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Information and communication technology (ICT) use among older adults has been on the rise in recent years. However, the predictors and mechanisms behind older adults’ acceptance and use of ICT are not clear.
This study aimed to systematically describe ICT usage among Czech older adults and to evaluate the factors influencing their ICT use and readiness to use digital technology to promote health (eHealth readiness). The primary focus was on psychological factors and the role of persons close to older adults.
The research utilized cross-sectional survey data from a quota-based sample of Czech older adults (>50 years) and persons close to them further referred to as
Czech older adults’ use of ICT is low with the exception of cell phone usage (cell phone usage by 173/250, 69.2%; other devices used by 50/250, 20.0% of older adults or less). Apart from age (β=−.21;
This study provided the first systematic examination of Czech older adults’ ICT usage and eHealth readiness. Novel predictors (NFCC and close persons’ variables) were evaluated and yielded actionable results. More research is needed to clarify the role of persons close to older adults.
Older adults (>65 years) are the fastest-growing segment of the population, estimated to account for more than 25% of the total population by 2050 and outnumbering the youngest segment of children under the age of 15 years by 2045 [
Considering the position of the Czech Republic within the European Union (EU), rates of ICT usage are slightly below the average for member countries of the EU. For instance, the percentage of households connected to the internet in the Czech Republic is 81.7% as compared with the EU average of 85.4%. The disparities are much more evident in the older segment of the population and for new technological developments such as smartphones and mobile internet. Only 13.1% of Czech adults aged between 55 and 74 years use mobile phones to connect to the internet, which stands in sharp contrast to the average of 34.2% for EU member countries. Within the EU, the Netherlands, Denmark, and Luxembourg rank the highest in terms of older adults using mobile internet, with 61.8%, 61.4%, and 61.1%, respectively. Curiously, the Czech Republic lags behind even when compared with similar countries (by gross domestic product per capita) such as Hungary, Croatia, and Slovakia with 25.2%, 24.2%, and 18.6% of older mobile internet users, respectively [
The use of ICT has been argued to have the potential to positively influence older adults’ well-being, decrease loneliness, and increase social support and integration into society [
Nevertheless, a vital premise for the effectiveness of eHealth interventions is the initial acceptance of the intervention or program as well as adherence to it over time. Uptake of health-promoting technologies is very low even in carefully conducted research conditions [
From a socio-ecological perspective, ICT acceptance among older adults could be best explained by the interaction of factors at multiple levels. At the individual level, ICT use and adoption of new technological developments has been consistently associated with sociodemographic characteristics including age [
Among psychological characteristics, explanations of technology use focus on individual differences in motivations to use technology (eg, technology acceptance model and its variations or hedonic motivation) [
Going beyond individual characteristics, other factors in older adults’ environments that have been found to affect technology usage of older adults include accessibility, financial support, hardware/software capacity and compatibility, and importantly also support, training, and assistance from others [
The objective of this study was to examine ICT use and eHealth readiness of Czech older adults and to evaluate the influence of psychological factors on older adults’ readiness to use eHealth technology, while considering the role of older adults’ close persons (eg, children and friends). It was hypothesized that older adults with a high NFCC would perceive more barriers to using ICT, use it less, and exhibit lower eHealth readiness. It was also hypothesized that close persons’ eHealth readiness would be positively related to older adults’ ICT use and eHealth readiness. Finally, as ICT use has been consistently demonstrated to decline with age and age has been shown to be an important predictor of ICT-related attitudes and behavior, the explanatory models tested considered the effect of age when evaluating the associations among NFCC, perceived barriers, current ICT use, and eHealth readiness.
The study was approved by the University Ethical Committee. A total of 250 Czech older adults and the persons close to them (close persons, N=250 dyads) participated in this research. The data were collected between September and November 2017. Participants were recruited and surveyed through a professional marketing and social research agency using stratified quota sampling. The quotas were set based on most recent census data (Czech Statistical Office) to correspond with the underlying population of adults aged 50 years and older based on region (representation of all 14 regions within the Czech republic with a quota based on resident population within each region), gender, age (50-59 years, 60-69 years, 70 years and older), education, and city size (categorized by number of inhabitants). The resulting primary sample of older adults is thus representative of the overall Czech population of adults of 50 years and older in terms of distribution by age, gender, education, region, and city size. Professional interviewers located in various regions were given quota breakdowns and conducted in-person questionnaires with corresponding participants until the quota was met. Each older respondent from the primary sample identified a close person (such as an adult child, a partner, or a friend) who at least occasionally helps them with day-to-day activities (eg, shopping, doctor’s visits, household chores, or running errands) with whom they are in contact at least once a week. All close persons were subsequently interviewed either in person or by telephone. The data from the primary sample of older adults were collected through standardized, structured face-to-face interviews (approximately 45 min in length) in the households of respondents with the help of a tablet and a questionnaire software. The data were collected at one time point and are cross-sectional.
