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Because the internet has become a primary means of communication in the long-term care (LTC) and health care industry, an elevated understanding of market segmentation among LTC consumers is an indispensable step to responding to the informational needs of consumers.
This exploratory study was designed to identify underlying market segments of the LTC consumers who seek Web-based information.
Data on US adult internet users (n=2018) were derived from 2010 Pew Internet and America Life Project. Latent class analysis was employed to identify underlying market segments of LTC Web-based information seekers.
Web-based LTC information seekers were classified into the following 2 subgroups: heavy and light Web-based information seekers. Overall, 1 in 4 heavy Web-based information seekers used the internet for LTC information, whereas only 2% of the light information seekers did so. The heavy information seekers were also significantly more likely than light users to search the internet for all other health information, such as a specific disease and treatment and medical facilities. The heavy Web-based information seekers were more likely to be younger, female, highly educated, chronic disease patients, caregivers, and frequent internet users in general than the light Web-based information seekers.
To effectively communicate with their consumers, providers who target Web-based LTC information seekers can more carefully align their informational offerings with the specific needs of each subsegment of LTC markets.
It is widely acknowledged that the internet has become a primary marketplace for virtually all industries. Accordingly, potential consumers are now able to access necessary information for their decision making [
Current research on Web-based information and knowledge exchange in the LTC marketplace reveals the criticality of these processes. For example, the 5-star rating system of the US Centers for Medicare and Medicaid Services (CMS) as a Web-based information exchange has significantly impacted the way LTC information is presented to the public and the way individuals perceive such information [
Additional research indicates that LTC providers also have an opportunity to promote their facilities by being responsive to the informational needs of consumers in areas that extend beyond the mainstream quality measures such as the Nursing Home Compare of the CMS. On the basis of focus groups and key informant interviews with persons aged 65 years and older, as well as family members of nursing home residents, a study found that there is a far greater breadth of informational needs than that which now exists on Nursing Home Compare [
In order for nursing homes and/or other LTC organizations to effectively communicate their informational contents to the prospective consumers who seek information via the internet, more refined marketing segmentation is needed. Research must move beyond mere demographic and/or socioeconomic data so that the psychographic, sociographic, and/or clinical informational needs of various subsets of consumers can be addressed. In this respect, an analytical approach that allows the identification of subgroup differences in the Web-based informational needs of consumers is useful [
The internet provides an opportunity for all marketplaces to function more optimally as a source of timely information. Yet, there is paucity of research that specifically examines how LTC information is accessed, and how the available Web-based information is in alignment with the information sought. This study seeks to initiate the process of remediating this void by advocating the use of market segmentation to better facilitate the exchange of Web-based information between LTC consumers and providers.
Building upon the insights from previous research that focused on health and medical information-seeking behaviors [
Variable-centered approaches are sound when examining relationships between variables and developing the initial segmentation basis for internet users. However, such methodologies also embody several limitations in circumstances when more detailed segmentation data are required. First, the estimation and interpretation of models with more than one outcome variable can be a challenging task. As such, variable-centered approaches are generally not suitable for the simultaneous examination of multiple internet users’ information-seeking behaviors (eg, the data we used for this study). Second, the extent to which such statistical models are capable of identifying the characteristics of target populations is also somewhat restricted. Specifically, the effect of one characteristic (eg, gender or education) on the outcome variable can only be examined while all other characteristics are held constant. Third, the traditionally used variable-centered approach measures an average effect of a predictor variable on the outcome variable by using the premise that all individuals were sampled from the same population. Such an approach explicitly bypasses underlying subpopulation differences. Finally, in conjunction with the first three limitations, variable-centered approaches do not clearly identify the consumer subpopulations to whom LTC providers must be responsive at a micro-level. Indeed, most studies on the information-seeking behavior of consumers fail to consider the need for providers to direct responsive
This study used one of the first publicly available consumer survey datasets that include the questions of internet search for LTC. This study was specifically designed to address the limitations of the currently dominant variable-centered approaches while building upon the findings from previous studies on Web-based health or medical information-seeking. Moreover, this inquiry broadens dialogue by employing LCA [
The use of the person-centered approach supports the profiling of the internet users who seek LTC information while simultaneously taking other factors into account. These factors include (1) a summary description of the multiple health information-seeking behaviors displayed and (2) the construction of a sociodemographic profile of the internet users by identified subgroups. When LTC providers better understand the informational needs of each subgroup, they can better respond to these needs via their website and/or other marketing materials. This study was designed to answer the questions listed below:
Who are the subgroups or unique market segments that search the internet for LTC information?
