Abstract
Background
In the era of digital health, online health consultation platforms have become vital resources. However, the complex information environment is often associated with defensive user behaviors. This study investigates the factors correlated with health information avoidance among middle-aged and older adults within the information overload environment of these platforms.
Methods
A cross-sectional survey was conducted in China, yielding 249 valid responses from individuals aged 50 and above. Drawing on the Stressor-Strain-Outcome (S–S-O) framework, the study examined the structural relationships between information overload (stressor), psychological strains (privacy concerns, techno-exhaustion, and fear of missing out), and health information avoidance (outcome).
Results
The findings demonstrated that information overload was positively associated with privacy concerns, techno-exhaustion, and fear of missing out. Among these psychological strains, techno-exhaustion showed a direct association with health information avoidance behavior. Furthermore, self-perceptions of aging moderated several relationships within the S–S-O framework, suggesting that aging-related attitudes may shape how environmental stressors relate to psychological strain.
Conclusions
Information overload environments show an indirect association with health information avoidance behaviors through heightened psychological strain. The results highlight the necessity for online health consultation platforms to prioritize aging-adaptive optimizations and psychological support mechanisms to mitigate user fatigue and promote digital health inclusion for middle-aged and older adults.
Keywords: Information overload, Health information avoidance, Self-perceptions of aging, Privacy concerns, Techno-exhaustion, Fear of missing out
Introduction
With the rapid development of digital health technologies, online health consultation (OHC) platforms, such as Dingxiang Doctor and Haodaifu in China, have become increasingly important channels for accessing health information and professional consultation. These platforms extend access to medical advice beyond traditional clinical settings and have been especially salient during public health emergencies, when timely and authoritative information is needed. As a complement rather than a substitute for offline care, OHC platforms enable users to obtain preliminary guidance, consultation, and follow-up support remotely [73, 80]. OHC platforms also differ from face-to-face encounters in how they structure interaction. Features such as pseudonym use, text-based communication, and the absence of physical co-presence may provide users with a stronger sense of psychological safety [64]. This perceived anonymity can be associated with greater willingness to disclose sensitive health-related information that might otherwise be withheld because of stigma or embarrassment. Such disclosure is clinically relevant because incomplete information may reduce the accuracy of physicians’ assessments [32].
The relevance of this topic is particularly evident in China. According to recent national statistics, the number of older adults in China was 280 million and older internet users exceeded 180 million by late 2023, indicating that digital engagement among later-life populations is no longer peripheral (CNNIC, 2024). At the same time, national reports also suggest that digital participation is uneven across activities: as of June 2025, China's social network users reached 1.107 billion, accounting for 98.6% of the country’s total internet users. The number of online healthcare users stood at 393 million, representing 35.0% of all internet users, which is markedly lower than the figure for social network users. (CNNIC, 2025). These figures underscore both the growing importance of OHC platforms for middle-aged and older adults and the continuing inequalities in how such platforms are used. In this context, information overload represents a particularly relevant challenge. Information overload refers to a condition in which the quantity, density, or complexity of information exceeds users’ ability to process it effectively [20]. In digital health environments, this challenge may be intensified by layered menus, system prompts, recommendations, advertisements, and rapidly updated content. Prior research has shown that perceived information overload is associated with reduced information engagement, increased stress, and lower-quality information processing among adults [41, 66, 68]. The samples in these studies already partially encompass middle-aged and older individuals, and consistent evidence across age-diverse samples suggests that the psychological mechanisms linking information overload to other factors are not confined to younger users.
Prior studies suggest that information-rich and system-demanding digital environments may be associated with psychological strain and resistance-related responses in later-life users. For example, Cao et al. (2020) found that overload in mobile health contexts was related to fatigue and resistance behavior among older users [12]. From a coping perspective, Beaudry argue that when users appraise technology as difficult to manage or psychologically taxing, they may adopt self-protective coping responses [6]. Relatedly, research on internet adoption among middle-aged and older adults has shown that usability demands, facilitating conditions, and confidence in using technology are central to continued engagement [51]. Taken together, these studies suggest that information overload in OHC contexts may be associated with strain-related responses rather than straightforward engagement.
Existing research has examined digital health use from several related but distinct perspectives. One stream has focused on the adoption of online health services, emphasizing factors such as trust, perceived usefulness, and perceived risk [37, 74]. Another has examined health information seeking and scanning, including the role of health status, motivation, and platform-related perceptions [22, 72, 81]. However, information seeking and OHC use are not identical. The former often refers to searching for pre-existing information, whereas the latter involves interactive consultation, disclosure, and reciprocal communication with healthcare professionals.
Research targeting middle-aged and older adults has expanded remarkably in recent years across interdisciplinary fields including health information science, digital health, public health and gerontology. However, limited attention has been paid to how information overload in OHC environments is associated with privacy concerns, techno-exhaustion, and health information avoidance among these users. This gap is especially important in China, where the rapid expansion of digital health services coexists with continuing differences in digital access, literacy, and confidence among later-life users [21, 47, 78].
In light of these issues, this study examines how information overload is associated with psychological strain and health information avoidance among adults aged 50 years and above who use OHC platforms in China. The choice of this age threshold reflects the Chinese institutional context, in which health-management needs and digital adaptation challenges often become more salient from the early retirement transition onward. The study addresses the following research questions:
RQ1: How is information overload associated with health information avoidance among middle-aged and older adults in OHC settings?
RQ2: How do privacy concerns, techno-exhaustion, fear of missing out, and self-perceptions of aging relate to this association?
Literature review
Information avoidance
Information avoidance refers to behavior intended to delay, limit, or prevent exposure to available information that is perceived as unpleasant, threatening, or cognitively burdensome [67]. Within the broader domain of information behavior, it is generally understood as a conscious and goal-directed strategy rather than a mere absence of information seeking. In health contexts, information avoidance has attracted increasing scholarly attention because it may restrict engagement with information relevant to prevention, self-care, and treatment.
Existing studies suggest that the antecedents of health information avoidance can be grouped into three broad categories: personal, informational, and environmental factors. Personal factors are often linked to emotional regulation, as individuals may avoid potentially useful information when it is associated with sadness, anxiety, fear, or anticipated distress [1, 61, 62]. Informational factors include perceived quality and overload, both of which may reduce users’ willingness to process content [18, 43]. Environmental factors include subjective norms, social climate, interpersonal pressure, and digital access barriers, all of which may shape avoidance-related tendencies [48, 77].
A growing body of work has focused specifically on information avoidance among middle-aged and older adults. Recent studies have examined this issue from the perspectives of mindfulness intervention, intergenerational support, and the informational, social, and psychological dynamics of social media environments [28, 59, 65]. Taken together, this literature suggests that information avoidance among middle-aged and older adults should not be understood as a uniform or purely individual tendency. Rather, it reflects the interaction between emotional regulation, social context, and the characteristics of specific information environments.
However, relatively limited research has examined health information avoidance in the context of OHC platforms. This context is distinct because users are not only exposed to large volumes of health information but may also need to evaluate content, navigate platform functions, and disclose personal information during consultation.
Information overload
Information overload refers to a condition in which the amount, density, or complexity of available information exceeds an individual’ s capacity to process it effectively [20]. In digital environments, overload has been associated with reduced information utility, lower-quality decision processes, and increased psychological strain [14, 56]. Research on this phenomenon spans multiple contexts, including social media, e-commerce, and healthcare [8, 9, 26].
