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Although digital health technologies (DHTs) help many people maintain a healthy life, including those of advanced age, these technologies are of little use to older adult populations if they are not being adopted in daily life. Thus, it is critical to identify ways to help older adults recognize and try new technologies and maintain their use of them to maximize the benefits of these technologies in a digital-based society.
Our study aimed (1) to assess the current usage of DHT among older adults in Hong Kong and (2) to examine how high and low levels of eHealth literacy in this group affects the relationship between the Technology Readiness and Acceptance Model (TRAM) and attitudes and intention toward DHT.
A total of 306 adults over 60 years of age in Hong Kong participated in this study. After conducting confirmatory factor analysis to validate the measurement model, the hypothesized model was tested using structural equation modeling.
Optimism was significantly related to perceived usefulness, while optimism, innovativeness, and discomfort were significantly associated with perceived ease of use. Both perceived usefulness and perceived ease of use were significantly linked to attitude toward the use of DHTs. Meanwhile, attitude significantly predicted usage intention. Additionally, the results revealed the differences in the relationships of the TRAM between participants with high and low levels of eHealth literacy. The influence of optimism and innovativeness on perceived ease of use was stronger for the higher-level group than for the lower-level group, and the influence of discomfort for the higher-level group was much weaker.
The findings provided partial support for the impact of eHealth literacy on encouraging older adults to use DHT and obtain health benefits from it. This study also suggests providing assistance and guidelines for older adults to narrow the aging-related technology gap and to further explore the associations of eHealth literacy, the TRAM, and actual behaviors.
Rapid advances in medical science and technology have made it possible to detect diseases much earlier and provide appropriate treatment for those that were previously considered incurable. These advances have also enabled a variety of advanced health-related services and treatment techniques to be received more comfortably and effectively, which may lead to increased life expectancy [
Among various technologies to help people maintain a healthy life, digital health technology (DHT), which applies digital transformation technology to the health care field and includes mobile health (mHealth) apps, wearable devices, electronic health records, and electronic medical records, is an innovative and efficient means to offer people a healthier life, particularly for older populations [
Although DHT is presented as an important way to achieve a healthy life and new technologies have been designed and developed to provide a better quality of life for older individuals, these technologies are purposeless unless older adults use them. Therefore, identifying ways to help older people recognize and try new technologies and maintain their use of these technologies is critical to enable them to benefit from these technologies in a digital-based society. In the last 3 decades, many studies based on various theoretical models and theories have been conducted to understand older adults' intention to use these technologies and to identify relevant precedents [
Among the technology adoption models, the TAM has been the most frequently used to understand people’s information technology adoption behaviors in the health care context as well as in other fields. The TAM, developed by [
As mentioned, the TAM has been empirically replicated to explain people’s behaviors with regard to adopting technologies in various fields, such as marketing, education, banking, social media, and health care [
As shown in
As one of the extended models of the TAM, the TRAM incorporates TR and people’s propensity to accept and use new technologies to achieve their goals at home and work [
Previous literature on technology acceptance in health care and services has studied various individual characteristics and external variables that could affect the relationships proposed by the TAM, but no research has incorporated TR along with PU and PEU on attitudes and intentions. The conceptual model (
Hypothesis 1: TR (H1-a: optimism, H1-b: innovativeness, H1-c: discomfort, and H1-d: insecurity) influences PU.
Hypothesis 2: TR (H2-a: optimism, H2-b: innovativeness, H2-c: discomfort, and H2-d: insecurity) influences PEU.
Hypothesis 3: PEU influences PU.
Hypothesis 4: PU influences attitudes toward the use of DHT.
Hypothesis 5: PEU influences attitudes toward the use of DHT.
Hypothesis 6: Attitudes toward the use of DHT influence continued usage intention.
Hypothesis 7: The level of eHealth literacy will influence the relationships between TR and the variables.
Research model.