What health, medical, and/or other knowledge is sought by the internet users who seek Web-based LTC information?
What are the sociodemographic and other characteristics of the internet users who search the internet for LTC information?
Based upon the answers to the above questions, recommendations can be made to LTC information providers regarding the type of information they should disseminate via Web-based resources.
Data from the 2010 Princeton Survey Research Associates International for the Pew Internet and American Life Project (Pew Internet) were used to answer the 3 described questions [
A series of survey items were included to assess key sociodemographic characteristics, internet use behaviors, and the health-related information sought by seekers of LTC information. Random digit dialing was used as a sampling strategy. Although not entirely representative of all of the US adult population, the data covered a large population of phone users [
The primary outcome of interest was a dichotomous measure indicating 2 identified latent classes (which is labeled as class 1 [light information seekers] vs class 2 [heavy information seekers, reference group]). Using LCA (described more in the next sections), these subgroups were identified based on a set of 15 Web-based health information-seeking behaviors with dichotomous responses (Yes or No; see
Posterior probabilities for the online information-seeking behaviors by the identified latent classes.
A variety of demographic, socioeconomic, health status, and caregiving status information was included for each model. Age was recorded in years. However, people older than 97 years were top-coded at 97. The more traditional demographic and socioeconomic segmentation variables were included as predictor variables. These included (1) gender (women vs men); (2) race or ethnicity (black vs white, Hispanic vs white, and others vs white); (3) marital status (married vs not married); and (4) employment status (employed vs not employed and retired vs not employed). These dichotomous measures were used for purposes of cross-classification. The number of people in each household was measured as the absolute count of total household members. Educational attainment was assessed based on a 5-point Likert-scale (1-5: None-Postgraduate degree). Household income was recorded using a 9-point Likert-scale ranging from less than US $10,000 to US $150,000 or more. Uneven rather than even increments were used. As a result, the income classes could not be treated as a continuous variable (eg, by US $10,000, US $25,000, and US $50,000). Self-rated health was recorded based on a 4-point Likert-scale (1-4: Poor-Excellent). However, a range of clinical variables, as well as other segmentation factors were included. The number of self-reported chronic conditions was counted based on following 6 major diseases: diabetes mellitus, hypertension, lung disease, cardiovascular disease, cancer, and/or other chronic diseases. Disabilities were accessed using 6 disability indicators based on difficulties with hearing, vision, memory, walking, dressing, and running errands. Two dichotomous measures of caregivers were used as follows: (1) caregivers for 1 or more parents vs noncaregivers; (2) caregivers for adults who were not parent(s) vs noncaregivers (reference group). Finally, internet usage was recorded based on 7-point Likert-scale (1-7: Never-Several times a day) either at home or at work. Internet users who reported “Never” but still used email were classified as internet users in this study. Accordingly, the study included the potentiality for many subsegments based on various permutations and combinations of the included categories.
Two primary areas of inquiry guided this research. At the first level, this study sought to identify unique underlying subgroups or market subsegments that used the internet to address their needs relative to informational LTC. This study also sought to identify the health- or medical information-seeking activities of consumers across various subgroups. Accordingly, the first part of the analysis focused on the identification of the latent classes of users.
