Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48977, first published .
Online Health Information Seeking and Preventative Health Actions: Cross-Generational Online Survey Study

Online Health Information Seeking and Preventative Health Actions: Cross-Generational Online Survey Study

Online Health Information Seeking and Preventative Health Actions: Cross-Generational Online Survey Study

Authors of this article:

Jayati Sinha1 Author Orcid Image ;   Nuket Serin2 Author Orcid Image

Original Paper

1Department of Marketing & Logistics, College of Business, Florida International University, Miami, FL, United States

2W. Fielding Rubel School of Business, Bellarmine University, Louisville, KY, United States

*all authors contributed equally

Corresponding Author:

Jayati Sinha, BSc, MBA, PhD

Department of Marketing & Logistics

College of Business

Florida International University

11200 SW 8th Street

Miami, FL, 33146

United States

Phone: 1 3053487573

Email: jsinha@fiu.edu


Background: The popularity of online health information seeking (OHIS) has increased significantly owing to its accessibility and affordability. To facilitate better health management, it is essential to comprehend the generational differences in OHIS behavior and preventative health actions after seeking online health information (OHI).

Objective: This study investigates the variations in OHIS and engagement in preventative health actions between 2 generations based on their technology use (digital natives [aged 18-42 years] and digital immigrants [aged ≥43 years]). Additionally, this research explores the mediating role of OHIS types on the generational effect on preventative health actions and the moderating role of OHI search frequency, gender, and the presence of chronic diseases on the generational effect on OHIS types and preventative health actions.

Methods: A preregistered online survey was conducted on the Prolific online data collection platform using stratified sampling of 2 generations (digital natives and digital immigrants) from the United States in November 2023. Overall, 3 types of OHIS were collected: health wellness information search, health guidance information search, and health management information search. A 1-way analysis of covariance tested the generational differences in types of OHIS and preventative health actions, and a 2-way analysis of covariance tested the moderating role of OHIS search frequency, gender, and the presence of chronic diseases using 7 control variables. The PROCESS Macro Model 4 was used to conduct mediation analyses, testing OHI search types as mediators. Linear regression analyses tested age as a predictor of OHIS and preventative health actions.

Results: The analysis of 1137 responses revealed generational differences in OHIS. Digital natives searched for health wellness information more frequently (P<.001), whereas digital immigrants searched for health guidance (P<.001) and health management information (P=.001) more frequently. There were no significant differences between the 2 generations regarding preventative health actions (P=.85). Moreover, all 3 types of OHIS mediated the relationship between generational differences and preventative health actions. Furthermore, as people aged, they searched for significantly less health wellness information (P<.001) and more health guidance (P<.001), and health management information (P=.003). Age was not a significant predictor of preventative health actions (P=.48). The frequency of OHI searches did not moderate the effect of generations on OHIS types and preventative health actions. Gender only moderated the relationship between generation and health guidance information search (P=.02), and chronic diseases only moderated the relationship between generation and health wellness information search (P=.03).

Conclusions: To the best of our knowledge, this study is the first to explore how 2 digital generations vary in terms of searching for OHI and preventative health behaviors. As the older adult population grows, it is crucial to understand their OHIS behavior and how they engage in preventative health actions to enhance their quality of life.

J Med Internet Res 2024;26:e48977

doi:10.2196/48977

Keywords



Background

Online health information seeking (OHIS) has become popular owing to its convenience and low-cost access [1]. Although the internet remains a popular source of health information [1], an age-based digital divide exists in OHIS behavior—young generations (such as Millennials) are more likely to seek online health information (OHI) than older generations (such as Baby Boomers) [2-7]. The gap between younger and older Americans has widened in recent years—the number of Americans aged ≥55 years grew by 27% between 2010 and 2020, whereas the number of Americans aged <55 years increased by only 1.3% during that time [8]. As the US population ages, the demand for health care and medical information increases for older generations [1,7]. However, older generations’ OHIS behavior, specifically types of health-related information searches, is not well understood [9], and it is unclear to what extent older and younger Americans engage in preventative health behaviors after seeking OHI [9-11]. Moreover, the increased availability of online medical information has made patient-centered medical care more common, allowing health consumers to make informed decisions about their health. Thus, understanding the generational differences in OHI search behavior and subsequent health-related actions is crucial for designing more effective communication methods to improve and maintain health. This study explored how 2 generations with different experiences with technology (digital natives and digital immigrants) differ in their OHI search activities and preventative health actions.

Digital Natives and Digital Immigrants

Generations are defined by the years of birth. Generational cohorts are groups of people who share similar cultural, historical, and technological experiences growing up and tend to develop similar beliefs, values, and behaviors as they age [12,13]. Generations are often defined by how they use and grow up with technology [14]. Indeed, the Technology Model of Generations [15] suggests that technology is the root cause of most generational differences and indirectly affects other sociocultural forces that shape each generation (such as a shift from collectivism to individualism and the introduction of a slow life strategy). Prensky [16] defined 2 generational groups based on their preferences for technology or digital devices: digital natives (born from 1980 onward) and digital immigrants (born before 1980). Digital natives were raised in a technology-driven world (such as smartphones, tablets, and laptops as commonplace items in daily life), whereas digital immigrants did not have the same experiences with technologies [15-20].

Exposure to digital environments (or lack thereof) affects individuals’ use, fluency, comfort level, literacy, and engagement with technology [21-25] as well as how they think and behave [26,27]. Digital natives tend to process digital information quickly, multitask more, and depend on technology to communicate and access information and are usually more comfortable with technology than their digital immigrant counterparts [28,29]. As digital technologies have been integrated into the lives of digital natives since the early days, they feel more at ease with OHI searches than digital immigrants [30,31]. Indeed, nearly 48% of Americans aged <30 years use the internet constantly compared with 22% of Americans aged 50 to 64 years and 8% of Americans aged >65 years [32].

Researchers have suggested studying 2 generations along a continuum based on user technology proficiency, digital literacy, or engagement with technology rather than the age-based classification of digital natives and digital immigrants [22,25]. Although differences in computer efficacy, socioeconomic status, health literacy, and technology accessibility might help explain some differences between digital natives and digital immigrants [33-35], a generation gap exists in attitude and behavior toward technology use [16,17]. Research shows that younger people prefer the internet as a source of health information [3,7,36], and with increased age, the information processing capacity for technology-enabled tasks diminishes [29]. Previous research has shown that negative attitudes toward internet use as well as privacy concerns about using technology decrease older adults’ OHIS behavior [9,35]. Given that digital natives grew up with digital technology and are familiar with it from a young age, their OHIS behavior is likely to differ from that of digital immigrants as each generation uses technology differently [32]. Accordingly, digital natives and immigrants may differ in their search for various types of OHI and engagement in preventive efforts after retrieving such information. Thus, this study focused on 2 generations that differ in their level of technology immersion: digital natives and digital immigrants [16].

Seeking OHI and Preventative Behaviors

Health consumers seek OHI for various reasons, including gaining knowledge about health issues, managing health problems, and making health decisions [37-39]. Research has shown that OHI seekers frequently search for disease-related information [40]. In addition, people seek information about symptoms, medication, treatment, exercise and fitness, and nutrition or diet [41-43]. However, only a few studies have examined what types of OHI older and younger generations seek [9,43,44]. For example, Chinese OHI seekers most often search for information about health science popularization to improve their health literacy, whereas medical concerns are the least explored type of health information [43]. A recent review of the literature on health information seeking indicates that older adults seek various types of OHI, including information about specific diseases, medications and treatments, disease symptoms, nutrition and exercise, medical resources, and interpersonal advice [9]. Compared with older adults, adolescents and young adults seek online information on physical and psychological well-being, sexual health, and culturally sensitive topics [9]. In addition, OHI searches are lower for German and Australian older adults aged >65 years [7,36]. Moreover, young adults seek specific health information online by joining social media groups or following relevant pages [44]. It has also been found that the frequency of OHIS is higher among female individuals [43,45,46] and younger people [3,7,36], whereas people with chronic diseases are more likely than healthy people to perform OHI searches [47,48]. Although racial disparities exist in OHIS behaviors [49-51], not all racial groups seek out information equally, and chronic diseases have been found to be more important than race or ethnicity in OHI search and use [52]. Moreover, political affiliation is associated with various health outcomes, including health information searches, vaccine adoption, and preventative health behaviors [53-56].

Despite the availability of a large amount of health information online [57], some OHI can be inaccurate or misleading, causing unnecessary distress and anxiety among health consumers [58,59]. The inaccuracy of OHI raises concerns about its quality [60]. For example, health consumers, especially young people, increasingly rely on social media as their first resource for health information. However, online content is often unverified and can be misleading [61,62]. Specifically, research has shown that older adults tend to be concerned about the credibility of OHI sources [63]. Moreover, low health literacy prevents people from accurately determining the credibility of OHI on social media [34,35,64,65]. In addition, health consumers’ trust in OHI is associated with their self-efficacy beliefs and behavioral outcomes [66], and their confidence in OHIS skills is positively related to the use of the retrieved information [67]. Previous studies have identified that credibility, usefulness, and trust most frequently affect older adults’ OHIS and health behaviors [9,39].

