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Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model

Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
Institute for Sensory and Innovation Research (ISI GmbH), Ascherberg 2, 37124 Rosdorf, Germany
Chair of Marketing and Retailing, Faculty of Economics and Business Administration, Chemnitz University of Technology, Reichenhainer Straße 39, 09126 Chemnitz, Germany
Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118 Magdeburg, Germany
Center for Behavioral Brain Sciences (CBBS), Universitätsplatz 2, 39106 Magdeburg, Germany
Chair in Health Services Research, School of Life Sciences, University of Siegen, Am Eichenhang 50, 57076 Siegen, Germany
Chair in Empirical Economics, Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
Research Campus STIMULATE, Otto-von-Guericke-University Magdeburg, Sandtorstraße 23, 39106 Magdeburg, Germany
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2022, 19(20), 13202;
Received: 30 August 2022 / Revised: 7 October 2022 / Accepted: 9 October 2022 / Published: 13 October 2022


Home monitoring examinations offer diagnostic and economic advantages compared to inpatient monitoring. In addition, these technical solutions support the preservation of health care in rural areas in the absence of local care providers. The acceptance of patients is crucial for the implementation of home monitoring concepts. The present research assesses the preference for a health service that is to be introduced, namely an EEG home-monitoring of neurological outpatients—using a mobile, dry-electrode EEG (electroencephalography) system—in comparison to the traditional long-time EEG examination in a hospital. Results of a representative study for Germany (n = 421) reveal a preference for home monitoring. Importantly, this preference is partially driven by a video explaining the home monitoring system. We subsequently analyzed factors that influence the behavioral intention (BI) to use the new EEG system, drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The strongest positive predictor of BI is the belief that EEG home-monitoring will improve health quality, while computer anxiety and effort expectancy represent the strongest barriers. Furthermore, we find the UTAUT model’s behavioral intention construct to predict the patients’ decision for or against home monitoring more strongly than any other patient’s characteristic such as gender, health condition, or age, underlying the model’s usefulness.

1. Introduction

The German public health sector faces challenges, resulting from an increasingly aging population, accompanied by financial pressure and also physician shortages, especially in rural areas [1]. In this context, telemedicine is gaining importance for the capabilities it offers in the assessment and management of diseases across different medical specialties [2]. Furthermore, these developments have been pushed forward since the COVID-19 pandemic began in 2019 [3,4]. Beyond telephone and video consultation between physicians and patients as common telemedical implementations, home monitoring devices in combination with an appropriate infrastructure for safely transferring data–e.g., from rural areas to hospitals or doctors’ offices–offer promising results [5].
Electroencephalography (EEG) is an important diagnostic tool in the neurological sector. During an EEG examination, electrical fields, which are generated by the ongoing neural activity of the brain, are recorded at the scalp using appropriate electrodes and amplifiers. Clinically the EEG reflects a correlate of general brain function see [6,7]. Accordingly, it serves as a standard method to diagnose patients suffering from neurologic diseases, such as epilepsy, stroke, dementia, occasional unconsciousness, concussion, and others. Under routine conditions the EEG is recorded at 21 different electrode sites (i.e., 21 EEG-channels) over a period of approximately 20 min [8,9] in a doctor’s office or in a neurological hospital department. However, given that many diagnostic questions (for instance epilepsy) require much longer recordings [7,9,10,11,12,13,14,15,16,17,18,19] and hospitalization of patients is expensive [11,12,20], the idea of mobile EEG devices that can be used at home arose in the seventies with the development of ambulatory cassette EEG recorders [21,22]. While during the eighties and nineties the further development and digitalization of mobile EEG devices improved their medical and diagnostic use [23,24,25,26,27,28], home monitoring was still cumbersome as the EEG systems could only be placed and removed by medical staff who had to add electrode gel or conductive paste. Only the development of “dry electrodes” which do not require a proper preparation of the skin before recording [29] was the starting point to create EEG systems that allow for a user-friendly and autonomous use of patients. There are several modern mobile EEG systems, see [30] for an exemplary overview, and studies that compare mobile systems with conventional EEG systems in clinical environments [31,32,33,34]. However, there is a lack of studies with mobile, patient-controlled, and dry-electrode EEG systems that meet the medical requirements and are used for home monitoring.
Due to the mentioned difficulties regarding medical care in rural regions in Germany, EEG home-monitoring has been proposed as an alternative [35,36] to the conventional inpatient monitoring and has been the motivator for our HOME (Home-Monitoring and Education) project.
We created the HOME project in order to develop an EEG-based home-monitoring concept for patients with neurological disorders. This was achieved using the Fourier ONETM (F1) (Nielsen Tele Medical GmbH, Magdeburg; now TeleMedi GmbH, Magdeburg), a new mobile EEG system that meets the technical and practical requirements we considered essential for this purpose, e.g., CE certification, wireless connectivity, dry electrodes, comfort, portability, and patient friendliness [37]. To meet the key goals of the HOME project, we conducted several studies. In a first step, the HOMEONE study confirmed the technical usability and efficacy of F1 when compared to conventional EEG systems under clinical conditions [38] and, in the second step, proved both the feasibility and diagnostic/therapeutic yield of EEG home-monitoring [39]. Additionally, the HOMEEPI study also confirmed the technical usability and efficacy of F1 when compared to conventional EEG systems but with a special focus that included only patients with suspected epilepsy [40]. Feedback regarding the comfort of F1 has been documented elsewhere [41].
In the case of home monitoring, patients must accept the burden of creating EEG recordings autonomously. As their personal commitment is essential for the implementation of this new medical care option, this led to the creation of our HOMETA study, designed with the objective of finding the preferences of both patients and potential patients (defined as non-patients further on) when faced with either a long-term EEG examination in a hospital (inpatient examination) or EEG home-monitoring, including the predictors behind the preference.
Taking the assumption that the preference for using EEG home-monitoring may depend on the acceptance of a health service that includes the autonomous use of an EEG system, we decided to investigate this aspect in more detail. In this regard, we incorporated a technology acceptance model in our study.
We opted for the Unified Theory of Acceptance and Use of Technology (UTAUT) model [42] which is based on the earlier Technology Acceptance Model (TAM) [43]. Both theories are widely used models in health technology acceptance studies according to several reviews [44,45]. Recent applications of the UTAUT model in the patients’ or potential patients’ acceptance area can be divided into two approaches: studies that (1) investigate the acceptance of telehealth services [46] or mobile health solutions [47,48,49] as a broad concept, and (2) refer to a specific service or product (e.g., apps) [50] or healthcare wearable devices [51,52]. To the best of our knowledge, there is no other study assessing preferences for an EEG home-monitoring based on patients’ acceptance regarding this specific health service. The HOMETA study we report here can bridge this gap.
Our research is based on an extended version of the UTAUT model [46] which was originally designed to investigate the acceptance of home telehealth services in general. However, besides some necessary changes, it seemed promising to apply this approach to assess several drivers that may influence the behavioral intention to use an EEG home-monitoring.

2. Methods

2.1. Procedure

The study followed a 2 (participant type: patient vs. non-patient) × 2 (introductory video: yes vs. no) between-subject design. In total, 488 participants completed the survey (gross sample). We excluded all individuals who needed less than 10 min to do so ( n = 62 ) and all those who failed the attention check question ( n = 5 ), resulting in a net sample of n = 421 participants with a mean age of 49.13 years and S D = 14.62 (55% males). Table 1 presents the participants characteristics of the remaining 421 participants.
Participants were asked to imagine the following scenario: their neurologist recommends a long-term EEG investigation after they have collapsed in a garden (Appendix A). The medical check-up in question can be performed as either inpatient monitoring or home monitoring. Subsequently, we presented both options, each with a picture of the corresponding EEG device and a description (Appendix B).

2.2. Recruitment

We used two different groups of participants for our study. First, a group of 40 randomly selected neurological patients of the University Hospital in Magdeburg (Germany) who already were confident with the home monitoring system. These participants were free to choose whether to fill-out the survey online ( n = 30 ) or offline ( n = 10 ). Inclusion criteria were a minimum age of 18 years. Second, a group of participants (non-patient) recruited by a professional German online panel provider (myonlinepanel GmbH), who also required a minimum age of 18 years as inclusion criteria. The selection of suitable participants from the online panel pool was based on an equal distribution of age, gender and living environment (urban or rural). Participants ( n = 381 ) recruited by the online panel provider did not know the EEG home-monitoring system. Additionally, we randomly allocated all online participants (i.e., patients and non-patients) to either the video condition or no-video condition. As a result, in the net sample ( n = 421 ), nearly half of participants ( n = 215 ) viewed a video covering the usage of an example mobile EEG device suitable for home monitoring, while in the no-video condition participants only received pictures and written descriptions of both options ( n = 206 ).
The resultant four groups (1) patient/video, (2) patient/no-video, (3) non-patient/video, and (4) non-patient/no-video did not differ significantly in terms of age, graduation, or vocational qualification level, smallest p = 0.223 , but did in gender: χ 2 6 = 19.50 ; p = 0.003 (for details see Table 1). Please note that we obtained one divers observation, when excluding, we got χ 2 3 = 3.71 ; p = 0.295 . Thus, gender is not significantly different across the four groups.

