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Epidemiology (193) Infodemiology and Infoveillance (1557) JMIR Theme Issue: COVID-19 Special Issue (2548) Public Health (247) Health Professionals' Training in eHealth, Digital Medicine, Medical Informatics (184)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67098, first published .
Factors Predicting Information Overload During the COVID-19 Pandemic in the Digital Age: Longitudinal Study

Factors Predicting Information Overload During the COVID-19 Pandemic in the Digital Age: Longitudinal Study

Factors Predicting Information Overload During the COVID-19 Pandemic in the Digital Age: Longitudinal Study

Authors of this article:

Hiroko Okada1 Author Orcid Image ;   Tsuyoshi Okuhara1 Author Orcid Image ;   Takahiro Kiuchi1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    Department of Health Communication, Schoolf of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Corresponding Author:

    Hiroko Okada

    Department of Health Communication, Schoolf of Public Health

    Graduate School of Medicine

    The University of Tokyo

    7-3-1 Hongo, Bunkyo-ku

    Tokyo, 113-8655

    Japan

    Phone: 81 3 5800 6549

    Email: okadahiroko-tky@umin.ac.jp


    Background: The human capacity to process information is limited. During the COVID-19 pandemic, people were exposed to a large amount of uncertain and complex health information. This situation made some people experience perceived information overload, which made them unable to adopt appropriate preventive behaviors.

    Objective: This study aimed to examine the individual characteristics, abilities, and attention to informational media that predict the perception of information overload during a pandemic.

    Methods: We conducted a longitudinal study with 2 time points, August 2020 and August 2021, among residents of Japan under a COVID-19 emergency declaration. The sample had the same proportions for sex, age, and prefecture as the general Japanese population. We used a web-based survey to measure sociodemographic characteristics, health literacy (HL), attention to 6 different types of information channels, and participants’ perception of information overload. Hierarchical multiple regression analysis was conducted with information overload as the objective variable.

    Results: A total of 784 participants responded to the survey at both time points, with a follow-up rate of 78.4% (784/1000). Hierarchical multiple regression analysis showed that younger age (β=−0.084, 95% CI −0.142 to −0.013), male sex (β=−0.163, 95% CI −0.008 to −0.003), lower HL (β=−0.084, 95% CI −0.114 to −0.011), paying less attention to television news (β=−0.118, 95% CI −0.038 to −0.001), and paying greater attention to social media (β=0.089, 95% CI 0.000-0.027) significantly predicted information overload 1 year after exposure to information during the pandemic.

    Conclusions: Public health communicators should aim to provide concise and understandable information in consideration of a target population that is vulnerable to information overload during a pandemic. A high level of attention to social media may increase the perception of information overload. By contrast, HL may reduce the cognitive load in information processing. Providing an environment during normal periods that allows people to develop the skills to critically interpret health information will help them to prepare for future infodemics.

    Interact J Med Res 2025;14:e67098

    doi:10.2196/67098

    Keywords

    COVID-19; health communication; information overload; social media; infobesity; infoxication; information anxiety; data overload; SARS-CoV-2; coronavirus; respiratory; infectious; pulmonary; pandemic; digital age; information age; longitudinal study; Japan; human capacity; informational media 


    Background

    The unexpected occurrence of the COVID-19 pandemic caused a serious and prolonged threat to human health and life. To cope with this threat, people consumed information from diverse media sources to stay informed about this emerging infectious disease [1,2]. At the 2020 Munich Security Conference, the World Health Organization (WHO) director general stated that the world was not only dealing with a pandemic, but also with an infodemic [3,4]. The COVID-19 pandemic occurred amid the ongoing proliferation of new media platforms, such as social media, where anyone can disseminate any type of information. Fake news, misinformation, and conspiracy theories spread faster than ever via these new media channels [3,5,6]. In addition, information during a pandemic is often uncertain. It has become apparent that ensuring that the public can adequately perceive information to protect their own lives and well-being during a pandemic is an enormous challenge within public health communication.

    Information Overload

    The human capacity to process information is limited [7-10]. Therefore, a rapid influx of information can impose a psychological burden. Information overload can be defined as a situation where the amount of information provided within a certain time frame exceeds an individual’s capacity to process the information [11]. Information overload affects people’s decision-making accuracy, time to reach a decision, and general performance [12]. More specifically, with information overload, a person can become overly selective, ignore large amounts of information, have difficulty understanding the relevance of details with respect to the overall perspective, and require more time and effort to acquire information [12]. Similarly, when people are faced with overly complex health information, they may not recognize the information as personally relevant or valuable, and may be unable to infer what information is needed to engage in appropriate behavior [13].

    Information Overload and Health Behavior

    People experiencing information overload tend to have negative attitudes toward health information [14,15]. Consequently, they are less motivated to seek out and digest such information, which ultimately reduces their health knowledge and associated skills [15,16]. In the field of health, the problem of information overload has been studied in recent years. For example, a study of cancer information seeking showed that information overload leads to the avoidance of cancer screening and a lack of appropriate preventive behavior [17]. A study of patients with newly diagnosed coronary artery disease suggested that information overload has a direct negative impact on their intention to read self-management materials [18]. Previous cross-sectional studies conducted during the COVID-19 pandemic showed that information overload is associated with vaccine hesitancy [19,20]. Information overload in the COVID-19 era has also been reported to cause negative emotions, such as anxiety and depression [21,22]. These findings show that information overload is a health communication concern as a factor that influences health behavior.

    Predictors of Information Overload: Theoretical Framework

    Cognitive load theory is an important framework for understanding the causes of information overload [23]. This theory was originally introduced in the field of educational psychology and has contributed to the development of educational environments for the retention of knowledge and information [24]. As an application of learning theory, it is also useful for understanding the process by which humans acquire knowledge from news and other sources. In this theory, cognitive load is composed of 3 conceptualized factors: intrinsic, extraneous, and germane load [24]. Intrinsic cognitive load is related to the natural complexity of the information or learning materials that must be understood with respect to a specific topic. Extraneous load can be expressed as the load imposed by information from suboptimal media. When extraneous cognitive load is high, the information seeker disproportionately uses their working memory (WM) resources to deal with the provided media rather than with information seeking itself, so the attention devoted to essential information is reduced. Germane load refers to the WM resources that the information seeker expends to deal with the intrinsic cognitive load of the information. Germane load is related to individual characteristics [24].

