Beyond Dichotomy and Stability: ICT Use Among Asian American Older Adults During the COVID-19 Pandemic

Abstract

This study examined the effect of the COVID-19 pandemic on information and communications technology (ICT) use and the typology of ICT users among older Chinese and Korean Americans. Survey data were collected from 513 Chinese and Korean older adults in New York City. We measured ICT use for social contact, grocery shopping, health care, and COVID-19 information seeking. In the study sample, ICT use for online shopping with others, contact with doctors, and telehealth significantly increased during the pandemic. Three groups of ICT users were identified: limited, users, expanding users, and active users. Older Chinese Americans and those with better English proficiency were more likely to be expanding and active ICT users. The patterns and heterogeneity of ICT use among older Asian Americans are multifaceted and dynamic beyond dichotomy and stability. The findings of this study offer helpful guidance for future development of ICT-based interventions for older Asian Americans.

Keywords

digital divide typology minority Chinese Korean

What this paper adds

• We expand knowledge on the digital divide among older adults during the COVID-19 pandemic by collecting and analyzing data on an understudied population, Asian Americans;

• We advance the literature by presenting a spectrum of technology use;

• We contribute to the understanding of the typology of older ICT users by examining the effects of ethnic culture and acculturation that were understudied in previous studies.

Applications of study findings

• More educational interventions are needed to support their ICT learning needs;

• Interventions that aim to improve older adults’ ICT skills may be of particular interest among older adults who are Koreans and have limited English proficiency.

• Practitioners should be aware of the existence of the limited user group, whose needs may be missed without community outreach and inclusive program design.

Introduction

The COVID-19 pandemic had substantially reshaped the behavior of older adult populations regarding their ICT use. In addition to their physical vulnerability, many preventive measures, such as social distancing and city lockdowns, placed older adults’ mental and psychological health at greater risk, such as social isolation and anxiety related to fears of infection (Llorente-Barroso et al., 2021). Besides ICT being a feasible solution to meet daily and health care needs (Fang et al., 2022), existing literature has consistently documented that it brings mental health benefits. Engaging in ICT regularly allows older adults to participate in meaningful and quality social relationships with family and friends and provides them with a sense of autonomy and connectedness, which could lower the risk of loneliness and depression (Bonsaksen et al., 2021; Wallinheimo & Evans, 2021).

Existing evidence has shown that the pandemic has significantly transformed the perception and behavior related to ICT use among older adult populations. Particularly, an increasing trend of ICT uses has been identified for telehealth (Choi et al., 2022; McCausland et al., 2022), social connection (Kung & Steptoe, 2023; Lin et al., 2022; McCausland et al., 2022), and functional (Kung & Steptoe, 2023) purposes. However, limited studies have examined to what extent ICT use is influenced by the pandemic among minority older Americans and whether the influence is heterogeneous in terms of the purpose and the range of ICT use. Furthermore, it is not clear whether the digital divide has widened or narrowed and what factors explain older adults’ ICT use patterns. To address these gaps, this study examined the typology of ICT use among older Asian Americans before and during the COVID-19 pandemic. The findings of this study can help create and strengthen inclusive ICT-based services for older adults in a post-pandemic society.

Digital Divide Among Older Adults: From Dichotomy to Spectrum

The digital divide refers to “the gap between individuals, households, business, and geographic areas at different socioeconomic levels with regards both to their opportunities to access ICTs and their use of the internet for a wide variety of activities” (OECD, 2001, p. 5). It has emerged as a crucial concern because ICT has been widely applied in many areas and profoundly boosted during the COVID-19 pandemic. In societies with fast and continuing digitalization, individuals who are not capable of using ICT are disadvantaged and possibly excluded from society, which is also called e-exclusion. Earlier studies on the digital divide focused on inequalities in access to electronic devices, such as the Internet, computers, and smartphones (e.g., Cavender & Bigham, 2011; Srinuan & Bohlin, 2011). Later studies have shifted their attention to inequality in digital skills or capability rather than accessibility (Vassilakopoulou & Hustad, 2021).