2.3. Measurement

After the introduction, participants indicated their preference between the two medical care variants via a Likert scale (‘Which of the two options would you prefer for a long-term EEG examination?’). The scale ranged from 1 = strong preference for inpatient monitoring to 7 = strong preference for home monitoring. This allowed us to obtain granular information on the merits of both medical care options. Furthermore, they filled out all the questions from the extended UTAUT model based on Cimperman et al. [46] for measuring the acceptance of EEG home-monitoring. This meant also incorporating 7-point Likert-scaled items ranging from 1 = totally disagree to 7 = totally agree. The model comprised the main UTAUT constructs of performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), which were extended by the constructs of perceived security (PS), computer anxiety (CA), and doctor’s opinion influence (DC) to identify individual drivers of behavioral intention to use home monitoring (BI). Appendix C presents all the measurement model details, including wording, translation, and validity assessment. For identifying the current health status, the German translation of the 36-item Short-Form Health Survey [53] was used, including for physical functioning (PF), role limitations due to physical health (PH), role limitations due to emotional problems (EP), energy/fatigue (EF), emotional well-being (EW), social functioning (SF), pain (P), general health (GH), and health change (HC). Appendix D presents construct wording and the reliability assessment. Finally, participants also provided socio-demographic information.

3. Results

3.1. Preference Analysis

Individuals report a higher relative preference for home monitoring compared to inpatient monitoring ( m e a n = 5.15 ,   S D = 2.01 , one-sample t-test against the scale midpoint t 420 = 11.73 ,   p < 0.001 ). Furthermore, no significant difference concerning preference is found to exist between patients and non-patients ( m e a n n o n p a t i e n t = 5.11 ,   S D = 2.01 , m e a n p a t i e n t = 5.55 ,   S D = 2.00 , F 1 ,   420 = 1.78 ,   p = 0.183 ). In line with our expectations we found that participants who saw the video reported a significantly higher relative preference for home monitoring ( m e a n v i d e o = 5.37 ,   S D = 1.83 , m e a n n o   v i d e o = 4.91 ,   S D = 2.15 , F 1 ,   420 = 5.57 ,   p < 0.001 ), This result illustrates the need for a proper explanation of the examination options. However, no significant interaction emerges between the participant type (patient vs. non-patient) and the video factor ( F 1 ,   420 = 0.681 ,   p = 0.410 ). Supporting the face validity of findings, a higher preference for home monitoring significantly correlates with individuals’ behavioral intention (BI) in the UTAUT model presented in the next section ( r = 0.49 ,   t 419 = 11.51 ,   p < 0.001 ) which is the case for the full sample as well as all four study groups separately. Based on these results, the next step focused on identifying drivers of participants’ BI to use home monitoring. Because our goal is to explain the BI construct’s variance rather than replicating perfectly the original variance covariance matrix, we opt for partial least squares structural equation modeling PLS-SEM [54,55,56]. This decision is in line with other authors in the field of research who usually apply PLS-SEM when implementing the UTAUT model [46,57,58,59,60], especially when their research is intended to predict and explain the key target construct or to identify the key construct’s main drivers [61,62].

3.2. Drivers of BI

We set up the extended UTAUT model with SmartPLS 3.0 [63] (Figure 1). Due to the identical results of the preference analysis above, as well as the sample size of the patient group ( n = 40 ), we pooled the patient and non-patient groups. Please note that we also checked the results for differences between the groups (patient type and video factor) by means of a multi-group analysis. However, no significant differences emerged that limit our results’ interpretation.
The analysis starts with an evaluation of the measurement model’s reliability and validity for which Appendix C presents the details. Overall, the measurement model raises no reliability or validity concerns. The main analysis evaluates the structural model and is based on 5000 bootstraps and two-tailed p-values a ( = 5 % ). Multicollinearity is not a problem among exogenous constructs as the inner variance inflation factors (VIFs) are all < 2.5 (Appendix C). Figure 1 presents the model with the direct path coefficients, t-values, and p-values. Additionally, Table 2 provides the indirect and total effects, together with full statistics.
Our main interest is to understand which exogenous constructs explain the final endogenous construct BI. Results show that there are three significant direct drivers and three significant indirect drivers. PE has the strongest direct effect ( b = 0.348 ,   p < 0.001 ), followed by EE ( b = 0.312 ,   p < 0.001 ) and FC ( b = 0.184 ,   p < 0.001 ). Thus, participants’ belief in an improvement of health quality is the main driver for their BI to favor home monitoring. Additional drivers include participants’ belief in there being less effort required to use home monitoring systems (higher EE scores indicate less expected effort) and to obtain technical support (FC). SI does not have a significant effect on BI ( b = 0.057 ,   p = 0.260 ), which stresses that social influence (e.g., friends and colleagues) is not a BI driver. CA (higher scores indicate the absence of computer anxiety) has the strongest significant indirect effect on BI via EE ( b = 0.397 ,   p < 0.001 ). Certainty regarding the safety of personal health information (PS) and confidence in doctors’ expertise (DC) also have significant indirect effects on BI via PE (respectively, b = 0.066 ,   p = 0.001 and b = 0.065 ,   p < 0.001 ).
Next, we checked via a multigroup analysis [64] for differences between patients vs. non-patients as well as between the video vs. no-video groups. For the first factor, we find one significant effect of FC on BI ( b n o n p a t i e n t p a t i e n t = p = 0.048 ). Nevertheless, both groups provide a significant effect of FC on BI. For patients, however, the effect of ‘belief in receiving technical support when using home monitoring’ on their behavioral intention is even stronger. Moreover, no differences exist between the video and no-video conditions (smallest p = 0.256 ).
To summarize the exogenous constructs’ explanatory power for BI, we implemented an importance-performance map analysis (IPMA), which portrays the exogenous constructs’ importance (i.e., their total effects in explaining BI on the x-axis) as well as their rescaled latent construct scores on the y-axis that represents the status quo for a construct from worst–0, to best–100 [65]. Figure 2 provides the resultant IPMA.
The x-axis in Figure 2 visualizes that, in total (sum of direct and indirect effects), EE has the largest effect on BI, followed by CA and PE. In contrast, SI, DC, and PS have only marginal effects. The y-axis however underlines that all three above-average important constructs also have high and above-average latent construct scores (i.e., the y-axis highlights a construct’s status quo). Thus, there is only limited room for possible improvements. Keep in mind that EE and CA are negatively scaled, with high values representing the absence of computer anxiety as well as low effort expectations. In conclusion, although PS only has a marginal influence in explaining BI, there is much room for improving the current status quo when it comes to the perceived security of EEG home-monitoring.
Next, we assessed the predictive validity of the PLS-SEM model in terms of its ability to predict the endogenous constructs’ indicators by means of their exogenous constructs’ indicators as compared to a classical OLS regression. This procedure is called PLSpredict in the literature [66,67]. Since we handle mediating constructs in our structural model (e.g., EE), we applied the direct antecedents [68] approach drawing on the R package seminr [69]. Specifically, we used 10 folds with 10 replications, which results in the root mean squared errors of prediction (RMSE) in Table 3.
Results in Table 3 indicate that only the minority of prediction errors of the PLS-SEM model is lower as compared with the linear OLS model. Thus, following conventional thresholds, the predictive power has to be regarded as rather low [70]. Therefore, we take additional measures to evaluate PLS model’s abilities in predicting the respondents’ relative preference for EEG home-monitoring vs. its stationary counterpart in the next section.

