Loneliness, Social Isolation, and Digital Health Literacy Among Older Women Living Alone in South Korea During the COVID-19 Pandemic

Minhwa Hwang; Gahye Kim; Seonghyeon Lee; Yeon-Hwan Park

doi:10.30773/pi.2024.0210

Psychiatry Investig > Volume 22(1); 2025 > Article

Hwang, Kim, Lee, and Park: Loneliness, Social Isolation, and Digital Health Literacy Among Older Women Living Alone in South Korea During the COVID-19 Pandemic

Original Article

Psychiatry Investigation 2025;22(1):75-83.

Published online: January 15, 2025

DOI: https://doi.org/10.30773/pi.2024.0210

Loneliness, Social Isolation, and Digital Health Literacy Among Older Women Living Alone in South Korea During the COVID-19 Pandemic

Minhwa Hwang¹

, Gahye Kim²

, Seonghyeon Lee³

, Yeon-Hwan Park^1,³

¹The Research Institute of Nursing Science, Seoul National University, Seoul, Republic of Korea

²College of Nursing, Eulji University, Uijeongbu, Republic of Korea

³College of Nursing, Seoul National University, Seoul, Republic of Korea

Correspondence: Yeon-Hwan Park, PhD College of Nursing and The Research Institute of Nursing Science, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea Tel: +82-2-740-8846, E-mail: hanipyh@snu.ac.kr

Received June 26, 2024 Revised October 23, 2024 Accepted December 9, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

While using digital technologies for social health is widely acknowledged, the relationship between loneliness, social isolation, and digital health literacy remains unknown. This study aimed to assess the levels of loneliness, social isolation, and digital health literacy in older women living alone and to explore the associations between these factors.

Methods

In August 2021, a cross-sectional survey was conducted on 145 older women living alone, selected using convenience sampling. The study used the University of California, Los Angeles Loneliness Scale (version 3), Lubben Social Network Scale-6, and Digital Health Technology Literacy-Assessment Questionnaire. The collected data were analyzed using descriptive statistics, the Mann-Whitney U test, the Kruskal-Wallis test, Spearman’s rho correlation, and a generalized linear model (GLM).

Results

Of the participants, 22.8% (n=33) reported moderate loneliness, 20.7% (n=30) reported severe loneliness, and 36.6% (n=53) were at risk for social isolation. The mean digital health literacy score among participants was 4.85 (SD=6.92), which is relatively low. Importantly, our findings using a GLM to control for covariates revealed a significant association between loneliness (B=-0.013, p=0.018) and digital health literacy.

Conclusion

The increased loneliness experienced during the pandemic may contribute to low digital health literacy, alienating these individuals from the benefits of digital technologies. Therefore, in developing digital health programs or policies, it is imperative to consider the psychosocial status of individuals, including loneliness, while enhancing digital health literacy.

Keywords: Loneliness, Social isolation, Health literacy, Digital divide, Aged

INTRODUCTION

As the global population ages, social isolation is becoming a significant issue. The prevalence of social isolation among community-dwelling older adults is approximately 25%, indicating that one in four older adults experiences social isolation [1]. Social isolation and loneliness are often used interchangeably with similar meanings but are distinct concepts. Social isolation refers to an objectively limited number of social relationships (family and friends), whereas loneliness refers to a subjective and distressing feeling resulting from a discrepancy between one’s desired and actual social relationships [2,3]. Social isolation and loneliness have been associated with significant health risks in older adults, including functional decline [4], cognitive impairment [5], frailty [6], an increased risk of cardiovascular disease [7], and all-cause mortality [8,9]. Living alone is a significant risk factor for both loneliness and social isolation [10,11]. Although women tend to outlive men, they are more likely to experience prolonged periods of functional limitations and older women have been found to report lower levels of subjective well-being [12,13]. Gender differences also play a role in loneliness, with older women generally reporting higher levels of loneliness than older men [12,14]. Older women tend to have lower socioeconomic and health status than older men [13]. Moreover, there is a bidirectional relationship between health status, loneliness, and social isolation. Poor physical and mental health is associated with loneliness, while declines in health and mobility can further contribute to loneliness and social isolation [15,16].