The population of this study comprised adults over 60 years of age in Hong Kong. A web-based survey method was used to collect data in this study via a convenience sampling method. A total of 357 participants completed the survey, of whom 306 provided usable responses. The demographic characteristics are shown in
Crosstab analysis for demographic characteristics.
Characteristics | Total, N | Group A (n=141), n | Group B (n=165), n | ||||||||||
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0.935 (1) | .33 | |||||||||||
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Male | 113 | 48 | 65 |
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Female | 193 | 93 | 100 |
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5.602 (2) | .06 | |||||||||||
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55-59 | 65 | 25 | 40 |
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60-65 | 154 | 67 | 87 |
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66 and older | 87 | 49 | 38 |
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10.180 (2) | .006 | |||||||||||
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Single | 46 | 30 | 16 |
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Married | 230 | 102 | 128 |
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Divorced or widowed | 30 | 9 | 21 |
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4.162 (5) | .53 | |||||||||||
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Elementary | 10 | 7 | 3 |
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High school | 68 | 30 | 38 |
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College degree | 56 | 25 | 31 |
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Junior high | 33 | 18 | 15 |
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Certificate | 57 | 23 | 34 |
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Graduate degree | 82 | 38 | 44 |
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12.240 (4) | .02 | |||||||||||
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Very low | 9 | 6 | 3 |
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Low | 53 | 32 | 21 |
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Medium | 190 | 87 | 103 |
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High | 48 | 15 | 33 |
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Very high | 6 | 1 | 5 |
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Information about participants’ internet usage.
Variables | Participants, n (%) | |
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1 hour | 43 (14) |
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2 hours | 92 (31) |
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3 hours | 53 (18) |
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4 hours | 39 (13) |
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≥5 hours | 71 (23) |
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Mobile | 280 (91) |
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PC | 182 (59) |
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PC, somewhere else | 38 (12) |
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Other | 12 (4) |
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Chatting | 144 (47) |
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Reading news | 81 (26) |
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Entertainment | 109 (36) |
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Games | 227 (74) |
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Shopping | 87 (28) |
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Searching health information | 152 (50) |
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Office/personal affairs | 134 (44) |
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Bank/finance transaction | 192 (63) |
aParticipants were able to choose more than 1 response.
To measure eHealth literacy, an eHealth literacy scale [
For TR [
PU, PEU, and attitude toward using digital health technology (ATDHT) are basic variables of the TAM. These variables were assessed by items developed by [
All responses were recorded on a 7-point Likert scale ranging from “strongly disagree” to “strongly agree.” First, the survey questionnaires were created in English because the original scales from the previous research were developed in English. The English version of the survey was then translated into Cantonese, which was the native language of the participants in this research, by an individual with a doctoral degree in sports management who also possessed a comprehensive understanding of public health literature and fluency in Cantonese and English. The Cantonese version was then back-translated into English by a different individual who possessed credentials similar to those of the individual who produced the Cantonese version. Finally, 10 potential participants who were above 60 years of age were recruited to check the questionnaire’s ease of use and clarity. As a result, the completed questionnaire was properly verified.
First, confirmatory factor analysis (CFA) was conducted to validate the posited relations between the observed variables and the underlying constructs in the measurement model. Various indexes, such as chi-square, the Steiger-Lind root mean square error of approximation (RMSEA), the Tucker–Lewis index (TLI), and the comparative fit index (CFI), were used to assess the absolute and comparative fit of the model. Second, composite reliability (CR), average variance extracted (AVE), and Cronbach α coefficients were calculated for the components of each measurement scale to check convergent validity, discriminant validity, and reliability. Finally, structural equation modeling (SEM) was applied to test the proposed hypotheses. The proposed model was also assessed by the same indexes used for the CFA.
The Research Ethics Committee of Hong Kong Baptist University in Hong Kong, China, approved the study (REC/20-21/0378).
To assess the validity and reliability of the measurement model, we applied a 2-step structural equation modeling approach [
Convergent validity is achieved by 2 criteria recommended by [
Confirmatory factor analysis for measurement modela.