First, an LCA was conducted using the 15-Web-based health-related and medical information-seeking behaviors. LCA is a special type of structural equation model (SEM) with unobserved or latent variable(s) [
As a result, the model with 2 latent classes was determined to be optimal (the posterior membership probabilities>0.95; entropy=0.84, and BLRT
Each latent class corresponded with an underlying subgroup of internet users who visit the Web in search of information about the LTC marketplace.
In terms of LTC Web-based information-seeking, the difference between these 2 classes was evident. The first latent class is characterized by a high probability of internet use behavior. As a result, class 1 users were labeled as
Latent class analysis model and analytic approaches.
Comparisons between the latent class analyses with different number of latent classes.
Model selection criteria | |||||
The minimum percentage of 1 class | 44.50 | 22.8 | 15.46 | 10.50 | 7.17 |
The mean posterior class membership probabilityb | >0.96 | >0.91 | >0.83 | >0.78 | >0.77 |
Entropy | 0.84 | 0.82 | 0.78 | 0.77 | 0.79 |
Bootstrap likelihood ratio test ( |
5057.04 (16)c | 1010.30 (16)c | 274.03 (16)c | 182.95 (16)c | 141.77 (16)c |
Akaike information criteria | 27,465.22 | 26,486.93 | 26,244.90 | 26,093.95 | 25,984.18 |
Bayesian information criteria | 27,639.12 | 26,750.59 | 26,598.32 | 26,537.13 | 26,517.12 |
a
bThe model with
c
Descriptive summary of internet users by the identified latent classes.
Characteristics | Latent class 1; heavy Web-based information seekers (n=1120) | Latent class 2; light Web-based information seekers (n=898) | |
Age in years, mean (SD) | 44.30 (15.97) | 45.03 (17.91) | |
Womena | 713 (63.70) | 479 (53.3) | |
Whiteb | 754 (67.31) | 547 (60.9) | |
Blackc | 173 (15.43) | 176 (19.6) | |
Latino | 132 (11.77) | 122 (13.6) | |
Others | 61 (5.49) | 53 (5.9) | |
Married (vs not married)c, n (%) | 604 (53.91) | 417 (46.4) | |
Number of household members, mean (SD) | 2.18 (0.92) | 2.19 (0.98) | |
High school or less | 41 (3.62) | 84 (9.3) | |
Vocational school | 246 (22.00) | 317 (35.3) | |
Some college or associated degree | 24 (2.17) | 25 (2.8) | |
Bachelor’s degree | 405 (36.18) | 279 (31.0) | |
Postgraduate degree | 404 (36.03) | 194 (21.6) | |
Employedb | 753 (67.19) | 541 (60.2) | |
Less than $10,000 | 50 (4.45) | 93 (10.3) | |
$10,000 to under $20,000 | 104 (9.28) | 90 (10.0) | |
$20,000 to under $30,000 | 97 (8.64) | 107 (12.0) | |
$30,000 to under $40,000 | 131 (11.69) | 120 (13.4) | |
$40,000 to under $50,000 | 104 (9.28) | 110 (12.3) | |
$50,000 to under $75,000 | 206 (18.42) | 152 (16.9) | |
$75,000 to under $100,000 | 168 (14.99) | 106 (11.9) | |
$100,000 to under $150,000 | 144 (12.83) | 69 (7.7) | |
$150,000 or more | 117 (10.42) | 52 (5.6) | |
Insureda, n (%) | 1009 (90.12) | 760 (84.6) | |
Excellent | 365 (32.55) | 299 (33.3) | |
Good | 609 (54.40) | 475 (53.0) | |
Only fair | 126 (11.27) | 105 (11.7) | |
Poor | 20 (1.78) | 18 (2.1) | |
Number of chronic conditionsc, mean (SD) | 0.21 (0.50) | 0.63 (0.93) | |
Number of disabilities, mean (SD) | 0.33 (0.75) | 0.33 (0.75) | |
Parents vs noncaregiversa, n (%) | 178 (15.91) | 77 (8.5) | |
Nonparents vs noncaregiversa, n (%) | 250 (22.31) | 127 (14.1) | |
Neverd | 22 (2.00) | 31 (3.4) | |
Less often | 11 (1.00) | 35 (3.9) | |
Every few weeks | 15 (1.34) | 39 (4.4) | |
1-2 days a week | 55 (4.90) | 92 (10.3) | |
3-5 days a week | 110 (9.80) | 125 (14.0) | |
About once a day | 147 (13.14) | 177 (19.7) | |
Several times a day | 760 (67.82) | 398 (44.4) |
a
b
c
dInternet usage: never, these respondents still used email and, therefore, classified as internet users.