Access to OHI has become critical for managing health and disease, and it generally leads to positive outcomes such as improved health outcomes and empowered patients [68,69]. Research shows that health information seeking improves patient involvement and satisfaction with medical decision-making and communication between patients and informal care providers [70,71]. Despite the potential of OHIS to affect health behaviors across different age groups [47,72], little research has examined whether different generations based on their technology adoption engage in preventative behaviors after seeking OHI in the same way. As a result, there is a clear need to understand what types of health information different digital generations seek online [9,11]. Thus, this study focused on generational differences based on the use of technology or digital devices in seeking out types of health information online and engaging in preventative health actions afterward.

Objectives

This study addressed the limitations of previous research on information seeking from a generational perspective. It focused on 2 generations that differ in technology proficiency (digital natives and digital immigrants) to examine how they differ in their OHI search behavior. This study aimed to determine the types of OHI that digital natives and digital immigrants seek as well as whether each generation varies in their engagement in preventative health actions after seeking different types of OHI. The following three research questions guided this study:

  1. How do generational differences influence different types of OHIS and preventative health actions?
  2. Do types of OHIS behaviors mediate the generational effect on preventative health actions?
  3. Do the frequency of OHI searches, gender, or the presence of a chronic disease moderate the generational effect on OHIS types and preventative health actions?

Study Design

We conducted an online survey to understand how OHIS behavior influences preventative health behaviors across 2 generations (digital natives and digital immigrants) and whether the frequency of OHI searches, gender, and the presence of a chronic disease moderate the effect. We used a 2-factor mixed factorial design where the first factor was a between-subject moderator (2 digital generations: natives vs immigrants) and the second factor was a within-subject moderator (frequency of OHI searches [frequent vs infrequent], gender [male vs female], or presence of a chronic disease [yes vs no]).

Ethical Considerations

The Bellarmine University Institutional Review Board (IRB) approved this study under the exempted research category (IRB 1092) before data collection, and general information about the nature of this study was included at the beginning of the survey as a means of informed consent. No formal informed consent was obtained. This study was preregistered on the AsPredicted platform (Wharton Credibility Lab) [73]. The online platform Prolific was used for data collection. Researchers using Prolific only had access to a user ID, and the respondents’ identities were not revealed. An online survey was designed using the Qualtrics survey design software (Qualtrics International Inc), which did not capture any identifying information. We provided respondents with a monetary reward (US $0.40) as an incentive for participation.

Data Collection

Study data were collected in November 2023 from Prolific, an online data collection platform, using samples of adults from the United States. We selected Prolific as an online data collection platform because of its high data quality compared with Amazon Mechanical Turk and CloudResearch [74]. We used a power analysis using the G*Power software (version 3.1; Heinrich-Heine-Universität Düsseldorf) to identify the appropriate sample size [75]. The power analysis results revealed that, for a small (0.15) effect size with α=.05 and a statistical power of 0.95, the total minimum sample size was approximately 768 respondents (4 groups and 7 covariates). We used a similar stratified sampling method [76] based on age, sex, and political affiliation to understand the OHIS behavior of adults in the United States. Specifically, we aimed to collect an age-, sex-, and political affiliation–stratified sample of 1200 respondents to ensure an adequate sample size after eliminating respondents based on the data exclusion criteria outlined in the preregistration. On the basis of the age and US political affiliation criteria, we aimed to recruit 300 digital natives (aged between 18 and 42 years) of Democratic affiliation, 300 digital natives of Republican affiliation, 300 digital immigrants (aged between 43 and 99 years) of Democratic affiliation, and 300 digital immigrants of Republican affiliation from Prolific for this study (a total of 1200). To ensure a sample representative of both sexes, we used balanced sample criteria for an even distribution of male and female respondents. Thus, an age-, sex-, and political affiliation–stratified sample was used to ensure an equal number of respondents in the 2 digital generation groups (digital natives vs digital immigrants), with an even distribution of gender (male and female) and political affiliation (Republican and Democratic). In addition, we recruited respondents from the United States using location criteria as well as those with a 100% approval rate from Prolific to ensure data quality. The final recorded responses were from 299 digital immigrants who were Democrats, 311 digital immigrants who were Republicans, 310 digital natives who were Democrats, and 309 digital natives who were Republicans (a total of 1229 responses were used for the analysis after merging 4 surveys). This deviation was due to the Prolific software and was outside the researchers’ control.

Data Exclusion

On the basis of the exclusion criteria outlined in the preregistration, we excluded 92 respondents in the following order—17 (18%) failed the attention check question, 8 (9%) did not complete the survey, 26 (28%) responded “never” for the frequency of OHIS, and 7 (8%) participants’ age and 34 (37%) participants’ political affiliation did not match for the respective surveys—from the sample, leaving 1137 (n=571, 50.22% digital natives and n=566, 49.78% digital immigrants) responses for analysis. The operationalization and coding of the focal study variables are shown in Table 1.

Table 1. The operationalization and coding of study variables.
VariablesOperationalization and codingReferences
GenerationDigital natives=aged between 18 and 42 years; digital immigrants=aged ≥43 yearsRansdell et al [18], 2011; Roth-Cohen et al [77], 2021
Frequency of OHISa“How frequently did you search for online health information in the past 12 months?” (“frequent,” “infrequent,” and “never”; 1=frequent; 2=infrequent; respondents who selected “never” were excluded)Xiong et al [43], 2021
HWISbThe sum of 3 types (sports and fitness, nutrition and diet, and general health knowledge; 1=yes; 0=no)Neter and Brainin [78], 2012; Xiong et al [43], 2021
HGIScThe sum of 2 types (medication guidance and disease consulting; 1=yes; 0=no)Neter and Brainin [78], 2012; Xiong et al [43], 2021
HMISdThe sum of 2 types (managing health conditions and participating in an online support group; 1=yes; 0=no)Neter and Brainin [78], 2012; Xiong et al [43], 2021
PHAeThe sum of 3 items (“After seeking health information and finding the information, did your health behavior change for the better? After seeking health information and finding the information, did you see a doctor? After seeking health information and finding the information, did you monitor your health yourself for any changes?”; 1=yes; 0=no)Taylor and Humphrey [79], 2022
Gender1=male; 2=femaleN/Af
Marital statusg1=married; 2=singleXiong et al [43], 2021
Employmenth1=unemployed; 2=employedXiong et al [43], 2021
Education level1=high school graduate or lower; 2=high school graduate; 2=some college or associate degree; 4=bachelor’s degree; 5=graduate degree or higherGordon and Crouch [80], 2019
Income1=<US $25,000; 2=US $25,000-$34,999; 3=US $35,000-$49,999; 4=US $50,000-$64,999; 5=US $65,000-$79,999; 6=US $80,000-$99,999; 7=≥US $100,000Gordon and Crouch [80], 2019
Race1=White; 2=Black or African American; 3=American Indian or Alaska Native; 4=Asian; 5=Native Hawaiian or other Pacific Islander; 6=some other raceJensen et al [81], 2021
Ethnicity1=Hispanic or Latino; 2=not Hispanic or LatinoJensen et al [81], 2021
Political affiliation1=Democrat; 2=Republican; 3=independent; 4=otherNaeim et al [55], 2021

aOHIS: online health information seeking.

bHWIS: health wellness information search.

cHGIS: health guidance information search.

dHMIS: health management information search.

ePHA: preventative health actions.

fN/A: not applicable.

gMarital status was divided into 2 categories: single (unmarried, divorced, or widowed) and married.

hEmployment was recorded as 2 groups: unemployed (unemployed, students, and retired) and employed.

Study Measures

We adapted the online survey measures based on previous research. Multimedia Appendix 1 contains a list of all the measures in the order in which they appeared in the survey.

Digital Generations: Natives and Immigrants

On the basis of previous research [18,77], we operationalized 2 digital generations based on age: digital natives (Generation Z and Millennials; aged between 18 and 42 years) and digital immigrants (Generation X and Baby Boomers; aged ≥43 years).

Frequency of OHIS

The frequency of OHIS was examined using a subjective assessment of how frequently respondents engaged in OHIS in the previous 12 months on a 3-point scale (frequent, infrequent, and never; adapted from the study by Xiong et al [43]). We excluded respondents who selected never. Those who chose frequent were grouped as frequent OHI seekers, and those who indicated infrequent were grouped as infrequent OHI seekers.

Health-Related Information

For health-related information, we asked respondents the following: “Please indicate whether you have searched for the following health-related information during the past 12 months.” We presented 7 types of health-related information with a dichotomized (yes or no) response (adapted from the studies by Xiong et al [43] and Neter and Brainin [78]). We grouped them into 3 search categories: health wellness information search (HWIS; sports and fitness, nutrition and diet, and general health knowledge), health guidance information search (HGIS; medication guidance and disease consulting), and health management information search (HMIS; managing health conditions and participating in an online support group). We created 3 scores by adding the responses in the 3 search categories (HWIS, HGIS, and HMIS).

Preventative Health Actions

Preventative health actions were measured using 3 items with a dichotomized (yes or no) response (“After seeking health information and finding the information, did your health behavior change for the better? After seeking health information and finding the information, did you see a doctor? After seeking health information and finding the information, did you monitor your health yourself for any changes?”; adapted from the study by Taylor and Humphrey [79]). We computed a preventative health actions score by summing the responses to the 3 items.