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Range of χ2 | Average variance extracted | Composite reliability | Cronbach α |
Optimism | 0.710-0.825 | 0.601 | 0.857 | .856 |
Innovativeness | 0.720-0.795 | 0.576 | 0.845 | .843 |
Discomfort | 0.521-0.756 | 0.509 | 0.806 | .759 |
Insecurity | 0.579-0.795 | 0.523 | 0.814 | .787 |
Perceived usefulness | 0.773-0.884 | 0.713 | 0.937 | .936 |
Perceived ease of use | 0.756-0.872 | 0.685 | 0.929 | .928 |
Attitude toward using digital health technology | 0.839-0.855 | 0.717 | 0.835 | .853 |
Continued usage intention | 0.567-0.845 | 0.541 | 0.852 | .840 |
a
Mean (SD) and correlation coefficients among variables.
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Optimism | Innovativeness | Discomfort | Insecurity | Perceived usefulness | Perceived ease of use | Attitude toward using DHTa | Continued usage intention | eHealth literacy |
Optimism |
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Innovativeness | .446** |
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Discomfort | −.122** | −.037 |
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Insecurity | −.183** | −.142* | .449** |
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Perceived usefulness | .470** | .465** | −.165** | −.165** |
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Perceived ease of use | .481** | .622** | −.228** | −.174** | .638** |
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Attitude toward using DHT | .491** | .413** | −.128* | −.169** | .572** | .551** |
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Continued usage intention | .522** | .507** | −.061 | −.129* | .640** | .597** | .593** |
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eHealth literacy | .405** | .486** | −.070 | −.077 | .385** | .492** | .432** | .363** |
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Mean (SD) | 3.834 (0.661) | 2.946 (0.856) | 3.048 (0.696) | 3.469 (0.748) | 3.393 (0.731) | 3.226 (0.739) | 3.675 (0.710) | 3.575 (0.639) | 3.417 (0.805) |
Skewness | −.275 | −.136 | −.276 | −.496 | −.400 | −.098 | −.648 | −.138 | −.587 |
Kurtosis | 3.667 | −3.303 | 3.456 | 3.166 | 3.219 | 3.043 | 4.055 | 3.307 | 3.589 |
aDHT: digital health technology.
*
**
Based on an appropriate measurement model, SEM was conducted to test the hypothetical causal relationships among the 8 latent variables. The results showed that our structural model had fairly acceptable fit indexes (
The hypothetical paths between TR and PU were not significant, except for the path from optimism to PU (H1-a). In the case of H2 regarding the effect of TR on PEU, all paths were significant except for the path from insecurity to PEU (H2-d). This means that H1 and H2 were partially supported. In other words, for adults over 60 years of age, optimism about DHT had a positive effect on PU (β=.265;
The results of testing hypotheses H3 to H6 associated with the TAM were as follows. The path from PEU to PU was statistically significant (β=.513;
Hypotheses testinga.
Hypothesis | Path | Estimate ( |
Group A ( |
Group B ( |
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a | Optimism to PUb | 0.265 (.002) | 0.192 (.12) | 0.264 (.02) |
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b | Innovativeness to PU | 0.018 (.80) | −0.019 (.86) | 0.113 (.31) |
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c | Discomfort to PU | −0.013 (.90) | −0.307 (.23) | 0.122 (.25) |
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d | Insecurity to PU | −0.028 (.71) | 0.109 (.58) | −0.103 (.18) |
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a | Optimism to PEUc | 0.331 (<.001) | 0.187 (.10) | 0.358 (.004) |
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b | Innovativeness to PEU | 0.559 (<.001) | 0.456 (<.001) | 0.678 (<.001) |
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c | Discomfort to PEU | −0.397 (<.001) | −0.646 (.01) | −0.254 (.04) |
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d | Insecurity to PEU | 0.121 (.12) | 0.363 (<.05) | −0.007 (.93) |
H3 | PU to PEU | 0.513 (<.001) | 0.567 (<.001) | 0.417 (<.001) | |
H4 | PU to ATDHTd | 0.401 (<.001) | 0.469 (<.001) | 0.343 (<.001) | |
H5 | PEU to ATDHT | 0.323 (<.001) | 0.298 (.01) | .310 (<.001) | |
H6 | ATDHT to continued usage intention | 0.553 (<.001) | 0.495 (<.001) | .506 (<.001) |
a
bPU: perceived usefulness.
cPEU: perceived ease of use.
dATDHT: attitude toward using digital health technology.
