Integrating the Situational Theory of Problem Solving and Technology Acceptance Model to Predict Intention to Practice Health Protective Behavior for Influenza-Like Illness Among TikTok Users: Cross-Sectional Study.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Can Li, Jen-Sern Tham, Ghazali Akmar Hayati Ahmad, Norliana Hashim, Jeong-Nam Kim
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引用次数: 0

Abstract

Background: Outbreaks of influenza-like illness (ILI) pose ongoing public health challenges, prompting widespread demand for timely and accessible health information. TikTok, a leading short video platform, has emerged as an overarching channel for disseminating health-related content, particularly in mainland China. While previous studies have examined health communication on social media, few have integrated complementary theoretical frameworks to understand how user perceptions and motivations jointly influence health behaviors.

Objective: This study integrates the situational theory of problem solving (STOPS) and the technology acceptance model (TAM) to examine the communicative actions and intentions of Chinese TikTok users to adopt health protective behaviors in response to ILI.

Methods: A cross-sectional web-based survey was conducted in China between June and July 2023 using convenience and snowball sampling. A total of 1109 valid responses were analyzed using partial least squares structural equation modeling. Constructs from STOPS (problem recognition, constraint recognition, involvement recognition, situational motivation, and communicative action in problem solving) and TAM (perceived usefulness, perceived ease of use, and attitude) were measured alongside risk perception and intention to engage in protective behaviors.

Results: Perceived usefulness (β=.344; P<.001) and ease of use (β=.359; P<.001) positively influenced the attitude toward using TikTok. Risk perception (β=.050, P=.02) had a small but significant impact on attitude. Situational motivation was positively predicted by risk perception (β=.154; P<.001), problem recognition (β=.153; P<.001), and involvement recognition (β=.248; P<.001) but negatively predicted by constraint recognition (β=-.265; P<.001). Both attitude (β=.390; P<.001) and situational motivation (β=.471; P<.001) significantly influenced communicative action, which in turn predicted intention to practice protective behaviors (β=.570; P<.001). Mediation analyses confirmed the partial mediating roles of attitude and situational motivation.

Conclusions: TikTok is an effective platform for public health communication in China, particularly for ILI-related content. Integrating the STOPS and TAM provides a robust framework for explaining how user perceptions and motivations translate into digital engagement and health protective intentions. These findings suggest that interventions should not only enhance technological usability and credibility but also tailor content to elevate perceived personal relevance and reduce psychological or contextual constraints. Future public health campaigns can benefit from engaging influencers, using participatory content formats, and targeting specific motivational cues to increase user involvement in health communication and behavioral change. Caution is warranted in generalizing these results because of the culturally specific and demographically skewed nature of the sample.

整合问题解决情境理论和技术接受模型预测TikTok用户对流感样疾病采取健康保护行为的意愿:横断面研究
背景:流感样疾病(ILI)的暴发构成持续的公共卫生挑战,促使人们对及时和可获取的卫生信息产生了广泛的需求。领先的短视频平台TikTok已成为传播健康相关内容的主要渠道,尤其是在中国大陆。虽然之前的研究考察了社交媒体上的健康传播,但很少有研究整合互补的理论框架来理解用户感知和动机如何共同影响健康行为。目的:本研究结合问题解决情境理论(情境理论)和技术接受模型(技术接受模型),研究中国TikTok用户应对ILI时采取健康保护行为的交际行为和意图。方法:于2023年6月至7月在中国开展基于网络的横断面调查,采用便利抽样和滚雪球抽样。采用偏最小二乘结构方程模型对1109份有效响应进行分析。stop(问题识别、约束识别、参与识别、情境动机和问题解决中的沟通行为)和TAM(感知有用性、感知易用性和态度)的构念与风险感知和参与保护行为的意图一起被测量。结果:感知有用性(β=.344;结论:TikTok是中国公共卫生传播的有效平台,特别是与i相关的内容。综合stop和TAM为解释用户感知和动机如何转化为数字参与和健康保护意图提供了一个强有力的框架。这些发现表明,干预措施不仅应该提高技术的可用性和可信度,还应该定制内容,以提高感知到的个人相关性,减少心理或情境限制。未来的公共卫生运动可以受益于吸引有影响力的人,使用参与式内容格式,并针对特定的动机线索,以增加用户对健康传播和行为改变的参与。由于样本的文化特殊性和人口统计学倾斜的性质,在概括这些结果时需要谨慎。
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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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