3.3. Impact of BI Score, Health Measurement, Age, and Gender on Preference

Finally, we performed a regression analysis (OLS) to assure that BI is a real predictor for individuals’ preference between home monitoring and inpatient monitoring beyond control variables. For this purpose, we set up regression models with varying sets of predictors (Table 4). Model 1 utilizes solely the BI construct score from PLS-SEM, whereas Model 2 extends the model according to the participants’ health status. Afterward, Model 3 includes demographics. Lastly, Model 4 draws on all previous predictors along with the participant type (patient vs. non-patient) as well as the video factor (yes vs. no). For the health status, we obtained construct scores by simply averaging individual items, after reverse coding if necessary. The results point out a significant overall model for all four models, with Model 4 resulting in F 14 ,   405 = 11.151 , p < 0.001 . Across all models, BI is a robust predictor of participants’ preferences. Model 4 indicates that the video factor is another significant predictor ( b = 0.476 , t = 2.772 , p < 0.006 ). Specifically, the preference for choosing home monitoring over inpatient monitoring increases when an introductory video is being watched. Nevertheless, a summary of Table 4 shows that the extension of Model 1 to Model 4 does not result in a significantly enhanced explanation of the dependent variable ( a d j u s t e d   R M o d e l   1 2 = 0.234 vs. a d j u s t e d   R M o d e l   4 2 = 0.253 . Consequently, BI from the extended UTAUT model seems to be the main driver for a home monitoring preference. (Appendix E presents detailed regression analysis information).

4. Discussion

4.1. Theoretical Implications

Our analysis of the HOMETA study shows that the relative preference for EEG home-monitoring compared to inpatient monitoring does not statistically significantly depend on participant type (patient or non-patient), age, gender, health status, graduation, or vocational qualification. Importantly, watching an introductory video about the use of a mobile EEG system necessary for home monitoring does positively influence an individual’s preference for choosing home monitoring over inpatient monitoring. This could lead us to assume that the provision of more information in advance regarding how to handle a medical device as part of a telemedical examination could help to increase acceptance. However, the most important predictor of preference regarding home monitoring is the behavioral intention to use this health care option, which is the final endogenous construct of our extended UTAUT model.
After confirmation of the UTAUT model’s reliability and validity, the analysis confirmed three significant direct and three significant indirect drivers of BI. Predictor PE is the strongest direct driver–in line with previous studies [71,72,73]–closely followed by EE. Effort expectancy (EE) has a similar influence on BI as expected benefit and improvement of health (PE), perhaps because the home-monitoring examination includes the autonomous use of an unknown medical device, which could prove more challenging than other telehealth services requiring only the use of desktop or mobile applications, for example, which might be more familiar. In this context, the level of trust that individuals have in user-friendly applications is even more important. The third direct predictor of BI is FC. According to our multigroup analysis, the belief that technical support will be available in case of problems during the performance of the home-monitoring examination has a stronger impact on BI for patients than for non-patients. Patients we recruited for our study are already familiar with the EEG home-monitoring system and, for this reason, are more willing to carry out EEG home-monitoring if they can count on support in the event of necessity. Finally, SI does not have an influence on BI, which is in line with a previous study of the acceptance of home telehealth services by older users [46]. Even though our study is not limited to elderly people, the opinions of peers, colleagues, and family are not found to be important when it comes to decisions of whether to use home monitoring or not. Considering that the UTAUT model was originally created for users’ acceptance of technology in a working environment [42] and not in a private space, as well as with the knowledge of previous studies affirming the impact of background situations on SI [46,74], the opinions of others do not seem to be a key decision-making criterium when it comes to pursuing health improvements.
The factors of CA, PS, and DC do have an influence on EE or PE, making them indirect BI drivers. The strongest indirect predictor is the absence of computer anxiety. In this context, our homemade examination includes the use of a mobile application installed on a provided tablet, which makes this aspect important for our analysis. Decreasing computer anxiety increases the belief in ease of use and results in the behavioral intention to use the home-monitoring system. DC and PS support the increase of ‘belief in an improvement of personal health’ directly and on BI indirectly. In summary, trust in the safety of personal health data and in doctors’ expertise do both have an indirect impact on BI, but a much lower one than decreasing computer anxiety.
An additional IPMA analysis clarifies that in total, the constructs EE, CA, and PE are most important in explaining the respondents BI to use EEG home-monitoring. However, this analysis also confirms that all three constructs have above-average status-quo, leaving only marginal potential for improvements. In contrast, although PS only have marginal impact on BI, this construct provides the highest potential for learning. Thus, proper communication of facts about data security should not be neglected.
Due to the lack of studies assessing the acceptance of EEG home-monitoring, we compare the results of the direct predictors in our study with those of two studies that investigate the acceptance of wearable healthcare devices [51,52]. In line with our results, both studies report PE to have a direct effect on BI. In contrast to the results reported here, in [51] EE and FC do not have any influence on BI. Regarding these differences, we assume the task complexity and the necessity of a patient’s commitment (when implementing the EEG home-monitoring) are possible causes of why we found EE and FC to have a stronger effect. Another difference to our results is that both studies report a positive effect of SI on BI. This difference might be explained—at least to some extent—by the device used. While the use of wearables depends on a high level of awareness within the group of (potential) customers, this awareness is not necessary for the use of a medical device such as the mobile EEG device used in our study. Here, the use of the medical device is based on a doctor’s prescription.
In terms of theoretical implications, we conclude from our study: (1) the preference for EEG home-monitoring compared to inpatient monitoring depends on the behavioral intention to use home monitoring and can be assessed by using an extended UTAUT model [46]. We support the conclusion of [46] who also found six relevant predictors in their study on older users’ home telehealth services acceptance behavior. More specifically, similar to [46], we (2) found three significant direct drivers (PE, EE, and FC) and three significant indirect drivers (DC, CA, and PS). The (3) insignificant effect of social influence is also in line with previous research and can be related to the origin of the UTAUT model. The model was originally developed to examine technology acceptance within organizations, e.g., companies. Here, it is plausible to assume that the opinion of the social environment, such as that of colleagues, has a major influence. However, since medical devices are prescribed by doctors, it is understandable that the influence of the social environment is less important. Therefore, future investigations can take this aspect into account.

4.2. Practical Implications

The current HOMETA study is part of the HOME project, which aims to provide evidence of diagnostic and therapeutic yield (“change of management”) of EEG home-monitoring neurological outpatients [37]. To meet the key goals of the project, it was necessary to confirm the technical usability and efficacy of the new EEG device (see [38,40]) but also to demonstrate the feasibility and diagnostic/therapeutic yield of EEG home-monitoring neurological patients (see [39]). In order to establish EEG home-monitoring as a new health service [75] and, thus, to gain practical relevance, the patients’ acceptance of this new health service is of crucial importance. In this regard, the HOMETA study results provide practical implications that have to be taken into consideration for the design and implementation of EEG home-monitoring as a standard alternative to inpatient monitoring.
The influence of CA and EE on BI suggests that when patients decide to use an EEG home-monitoring system autonomously they have to feel comfortable and secure, without fear of failing. This situation could be achieved via a user-friendly system design and adequate training in advance. At the time of taking a decision for or against the home-based examination, patients could be shown a short video about the EEG system that, as demonstrated in our study, can have a positive effect on the decision-making process. That means, providing the necessary information at the right time and in a way that patients can understand easily is particularly important to gain acceptance for such a health service. In this regard, we agree with [46] that physicians have a special role in this context. As social agents, physicians have to promote this health service by prescribing the use of EEG home-monitoring (see [46] p. 29).
In addition, the impact of FC on BI suggests that available support in case of problems also plays an important role. While doctors’ expertise and data safety are of less importance in comparison to other factors, they still influence BI. For this reason, the home-monitoring concept should be aligned to suit, e.g., through the provision of sufficient information on the examination procedure and data security. According to our study, the opinions of peers, friends, and family do not influence decision-making processes on home-monitoring use.
Based on the current HOMETA results and the results derived previously throughout the HOME project we summarize that EEG home-monitoring neurological patients can be well integrated into outpatient care. This new health service could be considered as an alternative for some cases of expensive inpatient monitoring, if the patients’ information needs are considered and if the positive effects on patients’ health are highlighted.

4.3. Limitations and Further Research

Despite these interesting findings, the study does have some limitations. For example, the survey relied upon an online structure (the home-monitoring system video should be shown randomly), largely for organizational reasons (use of an online panel and carried out during the COVID-19 pandemic). In taking this approach, although we drew participants from all age groups, we were unable to include people who partially or totally refused to embrace computer technology. Furthermore, with the study implementation occurring at the time of the COVID-19 pandemic, it is likely that participants may have felt a more positive attitude to telemedicine than previously. In addition, it remains to be seen whether attitudes may shift again once the pandemic is in the past, so future research might do well to focus on this.
So far, the HOME project has mainly examined technical aspects (usability and feasibility of an EEG home-monitoring) and the perspective of patients (acceptance of an EEG home-monitoring). Future research should consider the perspective of physicians. For this purpose, the attitude of physicians to this health service could be analyzed in qualitative studies. At the same time, specific requirements from the physicians’ perspective can be examined in depth. Thus, the focus group research method could be applied in order to identify wishes, requirements and possible problems physicians may anticipate with an EEG home-monitoring of their patients.