Basic demographic information was collected (ie, age, gender, education, income, residence).
Participants provided information about the following devices: computer, laptop, cell phone, smartphone, and tablet. Participants were first asked to indicate the devices they own and use and subsequently report further details such as the daily usage time (hours/day) and length of use. This information was aggregated to form two measures of ICT use:
In addition, the use of the internet as a specific ICT-related technology was assessed. Participants were asked if they use the internet. Internet users were asked for further information about their
Participants chose from the following list of barriers generated from previous studies on the topic:
Both older adults and their close persons completed the eHealth Readiness scale by Bhalla et al [
NFCC was measured with the Czech version of the 15-item NFCC Scale [
Statistical analyses were performed using R (R Foundation for Statistical Computing), version 3.4.2 [
The proposed relationships were tested using structural equation modeling (SEM). The structural model was estimated using the maximum likelihood with robust SEs test statistics estimator. The model fit was evaluated using standard measures of model fit: the standardized root mean square residual (SRMR), which should be less than or approximately 0.08 [
First, we defined three latent variables. The latent variable of ICT use was specified as the combination of three manifest variables: number of used devices, total usage time of the devices, and internet usage time. The measurement model for eHealth readiness was a simple 1-factor model where all items were loaded on a single latent variable. eHealth readiness item 2 was excluded from the scale in both samples as it exhibited high residual correlations with other variables and worsened the overall fit of both the measurement model and the resulting structural model. Moreover, upon closer examination, the item (
On the basis of research questions and the hypothesized relationships, the structural model portrayed in
A simplified version of the tested model with path estimates. Ellipses indicate latent variables; rectangles indicate manifest variables; full colored arrows indicate significant relationships (green: positive; red: negative) whereas dashed arrows indicate nonsignificant relationships. eHealth: electronic health; ICT: information and communication technology; NFCC: need for cognitive closure.
The mean age in the older adult sample was 66.14 (SD 9.47) years; 55.2% (138/250) were women. The majority of the older adults were retired (188/250, 75.2%) and completed high school education (171/250, 68.4%). In the sample of close persons, the mean age was 46.30 (SD 13.51) years; 70.4% (176/250) were women. Regarding the relationship toward the older adult, 54.8% (137/250) of close persons were children, 18.0% (45/250) were other relatives, and 15.2% (38/250) were partners, the rest accounting for friends, acquaintances, or professional caretakers. Detailed demographic characteristics of both samples can be found in
The most widely used device among older adults was the cell phone, with 69.2% (173/250) of older adults using it on a daily basis. All the other devices were used by less than a fourth of the participants. Details on the mean daily usage (hours/day) of the ICT devices and internet among daily users as well as the total usage time and the total number of devices among all older adults can be found in
Differences based on gender were examined and are portrayed in
As for the aggregated ICT usage variables, there were significant differences based on education in the total usage time (
When comparing older adults with their close persons, close persons used on average more ICT devices (mean 2.03, SD 0.91; t249=−11.91;
Demographic characteristics of older adults and their close persons.
Characteristic | Older adults | Close persons | |||
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Mean (SD) | 66.14 (9.47) | 46.30 (13.51) | ||
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Range | 50-93 | 21-90 | ||
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Females | 138 (55.2) | 176 (70.4) | ||
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Males | 112 (44.8) | 74 (29.6) | ||
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Elementary | 55 (22.0) | 10 (4.0) | ||
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Secondary school (no diploma) | 101 (40.4) | 93 (37.2) | ||
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Secondary school (diploma) | 70 (28.0) | 108 (43.2) | ||
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University | 24 (9.6) | 39 (15.6) | ||
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Married | 88 (35.2) | 133 (53.2) | ||
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Divorced | 60 (24.0) | 47 (18.8) | ||
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Widowed | 89 (35.6) | 9 (3.6) | ||
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Single | 7 (2.8) | 44 (17.6) | ||
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Living with a partner | 6 (2.4) | 17 (6.8) | ||
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Employed | 51 (20.4) | 172 (68.8) | ||
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Retired | 188 (75.2) | 44 (17.6) | ||
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Unemployed | 11 (4.4) | 28 (11.2) | ||
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Students | N/Aa | 6 (2.4) |
aN/A: not applicable.
Descriptive characteristics for the information and communication technology use variables, electronic health readiness, and need for cognitive closure.