Control Variables

We tested 7 control variables. We controlled for information quality, trust, and search skills (adapted from the studies by Miller and Bell [30], Diviani et al [60], and Xiao et al [82]): the quality of OHI (“How would you rate the quality of health-related information on the Internet?” [1=very good quality; 7=very poor quality]), trust in OHI (“How much would you trust health-related information on the Internet?” [1=a lot; 7=not at all]), trust in US health care institutions (“How much would you trust healthcare institutions in the US?” [1=a lot; 7=not at all]), and respondents’ online health-related information search skills (“Please rate your health-related information search skills on the Internet.” [1=very good; 7=very poor]). In addition, we used 3 other control variables: race (“What is your race?” [White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiianor other Pacific Islander, and some other race]; adapted from the study by Jensen et al [81]), ethnicity (“What is your ethnicity?” [Hispanic or Latino and not Hispanic or Latino]; adapted from the study by Jensen et al [81]), and political affiliation (“What is your political affiliation?” [Democrat, Republican, independent, and other]; adapted from the study by Naeim et al [55]). We used these 3 variables as covariates in all analyses.

Sociodemographic Information

Demographic information included age, gender, annual household income, education, marital status, and employment status. We also asked respondents whether they went for a regular annual checkup (“Do you go for annual checkup regularly?” [yes or no]), whether they had health insurance (“Do you have health insurance?” [yes or no]), and whether they had at least one chronic disease (“Do you have at least 1 chronic disease?” [yes or no]), and descriptive statistics were reported for these measures.

Attention Check

To test whether respondents paid attention while completing the online survey, we asked the following: “If you are reading this, please do not answer this question and leave it blank.” (1=not at all true of me, 2=slightly true of me, 3=moderately true of me, 4=very true of me, and 5=extremely true of me), adapted from the study by Chugani and Irwin [83]. We excluded respondents who failed the attention check.

Statistical Analysis

We used SPSS (version 29; IBM Corp) to analyze the data. We conducted 5 sets of statistical analyses outlined in the preregistration while controlling for 7 factors (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). Specifically, we conducted 4 one-way analyses of covariance (ANCOVAs) using generation as an independent variable, 3 types of health information searches (HWIS, HGIS, and HMIS) and preventative health behavior (preventative health actions) as dependent variables, and 7 covariates. In addition, to test the effect of age (a continuous measure instead of 2 generations) on OHIS types (HWIS, HGIS, and HMIS) and preventative health actions, we performed 4 linear regression analyses using age as an independent variable, OHIS types (HWIS, HGIS, and HMIS) and preventative health actions as dependent variables, and 7 covariates.

To test whether search frequency moderated the effect of generation, we conducted 4 two-way ANCOVAs using generation and search frequency as independent variables, the 4 focal variables (HWIS, HGIS, HMIS, and preventative health actions) as dependent variables, and 7 control variables. To test whether gender moderated the effect of generation on the same 4 focal variables, we conducted 4 two-way ANCOVAs using generation and gender as independent variables and 7 control variables. In addition, to explore whether the presence of chronic diseases moderated the effect of generation on the 4 focal variables, we conducted 4 two-way ANCOVAs using generation and the presence of chronic diseases as independent variables and 7 control variables.

To test the types of health information searches as mediators, we used the bootstrapping approach as applied in the SPSS PROCESS (version 3.4) macro [84]. We also performed a descriptive analysis of sociodemographic characteristics.


Sociodemographic Characteristics of Respondents

Table 2 presents the detailed sociodemographic characteristics of the study sample by generation (digital natives and digital immigrants). The study sample comprised 49.52% (563/1137) female individuals and had an average age of 44.2 (SD 13.8) years. Most respondents were single (614/1137, 54%) and employed (838/1137, 73.7%). Approximately 31% (351/1137, 30.87%) of respondents had some college education or associate degrees, 13.1% (149/1137) were high school graduates, 40.37% (459/1137) had bachelor’s degrees, and 15.04% (171/1137) had graduate degrees or higher. Approximately 11.96% (136/1137) of respondents earned <US $25,000 a year, 9.32% (106/1137) earned between US $25,000 and US $34,999 a year, 15.04% (171/1137) earned between US $35,000 and US $49,999 a year, 12.31% (140/1137) earned between US $50,000 and US $64,999 a year, 11.79% (134/1137) earned between US $65,000 and US $79,999 a year, 10.91% (124/1137) earned between US $80,000 and US $99,999 a year, and 28.67% (326/1137) earned ≥US $100,000 a year. Most respondents reported being White (931/1137, 81.88%) and of non-Hispanic or non-Latino ethnicity (1041/1137, 91.56%). In addition, 50.22% (571/1137) of respondents reported Democrat as their political affiliation, 91.47% (1040/1137) had health insurance, 67.37% (766/1137) went for regular annual checkups, and 42.22% (480/1137) had at least one chronic disease.

Table 2. Sociodemographic characteristics of the respondents by generation (N=1137).
Sociodemographic characteristicOverallGeneration


Digital natives (n=571)Digital immigrants (n=566)
Age (years), mean (SD)44.2 (13.8)32.5 (6.0)56.0 (8.3)
Gender, n (%)

Male574 (50.5)288 (50.4)286 (50.5)

Female563 (49.5)283 (49.6)280 (49.5)
Marital status, n (%)

Married523 (46)211 (37)312 (55.1)

Single614 (54)360 (63)254 (44.9)
Employment, n (%)

Unemployed299 (26.3)142 (24.9)157 (27.7)

Employed838 (73.7)429 (75.1)409 (72.3)
Income (US $), n (%)

<25,000136 (12)70 (12.3)66 (11.7)

25,000-34,999106 (9.3)55 (9.6)51 (9)

35,000-49,999171 (15)82 (14.4)89 (15.7)

50,000-64,999140 (12.3)86 (15.1)54 (9.5)

65,000-79,999134 (11.8)64 (11.2)70 (12.4)

80,000-99,999124 (10.9)71 (12.4)53 (9.4)

≥100,000326 (28.7)143 (25)183 (32.3)
Educational level, n (%)

Lower than high school graduate7 (0.6)5 (0.9)2 (0.4)

High school graduate149 (13.1)81 (14.2)68 (12)

Some college or associate degree351 (30.9)167 (29.2)184 (32.5)

Bachelor’s degree459 (40.4)250 (43.8)209 (36.9)

Graduate degree or higher171 (15)68 (11.9)103 (18.2)
Race, n (%)

American Indian or Alaska Native8 (0.7)7 (1.2)1 (0.2)

Asian68 (6)52 (9.1)16 (2.8)

Black or African American97 (8.5)41 (7.2)56 (9.9)

Native Hawaiian or other Pacific Islander2 (0.2)2 (0.4)0 (0)

White931 (81.9)445 (77.9)486 (85.9)

Some other race31 (2.7)24 (4.2)7 (1.2)
Ethnicity, n (%)

Hispanic or Latino96 (8.4)67 (11.7)29 (5.1)

Not Hispanic or Latino1041 (91.6)504 (88.3)537 (94.9)
Political affiliation, n (%)

Democrat571 (50.2)289 (50.6)282 (49.8)

Republican566 (49.8)282 (49.4)284 (50.2)
Do you go for an annual checkup regularly?, n (%)

No371 (32.6)237 (41.5)134 (23.7)

Yes766 (67.4)334 (58.5)432 (76.3)
Do you have health insurance?, n (%)

No97 (8.5)64 (11.2)33 (5.8)

Yes1040 (91.5)507 (88.8)533 (94.2)
Do you have at least one chronic disease?, n (%)

No657 (57.8)395 (69.2)262 (46.3)

Yes480 (42.2)176 (30.8)304 (53.7)
Online health information search frequency, n (%)

Frequent615 (54.1)279 (48.9)336 (59.4)

Infrequent522 (45.9)292 (51.1)230 (40.6)

Health Information Searches by Generation

Three 1-way ANCOVAs using 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation) were conducted to determine whether there were significant differences in the 3 types of OHIS between the 2 generations (digital natives and digital immigrants). Table 3 presents the ANCOVA results. The 2 generations differed significantly in the 3 types of OHIS behaviors. Specifically, digital natives engaged in significantly more (P<.001) searches for health wellness information compared with digital immigrants. In contrast, digital immigrants carried out considerably more searches for health guidance information (P<.001) and health management information (P=.001) compared with digital natives.

Table 3. Summary statistics of outcome variables as a function of 2 generationsa.
Outcome variableGenerationF test (df)P valueη2

Digital natives (n=571), mean (SD)Digital immigrants (n=566), mean (SD)


HWISb2.41 (0.75)2.13 (0.82)28.22 (1, 1128)<.0010.024
HGISc0.76 (0.78)1.02 (0.81)29.11 (1, 1128)<.0010.025
HMISd0.70 (0.66)0.83 (0.61)10.45 (1, 1128).0010.009
PHAe1.94 (0.89)1.94 (0.90)0.04 (1, 1128).850.000

aA total of 4 analyses of covariance were conducted using 7 covariates: information quality, trust in online health information, trust in health care institutions, search skills, race, ethnicity, and political affiliation.

bHWIS: health wellness information search.

cHGIS: health guidance information search.

dHMIS: health management information search.

ePHA: preventative health actions.

Preventative Health Actions by Generation

A 1-way ANCOVA using generation as an independent variable and the same 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation) was conducted to determine whether there were significant differences in preventative health actions between the 2 generations. Table 3 presents the ANCOVA results. The 2 generations—digital natives and digital immigrants—did not differ significantly in their steps toward preventative health actions (P=.85).