More importantly, the digital skill divide may present a spectrum of inequality rather than a dichotomized difference (e.g., ICT users vs. nonusers). Existing evidence across several developed countries has shown variations of ICT use frequency, type, autonomy, and extent among older adults (Hargittai et al., 2019; Leukel et al., 2023). However, very few studies have measured ICT use outcomes as a multidimensional factor and considered older adults as a heterogenous group of ICT users. An overall picture of the heterogeneity in the purpose and type of online activities is missing. A typology analysis is useful to understand nuanced distinctions among subgroups of older ICT users, which could guide relevant services and technology innovation that benefit this population.

Asian American Older Adults: A Homogeneous or Heterogenous Group?

Having a clearer understanding of older Asians’ ICT use patterns is of particular importance, especially during the pandemic in the United States. A unique issue for older immigrants in ICT use is the language barrier (Safarov, 2021). It is estimated that more than 30% of Asian Americans have limited English proficiency (Budiman & Ruiz, 2021). Moreover, compared to other racial and ethnic groups, Asian Americans need to manage not only the mental health burden of infection risk, but also psychological distresses from the escalating incidence of racial discrimination and anti-Asian violence during the pandemic. For instance, from 2020 to 2021, anti-Asian hate crimes reported across New York City (NYC) increased by 361% (Yam, 2021) and very often, Asian American older adults were the target (Takamura et al., 2022). These compounding stressors may further prevent and disincentivize older Asian Americans from leaving their home and engaging in meaningful activities. During the pandemic, the prevalence of depressive symptoms for this vulnerable population has increased substantially (Ettman et al., 2020; Lozano et al., 2022).

Despite the potential health benefits brought by the use of ICT, a knowledge gap remains regarding ICT use and potential heterogeneity of use patterns among older Asian Americans. Although previous research has linked socioeconomic status (SES), psychological factors, health status, and social support to ICT use among minority older Americans (Choi & DiNitto, 2013; Gallo et al., 2022; Jung et al., 2010; Yoon et al., 2020), no within-group studies have examined various aspects of older Asians’ ICT uses (e.g., range, type, frequency, purpose, or capability) concurrently to assess the potential heterogeneity of ICT use in this group.

In the United States, Asian American older adults are usually considered one ethnic group, given common themes across Asian cultures and experiences of immigration, strong family bonds, and intergenerational ties, but this misperception conceals diversities in this population with respect to culture, religion, language, trauma exposure, SES, and acculturation (Ikels, 2004; E.-K. O. Lee, 2007; Mui & Kang, 2006). For example, in NYC, the metropolitan area with the largest Asian American population in the United States, Chinese and Koreans are the first and third largest Asian ethnic groups based on the 2010 census (New York City Government, 2012). In NYC, Chinese and Korean Americans have some similarities, including similar Confucianism-oriented family cultural values (Ikels, 2004), and face a similar language barrier, such that most older Koreans (88%) and Chinese (87%) have limited English proficiency. However, Korean older adults have better SES than their Chinese peers on average, evidenced by a lower poverty rate and lower likelihood of living in crowded households among Korean older adults (Asian American Federation, 2019a, Asian American Federation, 2019b). Prior studies also documented distinctions in social networks and supportive resources outside the family between older Chinese and Korean Americans. Comparing to older Korean Americans, older Chinese Americans reported higher levels of social capital indicated by norms, trust, partnership in the community, information sharing, and political participation (Kim et al., 2013). Given these disparities, it is reasonable to surmise that various types of ICT use exist among older Asian Americans. Failing to consider such group heterogeneity could undermine the opportunities to provide appropriate and effective social services and interventions to improve ICT usage targeting these ethnic groups.

