Skip to main content
Download PDF

Factors associated with internet use and health information technology use among older people with multi-morbidity in the United States: findings from the National Health Interview Survey 2018

BMC Geriatrics volume 22, Article number: 733 (2022) Cite this article

Abstract

Background

The number of older adults with physical multi-morbidity is increasing. As Internet-based eHealth and mHealth increasingly require patients to use technology, it is important to examine the use of Internet/health information technology (HIT) among older adults with physical multi-morbidity. Here we examine the distribution of physical multi-morbidity, Internet use, and HIT use, and further explored the factors associated with Internet use and HIT use among older adults with physical multi-morbidity.

Methods

One wave of data from the 2018 US National Health Interview Survey (NHIS) was analysed. We included respondents aged 65 years and older. We used 13 physical non-communicable diseases to measure physical multi-morbidity. Descriptive statistics and logistic regression models, with sociodemographic factors, health status, health insurance, health care service use, and satisfaction with health care as covariates, were used to examine the research questions.

Results

Of 72,746 respondents in NHIS, 7060 were eligible for our analysis. 5380 (76.2%) eligible respondents had physical multi-morbidity in this study. Overall, 60% of older adults reported using the Internet, with 38.9% using eHealth services (defined as looking up health information online, filling a prescription, scheduling an appointment with a health care provider, or communicating with a health care provider via email). Gender, age, marital status, region, race, education, and family income were significant factors associated with the Internet and HIT use among people with multi-morbidity. The study also showed that after adjusting for confounders, good health status, having Medicare, receiving home care from a health professional, and low satisfaction with health care were positive predictors of the Internet and HIT use.

Conclusions

In summary, our study found that Internet and HIT use among older patients with chronic diseases is far from the Healthy People 2030 target. Internet and HIT use vary depending on a number of sociodemographic factors. Relevant influencing factors should be fully considered in health education interventions promoted.

Peer Review reports

Background

The recognition that chronic diseases are a major challenge to global health is undisputed [1, 2]. In its 2021 report on the global chronic disease challenge, WHO pointed out that non-communicable diseases cause 41 million deaths per year, equivalent to 71% of all global deaths [3]. More seriously, physical multi-morbidity (the co-occurrence of two or more chronic diseases) is also on the rise worldwide [4]. People with multi-morbidity often have poorer health outcomes, such as increased mortality, disability, decreased physical function, and reduced quality of life [5].

In the USA, chronic diseases are the leading cause of poor health, disability, and death, and account for the majority of medical expenditures. About half (50.9%) of adults have at least one chronic disease, and 26% have two or more diseases [6]. On the one hand, the older adults in the United States are facing a persistently high prevalence of risk factors including the increasingly sedentary, unhealthy lifestyle and other behaviors, and on the other hand, they are faced with an increase in more ill problems caused by an increase in life expectancy [7, 8]. The large number and high incidence of chronic diseases have brought huge challenges and costs to public health and health care systems [8], so how to carry on scientific intervention to chronic disease is particularly important.

Adequate management of chronic diseases has proven to be resource-intensive and requires coordinated and dedicated efforts from teams of patients and healthcare providers [9, 10]. In recent years, technological advances, such as mHealth and eHealth are bringing about mechanisms for effective and affordable health care delivery and education [11]. Healthy People 2030 highlights the desire to increase the use of patient portals, particularly the proportion of persons who use health information technology (HIT) to track health care data or communicate with providers, and the proportion of persons who have access to quality (reliable and easy to use) digital health tools and information [12]. The Internet is and will continue to be a major source for people to receive health information as it plays an increasingly important role in health care and as individuals become more aware and receptive to new technologies such as the Internet [13]. Studies have shown that greater participation in the Internet and social media may be related to the improvement of health behaviors and health conditions, indicating the growing important of the Internet in health maintenance [14].