For middle-aged and older adults, information overload in digital health environments has been associated with lower active information engagement [38], stress and a diminished sense of control even when users adopt selective management strategies [71], and simplified information selection under high cognitive load [29]. Together, these studies indicate that overload is not simply a matter of quantity; it also concerns how platform environments shape users’ ability to evaluate, prioritize, and act on health information.
These findings indicate that information overload is not simply a matter of information quantity. It also concerns how platform environments shape users’ ability to prioritize, interpret, and act on health information. In OHC settings, this issue may be especially salient because overload is embedded in a broader interactive environment involving system prompts, recommendation mechanisms, multiple service options, and privacy-relevant disclosures.
Self-perceptions of aging
Self-perceptions of aging (SPA) refer to an individual’s subjective evaluation of, and beliefs and attitudes toward, their own aging process, encompassing appraisals of changes in physical functioning, cognitive capacity, social roles, and overall well-being across the life course [44]. As a core, time-varying construct in geropsychology, SPA captures meaningful heterogeneity in how middle-aged and older adults experience and interpret age-related changes, rather than reflecting a universal, monolithic experience of aging.
Extant research has identified a range of demographic, social, and individual factors associated with variability in SPA. Cross-sectional and longitudinal studies have found that older adults with advanced age, lower educational attainment, economic hardship, or poorer self-rated health are more likely to hold negative SPA, while greater social support, inclusive cultural norms around aging, and dispositional optimism correlate with more positive SPA [15, 16, 46, 70]. A robust body of evidence has further linked SPA to key later-life outcomes: positive SPA is associated with better physical and mental health, higher social engagement, and greater willingness to adopt and use new information technologies, while negative SPA correlates with elevated risk of depression, loneliness, and technology avoidance [36, 45, 57, 58]. Notably, SPA is not a fixed trait: it is shaped by ongoing experiences with technology, including perceived success or difficulty navigating digital health tools, which in turn shapes subsequent technology use behaviors [13].
Overall, the SPA literature suggests that later-life technology experiences are not homogeneous. Rather, aging-related self-perceptions may shape how users interpret demands, evaluate their own capabilities, and respond to digital environments. This makes SPA a theoretically relevant moderator in the relationship between information overload, psychological strain, and avoidance-related behaviors.
Research model and hypotheses
Theoretical basis: stressor-strain-outcome
The Stressor-Strain-Outcome (S–S-O) framework explains how environmental stressors are associated with psychological strain, which in turn relates to behavioral outcomes [42]. This framework has been widely applied to explain how stressful information and technology conditions correspond to emotional, cognitive, and behavioral responses [30, 60, 79]. Within the framework, a stressor refers to a threatening environmental stimulus perceived by an individual; strain represents the psychological, emotional, or cognitive responses associated with the individual’s appraisal of the stressor; and outcome refers to the behavioral consequences linked to the experience of strain. The S–S-O framework is most frequently applied to examine individual behavioral changes associated with exposure to negative environmental stimuli.
This study maps its core constructs onto the S–S-O framework as follows: information overload in OHC platforms is specified as the core environmental stressor; three interrelated psychological states (privacy concerns, techno-exhaustion, and fear of missing out) are specified as sequential strains; and health information avoidance is specified as the final behavioral outcome. In addition, drawing on Conservation of Resources (COR) theory [33], we introduce SPA as a key moderating variable, to examine whether aging-related self-perceptions shape the strength of associations between variables in the S–S-O framework. The following sections develop specific hypotheses for each path in the proposed model.
Pressure source (stressor): information overload
In this study, information overload is defined as a condition where individuals encounter difficulties in the routine processes of receiving, processing, and acting on health information, when confronted with excessive volume, density, or complexity of content on OHC platforms [14, 67]. OHC platforms provide users with access to a large volume of health information and multi-functional services, including symptom checking, physician consultation, disease and medication information, and appointment booking. However, not all information available on these platforms is relevant or actionable for individual users.
Drawing on Resource Limitation Theory [40], which posits that human mental resources are finite, with attention serving as the core mechanism for resource allocation, information overload in OHC settings represents a state where the scale, complexity, and update frequency of platform content exceed the cognitive processing thresholds of older adults. This imbalance between environmental demands and individual resources is associated with the transformation of objective information environments into perceived psychological stressors. Within the S–S-O framework, information overload acts as the triggering stimulus, with potential associations with subsequent psychological strains, and indirect links to health information avoidance as a behavioral outcome. The following hypotheses develop the specific associations between information overload and the three specified strain variables.
Response (strain): privacy concerns, techno-exhaustion, and fear of missing out
In the S–S-O framework, strain represents multi-faceted psychological reactions to environmental stressors. In the OHC context, we identify three specific strains to capture distinct dimensions of psychological response: privacy concerns (risk-related response), techno-exhaustion (cognitive response), and fear of missing out (affective response). Collectively, these states characterize the psychological experiences associated with information overload in OHC platforms.
When using OHC, users are often required to disclose personal and health-related information to access tailored recommendations and consultation services. While such data sharing facilitates personalized care, it may also heighten user attention to how personal information is collected, processed, and used. According to Communication Privacy Management (CPM) theory [54], individuals establish and manage privacy boundaries to regulate the disclosure and use of their personal information, with these boundaries dependent on users’ ability to understand and anticipate data flows within digital systems. In information-dense OHC environments, the volume of system prompts, recommendations, and functional features may make it more difficult for users to form clear mental models of platform operations, with perceived system opacity associated with blurred privacy boundaries and heightened uncertainty about data use. Under such conditions, users may become more attentive to potential privacy risks. Accordingly, the following hypothesis is proposed:
H1: Information overload on online health consultation platforms is positively associated with the privacy concerns.
Techno-exhaustion refers to fatigue arising from sustained interaction with information technologies [4, 69]. In feature-rich OHC environments, users may need to invest considerable time and attention to locate relevant health information. Prior research suggests that differences in prior experience, usability demands, and interface complexity can make such interactions more effortful for later-life users [17]. Higher levels of information overload may therefore correlate with greater techno-exhaustion among middle-aged and older adults. Accordingly, the following hypothesis is proposed:
H2: Information overload on online health consultation platforms is positively associated with the techno-exhaustion.
Fear of missing out refers to the concern that one may miss important or valuable information [55]. In health contexts, this concern may manifest as anxiety about overlooking relevant health-related information. In information-rich environments, the abundance of available content may increase uncertainty about whether important information has been overlooked. Accordingly, the following hypothesis is proposed:
H3: Information overload on online health consultation platforms is positively associated with the fear of missing out.
Beyond their direct links to information overload, the three strain variables are hypothesized to be interrelated. Privacy concerns may impose an additional cognitive and emotional burden during platform use, particularly when users feel uncertain about how their information is protected [24]. In OHC settings, this ongoing vigilance around privacy may coexist with the cognitive demands of navigating consultation interfaces and evaluating health information, with this combined burden associated with greater feelings of techno-exhaustion. Accordingly, the following hypothesis is proposed:
H4: Privacy concerns experienced by adults aged 50 years and above when using OHC platforms are positively correlated with techno-exhaustion.
Fear of missing out in health contexts reflects concern about overlooking information that may be relevant for current or future health management. In information-dense environments, this concern may encourage sustained monitoring and repeated engagement with platform content. Such continued attention may increase psychological and cognitive demands during use. Therefore, fear of missing out may be positively associated with techno-exhaustion among middle-aged and older adults [10]. Accordingly, the following hypothesis is proposed:
H5: Fear of missing out experienced by adults aged 50 years and above when using OHC platforms is positively related to their techno-exhaustion.