Present Study

Though the existence of the digital divide and increased importance of ICT in individuals’ daily lives during the pandemic are well documented, our literature review shows that the variation in ICT use is not clear among older Asian Americans. Therefore, this study addressed three questions: (a) Did older Chinese and Korean Americans increase their ICT use for social, shopping, and health purposes during the COVID-19 pandemic?; (b) What was the ICT use pattern among these Asian American older adults before and during the pandemic?; and (c) Are ethnicity and acculturation associated with the typology of ICT use?

Based on the existing literature on ICT use, we developed a conceptual framework to guide this study, as shown in Figure 1, and proposed three hypotheses:

Figure 1.

Classification of ICT users among Asian older Americans during the COVID-19 pandemic.

Hypothesis 1

Older Chinese and Korean Americans increased their ICT use for social contact, shopping, and health service purposes during the COVID-19 pandemic. There is no doubt that the pandemic increased the importance and use of ICT in many social domains, such as education, health care, public management, and civic engagement, among older Americans (Sixsmith et al., 2022). We hypothesized that older Chinese and Korean adults are more likely to use ICT for multiple purposes during the pandemic, including social contact, shopping, and health services, comparing to prior to the pandemic. Given the pandemic context, we also incorporated ICT use to gather pandemic-related information from the internet.

Hypothesis 2

Three are more than two groups of ICT users in terms of purpose and change during the pandemic among older Chinese and Korean Americans. Beyond the dichotomizing analysis of ICT use among older adults (users vs. nonusers), recent literature has shown the complicated variations of ICT use frequency, type, autonomy, and extent among older adults (Hargittai et al., 2019; Leukel et al., 2023). In addition, the COVID-19 pandemic may boost the diversification of ICT users among older Asian Americans, who were vulnerable because of the virus and anti-Asian hate crimes. Those who had actively used ICT may maintain or increase ICT use for certain purposes, such as for health care and shopping. Among the inactive users before the pandemic, some persons may start learning ICT and expanding their technology use for more purposes. Therefore, we postulated that digital heterogeneity in this population represents a spectrum with more than two groups. The groups on the spectrum are differentiated by the purpose of their ICT use and responses to environmental forces (i.e., COVID-19 pandemic).

Hypothesis 3

The group membership of ICT users among older Asian Americans is associated with ethnic identity and acculturation. For older Asian Americans, we argued that ethnicity and acculturation are additional important factors shaping their ICT use. Given the differences in social engagement between these two ethnic groups, we presumed that Chinese older adults would be more active in ICT use to maintain their larger social network and connections than Korean older adults. We also speculated that Asian older adults with higher levels of acculturation, regardless of their ethnicity, are more likely to use ICT for multiple purposes because they do not have the language barrier to learn and access online information and resources that are primarily expressed in English in the United States.

Methods

Survey and Data

We conducted a 20-minute survey with 513 Chinese and Korean adults aged 60 or older in NYC from May to August 2021 regarding their sociodemographic characteristics, acculturation, physical and mental health status, and ICT use before and during the pandemic. We collaborated with community agencies serving the Asian community in three NYC boroughs, Brooklyn, Manhattan, and Queens, where most Asian older adults resided. Study flyers were distributed both in person at routine services and special community events and electronically in messaging groups (e.g., WeChat and Kakao) for agency clients. We also encouraged participants to invite their friends, family members, and neighbors, who were not in contact lists at our collaborative agencies, but may meet the recruitment criteria, to participate in the survey. Participants could answer the survey either via an online survey platform or by calling the research team in their preferred language: simplified (Mandarin) or traditional (Cantonese) Chinese, Korean, or English. The study was approved by [blinded for peer review] Institutional Review Board.