Specifically, the prolonged implementation of physical distancing and stay-at-home measures due to the coronavirus disease-2019 (COVID-19) pandemic has worsened social isolation and loneliness among vulnerable populations, such as older adults living alone. COVID-19-related restrictions on social activities, along with reduced hospital visits and regular check-ups, have led to increased reliance on the Internet and digital technologies for social interaction, health information seeking, and disease management [17]. Digital health literacy is defined as the ability to find, understand, evaluate, and apply health information using digital technologies to promote health and enhance quality of life [18,19]. It is regarded as a “super determinant of health, [20]" as it plays a crucial role in influencing health outcomes, promoting health equity, and bridging the digital divide [21]. Digital health literacy is positively associated with health-promoting behaviors, which in turn positively influences health-related quality of life [22]. Additionally, it is linked to better perceived health status and the absence of psychological distress [23]. Groups with limited digital health literacy, shaped by sociodemographic factors such as age, education level, and living environment, and health-related factors such as perceived health status, may experience widening inequalities as digital health services continue to expand [24-26]. Particularly, older women are known to have less access to and lower usage of digital technology [27]. The rapid advancement and widespread adoption of digital technologies, accelerated by the COVID-19 pandemic, have further highlighted the increasing importance of digital health literacy.

Social isolation and loneliness are modifiable health determinants [28] that can be addressed through digital technology. Ambient-assisted living technologies, including robots, wearables, and smart homes, can help detect, predict, and alleviate loneliness and social isolation [29]. However, effective use of these technologies depends on the individual’s ability to engage with digital health technologies, requiring a certain level of digital health literacy. While several studies have explored the relationship between social isolation, loneliness, and health outcomes, there remains a lack of research focused specifically on the relationship between these factors and digital health literacy among older adults. Existing studies have primarily focused on young adults, examining the relationship between health literacy and loneliness or social isolation [30], as well as the effects of low health literacy and social isolation on mortality [31]. Other research has investigated the impact of loneliness and social isolation on health literacy in patients with cardiovascular diseases [32]. A study conducted among older Swedish adults explored the relationship between eHealth literacy, perceived health status, and psychological distress [23]; however, a limitation was that it measured loneliness only as a subdomain of psychological distress.

Thus, it is important to investigate the impact of social isolation and loneliness on the digital health literacy of older women living alone, who are particularly vulnerable owing to the COVID-19 pandemic. This investigation could provide a foundation for developing strategies to enhance digital health literacy in vulnerable populations and improve the effectiveness of digital health interventions. Therefore, this study aimed to assess the levels of loneliness, social isolation, and digital health literacy in older women living alone and to explore the associations between these factors.

METHODS

Study design and participants

A cross-sectional study was conducted among community-dwelling female older adults living alone in South Korea. Since 84% of the older population in Korea has been diagnosed with at least one chronic disease [33], and digital health literacy is significantly associated with health-promoting behaviors [22], we included participants who had been diagnosed with one or more chronic conditions by a physician as part of the selection criteria. Inclusion criteria were 1) women aged ≥65 years, currently living alone, 2) physician diagnosis of at least one chronic condition, and 3) no severe cognitive impairment (excluding a mini-mental state examination score of ≤17). The minimum number of participants required for a two-tailed test, medium effect size (f²=0.15), a significance level of α=0.05, and a power of 0.9 for the regression analysis calculated using the G*power 3.1.9.7 software (Heinrich-Heine-University, Düsseldorf, Germany) was 130. A total of 147 participants provided informed consent to participate in the study. After screening, two participants were excluded for not meeting the inclusion criteria, resulting in 145 completed surveys.

Data collection

The survey was conducted from August 10 to 13, 2021, in a city located in Gyeonggi-do, South Korea. The city where participants were recruited is a large city with a population of over 500,000, located near the capital, and had an older population ratio of 9.92% as of 2021 [34]. In collaboration with public health centers and branches, recruitment notices for research participants were disseminated in facilities such as public health and welfare centers. Participants were selected through convenience sampling. Those who provided consent to participate in the study completed a survey after screening for chronic diseases and cognitive function. This study adhered to the principles of the Declaration of Helsinki and was conducted after approval by the Institutional Review Board(IRB No. 2106/003-011) of the corresponding author’s institution. All participants were informed in detail about the purpose of the study and its procedures and were asked to provide written consent before responding to the survey.