Results (outcome): information avoidance
In this study, health information avoidance refers to behaviors intended to block or postpone the acquisition of available but potentially aversive health information on OHC platforms, including both active avoidance (e.g., intentional filtering of content) and passive avoidance (e.g., refraining from information seeking) [67]. This section develops hypotheses linking the three psychological strains to the outcome of health information avoidance.
Extant literature indicates that concerns about health data privacy correlate with individuals’ willingness to use digital health technologies [3, 5]. On OHC platforms, access to health information is primarily facilitated through consultation services that require the disclosure of personal health information. Middle-aged and older adults may avoid engaging with health information through these services if they have concerns about the recording and use of their health information, with such concerns associated with greater information avoidance behavior. Accordingly, the following hypothesis is proposed:
H6: Privacy concerns experienced by adults aged 50 years and above when using OHC platforms are positively associated with their health information avoidance.
Sustained effort to navigate information technologies and locate relevant health information correlates with user techno-exhaustion [53, 79]. This association may be particularly salient among middle-aged and older adults, whose diverse functional profiles and digital competencies may not always align with the cognitive demands of complex digital interfaces. When the attentional demands of technology use exceed an individual’s available psychological resources, heightened feelings of exhaustion may occur. Such exhaustion may diminish motivation to engage with digital tools, making the information-seeking process feel overly burdensome. As a proactive strategy to preserve limited mental and physical resources, middle-aged and older adults may engage in selective information avoidance. Accordingly, the following hypothesis is proposed:
H7: Techno-exhaustion experienced by adults aged 50 years and above when using OHC platforms correlates positively with their health information avoidance.
Fear of missing out is widely recognized as a correlate of digital behaviors [52, 55]. For middle-aged and older adults, fear of missing out often manifests as heightened motivation to maintain connectivity with health-related information perceived as critical. This motivation may encourage proactive, goal-directed seeking of information aligned with immediate health priorities. However, as middle-aged and older adults often employ selective attention strategies to manage finite cognitive resources, a strong focus on urgent or existing health concerns may be associated with intentional filtering or defensive avoidance of secondary information, such as long-term preventive health content. In this context, fear of missing out may correlate with selective information avoidance to maintain focus on high-priority health goals. Accordingly, the following hypothesis is proposed:
H8: Fear of missing out experienced by adults aged 50 years and above when using OHC platforms is positively linked to their health information avoidance.
Moderation: self-perceptions of aging
Drawing on COR theory [33], we introduce SPA as a critical moderating variable in the proposed model. COR theory posits that individuals seek to acquire, preserve, and protect valued resources, with the loss or threatened loss of resources associated with psychological stress. Within this framework, SPA can be viewed as an internal psychological resource that shapes how individuals appraise and respond to environmental stressors.
Positive SPA acts as a psychological buffer. Adults aged 50 years and above with optimistic views of aging often report higher self-efficacy and cognitive resilience, which may support more effective management of the demands of OHC platforms. For these individuals, greater psychological capital may neutralize the negative associations of environmental stressors such as information overload, and weaken the links between stressors and subsequent psychological strains [35]. Conversely, negative SPA may act as a stress amplifier. Older adults who view aging as a process of inevitable decline may be more susceptible to loss spirals of resource depletion [34]. When using digital technologies, they may be more likely to internalize age-related stereotypes of technological incompetence, with associated reductions in confidence and greater resistance to technology use [13]. In this context, negative SPA may correlate with heightened perceived pressure from environmental stressors, amplifying the links between information overload, psychological strains, and subsequent information avoidance.
By examining SPA as a moderator across the S–S-O framework, this study captures nuanced psychological mechanisms that shape whether OHC platform use results in sustained engagement or withdrawal. The core hypothesis is as follows:
H9: Self-perceptions of aging have a moderating effect on the relationship between information overload and information avoidance.
In conclusion, this study will analyze the influencing factors of information avoidance behavior based on the psychological strain of adults aged 50 years and above using OHC platforms. The constructed research model is shown in Fig. 1.
Fig. 1.
Proposed model
Methodology
Survey design and participants
The initial questionnaire was developed by synthesizing the theoretical framework and established literature. Subsequently, a panel of experts was consulted to conduct a content validity review, who were completely independent of the authors. None of the experts share any institutional affiliation, financial interest, or personal relationship with the authorship team. They were selected solely based on their professional expertise and experience in the field. This process involved refining the linguistic clarity of the items, resolving semantic ambiguities, and ensuring logical consistency across the measurement scales. Based on their expert feedback, the research instrument was refined and finalized for formal data collection.
Study participants were middle-aged and older adults aged 50 years and above residing in China. The Credamo platform was employed to develop the online questionnaire, and the finalized questionnaire was distributed to targeted respondents on the platform. A total of 300 questionnaires were collected, and 249 responses were identified as valid for statistical analysis. Specifically, we excluded respondents who demonstrated non-differentiated response patterns, such as straight-lining at extreme anchors (1 or 7), or those whose completion time was under 60 s. This final sample size (N = 249) adheres to the recommended thresholds for robust statistical estimation in SEM research [27, 31]. Each participant filled out the informed consent.
Measurement
The finalized questionnaire consisted of two sections. The first section collected demographic variables, including gender (1 = male, 2 = female), age (1 = 50–54 years, 2 = 55–60 years old, 3 = 61–64 years old, 4 = 65–70 years old, 5 = over 71 years old), educational level (1 = primary school and below, 2 = junior high school, 3 = high school, 4 = university undergraduate, 5 = postgraduate students, 6 = doctoral student), annual household income (1 = less than 50,000 yuan, 2 = 50–100,000 yuan, 3 = 100,000–200,000 yuan, 4 = more than 200,000 yuan), subjective health status (1 = good, 2 = general, 3 = bad), and Internet access frequency per week (1 = 1–2 days, 2 = 3–5 days, 3 = more than 5 days).
The second section included measurement scales for six core constructs: information overload, privacy concerns, fear of missing out, techno-exhaustion, information avoidance, and self-perceptions of aging. All constructs were measured using validated scales from previous literature. A latent variable approach in AMOS was adopted for data analysis. Each construct was modeled as a latent variable indicated by its corresponding items, and measurement errors were accounted for in the analytical process. All constructs except self-perceptions of aging were measured on a 7-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = neutral, 5 = somewhat agree, 6 = agree, 7 = strongly agree). Self-perceptions of aging were assessed with a binary item, with response options of “yes” and “no”.
The detailed measurement items are presented in Table 1.
Table 1.
