Background
E-health applications are considered as promising technologies that enable patients to positively participate in improving their state of health. These technologies support patients, e.g. in actively monitoring their own health condition and thereby participating in medical treatment and therapy decisions [
1]. E-health encompasses all the apps that serve for the treatment and care of patients using modern information and communication media. As an overall term, it summarizes a broad spectrum of technological apps that process health information electronically, exchange it via secure connections between the actors involved, and thereby support the medical and therapeutic treatment processes [
2]. As a subset of e-Health applications, mHealth supports health-related self-management by using mobile devices and health-related apps to monitor, measure, and analyze health-related data [
3,
4].
E-Health and mHealth technologies are currently used in the context of individual health promotion, to support lifestyle changes, for the diagnosis and treatment of diseases, and for more efficient health care in structurally deprived and resource-poor regions. [
5,
6]. For example, apps installed on smartphones and online-based video consultations offer patients the possibility of location-independent medical consultations as well as the exchange of health-related data with medical care providers [
7]. However, it was found that the use of mHealth and health-related apps in Germany is below average: Only 28% of the inhabitants in Germany who have access to the Internet are using these kinds of apps to manage their health. Another 13% who have used health apps have stopped using them at some point [
4,
8]. The results from a survey conducted in Germany between March and April 2020 shows that 33% of Germans have already used online booking for appointments [
9]. Only 2% of the study participants [
9] used an online-based video consultation with their doctor during the May survey period 2020, which was offered by every second outpatient care provider respectively 52% in May 2020 [
10]. Moreover, the use of digital health technologies depends on the extent to which individuals and patients have sufficient technical infrastructure and access to the Internet [
11]. The problem here is that access to the Internet and the availability of the necessary hardware are unevenly distributed in society. This situation is described by the term “digital divide” and refers to the fact that mostly socially deprived cohorts participate less in the digital transformation and benefit less from it [
11‐
13].
In addition, as part of the ongoing processes of increasing digitalization, users are faced with the challenge of checking the personal and health-related relevance of information made available by digital health technologies to ensure that they can subsequently use the information in such a way that they are able to take responsibility for managing their own state of health and thereby contribute to an independent improvement in their individual state of health [
8]. This means that, for example, simply owning a smartphone is not sufficient to ensure the adequate use of a health-related app and to be able to use digital health technologies appropriately and effectively [
6]. In addition, a sufficiently high level of health literacy is required among users [
6].
The exemplary results of previous studies show that the use of digital health technologies correlates with the level of health literacy and that high health literacy is associated with the more frequent use of Internet-based information searches on health-related topics and issues [
14,
15]. Furthermore, health literacy varies by sociodemographic characteristics, such as age, migration status, and subjectively perceived social status, and to be less pronounced in the cohorts of lower socioeconomic status [
16]. In particular, older people are considered to be an exemplary cohort who are less likely to use digital health technologies because they perceive their technical skills and their subjective assessment of digital competence to be insufficiently developed. [
17]. Accordingly, it can be assumed that inequalities in access to digital health technologies will continue to increase and that the potential of digital health technologies to reduce such inequalities must be utilized and exploited [
18,
19].
Since the outbreak of COVID-19 at the turn of 2019/2020, more than 170 million people worldwide in over 190 countries were infected with the COVID-19 virus, with more than 3.5 million deaths reported by the end of May 2021 [
20,
21]. In Germany, more than 3.6 million confirmed infections with COVID-19 occurred, resulting in more than 86.000 deaths [
22]. Almost all areas of life are affected by the impact of the COVID-19 pandemic [
23]. For example, the care of COVID-19 patients involves a high expenditure of medical, material, and human resources, reflected by the incidence that treatment capacities for COVID-19 patients were reserved by canceling or postponing elective surgeries and preventive examinations to provide necessary intensive care treatment capacities for COVID-19 patients [
24]. In addition to the expenditure of infrastructural and intensive care resources, healthcare professionals suffer from the physical and psychological stress associated with caring for COVID-19 patients, manifested in depression, distress, and subjectively poor perceived health [
25].
Pandemic-related impacts arising outside the health care system include the fear of an infection with COVID-19 and dying from COVID-19 in the general population, as well as feelings of helplessness and depression due to social distancing and isolation, and concerns about getting fired because of the significantly decreased national economy and thus being unable to provide a living for themselves or their families [
26‐
29]. In education, school and university closures occurred to contain the spread of the virus, including a transition to web-based lectures and homeschooling [
30,
31].
