Age Differences in the Use of Health Information Technology Among Adults in the United States: An Analysis of the Health Information National Trends Survey

Abstract

Background: Despite significant advancements in the use of health information technologies (HITs) in health care, older adults’ adoption of new technologies has consistently lagged behind that of younger adults. Objective: To compare ownership rates and preferences for utilizing technology for health information exchange among older and younger adults. Methods: Utilizing data from the 2017 and 2018 iterations of the Health Information National Trends Survey (n = 6789), we performed multivariable logistic regression while controlling for sociodemographic characteristics. Results: Older adults were less likely than younger adults to own technological devices such as smartphones, less likely to report finding these tools beneficial in monitoring their health, and less likely to use these tools in communicating with their health providers. However, these differences were substantially attenuated after adjustment for technology ownership. Discussion: Future research should aim to identify factors associated with access, usability, and adoption of HIT for managing care among older adults.

Keywords

older adult health information technology mobile devices

Introduction

Health information technology (HIT) refers to the constellation of technological tools used by healthcare providers, patients, insurance companies, government entities, and others to store, analyze, and share health information. HIT is being increasingly used in health care as patients become more willing to accept the integration of these tools into their care (Anstey Watkins et al., 2018; Kampmeijer et al., 2016; Onyeaka et al., 2020; Rasche et al., 2018), and it has been shown to enhance healthcare delivery (Pfaeffli Dale et al., 2015; Welch et al., 2015), improve health outcomes (Brennan et al., 2001, 2010; Pop-Eleches et al., 2011), and lower healthcare costs (Chaudhry et al., 2006; Shekelle et al., 2006). Recently a review evaluating the impact of HIT on quality and cost of care also showed that HIT increased both quality and cost-effectiveness of care (Sadoughi et al., 2018). There is strong evidence to suggest that HIT improves health outcomes among older adults (Cajita et al., 2017; Changizi & Kaveh, 2017; Kampmeijer et al., 2016; Müller et al., 2016).

Despite the demonstrated potential benefits of integrating HIT into healthcare, there are concerns that older individuals may be less able or willing to utilize these technologies to manage their health (Older Adults and Technology Use | Pew Research Center, n.d.). Prior research has identified certain barriers such as issues with familiarity, trust, privacy, computer proficiency, and sociodemographic factors that may limit the utility of HIT-based interventions in older age groups (Fischer et al., 2014; Older Adults and Technology Use | Pew Research Center, n.d.; Sakaguchi-Tang et al., 2017; Turner et al., 2015). This is particularly concerning, given the in creasing number of older adults in the United States (Kincannon et al., 2005). However, existing studies were performed some time ago, and as the internet and smartphones become more ubiquitous (Digital 2019 Global Internet Use Accelerates, 2019; Smartphone Users Worldwide 2020, n.d.), it is possible that preferences and rates of HIT use among older adults may have changed.

Understanding the current use, patterns, and preferences for HIT among older adults is critical. As HIT interventions become more prevalent, it is relevant to evaluate older adults’ receptivity to using health information technology to manage their health. Accordingly, we aim to examine rates of ownership and preferences for utilization of HIT among older adults compared to younger adults in the United States using a nationally representative sample.

Methods

Sample Population

For this study, we drew data from the Health Information National Trends Survey (HINTS), a large-scale, household interview survey of the US noninstitutionalized adults aged ≥18 years which has been periodically administered by the National Cancer Institute since 2003. The HINTS collects information from the general population to evaluate trends and patterns in utilization of health communication systems between providers and patients, specifically pertaining to access and usage. We utilized data from the fifth edition HINTS 5 Cycles 1 (2017) and 2 (2018).

In both iterations of the HINTS, the sampling technique consisted of two stages. In the first stage, a stratified sample of addresses was selected from a list of nonvacant residential addresses obtained from the Marketing Systems Group. In the second, an adult from each household was selected. The sampling frame was composed of two sampling strata: 1) low concentrations of minority populations and (2) high concentrations of minority populations. A total of 13,360 and 14,586 households received the 4-part questionnaire in Cycles 1 and 2, respectively, and the response rates were 32.4% (4328/13,360) for Cycle 1 and 32.9% (4799/14,586) for Cycle 2. Additional information about data collection and methodologies can be found in the corresponding methodology reports for HINTS 5 (Health Information National Trends Survey 5 (HINTS 5) Cycle 1 Methodology Report, n.d.). The sampling methodology allowed for weighting of the sample to provide population estimates.