By applying the TRAM to older adults in Hong Kong, this study (1) examined the factors affecting older adults’ intention to use DHT, (2) investigated their eHealth literacy level, and (3) explored the differences between participants with high and low levels of eHealth literacy regarding their DHT usage intentions. The findings are discussed below.
This study validated the TRAM in older adults in Hong Kong in terms of its construct validity, convergent validity, discriminant validity, and internal consistency. The results demonstrated that the TRAM is an appropriate and meaningful framework to predict older adults’ intentions to use DHT. Previous TRAM- or TAM-based studies mostly focused on health professionals’ acceptance of information and communication technology [
The participants in the current research expressed a considerable extent of optimism and low discomfort toward DHT usage. Meanwhile, limited innovativeness and a degree of insecurity were demonstrated among this group of people as well. In terms of the relationships between TR variables and PU/PEU, for optimism, the study results indicated that optimistic participants were more likely to perceive DHT as useful and easy to use. This finding was consistent with previous research [
For the relationships among the TAM variables, the results were as expected and aligned with previous studies [
This research investigated eHealth literacy among older adults in Hong Kong and found that marital status and perceived socioeconomic status can influence their eHealth literacy status. Married older adults were more likely to receive social support for the use of DHT devices, which could result in high eHealth literacy. Older adults with higher income could have more experience with DHT devices, which may also help develop high eHealth literacy. The correlation analysis also showed that eHealth literacy was significantly connected with all the variables except discomfort and insecurity. This might be because the current measurement for eHealth literacy is developed on the basis of Web 1.0 technology, which is a web-based environment, while new technologies (eg, social networking services or mobile internet) have been applied in current DHT [
Our findings are meaningful for older adults because older individuals with eHealth literacy can increase their interest in health and knowledge of health care, which leads to confidence in DHT and a positive attitude toward DHT [
Several limitations are recognized in this study. First, the participants of this study were recruited via a web-based survey, which means that the current study excluded older adults with less willingness to interact with IT. The generalizability of the research findings might be hindered by this sampling method. Related studies targeting people who are less willing to use IT are required because their health demands might be more pressing and they may receive less support than those who use IT. Second, the respondents of this study were older adults from Hong Kong. Considering the cultural specificity, the application of the study findings in other groups or areas should be further examined. Third, factors beyond the TRAM may also predict users’ intention of DHT usage, but they have not been considered in the current research. More in-depth studies that consider other potential influencers of DHT usage are desirable. Finally, the outcome variable of this study was DHT usage intention. Since a gap naturally exists between behavioral intentions and actual behavior and it is actual behavior that can influence individuals’ health status, studies that integrate behavior and identify more components in this relationship are warranted.
This study tested the TRAM in older Hong Kong adults and explored the difference in the relationships of the TRAM between participants with high and low levels of eHealth literacy. The findings provided partial support for the hypotheses, emphasizing the impact of eHealth literacy on encouraging older adults to use DHT and obtain health benefits from it. This study also suggests providing assistance and guidelines for this population to narrow the aging-related technology gap and to further explore the associations of eHealth literacy, the TRAM, and actual behavior.
attitude toward using digital health technology
average variance extracted
confirmatory factor analysis
comparative fit index
composite reliability
digital health technology
perceived ease of use
perceived usefulness
Root Mean Square Error of Approximation
structural equation modeling
Technology Acceptance Model
Tucker–Lewis index
technology readiness
Technology Readiness and Acceptance Model
The research team would like to thank the individuals who generously shared their time and experience for the purposes of this project.
None declared.