The Internet has been recognized as an increasingly popular channel for doctor's access, with more Americans using it to search for health information and perform other health-related activities, such as remotely filling prescriptions and using E-mail to communicate with their healthcare provider [15, 16]. This new form of communication provides broader and open access to information and advices on how to maintain or improve health and manage illness. The use of the Internet and health information may be particularly beneficial to older adults seeking health information [17, 18]. However, little is known about the proportion of older adults with physical multi-morbidity who use the Internet to search for health information and communicate with health care providers online [11, 19]. In addition, less attention has been paid to how HIT is used in older with physical multi-morbidity compares to US Healthy People 2030 objectives. In fact, this information is necessary because people with physical multi-morbidity often have complex management needs and potentially face barriers to HIT, but may benefit greatly from eHealth services [11].

In addition, exploring the association between sociodemographic factors and the Internet and HIT use by people with physical multi-morbidity may provide new insights to better promote this technological approach to care. It is well-known that patients who are older, of lower socioeconomic status, or with lower levels of education are less likely to engage in eHealth activities [19]. Studies have shown that compared with non-older groups, older patients are more dependent on their healthcare providers for information, rather than relying solely or heavily on the Internet for health information (and may not consider the Internet to be a credible, reliable, or easy to navigate resource) [20]. It is therefore important to understand the sociodemographic factors that influence the Internet and HIT use in patients with physical multi-morbidity to assist vulnerable populations and advance the progress of health equity.

Although the Internet is expected to become an additional complementary resource for current and future diseased groups, systematic analysis of the Internet usage patterns among the older adults, especially for patients with physical multi-morbidity, is still lacking. In response to these gaps in the literature, the current study described Internet and HIT use in older adults with physical multi-morbidity (objective 1). We further identified a range of factors associated with Internet and HIT use in older adults with physical multi-morbidity (objective 2).

Methods

Data and sample

Data from the 2018 US National Health Interview Survey (NHIS) public-use data files were downloaded from the CDC’s National Center for Health Statistics website, which contains a variety of health information Internet use [21]. As an annual, nationally representative, cross-sectional household survey, NHIS provides information on the health and health care access of the civilian, non-institutionalized US population [22]. The NHIS obtains data through a complex multistage sample design that involves the stratification and clustering of specific population subgroups. For each sampled family, a face-to-face interview is conducted with an adult family member, who answers questions about the demographic and health conditions of each family member. A detailed description of the objectives and methods of NHIS has been reported elsewhere [23]. Since these data are provided to the public in an identifiable format, this study was exempt from review by the Institutional Review Board. In the 2018 NHIS annual data, a sample of 72,746 adult respondents between the ages 18 and 85 was obtained (to protect confidentiality among the oldest adults, all age variables were top-coded to “85 years and older” (85 +) in the HNIS database). In this study, we focused on patients aged 65 years and older with physical multi-morbidity, and we excluded respondents who had missing values of dependent or independent variables.

Measures

Physical multi-morbidity

We defined multi-morbidity as the presence of two or more physical chronic non-communicable diseases [22]. We used 13 non-communicable diseases to measure physical multi-morbidity, including diagnosed hypertension, high cholesterol, coronary heart disease, angina pectoris, heart condition, stroke, chronic obstructive pulmonary disease, asthma, cancer, diabetes, failing kidneys, liver condition, and arthritis. We counted the number of non-communicable diseases for each participant to identify those who had physical multi-morbidity.

Internet use

Respondents were asked if they had used the Internet in the past year (Yes = 1 and No = 0).

Health information technology use

Respondents were asked if they had (1) looked up health information on the Internet, (2) filled a prescription on the Internet, (3) scheduled a medical appointment on the Internet, and (4) communicated with a health care provider by email in the past 12 months. In this study, HIT use refers to any of these 4 aspects.

Health status

In NHIS, self-rated health was obtained by asking respondents, “Would you say your health, in general, is excellent, very good, good, fair, or poor?” We assigned "excellent", "very good" and “good” to 1, "fair" to 2, and "poor" to 3.

Health insurance

Respondents were asked if they had Medicare, Medicaid, private health insurance, and veterans/military insurance coverage in the past 12 months (yes = 1 and no = 0 for each).