Questionnaire scale
| Variables | Items | Source |
|---|---|---|
| Information overload | IOV1-There is too much health information on health consult platforms, so I find it a burden to handle | [62] |
| IOV2-I find it hard to extract important health information from the excessive amount of information available to me on health consult platforms | ||
| IOV3-I find it hard to get the health information that is relevant to my needs from the excessive amount of information available to me on health consult platforms | ||
| Privacy concerns | PC1-My use of health consult platforms would cause me to lose control over the privacy of my health information | [63] |
| PC2-My use of health consult platforms would lead to a loss of privacy for me because my personal health information could be used without my knowledge | ||
| PC3-My use of health consult platforms might allow others to take control of my health information | ||
| Fear of missing out | FOMO1-I feel anxious when I don't have access to health information | [79] |
| FOMO2-I think I feel like I'm falling behind when I don't have access to health information | ||
| FOMO3-I felt anxious when I knew I was missing out on some important health information | ||
| FOMO4-I feel sad if I can't get health information because of other restrictions | ||
| Techno-exhaustion | TE1-Sometimes I feel tired when using health consult platforms | [11] |
| TE2-Sometimes I feel worn out from using health consult platforms | ||
| TE3-I feel indifferent about the reminders or alerts of new things from health consult platforms | ||
| Information avoidance | IAV1-I intentionally ignore some health information related to me on the health consult platforms | [30] |
| IAV2-When it comes to health, I don’t want to know more | ||
| IAV3-I use technical means to avoid some of the health information on health consultation platforms | ||
| Self-perceptions of aging | SPA1-Things keep getting worse as I get older | [7, 45] |
| SPA2-I have as much pep as I did last year | ||
| SPA3-As I get older, I am less useful | ||
| SPA4-I am as happy now as I was when I was younger | ||
| SPA5-As I get older, things change (better, worse, or the same) just as I perceive them to be |
Procedure
IBM SPSS 22.0 and AMOS 24.0 were utilized for all statistical analyses in this study. Initially, descriptive statistics were calculated for all demographic variables and core research constructs. Subsequently, reliability and validity tests were conducted for all measurement scales, including Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). The model fit of the proposed theoretical model was then evaluated, and hypothesis testing was performed for all hypothesized paths. Finally, multi-group analysis was conducted to examine the moderating effect of self-perceptions of aging on the relationships in the theoretical model.
Results
Descriptive statistics
Among the collected questionnaires, there were 105 males and 144 females, resulting in a relatively balanced gender ratio. In terms of age, there were a higher number of respondents aged 50–54 and 55–60, with 98 and 99 individuals respectively, accounting for 79.2% of the total. The number of respondents aged 61 and above gradually decreased. The majority of respondents, 148 individuals, had an undergraduate degree, representing 59.4% of the total. In terms of internet use frequency, 189 respondents accessed the internet 1–2 days per week (75.9%), 32 accessed the internet 3–5 days per week (12.9%), and 28 accessed the internet more than 5 days per week (11.2%). For annual household income, 110 respondents reported an income of 100,000–200,000 yuan (44.2%). Most respondents reported being in good health, with only 5 reporting poor health. In the study, respondents with a total score of 3 or above on the self-perceptions of aging questions were classified as having a positive attitude, while those with a total score below 3 were classified as having a negative attitude. There were 141 respondents with a positive attitude towards aging and 108 with a negative attitude. The specific cases were shown in Table 2.
Table 2.
Descriptive statistics of the questionnaires
| Items | Options | Frequency | Rate |
|---|---|---|---|
| Gender | Male | 105 | 42.2% |
| Female | 144 | 57.8% | |
| Age | 50–54 | 98 | 39.4% |
| 55–60 | 99 | 39.8% | |
| 61–64 | 34 | 13.7% | |
| 65–70 | 14 | 5.6% | |
| Over 71 | 4 | 1.5% | |
| Educational level | Primary and below | 2 | 0.8% |
| Junior high school | 11 | 4.4% | |
| High school | 68 | 27.3% | |
| Bachelor | 148 | 59.4% | |
| Master | 16 | 6.4% | |
| PhD and above | 4 | 1.7% | |
| Annual household income | Below 50,000yuan | 7 | 2.8% |
| 50,000–100,000yuan | 63 | 25.3% | |
| 100,000–200,000yuan | 110 | 44.2% | |
| Over 200,000yuan | 69 | 27.7% | |
| Subjective health status | Good | 145 | 58.2% |
| General | 99 | 39.8% | |
| Bad | 5 | 2.0% | |
| Internet access frequency per week | 1–2 days | 189 | 75.9% |
| 3–5 days | 32 | 12.9% | |
| Over 5 days | 28 | 11.2% | |
| Self-perceptions of aging | Positive | 141 | 56.6% |
| Negative | 108 | 43.4% |
Reliability and validity analysis
The reliability test mainly examined the reliability of the questionnaire. This study analyzed the reliability of the questionnaire using SPSS22.0. As can be seen from Table 3, the Cronbach's alpha coefficients for all variables were above 0.8 [2], indicating that these five variables had good internal reliability, and the scale demonstrated strong validity and stability.
Table 3.
Scales’ reliability and validity
| Variables | Items | Number | Cronbach's alpha | AVE | CR |
|---|---|---|---|---|---|
| Information overload | IOV1 | 3 | 0.864 | 0.692 | 0.870 |
| IOV2 | |||||
| IOV3 | |||||
| Information avoidance | IAV1 | 3 | 0.813 | 0.596 | 0.815 |
| IAV3 | |||||
| IAV4 | |||||
| Privacy concerns | PC1 | 3 | 0.908 | 0.767 | 0.908 |
| PC2 | |||||
| PC3 | |||||
| Techno-exhaustion | TE1 | 3 | 0.876 | 0.717 | 0.883 |
| TE2 | |||||
| TE3 | |||||
| Fear of missing out | FOMO1 | 4 | 0.854 | 0.598 | 0.856 |
| FOMO2 | |||||
| FOMO3 | |||||
| FOMO4 | |||||
| Total | / | 16 | 0.910 | / | / |
On this basis, the combined reliability was further verified. As can be seen from Table 3, the AVE values corresponding to all the five factors exceeded 0.5, while the CR values all exceeded 0.7, which indicated the good aggregate validity of the data in this analysis. As shown in Table 4, the square root of the AVE for all variables is greater than the correlation coefficient. As a result, the differential validity of this scale met the criteria and could be further analyzed.
Table 4.
Differential validity
| IOV | IAV | PC | TE | FOMO | |
|---|---|---|---|---|---|
| IOV | 0.832 | ||||
| IAV | 0.506 | 0.772 | |||
| PC | 0.731 | 0.484 | 0.876 | ||
| TE | 0.790 | 0.721 | 0.691 | 0.846 | |
| FOMO | 0.258 | 0.275 | 0.273 | 0.444 | 0.773 |
Bold diagonal values represent the square root of the average variance extracted (AVE) for each latent variable; off-diagonal values denote the correlation coefficients between different latent variables. According to the Fornell-Larcker criterion, discriminant validity is established when each diagonal value exceeds all off-diagonal values in its corresponding row and column
Model fit test
AMOS 24.0 was used to conduct a significance test of path coefficients for the research model on factors influencing information avoidance among middle-aged and older adults in an information overload environment. All indicators in the table fell within acceptable ranges[27], indicated a good fit for the research model in this study (Table 5).
Table 5.
Model fit degree
| Indicators | Results | Standard |
|---|---|---|
| CMIN/DF | 1.763 | < 3 |
| GFI | 0.925 | [0.7–1.0] |
| AGFI | 0.894 | [0.7–1.0] |
| CFI | 0.971 | [0.7–1.0] |
| RMSEA | 0.055 | < 0.08 |
Hypothesis test
After testing the overall fit of the model, each hypothesis of the model should be tested separately, which involved analyzing the composite reliability (C.R.), p-values, and the magnitude of path coefficients between variables. Results of the study hypothesis were shown in Table 6. Information overload had positive influence on privacy concerns (β = 0.881, p < 0.001), techno-exhaustion (β = 0.734, p < 0.001), and fear of missing out (β = 0.288, p < 0.001), supporting hypotheses H1, H2 and H3. Privacy concerns and fear of missing out had a positive effect on techno-exhaustion, in which the impact of privacy concerns (β = 0.230, p = 0.005) was less than that of fear of missing out (β = 0.284, p < 0.001), namely H4 and H5 were supported. Of the three paths influencing information avoidance, only the impact of techno-exhaustion (β = 0754, p < 0.001) was significant, while the effects of privacy concerns and fear of missing out were not significant, that is, H7 was supported, while H6 and H8 were not supported.