Measurements

ICT use was proxied with 14 variables indicating four purposes. We adapted questions on changes in ICT use for social contact, grocery shopping, and health care before and during the pandemic from the National Health and Aging Trends Study (2021) COVID-19 Questionnaire. For social contact, we asked respondents to report whether and how frequently they used social media messages to contact family or friends (social messaging) and whether and how frequently they used video calls for social contact (video chat) before and during the pandemic. Similarly, the purposes of shopping were measured by whether and how often they ordered groceries online alone (online shopping by self) and with someone (online shopping with others). Based on the distribution of each variable, we recoded ICT use for social contact as a categorical variable (1 = at least daily/a few times a week, 2 = about once a week/less than once a week, 3 = never) and recoded ICT use for shopping measures into dichotomous variables (1 = used ICT, 0 = did not use ICT). For health care, respondents were also asked whether they have used email, texts, or portal messages to contact doctors (e-contact with doctors), and used telehealth (telehealth) before and during the pandemic (1 = yes, 0 = no). In addition to these three purposes, we also tested ICT use for COVID-19 information seeking. We asked participants to report whether they used the internet to search for information related to the pandemic, such as infection cases, prevention, and testing or vaccination sites (online information related to COVID), and whether they used social media to post, comment, or review anything related to the pandemic (social media use related to COVID); response options were yes and no.

The key independent variables to predict the typology of ICT users are ethnicity ((1 = Chinese, 0 = Korean) and acculturation. Acculturation was measured by the length of stay in the United States (less than 10 years, 11–20 years, 21–29 years, 31–40 years, and more than 40 years) and English proficiency, which was measured by a 3-item scale assessing English speaking, reading, and writing skills (Cronbach’s alpha = .94). The scale has been validated in the Asian American population (Kang et al., 2016). Each item is scored on a 4-point Likert scale (from 1 = not at all to 4 = very well); responses are summed, and scores range from 3 to 12. Higher scores indicate better English proficiency.

We included sociodemographic characteristics, social support, health status, and technology-related characteristics as covariates in predicting the typology of ICT users based on previous literature (Han & Nam, 2021; Hänninen et al., 2021; Leukel et al., 2023; Mitzner et al., 2010; van Deursen & Helsper, 2015; C. Lee & Coughlin, 2015; Vassilakopoulou & Hustad, 2021; Yu et al., 2016). Sociodemographic variables included age (60–69, 70–79, >80), gender (1 = female, 0 = male), marital status (1 = married, 0 = single, divorced, or widowed), highest level of education (never attended school, elementary school, middle or high school, associate degree or beyond), and difficulty with living (very hard, somewhat hard, not very hard at all, and don’t know or decline to answer). Social support was measured by whether the participants received emotional support from family members and friends at least once a week (1 = yes, 0 = no).

Physical and mental health includes the number of chronic illnesses, difficulty in activities of daily living (ADL), difficulty in instrumental activities of daily living (IADL), depression, and loneliness. ADL scores ranged from 0 to 6 (Cronbach’s alpha = .90) and indicated difficulty in bathing, dressing, toileting, transferring, continence, and feeding (Katz et al., 1970). IADL scores ranged from 0 to 7 (Cronbach’s alpha = .88) and reflected difficulty in preparing meals, housework such as cleaning and washing dishes, shopping for groceries, managing money, answering or making a phone call, traveling and commuting, and taking medications, that were adapted from Lawton IADL scale (Lawton, 1988). Depression was assessed by the Patient Health Questionnaire-9, a self-report scale with nine items measuring depressive symptoms that has been validated and used among older Chinese and Koreans (Park et al., 2010; Wang et al., 2014). Respondents were asked to rate each item from 0 (not at all) to 3 (nearly every day). We created composite scores ranging from 0 to 27; higher scores indicate more depressive symptoms (Cronbach’s alpha = .85). Loneliness was measured by the UCLA 3-Item Loneliness Scale (Hughes et al., 2004). Respondents rated each item from 1 (hardly ever) to 3 (often). Their responses were summed, with higher scores reflecting greater levels of loneliness (Cronbach’s alpha = .86).