Health care use

Respondents were asked if they (1) saw/talked to a medical specialist, (2) saw/talked to a general doctor, (3) received home care from a health professional, and (4) received health care 10 or more times in the past 12 months (yes = 1 and no = 0 for each).

Sociodemographic factors

A set of sociodemographic factors were included: (1) gender, (2) age (65—74, 75—84, and ≥ 85 years), (3) marital Status (Currently married, Widowed, Divorced/separated, Never married), (4) region (Northeast, Midwest, South, West), (5) race (Hispanic, Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian, Non-Hispanic All other race groups), (6) highest education (High school and below, Some college, College, and Master degree and above), (7) the percentage of family income to the federal poverty guidelines (FPG) (< 200% FPG, 200%-399% FPG, ≥ 400% FPG).

Data analysis

Statistical analyses were performed using the SPSS statistics 22.0 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp). Some covariates contained missing values, and the proportion of missing values was less than 5% [22]. Thus, we replaced the missing data with the mean of their integrity items. Frequency and percentages were calculated to describe sociodemographic parameters and level distributions among respondents. We used logistic regression models with Internet and HIT use as dependent variables to determine factors that affect use of Internet and HIT among older adults with multi-morbidity. We report associations as unadjusted rates (uORs) and odds ratios adjusted (aORs) for gender, age, marital status, region, race, highest education, family income, health status, health insurance, and health care use. A significance level of 95% was used with α = 0.05.

Results

Table 1 presents the socio-demographic characteristics of all respondents (n = 7060) and who with Internet use (n = 4061), HIT use (n = 3030), and multi-morbidity (n = 5380). The median age of all respondents was 74 years (IQR 65–85) in 2018. 2966 (42.1%) respondents were male, 4094 (57.9%) were female, and 3261 (46.2%) were currently married. About one-third (37.1%) respondents were living in the south area in the US, 5468 (77.5%) of them were non-hispanic white, 3017 (42.7%) respondents had high school education or below, and 2728 (38.6%) respondents had family incomes as a percentage of FPG of 200%—399%. More than 70% (5551,78.6%) of respondents reported good health, have health insurance (Medicare) (6284, 89.0%), and 6090 (86.3%) reported seeing/talking to a general doctor in the past 12 months. In addition, the proportion of older adults with physical multi-morbidity increased substantially with age. In 2018, the overall prevalence of physical multi-morbidity was 76.2%, with 3016 respondents aged 65–74 years having physical multi-morbidity and 656 older adults aged 85 years and above having physical multi-morbidity.

Table 1 Socio-demographic characteristics of all respondents and who with Internet use, HIT use, and multi-morbidity in 2018
Full size table

As shown in Table 2, in general, approximately 60.0% of the respondents used the Internet. Among those who used the Internet, the majority (45.9%) used it with a frequency of every day, and only 1% (74) of users with a frequency of once a month. Although Internet users’ most common HIT use was for health information seeking (38.9%), between 9.2% and 13.1% also engaging in the other 3 types of HIT use. In addition, about 59.1% of people with physical multi-morbidity used the Internet. Of these, about half (44.6%) used daily, and only 1.2% (63) of users used once a month. The HIT use of the people with multi-morbidity was generally consistent with all respondents, with the majority (38.9%) seeking health information and between 9.5% and 13.8% of Internet users also engaging in the other 3 types of HIT use.