As in prior studies (Centers for Disease Control and Prevention (CDC), 2003; Rabe et al., 2020), we identified older individuals by dichotomizing age into <65 years (younger group) and ≥65 years (older group). “Missing data” or “Inapplicable” response type for this question was considered missing values for age. These 2 versions of the HINTS resulted in a sample of 6789 total individuals who answered relevant questions and for whom demographic data were collected, and this was the sample size used for this analysis.

Measures

Outcome variables

The main dependent variables were sets of questions about (1) HIT ownership rates, perceived usefulness and (2) the exchange of information with providers using electronic means. The questions that were included in the analysis and their responses are outlined below and in the corresponding Tables.

Independent variables

The key independent variable was self-reported age, which was categorized into a binary variable; <65 years (younger adults) and ≥65 years (older adults).

Control variables

Following a previous study that examined factors associated with utilization of health services (Bhuyan et al., 2016), the specific sociodemographic variables included in the present study were gender, marital status, income, education, health insurance, race, employment status, and number of comorbid conditions. The number of comorbid conditions was measured from the respondents’ responses to whether or not they had diabetes, cancer, high blood pressure or hypertension, heart conditions (heart attack, angina, or congestive heart failure), arthritis, or respiratory diseases (chronic lung disease, asthma, emphysema, or chronic bronchitis). The comorbidity variable was then categorized as 0 comorbidity, 1 comorbidity, or 2 or more comorbidities.

Statistical Analysis

Basic descriptive demographic statistics, including unweighted and weighted percentages, for each group were conducted. Differences between older adults and younger population for each sample characteristic were assessed using the chi-square analysis. To examine the relationships between age and use of HIT, multivariable logistic regression was performed. Both unadjusted and adjusted models were created. When examining predictors of mobile device use, the adjusted model included gender, race, employment status, marital status, health insurance status, income, education, and number of comorbidities as covariates. Two adjusted models were created to examine the age differences in HIT use to access medical information and communicate with providers. The first adjusted model (Model 1) included gender, race, employment status, marital status, health insurance status, income, education, and number of comorbidities as covariates. The second (Model 2) additionally included smartphone, tablet, and internet use as covariates, given previous studies indicating that digital technology use differs by age (Bender et al., 2014; Carroll et al., 2017). All analyses were weighted to account for the sampling design of the HINTS and to obtain nationally representative parameter estimates. Replicate weights, based on the jackknife replication method (Rust & Rao, 1996), were applied to obtain accurate variance estimates. To account for multiple comparisons, a Bonferroni correction (Curtin & Schulz, 1998) was applied (.05/14 = .0036), and statistical significance was set at a p-value of .0036. Data were analyzed using the Stata 14.0 statistical software (svy program).

Results

A total of 6789 participants were included in this analysis (see Table 1). The mean age of the respondents was 43.0 years for the younger population and 74.4 years for the older adults’ group. Within this sample, 2301 participants were aged 65 years or more. Older adults were more likely to be white, females, married, have health insurance, and have two or more comorbidities. In addition, 3.3% (226/6789) of observations contained some missing data and were therefore excluded from the final analysis. Overall, rates of ownership of digital devices decreased with increasing age as depicted in Figure 1. In the unadjusted models, there was a statistically significant association between age, ownership of technological tools, and utilization of the HIT to manage care (see Table 2). This relationship remained after adjustment, with older individuals reporting lower odds of owning smartphones (OR = .16; 95% CI .11, .22), tablet computers (OR = .43; 95% CI .33, .56), and mobile health applications (OR = .54; 95% CI .42, .70). Additionally, they were also less likely to have used their smartphones or tablets to track progress on a health-related goal (OR = .32; 95% CI .24, .42), decide on how to treat an illness (OR = .59; 95% CI .44, .78), or to discuss with their healthcare provider (OR = .57; 95% CI .42, .77).

Table 1.

Sample Population Demographic Characteristics: Sample N = 6789.

