Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69554, first published .
Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study

Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study

Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study

Authors of this article:

Chenyu Gu1 Author Orcid Image ;   Liquan Qian2 Author Orcid Image ;   Xiaojie Zhuo2 Author Orcid Image

Original Paper

1School of Journalism and Communication, Minjiang University, Fuzhou, China

2School of Arts and Media, Wuhan College, Wuhan, China

Corresponding Author:

Xiaojie Zhuo, PhD

School of Arts and Media

Wuhan College

No. 333 Huangjiahu Avenue

Jiangxia District, Hubei Province

Wuhan, 430212

China

Phone: 86 180 5922 1673

Email: 3074@mju.edu.cn


Background: The global aging population and rapid development of digital technology have made health management among older adults an urgent public health issue. The complexity of online health information often leads to psychological challenges, such as cyberchondria, exacerbating health information avoidance behaviors. These behaviors hinder effective health management; yet, little research examines their mechanisms or intervention strategies.

Objective: This study investigates the mechanisms influencing health information avoidance among older adults, emphasizing the mediating role of cyberchondria. In addition, it evaluates the effectiveness of mindfulness meditation as an intervention strategy to mitigate these behaviors.

Methods: A mixed methods approach was used, combining quantitative and qualitative methodologies. Substudy 1 developed a theoretical model based on self-determination theory to explore internal (positive metacognition and health self-efficacy) and external (subjective norms and health information similarity) factors influencing health information avoidance, with cyberchondria as a mediator. A cross-sectional survey (N=236) was conducted to test the proposed model. Substudy 2 involved a 4-week mindfulness meditation intervention (N=94) to assess its impact on reducing health information avoidance behaviors.

Results: Study 1 showed that positive metacognition (β=.26, P=.002), health self-efficacy (β=.25, P<.001), and health information similarity (β=.29, P<.001) significantly predicted health information avoidance among older adults. Cyberchondria mediated these effects: positive metacognition (effect=0.106, 95% CI 0.035-0.189), health self-efficacy (effect=0.103, 95% CI 0.043-0.185), and health information similarity (effect=0.120, 95% CI 0.063-0.191). Subjective norms did not significantly predict health information avoidance (β=‒.11, P=.13), and cyberchondria did not mediate this relationship (effect=‒0.045, 95% CI ‒0.102 to 0.016). Study 2 found that after the 4-week mindfulness intervention, the intervention group (group 1: n=46) exhibited significantly higher mindfulness levels than the control group (group 2: n=48; Mgroup1=4.122, Mgroup2=3.606, P<.001) and higher levels compared with preintervention (Mt2=4.122, Mt1=3.502, P<.001, where t1=preintervention and t2=postintervention). However, cyberchondria levels did not change significantly (Mt1=2.848, Mt2=2.685, P=.18). Nevertheless, the results revealed a significant interaction effect between mindfulness and cyberchondria on health information avoidance (effect=‒0.357, P=.002, 95% CI ‒0.580 to ‒0.131), suggesting that mindfulness intervention effectively inhibited the transformation of cyberchondria into health information avoidance behavior.

Conclusions: This study reveals the role of cyberchondria in health information avoidance and validates mindfulness meditation as an effective intervention for mitigating such behaviors. Findings offer practical recommendations for improving digital health information delivery and health management strategies for older adults.

JMIR Public Health Surveill 2025;11:e69554

doi:10.2196/69554

Keywords



Background

With the global aging population and the rise of digital health information, the health information behavior of older adults has become a critical focus [Zhao YC, Zhao M, Song S. Online health information seeking behaviors among older adults: systematic scoping review. J Med Internet Res. 2022;24(2):e34790. [FREE Full text] [CrossRef] [Medline]1]. The adoption of internet and age-friendly technologies has enabled older adults to seek health information online. However, the overwhelming volume and inconsistent quality of information, combined with lower information literacy among older adults, often hinder efficient access to reliable health information, posing challenges to their health management.

Health information avoidance is a growing concern [Cheng X, Huang X, Yang B, Xiao Y. Unveiling the paradox of technostress: Impacts of technology-driven stressors on the elderly's avoidance behaviors. Inf. Manag. 2023;60(8):103875. [CrossRef]2]. It involves individuals deliberately avoiding health-related information, often driven by anxiety and uncertainty, which can lead to severe health consequences [Barbour JB, Rintamaki LS, Ramsey JA, Brashers DE. Avoiding health information. J Health Commun. 2012;17(2):212-229. [CrossRef] [Medline]3]. Given their greater need for health information, avoidance behaviors in older adults can hinder effective health management and delay timely diagnosis or treatment [Sun Y, Feng Y, Shen X, Guo X. Fear appeal, coping appeal and mobile health technology persuasion: a two-stage scenario-based survey of the elderly. Inf. Technol. People. 2022;36(1):362-386. [CrossRef]4]. However, research on its causes and interventions remains limited. Cyberchondria, characterized by excessive health information searching, is another critical issue. It disrupts daily activities and exacerbates health anxiety, despite being initially aimed at reducing uncertainty [Starcevic V, Berle D. Cyberchondria: towards a better understanding of excessive health-related Internet use. Expert Rev Neurother. 2013;13(2):205-213. [CrossRef] [Medline]5]. While it has been linked to problems like internet addiction and sleep disorders, its connection to health information avoidance remains underexplored [Vismara M, Varinelli A, Pellegrini L, Enara A, Fineberg NA. New challenges in facing cyberchondria during the coronavirus disease pandemic. Curr Opin Behav Sci. 2022;46:101156. [FREE Full text] [CrossRef] [Medline]6,Zheng H, Chen X, Jiang S, Sun L. How does health information seeking from different online sources trigger cyberchondria? The roles of online information overload and information trust. Inf. Process. Manag. 2023;60(4):103364. [CrossRef]7]. This study posits that cyberchondria amplifies anxiety, which in turn motivates information avoidance as a self-protection mechanism [Chae J. Who avoids cancer information? Examining a psychological process leading to cancer information avoidance. J Health Commun. 2016;21(7):837-844. [CrossRef] [Medline]8]. Viewing cyberchondria as a psychological driver of health information avoidance offers valuable insights into this behavior.

This study introduces mindfulness meditation as a potential intervention. Rooted in Buddhist traditions, mindfulness has been shown to reduce anxiety and improve emotional regulation and cognitive flexibility [Linardon J. Rates of attrition and engagement in randomized controlled trials of mindfulness apps: Systematic review and meta-analysis. Behav Res Ther. 2023;170:104421. [FREE Full text] [CrossRef] [Medline]9,Creswell JD. Mindfulness Interventions. Annu Rev Psychol. 2017;68:491-516. [CrossRef] [Medline]10]. By applying mindfulness meditation with older adults, this study evaluates its effects on cyberchondria and health information avoidance, providing a cost-effective health management strategy.

In summary, this study aims to explore the psychological mechanisms underlying health information avoidance among older adults, with a particular focus on cyberchondria as a mediating factor. Drawing from self-determination theory, we propose a model that incorporates both internal (positive metacognition and health self-efficacy) and external (subjective norms and health information similarity) factors to explain how they contribute to health information avoidance. Furthermore, we aim to evaluate the effectiveness of mindfulness meditation as a practical intervention to mitigate the impact of cyberchondria on information avoidance behaviors. This study seeks to address the following two research questions:

  1. What are the formation pathways of health information avoidance among older adults?
  2. Can mindfulness meditation effectively intervene in cyberchondria and reduce health information avoidance behaviors among older adults?

By addressing these questions, this research intends to contribute theoretical and practical insights to enhance older adults’ health management capabilities in the digital era.

Theory Background and Hypothesis Development

Health Information Avoidance Among Older Adults

In the context of global digitalization and an aging population, the health information behavior of older adults has become a critical topic in health management [Davodi SR, Zendehtalab H, Zare M, Behnam Vashani H. Effect of health promotion interventions in active aging in the elderly: a randomized controlled trial. Int J Community Based Nurs Midwifery. 2023;11(1):34-43. [FREE Full text] [CrossRef] [Medline]11]. Health information avoidance, the deliberate decision to avoid health-related information, is particularly prevalent among older adults due to lower information literacy, anxiety about health issues, and fear of disease severity [Howell JL, Shepperd JA. Reducing health-information avoidance through contemplation. Psychol Sci. 2013;24(9):1696-1703. [CrossRef] [Medline]12]. This behavior poses significant risks, such as missed medical advice or delayed disease prevention and treatment, as older adults often experience poorer health than younger individuals [Zhao YC, Zhao M, Song S. Online health information seeking behaviors among older adults: systematic scoping review. J Med Internet Res. 2022;24(2):e34790. [FREE Full text] [CrossRef] [Medline]1].