    Not all individuals experience information overload. The relationship between age and cognitive function, an individual characteristic, has been the focus of research in the fields of neuroscience and gerontology [25-27]. Related studies have shown that cognitive function declines with age in adulthood [28,29]. This suggests that the cognitive load from processing complex information in adulthood is greater with older age. However, a cross-sectional study conducted in Korea during the COVID-19 pandemic found no association between age and information overload [30]. The relationship between age and information overload in the context of health information and pandemics has not been clarified. However, evidence from the field of neuroscience shows that the cognitive load of information increases with age. It is likely that older people were more likely to perceive information overload during the pandemic, when they were exposed to a large amount of complex health information.

    Our first hypothesis (hypothesis 1) is that older adults are more likely to experience information overload 1 year later in a pandemic information environment.

    Previous studies have indicated that men and women have different psychological adaptations to extreme situations, such as pandemic lockdowns [31]. The WHO has emphasized the differential impact of a pandemic on men and women and the need for research on the effects of the COVID-19 pandemic from a sex-based perspective [32]. The relationship between information overload and sex has been reported in contexts of exposure to news. These studies have shown that women tend to experience greater information overload than men owing to news exposure [4,33,34]. It has been found that men and women use different cognitive resources with respect to news story structures, with women having slower reaction times to news stories [35]. The sex differences in cognitive load are also supported by research reporting functional magnetic resonance imaging data [26]. When men and women were given the same verbal and visuospatial WM tasks, women experienced greater task demands and higher cognitive load than men. However, as mentioned, the relationship between sex and information overload in the context of health information and pandemics remains unclear. Studies conducted in the context of exposure to early COVID-19 and cancer information reported no significant differences in the perception of information overload between men and women [36]. These studies also had limitations, such as having a cross-sectional design or having limited participant demographics. In the context of the COVID-19 pandemic, news continues to be disseminated daily. Evidence regarding the processing of WM tasks and news suggests that women may be more likely to recognize information overload in the information environment within a pandemic context.

    Our first hypothesis (hypothesis 2) is that women are more likely to experience information overload 1 year later in a pandemic information environment.

    Health information frequently contains complex medical wording [37,38]. This can especially affect the processing of information, especially for people with reading and writing difficulties [39]. Furthermore, the information released during a pandemic is fraught with uncertainty and is continually revised as new findings emerge. People must engage in repeated information processing each time information is updated. The intrinsic cognitive load caused by this complexity of information is affected by the learner’s previous psychological state and knowledge [23]. Therefore, it has been reported that having relevant previous specialist knowledge reduces the learner’s intrinsic cognitive load [40]. The degree to which an individual is able to find, understand, and use information and services that are useful for making decisions and taking action regarding their health is referred to as health literacy (HL). There are 3 aspects to HL. Functional HL refers to basic reading and writing skills, communicative HL refers to the ability to extract information from various forms of communication and apply it to changing situations, and critical HL refers to the ability to critically evaluate and use information to exert greater influence [41]. HL has been noted to be related to the experience of information overload as well as health status [41]. For example, previous studies conducted in the context of information seeking regarding cancer and weight management have reported that lower HL correlates with a higher perception of health information overload [42-44]. A cross-sectional study conducted in the United States during the COVID-19 pandemic also reported that individuals with lower HL were more likely to perceive information overload [45]. It is rational to consider that longitudinal analyses would similarly find that high levels of HL are likely to prevent information overload.

    Our first hypothesis (hypothesis 3) is that people with higher HL are less likely to experience information overload 1 year later in a pandemic information environment.

    When faced with an unknown infectious disease, people pay attention to information provided through various types of media to fill gaps in their knowledge [1,2]. It has been reported that processing information presented by diverse media sources is cognitively difficult [46]. There have been various reports on the impact of social media on users’ cognitive load. For example, there are reports on intrinsic cognitive load factors, such as fear of missing out (FOMO), privacy concerns, anxiety, and depression [47]. There have also been reports of extrinsic cognitive load factors, such as parental influence, cyberbullying, complexity, technology-related factors, and social overload [48]. In a study examining news attention and information overload, television viewing lowered the perception of information overload, whereas the use of a PC or Facebook increased perceptions of information overload [33]. During the COVID-19 pandemic, social media has been used daily as a platform for information exchange [49]. Social media users are constantly searching, receiving, identifying, reading, and processing a continuous flow of information. Users have been reported to experience a drain on cognitive energy, feeling overwhelmed and fatigued by the cognitive demands of social media [48,50]. Several cross-sectional studies have reported that people who follow COVID-19-related information on social media are more likely to experience information overload [51,52]. It is rational to assume that a longitudinal analysis would find that attention to social media leads to a greater perception of information overload than exposure to old media.

    Our first hypothesis (hypothesis 4) is that greater exposure to social media information increases the experience of information overload 1 year later in a pandemic information environment.

    The information flow during a pandemic differs in quantity and quality compared with that during a normal search for information on diseases, such as cancer. There is a need to identify the characteristics of individuals and information channels that predict information overload during a pandemic. Combined with skills that can reduce cognitive load owing to complex information, this information would provide important resources for the design of public health communication strategies to increase appropriate preventive behaviors. However, to the best of our knowledge, no previous studies have longitudinally examined the predictors of information overload during the COVID-19 pandemic.

    This Study

    The purpose of this study was to longitudinally examine predictors of information overload during the COVID-19 pandemic in terms of individual characteristics, health information–handling skills, and exposure to information as an available resource for information processing. Identifying the factors that influence information overload is essential to the theoretical understanding of these associations and to improving the efficiency of public health communication.


    Study Design

    We conducted a longitudinal study in Japan using a web-based survey administered at 2 time points: in August 2020, soon after the WHO’s declaration of the COVID-19 pandemic, and 1 year later, in August 2021. The state of emergency declared by the Japanese government was in effect at both times.

