Consumer Health Information Seeking on the Internet the State of the Art

Public Interest Argument

We alive in a digital age that has changed the mural of wellness information. People are living longer and with chronic diseases that require being up-to-date on their disease-specific wellness information. This changing health data landscape has increased the need for easily accessible health information such every bit is available on the Spider web. Nevertheless, there are many people who do not possess the skill needed to search for information on the web or the devices required to appraise the web. This written report showed that while the Web is an easily bachelor source of wellness information, information technology could also create inequalities in health information accessibility peculiarly among the elderly, those with low income, minimal education, and of certain ethnicities. Many people still rely on culling sources of health information such as personal networks, traditional media, and health intendance providers. Hence, the Spider web should non be considered a substitute for alternative health information sources. Doing so, might create disproportionate access to wellness information essential for health decisions.

1. Introduction

It is no news that we live in a digital age. A study establish that one in three US adults use the net to diagnose or learn most a health concern (Flim-flam & Duggan, 2013). This growth in internet utilize and increase in health data bachelor on the web is changing the landscape of health information. Given that information is a valuable factor that mediates the relationship between social status and health, the changing health information landscape could potentially assist reduce social inequalities in health (Link & Phelan, 1995; Rains, 2008). However, there is also a nagging question of whether the Spider web is reducing or perpetuating disparities in wellness information availability and use for making health decisions, especially if just those with access to alternative sources of health information are the same ones with access to online wellness information (Percheski & Hargittai, 2011).

Wellness information seeking behavior (HISB) refers to the means in which individuals seek data about their health, risks, illnesses, and health-protective behaviors (Lambert & Loiselle, 2007; Mills & Todorova, 2016). Previous research on HISB have either focused just on wellness information seeking online or used a cross sectional research pattern arroyo. Nosotros add to this literature by examining, over a period of four years, what factors—including Internet skills, health status, overall health perception, cancer family history, socioeconomic, and individual factors—are associated with use of different sources of health data. We also report demographic (eastward.m. gender, educational level, and socioeconomic status (SES)) trends in HISB over the 4 year period.

Even though 86% of the Usa population are connected online, studies have establish that there are even so many people who prefer to use traditional media (such as library, books, brochure, magazines) or healthcare professionals equally their primary source of wellness information (Baker, Wagner, Singer, & Bundorf, 2003; Cotten & Gupta, 2004; Dolan, Iredale, Williams, & Ameen, 2004; Dutta-Bergman, 2004, 2005; Rains, 2007). Trust, confidence in information source, and access are some of the main factors highlighted as motivators for preferring i source of information over the other (Rains, 2007). Studies have also showed that many of those who use these other (traditional media and health intendance professional) health data sources also turn to the internet as an alternative information source in order to gain a perspective unlike from what they read or heard from a traditional media source, from what they heard from a healthcare practitioner, or from an unsatisfactory doctor-patient interaction (Rains, 2007). The principal advantages and attractions for health information seeking online take been found to include access, anonymity, potential for interactivity, and social support (Cline & Haynes, 2001).

Online HISB is explained by both psychological and social factors (Mills & Todorova, 2016; Wang, Viswanath, Lam, Wang, & Chan, 2013). Wellness anxiety, cocky-efficacy, net-efficacy, and neuroticism have been identified as psychological factors that predict utilize of the internet/web for health data seeking (Eastin & Guinsler, 2006; Lagoe & Atkin, 2015). Social, demographic, and lifestyle factors linked to online health information seeking include being female, higher educational accomplishment, and historic period.(Hesse et al., 2005; Lambert & Loiselle, 2007; Rains, 2008; Wang et al., 2013; Weaver et al., 2010).

A study of health data seeking behavior found that age, education, literacy, and accessibility had a positive influence on the use of library and print materials for seeking health data amid this population (Gollop, 1997). With the advent of e-wellness and e-technologies, very few recent studies take examined the characteristics of those who seek health information from sources other than the Web (such as traditional media, healthcare providers, and personal network). Thus, there is a paucity of studies examining the salient predictors of using traditional media sources or relying on healthcare professionals every bit a primary source of health information.