Table 6.
Hypothesis testing
| hypothesis | Path | β | S.E | C.R | p | Results |
|---|---|---|---|---|---|---|
| H1 | PC < -IOV | 0.881 | 0.089 | 9.856 | *** | Support |
| H2 | TE < -IOV | 0.734 | 0.111 | 6.622 | *** | Support |
| H3 | FOMO < -IOV | 0.288 | 0.078 | 3.682 | *** | Support |
| H4 | TE < -PC | 0.230 | 0.082 | 2.825 | 0.005 | Support |
| H5 | TE < -FOMO | 0.284 | 0.063 | 4.550 | *** | Support |
| H6 | IAV < -PC | −0.025 | 0.095 | −0.265 | 0.791 | Not support |
| H7 | IAV < -TE | 0.754 | 0.106 | 7.101 | *** | Support |
| H8 | IAV < -FOMO | −0.063 | 0.085 | −0.731 | 0.465 | Not support |
***indicates p < 0.001
**indicates p < 0.01
*indicates p < 0.05
Moderation effect of self-perceptions of aging
In the questionnaire, we measured the respondents’ self-perceptions of aging through five questions. We coded the collected questionnaire data and summed them to distinguish their self-perceptions of aging according to their scores. Respondents with a total score of 3 or higher were classified as having a positive attitude, and those with a score below 3 were classified as having a negative attitude. The sample was split into the positive SPA group (n = 141) and negative SPA group (n = 108) based on total SPA scores, and path coefficient analyses were conducted separately for the two groups.
Multi-group analysis results showed that the association between information overload and fear of missing out was not significant in the positive SPA group (β = 0.191, p = 0.085 > 0.01), while a significant positive association was observed in the negative SPA group (β = 0.331, p = 0.006 < 0.01). The association between privacy concerns and techno-exhaustion was not significant in the positive SPA group (β = 0.178, p = 0.099 > 0.01), while a significant positive association was detected in the negative SPA group (β = 0.265, p = 0.013 < 0.05). The association between fear of missing out and techno-exhaustion was significant in the positive SPA group (β = 0.303, p = 0.001 < 0.01), while the corresponding association was significant in the negative SPA group (β = 0.272, p = 0.019 < 0.05). No significant moderating effect of SPA was observed on the direct path between information overload and health information avoidance in either group. Overall, SPA exhibited a partial moderating effect on the associations between variables in the S–S-O framework, so H9 was partially supported.
Discussion
Against the backdrop of digital health transformation and population aging in China, this study examined the associations between information overload, multi-dimensional psychological strains, and health information avoidance among Chinese middle-aged and older adults aged 50 years and above in OHC settings, with a focus on the moderating role of SPA based on the S–S-O framework. This section interprets the core findings of the study, aligns the results with existing empirical research and theoretical frameworks, outlines theoretical contributions and practical implications, and notes limitations of the study with corresponding directions for future research.
Main findings
This study observed a significant positive association between information overload in OHC platforms and all three psychological strains (privacy concerns, techno-exhaustion, and fear of missing out) among Chinese middle-aged and older adults. These findings align with results from prior cross-context research. For example, Swar et al. (2017) identified a positive association between information overload and negative psychological states in online healthcare information search scenarios [66], and Cao et al. (2020) reported a link between system and information overload and fatigue-related responses among older users of mobile health applications [12]. While most existing studies focused on younger populations or general social media contexts [75, 76], this study extends these findings to the interactive OHC setting among Chinese middle-aged and older adults, providing empirical evidence for the generalizability of the overload-strain association in this specific group and context.
In addition, this study observed significant positive associations between privacy concerns, fear of missing out, and techno-exhaustion. These results are consistent with existing research in digital behavior. Fox et al. (2021) noted that sustained privacy-related vigilance correlates with increased cognitive burden during digital platform use, and Fu et al. (2020) found a link between fear of missing out and heightened fatigue from sustained social media engagement [23, 25]. For Chinese middle-aged and older adults in this study, the dual demands of navigating complex OHC interfaces and maintaining vigilance around personal health data disclosure correlated with greater techno-exhaustion, as did sustained attention to health information driven by fear of missing out. These findings enrich existing research by verifying the interrelationships between multi-dimensional psychological strains among middle-aged and older adults in Chinese digital health settings.
Regarding the association between psychological strains and health information avoidance, only techno-exhaustion showed a significant positive association with health information avoidance in this study, while the direct associations between privacy concerns, fear of missing out, and information avoidance were not statistically significant. The significant link between techno-exhaustion and information avoidance aligns with a large body of existing research: Dai et al. (2020) and Guo et al. (2020) both observed a positive association between technology-related fatigue and information avoidance behaviors in digital environments, and Lipsey and Shepperd (2019) noted that older adults are more likely to adopt resource-preserving behaviors such as information avoidance when facing cognitively demanding tasks [18, 30, 49]. This finding confirms the core position of techno-exhaustion in the S–S-O framework for this study group, as the most direct correlate of health information avoidance.
For the non-significant direct association between privacy concerns and information avoidance, this result differs from some prior studies conducted in Western contexts [3, 5] but can be interpreted through the privacy calculus framework [19] and the specific health needs of Chinese middle-aged and older adults. Existing literature suggests that individuals weigh perceived benefits against potential risks when making decisions about information disclosure and platform engagement. For the middle-aged and older adults in this study, the perceived health benefits of accessing professional medical guidance via OHC platforms may outweigh concerns about privacy risks, which corresponds with findings from Jin and Qu (2025) on Chinese older adults’ online medical help-seeking behaviors [39]. This result highlights the need for context-specific examination of privacy-related behaviors in digital health research, rather than generalization across populations and cultural settings.
For the non-significant direct association between fear of missing out and information avoidance, this finding differs from studies focused on younger social media users [52, 55]. A possible explanation lies in the differences in health information needs between the study group and younger populations. Chinese middle-aged and older adults in this study often have more targeted, condition-specific health information needs, rather than the broad, socially driven information engagement seen in younger groups [50]. Their information-seeking behaviors are more likely to focus on existing health conditions, rather than general preventive information, which may reduce the direct link between fear of missing out and broad information avoidance. This finding adds to the understanding of age-related heterogeneity in fear of missing out and its association with health information behaviors.
Finally, this study found that SPA partially moderated the associations between variables in the S–S-O framework. Specifically, the association between information overload and fear of missing out, and the association between privacy concerns and techno-exhaustion, were significant only in the negative SPA group, while the association between fear of missing out and techno-exhaustion was significant in both groups, with a stronger coefficient in the positive SPA group. These findings align with core propositions of Conservation of Resources (COR) theory [33] and existing SPA research. [35] noted that positive SPA acts as a psychological buffer against technology-related stress, while [13] found that negative SPA correlates with heightened sensitivity to technology-related difficulties and age-related stereotypes [13, 35]. For the middle-aged and older adults in this study, positive SPA correlated with greater resilience to privacy-related stress, while negative SPA correlated with heightened sensitivity to information overload and related risks. These findings extend existing SPA research, which has focused primarily on technology adoption and general health behaviors [57, 58], to the specific domain of health information avoidance in OHC settings.
Theoretical contributions
This study makes three core theoretical contributions to the fields of aging research, digital health information behavior, and health management.