5. Conclusions

The aim of the HOMETA study was to examine neurological patients’ preferences regarding EEG home-monitoring compared to inpatient monitoring, and to gain a better understanding of the predictors behind the preferences. The HOMETA results complement the previous results of the HOME project and, thus, contribute to evaluating EEG home-monitoring for neurological patients as a new health service and to demonstrating how this service can be integrated into outpatient care.
For this purpose, we used an extended version of the UTAUT model to assess factors considered relevant for developing an EEG home-monitoring concept that patients will accept as an alternative to inpatient monitoring. In addition to the factors incorporated in the extended UTAUT model, we considered several control variables, such as gender, age, and health status, to evaluate which factors have to be taken into consideration when developing a strategy to implement EEG home-monitoring as a new health service. In this regard, we recruited (1) 40 patients from the University Hospital in Magdeburg (Germany), and (2) 381 non-patients from an online panel provider. Approximately half of the total 421 participants were randomly assigned into either a video-condition or a no-video condition.
Our study shows behavioral intention (BI) to be the main driver behind preferences for home-monitoring examination, which is applicable for choosing an autonomous use of a medical device over inpatient monitoring. BI is the final endogenous construct of the UTAUT model for which we can confirm the validity in the context of EEG home-monitoring. The behavioral intention to use this approach depends on various views and beliefs, such as: (1) home-monitoring improves health, (2) the home-monitoring system is easy to use, (3) there will be technical support in case of issues during the home recording, (4) I am not afraid of using the home-monitoring system, (5) my health data are safe, and (6) the doctor is an expert. Meeting these expectations is a crucial task when creating and designing a home-monitoring concept, which can be achieved by implementing user-friendliness, patient training, and provision of comprehensive information covering the new medical care option.

Author Contributions

Conceptualization, U.B., M.L., H.H. and T.N.; data curation, U.B., F.K., M.L. and T.N.; formal analysis, F.K. and M.L.; funding acquisition, H.H. and T.N.; investigation, U.B., F.K., M.L., R.D., H.H. and T.N.; methodology, F.K., M.L., H.H. and T.N.; project administration, U.B. and A.-K.B.; resources, H.H.; software, F.K. and M.L.; supervision, H.H. and T.N.; validation, M.L., A.-K.B., R.D., H.H. and T.N.; visualization, F.K. and M.L.; Writing—original draft, U.B. and F.K.; Writing—review and editing, M.L. and T.N. All authors have read and agreed to the published version of the manuscript.


The study is funded through the research consortium “Autonomie im Alter” by the State of Saxony-Anhalt and the European Union, European Regional Development Fund (ERDF), grant ID ZS/2019/03/97871.

Institutional Review Board Statement

Ethical approval was obtained from the Ethics Committee of the Faculty of Medicine of the Otto-von-Guericke-University Magdeburg, Germany (Reference number: 64/20).

Informed Consent Statement

All patients and participants gave their informed consent for inclusion before they participated in the study.

Data Availability Statement

The corresponding datasets of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

Since July 2022, Ulrike Baum has worked as a Technical Project Manager at Telemedi GmbH. The research reported here was conducted and evaluated until 06/2022 during her full-time employment at the University Department of Neurology, Otto-von-Guericke-University Magdeburg (Germany). She declares that the reported research is unrelated to her current employment at TeleMedi GmbH. She has NO conflicts of interest to declare. Frauke Kühn works as a Sensory Project Manager at isi GmbH, a professional Market Research Institute. She declares that the research reported in this study (as well as the whole HOME project) was not related to her work at isi GmbH. She has NO conflicts of interest to declare. The authors declare no conflict of interest.

Appendix A. Survey Scenario

Please Imagine the Following Situation

You are sitting in the garden with your friends on a warm and sunny day. You are thirsty and get up to get something to drink. The next thing you know, you wake up lying on the ground without remembering what happened. Your friends tell you that you suddenly collapsed and were unresponsive for a short time. Although you feel fine again and have suffered no injuries due to a soft landing on the grass, the next day you go to your family doctor, who refers you to a neurologist (specialist in neurology). Based on your history, your neurologist recommends that you have electroencephalography (EEG) examination. During this examination, electrodes are placed on your scalp to record the electrical impulses of brain activity. There is the possibility of performing this examination over a short period of time (approximately 20 min) or over a longer period (several hours). In your case, the neurologist suggests a long-term measurement in order to be able to evaluate for a longer period of time. This long-term EEG can be performed either in a hospital or in your home.

Appendix B. Presentation of Inpatient Monitoring and Home-Monitoring Options

Appendix B.1. Option 1: EEG Inpatient Monitoring

You are hospitalized and admitted for 24 h. The examination proceeds as follows: A medical-technical assistant places the electrodes on your scalp for long-term EEG measurement and then starts the EEG recording. You are not allowed to attach or detach the electrodes by yourself.
Figure A1. Option 1: EEG Inpatient Monitoring.
Figure A1. Option 1: EEG Inpatient Monitoring.
Ijerph 19 13202 g0a1
The wearing time of the electrodes depends on your neurologist’s instructions. You can move around while wearing the electrodes, but should only engage in moderate activities (i.e., no sports, etc.). At the end of the wearing time, the medical-technical assistant stops the recording and removes the electrodes. Following your hospital discharge, you will need to attend a meeting at your neurologist’s office where the EEG results will be discussed with you.

Appendix B.2. Option 2: EEG Home-Monitoring

You are at your home and a nursing service supplies you with a mobile EEG system with accessories. The nursing service also provides you with detailed instructions on how to place and remove the EEG cap autonomously and how to start the EEG recording. The electrodes are already positioned within the cap and, with the help of a supplied tablet (mini-computer), you can start the recording. After the nursing service has said goodbye, you can put on the cap by yourself for the long-term EEG measurement and start the recording with the tablet (mini-computer). While you are wearing the mobile EEG cap, you can move around, but should only engage in moderate activities (i.e., no sports, etc.). The recorded EEG data is sent to the server of your neurologist in an encrypted way via an included router. Compliance with all data privacy directives and safety regulations is warranted. Your neurologist informs you about the necessary wearing time of the mobile EEG cap. After completion of the wearing time, you stop the EEG recording and then remove the mobile EEG cap by yourself. In agreement with you, the nursing service collects the mobile EEG system from your home. Your neurologist will discuss the EEG results with you during a follow-up appointment in the doctor’s office.
Figure A2. Option 2: EEG Home-Monitoring.
Figure A2. Option 2: EEG Home-Monitoring.
Ijerph 19 13202 g0a2