Although avoidance may temporarily reduce stress, it often exacerbates anxiety by failing to address underlying health concerns and increasing uncertainty [Leyva B, Taber JM, Trivedi AN. Medical care avoidance among older adults. J Appl Gerontol. 2020;39(1):74-85. [CrossRef] [Medline]13,Case D, Andrews JE, Johnson JD, Allard SL. Avoiding versus seeking: the relationship of information seeking to avoidance, blunting, coping, dissonance, and related concepts. J Med Libr Assoc. 2005;93(3):353-362. [FREE Full text] [Medline]14]. It also undermines proactive health management, reducing engagement in health-promoting behaviors and increasing the risk of chronic diseases [Howell JL, Lipsey NP, Shepperd JA. Health information avoidance. The Wiley Encyclopedia of health psychology. 2020:279-286. [CrossRef]15]. Despite growing research, key gaps remain, particularly in understanding how external environments interact with psychological factors and in developing targeted interventions for older adults. Cyberchondria, defined as repeated health searches that heighten anxiety, offers a valuable framework for understanding avoidance behaviors [Schenkel SK, Jungmann SM, Gropalis M, Witthöft M. Conceptualizations of cyberchondria and relations to the anxiety spectrum: systematic review and meta-analysis. J Med Internet Res. 2021;23(11):e27835. [FREE Full text] [CrossRef] [Medline]16]. It highlights the uncertainty and negative emotions triggered by health information, which align with the drivers of avoidance. Incorporating this framework can help explain the anxiety-avoidance cycle in older adults and support the development of targeted interventions to break this cycle.

Cyberchondria

Cyberchondria refers to compulsive and excessive searches for health-related information, often intended for self-reassurance. However, these searches usually provide only temporary relief, with anxiety often worsening during and after the process. Despite its negative consequences, such as harm to mental health, overmedicalization, and susceptibility to medical scams, cyberchondria has become a significant public health concern with the rise of mobile internet use [Vismara M, Caricasole V, Starcevic V, Cinosi E, Dell'Osso B, Martinotti G, et al. Is cyberchondria a new transdiagnostic digital compulsive syndrome? A systematic review of the evidence. Compr Psychiatry. 2020;99:152167. [FREE Full text] [CrossRef] [Medline]17,Tyrer P, Cooper S, Tyrer H, Wang D, Bassett P. Increase in the prevalence of health anxiety in medical clinics: possible cyberchondria. Int J Soc Psychiatry. 2019;65(7-8):566-569. [FREE Full text] [CrossRef] [Medline]18].

Research on cyberchondria highlights 2 main areas. First, contributing factors include low self-esteem, anxiety sensitivity, low tolerance for uncertainty, and high neuroticism [Bajcar B, Babiak J. Self-esteem and cyberchondria: The mediation effects of health anxiety and obsessive–compulsive symptoms in a community sample. Curr Psychol. 2019;40(6):2820-2831. [CrossRef]19-Bajcar B, Babiak J. Neuroticism and cyberchondria: The mediating role of intolerance of uncertainty and defensive pessimism. Pers. Individ. Differ. 2020;162:110006. [CrossRef]21]. Second, its negative impacts include reduced trust in doctors, increased self-treatment behaviors, medical errors, lower quality of life, and wasted health care resources [Khan AW, Pandey J. Dark side consequences of cyberchondria: an empirical investigation. Aslib J. Inf. Manag. 2022;74(5):801-817. [CrossRef]22,Mathes BM, Norr AM, Allan NP, Albanese BJ, Schmidt NB. Cyberchondria: Overlap with health anxiety and unique relations with impairment, quality of life, and service utilization. Psychiatry Res. 2018;261:204-211. [CrossRef] [Medline]23]. It has also been linked to problematic internet use [Fergus TA, Spada MM. Cyberchondria: Examining relations with problematic Internet use and metacognitive beliefs. Clin Psychol Psychother. 2017;24(6):1322-1330. [CrossRef] [Medline]24]. Despite these findings, little research has examined the link between cyberchondria and health information avoidance. This study aims to fill this gap by exploring how cyberchondria contributes to avoidance behaviors, providing new insights into health behaviors [Brown RJ, Skelly N, Chew-Graham CA. Online health research and health anxiety: A systematic review and conceptual integration. Clin. Psychol.: Sci. Pract. 2020;27(2):e12299. [CrossRef]25,Starcevic V. Keeping Dr. Google under control: how to prevent and manage cyberchondria. World Psychiatry. 2023;22(2):233-234. [FREE Full text] [CrossRef] [Medline]26]. Understanding this connection is crucial for designing interventions to help vulnerable populations better manage health information and reduce the negative effects of cyberchondria.

Self-Determination Theory

Self-determination theory (SDT), proposed by Deci and Ryan [Deci EL, Ryan RM. Self-determination theory. Handbook of theories of social psychology. 2012;1(20):416-436. [CrossRef]27], explains human motivation and behavior through the fulfillment of 3 psychological needs: competence, relatedness, and autonomy. Behavior is influenced by internal motivation, driven by psychological cognition, and external motivation, shaped by external pressures or rewards [Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68-78. [CrossRef] [Medline]28]. This dual perspective has been widely applied in health psychology, effectively explaining behaviors such as the adoption of personal health record systems driven by autonomous traits (internal factors) and compulsive social media use influenced by social interaction (external factors) [Gillison FB, Rouse P, Standage M, Sebire SJ, Ryan RM. A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective. Health Psychol Rev. 2019;13(1):110-130. [CrossRef] [Medline]29-Ali F, Zhang Q, Tauni MZ, Butt AH, Ahsan T. Contingent self-esteem, social interaction fears, and compulsive WeChat usage. Behav. Inf. Technol. 2022;42(6):1-12. [CrossRef]31].

Although extensively applied, SDT has not been used to explain cyberchondria. By integrating internal motivations and external environments, SDT provides a valuable framework for understanding its development and guiding effective health interventions [Li Y, Wang X. Seeking health information on social media: a perspective of trust, self-determination, and social support. j organ end user com. 2018;30(1):1-22. [CrossRef]32]. Responding to calls for further exploration of cyberchondria’s antecedents and consequences [Laato S, Islam AKMN, Islam MN, Whelan E. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? Eur. J. Inf. Syst. 2020;29(3):288-305. [CrossRef]33], this study uses SDT to analyze the factors influencing cyberchondria and its connection to health information avoidance.

Cyberchondria and Health Information Avoidance

Cyberchondria is characterized by compulsive and frequent internet-based searching for health-related information, which intensifies individuals’ anxiety and concerns about their health. This behavior often leads to cognitive and emotional responses, such as excessive worry about illness and overinterpretation of symptoms, ultimately impacting health behaviors [Yalçın İ, Boysan M, Eşkisu M, Çam Z. Health anxiety model of cyberchondria, fears, obsessions, sleep quality, and negative affect during COVID-19. Curr Psychol. 2024;43(9):8502-8519. [FREE Full text] [CrossRef]34]. While cyberchondria has been linked to behaviors like internet addiction, evidence suggests that internet addiction may eventually result in information avoidance. Individuals with cyberchondria often experience heightened anxiety and negative emotional responses when exposed to health-related information [Zhu X, Zheng T, Ding L, Zhang X. Exploring associations between eHealth literacy, cyberchondria, online health information seeking and sleep quality among university students: A cross-section study. Heliyon. 2023;9(6):e17521. [FREE Full text] [CrossRef] [Medline]35]. These negative emotions reinforce their fear of health issues, leading to a hypersensitivity toward disease-related information. Based on protection motivation theory, individuals may perceive excessive health information as a source of emotional distress and adopt avoidance behaviors to reduce anxiety and stress [Sweeny K, Melnyk D, Miller W, Shepperd JA. Information avoidance: who, what, when, and why. Rev. Gen. Psychol. 2010;14(4):340-353. [CrossRef]36]. Furthermore, individuals with cyberchondria often interpret ambiguous or uncertain information as a potential threat. This heightened threat perception can result in strong psychological resistance, causing them to proactively reduce exposure to health information to avoid discomfort. Over time, this behavior manifests as health information avoidance [McMullan RD, Berle D, Arnáez S, Starcevic V. The relationships between health anxiety, online health information seeking, and cyberchondria: Systematic review and meta-analysis. J Affect Disord. 2019;245:270-278. [CrossRef] [Medline]37]. In light of these mechanisms, the following hypotheses are proposed in this study:

H1: Cyberchondria positively influences the health information avoidance.

Internal Factors: Positive Metacognition and Health Self-Efficacy

Next, we explore the factors that lead to cyberchondria in older adults. According to the SDT mentioned earlier, an individual’s behavioral intention is influenced by internal cognitive factors. This study selects positive metacognition as one of the key internal factors influencing cyberchondria. Metacognition is commonly defined as “thinking about thinking” and encompasses the beliefs, strategies, and methods individuals use to regulate their internal cognitive processes [Spada MM, Nikcević AV, Moneta GB, Wells A. Metacognition as a mediator of the relationship between emotion and smoking dependence. Addict Behav. 2007;32(10):2120-2129. [CrossRef] [Medline]38]. Metacognitive beliefs can be categorized as either positive or negative. Positive metacognition refers to the belief that focusing on health issues and extensively thinking about them enhances one’s sense of safety and control. This construct is widely used to explain health anxiety [Nadeem F, Malik N, Atta M, Ullah I, Martinotti G, Pettorruso M, et al. Relationship between health-anxiety and cyberchondria: role of metacognitive beliefs. J Clin Med. 2022;11(9):2590. [FREE Full text] [CrossRef] [Medline]39].