    Data Collection

    A sample was drawn from a panel of 2.2 million Japanese residents using the platform of the research firm Rakuten Insight, Inc. This study was conducted as part of a multipurpose longitudinal survey to examine multiple research questions during the pandemic. We used nonprobabilistic quota sampling for age, gender, and place of residence to match the Japanese population distribution. The sample had a similar distribution for sex, age, and prefecture as the general Japanese population. People eligible to participate in the study were those (1) who lived in areas where a state of emergency had been declared at our study time points, (2) who were not infected with COVID-19 and had no family members who were infected, and (3) who were aged 18 years or older.

    We conducted the first survey (T1) on August 15 and 16, 2020, and the second survey (T2) on August 15 to 17, 2021. The 1820 members of the survey panel who met the inclusion criteria were invited via email to respond to the screening questions. As part of the screening process, participants were required to read a web page that outlined our research. After agreeing to participate, participants were given a URL to access the online questionnaire, and then they completed the questionnaire manually. This survey was a closed survey that required participants to enter their ID and password to complete. The survey form developed in this study was sent to 10 participants who had been recruited using the convenience sampling method, and we checked for any items that were difficult to answer or inappropriate before the main survey. Using adaptive questioning, we minimized the number of questions displayed to individuals. All questions displayed were mandatory, and the next question was displayed after the participant had completed the previous question. There was a page where participants could review their answers after completing all the questions, and they could make corrections before submitting their answers.

    The T1 closed when 1000 participants from 8 prefectures (Chiba, Hokkaido, Hyogo, Kanagawa, Kyoto, Osaka, Saitama, and Tokyo) completed the questionnaire. The T2 was conducted among the participants recruited in T1. The areas surveyed were under a state of emergency during both survey periods. An analysis was conducted among the 784 participants who completed both surveys. The follow-up rate was 78.4% (784/1000).

    Measurements

    Independent Variables
    Sociodemographic Data

    Data on age (continuous), sex (0=male, 1=female), level of education (categorical), household income (categorical), history of infection (binary), and cohabiting family (binary) were collected during T1.

    Health Literacy

    HL was measured using the 5-item version of the Communicative Critical Health Literacy instrument developed by Ishikawa et al [53]. This instrument uses a 5-point Likert scale ranging from 1 (completely disagree) to 5 (strongly agree) and was applied in T1. The score was the average of 5 items (continuous variables), with higher scores indicating higher HL. The SD in this study was 0.6. The Cronbach α in this study was 0.88.

    Attention Paid to Information Channels About the COVID-19 Pandemic

    The T1 assessed the frequency of exposure to 7 types of media information channels: newspapers, television news, television tabloid shows, news websites, official websites of the government and medical professional organizations, video-sharing sites, and social media. For each channel, respondents were asked a single question about the extent to which they had paid attention to COVID-19–related information on that media channel over the past week (eg, “How much attention did you pay to information about COVID-19 in newspapers?”). Each item was rated on a 10-point scale that ranged from 1 (paid no attention at all) to 10 (paid a great deal of attention), which was adapted from a previous study [53]. The score was a continuous variable ranging from 1 to 10, with higher scores indicating greater attention to that information channel.

    COVID-19 Information Overload

    Information overload was assessed using the Corona Information Overload Scale developed by Sarkhel et al [54]. This scale is an adaptation of the Cancer Information Overload scale, modified for the COVID-19 setting [54]. Example items include statements, such as “There are so many different recommendations for the prevention of COVID-19 that it is difficult to know which ones I should follow.” The scale comprises 8 items, with each item rated on a 4-point Likert scale ranging from 1 (do not at all agree) to 4 (very much agree). The score is the average of 8 items (continuous variables), with higher scores indicating a greater perception of information overload. Information overload was assessed at T2. The SD in this study was 0.4. The Cronbach α in this study was 0.89.

    Ethical Considerations

    A protocol for this study was approved by the ethics review committee of the Graduate School of Medicine, University of Tokyo, based on guidelines for clinical trials involving human subjects (11270). Participants were provided with an explanation of the study and completed a web-based consent form. All participants were given the right to withdraw from the study at any time during the study period. Data were provided to researchers by the research company in a linked but anonymized form. Participants in the online survey were offered points that could be used as 1 yen (US $0.009) per point on the Rakuten e-commerce site as an incentive.

    Statistical Analysis

    Descriptive statistics were calculated for each sociodemographic variable. We then performed single linear regression analysis as a crude analysis, with the objective variables being information overload, sociodemographic characteristics, HL, and attention to media information.

    Hierarchical multiple linear regression analysis was performed as an autoregressive model. In model 1, we included information overload (T2) as the outcome variable and sociodemographic characteristics, including age (continuous; sex, 0=male, 1=female), level of education (categorical), household income (categorical), history of infection (binary), and cohabiting family (binary), as independent variables. In addition to the variables in model 1, we included HL (T1) as an independent variable in model 2. In addition to the variables in model 2, we included various information channels (T1: news, television tabloid shows, newspapers, news websites, nonnews websites, and social media) as independent variables in model 3. All variables entered as independent variables for all models were forced into the model based on previous studies or theory as variables associated with information overload.

    No missing values were observed because the online survey was designed such that respondents could not proceed to the next question if values were missing. Participants who only responded to T1 were not included in our analysis. All tests were 2-sided, and the significance level was set at 5%. We used IBM SPSS (version 25; IBM Corp) for the analysis.


    Participants’ characteristics are presented in Table 1. The breakdown of characteristics by sex, age, and prefecture of residence was similar to that of the general population in Japan, as stated in the Methods section. The minimum age was 21 years and the maximum age was 76 years. The proportions of participants with an education level lower than a university degree and with a university degree or higher were similar. Approximately half of the participants were full-time employees. The demographic characteristics of participants who were included in the analysis did not differ statistically from the characteristics of those who were excluded owing to not having completed T2.

