk beliefs (β=0.442; P<.001). Trusting beliefs negatively impacted risk beliefs (β=-o.362; P<.001) and positively affected the intention to disclose personal health information (β=o.266; P<.001). Risk beliefs negatively influenced the intention to disclose (β=-0.281; P<.001). The model explained 41.5% of the variance in the intention to disclose personal health information (R²=0.415). In parallel with pregnant individuals' willingness to share, we find that they have heightened privacy concerns and their use of social media for information seeking is largely impacted by their trust in the platforms. This heightened concern significantly affects both their trusting beliefs, making them less inclined to trust social media companies, and their risk beliefs, leading them to perceive greater risks in sharing personal health information. However, within this population, an increase in trust toward social media companies leads to a more substantial decrease in perceived risks than what has been previously observed in the general population.

Conclusions: We find that more than half of the pregnant individuals are open to sharing their personal health information to receive personalized content about health via social media, although they have more privacy concerns than the general population. This study emphasizes the need for policy regarding the protection of health data on social media for the pregnant population and beyond.

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隐私问题与个性化健康内容——孕妇在社交媒体上分享个人健康信息的意愿:调查研究。
背景:孕妇通常无法立即获得护理提供者的帮助,因此经常转向基于网络的信息来源,以满足其不断变化的身心健康需求。社交媒体作为新闻和信息的来源越来越突出,尽管存在隐私问题和对孕妇群体构成的独特风险。目的:本研究调查了患者在多大程度上愿意向社交媒体公司披露个人健康信息,以换取更个性化的健康内容。方法:我们于2023年设计并部署了一项全球孕妇电子调查。我们使用经典的互联网用户信息隐私关注(IUIPC)模型来研究隐私关注如何调节怀孕个体在出于健康目的使用社交媒体时对风险和信任的行为和信念。采用偏最小二乘结构方程模型对结果进行分析。结果:在发起调查的317名受访者中,84%(265/317)的受访者留在了研究中,提供了完整的回复。其中54.7%(145/265)的人表示愿意提供自己的个性化健康信息,以便通过社交媒体接收个性化健康内容,26%(69/265)的人表示不确定,19.3%(51/265)的人表示反对。我们估计的IUIPC模型结果具有统计意义,并且在质量上与以前在电子商务环境中发现的针对一般人群的经典IUIPC模型一致。结构模型显示,隐私关注(IUIPC)负向影响信任信念(β=-0.408;结论:我们发现,超过一半的孕妇愿意通过社交媒体分享自己的个人健康信息,以接收个性化的健康内容,尽管她们比一般人群更关注隐私。这项研究强调,需要制定政策,保护孕妇和其他人群在社交媒体上的健康数据。
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来源期刊
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
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