Technology-related characteristics include technology support, technology stress, and self-conducting online questionnaire. Technology support was measured by whether they received help learning a new technology during the pandemic. The participants were asked, “During the COVID-19 outbreak, have you learned a new technology or program to go online?” and “Has anyone helped you with that or did you learn that on your own?” New technology or programs included a smartphone, computer, tablet, or programs such as Zoom or FaceTime. We recoded their responses into three categories: didn’t learn new technology, learned a new technology by self, and learned a new technology with help from others. Technology stress was measured by a 3-item subscale from a 16-item scale developed by Nimrod (2018) to understand older adults’ stress regarding technology. The original scale includes five factors to measure different technology stressors: overload, invasion, complexity, privacy, and inclusion. We used the complexity subscale to capture respondents’ potential sense of incompetence regarding learning, using, and mastering a new technology (Cronbach’s alpha = .92). An example item is: “The constant developments and upgrades in the technology are a burden for me.” Participants were asked to rate each item from 1 (strongly disagree) to 5 (strongly agree). The scores were added, and higher numbers indicate higher levels of stress on complexity of technology. Lastly, we also controlled for whether the respondents completed the survey by telephone interview (coded as 1) or self-conducting online questionnaire that may reflects their technology skills and experiences.

Analysis Strategies

To test Hypothesis 1, we conducted t-tests to explore significant differences in ICT use for social contact, shopping and health services before and during the pandemic. For Hypothesis 2, we carried out latent class analysis (LCA) to identify the classification of ICT users among older Asian Americans based on the 14 variables of ICT use for four purposes. We fitted LCA models with two to five classes. We determined the optimal class based on the interpretability of each class and model fit statistics, including Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and p-value for Vuong-Lo-Mendell-Rubin likelihood ratio test for models with k classes versus k-1 classes. Larger decreases in AIC and BIC between two models indicate improvements in model fit. A higher entropy reflects a better quality of classification. To examine Hypothesis 3, we used multinomial logistic regression to estimate the effects of ethnicity and acculturation on the predicted class membership by LCA while controlling for sociodemographic, social support, health status, and technology-related characteristics. A small amount (7%, n = 37) of missing values across predictors in regression analysis were addressed by multiple imputation. Data cleaning and bivariate and regression analysis were conducted in Stata 17.0, and LCA was done in MPlus 8.7.

Results

Table 1 shows the descriptive and t-test results to compare ICT use before and during the pandemic. The older Chinese and Korean Americans significantly increased their online shopping with others (15%–20%, p < .05), e-contact with doctors (9%–21%, p < .01), and telehealth (10%–33%, p < .01) during the pandemic.

Table 1.

Characteristics of Study Sample (N = 513).

	% Or M (SD)
Demographics
Age
60–69	51.66
70–79	35.67
>80	12.67
Female	69.73
Married	66.21
Ethnicity
Chinese	76.61
Korean	23.39
Socioeconomic status
Education
Never go to school or elementary school	25.29
Middle or high school	43.92
Associate degree or more	30.78
Difficulty with living
Very	13.65
Little	47.37
None	33.33
Don’t know or decline to answer	5.65
Social support
Receive emotional support from family and friends at least once a week	68.32
Acculturation
Length of stay in United States (years)
<10	13.98
11–20	22.05
21–29	25.39
31–40	26.18
>40	12.4
English skills (range: 3–12)	4.61 (2.04)
Physical and mental health
Chronic illnesses	1.43 (1.21)
Activities of daily living (range: 2–18)	6.56 (1.63)
Instrumental activities of daily living (range: 1–21)	9.90 (3.51)
Depression (range: 0–27)	5.59 (5.00)
Loneliness (range: 3–9)	4.61 (1.84)
Technology-related characteristics
Technology support
Didn’t learn new technology	57.79
Learned new technology by self	10.26
Learned new technology with help from others	31.95
Technology stress (range: 0–15)	10.48 (3.53)
Telephone/online survey
Self-administered online questionnaire	67.44
Telephone interview	32.56
Technology use
Use email, texts, or social media messages to contact family or friends
Before COVID-19
At least daily/a few times a week	58.1
About once a week/less than once a week	26.88
Never	15.02
During COVID-19
At least daily/a few times a week	51.48
About once a week/less than once a week	33.53
Never	14.99
Use video calls to contact family or friends
Before COVID-19
At least daily/a few times a week	35.45
About once a week/less than once a week	41.19
Never	23.37
During COVID-19
At least daily/a few times a week	31.49
About once a week/less than once a week	44.75
Never	23.76
Order groceries online by self
Before COVID-19	16.08
During COVID-19	18.90
Order groceries online with someone else*
Before COVID-19	15.28
During COVID-19	20.16
Use email, texts, or portal messages to contact doctors**
Before COVID-19	9.31
During COVID-19	21.54
Use telehealth**
Before COVID-19	10.43
During COVID-19	33.73
Use the internet to get information of COVID-19	68.06
Use social media to post, comment, or review anything related to COVID	52.77