We also tested whether OHI searches mediated the generational effects on preventative health actions and conducted 3 mediation analyses (one for each type of OHIS, namely, HWIS, HGIS, and HMIS). We selected the PROCESS Macro Model 4 for mediation analysis (simple mediation), with 20,000 bootstraps and 95% CIs with generation as an independent variable, preventative health actions as a dependent variable, OHI search types as the mediator, and 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). The relationship between generation and preventative health actions was mediated (as the 95% CI did not include 0 for all 3 OHIS types) by each type of OHIS. Table 4 presents the mediation analysis results.

Table 4. Mediation analysis resultsa.
Indirect effectCoefficient (SE)Bootstrapping 95% CI
HWISb

Total indirect effect–0.08 (0.02)–0.12 to –0.05

Generation and HWIS–0.25 (0.05)–0.35 to –0.16

HWIS and PHAc0.31 (0.03)0.25 to 0.38
HGISd

Total indirect effect0.08 (0.02)0.05 to 0.11

Generation and HGIS0.26 (0.05)0.17 to 0.36

HGIS and PHA0.29 (0.03)0.23 to 0.36
HMISe

Total indirect effect0.06 (0.02)0.02 to 0.11

Generation and HMIS0.12 (0.04)0.05 to 0.20

HMIS and PHA0.52 (0.04)0.44 to 0.59

aA total of 3 mediation analyses (PROCESS Macro Model 4) were conducted using 7 covariates: information quality, trust in online health information, trust in health care institutions, search skills, race, ethnicity, and political affiliation.

bHWIS: health wellness information search.

cPHA: preventative health actions.

dHGIS: health guidance information search.

eHMIS: health management information search.

Age as a Predictor of Health Information Searches and Preventative Health Actions

To test the effect of age on OHIS types (HWIS, HGIS, and HMIS) and preventative health actions, we conducted 4 linear regression analyses using age as an independent variable, OHIS types (HWIS, HGIS, and HMIS) and preventative health actions as dependent variables, and 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). Table 5 presents the linear regression results. Age was a significant negative predictor of HWIS behavior; as people aged, they searched for significantly less health wellness information (P<.001). In contrast, age was a significant positive predictor of HGIS and HMIS behaviors; as people aged, they searched for significantly more health guidance information (P<.001) and health management information (P=.003). However, age was not a significant predictor of preventative health actions (P=.48).

Table 5. Linear regression results.

Unstandardized coefficient (SE)t testP value
HWIS a,b

Age (years)–0.01 (0.002)–7.69<.001

Information quality–0.04 (0.03)–1.33.18

Trust in online health information0.01 (0.03)0.40.69

Trust in health care institutions0.01 (0.02)0.27.78

Search skills0.01 (0.02)0.62.53

Race0.06 (0.02)2.91.004

Ethnicity–0.11 (0.09)–1.29.20

Political affiliation0.05 (0.05)1.15.25

Constant2.95 (0.21)14.13<.001
HGISc,d

Age (years)0.01 (0.002)5.08<.001

Information quality–0.04 (0.03)–1.13.26

Trust in online health information0.02 (0.03)0.67.51

Trust in health care institutions0.01 (0.02)0.59.56

Search skills–0.01 (0.02)–0.64.53

Race0.04 (0.02)1.52.13

Ethnicity0.03 (0.09)0.32.75

Political affiliation0.01 (0.05)0.24.81

Constant0.43 (0.22)2.00.045
HMISe,f

Age (years)0.004 (0.001)2.96.003

Information quality0.01 (0.03)0.47.64

Trust in online health information–0.06 (0.03)–2.20.03

Trust in health care institutions0.02 (0.02)1.21.23

Search skills–0.02 (0.02)–0.99.32

Race0.02 (0.02)1.25.21

Ethnicity0.08 (0.07)1.11.27

Political affiliation–0.01 (0.04)–0.26.79

Constant0.56 (0.17)3.25.001
PHAg,h

Age (years)–0.001 (0.002)–0.71.48

Information quality–0.004 (0.04)–0.10.92

Trust in online health information–0.04 (0.04)–1.24.22

Trust in health care institutions–0.02 (0.02)–0.78.44

Search skill–0.04 (0.02)–1.69.09

Race0.02 (0.03)0.77.44

Ethnicity0.09 (0.10)0.95.35

Political affiliation–0.07 (0.05)–1.27.21

Constant2.24 (0.24)9.37<.001

aHWIS: health wellness information search.

bModel summary: R2=0.07; F8,1128=11.34; P<.001.

cHGIS: health guidance information search.

dModel summary: R2=0.03; F8,1128=4.00; P<.001.

eHMIS: health management information search.

fModel summary: R2=0.02; F8,1128=2.75; P=.005.

gPHA: preventative health actions.

hModel summary: R2=0.02; F8,1128=3.01; P=.002.

Search Frequency as a Moderator

To examine whether the frequency of OHIS (frequent vs infrequent) moderated the effect of generation on OHIS type and preventative health actions, we conducted four 2-way ANCOVAs using generation, search frequency, and interaction term as independent variables and 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). Table 6 presents the ANCOVA results. As expected, frequent OHI seekers, compared with infrequent OHI seekers, performed significantly more searches for all 3 types of health information (P<.001 for HWIS, HGIS, and HMIS) and engaged in significantly more preventative health behaviors (preventative health actions: P<.001). However, the interaction effects of generation and search frequency were not significant for all analyses. Thus, the frequency of OHIS did not moderate the relationship between generation and both type of OHIS and preventative health actions.

Table 6. Summary statistics of outcome variables as a function of 2 generations by online health information (OHI) search frequencya.
Outcome variablesFrequent, mean (SD)Infrequent, mean (SD)F test (df)P valueη2
HWISb

GenerationN/AcN/A45.24 (1, 1126)<.0010.04

OHI search frequencyN/AN/A100.99 (1, 1126)<.0010.08

Generation × OHI search frequencyN/AN/A2.18 (1, 1126).140.002

Digital natives2.62 (0.62)2.22 (0.82)N/AN/AN/A

Digital immigrants2.34 (0.72)1.81 (0.86)N/AN/AN/A
HGISd

GenerationN/AN/A18.37 (1, 1126)<.0010.02

OHI search frequencyN/AN/A200.91 (1, 1126)<.0010.15

Generation × OHI search frequencyN/AN/A0.59 (1, 1126).440.001

Digital natives1.10 (0.78)0.44 (0.64)N/AN/AN/A

Digital immigrants1.26 (0.78)0.67 (0.73)N/AN/AN/A
HMISe

GenerationN/AN/A3.68 (1, 1126).060.003

OHI search frequencyN/AN/A211.86 (1, 1126)<.0010.16

Generation × OHI search frequencyN/AN/A0.35 (1, 1126).560.000

Digital natives0.97 (0.64)0.45 (0.57)N/AN/AN/A

Digital immigrants1.03 (0.56)0.53 (0.55)N/AN/AN/A
PHAf

GenerationN/AN/A2.25 (1, 1126).130.002

OHI search frequencyN/AN/A125.21 (1, 1126)<.0010.10

Generation × OHI search frequencyN/AN/A0.50 (1, 1126).480.000

Digital natives2.22 (0.78)1.68 (0.90)N/AN/AN/A

Digital immigrants2.19 (0.80)1.57 (0.92)N/AN/AN/A

aA total of 4 analyses of covariance were conducted using 7 covariates: information quality, trust in OHI, trust in health care institutions, search skills, race, ethnicity, and political affiliation.

bHWIS: health wellness information search.

cN/A: not applicable.

dHGIS: health guidance information search.

eHMIS: health management information search.

fPHA: preventative health actions.

Gender as a Moderator

To examine whether gender (male vs female) moderated the effect of generation on OHIS type (HWIS, HGIS, and HMIS) and preventative health actions, we conducted four 2-way ANCOVAs using generation, gender, and interaction term as independent variables and 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). Table 7 presents the ANCOVA results. The interaction effect of generation and gender was significant only for HGIS (P=.02); among digital natives, female individuals, compared with male individuals, searched for significantly more health guidance information, whereas no significant differences among digital immigrants were observed between male and female individuals for HGIS. Moreover, the interaction effect of generation and gender was not significant for HWIS, HMIS, or preventative health actions. Thus, gender only moderated the relationship between generation and HGIS.