Moreover, social epidemiological studies from the United States and the United Kingdom suggest that the risk of being infected with COVID-19, experiencing a severe illness course, or dying from the virus is more pronounced in socially or socioeconomically deprived populations compared to cohorts of higher socioeconomic status [
32]. In addition, as regards the higher risk of contracting the disease when socioeconomic stratification is taken into consideration [
32], the elderly are considered as a particularly vulnerable group of people during the COVID-19 pandemic [
33,
34]. During the pandemic, the above-mentioned potentials of digital health technologies become apparent. Here, e-Health related technologies facilitate the exchange of treatment-relevant patient data between care providers or between patients and care providers by providing documents tailored to the individual needs of patients, thus enabling patient-centered care and treatment [
35]. In combination with social distancing measures, these types of technologies open new ways to provide health services and support [
36]. Digital health technologies / video consultations and conferences are suitable, for example, for location-independent health care and the monitoring of those who are chronically ill, for example, diabetics and pregnant women, since the risk of infection can be decreased, especially for these vulnerable cohort, by reducing contacts [
36,
37]. Furthermore, digital health technologies can be used to disseminate trusted information about COVID-19 to the general population, which can improve people’s understanding of the disease and the provision of relevant healthcare services [
38]. Some e-Health related technologies combine these functions by using an integrated tool for education, self-assessment, symptom monitoring and self-triage for COVID-19 for asymptomatic and symptomatic COVID-19 patients. It could be seen that unnecessary patient visits due to COIVD-19 were avoided to provide treatment capacities for emergency treatments and to contain the spread of the virus by the surveillance of COVID-19 infections supported by these kind of technologies [
39,
40]. Nevertheless, Tebeje and Klein [
35] concluded that most of present digital technologies which are used to overcome COVID-19 failed to provide information regarding cost-effectiveness and effectiveness.
Besides the potentials of these kinds of digital health technologies, there is the risk that the use of digital health technologies could generate new access barriers and inequalities. The elderly, in particular, are generally considered to be the population group with the poorest access to Internet-based technologies in this regard. [
41]. The study by van Deursen [
34] is an example of these barriers and inequalities regarding access to online-based information offerings about the virus in divergent social populations during COVID-19. The study investigated the information search on COVID-19-related information using the Internet during the COVID-19 pandemic. This study investigated the degree of need in the Dutch population to seek information about COVID-19 using the Internet and the influence of sociodemographic factors on COVID-19-related information seeking. The results showed that socioeconomically deprived as well as older cohorts are less able to use the Internet to obtain information about COVID-19-related topics than individuals with higher socioeconomic backgrounds. The relevant determinants here include lower levels of formal education, older age, poorer general literacy skills, and pre-existing physical infirmities. Therefore, the COVID-19 pandemic can be seen as reinforcing already existing digital inequalities [
34]. Accordingly, during the COVID-19 pandemic, in particular, individuals need to be provided with evidence-based information about the virus so that they adopt preventive behaviors to counteract the fears about the virus and intentionally disseminated misinformation [
42].
Therefore, it can be stated that pre-existing social and health inequalities are perpetuated in the digital setting and era and, accordingly, further empirical surveys are needed to better understand the influence of socio-demographic factors and health literacy on the use of digital health technologies. Furthermore, the current empirical research projects do not sufficiently consider the multicausal network of needs, attitudes, and reservations toward digital health technologies and the resulting target-group-specific and needs-based differentiation of eHealth technologies [
11,
43].
Accordingly, this study pursues answering the question of whether or to what extent the use of digital health technologies has changed since the shutdown in Germany to control the COVID-19 pandemic and what influence divergent sociodemographic factors, subjective perceptions, and personal health literacy skills have on the use of digital health technologies, such as the online booking of medical appointments and medications, online-based video consultations with health care providers, and the transmission of health-related data via an app to health insurers.
The associated research hypotheses are as follows:
Methods
Study design
The results obtained are based on a partial analysis of the cross-sectional survey “Digital divide in relation to health literacy during the COVID-19 pandemic”. 7.239 people between 18- and 74-years old living in Germany were invited to complete an online questionnaire. A total of 1.953 people participated, and 1.570 individuals were included in the sample. The composition of the quota sample corresponds to the current distribution of age, gender, and residence in a federal state (not crossed) according to the Eurostat 2018 database. In the sample, the proportion of people with low education is larger than the national average. The study participants were recruited via Respondi AG, which is an external provider of online surveys. The external provider ensured anonymity and data protection guidelines in accordance with the General Data Protection Regulation (GDPR) at all times.