    Table 1. The characteristics of the study participants (N=784).
    Participant characteristicsValues
    Age (y), mean (SD)47.14 (12.8)
    Sex,n (%)

    		Male396 (50.5)

    		Female388 (49.5)
    Education,n (%)

    		Junior high school12 (1.5)

    		High school156 (19.9)

    		Vocational school or junior college175 (22.3)

    		University379 (48.3)

    		Graduate school62 (7.9)
    Household income,n (%)

    		<JP ¥2,000,000 (<US $15,000)64 (8.2)

    		JP ¥2,000,000-¥6,000,000 (US $15,000-$43,500)320 (49)

    		>JP ¥6,000,000 (>US $43,500)334 (42.6)

    		Unclear66 (8.4)
    Previous infection, n (%)21 (2.7)
    Cohabiting with family, n (%)613 (78.2)
    Health literacya, mean (SD)3.822 (0.6)

    aHealth literacy was measured by the Communicative Critical Health Literacy.

    Table 2 shows the results of the single regression analysis of sociodemographic characteristics, HL, and attention to media information channels in terms of COVID-19 information overload. Significant simple associations with information overload were found for age, sex, education, HL, television news, television tabloid shows, newspapers, and social media.

    Table 2. Univariate associations between information overload and explanatory variables (N=784).

    		Bβ (SE; 95% CI)P valuea
    Age (y)−0.006−0.154 (0.001; −0.008 to −0.003)<.001
    Sex−0.061−0.067 (0.033; −0.125 to 0.003).06
    Education−0.009−0.018 (0.017; −0.043 to 0.025).61
    Household income−0.052−0.070 (0.027; −0.104 to 0.000).051
    Cohabiting with family−0.057−0.051 (0.040; −0.134 to 0.021).15
    Previous infection−0.286−0.059 (0.174; −0.627 to 0.054).10
    Health literacyb−0.082−0.111 (0.026; −0.133 to −0.030).002
    Television news−0.030−0.186 (0.006; −0.042 to −0.019)<.001
    Television tabloid show−0.022−0.137 (0.006; −0.032 to −0.011)<.001
    Newspaper−0.019−0.138 (0.005; −0.029 to −0.009)<.001
    News website−0.017−0.093 (0.006; −0.030 to −0.004).009
    Nonnews website−0.010−0.048 (0.007; −0.025 to 0.005).18
    Social media0.0110.068 (0.006; 0.000 to 0.021).06

    aThe estimates were calculated by simple regression analysis.

    bHealth literacy was measured by the Communicative Critical Health Literacy instrument.

    Table 3 shows the results of hierarchical multiple regression analysis. The results of model 1 showed that younger age (β=−0.084, 95% CI −0.142 to −0.013) was significantly more highly correlated with information overload 1 year later. For sex, information overload was significantly higher for men than for women (β=−0.163, 95% CI −0.008 to −0.003). Lower household income was associated with significantly higher information overload (β=−0.083, 95% CI −0.115 to −0.009). The model 2 results showed that the lower the HL, the higher the information overload (β=−0.084, 95% CI −0.114 to −0.011), which was significant. As the results of model 3 show, information overload was significantly higher for those who paid less attention to television news (β=−0.118, 95% CI −0.038 to −0.001) and for those who paid greater attention to social media (β=0.089, 95% CI 0.000-0.027).

    Table 3. Multivariate associations between information overload and sociodemographic characteristics, health literacy, and COVID-19 information attention (N=784)a.

    		Model 1Model 2Model 3

    		Bβ (SE; 95% CI)P valuebBβ (SE; 95% CI)P valuebBβ (SE; 95% CI)P valueb
    Age (y)−0.006−0.158 (0.001; −0.008 to −0.003)<.001−0.005−0.145 (0.001; −0.008 to −0.003)<.001−0.003−0.078 (0.001; −0.006 to 0.000).054
    Sex0.0760.083 (0.033; 0.011 to 0.140).0220.0740.081 (0.033; 0.009 to 0.138).0250.0720.079 (0.033; 0.008 to 0.136).03
    Education−0.016−0.033 (0.018; −0.051 to 0.019).373−0.012−0.026 (0.018; −0.048 to 0.023).488−0.004−0.008 (0.018; −0.039 to 0.031).82
    Household income−0.059−0.080 (0.029; −0.116 to −0.003).040−0.056−0.076 (0.029; −0.113 to 0.000).051−0.051−0.069 (0.029; −0.107 to 0.005).08
    Cohabiting with family−0.012−0.011 (0.042; −0.093 to 0.070).778−0.015−0.014 (0.042; −0.097 to 0.066).7140.0100.009 (0.042; −0.072 to 0.092).80
    Previous infection−0.221−0.046 (0.172; −0.559 to 0.117).199−0.209−0.043 (0.172; −0.546 to 0.128).224−0.210−0.043 (0.171; 0.546 to 0.127).22
    Health literacyc—d——−0.062−0.083 (0.026; −0.113 to −0.01).019−0.061−0.082 (0.026; −0.112 to 0.009).02
    Television news——————−0.019−0.117 (0.010; −0.038 to 0.000).045
    Television tabloid show——————0.0020.011 (0.009; −0.015 to 0.018).84
    Newspaper——————−0.006−0.042 (0.006; −0.017 to 0.006).32
    News website——————−0.007−0.036 (0.008; −0.022 to 0.008).39
    Nonnews website——————−0.012−0.056 (0.009; −0.030 to 0.007).22
    Social media——————0.0140.089 (0.007; 0.000 to 0.027).048

    aFor model 1, R2 was 0.039, ΔR2 was 0.039, F6,777=5.292, and P value was <.001. For model 2, R2 was 0.046, ΔR2 was 0.007, F1,776=5.490, and P value was .02. For model 3, R2 was 0.072, ΔR2 was 0.026, F1,770=3.532, and P value was .002.

    bThe estimates were calculated by hierarchical multiple regression analysis.

    cHealth literacy was measured by the Communicative Critical Health Literacy instrument.

    dNot applicable.


    Overview

    In this study, we examined predictors of information overload during the COVID-19 state of emergency declaration in Japan. The results suggest that predictors of information overload include individual characteristics, skills for handling health information, and the information channels to which individuals are exposed. These results are important findings for developing public health communication strategies that take into account people who are more likely to perceive information overload during a pandemic.