First, this study extends the application of the S–S-O framework to the understudied context of OHC use among Chinese middle-aged and older adults aged 50 years and above. While the S–S-O framework has been widely used in organizational behavior and youth digital behavior research [60, 79], its application to older adults’ health information behaviors remains limited. This study verifies the applicability of the framework in this context, delineates the sequential associations from information overload (stressor) to multi-dimensional psychological strains, and finally to health information avoidance (outcome), providing a robust theoretical lens for understanding older adults’ digital health information behaviors.
Second, this study clarifies the core role of techno-exhaustion in the association between environmental stressors and health information avoidance, and addresses inconsistent findings in existing research regarding the role of privacy concerns and fear of missing out. By examining the interrelationships between three distinct psychological strains, this study moves beyond single-variable explanations of information avoidance in prior research, and provides empirical evidence for the indirect pathways of privacy concerns and fear of missing out via techno-exhaustion. This enriches the theoretical understanding of the psychological mechanisms underlying health information avoidance among middle-aged and older adults.
Third, this study integrates COR theory and SPA research into the S–S-O framework, revealing the heterogeneous responses to information overload among middle-aged and older adults with different aging-related self-perceptions. While existing research has documented the association between SPA and technology adoption, few studies have examined its moderating role in the stressor-strain-outcome process of health information behavior. This study expands the theoretical boundaries of SPA research by linking aging-related self-perceptions to digital health information behaviors, and highlights the importance of incorporating age-specific psychological traits into research on older adults’ digital behaviors.
Limitations and suggestions
Despite its contributions, this study has several limitations that provide avenues for future research.
First, the sample was exclusively collected within the Chinese cultural context, which may limit the generalizability of the findings to older populations in other regions with different digital infrastructures or aging-related norms. Future studies should consider cross-cultural or multi-regional comparative analyses to enhance the external validity of the model.
Second, the current study primarily focuses on negative psychological strains within the S–S-O framework. However, positive psychological factors, such as digital resilience or technology optimism, might play a countervailing role in mitigating information avoidance. Future research could enrich the model by incorporating these positive emotional and cognitive dimensions to provide a more holistic understanding of older adults’ digital behavior.
Third, although basic demographic data were collected, this study did not include these factors as control variables in the structural model. Future investigations should incorporate demographics-such as education level, income, and chronic health status-as control variables to eliminate potential confounding effects and more precisely isolate the impact of psychological stressors.
Finally, the cross-sectional design of this study limits our ability to draw definitive causal inferences among the variables. Although the S–S-O framework provides a robust and well-established theoretical foundation for the hypothesized sequence-moving from information overload to psychological strains and ultimately to avoidance behavior-alternative directional relationships remain plausible. For instance, older adults experiencing high levels of techno-exhaustion may become more hypersensitive to digital environments, thereby perceiving a higher degree of information overload. Consequently, future research should employ longitudinal designs or experimental methods to further validate the temporal precedence and establish rigorous causal linkages within the proposed model.
Authors’ contributions
JA and WWZ designed the study; JA, JLA,WWZ, PH and CL revised this manuscript; KXW wrote this manuscript and conducted the data analysis; ZYX collected data. All authors read and approved the final manuscript.
Funding
This research was supported by the National Natural Science Foundation of China (NSFC) (Grant Number 72171124 & 72271128), a project of Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant Number KYCX24_1103), and Project Funding for the Innovation Team in Philosophy and Social Sciences of Jiangsu Universities (Digital Intelligence-driven Health Big Data Management Innovation Team).
Data availability
The data sets used and analyzed in this study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the ethics committee of First People’s Hospital of Changshu City, in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Jing An, Email: 13776855935@163.com.
Weiwei Zhu, Email: kirbyzhu@163.com.
References
- 1.Agustina E, Dodd RH, Waller J, Vrinten C. Understanding middle-aged and older adults’ first associations with the word “cancer”: a mixed methods study in England. Psychooncology. 2018;27(1):309–15. 10.1002/pon.4569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull. 1988;103(3):411–23. 10.1037/0033-2909.103.3.411. [DOI] [Google Scholar]
- 3.Appari A, Johnson ME. Information security and privacy in healthcare: current state of research. Int J Internet Enterprise Manag. 2010;6(4):1–1. 10.1504/IJIEM.2010.035624. [DOI] [Google Scholar]
- 4.Ayyagari R, Grover V, Purvis R. Technostress: technological antecedents and implications. MIS Q. 2011;35(4):831–58. 10.2307/41409963. [DOI] [Google Scholar]
- 5.Bansal G, Zahedi F“, Gefen D. The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decis Support Syst. 2010;49(2):138–50. 10.1016/j.dss.2010.01.010. [DOI] [Google Scholar]
- 6.Beaudry A, Pinsonneault A. Understanding user responses to information technology: a coping model of user adaptation. MIS Q. 2005;29(3):493–524. 10.2307/25148693. [DOI] [Google Scholar]
- 7.Boeder J, Tse DCK. Measuring self-perceptions of aging: Differences between measures when predicting health outcomes. J Gerontol: Series B. 2021;76(5):825–35. 10.1093/geronb/gbaa064. [DOI] [PubMed] [Google Scholar]
- 8.Branco F, Sun M, Villas-Boas JM. Too much information? Information provision and search costs. Mark Sci. 2016;35(4):605–18. 10.1287/mksc.2015.0959. [DOI] [Google Scholar]
- 9.Breyton M, Schultz É, Smith A“, Rouquette A, Mancini J. Information overload in the context of COVID-19 pandemic: a repeated cross-sectional study. Patient Educ Couns. 2023;110:107672. 10.1016/j.pec.2023.107672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Bright LF, Logan K. Is my fear of missing out (FOMO) causing fatigue? Advertising, social media fatigue, and the implications for consumers and brands. Internet Res. 2018;28(5):1213–27. 10.1108/IntR-03-2017-0112. [DOI] [Google Scholar]
- 11.Cao X, Masood A, Luqman A, Ali A. Excessive use of mobile social networking sites and poor academic performance: antecedents and consequences from stressor-strain-outcome perspective. Comput Hum Behav. 2018;85:163–74. 10.1016/j.chb.2018.03.023. [DOI] [Google Scholar]