Measurements

Loneliness was assessed using the University of California, Los Angeles (UCLA) Loneliness Scale version 3. Russell [35] developed and revised the scale, which comprised 20 questions with responses on a 4-point Likert scale (1=never, 2=rarely, 3=sometimes, 4=always). This study used the Korean version of the questions translated and validated by Jin and Hwang [36]. The overall score was the sum of the scores of each question, ranging from 20-80 points. Higher scores indicate greater loneliness. We categorized loneliness scores into three levels based on previous studies; 20-34 as mild loneliness, 35-49 as moderate loneliness, and 50-80 as severe loneliness [37]. Upon developing the scale, reliability across different samples ranged from Cronbach’s α=0.89-0.94 [35]. The reliability of the Korean version was 0.93 in the validation study [36] and 0.90 in this study, which is satisfactory.

Social Isolation was measured using the Lubben Social Network Scale-6 (LSNS-6). The LSNS-6 is an instrument designed to screen for social isolation by quantifying the number of social contacts and perceived social networks based on family and friend relationships. This scale comprises six questions; three questions about family relationships and three questions about friend relationships [38]. Scoring ranges from 0-30, with an overall score ≤12 indicating a risk of social isolation. Higher scores indicate better social networks. The reliability of the scale was Cronbach’s α=0.83 when it was developed [38] and Cronbach’s α=0.87 as it was validated with Korean older adults in a validation study with Asian older adults [39]. In this study, Cronbach’s alpha=0.78, which is acceptable.

Digital health literacy was assessed using the Digital Health Technology Literacy Assessment Questionnaire (DHTL-AQ) [19]. The DHTL-AQ is divided into two domains; digital functional literacy and digital critical literacy. The former comprises three categories: “information and communications technology (ICT) terms,” “ICT icons,” and “use of an app,” while the latter focuses on “evaluating reliability and relevance of health information.” In digital functional literacy, “ICT terms” consists of 11 items related to Internet use on computers and smartphones, including terms such as “application,” “update,” and “Bluetooth.” “ICT icons” includes nine items that match icons of Internet-related terms on computers and smartphones with their corresponding terms. “Use of an app” includes nine items that assess how well individuals can independently perform functions available in apps on smartphones, tablets, and smart pads. In digital critical literacy, “evaluating the reliability and relevance of health information” uses five items to assess cognitive abilities in critically analyzing and effectively utilizing digital health information for making health-related decisions. The scale is dichotomous, with 34 questions. The cutoff point is 22, allowing scores of ≥22 to be classified as high and those of <22 as low. During the development of DHTL, the internal consistency of the scale was excellent (Cronbach’s α=0.95) [19], and it was also satisfactory in this study (Cronbach’s α=0.94).

Characteristics of participants

Health-related characteristics included a number of chronic diseases and perceived health status. Perceived health status was assessed using a scale developed by Speake et al. [40], which included three questions rated on a 5-point Likert scale. Higher scores, ranging from 3 to 15 points, indicate a better perceived health condition. In this study, Cronbach’s α was 0.73. Digital-related characteristics were assessed using three questions from the 2020 Digital Divide survey in South Korea [41]. These questions included smartphone ownership (yes, no), home Internet access (yes, no), and Internet use. We categorized Internet users as those who had used the Internet in the past 30 days and non-Internet users as those who had used the Internet for ≥30 days or had never used the Internet. Sociodemographic characteristics, including age and education, were assessed. Age was measured as a continuous variable and categorized into 65-74 years, 75-84 years, and ≥85 years. Education was also measured as a continuous variable and categorized into “middle school graduate or less” and “more than middle school graduate.”