The data collection took place from April 29 to May 8, 2020. The timing of the study fell within the period when the first relaxations came into effect on April 20, 2020 after the shutdown of March 22, 2020 [
44]. The questionnaire was broad in scope and captured the impact of the COVID-19 pandemic in different areas of life as well as health care, aspects of health, and as a focus, health literacy. For the purposes of this study, however, the focus will be on the use of digital health care technologies.
In the study period the numbers of infections were significantly lower with approximately 167.000 infected persons and 7.300 deaths [
45] compared to the previously described situation at the time of submission in 2021. The study is a snapshot of the very specific circumstances regarding social life, COVID-19 exposure and burden as well as coping capabilities of the population and society.
Measurement and operationalization
Use of digital health technologies before and since the shutdown to control the COVID-19-pandemic
The following three e-health services in outpatient care that are offered in Germany were examined: Use of online-based booking of doctor's appointments and medications, use of video consultation, and transmission of health-related data via an app to a health insurance company. The questions were closed-ended wherein the participants could answer either Yes or No. The study participants, who declared that they had already used a digital health service before or during the COVID-19 pandemic shutdown, were classified as users. All the items were combined into one score, with a range of values from 0 to 3.
Analogous health care services in the ambulatory health care sector during COVID-19
The following variables were recorded under the general term analogous care situation in outpatient health care: Interruption of ongoing treatment due to the fear of a COVID-19 infection, difficulty in obtaining a medical appointment, cancellation of a medical appointment due to the fear of a COVID-19 infection, and cancellation of a medical appointment to protect relatives from a COVID-19 infection. The aforementioned variables depict care situations that occurred in outpatient analog medical care during the COVID-19 pandemic shutdown, thereby potentially influencing the use of digital health technologies. The aforementioned variables were recorded during the study on a four-point Likert scale (1 = Do not agree at all to 4 = Fully agree). The response categories Do not agree at all and Rather does not agree were recoded into the variable Not appeared. The response categories Fully Agree and Rather agree were recoded into the category Appeared.
Sociodemographics
The following sociodemographic factors were included: Age, gender, migration status, education,, and subjective social status (SSS).
In contrast to the usual classification, a distinction is made here between the age groups based on generations in order to identify potential generational differences / effects regarding the usage of these digital health technologies [
46]. The age generations are composed as follows: Traditionalists (born 1922–1955); Baby Boomers (born 1956–1965); Generation X (born 1966–1980); Generation Y (born 1981–1995); Generation Z (born 1996 and later). The educational level was collected using the CASMIN educational classification system [
47]. It was followed by categorization into low, medium, and high education groups [
48]. SSS was collected based on self-assessment. This was done by individual assignment on a ladder with levels from 1 (lowest social status) to 10 (highest social status) [
49]. Following Höbel et al. [
50], the study participants were assigned a low (scale values 1–4), medium (scale values 5–6), and high (scale values 7–10) SSS.
The classification into the migration status category followed the recommendation of Schenk et al. [
51]. A migration status exists if both parents were born in another country or the respondent has not lived in Germany since birth and at least one parent was born abroad, or their native language is not German.
The residence was categorized as follows: Rural area (up to 5.000 inhabitants); Small town (5.001–20.000 inhabitants); Medium-sized city (20.001–100.000 inhabitants); Urban city (more than 100.000 inhabitants) [
52]
The operationalization of the variable subjective health was based on the subjective assessment (poor, less good, satisfactory, good to very good) of the personal health status [
53]. For the subsequent analysis, two categories were formed, which were operationalized as follows: less good/bad; good/very good health. In addition, the study participants were asked whether they were suffering from a chronic illness. The answer was given here with the help of the dichotomous expressions of yes or no.
Subjectively perceived restriction of the life situation due to COVID-19
The participants were asked about their subjective perception of the COVID-19 pandemic and the extent to which they felt burdened, threatened, or restricted by COVID-19, which they indicated on a five-point Likert scale (1 = not at all to 5 = very much).