Across identifying psychological and social factors that influence use of ane information source over another, it is important to examine the context in which health information seeking occurs (Lambert & Loiselle, 2007; Mills & Todorova, 2016). For example, an individual's HISB could be motivated by perception of their health, their current health status, and family health history. As Lagoe and Atkin (2015) indicated in their written report, examining the influence of these antecedents to information seeking provides a more robust understanding of HISB among US adults.

An important component of preventive health exercise is the provision and availability of information regarding risks to health and promotional measures for enhancing health status. With more people living longer and a changing US racial/ethnic demographic, there is a need to examine the factors (across socio-demographic characteristics) associated with use of the Internet, traditional media, or healthcare professional as sources of healthcare information. Hence, we aimed to fill this cognition gap and farther aggrandize agreement of linkages between HISB and overall wellness perceptions, health status, and cancer family unit history and how, if at all, these associations have changed over a period of four years.

ii. Methods

2.ane. Data collection

The Health Data National Trends Survey (HINTS) is a biennial, cantankerous-sectional survey used to appraise the affect of the health information surroundings. HINTS is a nationally-representative sample of non-institutionalized US adults. "Specifically, HINTS measures how people access and use wellness information; how people utilise it to manage health and health information; and the degree to which people are engaged in healthy behaviors" (HINTS Study, 2017). For this report nosotros used the four cycles in HINTS 4 to measure out modify over time in data seeking behaviors among the US population. Data collection for all 4 cycles was through mailed questionnaire. The post-obit are the response rates for HINTS iv iterations: HINTS 4 Bike 1 (2011), 37%; HINTS 4 Cycle 2 (2012) forty%; HINTS 4 Cycle 3 (2013), 35%; and HINTS 4 Bicycle 4 (2014), 37%. HINTS sample design was a 2-stage stratified sample with addresses selected from a comprehensive United States Mail service national residential file, and individual respondents were selected per each household in the sample. More details on survey design, sampling strategies and methodology of the HINTS are published elsewhere (Finney et al., 2012; Nelson et al., 2004).

2.ii. Measures

two.2.1. Wellness information-seeking

To examine the dissimilar avenues through which wellness data can exist obtained, participants were asked to written report on "where they looked offset," the virtually recent time they looked for information about health or medical topics. The original question had several options, just for the purposes of this study responses were recoded into four categories: Internet; Family unit and Friend/Co-worker; Wellness intendance professionals (doctor, complimentary practitioner); Traditional media (Books, Brochures, magazines, phone info, and library). These categories were coded as binary variables, with a "0" indicating that participants did non utilize this source starting time and a "1" if they did.

The socio-demographic variables that were included in this written report were participant historic period in years, income ranges, body mass alphabetize (BMI), education, gender, race/ethnicity and marital status. Other health-related variables analyzed were participants' current wellness condition, cancer family history and perception of overall health.

two.3. Analytic strategy

Several logistic regressions were conducted to assess the relationship between the demographic and independent variables on the use of the internet, family unit/friend/co-worker, healthcare professionals, and traditional media every bit a source of wellness information. Subsequently cleaning and preparing the data for assay, information technology was observed that appro ximately 4% of the data was missing from each of the four cycles. As this is less than the five% cutting-off set forth by Little and Rubin, we can assume that the results are reliable. Jackknife adjustments using replicates of 50 were also included in each model to account for the type of survey.

3. Results

three.1. Social and demographic characteristics

Tabular array 1 shows descriptive information for the sample beyond the four cycles. The majority of participants in the sample were female person and white. Pedagogy level was recoded into four groups with the largest percentage of participants having a college caste. Socioeconomic status was also recoded with the largest percentage of participants having an income ranging from $20,000 to $74,999. For the purposes of the analyses and to understand the overall impact of education and SES, the original variables were used equally ordinal variables. Finally, the majority of participants were in proficient wellness condition, although the majority also indicated a family unit history of cancer. Breakup of the variables are presented in Table 1.