    Individual Characteristics That Predict Information Overload

    The results of this study did not support our hypothesis that older people are more likely to experience information overload during a pandemic. There are 2 possible reasons why younger people were more likely to experience information overload during the prolonged COVID-19 pandemic conditions. First, young people had fewer health problems before the pandemic than older people. As a result, younger people may have had less experience searching for and processing health information themselves. Previous studies have reported that people with less experience in information processing are more likely to perceive information overload [12,14]. When appropriate resources are lacking, it becomes difficult to integrate new information with an individual’s existing knowledge; therefore, the individual does not know how to react to new information [14]. Second, new media channels, such as social media, have developed rapidly in recent years. Younger people are likely to be exposed to older types of media but also use multiple new media channels [55]. During the COVID-19 pandemic, people were exposed to a greater volume of information from a variety of sources, media, and platforms than they were during previous pandemics [52]. This diversity and complexity of platforms can lead to exposure to irrelevant information and the need to allocate extra WM [40,48,56]. This may have resulted in a greater cognitive burden on younger people. The results of this study suggest that, in the special information environment of a pandemic, young people may experience greater cognitive load than older people.

    Contrary to our hypothesis, men were more likely to perceive information overload than women in this study. Although previous studies have demonstrated sex differences in behaviors related to the COVID-19 pandemic, the relationship between information overload and sex has been unclear [31,36]. During a prolonged pandemic, people are affected psychologically and behaviorally by various external triggers. Similar to information overload, one of these triggers is pandemic fatigue, which is more often perceived by men [57]. Pandemic fatigue has been described by the WHO as a loss of motivation owing to the chronicity of a pandemic situation and reportedly comprises 2 concepts: neglect of preventive behaviors and boredom with overexposure to information [58]. Fatigue from repeated exposure to similar information is reportedly associated with information overload [59]. It is possible that men are more vulnerable than women to fatigue owing to repeated exposure to similar information.

    In future pandemics, more diverse information may be disseminated than during the COVID-19 pandemic, and more people may perceive information overload. As the results of this and previous studies have shown, younger people and men may not be able to acquire appropriate and correct knowledge owing to information overload and may have difficulty adopting appropriate preventive behaviors. It is necessary to consider information provision strategies that are appropriate for these populations.

    Effect of Health Information–Handling Skills With Information Overload

    The results of this study support the hypothesis that people with higher levels of HL are less likely to experience information overload. This finding is consistent with the results of 2 cross-sectional studies conducted in the United States during the COVID-19 pandemic [45,59]. Those results suggest that previous skills in handling health information may reduce the intrinsic cognitive load of complex information in a special information environment, such as a pandemic [40]. The HL scale used in our study measured 2 aspects of HL: communicative literacy, which is the ability to extract information and derive meaning, and critical literacy, which is the ability to critically analyze information. It has been reported that critical thinking is an important skill in mitigating information overload [60]. Critical thinking is closely related to information mastery competencies, such as problem-solving and self-management [61].

    As a skill that individuals can develop in advance, HL is an important resource to help combat information overload during a pandemic. HL can be improved through intervention [62]. Therefore, education in developing the skills needed to appropriately process health information should be promoted. Although not addressed in this study, functional literacy is also an important aspect of HL. The variance in functional literacy among Japanese people is small, and it is not suitable for predicting health-related behavior and outcomes. However, additional findings on this aspect will lead to the development of information provision strategies for people with limited functional HL [63].

    Information Channels That Predict Information Overload

    Regarding the association between the degree of attention to each information channel and information overload, our study results support our hypothesis that greater attention to social media information is a predictor of information overload. In addition, we found that survey respondents who were attentive to television news had a significantly lower perception of information overload. This result can be explained by the characteristics of the information provided by both types of media, as follows. First, the characteristics of the media content differ between these types of media. Television news involves mainly objective facts that have been scrutinized by the news organization, whereas social media information involves mainly subjective perspectives and interpretations by individuals. The 2018 Edelman Trust Barometer Special Report concluded that only 41% of people trust social media [64]. When obtaining information from another individual on social media, there is a cognitive load involved in determining whether that information is worth believing [55]. Concerns about the quality of information may contribute to increasing the cognitive load in information processing [43,65]. A cross-sectional study conducted in the United States during the COVID-19 pandemic reported that individuals receiving information through 2 channels, social media and interpersonal communication, were less likely to perceive information overload than those receiving information through social media alone [65]. Social media information may reduce the cognitive load when provided through interpersonal communication. Second, the originator of information differs among these media sources. Television news is transmitted from a news broadcasting agency by professional news readers, whereas social media information can be transmitted by anyone, including laypersons, and is often put forth by people who are closer and more connected to the individual receiving the information. One cause of the intrinsic cognitive load with social media is FOMO, which can occur when people exchange information among connected individuals [66]. FOMO is defined as a person’s experience of pervasive anxiety that others may be having rewarding experiences that they themselves are not experiencing, and is characterized by a desire to continuously connect with what others are doing [67]. This makes it difficult for some individuals to reduce or stop using social media for fear of missing important information and activity on social media [68,69]. From the perspective of cognitive load, personal informational media to which the user has an emotional attachment (eg, social media) may involve a high cognitive load, whereas informational media that provides objective information with no emotional attachment (eg, television news) may produce a lower cognitive load.

    On the basis of the results of this research and the findings of previous studies, we propose several approaches to reducing information overload during a pandemic from the perspectives of intrinsic, extrinsic, and germane cognitive load. First, health information about a pandemic is uncertain and changes over time, which means it has a high intrinsic load. One factor that reduces intrinsic load is previous specialist knowledge and skills. Encouraging people to develop skills, such as HL, information literacy for reading information critically, and slow reading under normal circumstances, will help to reduce the cognitive load caused by the complexity of information during a pandemic [60,70]. Approaches to reducing extrinsic load using technological methods have been proposed. These include methods that improve conciseness, consistency, and understandability of information content, as well as increasing findability, a technique that makes it easier for users to find content based on their needs [60,71]. Finally, for people who have characteristics that make them more susceptible to cognitive overload from complex health information during a pandemic, it may be possible to provide information that is less likely to cause overload by segmentation according to sex and age. These approaches can be adopted as part of an information delivery strategy by public health communicators to mitigate information overload during a pandemic.