Appendix C. Measurement Information for the Extended UTAUT Model

The SEM quality assessment starts with evaluating the model’s reliability. To begin with, internal consistency reliability holds with all Cronbach’s α values higher than 0.7 (Table A1). Almost all outer loadings exceed 0.7, ensuring indicator reliability. Additionally, although one item of DC possesses an outer loading of 0.566, convergent validity holds for all constructs with an average variance extracted (AVE) above 0.5.
Table A1. Item wording and PLS measurement model results.
Table A1. Item wording and PLS measurement model results.
ConstructItemLoadingEnglish WordingGerman Wording
Performance Expectancy (PE)–the degree to which an individual believes that using EEG home-monitoring will help him or her increase their health performance/quality
AVE = 0.702
α = 0.894
C.R. = 0.922
PE10.816I find that using EEG home-monitoring would be helpful in monitoring my health.Ich denke, dass die Durchführung eines EEG-Home-Monitorings bei der Diagnostik meines Krankheitsbildes und der Überwachung meiner Gesundheit hilfreich ist.
PE20.811I find that using EEG home-monitoring would make me feel safer in my daily life.Ich denke, dass ich mich durch die Durchführung eines EEG-Home-Monitorings sicherer fühle, was meine Gesundheit betrifft.
PE30.838EEG home-monitoring could enhance the level of convenience in accessing medical care services.Die Möglichkeit, eine EEG-Untersuchung zu Hause durchführen zu können (EEG-Home-Monitoring), könnte den Zugang zur medizinischen Versorgung erleichtern.
PE40.829EEG home-monitoring could enhance the quality of my life.Das EEG-Home-Monitoring als Untersuchung in der Häuslichkeit könnte meine Lebensqualität verbessern.
PE50.894Overall, I find that EEG home-monitoring would be highly useful.Insgesamt finde ich das EEG-Home-Monitoring sehr nützlich.
Effort Expectancy (EE)–the degree of ease associated with the use of EEG home-monitoring
AVE = 0.819
α = 0.926
C.R. = 0.948
EE10.896I find that using EEG home-monitoring would be simple.Ich finde, dass die Durchführung des EEG-Home-Monitorings einfach wäre.
EE20.928I find that using EEG home-monitoring would be easy to learn.Ich finde, dass die Durchführung des EEG-Home-Monitorings leicht zu erlernen wäre.
EE30.920I find that EEG home-monitoring would be easily understandable and clear for me.Ich finde, dass das EEG-Home-Monitoring für mich leicht verständlich und klar wäre.
EE40.876Overall, I find that using EEG home-monitoring would be convenient.Insgesamt finde ich die Verwendung des EEG-Home-Monitorings praktisch.
Social Influence (SI)–influence of peers and colleagues’ opinions
AVE = 0.808
α = 0.881
C.R. = 0.927
SI10.870Peers and colleagues would support me in using EEG home-monitoring.Gleichaltrige und Kollegen würden mich bei der Entscheidung, das EEG-Home-Monitoring durchzuführen, unterstützen.
SI20.915People who influence my behavior would support my use of EEG home-monitoring.Menschen, die mich beeinflussen, würden mich bei der Entscheidung, das EEG-Home-Monitoring durchzuführen, unterstützen.
SI30.911People who are important to me would support my use of EEG home-monitoring.Menschen, die mir wichtig sind, würden mich bei der Entscheidung, das EEG-Home-Monitoring zu durchzuführen, unterstützen.
Facilitating Conditions (FC)–technical support for using EEG home-monitoring
AVE = 0.741
α = 0.824
C.R. = 0.895
FC10.870I believe that guidance will be available to me when deciding whether to use EEG home-monitoring.Ich gehe davon aus, dass ich bei meinem Neurologen ein Aufklärungsgespräch erhalten werde, wenn ich entscheiden muss, ob ich das EEG-Home-Monitoring durchführen möchte.
FC20.912I believe that specialized instructions concerning the use of EEG home-monitoring will be available to me.Ich gehe davon aus, dass mir spezielle Anweisungen zur Verwendung des EEG-Home- Monitorings zur Verfügung stehen.
FC30.796I believe that specific persons (or a group) will be available for assistance with EEG home-monitoring difficulties (e.g., nursing service or a call center).Unabhängig von der Einweisung durch den Pflegedienst gehe ich davon aus, dass ich während der Nutzung des EEG-Home- Monitorings Unterstützung bei Anwendungsschwierigkeiten erhalten werde (z. B. durch den Pflegedienst oder ein Callcenter).
Computer Anxiety (CA)–anxiety concerning the use of EEG home-monitoring option
AVE = 0.674
α = 0.838
C.R. = 0.892
CA10.824Anyone can learn to use a mobile EEG cap if they are patient and motivated.Jeder kann lernen, eine mobile EEG-Haube zu verwenden, wenn er geduldig und motiviert ist.
CA2 0.855I do not hesitate to use a mobile EEG cap for fear of making mistakes.Ich zögere nicht, eine mobile EEG-Haube zu verwenden, weil ich keine Angst habe, Fehler zu machen.
CA30.855If given the opportunity, I would like to learn about and use the mobile EEG cap.Wenn ich die Gelegenheit dazu hätte, würde ich gerne die mobile EEG-Haube kennenlernen und nutzen.
CA40.744I feel that computers are necessary tools in both educational and work settings.Ich bin der Meinung, dass Computertechnik in der Medizin notwendiges Werkzeug ist.
Perceived Security (PS)–the degree to which using information technology enables the administration of personal health information
AVE = 0.869
α = 0.950
C.R. = 0.964
PS10.920I would feel secure sending personal health information using the Internet and computers.Ich würde mich sicher fühlen, persönliche Gesundheitsinformationen über das Internet zu senden.
PS20.916The Internet offers a secure means through which to send sensitive personal information.Das Internet ist ein sicheres Medium, um vertrauliche persönliche Informationen zu senden.
PS30.950I would feel totally safe providing sensitive personal information about myself over the Internet.Ich würde mich absolut sicher fühlen, sensible persönliche Informationen über das Internet bereitzustellen.
PS40.943Overall, using the EEG cap and an Internet connection is a safe way to transmit sensitive personal health information.Insgesamt ist die Verwendung der EEG-Haube und einer Internetverbindung eine sichere Möglichkeit, vertrauliche persönliche Gesundheitsinformationen zu übertragen.
Doctor’s Opinion (DC)–doctor’s expert power influence
AVE = 0.683
α = 0.919
C.R. = 0.937
DC10.877I trust my doctor’s judgment.Ich vertraue dem Urteil meines Arztes.
DC20.907The doctor’s expertise makes him/her more likely to be right.Aufgrund des medizinischen Fachwissens des Arztes ist es wahrscheinlicher, dass er/sie recht hat.
DC30.906The doctor has a lot of experience and usually knows best.Der Arzt hat viel Erfahrung und weiß es normalerweise am besten.
DC40.871The doctor’s knowledge usually makes him/her right.Durch sein Wissen hat der Arzt für gewöhnlich recht.
DC50.867I trust my doctor’s judgment about the use of EEG home-monitoring.Ich vertraue dem Urteil meines Arztes, was den Einsatz des EEG-Home-Monitorings betrifft.
DC60.566In the case of deciding to use EEG home-monitoring, I don’t know as much about what is required as the doctor does.Wenn ich mich für das EEG-Home-Monitoring entscheide, kenne ich mich damit nicht so gut aus wie der Arzt.
DC70.734Doctors are intelligent.Ärzte sind klug.
Behavioral Intention to Use (BI)–the degree to which an individual intends to use EEG home-monitoring
AVE = 0.869
α = 0.950
C.R. = 0.964
BI10.913Assuming there was a medical need to perform EEG home-monitoring, I would use it.Angenommen es bestünde die medizinische Notwendigkeit, das EEG-Home-Monitoring durchzuführen, würde ich es verwenden.
BI20.928I assume that in the future I would regularly use EEG home-monitoring if it was medically necessary.Ich gehe davon aus, dass ich bei Notwendigkeit in Zukunft regelmäßig das EEG-Home-Monitoring verwenden würde.
BI30.937I intend to use EEG home-monitoring in the future if medical necessity exists.Ich beabsichtige, in Zukunft das EEG-Home-Monitoring zu verwenden, falls die Notwendigkeit besteht.
BI40.950Providing I had access to EEG home-monitoring, I would use the services when needed.Wenn ich Zugang zur Verwendung des EEG-Home-Monitorings hätte, würde ich die Dienste bei Notwendigkeit nutzen.
Note. All items are scored on a 7-point Likert scale and range from 1 (totally disagree) to 7 (totally agree). AVE = average variance extracted, α = Cronbach’s Alpha, C.R. = composite reliability.
Next, we use the Fornell–Larcker criterion [76] to assess discriminant validity. Table A2 points out that the square roots of the average variance extracted on the diagonal (marked in gray) are higher for each of the constructs than the inter-construct correlations to any other latent construct (presented in the column below the diagonal values). Furthermore, Heterotrait-Monotrait Ratios in the upper triangular matrix confirm discriminant validity for the reflectively measured constructs [77]. Specifically, the 95% CIs do not include 1 (HTMTinference < 1). For the sake of completeness, Table A3 presents variance inflation factors (VIF) regarding each endogenous construct in the model. All values are below 5, thus, no issues with multicollinearity among exogenous constructs can be identified.
Table A2. Discriminant validity measurement.
Table A2. Discriminant validity measurement.