Table 2 Frequency of internet and health information technology use among respondents and people with multi-morbidity
Full size table

Factors that affect Internet and HIT use among people with multi-morbidity were identified in this study. In controlling for other factors, the multivariate logistic regression output shown in Table 3 revealed that female, living in the west area, non-hispanic white, higher education (Some college, College, and Master degree and above), higher family income (200%-399% and ≥ 400%) were significant factors that affect the use of Internet. Older people with advanced age (85 + group: aOR = 0.18, CI = 0.15–0.23, P < 0.001) or widowed patients (aOR = 0.66, CI = 0.56–079, P < 0.001) were less likely to use the Internet. Among factors such as health status and health service utilization, respondents with poor health status (aOR = 0.53, CI = 0.40–0.70, P < 0.001), those without private health insurance (aOR = 0.79, CI = 0.69–0.92, P = 0.002) and those who had not talked to a medical specialist in the past year (aOR = 0.79, CI = 0.69–0.91, P = 0.001) were less likely to use the Internet. Those without Medicaid (aOR = 1.87, CI = 1.43–2.44, P < 0.001), not received home care from health professional (aOR = 1.73, CI = 1.37–2.18, P < 0.001), and those with low satisfaction with health care (aOR = 1.61, CI = 1.10–2.36, P = 0.014) were more likely to use the Internet.

Table 3 Factors influencing internet and HIT use among people with multi-morbidity, results of bivariate and multivariate logistic regression (n = 5380)
Full size table

In the analysis of factors influencing any HIT use in people with multi-morbidity, it was found that female, non-hispanic white, higher educational (Some college, College, and Master degree and above), and higher family income (200%-399% and ≥ 400%) were significant factors influencing any HIT use. Older adults (85 + group: aOR = 0.21, CI = 0.16–0.26, P < 0.001) or widowed patients (aOR = 0.75, CI = 0.64–089, P = 0.001) were less likely to use any HIT. Among the factors of health status and health care utilization, respondents with poor health status (aOR = 0.59, CI = 0.43 -0.79, P = 0.001), those who had not talked to a medical specialist in the past year (aOR = 0.62, CI = 0.54–0.71, P = 0.001) and those who had not received care 10 + times in the past year (aOR = 0.83, CI = 0.70–0.98, P = 0.024) were less likely to use HIT. Those without Medicaid (aOR = 1.95, CI = 1.46–2.61, P < 0.001), not received home care from a health professional in the past year (aOR = 1.40, CI = 1.11–1.76, P = 0.005), and with low satisfaction with health care (aOR = 1.56, CI = 1.09–2.25, P = 0.016) were more likely to use HIT.

Discussion

To our knowledge, this is the most recent study using nationally representative data to report on factors associated with Internet use and HIT use affecting older adults with physical multi-morbidity in the U.S. We found that physical multi-morbidity is common among people aged 65 years and older (76.2%). At the same time, older adults are increasingly interested in health information, and the Internet has become an important source in recent years [24]. 60% of the older adults use the Internet, and 38.9% of them rely on it for health information, similar to a previous study [25, 26]. Among older Internet users, 45% of them use the Internet regularly every day. Seeking health information on the Internet was found to be the most frequently reported HIT use behavior among older adults. Following that, communicating with health care providers by email, filling a prescription on the Internet, and scheduling medical appointments on the Internet also accounted for a percentage of Internet use among older Americans. The increasing popularity of HIT among the older adults may, on the one hand, be a natural consequence of information technology developments, given the increasing popularity of the Internet and the ease of use of android, IOS, and other handheld devices [24]. On the other hand, we consider that some older adults with multi-morbidity have limited physical conditions, so online consultation is a more convenient way.

For people with multi-morbidity, the prevalence of their Internet and HIT use varied across sociodemographic factors. Specifically, females were significantly more likely to report using the Internet and HIT. The reasons for this gender difference are unclear, but similar gender-related internet use patterns have been documented in US cancer survivors [27, 28]. The reason may be that the different the role taken up by women in society and the different stressors they face make them more active in obtaining information and seeking social support than men [29], who tend to be more passive information gatherers than active information seekers [30]. Therefore, these differences should be taken into account when designing interventions for Internet and HIT use among people with multi-morbidity. In terms of race, non-Hispanic whites have always been the predominant group using the Internet and HIT. Minority groups such as non-Hispanic Blacks and Hispanics have low participation in HIT, likely due to language and cultural barriers. Also, racial/ethnic minorities have been reported to have more chronic health problems than non-Hispanic whites, which may be a barrier to HIT use [31, 32].