The findings from the analysis reveal a contrariety. Although a priori reasoning would suggest that people in need of LTC and/or individuals with a chronic disease would be more compelled to use the internet for information-seeking, this was not the case. As mentioned, 2 latent classes existed among the study participants: heavy Web-based information seekers (n=1120) and light Web-based information seekers (n=898). The heavy Web-based information seekers were more likely to be women (independent of race or ethnicity). These women were most often married, highly educated, employed, economically upper class, insured, less chronically ill, and in general, more active internet users.
Unsurprisingly, the heavy Web-based information seekers (15.91%, 178/1120) were more likely to be caregivers than the light Web-based information seekers (8.5%, 77/898). In this study, about 25% of the heavy Web-based information seekers reportedly looked for LTC information on the Web, whereas only about 2% of light Web-based information seekers did so (see
The results of the binary logistic regression were predictive of the latent class membership. This analysis revealed 8 statistically significant predictors (see
Finally, the adults who used the internet more often also tended to be in the category of heavy Web-based health-related as well as LTC information seekers. Overall, individuals who had health issues (either their own or someone else’s) and/or caregiving responsibilities and particular characteristics (eg, gender and higher socioeconomic status) were significantly more active in terms of Web-based health and medical and LTC information-seeking behaviors.
Estimated odds ratios from proportional odds binary logistic regression on the heavy Web-based information seekers (class 1) versus light Web-based information seekers (class 2).
Variables | Odds ratio (SE) |
Age (years) | 0.98 (0.01)a |
Women (vs men) | 1.90 (0.13)a |
Black (vs white) | 0.77 (0.17) |
Latino (vs white) | 0.89 (0.19) |
Others (vs white) | 0.64 (0.29) |
Married (vs not married) | 1.17 (0.15) |
Number of household members | 0.94 (0.07) |
Educational attainment | 1.34 (0.05)a |
Employed (vs not employed) | 1.02 (0.17) |
Retired (vs not employed) | 1.27 (0.26) |
Annual household income | 1.09 (0.03)b |
Insured (vs uninsured) | 1.09 (0.19) |
Self-rated health | 0.96 (0.10) |
Number of chronic conditions | 1.30 (0.08)a |
Number of disabilities | 1.06 (0.09) |
Caregivers for adults (parent vs noncaregivers) | 1.94 (0.20)a |
Caregivers for adults (nonparent vs noncaregivers) | 1.82 (0.17)a |
Internet usage | 1.20 (0.05)a |
a
b
This exploratory study analyzed a large dataset of the internet users and identified the primary segments of users, as well the subsets within each larger segment of the adults who looked on the Web for LTC and other health and medical information. However, the implications of this study extend far beyond the defined areas of inquiry. Although multiple sources of LTC information exist on the Web, LTC providers as the
One important finding from this study is the unobserved latent class memberships among the heavy and light Web-based information seekers. The latent class membership is informative of Web-based LTC of multiple individuals and other health and medical information-seeking behaviors. That is, when individuals seek LTC information on Web, there is a significantly greater chance that they also use the internet to look for other health and medical information. Moreover, with the traditional variable-centered approach, findings are limited to the associations between two variables at a time while holding all other variables or covariates constant (ie, assuming all other variables are the same). Two practical implications can be drawn from the finding.