Research has shown that positive metacognition effectively predicts attentional biases toward health-related information. Individuals with strong positive metacognitive tendencies are more vigilant about their health and more likely to seek health-related information [Kaur A, Butow PN, Thewes B. Do metacognitions predict attentional bias in health anxiety? Cogn Ther Res. 2011;35(6):575-580. [CrossRef]40]. Such individuals often believe that worrying about their health and gaining more knowledge about illnesses provide a sense of security. This biased thinking pattern drives compulsive health information-seeking behaviors, which are hallmarks of cyberchondria [Starcevic V, Baggio S, Berle D, Khazaal Y, Viswasam K. Cyberchondria and its relationships with related constructs: a network analysis. Psychiatr Q. 2019;90(3):491-505. [FREE Full text] [CrossRef] [Medline]41]. However, excessive information-seeking often leads to heightened anxiety, cognitive overload, and feelings of inadequacy in managing one’s health, which can result in health information avoidance as a coping mechanism. Therefore, this study hypothesizes that positive metacognition positively influences an individual’s likelihood of developing cyberchondria, which, in turn, leads to health information avoidance. Based on this logic, the following research hypotheses are proposed:

H2a: Positive metacognition positively influences cyberchondria.

H2b: Cyberchondria mediates the effect of positive metacognition on health information avoidance.

The concept of self-efficacy was first introduced by Bandura [Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191-215. [CrossRef] [Medline]42] in 1977, referring to an individual’s confidence and belief in their ability to achieve specific behavioral goals. Ajzen [Ajzen I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991;50(2):179-211. [CrossRef]43] later incorporated it into the theory of planned behavior, which suggests that self-efficacy is a significant predictor of an individual’s behavioral intention. Numerous studies have since confirmed that self-efficacy is an important component of the internal cognitive system, significantly affecting personal behavior and decision-making [Aminuddin HB, Jiao N, Jiang Y, Hong J, Wang W. Effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, health-related quality of life and clinical outcomes in patients with type 2 diabetes: a systematic review and meta-analysis. Int J Nurs Stud. 2021;116:103286. [CrossRef] [Medline]44]. Self-efficacy has been widely studied in the field of health behaviors [Simonetti V, Durante A, Ambrosca R, Arcadi P, Graziano G, Pucciarelli G, et al. Anxiety, sleep disorders and self-efficacy among nurses during COVID-19 pandemic: a large cross-sectional study. J Clin Nurs. 2021;30(9-10):1360-1371. [FREE Full text] [CrossRef] [Medline]45], where it is often referred to as health self-efficacy. According to Lee et al [Lee SY, Hwang H, Hawkins R, Pingree S. Interplay of negative emotion and health self-efficacy on the use of health information and its outcomes. Commun. Res. 2008;35(3):358-381. [CrossRef]46], health self-efficacy refers to an individual’s confidence in their ability to manage their health. Individuals with higher levels of health self-efficacy are more likely to feel capable of addressing health issues independently and have a stronger inclination toward self-healing. This self-healing tendency reduces communication with health care professionals and encourages individuals to search for health and treatment-related information for self-treatment [Essery R, Geraghty AWA, Kirby S, Yardley L. Predictors of adherence to home-based physical therapies: a systematic review. Disabil Rehabil. 2017;39(6):519-534. [CrossRef] [Medline]47]. Furthermore, the tendency toward self-healing has been shown to be significantly associated with cyberchondria [Khan AW, Pandey J. Dark side consequences of cyberchondria: an empirical investigation. Aslib J. Inf. Manag. 2022;74(5):801-817. [CrossRef]22]. Therefore, this study speculates that individuals with higher levels of health self-efficacy are more inclined to gather health-related information through internet-based channels, which may, in turn, lead to a tendency toward cyberchondria, causing these users to become trapped in a health information bubble. Based on this reasoning, the following research hypotheses are proposed:

H3a: Health self-efficacy positively influences cyberchondria.

H3b: Cyberchondria mediates the effect of health self-efficacy on health information avoidance.

External Factors: Health Information Similarity and Subjective Norms

Health information similarity refers to the consistency or resemblance in the content and structure of health information encountered on digital platforms, often driven by personalized recommendation algorithms tailored to users’ preferences and behaviors [Liang H, Fu K. Information overload, similarity, and redundancy: unsubscribing information sources on Twitter. J Comput-Mediat Comm. 2016;22(1):1-17. [CrossRef]48,Cinelli M, De Francisci Morales G, Galeazzi A, Quattrociocchi W, Starnini M. The echo chamber effect on social media. Proc Natl Acad Sci U S A. 2021;118(9):e2023301118. [FREE Full text] [CrossRef] [Medline]49]. These algorithms frequently expose users to overlapping health content, which can create cognitive confusion and negative emotional responses. For instance, similar symptom descriptions across diseases make it difficult to differentiate conditions, increasing uncertainty and anxiety [Malloch YZ, Zhang J. Seeing others receive support online: effects of self-disclosure and similarity on perceived similarity and health behavior intention. J Health Commun. 2019;24(3):217-225. [CrossRef] [Medline]50]. This anxiety often triggers more frequent searches, leading to information overload and reinforcing a cycle of heightened anxiety.

While previous studies link information similarity to health anxiety, its impact on cyberchondria remains underexplored. Repeated exposure to overlapping health content can cause individuals to misinterpret ordinary symptoms such as severe illnesses, further amplifying uncertainty and intensifying cyberchondria [Deng Z, Deng Z, Fan G, Wang B, Fan W, Liu S. More is better? Understanding the effects of online interactions on patients health anxiety. Asso for Info Science & Tech. 2023;74(11):1243-1264. [CrossRef]51]. This exacerbates excessive information searching and, in turn, contributes to health information avoidance. Based on this reasoning, the following hypotheses are proposed:

H4a: Health information similarity positively influences cyberchondria.

H4b: Cyberchondria mediates the effect of health information similarity on health information avoidance.

Subjective norms refer to the perceived social pressure or expectations from others, reflecting the social acceptance of a particular behavior. Both the risk information seeking and processing model and the theory of planned behavior emphasize that social factors (subjective norms) significantly influence an individual’s intention to seek health information [Griffin RJ, Dunwoody S, Neuwirth K. Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ Res. 1999;80(2 Pt 2):S230-S245. [CrossRef] [Medline]52]. In this study, subjective norms specifically refer to the expectations from important others, such as family and friends, regarding an individual’s health information-seeking behavior. When individuals perceive strong interest or encouragement from their social circle, subjective norms can create pressure to continue searching for health information. This social pressure often leads individuals to feel a heightened sense of responsibility, causing them to persist in health information searches despite experiencing anxiety and discomfort. Such pressure can exacerbate anxiety, trapping individuals in a cycle of frequent searches and information overload [Zheng H, Kyung Kim H, Joanna Sin S, Theng Y. A theoretical model of cyberchondria development: Antecedents and intermediate processes. Telemat Inform. 2021;63:101659. [CrossRef]53]. Furthermore, subjective norms can make it difficult for individuals to exercise self-restraint, increasing doubts about their health and ultimately contributing to cyberchondria.

Subjective norms may also worsen cyberchondria by influencing self-perception. When individuals believe that those around them place great importance on health information searching, they may see not engaging in such behavior as irresponsible, further intensifying their psychological burden and health anxiety [Honora A, Wang K, Chih W. How does information overload about COVID-19 vaccines influence individuals' vaccination intentions? The roles of cyberchondria, perceived risk, and vaccine skepticism. Comput Human Behav. 2022;130:107176. [FREE Full text] [CrossRef] [Medline]54]. To alleviate this emotional distress, individuals may choose to avoid health information altogether, attempting to reduce the psychological burden caused by social pressure. Based on this reasoning, the following hypotheses are proposed:

H5a: Subjective norms positively influence cyberchondria.

H5b: Cyberchondria mediates the effect of subjective norms on health information avoidance.

Mindfulness as a Proactive Psychological Intervention Method

The resource model of self-control and ego depletion theory posits that self-control tasks, such as compulsive health information searches, deplete cognitive resources (“ego depletion”), impairing subsequent performance and leading to avoidance of related activities like processing additional health information [Inzlicht M, Schmeichel BJ. What is ego depletion? Toward a mechanistic revision of the resource model of self-control. Perspect Psychol Sci. 2012;7(5):450-463. [CrossRef] [Medline]55,Reinecke L, Hartmann T, Eden A. The guilty couch potato: the role of ego depletion in reducing recovery through media use. J Commun. 2014;64(4):569-589. [CrossRef]56]. However, self-control can be improved through consistent training. Mindfulness, rooted in Buddhist traditions, is defined as present-focused attention and nonjudgmental acceptance [Ma J, Zhao D, Xu N, Yang J. The effectiveness of immersive virtual reality (VR) based mindfulness training on improvement mental-health in adults: a narrative systematic review. Explore (NY). 2023;19(3):310-318. [FREE Full text] [CrossRef] [Medline]57]. It includes 4 elements: being present, attention, awareness, and acceptance [Shapiro SL, Carlson LE, Astin JA, Freedman B. Mechanisms of mindfulness. J Clin Psychol. 2006;62(3):373-386. [CrossRef] [Medline]58]. Mindfulness training enhances self-control, reduces cognitive depletion, and improves emotional regulation, making it effective for addressing anxiety, depression, and stress [Cullen M. Mindfulness-based interventions: an emerging phenomenon. Mindfulness. 2011;2(3):186-193. [CrossRef]59,Hathaisaard C, Wannarit K, Pattanaseri K. Mindfulness-based interventions reducing and preventing stress and burnout in medical students: a systematic review and meta-analysis. Asian J Psychiatr. 2022;69:102997. [CrossRef] [Medline]60].