In addition, this study found a strong association between education level and family income on the Internet and HIT use, which is consistent with a Norwegian study that analyzed e-health service use among patients with type 1 and type 2 diabetes and found a positive correlation between education level and search engine use [19]. This may be due to the preferential access and awareness of the Internet, good thinking and economic level of people with higher education levels and household incomes, which provide them with a larger and more comprehensive access to the Internet. Finally, age and widowed marital status are hindering factors for Internet and HIT use. On the one hand, some surveys showed that older adults usually have some degree of cybersecurity concerns and digital skills anxiety [33], further limiting their acceptance and use of new technologies such as the Internet in their sickly physical condition, and on the other hand, widowhood deepens older adults' sense of isolation [34], which may also be detrimental to enhancing their acceptance.

These findings emphasize that health education and interventions to promote the use of the Internet and HIT in older people with multi-morbidity must take into account sociodemographic factors. Older adults with chronic diseases are often unfamiliar with health technologies and may be adversely affected by HIT used in telemedicine and e-health services [35]. Efforts to help them improve their ability to browse and use the Internet and to design age-friendly electronic devices may increase their use of HIT and thus improve their health.

Another key finding of this study is that among factors such as health status and health care utilization, multi-morbidity individuals with poorer health and those who had not talked to a medical professional in the past year were less likely to use the Internet and HIT. This may be because their poor physical condition does not allow them to focus more on new technology and access to new information, and the lack of conversations with doctors may also make them miss the opportunity to be passed on information about HIT use. In addition, we also noticed that patients without Medicaid were more likely to use the Internet and HIT. The American Health Insurance Association describes Medicaid as "a government insurance program for people of all ages with insufficient income and resources to pay for health care [36]." Therefore, this study speculates that this may be a reflection of the fact that people without Medicaid may be financially well off themselves and have more convenient access to the Internet and HIT.

Based on the results of this study, we also found that people with low satisfaction with health care services were more likely to use the Internet and HIT, and this is consistent with the findings of De Rosis S et al. [37]. Patients' low satisfaction with healthcare services is often due to the fact that the healthcare experience did not meet their psychological expectations or that the healthcare services did not adequately address their health problems. As a result, this group of patients may choose the Internet or HIT as a complementary form of service to their consultation. This also suggests that health service providers need to pay more attention to the integration of online and offline healthcare service delivery methods so that patients can be supplemented and re-visited by online treatment in a timely manner when they encounter inadequate offline treatment.

As the Internet plays an increasingly essential role in healthcare, to bridge the digital divide, action is needed to connect and raise awareness among older people, especially those with comorbidities. Hospital systems and providers are increasingly using mobile web-based healthcare services, such as SMS, a popular mobile media platform, as a possible option for health interventions. Mayberry's study of diabetics found that patients with chronic diseases may have easier access to interventions using SMS services than Internet-based platforms, as they use Internet-dependent interventions less than those conducted through SMS text messages [38].

The newly released Healthy People 2030 target revises the key targets for HIT use, the most relevant of which is to increase the proportion of adults who use information technology to track health data or communicate with providers to 87.3% [11]. While these goals reflect the general desire of the general population to increase the use of technology, it remains to be seen whether patients with comorbidities will meet these standards. At the same time, more research on the Internet and HIT use and access among people with chronic diseases is needed to help these populations achieve these goals.

In addition, some previous studies indicated that although many patients have access to the Internet, they may not choose to use HIT resources. This suggests that online forums that disseminate reliable health information and encourage active patient participation can be used to increase patient satisfaction with HIT and increase overall use. The accessibility and convenience of people looking for health information online have increased in the last decade [39, 40], but more research is needed to thoroughly investigate the potential benefits of using HIT in improving the health of older adults. Future research needs to further clarify the information content searched by the older adults on the Internet, such as disease types, treatment strategies, and prescription categories, so as to further information intervention for older patients.