Behavioral Intention (BI)Computer Anxiety (CA)Doctor’s Opinion (DC)Effort Expectancy (EE)Facilitating Conditions (FC)Performance Expectancy (PE)Perceived Security (PS)Social Influence (SI)
Behavioral intention (BI)0.932[0.799;
Computer anxiety
Doctor’s opinion (DC)0.5200.4670.826[0.345;
Effort expectancy (EE)0.7220.8030.4320.905[0.582;
Facilitating conditions (FC)0.6020.6470.5340.5950.861[0.537;
Performance expectancy (PE)0.7250.7020.4860.7120.5530.838[0.410;
Perceived security (PS)0.3940.4100.3190.3760.2550.4610.932[0.331;
Social influence (SI)0.5420.6020.4170.5860.4790.5610.3980.899
Notes: Gray main diagonal ( A V E 2 ) and lower triangular matrix (Pearson correlation) present Fornell–Larcker criterion. Upper triangular matrix presents the Heterotrait-Monotrait Ratio of correlations (95% CIs).
Table A3. Collinearity Check.
Table A3. Collinearity Check.
Variance Inflation Factors (VIF)Behavioral Intention (BI)Effort Expectancy (EE)Performance Expectancy (PE)
Computer anxiety (CA) 1.203
Doctor’s opinion (DC) 1.274
Effort expectancy (EE)2.470 1.333
Facilitating conditions (FC)1.678
Performance expectancy (PE)2.405
Perceived security (PS)1.3221.2031.207
Social influence (SI)1.721

Appendix D. Items Wording and Scale Quality Assessment of 36-Short Form Health Survey

English WordingGerman Wording
Physical functioning α = 0.946
(0 = yes, limited a lot; 50 = yes, limited a little; 100 = no, not limited at all)
The following items are about activities you might carry out during a typical day. Does your health now limit you in these activities? If so, how much?Im Folgenden sind einige Tätigkeiten beschrieben, die Sie vielleicht an einem normalen Tag ausüben. Sind Sie durch Ihren derzeitigen Gesundheitszustand bei diesen Tätigkeiten eingeschränkt? Wenn ja, wie stark?
vigorous activities, such as running, lifting heavy objects, participating in strenuous sportsanstrengende Tätigkeiten, z.B. schnell laufen
moderate activities, such as moving a table, pushing a vacuum cleaner, bowling, or playing golfmittelschwere Tätigkeiten, z.B. einen Tisch verschieben, staubsaugen, kegeln, Golf spielen
lifting or carrying groceriesEinkaufstaschen heben oder tragen
climbing several flights of stairsmehrere Treppenabsätze steigen
climbing one flight of stairseinen Treppenabsatz steigen
bending, kneeling, or stoopingsich beugen, knien, bücken
walking more than a milemehr als 1 Kilometer zu Fuß gehen
walking several blocksmehrere Straßenkreuzungen weit zu Fuß gehen
walking one blockeine Straßenkreuzung weit zu Fuß gehen
bathing or dressing yourselfsich baden oder anziehen
Physical health α = 0.888
(0 = yes, 100 = no)
During the past 4 weeks, have you experienced any of the following problems with your work or other regular daily activities as a result of your physical health?Hatten Sie in den vergangenen 4 Wochen aufgrund Ihrer körperlichen Gesundheit irgendwelche Schwierigkeiten bei der Arbeit oder anderen alltäglichen Tätigkeiten im Beruf bzw. zu Hause?
cut down the amount of time you spent on work or other activitiesIch konnte nicht so lange wie üblich tätig sein.
accomplished less than you would likeIch habe weniger geschafft als ich wollte.
were limited in the kind of work or other activitiesIch konnte nur bestimmte Dinge tun.
had difficulty performing the work or other activities (for example, it took extra effort)Ich hatte Schwierigkeiten bei der Ausführung.
Emotional problems α = 0.909
(0 = yes, 100 = no)
During the past 4 weeks, have you experienced any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?Hatten Sie in den vergangenen 4 Wochen aufgrund seelischer Probleme irgendwelche Schwierigkeiten bei der Arbeit oder anderen alltäglichen Tätigkeiten im Beruf bzw. zu Hause (z.B., weil Sie sich niedergeschlagen oder ängstlich fühlten)?
cut down the amount of time you spent on work or other activitiesIch konnte nicht so lange wie üblich tätig sein
accomplished less than you would likeIch habe weniger geschafft als ich wollte.
didn’t complete work or other activities as carefully as usualIch konnte nicht so sorgfältig wie üblich arbeiten.
Energy/fatigue α = 0.873
(100 = all of the time, 80 = most of the time, 60 = a good portion of the time, 40 = some of the time, 20 = a little of the time, 0 = none of the time)
These questions are about how you feel and how things have been with you during the past four weeks. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the past four weeks...In diesen Fragen geht es darum, wie Sie sich fühlen und wie es Ihnen in den vergangenen 4 Wochen gegangen ist. Wie oft waren Sie in den vergangenen 4 Wochen…
did you feel full of pep?...voller Schwung?
did you have a lot of energy?...voller Energie?
did you feel worn out? (R)...erschöpft? (R)
did you feel tired? (R)...müde? (R)
Emotional well-being α = 0.882
(100 = none of the time, 80 = a little of the time, 60 = some of the time, 40 = a good portion of the time, 20 = most of the time, 0 = all of the time)
These questions are about how you feel and how things have been with you during the past four weeks. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the past four weeks...In diesen Fragen geht es darum, wie Sie sich fühlen und wie es Ihnen in den vergangenen 4 Wochen gegangen ist. Wie oft waren Sie in den vergangenen 4 Wochen…
have you felt like a very nervous person?...sehr nervös?
have you felt so down in the dumps that nothing could cheer you up?so niedergeschlagen, dass Sie nichts aufheitern konnte?
have you felt calm and peaceful? (R)...ruhig und gelassen? (R)
have you felt downhearted and blue?...entmutigt und traurig?
have you been a happy person? (R)... glücklich? (R)
Social functioning α = 0.895
(Item 1: 100 = not at all, 75 = a little bit, 50 = moderately, 25 = quite a bit, 0 = extremely. Item 2: 0 = all of the time, 25 = most of the time, 50 = some of the time, 75 = a little bit of the time, 100 = none of the time)
During the past four weeks, to what extent have your physical health or emotional problems interfered with your normal social activities with family, friends, neighbors, or groups?Wie sehr haben Ihre körperliche Gesundheit oder seelischen Probleme in den vergangenen 4 Wochen Ihre normalen Kontakte zu Familienangehörigen, Freunden, Nachbarn oder zum Bekanntenkreis beeinträchtigt?
During the past four weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)? (R)Wie häufig haben Ihre körperliche Gesundheit oder seelischen Probleme in den vergangenen 4 Wochen Ihre Kontakte zu anderen Menschen (Besuche bei Freunden, Verwandten usw.) beeinträchtigt?
Painα = 0.903
(Item 1: 100 = none, 80 = very mild, 60 = mild, 40 = moderate, 20 = severe, 0 = very severe. Item 2: 100 = not at all, 75 = a little bit, 50 = moderately, 25 = quite a bit, 0 = extremely)
How much bodily pain have you had during the past four weeks?Wie stark waren Ihre Schmerzen in den vergangenen 4 Wochen?
During the past four weeks, how much did pain interfere with your normal work (including both work outside the home and housework)?Inwieweit haben die Schmerzen Sie in den vergangenen 4 Wochen bei der Ausübung Ihrer Alltagstätigkeiten zu Hause und im Beruf behindert?
General health α = 0.812
(Item 1: 100 = excellent, 75 = very good, 50 = good, 25 = fair, 0 = poor. Item 2–5: 100 = definitely true, 75 = mostly true, 50 = don’t know, 25 = mostly false, 0 = definitely false)
In general, would you say that your health is:Wie würden Sie Ihren Gesundheitszustand im Allgemeinen beschreiben?
I seem to get sick a little easier than other people (R)Ich scheine etwas leichter als andere krank zu werden.
I am as healthy as anybody I knowIch bin genauso gesund wie alle anderen, die ich kenne.
I expect my health to get worse (R)Ich erwarte, dass meine Gesundheit nachlässt.
My health is excellentIch erfreue mich ausgezeichneter Gesundheit.
Health change
(100 = Much better now than one year ago, 75 = Somewhat better now than one year ago, 50 = Approximately the same, 25 = Somewhat worse now than one year ago, 0 = Much worse now than one year ago)
Compared to one year ago, how would you rate your health in general now?Im Vergleich zum vergangenen Jahr, wie würden Sie Ihren derzeitigen Gesundheitszustand beschreiben?