Note. *p < .01; **p < .001 in Vuong-Lo-Mendell-Rubin likelihood ratio test for differences between ICT use before and during the pandemic.

We selected a three-class model as our final model in the LCA. As shown in Table 2, the three-class model had the highest decrease in AIC (304.729) and BIC (224.163) compared to the differences between the four-class and three-class models (ΔAIC = 239.568, ΔBIC = 159.004) and between the five-class and four-class models (ΔAIC = 118.626, ΔBIC = 38.06). Although the three-class model did not have the best separation of classes statistically (indicated by entropy), it had the best interpretability than other models.

Table 2.

Comparison of LCA Models With Two to Five Classes.

Classes	df	AIC	BIC	Entropy	p
2	82804	8359.328	8516.218	.828	<.001
3	82791	8054.599	8292.055	.818	.160
4	82780	7815.031	8133.051	.862	<.001
5	82762	7696.405	8094.991	.876	.114

Note. AIC = Akaike information criterion, BIC = Bayesian information criterion; p-value for Vuong-Lo-Mendell-Rubin likelihood ratio test for N-1 classes (H0) versus N classes.

Table 3 reports the prevalence and item-response probabilities of each group in the three-class model. The first group was “limited users,” accounting for 22.97% of the sample. Comparing to the other two groups, older adults in this group were less likely to use ICT for social contact, shopping, health care or COVID-19 related purposes before and during the pandemic. The second and largest group was “expanding users” (52.81%), who were characterized by increasing their ICT use during the pandemic. Their probabilities of using ICT for all purposes were higher than the limited group before the pandemic, and they used ICT for more purposes during the pandemic, especially telehealth (likelihoods range from .02 to .27). Most older adults in this group also used ICT for COVID-19 information seeking. The third group was “active users,” accounting for 24.22% of the sample. Active users also had the highest probabilities of shopping online before and during the pandemic and using ICT for COVID-19 information seeking. Moreover, they had larger increases in the probabilities of using ICT for health care (from 31% to 58% for e-contact with doctors and from 30% to 60% for telehealth), compared to expanding users.

Table 3.

Three-Class Model of Technology Use Among Chinese Older Immigrants.

	Inactive user	Expanding user	Active user
Latent class prevalence	22.97%	52.81%	24.22%
Item-response probabilities
Use email, texts, or social media messages to contact family or friends before COVID-19
	.133	.781	.566
	.247	.212	.417
	.620	.007	.017
Use email, texts, or social media messages to contact family or friends after COVID-19
	.086	.720	.472
	.276	.280	.519
	.639	.000	.009
Use video calls to contact family or friends before COVID-19
	.030	.448	.461
	.310	.418	.499
	.660	.134	.040
Use video calls to contact family or friends after COVID-19
	.027	.395	.414
	.264	.477	.556
	.709	.128	.029
Ordered groceries online by self before COVID-19	.013	.047	.557
Ordered groceries online by self after COVID-19	.023	.081	.593
Ordered groceries online with someone else before COVID-19	.041	.032	.530
Ordered groceries online with someone else after COVID-19	.046	.117	.536
Use email, texts, or portal messages to contact doctors before COVID-19	.038	.019	.313
Use email, texts, or portal messages to contact doctors after COVID-19	.018	.135	.583
Use telehealth before COVID-19	.054	.037	.301
Use telehealth after COVID-19	.212	.273	.595
Use internet to get information of COVID-19	.300	.768	.855
Use social media to post, comment, or review anything related to COVID-19	.083	.641	.706