Data analysis

Descriptive statistics determined the characteristics of study participants and their digital health literacy levels. Due to the non-normal distribution of digital health literacy, we used non-parametric methods, such as the Mann-Whitney U and Kruskal-Wallis tests. Spearman’s rho correlation examined the relationship between digital health literacy, perceived health status, and the number of chronic diseases. A generalized linear model (GLM) with a gamma distribution was used to identify factors associated with digital health literacy, with scores transformed by adding 1. Statistical significance was set at p<0.05, and data analysis was performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).

RESULTS

Descriptive analysis

The average age of the participants was 77.50 years, with those aged 75-84 years accounting for 64.1% of the total participants. The average years of education was 5.63, and 126 participants (86.9%) had an education level of middle school or less. Approximately 70% of all participants owned a smartphone, and >50% of the older women living alone had no home Internet connection. There were 44 participants (approximately 30%) who had used the Internet in the past 30 days. Participants had an average of 4.46 chronic diseases. The disease with the highest prevalence was hypertension (108, 74.5%), followed by hyperlipidemia (71, 49.0%), osteoarthritis (72, 49.7%), back pain (47, 32.4%), osteoporosis (45, 31.0%), and other conditions (126, 86.9%). The average perceived health score was 8.67/15, indicating relatively good health (Table 1).

Among the older women living alone, the average loneliness score was 36.45 out of 80 (SD=13.27), indicating a significant difference in levels of loneliness among the participants. Of the 145 participants, 33 (22.8%) reported moderate loneliness, 30 (20.7%) reported severe loneliness, and 53 (36.6%) were at risk of social isolation. The participants’ average digital health literacy score was 4.85 (SD=6.92), with scores ranging from 0-32 (Table 2). Of the 145 participants, only three had a high level of digital health literacy, scoring above the DHTL cutoff point of 22. Their mean score of DHTL was 28.67 points (SD=3.51). Most female older adults living alone (143, 98.62%) showed low digital health literacy. The subcategories with the lowest and highest mean scores were “evaluating the reliability and relevance of health information” and “use of an app.” In the ICT term category, the lowest response item was “wearable device (2, 1.4%),” and the highest response item was “update (synchronization) (29, 20.0%).” In the “evaluating reliability and relevance of health information” category, the lowest response question was “I can check if the same information is provided by other websites or on the Internet (16, 11.0%),” and the highest response question was “I can judge whether the information on the Internet or digital health was used for commercial benefit (31, 21.4%).” In the ICT icons category, the lowest response item was “social media (5, 3.4%),” and the highest response item was “voice assistant (30, 20.7%).” In the “use of an app” category, the lowest response item was “I can find more reliable apps by comparing different apps (17, 11.7%),” and the highest response item was “I can set preferences (sound, security, display, and notifications) for the app (58, 40%).”

Association among digital health literacy, social isolation, loneliness, and covariates

Differences in DHTL scores by digital-related characteristics, such as smartphone ownership, home Internet access, and Internet use, were all statistically significant (Table 3). Participants who owned a smartphone (p<0.001), had home Internet access (p<0.001), and were Internet users (p<0.001) had significantly higher mean DHTL scores than those who did not. There was no statistically significant difference between DHTL scores based on social isolation and loneliness classification (Table 3). Correlation analysis revealed significant correlations between DHTL scores and several continuous variables, including age (ρ=-0.537, p<0.001), education (ρ=0.373, p<0.001), number of chronic diseases (ρ=-0.171, p=0.040), perceived health status (ρ=0.216, p=0.009), and loneliness scores (ρ=-0.190, p=0.022), whereas social isolation did not show a significant correlation (Table 4).

According to the results of GLM, age (B=-0.069, p<0.001), education (B=0.051, p=0.003), perceived health status (B=0.052, p=0.030), home Internet access (B=0.917, p=0.001), smart-phone ownership (B=0.306, p=0.044), and loneliness (B=-0.013, p=0.018) were identified as factors influencing digital health literacy (Table 5).