Appendix E. Detailed Information about Regression Analysis

Model 1BStd. ErrorBetatpCI
(Constant)5.1470.086 60.0970.0004.9795.316
BI score0.9730.0860.48511.3640.0000.8051.142
Model 2BStd. ErrorBetatpCI
(Constant)5.3980.408 13.2230.0004.5966.201
BI score1.0150.0870.50611.6050.0000.8431.187
Physical functioning−0.0020.005−0.019−0.2940.769−0.0120.009
Physical health0.0040.0040.0821.1240.262−0.0030.012
Emotional problems0.0040.0030.0781.1840.237−0.0030.011
Energy /fatigue−0.0030.007−0.035−0.4500.653−0.0170.011
Emotional well-being−0.0080.008−0.078−0.9200.358−0.0240.009
Social functioning−0.0100.005−0.135−1.9350.054−0.0210.000
General health0.0090.0060.0921.3500.178−0.0040.022
Health change0.0030.0050.0260.5560.579−0.0070.013
Model 3BStd. ErrorBetatpCI
(Constant)5.6360.631 8.9350.0004.3966.877
BI score1.0120.0870.50511.5750.0000.8401.184
Physical functioning−0.0030.006−0.032−0.4830.629−0.0140.008
Physical health0.0040.0040.0680.9290.354−0.0040.011
Emotional problems0.0050.0030.0881.3320.184−0.0020.011
Emotional well-being−0.0060.008−0.066−0.7750.439−0.0230.010
Social functioning−0.0100.005−0.134−1.9190.056−0.0210.000
General health0.0060.0070.0590.8500.396−0.0070.019
Health change0.0020.0050.0170.3630.716−0.0080.012
Model 4BStd. ErrorBetatpCI
(Constant)53910.634 8500041446637
BI score10070.0870.50311,56300.8351178
Physical functioning−0.0040.005−0.046−0.7050.481−0.0150.007
Physical health0.0040.0040.07710540.292−0.0040.012
Emotional problems0.0050.0030.09213920.165−0.0020.012
Emotional well-being−0.0060.008−0.061−0.7260.469−0.0220.01
Social functioning−0.010.005−0.126−18280.068−0.020.001
General health0.0050.0070.0560.8110.418−0.0080.018
Health change0.0020.0050.0160.3430.732−0.0080.012
Participant type0.3310.2990.04811070.269−0.2570.919