Table 4 reports the results of multinomial logistic regression to predict the group membership of ICT users based on the three-class LCA model. Older Chinese Americans were more likely to be expanding and active users than their Korean counterparts. Those with better English proficiency were more likely to be active users than those with limited English proficiency. In addition, younger age, higher educational degrees, better economic status, emotional support, less functioning limitations, technology support, and less technology stress were significantly associated with being an expanding or active ICT user.

Table 4.

Multinomial Logistic Regression Results for Variables Predicting Three-Class Model.

	Expanding User		Active User
	RRR	CI	RRR	CI
Ethnicity
Chinese	3.33**	1.38, 8.05	15.03***	5.17, 43.72
Acculturation
Length of stay in United States
11–20	1.09	.45, 2.68	.39	.13, 1.24
21–29	.84	.35, 2.05	.42	.14, 1.26
31–40	.99	.40, 2.47	.26*	.08, .83
>40	1.32	.42, 4.20	1.29	.29, 5.72
English skills	1.14	.93, 1.40	1.42**	1.12, 1.81
Sociodemographic
Age
70–79	1.17	.65, 2.12	.30**	.13, .68
>80	.51	.22, 1.20	.36+	.11, 1.21
Female	1.44	.81, 2.56	1.06	.50, 2.28
Married	1.41	.80, 2.50	1.22	.58, 2.58
Education
Middle or high school	2.32**	1.23, 4.38	3.11*	1.23, 7.82
Associate degree or beyond	3.11**	1.37, 7.06	4.63**	1.57, 13.64
Difficulty with living
Very	1.20	.51, 2.84	1.20	.35, 4.12
Little	1.04	.56, 1.93	2.92*	1.21, 7.01
Don’t know or decline to answer	.27*	.09, .86	.12**	.02, .60
Social support
Emotional support from network	2.38**	1.33, 4.27	3.75***	1.78, 7.92
Physical and mental health
Chronic illnesses	1.25+	.97, 1.61	1.30	.94, 1.81
ADL	.97	.80, 1.17	1.30+	1.00, 1.70
IADL	.88*	.80, .97	.83*	.71, .97
Depression	.95	.90, 1.02	.93	.85, 1.03
Loneliness	1.14	.97, 1.33	1.18	.92, 1.51
Technology-related characteristics
Technology support
Learned new technology by self	3.96+	.85, 18.41	9.35**	1.85, 47.36
Learned new technology with help	2.42*	1.23, 4.74	4.96***	2.14, 11.48
Technology stress	.96	.87, 1.06	86*	.76, .97
Completed survey by phone call	.45*	.22, .91	.05***	.02, .18

Note. Observations: 513. Results are based on 5 multiple imputed datasets. Comparisons are to inactive user class. Reference categories were 60–69 for age, didn’t learn new technology for technology support; never attended school or attended elementary school for education; none for difficulty with living; and 10 years for length of stay in the United States. ADL = activities of daily living; CI = confidence interval; IADL = instrumental activities of daily living; RRR = relative risk ratio.

*p < .05. **p < .01. ***p < .001.

Discussion

Focusing on older Chinese and Korean Americans, this study examined changes in ICT use during the COVID-19 pandemic and identified the typology of ICT users in this population. We found that the pattern of ICT use among older Asian Americans is multifaceted and dynamic rather than dichotomous and static. In addition, the group membership of ICT users was associated with Chinese or Korean ethnicity and English proficiency.