Table 7. Summary statistics of outcome variables as a function of 2 generations by gendera.
Outcome variablesMale, mean (SD)Female, mean (SD)F test (df)P valueη2
HWISb

GenerationN/AcN/A28.68 (1, 1126)<.0010.03

GenderN/AN/A8.54 (1, 1126).0040.01

Generation × genderN/AN/A0.00 (1, 1126)>.990.000

Digital natives2.49 (0.73)2.34 (0.77)N/AN/AN/A

Digital immigrants2.19 (0.85)2.06 (0.80)N/AN/AN/A
HGISd

GenerationN/AN/A29.71 (1, 1126)<.0010.03

GenderN/AN/A12.18 (1, 1126)<.0010.01

Generation × genderN/AN/A5.38 (1, 1126).020.01

Digital natives0.63 (0.78)0.90 (0.77)N/AN/AN/A

Digital immigrants0.99 (0.79)1.05 (0.83)N/AN/AN/A
HMISe

GenerationN/AN/A10.69 (1, 1126).0010.01

GenderN/AN/A10.51 (1, 1126).0010.01

Generation × genderN/AN/A1.65 (1, 1126).200.001

Digital natives0.62 (0.66)0.79 (0.66)N/AN/AN/A

Digital immigrants0.79 (0.60)0.86 (0.61)N/AN/AN/A
PHAf

GenerationN/AN/A0.04 (1, 1126).850.000

GenderN/AN/A1.13 (1, 1126).290.001

Generation × genderN/AN/A0.69 (1, 1126).410.001

Digital natives1.90 (0.89)1.99 (0.88)N/AN/AN/A

Digital immigrants1.93 (0.91)1.95 (0.89)N/AN/AN/A

aA total of 4 analyses of covariance were conducted using 7 covariates: information quality, trust in online health information, trust in health care institutions, search skills, race, ethnicity, and political affiliation.

bHWIS: health wellness information search.

cN/A: not applicable.

dHGIS: health guidance information search.

eHMIS: health management information search.

fPHA: preventative health actions.

Presence of Chronic Diseases as a Moderator

To examine whether the presence of chronic diseases (yes vs no) moderated the effect of generation on OHIS types (HWIS, HGIS, and HMIS) and preventative health actions, we conducted four 2-way ANCOVAs using generation, presence of chronic diseases, and interaction term as independent variables and 7 covariates (quality of OHI, trust in OHI, trust in health care institutions, OHI search skills, race, ethnicity, and political affiliation). Table 8 presents the ANCOVA results. The interaction effects of generation and presence of chronic diseases were significant only for HWIS (P=.03). Specifically, digital natives with at least one chronic disease searched for more health wellness information than those without chronic diseases, whereas no significant differences among digital immigrants were observed between the presence or absence of a chronic disease for HWIS. Moreover, the interaction effects of generation and presence of chronic diseases were not significant for HGIS, HMIS, or preventative health actions. Thus, the presence of chronic diseases only moderated the relationship between generation and HWIS.

Table 8. Summary statistics of outcome variables as a function of 2 generations by presence of chronic diseasesa.
Outcome variablesChronic disease (no), mean (SD)Chronic disease (yes), mean (SD)F test (df)P valueη2
HWISb

GenerationN/AcN/A33.26 (1, 1126)<.0010.03

Presence of chronic diseasesN/AN/A1.95 (1, 1126).160.002

Generation × presence of chronic diseasesN/AN/A5.06 (1, 1126).030.004

Digital natives2.36 (0.77)2.53 (0.70)N/AN/AN/A

Digital immigrants2.15 (0.85)2.11 (0.81)N/AN/AN/A
HGISd

GenerationN/AN/A7.83 (1, 1126).010.01

Presence of chronic diseasesN/AN/A124.97 (1, 1126)<.0010.10

Generation × presence of chronic diseasesN/AN/A0.89 (1, 1126).350.001

Digital natives0.58 (0.72)1.16 (0.78)N/AN/AN/A

Digital immigrants0.76 (0.79)1.24 (0.77)N/AN/AN/A
HMISe

GenerationN/AN/A0.62 (1, 1126).430.001

Presence of chronic diseasesN/AN/A112.81 (1, 1126)<.0010.09

Generation × presence of chronic diseasesN/AN/A0.28 (1, 1126).600.000

Digital natives0.57 (0.65)1.00 (0.60)N/AN/AN/A

Digital immigrants0.62 (0.57)1.00 (0.59)N/AN/AN/A
PHAf

GenerationN/AN/A1.50 (1, 1126).220.001

Presence of chronic diseasesN/AN/A14.88 (1, 1126)<.0010.01

Generation × presence of chronic diseasesN/AN/A1.30 (1, 1126).260.001

Digital natives1.85 (0.88)2.15 (0.86)N/AN/AN/A

Digital immigrants1.86 (0.85)2.01 (0.94)N/AN/AN/A

aA total of 4 analyses of covariance were conducted using 7 covariates: information quality, trust in online health information, trust in health care institutions, search skills, race, ethnicity, and political affiliation.

bHWIS: health wellness information search.

cN/A: not applicable.

dHGIS: health guidance information search.

eHMIS: health management information search.

fPHA: preventative health actions.


Principal Findings

In this study, we examined how the experiences with technology of 2 generations (digital natives and digital immigrants) affected their OHIS and preventative health behaviors using 7 control variables (information quality, trust in OHI, trust in health care institutions, search skills, race, ethnicity, and political affiliation). We found that digital natives searched more for health wellness information than digital immigrants, whereas digital immigrants searched more for health management and health guidance information than digital natives. However, after seeking OHI, the 2 generations did not differ significantly in their steps toward preventative health actions. We also found that OHI search types served as a mediator between generational differences and preventive health outcomes, whereas the search frequency of OHI did not moderate the relationship between generation and type of OHIS. In addition, gender only moderated the relationship between generation and HGIS; in contrast, the presence of chronic diseases only moderated the relationship between generation and HWIS. Further analysis (using age as a continuous measure) revealed that, as respondents’ ages increased, they sought significantly less health and wellness information but significantly more health management and health guidance information. However, age was not found to be a significant predictor of preventative health actions.

Comparison With Prior Work

Previous research on OHIS has found that younger people are more prone to seeking OHI than older people [2-7]. The findings of this study are consistent with those of previous research indicating that younger generations (digital natives; aged between 18 and 42 years) are more likely to search for OHI than older generations (digital immigrants; aged ≥43 years). In addition, this study revealed generational differences in the types of OHI search behaviors.

In total, 3 types of OHIS (HWIS, HGIS, and HMIS) were used to analyze generational differences in search behavior. The findings revealed that digital natives (the younger generation) conducted more HWISs than digital immigrants (the older generation). The opposite was true of HGIS and HMIS—digital immigrants (the older generation) conducted more of these searches than digital natives (the younger generation). Some of these findings confirm previous research, whereas others contradict it. For example, research has shown that older adults tend to search for health information related to specific diseases, medications, and treatment guidance [9]. Similarly, this study found that digital immigrants conducted more HGI searches (namely, medication guidance and disease consulting) than digital natives. In addition, the results revealed that digital immigrants conducted more HMISs (such as managing health conditions and participating in online support groups) than digital natives. However, unlike previous research indicating that older adults tend to seek information related to nutrition and exercise [9], this study found that younger generations conducted more HWISs (namely, sports and fitness, nutrition and diet, and general health knowledge). These findings deviate from those of previous research, which shows that young adults seek health information online by joining social media groups and following pages relevant to their condition [44].

Previous findings have highlighted that OHIS is associated with positive health behaviors and health outcomes [85-91]. Although digital natives and digital immigrants did not differ in their preventative behaviors, this study found that the type of health information searches drove the effect of generational differences on preventive health outcomes by demonstrating the indirect link between OHIS and preventative health behaviors after retrieving all 3 types of OHI. This finding is significant as both generations reported engaging in preventive actions after seeking specific types of OHI. In addition, previous studies have established that the frequency of OHIS is higher among younger people [3,7,67]. However, the frequency of OHIS did not moderate the relationship between younger and older generations and types of OHI searches. The findings indicate that both frequent and infrequent OHI searchers from both generations in our sample conducted similar levels of 3 types of health information searches online, a departure from previous findings.

Furthermore, previous research has shown gender to be a significant predictor of OHIS behavior [92]. However, our analysis shows that gender only moderated the relationship between generation and searches for health guidance information (HGIS) but not searches related to health management information (HMIS) and health wellness information (HWIS). Similar to previous studies showing the association between people facing a health threat and health information–seeking behavior [93], we also found that the presence of at least one chronic disease moderated the relationship between generation and searches for health guidance information (HGIS) but not searches related to health management information (HMIS) and health wellness information (HWIS).

Although existing studies show that age, gender, ethnic background, political affiliation, and health conditions are significant predictors of OHIS behavior [3,7,92-96], there is a lack of research on whether generational differences affect the types of OHIS behavior or preventative behavior afterward [9]. Thus, our findings add to the OHIS behavior literature by illustrating that a single factor—generational cohorts that differ in their level of technology immersion—influences information-seeking and preventative health behaviors. This study furthers the understanding of OHI search behavior by differentiating between types of OHI search behavior across 2 generations.

Limitations and Future Directions

This research is limited in several ways, indicating directions for future inquiry. First, we used stratified sampling to recruit 2 generations of American adults using an online data collection platform (Prolific). Specifically, we collected a stratified sample based on age, sex, and political affiliation using the United States as the location criterion. Although online data collection methods have many advantages, they are less likely to be used by those who do not have internet access. Thus, our findings are limited to digital natives and immigrants in this population sample. Future research could use in-person recruitment and focus groups to test the differences in OHIS and preventative health behaviors among different generations. Although we ensured an equal number of respondents in 2 digital generation groups (digital natives vs digital immigrants) with an even distribution of gender (male and female) and political affiliation (Republican and Democratic) and used 7 relevant control variables (information quality, trust in OHI, trust in health care institutions, search skills, race, ethnicity, and political affiliation) to show the effect of 2 generations on OHIS and preventative health behaviors, future research could use a more representative sample to test the validity of the relationships.