In the context of cyberchondria, individuals experience intense anxiety due to excessive focus on health information, leading to repeated searches that amplify uncertainty and anxiety. This cycle depletes cognitive resources, increasing health information avoidance. Mindfulness training helps break this cycle by enhancing awareness and acceptance, thereby reducing anxiety and distress [Toivonen KI, Zernicke K, Carlson LE. Web-based mindfulness interventions for people with physical health conditions: systematic review. J Med Internet Res. 2017;19(8):e303. [FREE Full text] [CrossRef] [Medline]61]. Improved emotional regulation allows individuals to manage anxiety without excessive searching, disrupting the “anxiety–information searching–greater anxiety–information avoidance” loop. In addition, mindfulness promotes cognitive flexibility, helping individuals identify and resist cognitive biases common in cyberchondria, such as misinterpreting ordinary symptoms as severe illnesses [Yogeswaran V, El Morr C. Effectiveness of online mindfulness interventions on medical students' mental health: a systematic review. BMC Public Health. 2021;21(1):2293. [FREE Full text] [CrossRef] [Medline]62]. By encouraging observation over reaction, mindfulness reduces health anxiety and prevents avoidance behaviors stemming from excessive concern about health information.

H6a: Mindfulness training significantly reduces individuals’ levels of cyberchondria.

H6b: Mindfulness training negatively moderates the positive effect of cyberchondria on health information avoidance.

Conceptual Model

In summary, this study consists of 2 substudies. Study 1 aims to explore the causes of cyberchondria among older adults in the digital age and the impact model of health information avoidance behavior among older adults. Study 2 aims to validate the intervention effects of mindfulness training on cyberchondria and health information avoidance behavior in older adults. The specific research models are shown in Figure 1.

Figure 1. Conceptual model of health information avoidance among older adults.

Study 1

Participants and Procedure

Participants for study 1 were primarily recruited through health communities and social media platforms commonly used by older adults, such as WeChat Moments (Tencent), Douyin (the Chinese version of TikTok, ByteDance), and Jinri Toutiao (Today’s Headlines). The target population included individuals aged 60 years and older who were capable of independently completing internet-based questionnaires. To enhance sample diversity and ensure an adequate sample size, additional outreach was conducted through local community networks, such as senior activity centers.

Data for study 1 was collected through internet-based survey platform. Before the survey began, the researchers clearly informed all participants about the purpose of the study and assured them that all responses would be strictly confidential and used solely for academic purposes. The survey was conducted anonymously, with no personal information being disclosed. Participants were also informed of the importance of providing truthful responses and were reminded of their right to withdraw from the survey at any time without any conditions. Informed consent was obtained from all participants.

Initially, 328 questionnaires were collected, with 92 invalid responses excluded, resulting in 236 valid questionnaires (mean age 63.93 years; 91, 38.56% males; 145, 61.44% females). Demographic details are provided in the

Multimedia Appendix 1

A: Demographic information B: Measures C: Study 2 Stimulus materials D: Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

DOCX File , 26 KBMultimedia Appendix 1. The response rate of valid questionnaires was 71.95%. The criteria for selecting valid questionnaires were correctly answering screening questions, a total response time of more than 1 minute, and not selecting the same response option for 9 consecutive items.

Measurements

Study 1 measured the following latent variables: health information avoidance [Howell JL, Shepperd JA. Establishing an information avoidance scale. Psychol Assess. 2016;28(12):1695-1708. [CrossRef] [Medline]63], cyberchondria [Jokić-Begić N, Mikac U, Čuržik D, Sangster Jokić C. The development and validation of the short Cyberchondria Scale (SCS). J Psychopathol Behav Assess. 2019;41(4):662-676. [CrossRef]64], positive metacognition [Bailey R, Wells A. Development and initial validation of a measure of metacognitive beliefs in health anxiety: The MCQ-HA. Psychiatry Res. 2015;230(3):871-877. [CrossRef] [Medline]65], health self-efficacy [Sheer VC. A meta-synthesis of health-related self-efficacy instrumentation: problems and suggestions. J Nurs Meas. 2014;22(1):77-93. [CrossRef] [Medline]66], subjective norms, and health information similarity [Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Applied Social Pyschol. 2006;32(4):665-683. [CrossRef]67,Walsh G, Mitchell V. Consumer vulnerability to perceived product similarity problems: scale development and identification. J. Macromarketing. 2005;25(2):140-152. [CrossRef]68]. The detailed measurement scales can be found in

Multimedia Appendix 1

A: Demographic information B: Measures C: Study 2 Stimulus materials D: Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

DOCX File , 26 KBMultimedia Appendix 1. To ensure validity, all measurement tools were adapted from established research and adjusted to fit the context of health information dissemination. The average score of the items in each scale was used to represent each latent variable, and all questionnaires were measured using a 5-point Likert scale. A pretest was also conducted with 10 volunteers to ensure that all items were clearly stated and accurately understood.

Measurement of Model

Since this study is exploratory in nature, the research model, although built based on theoretical and logical reasoning, does not originate from an existing model. Therefore, it is appropriate to use “Smart PLS” for partial least squares (PLS) path analysis () to test the research model in this context [Hair JF, Sarstedt M, Ringle CM, Mena JA. An assessment of the use of partial least squares structural equation modeling in marketing research. J. of the Acad. Mark. Sci. 2011;40(3):414-433. [CrossRef]69].

Reliability and validity testing. As shown in Table 1, the factor loadings for all measurement items ranged from 0.728 to 0.950, indicating that all items met the retention criteria. In addition, the Cronbach α values for each latent variable ranged from 0.870 to 0.915, and the composite reliability (CR) values for all latent variables were greater than the acceptable threshold of 0.7, indicating that the reliability of the scales was satisfactory. Furthermore, the average variance extracted (AVE) values for all variables exceeded the standard acceptance value of 0.5, demonstrating adequate convergent validity. The variance inflation factor (VIF) values for all factors were below 10, indicating that there were no multicollinearity issues in the scales used in this study [Hair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C. Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus Res. 2018;12(1):115-142. [CrossRef]70,Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 1981;18(3):382-388. [CrossRef]71].

Table 1. Reliability and convergent validity results of latent variables.
Latent variable and itemsFactor loadingsVIFaCronbach α valuesCRbAVEc
Health information avoidance.912.925.739

HIAd 1.8412.608



HIA 2.9033.805



HIA 3.8032.056



HIA 4.8542.461



HIA 5.8942.929


Cyberchondria.915.916.796

CCe 1.9043.421



CC 2.9123.329



CC 3.8983.127



CC 4.8542.202


Positive metacognition.898.884.710

PMf 1.8472.528



PM 2.8542.408



PM 3.7971.955



PM 4.8692.744



PM 5.8442.373


Health self-efficacy.870.903.718

HSg 1.8732.145



HS 2.8232.053



HS 3.8512.377



HS 4.8432.100


Subjective norm.912.936.850

SNh 1.9443.727



SN 2.8822.521



SN 3.9403.908


Health information similarity.880.927.731

ISi 1.7282.246



IS 2.8772.815



IS 3.8502.239



IS 4.9504.355


aVIF: variance inflation factor.

bCR: composite reliability.

cAVE: average variance extracted.

dHIA: health information avoidance.

eCC: cyberchondria.

fPM: positive metacognition.

gHS: health self-efficacy.

hSN: subjective norm.

iIS: health information similarity.

Next, discriminant validity among the variables was verified, and the specific results are shown in Table 2. The square root of the AVE for all variables (on the diagonal) was greater than the Pearson correlation coefficients between the variables, indicating that the scales had satisfactory discriminant validity. A common method bias test was also conducted using the single-factor test. The variance explained by the first unrotated factor was 31.999%, which is below the critical threshold of 40%, indicating that common method bias is not an issue in this study [Podsakoff PM, MacKenzie SB, Lee J, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. [CrossRef] [Medline]72].

Table 2. Discriminant validity analysis of constructs in study 1.
ConstructsHIAaCCbHScPMdSNeISf
HIA.860gh
CC.410.892g
HS.346.382.848g
PM.212.343.184.843g
SN.158.137.019.651.922g
IS.339.435.291.371.246.855g

aHIA: health information avoidance.

bCC: cyberchondria.

cHS: Health self-efficacy.

dPM: Positive metacognition.

eSN: subjective norm.

fIS: health information similarity.

gsquare roots of the average variance extracted.

hNot applicable.

Model Testing

The PLS algorithm was used to calculate the R² values for each variable, all of which were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model [Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2014;19(2):139-152. [CrossRef]73]. Blindfolding was then conducted, and the results showed that the Stone-Geisser Q² values for all variables were greater than 0, indicating that the model effectively predicts the relationships between variables [Dijkstra TK, Henseler J. Consistent and asymptotically normal PLS estimators for linear structural equations. Comput Stat Data Anal. 2015;81:10-23. [CrossRef]74]. In addition, the SRMR value was 0.054, which is below the standard threshold of 0.08, the NFI value was 0.931, and the RMS θ value was 0.103. These results demonstrate that the model has good fit [Hair JF, Risher JR, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019;31(1):2-24. [CrossRef]75].