Demographic Variable	Total (n = 6563)%a	Older Adult Population (n = 2301)%	Younger Adult Population (n = 4262)%	Test Statistic	p-value
Gender
Male	49.2	44.5	50.3	43.86	<.001
Female	50.8	55.5	49.7
Marital status
Married	54.0	57.1	53.2	4.89	.029
Not married/Divorced	46.0	42.9	46.8
Employment
Yes	58.3	11.6	69.4	1134.0	<.001
No	41.7	88.4	30.56
Education
High school or less	31.3	40.1	29.2	27.42	<.001
Some college	36.3	37.2	36.1
College graduate	32.4	22.7	34.7
Household income
<$20,000	17.32	21.75	16.4	20.22	<.001
$20,000–$34,999	12.03	18.63	10.6
$35,000–$49,999	14.20	16.70	13.7
$50,000–$74,999	18.45	19.69	18.2
$75,000 or more	38.00	23.23	41.1
Race
White	65.4	79.6	62.3	51.40	<.001
African American	10.4	7.2	11.1
Hispanic	15.9	8.6	17.4
Others	8.3	4.6	9.2
Health insurance
Yes	91.6	98.7	89.9	104.70	<.001
No	8.4	1.3	10.1
Comorbidities
No comorbidity	44.1	11.3	51.9	351.61	<.001
1 comorbidity	26.6	25.3	26.9
2 or more comorbidities	29.3	63.4	21.2

Weighted percentages %.

Figure 1.

Ownership rates of smartphones, health apps, and tablets by age groups.

Table 2.

Unadjusted and Adjusted Odds Ratios (ORs) for Ownership and Use of Mobile Devices in Accessing Health Among Older Versus Younger Adults.

Question	Yes (%)		Unadjusted OR 95% CI	p-value	Adjusted OR, 95% CIa	p-value
Question	Older Adult	Younger Adult	Unadjusted OR 95% CI	p-value	Adjusted OR, 95% CIa	p-value
Please indicate if you have a smartphone.	47.85	87.52	.13 (.11, .16)	<.001	.16 (.11, .22)	<.001
Please indicate if you have a tablet computer.	42.33	64.40	.41 (.34, .48)	<.001	.43 (.33, .56)	<.001
On your tablet or smartphone, do you have any apps related to health and wellness?	30.99	49.90	.45 (.37, .55)	<.001	.54 (.42, .70)	<.001
Do you ever go online to access the internet or world wide web, or to send and receive e-mail?	61.38	87.89	.22 (.18, .27)	<.001	.26 (.18, .36)	<.001
Has your tablet or smartphone helped you track progress on a health-related goal, such as quitting smoking, losing weight, or increasing physical activity?	20.84	47.36	.29 (.24, .35)	<.001	.32 (.24, .42)	<.001
Has your tablet or smartphone helped you make a decision about how to treat an illness or condition?	24.72	39.96	.49 (.40, .61)	<.001	.59 (.44, .76)	<.001
Has your tablet or smartphone helped you in discussions with your healthcare provider?	26.20	33.64	.70 (.57, .87)	.001	.57 (.42, .77)	<.001

Model was adjusted for gender, race, marital status, employment, education level, income, health insurance status, and number of comorbidities.

Table 3 displays the unadjusted and adjusted ORs for use of electronic devices to exchange information with a health provider. After adjustment for sociodemographic characteristics and number of comorbidities (Model 1), individuals aged 65 years and above were less likely to use electronic devices to look for health information (OR = .35, 95% CI .26, .48), communicate with their physician (OR = .51, 95% CI .40, .66), look up test results (OR = .60, 95% CI .46, .80), and track health charges (OR = .59, 95% CI .47, .74). The likelihood of finding online medical records useful and being confident that safeguards exist to protect online medical records did not differ by age group. Older adults were 57% less likely (OR = .43, 95% CI .28, .67) to withhold information from their health providers due to concerns about privacy. However, after controlling for technology ownership (smartphone, internet, and tablet ownership; Model 2), these results were attenuated substantially and several relationships did not meet statistical significance, though older adults remained significantly less likely to withhold information from their providers.

Table 3.

Unadjusted and Adjusted Odds Ratios (ORs) for Use of Electronic Devices to Exchange Information with Health Providers Among Older Adults’ Versus Younger Population.