Characteristic | Proportion of daily users (N=250), n (%) | Entire sample | Females | Males | |||
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Mean (SD) | Range | Mean (SD) | Range | Mean (SD) | Range |
Cell phone usage (hours/day) | 173 (69.2) | 1.89 (3.31) | 0.1-24.0 | 2.21 (4.22) | 0.1-24.0 | 1.54 (1.82) | 0.1-10.0 |
Smartphone usage (hours/day) | 19 (7.6) | 2.05 (1.92) | 0.5-8.0 | 1.95 (1.61) | 0.5-6.0 | 2.17 (2.32) | 0.5-8.0 |
Personal computer usage (hours/day) | 50 (20.0) | 3.16 (2.52) | 1.0-10.0 | 3.00 (2.49) | 1.0-8.0 | 3.29 (2.60) | 1.0-10.0 |
Laptop usage (hours/day) | 39 (15.6) | 2.64 (1.70) | 0.1-7.0 | 2.49 (1.55) | 0.1-6.0 | 2.81 (1.89) | 0.5-7.0 |
Tablet usage (hours/day) | 7 (2.8) | 1.71 (0.76) | 1.0-3.0 | 1.33 (0.58) | 1.0-2.0 | 2.00 (0.82) | 1.0-3.0 |
Internet usage (hours/day) | 99 (39.6) | 2.24 (1.98) | 0.1-10.0 | 2.23 (1.92) | 0.1-10.0 | 2.47 (2.02) | 0.2-10.0 |
Total usage time (hours/day) | N/Aa | 2.43 (3.26) | 0-16 | 2.26 (3.25) | 0-16 | 2.64 (3.28) | 0-14 |
Number of devices | N/A | 1.24 (0.73) | 0-4 | 1.15 (0.71) | 0-4 | 1.35 (0.74) | 0-4 |
Number of barriers | N/A | 1.47 (1.20) | 0-6 | 1.59 (1.25) | 0-6 | 1.32 (1.13) | 0-5 |
Need for cognitive closure | N/A | 3.56 (0.56) | 1.80-5.00 | 3.60 (0.54) | 2.13-4.80 | 3.51 (0.59) | 1.80-5.00 |
eHealthb readiness | N/A | 15.56 (7.55) | 6-33 | 15.03 (7.49) | 6-33 | 16.21 (7.60) | 6-32 |
eHealth readiness close persons | N/A | 22.76 (7.28) | 6-36 | 23.07 (7.07) | 6-36 | 22.38 (7.55) | 6-35 |
aN/A: not applicable.
beHealth: electronic health.
The evaluated structural model with standardized estimates of the regression paths is depicted in
Estimates for the direct effects are displayed in
Indirect effect of NFCC on ICT use through perceived barriers was negative and significant (β=−.049;
The evaluated model explained 63.7% of the variance of older adults’ eHealth readiness, 21.2% of variance of ICT use, and 8.5% of variance of the number of perceived barriers.
Estimates of direct effects.
Regression path | Estimate Ba | SE | 95% CI | Standardized estimate β | |
NFCCb –> eHealthc Readiness | −0.187 | 0.117 | .11 | −0.415 to 0.042 | −.091 |
NFCC –> Barriers | 0.750 | 0.301 | .01 | 0.160 to 1.339 | .232 |
NFCC –> ICTd use | −0.055 | 0.574 | .92 | −1.180 to 1.070 | −.008 |
Barriers –> eHealth Readiness | −0.034 | 0.028 | .23 | −0.090 to 0.022 | −.053 |
Barriers –> ICT use | −0.429 | 0.133 | .001 | 0.689 to −0.169 | −.210 |
ICT use –> eHealth Readiness | 0.201 | 0.034 | <.001 | 0.134 to 0.268 | .647 |
eHealth Readiness Close Persons –> eHealth Readiness | 0.118 | 0.075 | .12 | −0.028 to 0.264 | .080 |
eHealth Readiness Close Persons –> ICT use | 0.730 | 0.432 | .09 | −0.117 to 1.577 | .154 |
Age –> eHealth Readiness | −0.017 | 0.004 | <.001 | −0.025 to −0.008 | −.207 |
Age –> Barriers | 0.019 | 0.007 | .008 | 0.005 to 0.034 | .151 |
Age –> ICT use | −0.089 | 0.017 | <.001 | −0.122 to −0.056 | −.342 |
Age –> NFCC | 0.005 | 0.004 | .24 | −0.003 to 0.012 | .117 |
aNonstandardized estimate of direct effects (as compared with the standardized estimate β).
bNFCC: need for cognitive closure.
ceHealth: electronic health.
dICT: information and communication technology.