- 12.Cao Y, Li J, Qin X, Hu B. Examining the effect of overload on the MHealth application resistance behavior of elderly users: an SOR perspective. Int J Environ Res Public Health. 2020;17(18):18. 10.3390/ijerph17186658. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Caspi A, Daniel M, Kavé G. Technology makes older adults feel older. Aging Ment Health. 2019;23(8):1025–30. 10.1080/13607863.2018.1479834. [DOI] [PubMed] [Google Scholar]
- 14.Chan YM. Predictors of health information overload within internet users. Int J Inform Commun Technol Res. 2012;2(12):881–7. [Google Scholar]
- 15.Chang E-S, Kannoth S, Levy S, Wang S-Y, Lee JE, Levy BR. Global reach of ageism on older persons’ health: a systematic review. PLoS ONE. 2020;15(1):e0220857. 10.1371/journal.pone.0220857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cheng S-T. Self-perception of aging and satisfaction with children’s support. J Gerontol: Series B. 2017;72(5):782–91. 10.1093/geronb/gbv113. [DOI] [PubMed] [Google Scholar]
- 17.Czaja SJ, Charness N, Fisk AD, Hertzog C, Nair SN, Rogers WA, et al. Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (CREATE). Psychol Aging. 2006;21(2):333–52. 10.1037/0882-7974.21.2.333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dai B, Ali A, Wang H. Exploring information avoidance intention of social media users: a cognition–affect–conation perspective. Internet Res. 2020;30(5):1455–78. 10.1108/INTR-06-2019-0225. [DOI] [Google Scholar]
- 19.Dinev T, Hart P. An extended privacy calculus model for E-commerce transactions. Inf Syst Res. 2006;17(1):61–80. 10.1287/isre.1060.0080. [DOI] [Google Scholar]
- 20.Eppler MJ, Mengis J. The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf Soc. 2004;20(5):325–44. 10.1080/01972240490507974. [DOI] [Google Scholar]
- 21.Fang Z, Liu Y, Peng B. Empowering older adults: bridging the digital divide in online health information seeking. Hum Soc Sci Commun. 2024;11(1):1748. 10.1057/s41599-024-04312-7. [DOI] [Google Scholar]
- 22.Ferraris G, Monzani D, Coppini V, Conti L, Maria Pizzoli SF, Grasso R, et al. Barriers to and facilitators of online health information-seeking behaviours among cancer patients: a systematic review. Digit Health. 2023;9:20552076231210664. 10.1177/20552076231210663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Fox G, Clohessy T, Van Der Werff L, Rosati P, Lynn T. Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Comput Hum Behav. 2021;121:106806. 10.1016/j.chb.2021.106806. [DOI] [Google Scholar]
- 24.Fox G, James TL. Toward an understanding of the antecedents to health information privacy concern: a mixed methods study. Inf Syst Front. 2021;23(6):1537–62. 10.1007/s10796-020-10053-0. [DOI] [Google Scholar]
- 25.Fu S, Li H, Liu Y, Pirkkalainen H, Salo M. Social media overload, exhaustion, and use discontinuance: examining the effects of information overload, system feature overload, and social overload. Inf Process Manag. 2020;57(6):102307. 10.1016/j.ipm.2020.102307. [DOI] [Google Scholar]
- 26.Furner CP, Zinko RA. The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: an empirical investigation. Electron Mark. 2017;27(3):211–24. 10.1007/s12525-016-0233-2. [DOI] [Google Scholar]
- 27.Gefen D, Straub D, Boudreau M-C. Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst. 2000;4:1–78. 10.17705/1CAIS.00407. [DOI] [Google Scholar]
- 28.Gu C, Qian L, Zhuo X. Mindfulness intervention for health information avoidance in older adults: mixed methods study. JMIR Public Health Surveill. 2025;11:e69554. 10.2196/69554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Guo R, Azlan AA, Mohamad E. Mechanisms of short video selection behavior in elderly hypertensives under health information overload: a cognitive load theory. Future Technology. 2025;4(3):107–18. 10.55670/fpll.futech.4.3.11. [DOI] [Google Scholar]
- 30.Guo Y, Lu Z, Kuang H, Wang C. Information avoidance behavior on social network sites: information irrelevance, overload, and the moderating role of time pressure. Int J Inf Manage. 2020;52:102067. 10.1016/j.ijinfomgt.2020.102067. [DOI] [Google Scholar]
- 31.Hair JF, editor. A primer on partial least squares structural equation modeling (PLS-SEM). Sage; 2014. [Google Scholar]
- 32.He Y, Guo X, Wu T, Vogel D. The effect of interactive factors on online health consultation review deviation: an empirical investigation. Int J Med Inform. 2022;163:104781. 10.1016/j.ijmedinf.2022.104781. [DOI] [PubMed] [Google Scholar]
- 33.Hobfoll SE. Conservation of resources: a new attempt at conceptualizing stress. Am Psychol. 1989;44(3):513–24. 10.1037/0003-066X.44.3.513. [DOI] [PubMed] [Google Scholar]
- 34.Hobfoll SE. The influence of culture, community, and the nested-self in the stress process: advancing conservation of resources theory. Appl Psychol Int Rev. 2001;50(3):337–70. 10.1111/1464-0597.00062. [DOI] [Google Scholar]
- 35.Hong S-J, Lui CSM, Hahn J, Moon JY, Kim TG. How old are you really? Cognitive age in technology acceptance. Decis Support Syst. 2013;56:122–30. 10.1016/j.dss.2013.05.008. [DOI] [Google Scholar]
- 36.Hu RX, Li LW. Social disconnectedness and loneliness: do self-perceptions of aging play a role? J Gerontol B Psychol Sci Soc Sci. 2022;77(5):936. 10.1093/geronb/gbac008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Hung M-C, Jen W-Y. The adoption of mobile health management services: an empirical study. J Med Syst. 2012;36(3):1381–8. 10.1007/s10916-010-9600-2. [DOI] [PubMed] [Google Scholar]
- 38.Jensen JD, Liu M, Carcioppolo N, John KK, Krakow M, Sun Y. Health information seeking and scanning among US adults aged 50-75 years: testing a key postulate of the information overload model. Health Informatics J. 2017;23(2):96–108. 10.1177/1460458215627290. [DOI] [PubMed] [Google Scholar]
- 39.Jin H, Qu Y. Association between intergenerational support, technology perception and trust, and intention to seek medical care on the internet among Chinese older adults: cross-sectional questionnaire study. J Med Internet Res. 2025;27:e65065. 10.2196/65065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Kahneman D. Attention and effort. Prentice-Hall; 1973. [Google Scholar]
- 41.Kim HK, Ahn J, Atkinson L, Kahlor LA. Effects of COVID-19 misinformation on information seeking, avoidance, and processing: a multicountry comparative study. Sci Commun. 2020;42(5):586–615. 10.1177/1075547020959670. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Koeske GF, Kirk SA, Koeske RD. Coping with job stress: which strategies work best? J Occup Organ Psychol. 1993;66(4):319–35. 10.1111/j.2044-8325.1993.tb00542.x. [DOI] [Google Scholar]
- 43.Kusi GA, Rumki MZA, Quarcoo FH, Otchere E, Fu G. The role of information overload on consumers’ online shopping behavior. J Business Manag Stud. 2022;4(4):162–78. 10.32996/jbms.2022.4.4.16. [DOI] [Google Scholar]
- 44.Levy BR. Mind matters: cognitive and physical effects of aging self-stereotypes. J Gerontol B Psychol Sci Soc Sci. 2003;58(4):P203–11. 10.1093/geronb/58.4.P203. [DOI] [PubMed] [Google Scholar]
- 45.Levy BR, Myers LM. Preventive health behaviors influenced by self-perceptions of aging. Prev Med. 2004;39(3):625–9. 10.1016/j.ypmed.2004.02.029. [DOI] [PubMed] [Google Scholar]
- 46.Levy BR, Slade MD, Kunkel SR, Kasl SV. Longevity increased by positive self-perceptions of aging. J Pers Soc Psychol. 2002;83(2):261–70. 10.1037/0022-3514.83.2.261. [DOI] [PubMed] [Google Scholar]