Health data seeking in the digital age: An analysis of health information seeking behavior among United states adults

Published online:

xiii March 2017

Tabular array 1. Descriptive information for participants in the Health Information National Trend Survey (HINTS) 4

3.2. Health data seeking trend

To examine HISBs over the iv year period examined in this study, the percentage of participants using each of the sources of health information was plotted as shown in Figure 1. Overall, a greater per centum of participants reported using the internet as the beginning identify they go for health information compared to family/friend/co-workers, health care professionals, and traditional media. This is especially true in bicycle 4. Only a minor per centum of participants reported using family unit/friends/coworkers for health data and this remained relatively abiding across the four cycles. The use of health intendance professionals increased from bicycle i to wheel 2 but declined across the concluding two cycles. Finally, the utilise of traditional media consistently declined beyond the four cycles.

Health information seeking in the digital age: An analysis of health information seeking behavior amid US adults

Published online:

13 March 2017

Figure 1. Participants' report of where they sought information offset the about recent time they looked for information about wellness or medical topics.

Figure 1. Participants' report of where they sought information outset the almost recent fourth dimension they looked for data about health or medical topics.

3.two.1. Health information seeking on the Internet

The overall models predicting use of the internet as a source of health information across historic period, race, teaching, gender, socioeconomic condition, health condition (cancer status), family history of cancer, wellness perception, and internet skill variables were significant for the 4 cycles. The overall model fit for cycle 1 was χ ⁱⁱ(11) = 594.27, p < 001; cycle 2 was χ ²(11) = 603.51, p < 001; cycle iii was χ ²(11) = 483.59, p < 001; and cycle 4 was χ ⁱⁱ(11) = 555.34, p < 001. Across all 4 cycles, participants who were younger had college SESand higher net skills, and were more probable to report using online sources for wellness information. Educational activity level besides became significant (p < .05) at cycle 2 and remained pregnant (p < .05) through cycle 4. Participants with more educational activity were more likely to use the net equally a source of health data. The odds ratio and significance are presented in Table ii.

Wellness information seeking in the digital age: An assay of health information seeking behavior among United states adults

Published online:

xiii March 2017

Table 2. Multiple logistic regression models assessing trends in the determinants of health information seeking from different sources

three.ii.2. Health information seeking from family unit, friend, and co-worker

The overall models predicting the apply of family, friends, and co-workers every bit a source of health information beyond age, race, education, gender, socioeconomic status, health condition (cancer status), family history of cancer, health perception, and internet skill variables were pregnant for the four cycles. The overall model fit for cycle 1 was χ ^two(11) = 41.twenty, p < 001; cycle 2 was χ ²(11) = 55.05, p < 001; cycle 3 was χ ²(11) = 32.07, p < 001; and cycle 4 was χ ²(11) = 48.93, p < 001. Of the predictor variables, merely net skill was significant across the four cycles. Participants with less internet skill were more likely to use family, friends, and co-workers every bit a source of health information. In addition, gender was a pregnant (p < .05) predictor in cycle 1. Female participants were less likely to use family, friends, and co-workers as a source of health information compared to male participants. Finally, teaching level and participants who identified as black became meaning (p < .05) predictors in cycle iv. Black participants were less likely to utilise family unit, friends, and co-workers as a source of health information compared to non-black participants. In improver, participants with more education were less likely to employ family unit, friends, and co-workers as a source of wellness information. Odds ratios and p-values are presented in Table two.

3.2.3. Health data seeking from healthcare professionals

The overall models predicting the use of healthcare professionals as a source of health information from age, race, instruction, gender, socioeconomic status, health condition (cancer condition), family history of cancer, wellness perception, and internet skill were significant across the iv cycles. The overall model fit for wheel 1 was χ ²(11) = 192.35, p < 001; bicycle ii was χ ²(11) = 213.68, p < 001; bike 3 was χ ^two(11) = 167.13, p < 001; and cycle iv was χ ²(xi) = 209.75, p < 001. Of the predictor variables, only historic period and internet skill were significant across the iv cycles. Older participants and participants with less internet skill were more than likely to use healthcare professionals as a source of health data. In addition, education level and gender were meaning (p < .05) predictors in cycle 1. Participants with less instruction and female participants were less likely to utilise healthcare professionals every bit a source of wellness data. Finally, white and Hispanic participants, gender, and family history of cancer became significant (p < .05) in wheel 4. White participants were less likely to utilise healthcare professionals as a source of health information compared to non-white participants. In contrast, Hispanic participants were more than likely to use healthcare professionals as a source of health information compared to non-Hispanic participants. Finally, female participants were less likely to utilise healthcare professionals as a source of health information compared to male participants, and participants with family history of cancer were more likely to utilize healthcare professionals. Odds ratios and p-values are presented in Table 2.