    Limitations

    Several limitations should be considered when interpreting the results of this study. First, because this was an observational study, a causal relationship between individual characteristics or attention to media information and information overload cannot be established from these results. Therefore, we relied on previous studies and existing theories in our discussion. However, given the considerable difficulty in conducting a randomized controlled trial during a public health crisis, the results of this study represent important findings. Second, study participants were registered members of a survey panel recruited by an internet company, and their educational background tended to be higher than that of the general Japanese public. We adjusted for the influence of educational background using multiple regression analysis; however, there is a possibility that there are still unadjusted factors related to self-selection. In addition, considering the R2 scores of each regression model, there may be unknown variables that can better explain information overload. Finally, single-item, unvalidated scales were used in this study to assess the degree of attention to media information. We adopted items that have been commonly used in previous studies to enable comparisons with results from other infectious disease pandemics and findings from other countries. In the future, we anticipate the development of scales that will measure these factors.

    Conclusions

    During the COVID-19 pandemic, people have been exposed to a great deal of information through multiple media channels, constituting an infodemic. The results of this study have longitudinal implications in terms of the characteristics of individuals and types of informational media that can predict information overload in a specific informational environment. Public health communicators should consider younger people, men, and people with low HL when developing public health communication strategies during a pandemic. High levels of social media attention may increase the perception of information overload. Therefore, public health communicators can incorporate technical approaches to prevent information overload during a pandemic, such as improving the findability of appropriate information and preparing concise, understandable content. Moreover, providing opportunities for educating the public about critical reading of health information during normal periods will help individuals to prepare for future infodemics during which anyone can disseminate information.

    Acknowledgments

    This work was supported by the Japan Society for the Promotion of Science KAKENHI (grant 20K10397). The authors thank Analisa Avila, MPH, ELS, of Edanz for editing a draft of this paper.

    Data Availability

    The datasets generated or analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    HO and TO contributed to conceptualization and methodology. HO contributed to data curation, formal analysis, and writing the original draft. TK contributed to supervision. HO, TO, and TK contributed to writing, reviewing, and editing.

    Conflicts of Interest

    None declared.