Study 2

Participants and Procedure

The required sample size for the intervention study was estimated using GPower, with an effect size greater than 0.8 considered appropriate for the experimental study. Setting α=.05, 1-β=.8, and effect size=0.4, the minimum required sample size was calculated to be 44 [Cohen J. Statistical power analysis for the behavioral science. Cambridge. Academic Press; 2013. 76].

The mindfulness training required participants to invest a significant amount of time, making it challenging to recruit a sufficient number of participants. To address this difficulty, our research team collaborated with students from 3 courses at 3 universities, encouraging them to invite their older family members (older than 60 years old and capable of independently using social media) to participate in the mindfulness training program. To motivate the students, the researchers offered course credits and a reward of approximately US $20. To improve the quality of the mindfulness training and the responses to the survey, the researchers informed participants before the experiment began that their responses would be kept strictly confidential and used only for academic purposes. Participants were also informed of the importance of honest responses and were made aware of their right to withdraw from the study at any time without conditions. Informed consent was obtained from all participants. The research was conducted in accordance with the standards of the Declaration of Helsinki.

We initially recruited 101 participants and divided them into 2 groups: group 1 (mindfulness training group, n=51) and group 2 (control group, n=50). The research process was conducted in 3 stages.

Stage 1 (t1)

At the start of the study, a survey measured participants’ levels of cyberchondria, health information avoidance, state mindfulness (Brown and Ryan [Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84(4):822-848. [CrossRef] [Medline]77]), and demographic variables (gender, age, income, and education). An independent samples t test showed no significant differences between the 2 groups for cyberchondria (Mgroup1=2.848, Mgroup2=3.110, P=.12, t99=–1.557), health information avoidance (Mgroup1=3.063, Mgroup2=3.384, P=.11, t99=–1.631), or state mindfulness (Mgroup1=3.502, Mgroup2=3.524, P=.77, t99=–.297).

Stage 2

Mindfulness training started 1 week after the initial survey to avoid bias from survey content. Group 1 underwent a short-term intervention based on the mindfulness-based stress reduction (MBSR) program (Kabat-Zinn [Kabat-Zinn J. Mindfulness-based interventions in context: past, present, and future. Clinical Psychology: science and practice. 2003;10(2):144-156. [CrossRef]78]), while group 2 received no intervention. The training included five 10-minute audio sessions focusing on breathing, bodily sensations, thoughts, and emotions. Participants were instructed to complete a daily session at noon with randomly assigned recordings and record their practice (refer to

Multimedia Appendix 1

A: Demographic information B: Measures C: Study 2 Stimulus materials D: Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

DOCX File , 26 KBMultimedia Appendix 1 for audio excerpts).

Stage 3 (t2)

Following 4 weeks of training, consistent with previous findings on mindfulness duration (Rooks et al [Rooks JD, Morrison AB, Goolsarran M, Rogers SL, Jha AP. “We Are Talking About Practice”: the influence of mindfulness vs. relaxation training on athletes‘ attention and well-being over high-demand intervals. J Cogn Enhanc. 2017;1(2):141-153. [CrossRef]79]), 5 participants from group 1 and 2 from group 2 withdrew or failed to complete the study, leaving a final valid sample of 46 in group 1 and 48 in group 2. Cyberchondria, health information avoidance, and state mindfulness levels were reassessed. Reanalysis of initial data showed no significant differences between retained participants, ensuring data integrity.

Ethical Considerations

This study strictly adhered to the ethical principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the School of Journalism and Communication at Minjiang University (Ref: MJUCER20240107). Both substudies were conducted in strict accordance with relevant ethical guidelines to protect participants’ rights and ensure data confidentiality.

All participants provided informed consent before participating in the study. They were fully informed about the study’s objectives, procedures, data usage policies, and their right to withdraw from the study at any time without penalty. As the data were collected through questionnaires, the consent forms were obtained electronically.

To protect privacy and data security, all participant data were anonymized and deidentified during both the data collection and analysis processes. No personally identifiable information was recorded during the study, and all data were securely stored in an encrypted system to prevent unauthorized access.

Participants received appropriate compensation based on their involvement in the study. In substudy 1, a cross-sectional survey, participants were rewarded US $2 upon completing the questionnaire. In substudy 2, a 4-week longitudinal survey, participants received a gift valued at approximately US $20 after completing the study. These compensations were intended to acknowledge participants’ time and effort while avoiding undue influence on their decision to participate. This manuscript and its supplementary materials do not include any images or materials that could identify participants.


Study 1

There were no missing values in the data. A bootstrapping test with a sample size of 5000 was conducted on the collected data to explore the path coefficients and their significance within the model. The final test results are presented in Tables 3 and 4.

The results of the data analysis indicate that cyberchondria exacerbates health information avoidance among older adults (β=.41, P<.001), supporting H1. From the perspective of internal factors, both positive metacognition and health self-efficacy have a positive influence on cyberchondria (β=.26, P=.002; β=.25, P<.001), which in turn further increases health information avoidance behavior (95% Boot CI 0.035-0.189; 95% Boot CI 0.043-0.185). Thus, H2a, H2b, H3a, and H3b are all supported. Regarding external environmental factors, the similarity of health information also significantly positively affects cyberchondria (β=.29, P<.001) and leads to health information avoidance through cyberchondria (95% Boot CI 0.063-0.191), supporting H4a and H4b. However, subjective norms did not show a significant direct relationship with cyberchondria (β=–.11, P=.13), and cyberchondria did not mediate the relationship between subjective norms and health information avoidance (95% Boot CI –0.102 to 0.016), leading to the rejection of H5a and H5b.

The results of study 1 indicate that cyberchondria significantly exacerbates health information avoidance among older adults, influenced by both internal factors (positive metacognition and health self-efficacy) and external factors (health information similarity). However, subjective norms did not have a significant effect on cyberchondria, suggesting that external social pressure is not a major driver of health information avoidance behavior in older adults. Based on these findings, the next study will focus on validating whether mindfulness training can effectively intervene in cyberchondria and the resulting health information avoidance behavior. The specific findings of study 1 are shown in Figure 2.

Table 3. Hypothesis testing results of study 1 (direct effect).
Direct effectβ valuest test (df)P values
H1: CCa→HIAb.4107.016 (226)<.001c
H2a: PMd→CC.2593.035 (226).002e
H3a: HSf→CC.2513.453 (227).001e
H4a: ISg→CC.2934.344 (227)<.001c
H5a: SNh→CC-.109–1.507 (228).13

aCC: cyberchondria.

bHIA: health information avoidance.

cP<.001.

dPM: positive metacognition.

eP<.01.

fHS: health self-efficacy.

gIS: health information similarity.

hSN: subjective norm.

Table 4. Hypothesis testing results of study 1 (indirect effect).
Indirect effectEffect valuet values (df)95% CIP values
H2b: PMa→CCb→HIAc0.1062.697 (220)0.035 to 0.189.007d
H3b: HSe→CC→HIA0.1032.821 (221)0.043 to 0.185.005d
H4b: ISf→CC→HIA0.1203.691 (221)0.063 to 0.191<.001g
H5b: SNh→CC→HIA–0.0451.459 (222)–0.102 to 0.016.15

aPM: positive metacognition.

bCC: cyberchondria.

cHIA: health information avoidance.

dP<.01.

eHS: health self-efficacy.

fIS: health information similarity.

gP<.001.

hSN: subjective norm.

Figure 2. Result model of study 1: path analysis.**P<.01, ***P<.001, n.s. P>.05.

Study 2

Before hypothesis testing, the effectiveness of the mindfulness intervention was first examined. To control for the effects of covariates such as age, gender, and education level, an ANCOVA (analysis of covariance) was conducted to test the manipulation of state mindfulness. The analysis showed that the state mindfulness score of the intervention group was significantly higher than that of the control group (Mgroup1=4.122, Mgroup2=3.606, F1,93=16.866, P<.001). Subsequently, the within-group differences in t1 and t2 for both the intervention and control groups were compared. The results showed that the state mindfulness level of the intervention group at t2 was significantly higher than at t1 (Mt2=4.122, Mt1=3.502, F1,96=85.024, P<.001), while there was no significant difference between t1 and t2 in the control group (Mt2=3.524, Mt1=3.583, F1,97=.222, P=.64). In summary, the manipulation of state mindfulness was successful.

Next, ANCOVA was conducted to test H6a, with age, gender, education level, and income level as covariates. The results showed that there was no significant difference in the level of cyberchondria between t1 and t2 in the intervention group (Mt1=2.848, Mt2=2.685, F1,96=1.835, P=.18), indicating that H6a was not supported, meaning that mindfulness training did not improve the level of cyberchondria. Subsequently, H6b was tested using Hayes’ suggested Bootstrap method with SPSS (IBM Corp) process model 1 to examine the interaction effect, with 5000 bootstrap samples and a 95% CI. The data analysis showed that the interaction effect of mindfulness level and cyberchondria on health information avoidance was significant (effect=‒0.357, P=.002, 95% CI ‒0.580 to ‒0.131). Thus, H6b was supported.

In conclusion, the results of study 2 supported H6b but rejected H6a. Although mindfulness training did not directly reduce the level of cyberchondria, it effectively inhibited the transformation of cyberchondria into health information avoidance behavior (Figure 3).