3.two.4. Wellness information seeking from traditional media

The overall models predicting the utilize of traditional media as a source of health information beyond age, race, teaching, gender, socioeconomic status, health condition (cancer status), family history of cancer, health perception, and internet skill variables were significant for the four cycles. The overall model fit for cycle ane was χ ^two(11) = 177.60, p < 001; cycle two was χ ²(eleven) = 130.15, p < 001; cycle three was χ ²(11) = 142.72, p < 001; and cycle four was χ ²(11) = 178.37, p < 001. Of the predictor variables, only age and internet skill were significant across the four cycles. Older participants and participants with less cyberspace skill were more probable to employ traditional media every bit a source of wellness information. In addition, socioeconomic status became a significant (p < .05) predictor in cycles 3 and 4. Participants from lower SES were more than likely to use traditional media as a source of wellness information. Table two presents odds ratios for all the determinants.

4. Discussion

The electric current study examined what set of characteristics, including age, teaching, socioeconomic status, cancer family history, current health condition, and perception of health are associated with HISBs among a sample of United states adults. Findings from the study indicate that there is an age, socioeconomic, and ethnic divide among US adults' HISB. This finding is one supported past previous studies which have found that in that location are digital disparities in wellness information seeking especially with regards to historic period and SES (Lorence & Park, 2007; Massey, 2016; Rains, 2008). The study results also highlight that existence younger, more educated, and having a college SES were predictors of internet utilise for wellness information seeking (Tennant et al., 2015). Existence older, having low internet skill, and beingness Hispanic were determinants of using a wellness care provider or traditional media, such as print and magazines, equally source of wellness information. Having lower SES was also a determinant of using traditional media as a source of health information.

Contrary to previous studies that have shown that certain sub-populations might be disadvantaged with regards to net use for health information (Lee, Boden-Albala, Larson, Wilcox, & Bakken, 2014), nosotros did not observe whatsoever association betwixt race/ethnicity and health information seeking on the internet. This finding may exist due, in part, to the written report sample which is predominantly female and White. Nevertheless, findings from this study indicate that having more education, college SES, being younger, and having net skill were factors associated with health information seeking on the internet. This finding further supports the notion that there is a digital disparity which is widening (Hargittai & Hinnant, 2008; Lorence & Park, 2007; Massey, 2016). The internet is a practical and price efficient health information source and by the virtue of its ubiquity, it is expected to provide individuals, families, and caregivers admission to data that otherwise might be inaccessible (Massey, 2016). Withal, with this ongoing digital revolution, and the health sector relying increasingly on electronic health data and records, our findings suggest that, a vast bulk of the United states population with little education, lower SES, and depression internet skill are at a disadvantage and non benefiting from the revolution. 1 of the implications of this finding is that although ane of the main goals of making health information available online is to reduce the inequalities in health information accessibility and availability, the sole reliance of health practitioners on digital health information in a context of uneven improvidence of health technologies can perpetuate or increase inequalities. This is specially the case if those who already have admission to alternative sources of information are also the ones who are able to access online sources of wellness data (Weaver et al., 2009).

This study's findings also propose that certain groups of people still rely on traditional media (e.chiliad. print, magazines) for health information, despite the fact that employ of print fabric is declining. Across the four waves (years) of the written report, being older, having low SES and depression cyberspace skills were consistently associated with seeking wellness information from traditional media. This finding, supported by other studies (Cotten & Gupta, 2004; Rains, 2007), is apropos. The group of people who rely on this medium of health information (older people, low SES, and low net skill) are those that are more probable to have health issues requiring upwardly-to-date health information. Relying on a source of information rapidly going extinct and also more likely to be outdated presents a greater risk for their health and wellbeing. In add-on, with the time and price involved in updating and developing print materials, this group is farther at a disadvantage especially with healthcare providers increasingly transitioning to cyberspace technologies for disseminating cutting edge health information. Healthcare providers and organizations will do well to invest in developing culturally and linguistically advisable print resource for this older population with low SES and depression cyberspace skill as they transition, howbeit slowly, to internet technologies for their health information.