    1. Kusama T, Kiuchi S, Takeuchi K, Ikeda T, Nakazawa N, Kinugawa A, et al. Information usage and compliance with preventive behaviors for COVID-19: a longitudinal study with data from the JACSIS 2020/JASTIS 2021. Healthcare (Basel). Mar 13, 2022;10(3):521. [FREE Full text] [CrossRef] [Medline]
    2. Uchibori M, Ghaznavi C, Murakami M, Eguchi A, Kunishima H, Kaneko S, et al. Preventive behaviors and information sources during COVID-19 pandemic: a cross-sectional study in japan. Int J Environ Res Public Health. Nov 04, 2022;19(21):14511. [FREE Full text] [CrossRef] [Medline]
    3. No authors listed. The COVID-19 infodemic. Lancet Infect Dis. Aug 2020;20(8):875. [CrossRef]
    4. Ji Q, Ha L, Sypher U. The role of news media use and demographic characteristics in the possibility of information overload prediction. Int J Commun. 2014;8. [FREE Full text]
    5. Apuke OD, Omar B. Social media affordances and information abundance: enabling fake news sharing during the COVID-19 health crisis. Health Informatics J. Jul 10, 2021;27(3):14604582211021470. [FREE Full text] [CrossRef] [Medline]
    6. Balakrishnan V, Ng WZ, Soo MC, Han GJ, Lee CJ. Infodemic and fake news - a comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: a scoping review. Int J Disaster Risk Reduct. Aug 2022;78:103144. [CrossRef] [Medline]
    7. Lang A. The limited capacity model of mediated message processing. J Commun. 2000;50(1):46-70. [FREE Full text] [CrossRef]
    8. Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev. Mar 1956;63(2):81-97. [CrossRef]
    9. Malhotra N. Information load and consumer decision making. J Consum Res. Mar 1982;8(4):419-430. [CrossRef]
    10. Simon HA. Information processing models of cognition. Annu Rev Psychol. Jan 1979;30(1):363-396. [CrossRef] [Medline]
    11. Sweller J. Cognitive load during problem solving: effects on learning. Cognit Sci. Feb 11, 2010;12(2):257-285. [CrossRef]
    12. Eppler MJ, Mengis J. The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf Soc. Nov 2004;20(5):325-344. [CrossRef]
    13. Davis TC, Williams MV, Branch WT. Explaining illness to patients with limited literacy. In: Whaley BB, editor. Explaining Illness: Research, Theory, and Strategies. Mahwah, NJ. Lawrence Erlbaum Associates; 1999:171-194.
    14. Eppler M. Information quality and information overload: the promises and perils of the information age. In: Cantoni L, Danowski JA, editors. Communication and Technology. Berlin, Germany. De Gruyter Brill; 2015.
    15. Jensen JD, Carcioppolo N, King AJ, Bernat JK, Davis L, Yale R, et al. Including limitations in news coverage of cancer research: effects of news hedging on fatalism, medical skepticism, patient trust, and backlash. J Health Commun. May 2011;16(5):486-503. [FREE Full text] [CrossRef] [Medline]
    16. Kalyuga S, Ayres P, Chandler P, Sweller J. The expertise reversal effect. Educ Psychol. Jun 08, 2010;38(1):23-31. [CrossRef]
    17. Chae J, Lee CJ, Jensen JD. Correlates of cancer information overload: focusing on individual ability and motivation. Health Commun. Oct 29, 2016;31(5):626-634. [CrossRef] [Medline]
    18. Liu CF, Kuo KM. Does information overload prevent chronic patients from reading self-management educational materials? Int J Med Inform. May 2016;89:1-8. [CrossRef] [Medline]
    19. Chen X, Lee W, Lin F. Infodemic, institutional trust, and COVID-19 vaccine hesitancy: a cross-national survey. Int J Environ Res Public Health. Jun 30, 2022;19(13):8033. [FREE Full text] [CrossRef] [Medline]
    20. Lin F, Chen X, Cheng EW. Contextualized impacts of an infodemic on vaccine hesitancy: the moderating role of socioeconomic and cultural factors. Inf Process Manag. Sep 2022;59(5):103013. [FREE Full text] [CrossRef] [Medline]
    21. Wang J, Huang X, Wang Y, Wang M, Xu J, Li X. COVID-19 information overload, negative emotions and posttraumatic stress disorder: a cross-sectional study. Front Psychiatry. May 13, 2022;13:894174. [FREE Full text] [CrossRef] [Medline]
    22. Guo N, Zhao SZ, Weng X, Wu Y, Luk TT, Wong JY, et al. Associations of COVID-19 online information sources and information overload with psychological distress symptoms: a population-based study. Transl Behav Med. Jul 29, 2021;11(7):1330-1338. [CrossRef] [Medline]
    23. Sweller J. Cognitive load theory. In: Mestre JP, Ross BH, editors. The Psychology of Learning and Motivation: Cognition in Education. Cambridge, MA. Elsevier Academic Press; 2011.
    24. Sweller J. Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ Psychol Rev. Apr 23, 2010;22(2):123-138. [CrossRef]
    25. Mathias JL, Lucas LK. Cognitive predictors of unsafe driving in older drivers: a meta-analysis. Int Psychogeriatr. Aug 01, 2009;21(4):637-653. [CrossRef]
    26. Nooyens AC, Wijnhoven HA, Schaap LS, Sialino LD, Kok AA, Visser M, et al. Sex differences in cognitive functioning with aging in the Netherlands. Gerontology. Jan 4, 2022;68(9):999-1009. [CrossRef] [Medline]
    27. Van Gerven PW, Paas FG, Van Merriënboer JJ, Schmidt HG. Cognitive load theory and aging: effects of worked examples on training efficiency. Learn Instr. Feb 2002;12(1):87-105. [CrossRef]
    28. Salthouse TA, Babcock RL. Decomposing adult age differences in working memory. Dev Psychol. 1991;27(5):763-776. [CrossRef]
    29. Wingfield A, Stine EA, Lahar CJ, Aberdeen JS. Does the capacity of working memory change with age? Exp Aging Res. Sep 27, 1988;14(2-3):103-107. [CrossRef] [Medline]
    30. Hong H, Kim HJ. Antecedents and consequences of information overload in the COVID-19 pandemic. Int J Environ Res Public Health. Dec 12, 2020;17(24):9305. [FREE Full text] [CrossRef] [Medline]
    31. Tadiri CP, Gisinger T, Kautzy-Willer A, Kublickiene K, Herrero MT, Raparelli V, et al. The influence of sex and gender domains on COVID-19 cases and mortality. CMAJ. Sep 08, 2020;192(36):E1041-E1045. [FREE Full text] [CrossRef] [Medline]
    32. Gender and COVID-19. World Health Organization. May 14, 2020. URL: https:/​/iris.​who.int/​bitstream/​handle/​10665/​332080/​WHO-2019-nCoV-Advocacy_brief-Gender-2020.​1-eng.​pdf [accessed 2025-08-13]
    33. Holton AE, Chyi HI. News and the overloaded consumer: factors influencing information overload among news consumers. Cyberpsychol Behav Soc Netw. Nov 2012;15(11):619-624. [CrossRef] [Medline]
    34. York C. Overloaded by the news: effects of news exposure and enjoyment on reporting information overload. Commun Res Reports. Oct 2013;30(4):282-292. [CrossRef]
    35. Sternadori MM, Wise K. Men and women read news differently: the effects of story structure on the cognitive processing of text. J Media Psychol. Jan 2010;22(1):14-25. [CrossRef]
    36. 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. May 2023;110:107672. [FREE Full text] [CrossRef] [Medline]
    37. Eysenbach G, Powell J, Kuss O, Sa ER. Empirical studies assessing the quality of health information for consumers on the world wide web: a systematic review. JAMA. May 22, 2002;287(20):2691-2700. [CrossRef] [Medline]
    38. Walsh TM, Volsko TA. Readability assessment of internet-based consumer health information. Respir Care. Oct 2008;53(10):1310-1315. [Medline]