Figure 3. The Interaction effect of mindfulness and cyberchondria on health information avoidance.

Principal Findings

Cyberchondria and health information avoidance have become increasingly pressing public health issues in the digital age, posing significant threats to the health of older adults. Therefore, exploring their underlying mechanisms and potential intervention strategies is of great theoretical and practical significance. Based on the self-determination theory, this study conducted 2 substudies to explore the causes of cyberchondria and health information avoidance among older adults, as well as the intervention effects of mindfulness training.

Internal and External Factors Leading to Health Information Avoidance: Cyberchondria as the Mediator

The results reveal that internal factors (positive metacognition and health self-efficacy) and external factors (health information similarity) significantly predict cyberchondria among older adults, leading to health information avoidance.Positive metacognition fosters overthinking and health-related anxiety, as excessive focus on health concerns amplifies uncertainty. Previous research links positive metacognition to problematic behaviors [Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav. 2018;85:83-87. [CrossRef] [Medline]80], and this study confirms its role in exacerbating cyberchondria and health information avoidance. Health self-efficacy also influences cyberchondria. Although high self-efficacy promotes confidence in managing health, ambiguous health information undermines this confidence, driving excessive searches and heightening anxiety. This aligns with self-efficacy theory, where unresolved issues lead to repetitive checking and stress [Muris P. Relationships between self-efficacy and symptoms of anxiety disorders and depression in a normal adolescent sample. Pers. Individ. Differ. 2002;32(2):337-348. [CrossRef]81].

Health information similarity indirectly contributes to avoidance by increasing anxiety and cognitive overload. Repeated exposure to similar information, often driven by algorithms, makes it difficult to differentiate between diseases, heightening uncertainty and driving avoidance behaviors through cyberchondria [Ahmed A, Aziz S, Toro CT, Alzubaidi M, Irshaidat S, Serhan HA, et al. Machine learning models to detect anxiety and depression through social media: a scoping review. Comput Methods Programs Biomed Update. 2022;2:100066. [FREE Full text] [CrossRef] [Medline]82]. However, subjective norms did not significantly affect cyberchondria, suggesting that older adults rely more on personal judgment and experience rather than external social influences [Ahn J, Kahlor LA. No regrets when it comes to your health: anticipated regret, subjective norms, information insufficiency and intent to seek health information from multiple sources. Health Commun. 2020;35(10):1295-1302. [CrossRef] [Medline]83,Heid AR, Zarit SH, Fingerman KL. "My Parent is so stubborn!"-perceptions of aging parents' persistence, insistence, and resistance. J Gerontol B Psychol Sci Soc Sci. 2016;71(4):602-612. [FREE Full text] [CrossRef] [Medline]84]. This finding underscores the need for interventions targeting individual-level factors rather than external pressures.

Mindfulness Training as an Effective Intervention for Cyberchondria and Health Information Avoidance

The results also indicate that while mindfulness training did not directly reduce the level of cyberchondria, it significantly mitigated the transition from cyberchondria to health information avoidance behavior. Mindfulness meditation, by enhancing emotional regulation and cognitive flexibility, helps older adults avoid being dominated by anxiety when faced with health information, even though it may not directly prevent the onset of cyberchondria. This effectively reduces their tendency to avoid health information. These findings are consistent with previous studies on the effects of mindfulness, supporting the conclusion that mindfulness training can help individuals better accept current emotions and reduce fear of future uncertainties [Tudor K, Maloney S, Raja A, Baer R, Blakemore S, Byford S, MYRIAD Team, et al. Universal mindfulness training in schools for adolescents: a scoping review and conceptual model of moderators, mediators, and implementation factors. Prev Sci. 2022;23(6):934-953. [FREE Full text] [CrossRef] [Medline]85]. Furthermore, our findings emphasize the role of mindfulness training in regulating health information behavior, demonstrating that it not only improves the emotional state of older adults but also increases their acceptance of health information, thereby reducing avoidance behaviors. This highlights the potential of mindfulness training to effectively break the vicious cycle of “anxiety–information search–greater anxiety–information avoidance.” Mindfulness can enable older adults to maintain a more balanced perspective when encountering health-related information, which prevents anxiety from escalating into avoidant behavior.

Implications and Future Directions

This Study Makes Three Theoretical Contributions

First, it makes significant contributions to SDT by deepening the understanding of how internal and external factors influence health behaviors through psychological mechanisms. It validates the mediating role of cyberchondria in health information avoidance among older adults and demonstrates how positive metacognition, health self-efficacy, and health information similarity interact within this process. This fills a critical gap in previous research by uncovering the mediating mechanisms of health information avoidance and provides new insights into the cognitive-emotional processes of older adults, an information-vulnerable population. Furthermore, this study extends the applicability of SDT by highlighting the dynamic balance between intrinsic motivation and external support in older adults navigating complex digital health environments. In addition, this study systematically explores the role of mindfulness interventions in managing health information behaviors. While mindfulness did not directly reduce cyberchondria, it significantly inhibited its progression to information avoidance. By enhancing older adults’ capacity to cope with uncertainty and anxiety, mindfulness fosters a more open attitude toward health information. This expands mindfulness beyond emotional regulation to include its impact on complex health decision-making, providing theoretical support for its application in public health interventions. Finally, this study challenges the universal applicability of subjective norms in shaping health behaviors, as proposed in the theory of planned behavior [Korn L, Haynie DL, Luk JW, Sita K, Simons-Morton BG. Attitudes, subjective norms, and perceived behavioral control associated with age of first use of cannabis among adolescents. J Sch Health. 2021;91(1):50-58. [CrossRef] [Medline]86]. It finds that older adults rely more on autonomous judgment than societal expectations due to their life experience and independence, emphasizing the importance of considering demographic differences and group heterogeneity in health behavior research.

Practically, This Study Offers Three Recommendations

First, for internal factors (eg, positive metacognition and excessive health self-efficacy), psychological education and cognitive behavioral therapy can help older adults reduce overfocus on health risks and avoid unnecessary health information searches [Podina IR, Cosmoiu A, Rusu P, Chivu AC. Positive thinking is not adaptive thinking: a cognitive-behavioral take on interpretation bias modification for social anxiety. J Rat-Emo Cognitive-Behav Ther. 2020;38(3):424-444. [CrossRef]87]. Second, optimizing recommendation algorithms on health information platforms to reduce repetitive content and providing personalized information tailored to older adults’ health conditions can alleviate anxiety caused by information overload and similarity. Finally, enhancing digital health literacy through health seminars and digital skills training, combined with mindfulness programs, can offer a comprehensive intervention strategy to address health information avoidance. These measures collectively aim to reduce health anxiety, improve engagement with health information, and enable older adults to achieve proactive health management.

Limitations

This study provides valuable insights into the causes of cyberchondria and health information avoidance among older adults and evaluates mindfulness meditation as an intervention. However, several limitations should be noted. First, the 4-week mindfulness intervention, while effective in the short term, does not address the sustainability of its effects. Future research should explore longer interventions and conduct follow-ups to assess lasting impacts. Combining mindfulness with approaches such as health education or social support may yield stronger and more enduring outcomes. In addition, while positive metacognition and health self-efficacy emerged as critical internal factors, specific intervention strategies for addressing these were not explored. Future research should investigate techniques such as cognitive behavioral therapy and health coaching to mitigate their negative impacts and further reduce health information avoidance. Expanding on these areas will help refine interventions and improve health management strategies for older adults.

Conclusion

This study used 2 substudies to explore the causes of cyberchondria and health information avoidance among older adults, as well as the intervention effects of mindfulness meditation. Study 1 constructed an influence model based on self-determination theory, using a cross-sectional survey to verify that internal factors such as positive metacognition (β=.26, P=.002) and health self-efficacy (β=.25, P<.001), as well as external factors such as health information similarity (β=.29, P<.001), were significant predictors of cyberchondria among older adults, which in turn led to health information avoidance behavior (β=.41, P<.001). However, subjective norms did not show a significant association with cyberchondria among older adults (β=–.11, P=.13). Study 2 conducted a 4-week mindfulness intervention, demonstrating the effectiveness of mindfulness in inhibiting the transition from cyberchondria to health information avoidance (effect=–0.357, P=.002, 95% CI –0.580 to –0.131]). However, it was also found that mindfulness did not directly reduce the occurrence of cyberchondria (P=.18).

Acknowledgments

The authors thank all the participants in this study. This research was funded by Minjiang University Research Start-up Funding (grant 324-32404314). The authors confirm that no generative AI tools, including ChatGPT or similar models, were used in the writing, editing, or preparation of this manuscript. All content was developed independently by the authors, based on original research and analysis.

Data Availability

The dataset of this study is accessible through science data bank website. In compliance with ethical guidelines and to protect participant privacy, all personally identifiable information has been removed from the dataset. Researchers are welcome to access the data through the provided link. For additional inquiries or specific questions about the dataset, please contact the corresponding author.

Authors' Contributions

CG contributed to conceptualization, methodology, software, formal analysis, data curation, writing-original draft preparation, writing-review and editing, visualization, and project administration. XZ handled methodology, validation, formal analysis, and resources. LQ managed validation, investigation, resources, visualization, and project administration.

Conflicts of Interest

None declared.