The comparative analysis results partially support Hypothesis 1. Older Chinese and Korean Americans increased their ICT use for online shopping, e-contact with doctors, and telehealth. Unlike the low rate of ICT use for shopping and health care services, most older adults (76%–85%) in our sample had already used ICT for social contact, leaving little room to increase during the pandemic. This may be due to different levels of capability and sense of security and necessity required for using ICT for different purposes. Communication with loved ones is often seen as socially and mentally important and yet requires lower comprehension skills, whereas online shopping and health care services demand higher technical skills and may cause fears of being a victim of cybercrime, such as online payment fraud and privacy issues. During the pandemic, more older adults used ICT for shopping and health care services, likely due to public lockdowns and worries about virus infection. In particular, the increase in ICT use for health care services was robust. More than half of these Asian older adults used ICT to search for information on virus prevention. These results indicate that health-related ICT use might be increasingly important for older ICT users.

The three-class LCA model supports Hypothesis 2. Of special interest are expanding users, who largely increased their ICT use for shopping and health care services during the pandemic. The existence of this group as the largest group in our sample indicates the improvement in ICT use among at least half of these older Asian Americans (52.20%). Compared to limited users, expanding users may have more interest in ICT, yet they may need more support than active users. Given its prevalence, this group of older adults might be the target customers of future technology support services, and their needs and skills should be considered in future ICT program design. However, we don’t know whether their growth in ICT use will continue. In future research, it is worth following the trajectory of ICT use in this group.

The regression results partially support Hypothesis 3. It is not surprising to find that older Chinese Americans were more likely to be expanding and active ICT users given their stronger social network and engagement than older Korean Americans (Kim et al., 2013). Similarly, it is not hard to understand that older Asian Americans with better English proficiency were more likely to be active ICT users given the primary language of most software and websites in the United States. However, we did not find a clear association between the length of stay and ICT use patterns among these older Asian Americans. As suggested by other studies, the length of stay may need to combine with variables (e.g., age at immigration created by the interaction term of length of stay by age) to understand older immigrants’ behavior and well-being (Guo et al., 2019; Lee, 2011).

Several limitations should be acknowledged in this study. First, because our study had a cross-sectional design, we could not assess how ICT use changed over time in the sample. Second, there might be selection bias. We collaborated with community agencies for recruitment, and those who joined our survey may have been more likely to engage in virtual communication and social activities provided by those agencies. Last, because ICT use was self-reported, respondents may have over- or underreported their use.

Despite these limitations, our analyses have strong implications for future research. First, because more interventions have now shifted to remote delivery models, more studies are needed to understand Asian older adults’ concerns and challenges (e.g., language barriers, culturally sensitive services and information, heterogeneous norms and preferences) regarding using ICT for health-related purposes, given the relatively low probability of using telehealth during the pandemic in our sample, even among expanding and active users. Second, to better understand the potential health benefits of ICT use among older adults, future studies could explore whether the typology of ICT use during the pandemic has long-term effects on health and mental health outcomes and whether the association varies by gender, age group, and SES. Last, it is also important to further investigate why older adults in the limited user group did not use ICT for shopping and health care services and how to better support them in the future.

The findings of this study offer helpful guidance for future development of ICT-based interventions among Asian older adults. Because more older adults are now open to using ICT for personal or health purposes following the pandemic, more educational interventions are needed to support their ICT learning needs. The multilingual design of apps and websites may boost the ICT use among older Asian Americans. Meanwhile, practitioners should be aware of the existence of the limited user group, whose needs may be missed without community outreach and inclusive program design. For example, ICT-based interventions to improve health knowledge among Asian older adults may consider adding a telephone- or radio-based option to accommodate the needs of older inactive users. Last, it may be possible to develop interventions that pair active and expanding users with limited users to form ICT learning groups, which may further increase the social engagement of Asian older adults.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Rutgers Institute for Health, Health Care Policy, and Aging Research (RCMAR Pilot Grant).

Ethical Statement

ORCID iDs

Jinyu Liu

Yifan Lou

Ethan Siu Leung Cheung

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