    39. Friedman DB, Hoffman-Goetz L. An exploratory study of older adults' comprehension of printed cancer information: is readability a key factor? J Health Commun. Jul 20, 2007;12(5):423-437. [CrossRef] [Medline]
    40. Hollender N, Hofmann C, Deneke M, Schmitz B. Integrating cognitive load theory and concepts of human–computer interaction. Comput Hum Behav. Nov 2010;26(6):1278-1288. [CrossRef]
    41. Healthy people 2030. Office of Disease Prevention and Health Promotion, Office of the Assistant Secretary for Health, Office of the Secretary, U.S. Department of Health and Human Services. URL: https://health.gov/healthypeople/priority-areas/health-literacy-healthy-people-2030 [accessed 2025-08-20]
    42. Jiang S, Beaudoin CE. Health literacy and the internet: an exploratory study on the 2013 HINTS survey. Comput Hum Behav. May 27, 2016;58(1):240-248. [FREE Full text] [CrossRef]
    43. Kim K, Lustria M, Burke D, Kwon N. Predictors of cancer information overload: findings from a national survey. Inf Res. 2007;12(4). [FREE Full text]
    44. Chan YM, Huang H. Weight management information overload challenges in 2007 HINTS: socioeconomic, health status and behaviors correlates. J Consum Health Internet. Apr 2013;17(2):151-167. [CrossRef]
    45. Yamamoto M, Krishnan A, Golden A, Owen G, Schell LM, Mata O, et al. Correlates of COVID-19 information overload and information seeking: evidence from a community survey. Am J Health Promot. May 13, 2024;38(4):503-512. [CrossRef] [Medline]
    46. Geiger S, Newhagen J. Revealing the black box: information processing and media effects. J Commun. 1993;43(4):42-50. [CrossRef]
    47. Dhir A, Kaur P, Chen S, Pallesen S. Antecedents and consequences of social media fatigue. Int J Inf Manage. Oct 2019;48:193-202. [FREE Full text] [CrossRef]
    48. Xiao L, Mou J. Social media fatigue -technological antecedents and the moderating roles of personality traits: the case of WeChat. Comput Hum Behav. Dec 2019;101:297-310. [CrossRef]
    49. De la Torre-Díez I, Díaz-Pernas FJ, Antón-Rodríguez M. A content analysis of chronic diseases social groups on Facebook and Twitter. Telemed J E Health. 2012;18(6):404-408. [CrossRef] [Medline]
    50. Lee AR, Son SM, Kim KK. Information and communication technology overload and social networking service fatigue: a stress perspective. Comput Hum Behav. Feb 2016;55:51-61. [CrossRef]
    51. Bala R, Srivastava A, Ningthoujam GD, Potsangbam T, Oinam A, Anal CL. An observational study in Manipur state, India on preventive behavior influenced by social media during the COVID-19 pandemic mediated by cyberchondria and information overload. J Prev Med Public Health. Jan 2021;54(1):22-30. [FREE Full text] [CrossRef] [Medline]
    52. Sun J, Lee SK. "No more COVID-19 messages via social media, please": the mediating role of COVID-19 message fatigue between information overload, message avoidance, and behavioral intention. Curr Psychol. Jun 02, 2023;42(24):1-15. [FREE Full text] [CrossRef] [Medline]
    53. Ishikawa H, Nomura K, Sato M, Yano E. Developing a measure of communicative and critical health literacy: a pilot study of Japanese office workers. Health Promot Int. Sep 2008;23(3):269-274. [CrossRef] [Medline]
    54. Sarkhel S, Bakhla AK, Praharaj SK, Ghosal MK. Information overload regarding COVID-19: adaptation and validation of the cancer information overload scale. Indian J Psychiatry. 2020;62(5):481-487. [FREE Full text] [CrossRef] [Medline]
    55. Wang Y, Dai Y, Li H, Song L. Social media and attitude change: information booming promote or resist persuasion? Front Psychol. Jun 24, 2021;12:596071. [FREE Full text] [CrossRef] [Medline]
    56. Islam AK, Laato S, Talukder S, Sutinen E. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecast Soc Change. Oct 2020;159:120201. [FREE Full text] [CrossRef] [Medline]
    57. Jia X, Ahn S, Carcioppolo N. Measuring information overload and message fatigue toward COVID-19 prevention messages in USA and China. Health Promot Int. Jun 01, 2023;38(3):daac003. [FREE Full text] [CrossRef] [Medline]
    58. Pandemic fatigue. World Health Organization. 2020. URL: https://iris.who.int/bitstream/handle/10665/335820/WHO-EURO-2020-1160-40906-55390-eng.pdf [accessed 2025-08-20]
    59. Mao B, Jia X, Huang Q. How do information overload and message fatigue reduce information processing in the era of COVID-19? An ability–motivation approach. J Inf Sci. Sep 05, 2022;50(5):1242-1254. [CrossRef]
    60. Koltay T. The bright side of information: ways of mitigating information overload. J Document. Jul 10, 2017;73(4):767-775. [CrossRef]
    61. Kam JA, Lee CJ. Examining the effects of mass media campaign exposure and interpersonal discussions on youth's drug use: the mediating role of visiting pro-drug websites. Health Commun. Jul 20, 2013;28(5):473-485. [CrossRef] [Medline]
    62. Sheridan SL, Halpern DJ, Viera AJ, Berkman ND, Donahue KE, Crotty K. Interventions for individuals with low health literacy: a systematic review. J Health Commun. Sep 30, 2011;16 Suppl 3(sup3):30-54. [CrossRef] [Medline]
    63. Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Prom Int. 2000;15(3):259-267. [CrossRef]
    64. 2018 Edelman Trust Barometer. Edelman. 2018. URL: https:/​/www.​edelman.com/​sites/​g/​files/​aatuss191/​files/​2018-10/​2018_Edelman_Trust_Barometer_Global_Report_FEB.​pdf [accessed 2025-08-20]
    65. Huang Q, Mao B, Jia X, Peng W. COVID-19 information overload mediated the effects of cross-channel information differences on health information elaboration. J Health Commun. Jul 03, 2023;28(7):401-411. [CrossRef] [Medline]
    66. Adams SK, Williford DN, Vaccaro A, Kisler TS, Francis A, Newman B. The young and the restless: socializing trumps sleep, fear of missing out, and technological distractions in first-year college students. Int Jt Adolesct Youth. May 11, 2016;22(3):337-348. [CrossRef]
    67. Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. Jul 2013;29(4):1841-1848. [CrossRef]
    68. González-Padilla DA, Tortolero-Blanco L. Social media influence in the COVID-19 pandemic. Int Braz J Urol. Jul 2020;46(suppl.1):120-124. [FREE Full text] [CrossRef] [Medline]
    69. Shen Y, Zhang S, Xin T. Extrinsic academic motivation and social media fatigue: fear of missing out and problematic social media use as mediators. Curr Psychol. Nov 26, 2020;41(10):7125-7131. [CrossRef]
    70. Bawden D, Robinson L. The dark side of information: overload, anxiety and other paradoxes and pathologies. J Inf Sci. Nov 21, 2008;35(2):180-191. [CrossRef]
    71. Forte A, Andalibi N, Park T, Willever-Farr H. Designing information savvy societies: an introduction to assessability. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014. Presented at: CHI '14; April 26-May 1, 2014; Toronto, ON. [CrossRef]

    ‎
    FOMO: fear of missing out
    HL: health literacy
    T1: first survey
    T2: second survey
    WHO: World Health Organization
    WM: working memory

    Edited by T de Azevedo Cardoso; submitted 02.Oct.2024; peer-reviewed by K Masuko, A Xiong; comments to author 10.Jan.2025; revised version received 14.Feb.2025; accepted 30.Jul.2025; published 30.Oct.2025.

    Copyright

    ©Hiroko Okada, Tsuyoshi Okuhara, Takahiro Kiuchi. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 30.Oct.2025.

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