Healthcare professionals take traditionally been the primary source of wellness information (Hesse et al., 2005). They served every bit gatekeepers in determining what health information their patients received. However, with the changing wellness data environment, the dynamic between patients and healthcare professionals is also changing. This written report's findings show that only those who were older, with lower education level and lower net skills used a health care professional as their primary source of health information. Further, in the 4th wave (2014), there were boosted determinants (beingness White, being Hispanic, and having a family unit history of cancer) which became significantly associated with using healthcare professionals as a chief source of health information. The interesting contribution of this finding is that even with the abundance of wellness information on the web and alternative sources of health information, those with a cancer family history sought health information first from a healthcare provider. Although research evidence supports the notion that people plow to the internet as their starting time source of health data (Ayers & Kronenfeld, 2007; Cotten & Gupta, 2004; Nguyen & Bellamy, 2006), our findings indicate that those with college risks of chronic diseases and atmospheric condition, such every bit cancer, rely on healthcare providers for health information. Given that in that location is an issue of trust in the accuracy of health data provided on the net (Massey, 2016), our findings are not surprising or unexpected as supported past other studies (Niederdeppe et al., 2007; Talosig-Garcia & Davis, 2005). A possible explanation for this finding could be these groups of people might require technical details on risk cess, prevention strategies, or handling, and healthcare professionals are the most reliable source for this type of information. Still, with increasing patient advancement, consumerism, and pharmaceutical companies' direct-to-consumer campaigns, the news media's increasing involvement in health and illness discussions, and apace evolving e-wellness technologies, in that location might be a change in this reliance on healthcare providers as the primary source of trusted health information specially for those with college risks for certain diseases and weather condition.

This study also examined the correlates of health information seeking from 1'due south personal network (family, friends, and co-workers). Findings signal that depression internet skill was the only determinant consistently and inversely associated with seeking health information from personal networks. However, in the in the fourth wave (2014), there were boosted determinants (being Black and low education level) that were associated with wellness information seeking from one'due south personal network. This finding indicates that those with low internet skills, low level of pedagogy, and of Black ethnicity were less likely to rely on their personal networks for their health information. Although nosotros are unable to determine what the correlates of seeking health data from personal networks are, ane can infer that this finding offers a glimpse into the characteristics of those who do not rely on surrogate seekers (those who seek information on behalf of others) as their chief source of health information.

In sum, although online access to and employ of wellness information can play a key part in effectively increasing people's noesis and also assistance in health-related decision-making, findings from this nationwide data bear witness that sub-populations may have more than challenges in benefitting from online health data. Findings advise that a detail group of people (i.e. those with more educational activity, who are younger, have higher SES, and are more internet skilled) with access to alternative health information sources, such equally a healthcare provider, is besides able to take advantage of the wellness data available on the Web. This indicates that sole/heavy reliance on e-technologies for disseminating health information may increment the likelihood of further perpetuating health disparities. Thus, in that location is a need for interventions and efforts focusing on developing ways to reduce this digital disparity and perhaps design e-wellness data services targeted at older adults, those with lower SES, lower educational level, and lower internet cocky-efficacy. More inquiry is also warranted to farther explore the different sub-populations and the factors associated with their HISBs.

five. Limitations

This study is not without limitations. Almost notably, it is not possible to infer causal relationships betwixt constructs or items in the survey, because HINTS is a cross-sectional survey collected annually. Additionally, while researchers tin examine trends over time at the national level for outcomes included in multiple iterations of the survey, one cannot assess change over fourth dimension at the individual level. Also, given that the sample was predominantly female person and White, information technology is possible that some associations could not be detected because of the lack of variability in the study sample.

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Source: https://www.tandfonline.com/doi/full/10.1080/23311886.2017.1302785

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