Second, this is the first study to our knowledge that has examined the differences in health information–seeking behaviors among older and younger Americans. This study focused on 2 generations that differ in their perception and use of technology: digital natives (born from 1980 onward) and digital immigrants (born before 1980). Although the use of 2 generational categories can simplify comparisons based on the ubiquity of technology in formative years, it has the potential to attenuate key variations within generational cohorts. Although our sample included nearly an equal number of digital natives and immigrants by age, sex, and political affiliation, most of our respondents were White, non-Hispanic or Latino Americans and skewed toward higher income brackets (nearly 40% made <US $50,000 a year; 413/1137, 36.32%) and educational levels (approximately 55% had a bachelor’s degree or higher; 630/1137, 55.41%). As such, our findings may overstate differences based on 2 generational cohorts, and future research could focus on recruiting more racial and ethnic minority groups and low-income individuals to assess the validity of our findings.

Third, we performed additional analyses using age as a continuous variable. Although using respondents’ age as a continuous variable shows results similar to those of categorizing them by generation, future research should test whether OHIS and preventative health behaviors vary among different generations, including microgenerations (such as Zillennials [14]). Thus, comparing how different generations in Western and non-Western countries differ in their OHIS and preventative health behaviors would be an exciting avenue to pursue. Moreover, technological exposure and norms [9] are likely to vary within 2 generations (digital immigrants and digital natives). Although we controlled for respondents’ self-reported online information search skills, other factors could be potential barriers that older adults face during OHIS, including self-efficacy and experience with information and communications technologies, perceived ease of use, and subjective norm perception [9,91,94].

Fourth, the statistical models used in the analyses did not account for some key variables. For example, previous research indicates that factors such as (but not limited to) health consumers’ perceived trust in health care professionals [97], ethnic background [3,94], and political ideology [95,96] are important predictors of OHIS and health behaviors. In addition, health information–seeking behavior has been shown to vary according to the perception of the credibility of information sources [34,35,64,65]. As our research did not account for these differences, the models used for the analyses may overstate differences based on generational cohorts.

Fifth, this study used self-reported OHIS and preventative health behavior measures, which might include biased responses, such as recall bias and social desirability bias [98,99]. Future research could benefit from using objective measures rather than relying on self-reports. For example, logged data of the OHI search history and daily diary study designs [100] would allow future researchers to measure types of health information searches and engagement in preventative health behaviors.

Finally, recent research suggests that some disadvantaged groups (older people and individuals of a lower socioeconomic status) lack the skills to use OHI and are often digitally marginalized [101]. Although this study did not explore digital marginalization, future research should address how lower technology skills might limit socially disadvantaged groups from participating in OHIS and managing their health.

Conclusions

This study used a generational cohort lens to examine how older and younger generations differ in their OHI search and preventative health behaviors. This study provides insights into differences in OHI search behavior between the 2 digital generations, suggesting that, compared with digital immigrants, digital natives tend to search for more wellness information and are less likely to search for health management or guidance information. Furthermore, this research identified that the types of OHI searches mediated the generational effect on preventive health outcomes. Although the study of OHI search behavior is a well-established research domain, older adults’ OHI searches and preventative health behaviors are poorly understood [9]. As the older adult population and the demand for health care and online medical information increase, it is important to understand older adults’ OHIS and preventative health behaviors. This research could also serve as a foundation for future studies to understand how these results may apply across various population groups, including disadvantaged and digitally marginalized groups.

Acknowledgments

The authors thank the study respondents. This research was supported in part by the Healthcare MBA Faculty Research Award. The funders had no role in any part of the study (conceptualization, design, analyses, and writing of the manuscript).

Data Availability

The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Survey questions.