DISCUSSION

In this study, 20.7% of older women living alone experienced severe loneliness, and 36.6% were at risk of social isolation. These rates are significantly higher than the prevalence of loneliness (4.1%) and social isolation (17.8%) reported in a study of South Koreans aged 15-74 years [13]. The high prevalence of loneliness and social isolation identified in this study is largely attributed to the effects of intensive social distancing measures implemented to contain the spread of COVID-19. A meta-analysis assessing the prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic reported rates of 28.6% and 31.2%, respectively [42], which closely aligns with the findings of this study. In that meta-analysis, studies conducted more than three months after the onset of the pandemic reported significantly higher prevalence estimates compared to those conducted at the pandemic’s outset [42]. This suggests that the prolonged nature of the pandemic, coupled with the continued enforcement of quarantine measures, has resulted in a cumulative effect of psychological distress, including loneliness and social isolation [42]. Previous research has indicated that loneliness increases with age and is more prevalent among women than men [9,43,44]. These findings can be explained by the stronger association between socioeconomic status, health status, and loneliness in older women compared to men [43]. Conversely, living alone has been identified as a factor associated with loneliness more significantly among older men than older women [44]. This discrepancy may be attributed to the fact that older women are more likely to actively build social networks, whereas older men often rely primarily on their spouses or partners for intimacy [44,45]. As this study focused exclusively on older women living alone during the COVID-19 pandemic, the findings may not be generalizable to the broader population. Thus, longitudinal studies are needed to evaluate the prevalence and associated factors of loneliness and social isolation in the older population, stratified by gender and age, across the periods before, during, and after the COVID-19 pandemic.

The rate of smartphone ownership and Internet use among older women living alone who participated in this study were lower than the national average for older adults in South Korea [46], and their digital health literacy was generally low. This discrepancy may be due to the differences in participant selection criteria between our study and previous research. In the DHTL development and validation study by Yoon et al. [19], smartphone ownership was one of the selection criteria, which was not the case in our study. As a result, participants with low mobile app task ability in Yoon et al.’s study had an average DHTL score (14.3), which was higher than the scores in our study. Our findings showed that participants who owned a smartphone and had home internet access demonstrated significantly higher digital health literacy, while internet use was not significantly associated with digital health literacy. Despite South Korea having one of the highest smartphone and internet penetration rates globally [47], the proportion of internet users in our study was less than half of the proportion of smartphone owners. This rate was also less than half of what was reported in a previous study comparing internet usage and factors related to eHealth literacy among older adults living in low-income areas in the United States and South Korea [48]. Given the significant differences in the proportion of individuals living alone and education levels between participants in that study and ours, there is an urgent need for largescale research to better understand internet usage and digital health literacy among older adults living alone. Although research on the direct link between smartphone ownership, internet access, and digital health literacy is limited, our findings align with previous studies that report significant relationships between smartphone use, internet access, and internet usage [49,50]. A study conducted in Chile showed that smartphones can expand internet access for underserved populations [50]. Smartphone-only users were mainly women and individuals with lower education and income, whereas those using both smartphones and computers engaged in a broader range of online activities [50]. Since this study did not fully examine internet usage patterns, future research should examine the relationship between digital health literacy and factors like the types of digital devices used for internet access, the number of functionalities utilized, and the user’s digital skill level.

Our study found that digital health literacy was lower among older women living alone who were older and had lower levels of education. This aligns with previous studies that have identified age and education level as predictors of eHealth literacy among older adults [24,51,52]. This can be explained by the decline in physiological and cognitive abilities that typically accompany aging, making it harder for older adults to access digital health information [51]. However, the impact of age and education on digital health literacy has shown inconsistent results depending on the study population. In one study comparing factors related to eHealth literacy between older and younger adult Internet users, 63% of the older participants had a college degree or higher, and no significant association was found between age, education level, and eHealth literacy [53]. Although economic status is significantly associated with eHealth literacy [51,54], research has found that education-related disparities have been found to be larger than those related to income [54]. Given that most participants in this study had an education level of middle school or lower, education likely played a significant role in influencing their digital health literacy. Since economic status was not a factor considered in this study, caution is required when interpreting these results. Therefore, it is essential to identify vulnerable older adults with low digital health literacy and to make efforts to improve their digital health literacy, taking their sociodemographic characteristics into account.