From these three items (Threat, Burden, Restriction due to COVID-19), a sum score was formed for the subjectively perceived restriction of the life situation, which could assume values between 3 and 15. Reliability analysis using Cronbach's alpha yielded a value of 0.793, which represents a sufficiently high inter-item correlation. This scale was designed as part of this study. The exclusion of any variable in the reliability analysis did not indicate an increase (Cronbach's alpha 0.600–0.784) of the inter-item correlation.
General health literacy, COVID-19-related health literacy, and digital literacy
The general health literacy (GHL) was determined by a sum of six items on a five-point Likert scale (5 =
very easy to 1 =
very difficult), referring to Schaeffer et al. 2016 [
54]. Therefore, the score can assume values between 6 and 30. A higher score indicates a higher GHL. Reliability analysis by Cronbach's alpha yielded a value of 0.895 and was, therefore, a sufficiently high internal consistency. COVID-19-related health literacy (COV-19-HL) was measured in the form of a scale sum value based on 10 items on a five-point Likert scale (1 =
Does not apply at all to 5 =
Applies completely). Conceptually, the measurement follows the recommendations of Okan et al. [
55]. Therefore, a score between a minimum of 10 and a maximum of 50 could be achieved. Accordingly, a higher score here also means better COV-19-HL. The reliability analysis using Cronbach's alpha yielded a value of 0.830. The digital literacy (DL) was also formed with the use of a sum score in accordance to [
56,
57] as a new scale for measuring the digital competence, which comprises the response options of a five-point Likert scale (1 =
Don't agree at all to 5 =
Agree completely) [
56,
57]. Consequently, the sum score can assume values between 11 and 55. Reliability analysis using Cronbach's alpha yielded a value of 0.893, which also represents a sufficiently high inter-item correlation. Here, the scale only focuses on the competencies "Information and media literacy" and "Digital problem solving". The competencies "Communication and Collaboration" and "Content Creation" were not operationalized in this study. Moreover, the assessment of digital literacy does not only include the Internet, but also digital media as a whole.
Data analysis
The statistical analysis included a descriptive presentation of the sample, a before-and-after comparison, and bivariate as well as multivariate procedures. The preparation of the data set and the subsequent data analysis were carried out with IBM SPSS 26.
The analysis of the before-after comparison of the dichotomous characteristic values regarding the use of the listed digital health technologies, based on the McNemar test, serves to test research hypothesis No. 1. Furthermore, an additional bivariate analysis is performed to test whether the extent of the outpatient analog care situation influences the use of the online-based booking of doctor appointments and medications, use of a video consultation, and transmission of patient-relevant data via an app to a health insurance company during the COVID-19 pandemic. To test research hypothesis No. 2, a bivariate analysis is conducted regarding the use of digital health services. The hypothesis was tested with the help of the stratification of sociodemographic characteristics. For the analysis, the variables of education level (CASMIN education classification system), SSS, subjective health status, chronic illness, gender, age generations, and migration status were enrolled. Since the requirements for parametric tests were not completely fulfilled, non-parametric test procedures (Kruskal–Wallis test/Wilcoxon-Mann–Whitney test) were used. To test research hypotheses No. 3 and No. 4, a block wise and inclusion-based binary logistic regression analysis is performed to determine the influence of sociodemographic factors, subjective feelings, and personal health literacy, in addition to the relative probability of using the addressed digital health technologies during the COVID-19 pandemic. Because of the low uptake of video consultation, this type of regression analysis was not performed as part of the multivariate analysis.
Previous results of an earlier conducted survey [
34] indicate that the seeking of digital information during COVID-19 is associated with socioeconomic and sociodemographic factors. Consequently, the first block of the model primarily includes sociodemographic influencing factors. In the second step, the model was expanded to include the variable of subjectively perceived restriction experienced by the study participants in the wake of the COVID-19 pandemic. Considering the previous blocks and their associated variables, GHL, COV-19-HL, and DL were included in the third model. In addition, in each of the following regression models, the reference groups are consistent and categorized as follows:
-
Gender (reference: male)
-
Age group (reference: traditionalists)
-
Migration status (reference: migration background existing)
-
Education (reference: low formal education)
-
Chronic illness (reference: no chronic illness)
-
Subjective social status (reference: low SSS)
-
Subjective health status (reference: less good/poor health)
-
Residence (reference: rural area)
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.