DOCX File , 20 KB

  1. Jacobs W, Amuta AO, Jeon KC. Health information seeking in the digital age: an analysis of health information seeking behavior among US adults. Cogent Social Sciences. Mar 13, 2017;3(1):1302785. [CrossRef]
  2. Brodie M, Flournoy RE, Altman DE, Blendon RJ, Benson JM, Rosenbaum MD. Health information, the internet, and the digital divide. Health Aff (Millwood). Nov 01, 2000;19(6):255-265. [CrossRef] [Medline]
  3. Din HN, McDaniels-Davidson C, Nodora J, Madanat H. Profiles of a health information-seeking population and the current digital divide: cross-sectional analysis of the 2015-2016 California health interview survey. J Med Internet Res. May 14, 2019;21(5):e11931. [FREE Full text] [CrossRef] [Medline]
  4. Levy H, Janke AT, Langa KM. Health literacy and the digital divide among older Americans. J Gen Intern Med. Mar 2015;30(3):284-289. [FREE Full text] [CrossRef] [Medline]
  5. Nguyen A, Mosadeghi S, Almario CV. Persistent digital divide in access to and use of the internet as a resource for health information: results from a California population-based study. Int J Med Inform. Jul 2017;103:49-54. [CrossRef] [Medline]
  6. Rao N, Tighe EL, Feinberg I. The dispersion of health information-seeking behavior and health literacy in a state in the Southern United States: cross-sectional study. JMIR Form Res. Jun 15, 2022;6(6):e34708. [FREE Full text] [CrossRef] [Medline]
  7. Weber W, Reinhardt A, Rossmann C. Lifestyle segmentation to explain the online health information-seeking behavior of older adults: representative telephone survey. J Med Internet Res. Jun 12, 2020;22(6):e15099. [FREE Full text] [CrossRef] [Medline]
  8. Frey WH. What the 2020 census will reveal about America: stagnating growth, an aging population, and youthful diversity. Brookings. 2021. URL: https:/​/www.​brookings.edu/​research/​what-the-2020-census-will-reveal-about-america-stagnating-growth-an-aging-population-and-youthful-diversity/​ [accessed 2022-11-01]
  9. Zhao YC, Zhao M, Song S. Online health information seeking behaviors among older adults: systematic scoping review. J Med Internet Res. Feb 16, 2022;24(2):e34790. [FREE Full text] [CrossRef] [Medline]
  10. Berkowitz EN, Schewe CD. Generational cohorts hold the key to understanding patients and health care providers: coming-of-age experiences influence health care behaviors for a lifetime. Health Mark Q. Apr 16, 2011;28(2):190-204. [CrossRef] [Medline]
  11. Ma X, Liu Y, Zhang P, Qi R, Meng F. Understanding online health information seeking behavior of older adults: a social cognitive perspective. Front Public Health. Mar 3, 2023;11:1147789. [FREE Full text] [CrossRef] [Medline]
  12. Ryder NB. The cohort as a concept in the study of social change. Am Sociol Rev. Dec 1965;30(6):843. [CrossRef]
  13. Wyatt S. Technological determinism is dead; long live technological determinism. In: Hackett EJ, Amsterdamska O, Lynch M, Wajcman J, editors. The Handbook of Science and Technology Studies. 3rd edition. Cambridge, MA. The MIT Press; 2008;165-180.
  14. Ward T. Zillennials: the newest micro-generation has a name. CNN Health. 2023. URL: https://www.cnn.com/2023/05/10/health/what-are-zillennials-wellness/index [accessed 2024-01-29]
  15. Twenge JM. Generations: the real differences between Gen Z, Millennials, Gen X, Boomers, and Silents—and what they mean for America’s future. Perspect Sci Christ Faith. Dec 2023;75(3):212-214. [CrossRef]
  16. Prensky M. Digital natives, digital immigrants part 1. Horiz. Sep 2001;9(5):1-6. [CrossRef]
  17. Prensky M. Digital natives, digital immigrants part 2: do they really think differently? Horiz. Nov 2001;9(6):1-6. [CrossRef]
  18. Ransdell S, Kent B, Gaillard-Kenney S, Long J. Digital immigrants fare better than digital natives due to social reliance. Br J Educ Technol. 2010;42(6):931-938. [FREE Full text] [CrossRef]
  19. Akçayır M, Dündar H, Akçayır G. What makes you a digital native? Is it enough to be born after 1980? Comput Human Behav. Jul 2016;60:435-440. [CrossRef]
  20. Kopalle PK, Kumar V, Subramaniam M. How legacy firms can embrace the digital ecosystem via digital customer orientation. J Acad Mark Sci. Nov 20, 2019;48(1):114-131. [FREE Full text] [CrossRef]
  21. Baham C, Kalgotra P, Nasirpouri Shadbad F, Sharda R. Generational differences in handling technology interruptions: a qualitative study. Eur J Inf Syst. May 10, 2022;32(5):858-878. [CrossRef]
  22. Kesharwani A. Do (how) digital natives adopt a new technology differently than digital immigrants? A longitudinal study. Inf Manag. Mar 2020;57(2):103170. [FREE Full text] [CrossRef]
  23. Lai KW, Hong KS. Technology use and learning characteristics of students in higher education: do generational differences exist? Br J Educ Technol. May 02, 2014;46(4):725-738. [FREE Full text] [CrossRef]
  24. Martins P, Picoto WN, Bélanger F. The effects of digital nativity on nonvolitional routine and innovative usage. Inf Technol People. Nov 08, 2022;36(7):2804-2825. [FREE Full text] [CrossRef]
  25. Vodanovich S, Sundaram D, Myers M. Research commentary—digital natives and ubiquitous information systems. Inf Syst Res. Dec 2010;21(4):711-723. [FREE Full text] [CrossRef]
  26. Colbert A, Yee N, George G. The digital workforce and the workplace of the future. Acad Manage J. Jun 2016;59(3):731-739. [FREE Full text] [CrossRef]
  27. Filho EJ, Gammarano ID, Barreto IA. Technology-driven consumption: digital natives and immigrants in the context of multifunctional convergence. J Strateg Mark. Aug 22, 2019;29(3):181-205. [CrossRef]
  28. Bennett S, Maton K, Kervin L. The ‘digital natives’ debate: a critical review of the evidence. Br J Educ Technol. Aug 19, 2008;39(5):775-786. [CrossRef]
  29. Tams S. Helping older workers realize their full organizational potential: a moderated mediation model of age and it-enabled task performance (Open Access). MIS Q. Mar 01, 2022;46(1):1-34. [FREE Full text] [CrossRef]
  30. Miller LM, Bell RA. Online health information seeking: the influence of age, information trustworthiness, and search challenges. J Aging Health. Apr 2012;24(3):525-541. [CrossRef] [Medline]
  31. Pang PC, Chang S, Pearce J, Verspoor K. Online health information seeking behaviour: understanding different search approaches. In: proceedings of the 18th Pacific Asia Conference on Information Systems. 2014. Presented at: PACIS '14; June 24-28, 2014;229; Chengdu, China. URL: https://aisel.aisnet.org/pacis2014/229
  32. Faverio M. Share of those 65 and older who are tech users has grown in the past decade. Pew Research Center. URL: https:/​/www.​pewresearch.org/​short-reads/​2022/​01/​13/​share-of-those-65-and-older-who-are-tech-users-has-grown-in-the-past-decade/​ [accessed 2022-10-27]
  33. Kennedy GE, Judd TS, Churchward A, Gray K, Krause KL. First year students' experiences with technology: are they really digital natives? Australas. J Educ Technol. Jan 16, 2008;24(1):1233. [FREE Full text] [CrossRef]
  34. Feufel MA, Stahl SF. What do web-use skill differences imply for online health information searches? J Med Internet Res. Jun 13, 2012;14(3):e87. [FREE Full text] [CrossRef] [Medline]
  35. Park E, Kwon M. Health-related internet use by children and adolescents: systematic review. J Med Internet Res. Apr 03, 2018;20(4):e120. [FREE Full text] [CrossRef] [Medline]
  36. Zajac IT, Flight IH, Wilson C, Turnbull D, Cole S, Young G. Internet usage and openness to internet-delivered health information among Australian adults aged over 50 years. Australas Med J. 2012;5(5):262-267. [FREE Full text] [CrossRef] [Medline]
  37. El Sherif R, Pluye P, Ibekwe F. Contexts and outcomes of proxy online health information seeking: mixed studies review with framework synthesis. J Med Internet Res. Jun 24, 2022;24(6):e34345. [FREE Full text] [CrossRef] [Medline]
  38. Thapa DK, Visentin DC, Kornhaber R, West S, Cleary M. The influence of online health information on health decisions: a systematic review. Patient Educ Couns. Apr 2021;104(4):770-784. [CrossRef] [Medline]
  39. Wang X, Shi J, Kong H. Online health information seeking: a review and meta-analysis. Health Commun. Sep 2021;36(10):1163-1175. [CrossRef] [Medline]
  40. Ramsey I, Corsini N, Peters MD, Eckert M. A rapid review of consumer health information needs and preferences. Patient Educ Couns. Sep 2017;100(9):1634-1642. [CrossRef] [Medline]
  41. Griffin RJ, Dunwoody S, Neuwirth K. Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environ Res. Feb 1999;80(2 Pt 2):S230-S245. [CrossRef] [Medline]
  42. Madrigal L, Escoffery C. Electronic health behaviors among US adults with chronic disease: cross-sectional survey. J Med Internet Res. Mar 05, 2019;21(3):e11240. [FREE Full text] [CrossRef] [Medline]
  43. Xiong Z, Zhang L, Li Z, Xu W, Zhang Y, Ye T. Frequency of online health information seeking and types of information sought among the general Chinese population: cross-sectional study. J Med Internet Res. Dec 02, 2021;23(12):e30855. [FREE Full text] [CrossRef] [Medline]
  44. Lim MS, Molenaar A, Brennan L, Reid M, McCaffrey T. Young adults' use of different social media platforms for health information: insights from web-based conversations. J Med Internet Res. Jan 18, 2022;24(1):e23656. [FREE Full text] [CrossRef] [Medline]
  45. Renahy E, Parizot I, Chauvin P. WHIST: a web-based survey on health information seeking on Internet in France, 2007. AMIA Annu Symp Proc. Oct 11, 2007.:1090-1091. [Medline]
  46. Baumann E, Czerwinski F, Reifegerste D. Gender-specific determinants and patterns of online health information seeking: results from a representative German health survey. J Med Internet Res. Apr 04, 2017;19(4):e92. [FREE Full text] [CrossRef] [Medline]
  47. Lee K, Hoti K, Hughes JD, Emmerton LM. Consumer use of "Dr Google": a survey on health information-seeking behaviors and navigational needs. J Med Internet Res. Dec 29, 2015;17(12):e288. [FREE Full text] [CrossRef] [Medline]
  48. Zhao YC, Zhao M, Song S. Online health information seeking among patients with chronic conditions: integrating the health belief model and social support theory. J Med Internet Res. Nov 02, 2022;24(11):e42447. [FREE Full text] [CrossRef] [Medline]
  49. Rooks RN, Wiltshire JC, Elder K, BeLue R, Gary LC. Health information seeking and use outside of the medical encounter: is it associated with race and ethnicity? Soc Sci Med. Jan 2012;74(2):176-184. [CrossRef] [Medline]
  50. Richardson A, Allen JA, Xiao H, Vallone D. Effects of race/ethnicity and socioeconomic status on health information-seeking, confidence, and trust. J Health Care Poor Underserved. Nov 2012;23(4):1477-1493. [CrossRef] [Medline]
  51. Finney Rutten LJ, Blake KD, Greenberg-Worisek AJ, Allen SV, Moser RP, Hesse BW. Online health information seeking among US adults: measuring progress toward a healthy people 2020 objective. Public Health Rep. 2019;134(6):617-625. [FREE Full text] [CrossRef] [Medline]
  52. Rooks RN, Kapral CG, Mathis AL. Chronic conditions may be more important than race or ethnicity in relation to health information seeking and use. J Aging Health. Apr 2019;31(4):611-630. [CrossRef] [Medline]
  53. Kannan VD, Veazie PJ. Political orientation, political environment, and health behaviors in the United States. Prev Med. Sep 2018;114:95-101. [CrossRef] [Medline]