Question	Yes (%)		Unadjusted		Model 1a		Model 2b
Question	Older Adult	Younger Adult	OR, (95% CI)	p-value	OR, (95% CI)	p-value	OR, (95% CI)	p-value
In the past 12 months have you used a computer, smartphone, or other electronic means to look for health or medical information for yourself?	49.65	76.18	.31 (.27, .36)	<.001	.35 (.26, .48)	<.001	.68 (.49, .94)	.020
In the past 12 months have you used a computer, smartphone, or other electronic means to use e-mail or the internet to communicate with a doctor or a doctor’s office?	24.22	38.23	.52 (.45, .60)	<.001	.51 (.40, .66)	<.001	.69 (.52, .92)	.012
In the past 12 months have you used a computer, smartphone, or other electronic means to track healthcare charges and costs?	19.69	35.80	.44 (.37, .52)	<.001	.59 (.47, .74)	<.001	.77 (.60, .99)	.038
In the past 12 months have you used a computer, smartphone, or other electronic means to look up medical test results?	26.89	34.90	.69 (.60, .78)	<.001	.60 (.46, 80)	<.001	.77 (.57, 1.04)	.085
In general, how useful is your online record in monitoring your health?	87.65	90.06	.78 (.48, 1.27)	.32	.73 (.32, 1.66)	.44	.74 (.31, 1.79)	.51
Have you ever kept information from your healthcare provider because you were concerned about the privacy or security of your medical record?	4.69	10.26	.43 (.31, .60)	<.001	.43 (.28, .67)	<.001	.38 (.22, .65)	.001
How confident are you that safeguards (including the use of technology) are in place to protect your medical records from being seen by people who are not permitted to see them?	80.17	79.88	1.02 (.85, 1.22)	.85	.93 (.69, 1.26)	.65	1.12 (.79, 1.58)	.53

Model 1 was adjusted for gender, race, education level, income, employment status, marital status, health insurance, and number of comorbidities.

Model 2 was adjusted for gender, race, education level, income, employment status, marital status, health insurance, number of comorbidities, smartphone ownership, tablet ownership, and internet use.

Discussion

In this analysis of data from the nationally representative HINTS dataset, older adults (≥65 years) were less likely to own mobile devices such as smartphones and tablets and also less likely to have health apps installed on their mobile devices than younger adults. These results support the literature that the disparities in HIT ownership and access between younger and older adults continue to exist (Asan et al., 2018; Carroll et al., 2017), with consistently lower rates of HIT ownership among older individuals. We observed that 47.9% of older adults owned smartphones, 42% owned tablets, 31% owned health apps, and 61% utilized the internet. Data from the Pew Research Center showed that in 2016, 67% of older adults used the internet, 42% owned smartphones, and 32% owned tablets (Tech Adoption Climbs Among Older Americans|Pew Research Center, n.d.). Our results indicate that while ownership rates of tech tools among older adults are on the rise, they still remain consistently lower than rates for younger adults.

Also, older adults were less likely to use HIT in managing their care. We found that they were 65% less likely to use electronic devices to look for health information, as well as track health costs (41%), communicate with a physician (49%), and view test results (40%) when compared to younger adults. However, these differences were attenuated after adjusting for ownership of technology devices (smartphones, tablets, and internet use) suggesting that access to these devices may play some role in driving the differences in HIT use across both age groups. These findings are consistent with earlier research which reported that among internet users, preference for internet-based health services between older and younger adults was fairly similar (Gordon & Crouch, 2019). However, our results further extend their findings and identify smartphone/tablet ownership as a factor that may contribute to the low rates of HIT adoption that we see in this population. While there will always be a group of older adults who may not own tech tools (Bhuyan et al., 2016; Gordon & Hornbrook, 2018), our results are in line with previous studies (Levine et al., 2016) suggesting that a fair proportion of older individuals are increasingly integrating technology to manage their care. These findings are relevant and of value for healthcare organizations and healthcare providers who may use HIT to communicate with patients.