- 47.Li Y, Liu C, Sun J, Zhang J, Li X, Zhang Z. The digital divide and cognitive disparities among older adults: community-based cohort study in China. J Med Internet Res. 2024;26:e59684. 10.2196/59684. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Link E. Information avoidance during health crises: predictors of avoiding information about the COVID-19 pandemic among German news consumers. Inf Process Manag. 2021;58(6):102714. 10.1016/j.ipm.2021.102714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Lipsey NP, Shepperd JA. Powerful audiences are linked to health information avoidance: results from two surveys. Soc Sci Med. 2019;225:51–9. 10.1016/j.socscimed.2019.01.046. [DOI] [PubMed] [Google Scholar]
- 50.Manafo E, Wong S. Exploring older adults’ health information seeking behaviors. J Nutr Educ Behav. 2012;44(1):85–9. 10.1016/j.jneb.2011.05.018. [DOI] [PubMed] [Google Scholar]
- 51.Niehaves B, Plattfaut R. Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide. Eur J Inf Syst. 2014;23(6):708–26. 10.1057/ejis.2013.19. [DOI] [Google Scholar]
- 52.Oberst U, Wegmann E, Stodt B, Brand M, Chamarro A. Negative consequences from heavy social networking in adolescents: the mediating role of fear of missing out. J Adolesc. 2017;55:51–60. 10.1016/j.adolescence.2016.12.008. [DOI] [PubMed] [Google Scholar]
- 53.Pang H, Ruan Y. Can information and communication overload influence smartphone app users’ social network exhaustion, privacy invasion and discontinuance intention? A cognition-affect-conation approach. J Retail Consum Serv. 2023;73:103378. 10.1016/j.jretconser.2023.103378. [DOI] [Google Scholar]
- 54.Petronio S. Boundaries of privacy: dialectics of disclosure. State University of New York Press; 2002. [Google Scholar]
- 55.Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. 2013;29(4):1841–8. 10.1016/j.chb.2013.02.014. [DOI] [Google Scholar]
- 56.Roetzel PG. Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development. Bus Res. 2019;12(2):479–522. 10.1007/s40685-018-0069-z. [DOI] [Google Scholar]
- 57.Sabatini S, Wilton-Harding B, Ballard C, Brooker H, Corbett A, Hampshire A, et al. Bidirectional associations of awareness of age-related change and attitudes toward own aging with social media use. J Gerontol B Psychol Sci Soc Sci. 2023;78(8):1349–59. 10.1093/geronb/gbad070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Schlomann A, Memmer N, Wahl H-W. Awareness of age-related change is associated with attitudes toward technology and technology skills among older adults. Front Psychol. 2022;13:905043. 10.3389/fpsyg.2022.905043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Shan Y, Huang S. Relationships between intergenerational support and the health information avoidance behavior of the elderly. Front Soc Sci Technol. 2021;3(1):1–5. 10.25236/FSST.2021.030114. [DOI] [Google Scholar]
- 60.Shi C, Yu L, Wang N, Cheng B, Cao X. Effects of social media overload on academic performance: a stressor–strain–outcome perspective. Asian J Commun. 2020;30(2):179–97. 10.1080/01292986.2020.1748073. [DOI] [Google Scholar]
- 61.Song S, Yao X, Wen N. What motivates Chinese consumers to avoid information about the COVID-19 pandemic?: the perspective of the stimulus-organism-response model. Inf Process Manag. 2021;58(1):102407. 10.1016/j.ipm.2020.102407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Soroya SH, Farooq A, Mahmood K, Isoaho J, Zara S. From information seeking to information avoidance: understanding the health information behavior during a global health crisis. Inf Process Manag. 2021;58(2):102440. 10.1016/j.ipm.2020.102440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Suh A, Li M. How the use of mobile fitness technology influences older adults’ physical and psychological well-being. Comput Hum Behav. 2022;131:107205. 10.1016/j.chb.2022.107205. [DOI] [Google Scholar]
- 64.Suler J. The online disinhibition effect. Cyberpsychol Behav. 2004;7(3):321–6. 10.1089/1094931041291295. [DOI] [PubMed] [Google Scholar]
- 65.Sun L, Wang Y. Mapping health information avoidance among older adults in China: informational, social and psychological dynamics in the social media landscape. Health Commun. 2026;0(0):1–19. 10.1080/10410236.2026.2616279. [DOI] [PubMed] [Google Scholar]
- 66.Swar B, Hameed T, Reychav I. Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search. Comput Hum Behav. 2017;70:416–25. 10.1016/j.chb.2016.12.068. [DOI] [Google Scholar]
- 67.Sweeny K, Melnyk D, Miller W, Shepperd JA. Information avoidance: who, what, when, and why. Rev Gen Psychol. 2010;14(4):340–53. 10.1037/a0021288. [DOI] [Google Scholar]
- 68.Tandoc EC, Kim HK. Avoiding real news, believing in fake news? Investigating pathways from information overload to misbelief. Journalism. 2023;24(6):1174–92. 10.1177/14648849221090744. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Tarafdar M, Tu Q, Ragu-Nathan TS. Impact of technostress on end-user satisfaction and performance. J Manag Inf Syst. 2010;27(3):303–34. 10.2753/MIS0742-1222270311. [DOI] [Google Scholar]
- 70.Tovel H, Carmel S, Raveis VH. Relationships among self-perception of aging, physical functioning, and self-efficacy in late life. J Gerontol B Psychol Sci Soc Sci. 2019;74(2):212–21. 10.1093/geronb/gbx056. [DOI] [PubMed] [Google Scholar]
- 71.Vivion M, Reid V, Dubé E, Coutant A, Benoit A, Tourigny A. How older adults manage misinformation and information overload—a qualitative study. BMC Public Health. 2024;24(1):871. 10.1186/s12889-024-18335-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Wang X, Shi J, Kong H. Online health information seeking: a review and meta-analysis. Health Commun. 2021;36(10):1163–75. 10.1080/10410236.2020.1748829. [DOI] [PubMed] [Google Scholar]
- 73.Wang X, Zhang Z, Zhao J, Shi Y. Impact of telemedicine on healthcare service system considering patients’ choice. Discrete Dyn Nat Soc. 2019;2019:1–16. 10.1155/2019/7642176. [DOI] [Google Scholar]
- 74.Wu JH, Wang SC, Lin LM. What drives mobile health care? An empirical evaluation of technology acceptance. Proceedings of the 38th Annual Hawaii International Conference on System Sciences. 2005:150a–150a. 10.1109/HICSS.2005.689. [DOI]
- 75.Yang Q, Pitafi AH. A moderated mediation investigation of the influence of enterprise social media visibility on work stress. Acta Psychol. 2023;241:104084. 10.1016/j.actpsy.2023.104084. [DOI] [PubMed] [Google Scholar]
- 76.Zhang H, Li H, Li C, Lu X. Disentangling user fatigue in WeChat use: the configurational interplay of fear of missing out and overload. Internet Res. 2024;34(7):160–86. 10.1108/INTR-07-2023-0565. [DOI] [Google Scholar]
- 77.Zhang J, Hu X, Wu D, Yan H. Exploring the influence mechanism of Chinese young researchers’ academic information avoidance behavior. J Acad Librariansh. 2023;49(2):102649. 10.1016/j.acalib.2022.102649. [DOI] [Google Scholar]
- 78.Zhang M, Osman K, Mahmud SND. Bridging the digital divide: Empowering older adults’ digital engagement in China’s university of the third age. J Adult Continuing Educ. 2025:14779714251394167. 10.1177/14779714251394167. [DOI]
- 79.Zhang S, Zhao L, Lu Y, Yang J. Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services. Inf Manag. 2016;53(7):904–14. 10.1016/j.im.2016.03.006. [DOI] [Google Scholar]
- 80.Zhang X, Guo X, Lai K, Yin C, Meng F. From offline healthcare to online health services: the role of offline healthcare satisfaction and habits. J Electron Commer Res. 2017;18(2):138–54. [Google Scholar]
- 81.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. 10.2196/34790. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data sets used and analyzed in this study are available from the corresponding author on reasonable request.