The study findings indicate that the loneliness experienced by older women living alone is associated with their digital health literacy and aligns with the findings of a previous study that examined the relationship between eHealth literacy, perceived health status, and psychological distress among Swedish older adults during the COVID-19 pandemic [23]. Additionally, previous research has indicated that loneliness or social isolation is related to health literacy, digital literacy, and digital health literacy [30-32,55]. This suggests that emotional fatigue resulting from loneliness may interfere with the process of learning to use new digital health technologies or the ability to find, understand, and evaluate health information using digital technology, indicating a negative relationship between loneliness and digital health literacy [56]. Furthermore, since loneliness is significantly related to declined cognitive functions, such as immediate and delayed recall, this may explain the negative association between loneliness and digital health literacy [57]. Since this study did not measure or consider variables related to learning motivation, learning ability, and cognitive function, their mediating effects should be explored through follow-up research. Nevertheless, the study findings imply that the psychosocial status of older adults should be considered in implementing digital health literacy programs or digital health interventions for the older population.

The present study found no significant association between the risk of social isolation and digital health literacy. This can be explained by the fact that the participants were female older adults living alone and that social isolation refers to a lack of objective social relationships or social support, unlike loneliness, which reflects subjective feelings. A study has shown that emotional problems, such as depression and loneliness, are more prevalent in women than in men [13]. The study investigating the relationship between social isolation, loneliness, and mental health status among Korean older adults found that men were significantly associated with social isolation, while women were related to loneliness [13]. However, another study demonstrated that young men are the most vulnerable to loneliness in individualistic cultures [58]. Therefore, it is essential to examine the relationship between social isolation, loneliness, and digital health literacy according to sex and age.

Nevertheless, this study has limitations. As a cross-sectional study, it is limited in its ability to establish causal relationships between variables. The digital health literacy instrument used in this study lacks validation across diverse population groups, which requires caution if comparing and interpreting results with those of previous studies.

In conclusion, the present study revealed that older women living alone are particularly vulnerable owing to sociodemographic factors, limited access to digital technology, and psychosocial conditions such as loneliness and social isolation. The findings suggest that loneliness is significantly associated with digital health literacy among older women living alone. The increased loneliness during the pandemic may have further reduced their digital health literacy, isolating them from the benefits of digital technologies. Therefore, when developing digital health programs or policies, it is essential to consider psychosocial factors, such as loneliness, in conjunction with efforts to improve digital health literacy.

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: all authors. Data curation: Minhwa Hwang, Gahye Kim, Seonghyeon Lee. Formal analysis: Minhwa Hwang. Funding acquisition: Yeon-Hwan Park. Investigation: Minhwa Hwang, Gahye Kim, Seonghyeon Lee. Methodology: all authors. Project administration: Yeon-Hwan Park. Resources: Minhwa Hwang, Gahye Kim, Seonghyeon Lee. Software: Minhwa Hwang, Gahye Kim, Seonghyeon Lee. Supervision: Yeon-Hwan Park. Validation: Yeon-Hwan Park. Visualization: Minhwa Hwang. Writing—original draft: Minhwa Hwang. Writing—review & editing: all authors.

Funding Statement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A2C2006222).

ACKNOWLEDGEMENTS

None

Table 1.

Characteristics of participants (N=145)

Characteristics	Value
Sociodemographic characteristics
Age (yr)	77.50±4.83
65-74	40 (27.6)
75-84	93 (64.1)
≥85	12 (8.3)
Years of education	5.63±3.68
Educational level
≤Middle school graduate	126 (86.9)
>Middle school graduate	19 (13.1)
Digital-related characteristics
Smartphone ownership
Yes	99 (68.3)
No	46 (31.7)
Home Internet access
Yes	52 (35.9)
No	93 (64.1)
Internet use
Internet user	44 (30.3)
Internet non-user	101 (69.7)
Health-related characteristics
Number of chronic diseases	4.46±2.51
Chronic disease
Hypertension	108 (74.5)
Hyperlipidemia	71 (49.0)
Osteoarthritis	54 (37.2)
Low back pain	47 (32.4)
Osteoporosis	45 (31.0)
Others	126 (86.9)
Perceived health status	8.67±2.71

Values are presented as number (%) or mean±standard deviation.