First, the volume of research on Web-based health and medical information is significantly greater than that of LTC information [
Second, LTC providers, depending on the nature of their services, can literally target the specific subpopulations identified in this study. For example, if the goal is to provide Web-based LTC information to older adults who may need LTC services at some point in the future, LTC providers can target light Web-based information seekers who can be identified based on a set of characteristics, including age, gender, educational attainment, household income level, number of chronic conditions, caregiving responsibilities, and general internet use. In other words, given that most of the light information seekers do not use the internet for health, medical, and LTC information, more aggressive marketing and outreach with the traditional health communication (eg, printed materials such as flyer and postcard) may be necessary.
Similarly, given the findings on the heavy Web-based information seekers, LTC providers can align their messages with non-LTC health and medical information sources. This is a highly effective strategy for reaching their audiences given that Web-based information seekers tend to simultaneously look for LTC and health and medical information. Finally, more practical strategies can also be used to better coordinate LTC providers and consumers’ health and medical information as additional research of this type is completed.
This study reconfirmed findings from other researchers, which indicated that older adults, despite their status as the primary LTC consumer segment, are significantly less likely to seek LTC information on the Web. Citing data from the Pew Internet Report, this research confirms that although internet use has been increasing among older adults, usage levels continue to remain below those of younger adults [
A greater informational exchange between this subsegment of LTC consumers and providers can potentially improve outcomes via better informed decision making in LTC preplanning before the emergence of aging-related severe cognitive and/or physical disabilities that require LTC services. That is, a knowledge informational gap in the LTC marketplace that can only be addressed when providers and consumers of LTC experience better coordination in the online demand for, and supply of, LTC information [
One study using data from 7609 Medicare beneficiaries in the 2011 National Health and Aging Trends Study, found that, in general, males are more likely to use the internet than females [
This study, as has been true with other analyses, also discovered that people with higher incomes and higher levels of education are more likely to access LTC information on Web. Yet, in some respects, people having lower income with disabilities that require LTC find themselves engaged in a more complex network of financial transactions as they engage in eligibility screening (eg, Medicaid), benefits establishment, and dual-eligibility [
The findings also reveal that persons with chronic diseases are more likely to engage in LTC and health-related information-seeking on the internet (arguably out of necessity). This finding on chronic conditions and Web-based information-seeking suggests that LTC providers can disseminate reliable information to prospective residents regarding their services for managing various chronicities. One study criticizes the internet as a source of health information based upon fragilities, complexity of the information, and the observed frequency of inaccurate information [
This exploratory study applied LCA as a tool for the segmentation of LTC internet information-seeking into relevant subsegments. Such a person-centered approach can potentially improve the operations of LTC marketplace. The analysis of a large pool of data of American adults identified two underlying market segments—heavy and light Web-based information seekers—according to their Web-based LTC and health and medical information-seeking behaviors. The study also revealed that the segmentation basis for LTC consumers includes but extends beyond demographic and socioeconomic variables such as age, gender, educational attainment, and household income level. Rather, chronic conditions, caregiving status, and general internet usage are predictive of class membership. Thus, identifying the latent classes with more or less usage of the internet for LTC and health and medical information was merely a starting point. The next step involves using the findings from this study to enhance Web-based communications between LTC providers and current and prospective LTC consumers. In this respect, this exploratory analysis contributed to the framework for future research and provided a foundation to generate greater dialogue regarding how various subsegments of LTC information seekers via the internet can be better linked with LTC service providers, the group that is best positioned to deliver information essential for decision-making.
Akaike information criteria
Bayesian information criteria
bootstrap likelihood ratio test
Centers for Medicare and Medicaid Services
latent class analysis
long-term care
structural equation model
None declared.