Limitations

Our study had some limitations. First, the NHIS data were self-reported, and the diagnoses of all chronic diseases may underestimate the true association. Secondly, the NHIS data used in this study is a cross-sectional survey. The study design made it impossible to draw conclusions about possible causal relationships between sociodemographic factors, physical multi-morbidity, and Internet and HIT use. Third, since there are other behavioral factors associated with HIT use that have not been considered, the significant associations observed in the study may be due to unadjusted residual confounders. Finally, the study excluded patients with mental health diseases, which may lead to the limitation of the research conclusions.

Conclusions

In summary, our study found that Internet and HIT use among older patients with chronic diseases is far from the Healthy People 2030 target. Internet and HIT use vary by some sociodemographic factors. Governments or communities can develop digital skills training and safety awareness campaigns based on the characteristics of geriatric patient networks and HIT use to alleviate geriatric patient skills and safety concerns. Also, medical staff can also encourage older patients to choose a suitable way of using HIT to facilitate their acquisition of health knowledge. It is expected that the use of the Internet and HIT tools will be effective in improving the quality and efficiency of care for patients with chronic or comorbidities, but further research is needed to determine the extent of these health benefits from the trend of increased use of the Internet and HIT.

Availability of data and materials

All material appearing in this manuscript is in the public domain and may be reproduced or copied without permission. The datasets used and analyzed during the current study are available in https://www.cdc.gov/nchs/nhis/new_nhis.htm.

Abbreviations

HIT:

Health information technology

NHIS:

National Health Interview Survey

uOR:

Unadjusted odds ratio

aORs:

Adjusted Odds Ratios

CI:

Confidence Interval

References

  1. Nugent R. Preventing and managing chronic diseases. British Medical Journal Publishing Group; 2019.

  2. Vos T, Barber RM, Bell B, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The lancet. 2015;386(9995):743–800.

    Article  Google Scholar 

  3. Organization WH. Noncommunicable diseases. 2021;2021.

  4. Nunes BP, Flores TR, Mielke GI, Thumé E, Facchini LA. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2016;67:130–8.

    Article  Google Scholar 

  5. Nguyen H, Chua KC, Dregan A, Vitoratou S, Prina AM. Factors Associated With Multimorbidity Patterns in Older Adults in England: Findings From the English Longitudinal Study of Aging (ELSA). J Aging Health. 2019;32(3):089826431989102.

    Google Scholar 

  6. Ward BW, Schiller JS. Prevalence of Multiple Chronic Conditions Among US Adults: Estimates From the National Health Interview Survey, 2010. Preventing Chronic Disease 2013;10.

  7. Lancet T. Tackling the burden of chronic diseases in the USA. Elsevier; 2009. p. 185.

  8. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45–52.

    Article  Google Scholar 

  9. Zhuo X, Zhang P, Barker L, Albright A, Thompson TJ, Gregg E. The lifetime cost of diabetes and its implications for diabetes prevention. Diabetes Care. 2014;37(9):2557–64.

    Article  Google Scholar 

  10. Hartz J, Yingling L, Powell-Wiley TM. Use of mobile health technology in the prevention and management of diabetes mellitus. Curr Cardiol Rep. 2016;18(12):1–11.

    Article  Google Scholar 

  11. Wang SY, Yeh H-C, Stein AA, Miller ER. Use of Health Information Technology by Adults With Diabetes in the United States: Cross-sectional Analysis of National Health Interview Survey Data (2016–2018). JMIR diabetes. 2022;7(1): e27220.

    Article  Google Scholar 

  12. Jackson DN, Trivedi N, Baur C. Re-prioritizing digital health and health literacy in healthy people 2030 to affect health equity. Health Commun. 2021;36(10):1155–62.

    Article  Google Scholar 

  13. Bender JL. The Internet as a Route to Health Information, Support, and Healthcare for Cancer Survivors. J Natl Compr Canc Netw. 2020;18(11):1586–8.

    Article  Google Scholar 

  14. Gordon NP, Hornbrook MC. Older adults’ readiness to engage with eHealth patient education and self-care resources: a cross-sectional survey. BMC Health Serv Res. 2018;18(1):220.