  54. Imburgia TM, Hendrix KS, Donahue KL, Sturm LA, Zimet GD. Predictors of influenza vaccination in the U.S. among children 9-13 years of age. Vaccine. Apr 25, 2017;35(18):2338-2342. [CrossRef] [Medline]
  55. Naeim A, Baxter-King R, Wenger N, Stanton AL, Sepucha K, Vavreck L. Effects of age, gender, health status, and political party on COVID-19-related concerns and prevention behaviors: results of a large, longitudinal cross-sectional survey. JMIR Public Health Surveill. Apr 28, 2021;7(4):e24277. [FREE Full text] [CrossRef] [Medline]
  56. Gyorda JA, Lekkas D, Price G, Jacobson NC. Evaluating the impact of mask mandates and political party affiliation on mental health internet search behavior in the united states during the COVID-19 pandemic: generalized additive mixed model framework. J Med Internet Res. Mar 03, 2023;25:e40308. [FREE Full text] [CrossRef] [Medline]
  57. Prestin A, Vieux SN, Chou WY. Is online health activity alive and well or flatlining? Findings from 10 years of the health information national trends survey. J Health Commun. 2015;20(7):790-798. [CrossRef] [Medline]
  58. 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]
  59. Starcevic V, Berle D. Cyberchondria: towards a better understanding of excessive health-related internet use. Expert Rev Neurother. Feb 2013;13(2):205-213. [CrossRef] [Medline]
  60. Diviani N, van den Putte B, Giani S, van Weert JC. Low health literacy and evaluation of online health information: a systematic review of the literature. J Med Internet Res. May 07, 2015;17(5):e112. [FREE Full text] [CrossRef] [Medline]
  61. Antheunis ML, Tates K, Nieboer TE. Patients' and health professionals' use of social media in health care: motives, barriers and expectations. Patient Educ Couns. Sep 2013;92(3):426-431. [CrossRef] [Medline]
  62. Levac JJ, O'Sullivan T. Social media and its use in health promotion. Interdiscip J Health Sci. Feb 10, 2010;1(1):47. [CrossRef]
  63. Robertson-Lang L, Major S, Hemming H. An exploration of search patterns and credibility issues among older adults seeking online health information. Can J Aging. Dec 2011;30(4):631-645. [CrossRef] [Medline]
  64. Chen X, Hay JL, Waters EA, Kiviniemi MT, Biddle C, Schofield E, et al. Health literacy and use and trust in health information. J Health Commun. 2018;23(8):724-734. [FREE Full text] [CrossRef] [Medline]
  65. Suarez-Lledo V, Alvarez-Galvez J. Prevalence of health misinformation on social media: systematic review. J Med Internet Res. Jan 20, 2021;23(1):e17187. [FREE Full text] [CrossRef] [Medline]
  66. Ye Y. A path analysis on correlates of consumer trust in online health information: evidence from the health information national trends survey. J Health Commun. Dec 10, 2010;15 Suppl 3(sup3):200-215. [CrossRef] [Medline]
  67. Dutta MJ, Bodie GD. Web searching for health: theoretical foundations and connections to health related outcomes. In: Web Search: Multidisciplinary Perspectives. Cham, Switzerland. Springer; 2008;235-254.
  68. Amante DJ, Hogan TP, Pagoto SL, English TM, Lapane KL. Access to care and use of the Internet to search for health information: results from the US National Health Interview Survey. J Med Internet Res. Apr 29, 2015;17(4):e106. [FREE Full text] [CrossRef] [Medline]
  69. Pluye P, El Sherif R, Gonzalez-Reyes A, Turcotte E, Schuster T, Bartlett G, et al. Outcomes of equity-oriented, web-based parenting information in mothers of low socioeconomic status compared to other mothers: participatory mixed methods study. J Med Internet Res. Nov 10, 2020;22(11):e22440. [FREE Full text] [CrossRef] [Medline]
  70. de Looper M, van Weert JC, Schouten BC, Bolle S, Belgers EH, Eddes EH, et al. The influence of online health information seeking before a consultation on anxiety, satisfaction, and information recall, mediated by patient participation: field study. J Med Internet Res. Jul 05, 2021;23(7):e23670. [FREE Full text] [CrossRef] [Medline]
  71. Chen YY, Li CM, Liang JC, Tsai CC. Health information obtained from the internet and changes in medical decision making: questionnaire development and cross-sectional survey. J Med Internet Res. Feb 12, 2018;20(2):e47. [FREE Full text] [CrossRef] [Medline]
  72. Ayers SL, Kronenfeld JJ. Chronic illness and health-seeking information on the internet. Health (London). Jul 2007;11(3):327-347. [CrossRef] [Medline]
  73. AsPredicted - Medical Information Search. Wharton Credibility Lab. URL: https://aspredicted.org/TPZ_21P [accessed 2024-02-19]
  74. Peer E, Rothschild D, Gordon A, Evernden Z, Damer E. Data quality of platforms and panels for online behavioral research. Behav Res Methods. Aug 29, 2022;54(4):1643-1662. [FREE Full text] [CrossRef] [Medline]
  75. Faul F, Erdfelder E, Lang A, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. May 2007;39(2):175-191. [CrossRef]
  76. Link E, Baumann E. A comparison of women's and men's web-based information-seeking behaviors about gender-related health information: web-based survey study of a stratified German sample. J Med Internet Res. May 17, 2023;25:e43897. [FREE Full text] [CrossRef] [Medline]
  77. Roth-Cohen O, Rosenberg H, Lissitsa S. Are you talking to me? Generation X, Y, Z responses to mobile advertising. Convergence. Oct 19, 2021;28(3):761-780. [CrossRef]
  78. Neter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. Jan 27, 2012;14(1):e19. [FREE Full text] [CrossRef] [Medline]
  79. Taylor KA, Humphrey Jr WF. Impact of medical blog reading and information presentation on readers' preventative health intentions: mixed methods, multistudy investigation. J Med Internet Res. Dec 22, 2021;23(12):e23210. [FREE Full text] [CrossRef] [Medline]
  80. Gordon NP, Crouch E. Digital information technology use and patient preferences for internet-based health education modalities: cross-sectional survey study of middle-aged and older adults with chronic health conditions. JMIR Aging. Apr 04, 2019;2(1):e12243. [FREE Full text] [CrossRef] [Medline]
  81. Jensen E, Jones N, Orozco K, Medina L, Perry M, Bolender B, et al. Measuring racial and ethnic diversity for the 2020 census. United States Census Bureau. 2021. URL: https:/​/www.​census.gov/​newsroom/​blogs/​random-samplings/​2021/​08/​measuring-racial-ethnic-diversity-2020-census.​html [accessed 2023-11-15]
  82. Xiao N, Sharman R, Rao HR, Upadhyaya S. Factors influencing online health information search: an empirical analysis of a national cancer-related survey. Decis Support Syst. Jan 2014;57:417-427. [CrossRef]
  83. Chugani S, Irwin JR. All eyes on you: the social audience and hedonic adaptation. Psychol Mark. Aug 26, 2020;37(11):1554-1570. [CrossRef]
  84. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis, First Edition: A Regression-Based Approach. New York, NY. The Guilford Press; 2018.
  85. Chisolm DJ. Does online health information seeking act like a health behavior?: a test of the behavioral model. Telemed J E Health. Mar 2010;16(2):154-160. [CrossRef] [Medline]
  86. Dutta-Bergman MJ. Health attitudes, health cognitions, and health behaviors among internet health information seekers: population-based survey. J Med Internet Res. May 28, 2004;6(2):e15. [FREE Full text] [CrossRef] [Medline]
  87. Bass SB, Ruzek SB, Gordon TF, Fleisher L, McKeown-Conn N, Moore D. Relationship of internet health information use with patient behavior and self-efficacy: experiences of newly diagnosed cancer patients who contact the National Cancer Institute's Cancer Information Service. J Health Commun. Mar 2006;11(2):219-236. [CrossRef] [Medline]
  88. Cocco AM, Zordan R, Taylor DM, Weiland TJ, Dilley SJ, Kant J, et al. Dr Google in the ED: searching for online health information by adult emergency department patients. Med J Aust. Oct 15, 2018;209(8):342-347. [CrossRef] [Medline]
  89. Bert F, Gualano MR, Brusaferro S, De Vito E, de Waure C, La Torre G, et al. Pregnancy e-health: a multicenter Italian cross-sectional study on internet use and decision-making among pregnant women. J Epidemiol Community Health. Dec 01, 2013;67(12):1013-1018. [FREE Full text] [CrossRef] [Medline]
  90. Abdul-Muhsin H, Tyson M, Raghu S, Humphreys M. The informed patient: an analysis of information seeking behavior and surgical outcomes among men with benign prostatic hyperplasia. Am J Mens Health. Jan 08, 2017;11(1):147-153. [FREE Full text] [CrossRef] [Medline]
  91. Stellefson M, Chaney B, Barry AE, Chavarria E, Tennant B, Walsh-Childers K, et al. Web 2.0 chronic disease self-management for older adults: a systematic review. J Med Internet Res. Feb 14, 2013;15(2):e35. [FREE Full text] [CrossRef] [Medline]
  92. Mirzaei A, Aslani P, Luca EJ, Schneider CR. Predictors of health information-seeking behavior: systematic literature review and network analysis. J Med Internet Res. Jul 02, 2021;23(7):e21680. [FREE Full text] [CrossRef] [Medline]
  93. Link E, Baumann E, Klimmt C. Explaining online information seeking behaviors in people with different health statuses: German representative cross-sectional survey. J Med Internet Res. Dec 10, 2021;23(12):e25963. [FREE Full text] [CrossRef] [Medline]
  94. Sedrak MS, Soto-Perez-De-Celis E, Nelson RA, Liu J, Waring ME, Lane DS, et al. Online health information-seeking among older women with chronic illness: analysis of the women's health initiative. J Med Internet Res. Apr 09, 2020;22(4):e15906. [FREE Full text] [CrossRef] [Medline]
  95. Jin X. Political ideology and differences in seeking COVID-19 information on the internet: examining the comprehensive model of information seeking. Online Inf Rev. Mar 13, 2023;47(7):1280-1301. [CrossRef]
  96. Baumgaertner B, Carlisle JE, Justwan F. The influence of political ideology and trust on willingness to vaccinate. PLoS One. Jan 25, 2018;13(1):e0191728. [FREE Full text] [CrossRef] [Medline]
  97. Lee K, Hoti K, Hughes JD, Emmerton L. Dr Google and the consumer: a qualitative study exploring the navigational needs and online health information-seeking behaviors of consumers with chronic health conditions. J Med Internet Res. Dec 02, 2014;16(12):e262. [FREE Full text] [CrossRef] [Medline]
  98. Baker M, Stabile M, Deri C. What do self-reported, objective, measures of health measure? J Hum Resour. Oct 01, 2004;XXXIX(4):1067-1093. [CrossRef]
  99. Onur I, Velamuri M. The gap between self-reported and objective measures of disease status in India. PLoS One. Aug 27, 2018;13(8):e0202786. [FREE Full text] [CrossRef] [Medline]
  100. Cho S, Kim S. Does a healthy lifestyle matter? A daily diary study of unhealthy eating at home and behavioral outcomes at work. J Appl Psychol. Jan 2022;107(1):23-39. [CrossRef] [Medline]
  101. Cheng C, Beauchamp A, Elsworth GR, Osborne RH. Applying the electronic health literacy lens: systematic review of electronic health interventions targeted at socially disadvantaged groups. J Med Internet Res. Aug 13, 2020;22(8):e18476. [FREE Full text] [CrossRef] [Medline]


ANCOVA: analysis of covariance
HGIS: health guidance information search
HMIS: health management information search
HWIS: health wellness information search
OHI: online health information
OHIS: online health information seeking


Edited by A Mavragani; submitted 14.05.23; peer-reviewed by S Neely; comments to author 13.06.23; revised version received 31.07.23; accepted 08.02.24; published 11.03.24.

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