Multimedia Appendix 1

A: Demographic information B: Measures C: Study 2 Stimulus materials D: Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

DOCX File , 26 KB

  1. Zhao YC, Zhao M, Song S. Online health information seeking behaviors among older adults: systematic scoping review. J Med Internet Res. 2022;24(2):e34790. [FREE Full text] [CrossRef] [Medline]
  2. Cheng X, Huang X, Yang B, Xiao Y. Unveiling the paradox of technostress: Impacts of technology-driven stressors on the elderly's avoidance behaviors. Inf. Manag. 2023;60(8):103875. [CrossRef]
  3. Barbour JB, Rintamaki LS, Ramsey JA, Brashers DE. Avoiding health information. J Health Commun. 2012;17(2):212-229. [CrossRef] [Medline]
  4. Sun Y, Feng Y, Shen X, Guo X. Fear appeal, coping appeal and mobile health technology persuasion: a two-stage scenario-based survey of the elderly. Inf. Technol. People. 2022;36(1):362-386. [CrossRef]
  5. Starcevic V, Berle D. Cyberchondria: towards a better understanding of excessive health-related Internet use. Expert Rev Neurother. 2013;13(2):205-213. [CrossRef] [Medline]
  6. Vismara M, Varinelli A, Pellegrini L, Enara A, Fineberg NA. New challenges in facing cyberchondria during the coronavirus disease pandemic. Curr Opin Behav Sci. 2022;46:101156. [FREE Full text] [CrossRef] [Medline]
  7. Zheng H, Chen X, Jiang S, Sun L. How does health information seeking from different online sources trigger cyberchondria? The roles of online information overload and information trust. Inf. Process. Manag. 2023;60(4):103364. [CrossRef]
  8. Chae J. Who avoids cancer information? Examining a psychological process leading to cancer information avoidance. J Health Commun. 2016;21(7):837-844. [CrossRef] [Medline]
  9. Linardon J. Rates of attrition and engagement in randomized controlled trials of mindfulness apps: Systematic review and meta-analysis. Behav Res Ther. 2023;170:104421. [FREE Full text] [CrossRef] [Medline]
  10. Creswell JD. Mindfulness Interventions. Annu Rev Psychol. 2017;68:491-516. [CrossRef] [Medline]
  11. Davodi SR, Zendehtalab H, Zare M, Behnam Vashani H. Effect of health promotion interventions in active aging in the elderly: a randomized controlled trial. Int J Community Based Nurs Midwifery. 2023;11(1):34-43. [FREE Full text] [CrossRef] [Medline]
  12. Howell JL, Shepperd JA. Reducing health-information avoidance through contemplation. Psychol Sci. 2013;24(9):1696-1703. [CrossRef] [Medline]
  13. Leyva B, Taber JM, Trivedi AN. Medical care avoidance among older adults. J Appl Gerontol. 2020;39(1):74-85. [CrossRef] [Medline]
  14. Case D, Andrews JE, Johnson JD, Allard SL. Avoiding versus seeking: the relationship of information seeking to avoidance, blunting, coping, dissonance, and related concepts. J Med Libr Assoc. 2005;93(3):353-362. [FREE Full text] [Medline]
  15. Howell JL, Lipsey NP, Shepperd JA. Health information avoidance. The Wiley Encyclopedia of health psychology. 2020:279-286. [CrossRef]
  16. Schenkel SK, Jungmann SM, Gropalis M, Witthöft M. Conceptualizations of cyberchondria and relations to the anxiety spectrum: systematic review and meta-analysis. J Med Internet Res. 2021;23(11):e27835. [FREE Full text] [CrossRef] [Medline]
  17. Vismara M, Caricasole V, Starcevic V, Cinosi E, Dell'Osso B, Martinotti G, et al. Is cyberchondria a new transdiagnostic digital compulsive syndrome? A systematic review of the evidence. Compr Psychiatry. 2020;99:152167. [FREE Full text] [CrossRef] [Medline]
  18. Tyrer P, Cooper S, Tyrer H, Wang D, Bassett P. Increase in the prevalence of health anxiety in medical clinics: possible cyberchondria. Int J Soc Psychiatry. 2019;65(7-8):566-569. [FREE Full text] [CrossRef] [Medline]
  19. Bajcar B, Babiak J. Self-esteem and cyberchondria: The mediation effects of health anxiety and obsessive–compulsive symptoms in a community sample. Curr Psychol. 2019;40(6):2820-2831. [CrossRef]
  20. Fergus TA. Anxiety sensitivity and intolerance of uncertainty as potential risk factors for cyberchondria: a replication and extension examining dimensions of each construct. J Affect Disord. 2015;184:305-309. [CrossRef] [Medline]
  21. Bajcar B, Babiak J. Neuroticism and cyberchondria: The mediating role of intolerance of uncertainty and defensive pessimism. Pers. Individ. Differ. 2020;162:110006. [CrossRef]
  22. Khan AW, Pandey J. Dark side consequences of cyberchondria: an empirical investigation. Aslib J. Inf. Manag. 2022;74(5):801-817. [CrossRef]
  23. Mathes BM, Norr AM, Allan NP, Albanese BJ, Schmidt NB. Cyberchondria: Overlap with health anxiety and unique relations with impairment, quality of life, and service utilization. Psychiatry Res. 2018;261:204-211. [CrossRef] [Medline]
  24. Fergus TA, Spada MM. Cyberchondria: Examining relations with problematic Internet use and metacognitive beliefs. Clin Psychol Psychother. 2017;24(6):1322-1330. [CrossRef] [Medline]
  25. Brown RJ, Skelly N, Chew-Graham CA. Online health research and health anxiety: A systematic review and conceptual integration. Clin. Psychol.: Sci. Pract. 2020;27(2):e12299. [CrossRef]
  26. Starcevic V. Keeping Dr. Google under control: how to prevent and manage cyberchondria. World Psychiatry. 2023;22(2):233-234. [FREE Full text] [CrossRef] [Medline]
  27. Deci EL, Ryan RM. Self-determination theory. Handbook of theories of social psychology. 2012;1(20):416-436. [CrossRef]
  28. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68-78. [CrossRef] [Medline]
  29. Gillison FB, Rouse P, Standage M, Sebire SJ, Ryan RM. A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective. Health Psychol Rev. 2019;13(1):110-130. [CrossRef] [Medline]
  30. Assadi V, Hassanein K. Consumer adoption of personal health record systems: a self-determination theory perspective. J Med Internet Res. 2017;19(7):e270. [FREE Full text] [CrossRef] [Medline]
  31. Ali F, Zhang Q, Tauni MZ, Butt AH, Ahsan T. Contingent self-esteem, social interaction fears, and compulsive WeChat usage. Behav. Inf. Technol. 2022;42(6):1-12. [CrossRef]
  32. Li Y, Wang X. Seeking health information on social media: a perspective of trust, self-determination, and social support. j organ end user com. 2018;30(1):1-22. [CrossRef]
  33. Laato S, Islam AKMN, Islam MN, Whelan E. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? Eur. J. Inf. Syst. 2020;29(3):288-305. [CrossRef]
  34. Yalçın İ, Boysan M, Eşkisu M, Çam Z. Health anxiety model of cyberchondria, fears, obsessions, sleep quality, and negative affect during COVID-19. Curr Psychol. 2024;43(9):8502-8519. [FREE Full text] [CrossRef]
  35. Zhu X, Zheng T, Ding L, Zhang X. Exploring associations between eHealth literacy, cyberchondria, online health information seeking and sleep quality among university students: A cross-section study. Heliyon. 2023;9(6):e17521. [FREE Full text] [CrossRef] [Medline]
  36. Sweeny K, Melnyk D, Miller W, Shepperd JA. Information avoidance: who, what, when, and why. Rev. Gen. Psychol. 2010;14(4):340-353. [CrossRef]
  37. McMullan RD, Berle D, Arnáez S, Starcevic V. The relationships between health anxiety, online health information seeking, and cyberchondria: Systematic review and meta-analysis. J Affect Disord. 2019;245:270-278. [CrossRef] [Medline]
  38. Spada MM, Nikcević AV, Moneta GB, Wells A. Metacognition as a mediator of the relationship between emotion and smoking dependence. Addict Behav. 2007;32(10):2120-2129. [CrossRef] [Medline]
  39. Nadeem F, Malik N, Atta M, Ullah I, Martinotti G, Pettorruso M, et al. Relationship between health-anxiety and cyberchondria: role of metacognitive beliefs. J Clin Med. 2022;11(9):2590. [FREE Full text] [CrossRef] [Medline]
  40. Kaur A, Butow PN, Thewes B. Do metacognitions predict attentional bias in health anxiety? Cogn Ther Res. 2011;35(6):575-580. [CrossRef]
  41. Starcevic V, Baggio S, Berle D, Khazaal Y, Viswasam K. Cyberchondria and its relationships with related constructs: a network analysis. Psychiatr Q. 2019;90(3):491-505. [FREE Full text] [CrossRef] [Medline]
  42. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191-215. [CrossRef] [Medline]
  43. Ajzen I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991;50(2):179-211. [CrossRef]
  44. Aminuddin HB, Jiao N, Jiang Y, Hong J, Wang W. Effectiveness of smartphone-based self-management interventions on self-efficacy, self-care activities, health-related quality of life and clinical outcomes in patients with type 2 diabetes: a systematic review and meta-analysis. Int J Nurs Stud. 2021;116:103286. [CrossRef] [Medline]