    Article  Google Scholar 

  15. Choi N. Relationship between health service use and health information technology use among older adults: analysis of the US National Health Interview Survey. J Med Internet Res. 2011;13(2): e33.

    Article  Google Scholar 

  16. Ramos TB, Bokehi LC, Oliveira EBd, Gomes MdSA, Bokehi JR, Castilho SRd. Information about benzodiazepines: what does the internet offer us? Ciência & Saúde Coletiva 2020;25:4351–60.

  17. Jiang Y, Sun P, Chen Z, Guo J, Wang S, Liu F, Li J. Patients’ and healthcare providers’ perceptions and experiences of telehealth use and online health information use in chronic disease management for older patients with chronic obstructive pulmonary disease: a qualitative study. BMC Geriatr. 2022;22(1):1–16.

    Article  Google Scholar 

  18. Falk Erhag H, Ahlner F, Rydberg Sterner T, Skoog I, Bergström A. Internet use and self-rated health among Swedish 70-year-olds: a cross-sectional study. BMC Geriatr. 2019;19(1):1–8.

    Article  Google Scholar 

  19. Hansen AH, Bradway M, Broz J, Claudi T, Henriksen Ø, Wangberg SC, Årsand E. Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes: cross-sectional study. J Med Internet Res. 2019;21(5): e13615.

    Article  Google Scholar 

  20. Pettus AJ, Mendez-Luck CA, Bergeron CD, Ahn SN, Towne SD, Ory MG, Smith ML. Internet-Based Resources for Disease Self-Care Among Middle-Aged and Older Women with Chronic Conditions. Journal of Women\"s Health 2017;26(3):222–33.

  21. NHIS Data, Questionnaires and Related Documentation [Internet]. 2021. Available from: https://www.cdc.gov/nchs/nhis/1997-2018.htm.

  22. Choi NG, DiNitto DM. Internet and Health Information Technology Use and Psychological Distress Among Older Adults With Self-Reported Vision Impairment: Case-Control Study. 2020;22(6): e17294.

    Google Scholar 

  23. Marmor S, Horvath KJ, Lim KO, Misono S. Voice problems and depression among adults in the United States. The Laryngoscope 2016;126(8):n/a-n/a.

  24. Nangsangna RD, Vroom Fd-C. Factors influencing online health information seeking behaviour among patients in Kwahu West Municipal, Nkawkaw, Ghana. Online journal of public health informatics 2019;11(2).

  25. Alicia HY, Jinmyoung C. Has the Digital Health Divide Widened? Trends of Health-Related Internet Use Among Older Adults From 2003 to 2011. Journals of Gerontology 2016(5):gbw100.

  26. Choi N. Relationship Between Health Service Use and Health Information Technology Use Among Older Adults: Analysis of the US National Health Interview Survey. Journal of medical Internet research 2011;13(2).

  27. Eric AB, Mohammed KA, Geneus CJ, Tobo BB, Wirth LS, Yang L, Nosayaba OP, Iratxe P. Correlates of health information seeking between adults diagnosed with and without cancer. Plos One. 2018;13(5):e0196446.

    Article  Google Scholar 

  28. Dee EC, Muralidhar V, Butler SS, Yu Z, Sanford NN. General and Health-Related Internet Use Among Cancer Survivors in the United States: A 2013–2018 Cross-Sectional Analysis. J Natl Compr Canc Netw. 2020;18(11):1468–75.

    Article  Google Scholar 

  29. Tamres LK, Janicki D, Helgeson VS. Sex differences in coping behavior: A meta-analytic review and an examination of relative coping. Personality And Social Psychology Review. 2002;6(1):2–30.

    Article  Google Scholar 

  30. Saab MM, Reidy M, Hegarty J, Omahony M, Drummond FJ. Men's information-seeking behavior regarding cancer risk and screening: A meta-narrative systematic review. Psycho-Oncology 2018;27.