Table 2.

Participants’ loneliness, social isolation, and digital health literacy (N=145)

Variables	Mean±SD	Median	Min	Max	Possible range
Loneliness (UCLA loneliness scale)
Mild loneliness (N=82)	27.09±3.87	27	20	34
Moderate loneliness (N=33)	39.42±4.19	37	35	48
Severe loneliness (N=30)	58.77±6.27	59	50	77
Total	36.45±13.27	32	20	77	20-80
Social isolation (LSNS-6)
Risk for social isolation (N=53)	7.36±2.98	8	0	11
No risk (N=92)	16.77±3.94	16	12	28
Family subscale	6.61±3.43	7	0	15	0-15
Friend subscale	6.72±3.65		0	15	0-15
Total	13.33±5.81	13	0	28	0-30
Digital health literacy (DHTL subdomain)
Digital functional literacy
ICT terms	0.99±2.05	0	0	9	0-11
ICT icons	1.21±2.05	0	0	9	0-9
Use of an app	1.81±2.73	0	0	9	0-9
Digital critical literacy
Evaluating reliability and relevance of health information	0.84±1.68	0	0	5	0-5
Total	4.85±6.92	2	0	32	0-34
High (N=3)	28.67±3.51	29	25	32
Low (N=142)	4.35±6.03	2	0	21

SD, standard deviation; UCLA, University of California, Los Angeles; LSNS-6, Lubben Social Network Scale-6; DHTL, digital health technology literacy; ICT, information and communication technology

Table 3.

Differences in DHTL scores according to digital-related characteristics, social isolation, and loneliness (N=145)

Characteristics	Digital health literacy	U	p
Digital-related characteristics
Smartphone ownership		1,051.00	<0.001
Yes (N=99)	6.47±7.56
No (N=46)	1.35±3.19
Home Internet access		702.50	<0.001
Yes (N=52)	10.33±8.33
No (N=93)	1.78±3.16
Internet use		790.00	<0.001
Internet user (N=44)	10.70±8.68
Internet nonuser (N=101)	2.30±3.85
Social isolation		2,499.50	0.795
Risk for social isolation (N=53)	3.81±5.01
No risk (N=92)	5.45±7.77
Loneliness		3.40^*	0.183
Mild (N=82)	5.83±7.80
Moderate (N=33)	4.24±5.92
Severe (N=30)	2.83±4.65

Data are presented as mean±standard deviation. The Mann-Whitney U test was used.

^* result of Kruskal-Walliss H test.

DHTL, digital health technology literacy

Table 4.

Correlations between DHTL and continuous variables (N=145)

Variables	1	2	3	4	5	6	7
1. DHTL	1
2. Age	-0.537^**	1
3. Education	0.373^**	-0.197^*	1
4. No. of chronic disease	-0.171^*	0.012	-0.123	1
5. Perceived health status	0.216^**	-0.036	0.161	-0.479^**	1
6. Social isolation	0.016	0.052	-0.091	-0.269^**	0.325^**	1
7. Loneliness	-0.190^*	0.051	-0.056	0.259^**	-0.425^**	-0.543^**	1

^* the correlation is significant at the 0.05 level (two-tailed);

^** the correlation is significant at the 0.01 level (two-tailed).

DHTL, digital health technology literacy

Table 5.

Generalized linear model for examining associated factors of digital health literacy (N=145)

Variables	B	SE	Wald	p
Constant	5.950	1.177	25.55	<0.001
Age	-0.069	0.014	25.70	<0.001
Education	0.051	0.017	9.10	0.003
Perceived health status	0.052	0.024	4.70	0.030
Number of chronic diseases	-0.011	0.026	0.18	0.669
Home Internet access (yes)	0.917	0.266	11.85	0.001
Smartphone ownership (yes)	0.306	0.152	4.06	0.044
Internet use (internet user)	-0.114	0.283	0.16	0.689
Loneliness	-0.013	0.006	5.63	0.018
Social isolation (risk for social isolation)	0.193	0.153	1.59	0.207
χ²=140.557 (p<0.001)
LL=-330.642, deviance/df=0.556

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