Furthermore, our results highlight access to technology as an important barrier to the use of HIT among older adults. This is in line with findings from one recent systematic review (Sakaguchi-Tang et al., 2017) which also reported that access to the technology remains a significant barrier to electronic portal use among older individuals. However, the age-related disparities in access to technology may not fully account for these differences, which may explain why the relationship between age and HIT use was not fully attenuated after controlling for smartphone, tablet, and internet ownership. It is possible that other barriers unique to older adults may also drive some of the differences in HIT use across both age groups. There is overwhelming evidence to suggest that several barriers may hinder the uptake and utilization of HITs among older adults. While some studies have demonstrated that usability and adoption of tech tools among older individuals depends on some personal factors such as perceived need, usefulness, and interest (Heart & Kalderon, 2013; Wildenbos et al., 2018), others have highlighted sociodemographic barriers such as education, income, and health status (Crouch & Gordon, 2019; Gell et al., 2015; Gordon & Hornbrook, 2018). Older adults have also been shown to require more help when utilizing the HIT (Crouch & Gordon, 2019; Gordon & Hornbrook, 2018), and research indicates that the likelihood of requiring assistance for using the HIT increased with age (Crouch & Gordon, 2019).

Interestingly, older adults were less likely than younger adults to withhold information from their healthcare providers due to concerns about the security or privacy of their medical records. This finding remained even among owners and users of technology. We found that 4.69% of older adults and 10.26% of younger adults withhold information from their providers due to privacy concerns. While the rates in younger adults are consistent with that reported in previous studies (Agaku et al., 2014), the reasons behind the lower rates of information nondisclosure among older adults are unclear. One possible explanation for the lesser concern on information privacy among older adults in our data could be the lower rates of technology ownership which may also reflect lower knowledge. Another explanation could be that some older individuals require technical assistance when navigating the HIT (Sakaguchi-Tang et al., 2017) and may therefore show less concern for privacy. Patient privacy and security of medical information are critical elements in today’s electronic healthcare ecosystem and concerns about privacy have been documented in the literature (Dimitropoulos et al., 2011; Hwang et al., 2012; Torous et al., 2018) However, our results indicate that a large proportion of adults reported high levels of confidence that protective measures exist to secure their electronic health information. These findings further support the evidence that consumers’ withholding of medical information may depend on other factors such as physician–patient relationship (Yang et al., 2020) and quality of care (Walker et al., 2017) and less on concerns over privacy of their electronic medical records. As the US population continues to age, it is relevant that future innovations should focus on methods to improve access to technology devices for older individuals and leverage this platform to deliver health care to this vulnerable population.

Strengths of this study include the large sample size which ensures that the study is able to highlight any differences between younger and older adults. Also, our results are based from recent data using the 2017 and 2018 iterations of the HINTS, a nationally representative sample of US adults. Further, our analysis utilized replicate weights to generate estimates for improved generalizability of the HINTS data and provided statistical correction for multiple comparisons.

Yet, the results of this study should be considered with the following limitations. First, this is a cross-sectional study that only highlights association and thus cannot offer information on causality. Second, given that the survey utilizes self-reported information and required participants to recall past behaviors, there is the possibility of recall bias by the respondents. Lastly, the response rate for both iterations of HINTS data used was 33%, suggesting the potential for selection bias.

Conclusion

HIT resources provide a relatively inexpensive and effective way for older adults to access information that can help them learn about and manage their health. However, this study found significant age differences in HIT ownership and use as older adults were significantly less likely to own or use these tools in managing their health. Furthermore, our results suggest that access to these tools may be a significant contributor to the lack of HIT use in this population. Given the established benefits of HIT (Brennan et al., 2010; Chaudhry et al., 2006; Sadoughi et al., 2018), efforts to reduce barriers to access and adoption of technology tools among older adults may result in improved health outcomes and reduce healthcare costs in this high-risk population.

Footnotes

Author’s Note

The abstract for these findings was accepted for presentation at the American Psychiatric Association annual conference 2020 in Philadelphia but was not presented.

Acknowledgments

We wish to thank the Cardiac Psychiatry Research Program, Department of Psychiatry, Massachusetts General Hospital, and the National Cancer Institute for making the HINTS data publicly available for use.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Time for analysis and article preparation was funded by the National Heart, Lung, and Blood Institute through grant K23HL123607 (to Dr. Celano). The content is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health. Dr. Celano has received honoraria for talks to Sunovion Pharmaceuticals on topics unrelated to this research.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Writing for this work was supported by the National Institute of Health [grant number K23HL123607].

ORCID iD

Henry K. Onyeaka

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