  31. López L, Green AR, Tan-Mcgrory A, King R, Betancourt JR. Bridging the Digital Divide in Health Care: The Role of Health Information Technology in Addressing Racial and Ethnic Disparities. Jt Comm J Qual Patient Saf. 2011;37(10):437–45.

    PubMed  Google Scholar 

  32. Gordon NP, Hornbrook MC. Differences in Access to and Preferences for Using Patient Portals and Other eHealth Technologies Based on Race, Ethnicity, and Age: A Database and Survey Study of Seniors in a Large Health Plan. J Med Internet Res. 2016;18(3): e50.

    Article  Google Scholar 

  33. Hong Y, Fu J, Kong D, Liu S, Luo Y. Benefits and barriers: a qualitative study on online social participation among widowed older adults in Southwest China. BMC Geriatrics 2021;21(1).

  34. Batista M, Rebelo JE, Carvalho R, Almeida MM, Lancman S. Widow's perception of their marital relationship and its influence on their restoration‐oriented everyday occupations in the first six months after the death of the spouse: A thematic analysis. Australian Occupational Therapy Journal 2019;66(6).

  35. Zhai YK, Zhu WJ, Cai YL, Sun DX, Zhao J. Clinical- and Cost-effectiveness of Telemedicine in Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis. Medicine. 2014;93(28): e312.

    CAS  Article  Google Scholar 

  36. Dong X, Gindling T, Miller NA. Effects of the Medicaid expansion under the Affordable Care Act on health insurance coverage, health care access, and use for people with disabilities. Disabil Health J. 2022;15(1): 101180.

    Article  Google Scholar 

  37. De Rosis S, Barsanti S. Patient satisfaction, e-health and the evolution of the patient–general practitioner relationship: Evidence from an Italian survey. Health Policy. 2016;120(11):1279–92.

    Article  Google Scholar 

  38. Mayberry LS, Lyles CR, Oldenburg B, Osborn CY, Parks M, Peek ME. mHealth Interventions for Disadvantaged and Vulnerable People with Type 2 Diabetes. Curr DiabRep. 2019;19(12):1–15.

    Google Scholar 

  39. Aydin MN, Perdahci NZ, editors. Analysis of the Patients and Physicians Connection Network on an online Health Information Platform. MIE; 2014.

  40. Menefee HK, Thompson MJ, Guterbock TM, Williams IC, Valdez RS. Mechanisms of communicating health information through Facebook: implications for consumer health information technology design. J Med Internet Res. 2016;18(8): e5949.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Institute of Hospital Management, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Wenbo He

  2. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Wenbo He

  3. West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Liujiao Cao

  4. Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Rui Liu

  5. Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu, Sichuan, China

    Rui Liu

  6. Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin, Heilongjiang, China

    Yi Wu

  7. West China Biomedical Big Data Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Wei Zhang

Authors
  1. Wenbo He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liujiao Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WBH and WZ conceptualized the study. WBH, LLC, RL, and YW collected and analyzed the data. WBH and LJC wrote the manuscript. WZ revised and finalized the manuscript. “The author(s) read and approved the final manuscript.”

Corresponding author

Correspondence to Wei Zhang.

Ethics declarations

Ethics approval and consent to participate

Informed consent was obtained from all subjects and/or their legal guardians. NHIS is approved by the Research Ethics Review Board of the National Center for Health Statistics and the U.S. Office of Management and Budget. All NHIS respondents provided oral consent prior to participation. Respondents were not directly involved in planning the study as it was done as a part of a regular demographic health survey. All procedures were performed in accordance with relevant guideline. All methods were carried out in accordance with STROBE Statement and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

He, W., Cao, L., Liu, R. et al. Factors associated with internet use and health information technology use among older people with multi-morbidity in the United States: findings from the National Health Interview Survey 2018. BMC Geriatr 22, 733 (2022). https://doi.org/10.1186/s12877-022-03410-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-022-03410-y

Keywords

  • Internet use
  • HIT
  • Physical multi-morbidity
  • Older adults

BMC Geriatrics

ISSN: 1471-2318

Contact us