  45. Simonetti V, Durante A, Ambrosca R, Arcadi P, Graziano G, Pucciarelli G, et al. Anxiety, sleep disorders and self-efficacy among nurses during COVID-19 pandemic: a large cross-sectional study. J Clin Nurs. 2021;30(9-10):1360-1371. [FREE Full text] [CrossRef] [Medline]
  46. Lee SY, Hwang H, Hawkins R, Pingree S. Interplay of negative emotion and health self-efficacy on the use of health information and its outcomes. Commun. Res. 2008;35(3):358-381. [CrossRef]
  47. Essery R, Geraghty AWA, Kirby S, Yardley L. Predictors of adherence to home-based physical therapies: a systematic review. Disabil Rehabil. 2017;39(6):519-534. [CrossRef] [Medline]
  48. Liang H, Fu K. Information overload, similarity, and redundancy: unsubscribing information sources on Twitter. J Comput-Mediat Comm. 2016;22(1):1-17. [CrossRef]
  49. Cinelli M, De Francisci Morales G, Galeazzi A, Quattrociocchi W, Starnini M. The echo chamber effect on social media. Proc Natl Acad Sci U S A. 2021;118(9):e2023301118. [FREE Full text] [CrossRef] [Medline]
  50. Malloch YZ, Zhang J. Seeing others receive support online: effects of self-disclosure and similarity on perceived similarity and health behavior intention. J Health Commun. 2019;24(3):217-225. [CrossRef] [Medline]
  51. Deng Z, Deng Z, Fan G, Wang B, Fan W, Liu S. More is better? Understanding the effects of online interactions on patients health anxiety. Asso for Info Science & Tech. 2023;74(11):1243-1264. [CrossRef]
  52. Griffin RJ, Dunwoody S, Neuwirth K. Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ Res. 1999;80(2 Pt 2):S230-S245. [CrossRef] [Medline]
  53. Zheng H, Kyung Kim H, Joanna Sin S, Theng Y. A theoretical model of cyberchondria development: Antecedents and intermediate processes. Telemat Inform. 2021;63:101659. [CrossRef]
  54. Honora A, Wang K, Chih W. How does information overload about COVID-19 vaccines influence individuals' vaccination intentions? The roles of cyberchondria, perceived risk, and vaccine skepticism. Comput Human Behav. 2022;130:107176. [FREE Full text] [CrossRef] [Medline]
  55. Inzlicht M, Schmeichel BJ. What is ego depletion? Toward a mechanistic revision of the resource model of self-control. Perspect Psychol Sci. 2012;7(5):450-463. [CrossRef] [Medline]
  56. Reinecke L, Hartmann T, Eden A. The guilty couch potato: the role of ego depletion in reducing recovery through media use. J Commun. 2014;64(4):569-589. [CrossRef]
  57. Ma J, Zhao D, Xu N, Yang J. The effectiveness of immersive virtual reality (VR) based mindfulness training on improvement mental-health in adults: a narrative systematic review. Explore (NY). 2023;19(3):310-318. [FREE Full text] [CrossRef] [Medline]
  58. Shapiro SL, Carlson LE, Astin JA, Freedman B. Mechanisms of mindfulness. J Clin Psychol. 2006;62(3):373-386. [CrossRef] [Medline]
  59. Cullen M. Mindfulness-based interventions: an emerging phenomenon. Mindfulness. 2011;2(3):186-193. [CrossRef]
  60. Hathaisaard C, Wannarit K, Pattanaseri K. Mindfulness-based interventions reducing and preventing stress and burnout in medical students: a systematic review and meta-analysis. Asian J Psychiatr. 2022;69:102997. [CrossRef] [Medline]
  61. Toivonen KI, Zernicke K, Carlson LE. Web-based mindfulness interventions for people with physical health conditions: systematic review. J Med Internet Res. 2017;19(8):e303. [FREE Full text] [CrossRef] [Medline]
  62. Yogeswaran V, El Morr C. Effectiveness of online mindfulness interventions on medical students' mental health: a systematic review. BMC Public Health. 2021;21(1):2293. [FREE Full text] [CrossRef] [Medline]
  63. Howell JL, Shepperd JA. Establishing an information avoidance scale. Psychol Assess. 2016;28(12):1695-1708. [CrossRef] [Medline]
  64. Jokić-Begić N, Mikac U, Čuržik D, Sangster Jokić C. The development and validation of the short Cyberchondria Scale (SCS). J Psychopathol Behav Assess. 2019;41(4):662-676. [CrossRef]
  65. Bailey R, Wells A. Development and initial validation of a measure of metacognitive beliefs in health anxiety: The MCQ-HA. Psychiatry Res. 2015;230(3):871-877. [CrossRef] [Medline]
  66. Sheer VC. A meta-synthesis of health-related self-efficacy instrumentation: problems and suggestions. J Nurs Meas. 2014;22(1):77-93. [CrossRef] [Medline]
  67. Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Applied Social Pyschol. 2006;32(4):665-683. [CrossRef]
  68. Walsh G, Mitchell V. Consumer vulnerability to perceived product similarity problems: scale development and identification. J. Macromarketing. 2005;25(2):140-152. [CrossRef]
  69. Hair JF, Sarstedt M, Ringle CM, Mena JA. An assessment of the use of partial least squares structural equation modeling in marketing research. J. of the Acad. Mark. Sci. 2011;40(3):414-433. [CrossRef]
  70. Hair JF, Ringle CM, Gudergan SP, Fischer A, Nitzl C, Menictas C. Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus Res. 2018;12(1):115-142. [CrossRef]
  71. Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 1981;18(3):382-388. [CrossRef]
  72. Podsakoff PM, MacKenzie SB, Lee J, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. [CrossRef] [Medline]
  73. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2014;19(2):139-152. [CrossRef]
  74. Dijkstra TK, Henseler J. Consistent and asymptotically normal PLS estimators for linear structural equations. Comput Stat Data Anal. 2015;81:10-23. [CrossRef]
  75. Hair JF, Risher JR, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019;31(1):2-24. [CrossRef]
  76. Cohen J. Statistical power analysis for the behavioral science. Cambridge. Academic Press; 2013.
  77. Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84(4):822-848. [CrossRef] [Medline]
  78. Kabat-Zinn J. Mindfulness-based interventions in context: past, present, and future. Clinical Psychology: science and practice. 2003;10(2):144-156. [CrossRef]
  79. Rooks JD, Morrison AB, Goolsarran M, Rogers SL, Jha AP. “We Are Talking About Practice”: the influence of mindfulness vs. relaxation training on athletes‘ attention and well-being over high-demand intervals. J Cogn Enhanc. 2017;1(2):141-153. [CrossRef]
  80. Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav. 2018;85:83-87. [CrossRef] [Medline]
  81. Muris P. Relationships between self-efficacy and symptoms of anxiety disorders and depression in a normal adolescent sample. Pers. Individ. Differ. 2002;32(2):337-348. [CrossRef]
  82. Ahmed A, Aziz S, Toro CT, Alzubaidi M, Irshaidat S, Serhan HA, et al. Machine learning models to detect anxiety and depression through social media: a scoping review. Comput Methods Programs Biomed Update. 2022;2:100066. [FREE Full text] [CrossRef] [Medline]
  83. Ahn J, Kahlor LA. No regrets when it comes to your health: anticipated regret, subjective norms, information insufficiency and intent to seek health information from multiple sources. Health Commun. 2020;35(10):1295-1302. [CrossRef] [Medline]
  84. Heid AR, Zarit SH, Fingerman KL. "My Parent is so stubborn!"-perceptions of aging parents' persistence, insistence, and resistance. J Gerontol B Psychol Sci Soc Sci. 2016;71(4):602-612. [FREE Full text] [CrossRef] [Medline]
  85. Tudor K, Maloney S, Raja A, Baer R, Blakemore S, Byford S, MYRIAD Team, et al. Universal mindfulness training in schools for adolescents: a scoping review and conceptual model of moderators, mediators, and implementation factors. Prev Sci. 2022;23(6):934-953. [FREE Full text] [CrossRef] [Medline]
  86. Korn L, Haynie DL, Luk JW, Sita K, Simons-Morton BG. Attitudes, subjective norms, and perceived behavioral control associated with age of first use of cannabis among adolescents. J Sch Health. 2021;91(1):50-58. [CrossRef] [Medline]
  87. Podina IR, Cosmoiu A, Rusu P, Chivu AC. Positive thinking is not adaptive thinking: a cognitive-behavioral take on interpretation bias modification for social anxiety. J Rat-Emo Cognitive-Behav Ther. 2020;38(3):424-444. [CrossRef]


ANCOVA: analysis of covariance
AVE: average variance extracted
CR: composite reliability
MBSR: mindfulness-based stress reduction
PLS: partial least squares
SDT: self-determination theory
VIF: variance inflation factor


Edited by A Mavragani; submitted 02.12.24; peer-reviewed by S You, Q Duan; comments to author 25.12.24; revised version received 29.12.24; accepted 30.12.24; published 28.01.25.

Copyright

©Chenyu Gu, Liquan Qian, Xiaojie Zhuo. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 28.01.2025.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.