This study evaluated the predictors of ICT use and readiness to use eHealth technology by older adults. The study considered the role of perceived barriers, the role of close persons’ eHealth readiness, and it was the first study to evaluate the influence of NFCC on ICT use and eHealth readiness of older adults. Although unable to definitively establish the direction of the studied relationships because of the cross-sectional nature of the data, the SEM analysis showed that apart from age, eHealth readiness was predicted by ICT use. Older adults who used ICT more in general were more ready to use technology for supporting health and healthy behaviors. Nevertheless, a reverse (higher eHealth readiness predicts more ICT use) or bidirectional relationship could also exist, but these were not specified and tested in the current model. Although the NFCC did not directly impact ICT use or eHealth readiness, NFCC exerted influence on ICT use and eHealth readiness indirectly through the number of perceived barriers. Individuals high in NFCC perceived more barriers to ICT. The number of barriers was, in turn, negatively related to their overall ICT use. Interestingly, there was evidence that significant others might influence older adults’ eHealth readiness, although the mechanisms remain unclear (the direct effect of close persons’ eHealth readiness on ICT use and eHealth readiness of older adults was not significant, but the total effect of close persons’ eHealth readiness on the older adults’ eHealth readiness was significant).
This study supported previous research [
A novel predictor yielding interesting results was the NFCC. This research built upon the pioneering studies by Chernikova et al [
The obtained results on the role of NFCC and perceived barriers on ICT use and readiness suggest interesting actionable implications. Researchers carrying out ICT use promoting interventions and programs might want to measure the level of NFCC of the participants in their programs. NFCC may then serve as a potential tailoring variable in the design of programs promoting ICT use, wherein depending on the individual’s level of NFCC, intervention components explicitly focusing on reducing the number of perceived barriers could be incorporated into the intervention. The respective strategies to mitigate barriers would depend on the specific barriers but could range from providing training with ICT devices to financial support to presenting older adults with available technology and how it can enhance one’s life. Effectively reducing perceived barriers (especially in high NFCC individuals) may then positively impact ICT acceptance and use. Moreover, NFCC has also been shown to be a situationally evocable state. This research suggests that when presenting a new ICT or eHealth device, application, or intervention to its potential users, it may be beneficial to lower their NFCC before and during the description of the device or intervention. This could be done, for example, by providing potential users with sufficient time when making decisions, by stressing the importance of forming an accurate judgment, or by aiding in the process of finding additional information on the matter before forming a judgement [
As for the role of significant others on older adults’ adoption and use of technology, which has been proposed by several theoretical approaches [
Considering the limited amount of research that has been conducted specifically on the relationship between NFCC and technology, this study should be regarded as an exploratory study, and the model should be confirmed and cross-validated on other samples to increase the validity of the results. A limitation of the present research is also the correlational nature of data, which does not allow for firm conclusions about the causality and direction of the relationships. Although the proposed relationships were theoretically construed and the proposed direction of causality seems theoretically plausible, further research should validate the findings in different older adult samples.
The sample size was rather low, considering the complexity of the evaluated models. It is also possible that an equally well-fitting structural model would result from a specification of different relationships. This further underscores the need for cross-validation and empirical testing of competing models in independent samples of older adults. Ideally, this research should involve studies with prospective, longitudinal, or experimental designs to allow for more definitive conclusions regarding the causality and time ordering of relationships under study.
Finally, little research has been conducted on the topic of ICT acceptance and use by Czech older adults—to our knowledge, we present the first systematic examination of ICT use and eHealth readiness in Czech older adults. Considering the rather low ICT usage and eHealth readiness in the current sample, it would be interesting to repeat the survey in a few years’ time to evaluate the changes. Similarly, comparing specific subsamples of the older adult population such as older adults who are physically active, older adults visiting university classes, and people living in homes for the elderly could yield information about further factors influencing ICT acceptance and use, helping shape ICT use promotion efforts and policies related to ICT adoption across the population spectrum of older adults.
This is the first study to systematically evaluate Czech older adults’ readiness to use technology for improving health, their related ICT use, and possible predictors, including NFCC and the role of significant others. Our results provide new insights into the predictors of older adults’ readiness to use eHealth technology, especially with respect to NFCC. eHealth readiness was found to be affected directly by age and actual ICT use and indirectly by the NFCC and the number of perceived barriers toward using technology. These results are directly applicable for researchers or organizations carrying out interventions promoting the use of eHealth devices and applications.
Future researchers are encouraged to validate the findings in various older adult samples and further clarify the role of older adults’ close persons. Additional studies, including prospective or experimental studies, are required to support the presented findings.
comparative fit index
electronic health
European Union
information and communication technology
mobile health
need for cognitive closure
root mean square error of approximation
structural equation modeling
standardized root mean square residual
Tucker-Lewis Index
These results are part of the project that has received funding from the EU’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie, and it is cofinanced by the South Moravian Region under grant agreement No. 665860. This material reflects only the author's attitudes, and the EU is not responsible for any possible use of the information contained in such material.
None declared.