  1. Sorg, H.; Ehlers, J.P.; Sorg, C.G.G. Digitalization in Medicine: Are German Medical Students Well Prepared for the Future? Int. J. Environ. Res. Public Health 2022, 19, 8308. [Google Scholar] [CrossRef] [PubMed]
  2. Hansen, A.; Herrmann, M.; Ehlers, J.P.; Mondritzki, T.; Hensel, K.O.; Truebel, H.; Boehme, P. Perception of the Progressing Digitization and Transformation of the German Health Care System Among Experts and the Public: Mixed Methods Study. JMIR Public Health Surveill. 2019, 5, e14689. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Knörr, V.; Dini, L.; Gunkel, S.; Hoffmann, J.; Mause, L.; Ohnhäuser, T.; Stöcker, A.; Scholten, N. Use of telemedicine in the outpatient sector during the COVID-19 pandemic: A cross-sectional survey of German physicians. BMC Prim. Care 2022, 23, 92. [Google Scholar] [CrossRef] [PubMed]
  4. Gerke, S.; Shachar, C.; Chai, P.R.; Cohen, I.G. Regulatory, safety, and privacy concerns of home monitoring technologies during COVID-19. Nat. Med. 2020, 26, 1176–1182. [Google Scholar] [CrossRef] [PubMed]
  5. Miller, J.C.; Skoll, D.; Saxon, L.A. Home Monitoring of Cardiac Devices in the Era of COVID-19. Curr. Cardiol. Rep. 2020, 23, 1. [Google Scholar] [CrossRef] [PubMed]
  6. Neurol, H.C. (Ed.) Chapter 9—Normal EEG Variants; Elsevier: Amsterdam, the Netherlands, 2019; ISBN 9780444640321. [Google Scholar]
  7. Beniczky, S.; Schomer, D.L. Electroencephalography: Basic biophysical and technological aspects important for clinical applications. Epileptic Disord. 2020, 22, 697–715. [Google Scholar] [CrossRef] [PubMed]
  8. Craciun, L.; Gardella, E.; Alving, J.; Terney, D.; Mindruta, I.; Zarubova, J.; Beniczky, S. How long shall we record electroencephalography? Acta Neurol. Scand. 2014, 129, e9–e11. [Google Scholar] [CrossRef]
  9. Sinha, S.R.; Sullivan, L.; Sabau, D.; San-Juan, D.; Dombrowski, K.E.; Halford, J.J.; Hani, A.J.; Drislane, F.W.; Stecker, M.M. American Clinical Neurophysiology Society Guideline 1: Minimum Technical Requirements for Performing Clinical Electroencephalography. J. Clin. Neurophysiol. 2016, 33, 303–307. [Google Scholar] [CrossRef][Green Version]
  10. Salinsky, M.; Kanter, R.; Dasheiff, R.M. Effectiveness of Multiple EEGs in Supporting the Diagnosis of Epilepsy: An Operational Curve. Epilepsia 1987, 28, 331–334. [Google Scholar] [CrossRef]
  11. Foley, C.M.; Legido, A.; Miles, D.K.; Chandler, D.A.; Grover, W.D. Long-term computer-assisted outpatient electroencephalogram monitoring in children and adolescents. J. Child Neurol. 2000, 15, 49–55. [Google Scholar] [CrossRef]
  12. Dash, D.; Hernandez-Ronquillo, L.; Moien-Afshari, F.; Tellez-Zenteno, J.F. Ambulatory EEG: A cost-effective alternative to inpatient video-EEG in adult patients. Epileptic Disord. 2012, 14, 290–297. [Google Scholar] [CrossRef]
  13. Faulkner, H.J.; Arima, H.; Mohamed, A. The utility of prolonged outpatient ambulatory EEG. Seizure 2012, 21, 491–495. [Google Scholar] [CrossRef][Green Version]
  14. Burkholder, D.B.; Britton, J.W.; Rajasekaran, V.; Fabris, R.R.; Cherian, P.J.; Kelly-Williams, K.M.; So, E.L.; Nickels, K.C.; Wong-Kisiel, L.C.; Lagerlund, T.D.; et al. Routine vs extended outpatient EEG for the detection of interictal epileptiform discharges. Neurology 2016, 86, 1524–1530. [Google Scholar] [CrossRef][Green Version]
  15. Siddiqi, M.; Ahmed, S.N. No Further Yield of Ambulatory EEG for Epileptiform Discharges Beyond 13 Hours. Neurodiagnostic J. 2017, 57, 211–223. [Google Scholar] [CrossRef]
  16. Kuo, J.; Lee-Messer, C.; Le, S. Optimal recording duration of ambulatory EEG (aEEG). Epilepsy Res. 2019, 149, 9–12. [Google Scholar] [CrossRef]
  17. Tutkavul, K.; Çetinkaya, Y. Optimum recording time of routine electroencephalogram for adults with epilepsy. Turk. J. Med. Sci. 2019, 49, 635–638. [Google Scholar] [CrossRef]
  18. Jamal Omidi, S.; Hampson, J.P.; Lhatoo, S.D. Long-term Home Video EEG for Recording Clinical Events. J. Clin. Neurophysiol. 2021, 38, 92–100. [Google Scholar] [CrossRef]
  19. Deutsche Gesellschaft für Klinische Neurophysiologie. 8. Empfehlungen für EEG-Langzeitableitungen. Available online: (accessed on 10 May 2022).
  20. Slater, J.D.; Eaddy, M.; Butts, C.M.; Meltser, I.; Murty, S. The real-world economic impact of home-based video electroencephalography: The payer perspective. J. Med. Econ. 2019, 22, 1030–1040. [Google Scholar] [CrossRef][Green Version]
  21. Ives, J.; Woods, J. 4-Channel 24 hour cassette recorder for long-term EEG monitoring of ambulatory patients. Electroencephalogr. Clin. Neurophysiol. 1975, 39, 88–92. [Google Scholar] [CrossRef]
  22. Ebersole, J.S. Ambulatory cassette EEG in epilepsy diagnosis. Yale J. Biol. Med. 1987, 60, 85–91. [Google Scholar]
  23. Bridgers, S.L.; Ebersole, J.S. The clinical utility of ambulatory cassette EEG. Neurology 1985, 35, 166–173. [Google Scholar] [CrossRef] [PubMed]
  24. Ebersole, J.S.; Bridgers, S.L. Direct comparison of 3 and 8 channel ambulatory cassette EEG with intensive inpatient monitoring. Neurology 1985, 35, 846–854. [Google Scholar] [CrossRef] [PubMed]
  25. Morris, G.L.; Galezowska, J.; Leroy, R.; North, R. The results of computer-assisted ambulatory 16-channel EEG. Electroenephalogr. Clin. Neurophysiol. 1994, 91, 229–231. [Google Scholar] [CrossRef]
  26. Morris, G.L. The clinical utility of computer-assisted ambulatory 16 channel EEG. J. Med. Eng. Technol. 1997, 21, 47–52. [Google Scholar] [CrossRef]
  27. Liporace, J.; Tatum IV, W.; Lee Morris III, G.; French, J. Clinical utility of sleep-deprived versus computer-assisted ambulatory 16-channel EEG in epilepsy patients: A multi-center study. Epilepsy Res. 1998, 32, 357–362. [Google Scholar] [CrossRef]
  28. Askamp, J.; van Putten, M.J.A.M. Mobile EEG in epilepsy. Int. J. Psychophysiol. 2014, 91, 30–35. [Google Scholar] [CrossRef]
  29. Lopez-Gordo, M.A.; Sanchez-Morillo, D.; Pelayo Valle, F. Dry EEG Electrodes. Sensors 2014, 14, 12847–12870. [Google Scholar] [CrossRef]
  30. Lau-Zhu, A.; Lau, M.P.H.; McLoughlin, G. Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Dev. Cogn. Neurosci. 2019, 36, 100635. [Google Scholar] [CrossRef]
  31. Fiedler, P.; Pedrosa, P.; Griebel, S.; Fonseca, C.; Vaz, F.; Supriyanto, E.; Zanow, F.; Haueisen, J. Novel Multipin Electrode Cap System for Dry Electroencephalography. Brain Topogr. 2015, 28, 647–656. [Google Scholar] [CrossRef]
  32. Marini, F.; Lee, C.; Wagner, J.; Makeig, S.; Gola, M. A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system. J. Neural Eng. 2019, 16, 54001. [Google Scholar] [CrossRef]
  33. Heijs, J.J.A.; Havelaar, R.J.; Fiedler, P.; van Wezel, R.J.A.; Heida, T. Validation of Soft Multipin Dry EEG Electrodes. Sensors 2021, 21, 6827. [Google Scholar] [CrossRef]
  34. Fiedler, P.; Fonseca, C.; Supriyanto, E.; Zanow, F.; Haueisen, J. A high-density 256-channel cap for dry electroencephalography. Hum. Brain Mapp. 2022, 43, 1295–1308. [Google Scholar] [CrossRef]
  35. Sauleau, P.; Despatin, J.; Cheng, X.; Lemesle, M.; Touzery-de Villepin, A.; N’Guyen The Tich, S.; Kubis, N. National French survey on tele-transmission of EEG recordings: More than a simple technological challenge. Neurophysiol. Clin. 2016, 46, 109–118. [Google Scholar] [CrossRef]
  36. Rosenow, F.; Audebert, H.J.; Hamer, H.M.; Hinrichs, H.; Keßler-Uberti, S.; Kluge, T.; Noachtar, S.; Remi, J.; Sotoodeh, A.; Strzelczyk, A.; et al. Tele-EEG: Current Applications, Challenges, and Technical Solutions. Klin. Neurophysiol. 2018, 49, 208–215. [Google Scholar] [CrossRef]
  37. Neumann, T.; Baum, A.K.; Baum, U.; Deike, R.; Feistner, H.; Hinrichs, H.; Stokes, J.; Robra, B.-P. Diagnostic and therapeutic yield of a patient-controlled portable EEG device with dry electrodes for home-monitoring neurological outpatients-rationale and protocol of the HOMEONE pilot study. Pilot Feasibility Stud. 2018, 4, 1–8. [Google Scholar] [CrossRef]
  38. Neumann, T.; Baum, A.K.; Baum, U.; Deike, R.; Feistner, H.; Scholz, M.; Hinrichs, H.; Robra, B.-P. Assessment of the technical usability and efficacy of a new portable dry-electrode EEG recorder: First results of the HOMEONE study. Clin. Neurophysiol. 2019, 130, 2076–2087. [Google Scholar] [CrossRef]
  39. Baum, U.; Baum, A.-K.; Deike, R.; Feistner, H.; Markgraf, B.; Hinrichs, H.; Robra, B.-P.; Neumann, T. Feasibility assessment of patient-controlled EEG home-monitoring: More results from the HOMEONE study. Clin. Neurophysiol. 2022, 140, 12–20. [Google Scholar] [CrossRef]
  40. Baum, U.; Baum, A.-K.; Deike, R.; Feistner, H.; Scholz, M.; Markgraf, B.; Hinrichs, H.; Robra, B.-P.; Neumann, T. Eignung eines mobilen Trockenelektroden-EEG-Gerätes im Rahmen der Epilepsiediagnostik. Klin. Neurophysiol. 2020, 51, 156–160. [Google Scholar] [CrossRef]
  41. Hinrichs, H.; Scholz, M.; Baum, A.K.; Kam, J.W.Y.; Knight, R.T.; Heinze, H.-J. Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications. Sci. Rep. 2020, 10, 5218. [Google Scholar] [CrossRef][Green Version]
  42. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef][Green Version]
  43. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319. [Google Scholar] [CrossRef]
  44. AlQudah, A.A.; Al-Emran, M.; Shaalan, K. Technology Acceptance in Healthcare: A Systematic Review. Appl. Sci. 2021, 11, 10537. [Google Scholar] [CrossRef]
  45. Yap, Y.-Y.; Tan, S.-H.; Choon, S.-W. Elderly’s intention to use technologies: A systematic literature review. Heliyon 2022, 8, e08765. [Google Scholar] [CrossRef]
  46. Cimperman, M.; Brenčič, M.M.; Trkman, P. Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. Int. J. Med. Inform. 2016, 90, 22–31. [Google Scholar] [CrossRef][Green Version]
  47. Hoque, R.; Sorwar, G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int. J. Med. Inform. 2017, 101, 75–84. [Google Scholar] [CrossRef]
  48. Duarte, P.; Pinho, J.C. A mixed methods UTAUT2-based approach to assess mobile health adoption. J. Bus. Res. 2019, 102, 140–150. [Google Scholar] [CrossRef]
  49. Liu, Y.; Lu, X.; Zhao, G.; Li, C.; Shi, J. Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns. Front. Psychol. 2022, 13, 944976. [Google Scholar] [CrossRef]
  50. Zhang, Y.; Liu, C.; Luo, S.; Xie, Y.; Liu, F.; Li, X.; Zhou, Z. Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey (Preprint); National Institutes of Health: Bethesda, MD, USA, 2019. [Google Scholar]
  51. Talukder, M.S.; Sorwar, G.; Bao, Y.; Ahmed, J.U.; Palash, M.A.S. Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol. Forecast. Soc. Chang. 2020, 150, 119793. [Google Scholar] [CrossRef]
  52. Wang, H.; Da, T.; Yu, N.; Qu, X. Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. Int. J. Med. Inform. 2020, 139, 104156. [Google Scholar] [CrossRef]
  53. Monika Bullinger, I.K. Fragebogen zum Allgemeinen Gesundheitszustand SF 36; Hogrefe-Verlag für Psychologie, GmbH & Co. KG.: Göttingen, Germany, 2011. [Google Scholar]
  54. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
  55. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; SAGE PUBLICATIONS: Thousand Oaks, CA, USA, 2021; ISBN 9781544396408. [Google Scholar]
  56. Sarstedt, M.; Hair, J.F.; Ringle, C.M.; Thiele, K.O.; Gudergan, S.P. Estimation issues with PLS and CBSEM: Where the bias lies! J. Bus. Res. 2016, 69, 3998–4010. [Google Scholar] [CrossRef]
  57. Arfi, W.B.; Nasr, I.B.; Kondrateva, G.; Hikkerova, L. The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technol. Forecast. Soc. Chang. 2021, 167, 120688. [Google Scholar] [CrossRef]
  58. Luyten, J.; Marneffe, W. Examining the acceptance of an integrated Electronic Health Records system: Insights from a repeated cross-sectional design. Int. J. Med. Inform. 2021, 150, 104450. [Google Scholar] [CrossRef] [PubMed]
  59. Zobair, K.M.; Sanzogni, L.; Houghton, L.; Islam, M.Z. Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling. PLoS ONE 2021, 16, e0257300. [Google Scholar] [CrossRef]
  60. Serrano, K.M.; Mendes, G.H.S.; Lizarelli, F.L.; Ganga, G.M.D. Assessing the telemedicine acceptance for adults in Brazil. Int. J. Health Care Qual. Assur. 2020. ahead-of-print. [Google Scholar] [CrossRef]
  61. Sarstedt, M.; Ringle, C.M.; Henseler, J.; Hair, J.F. On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Plan. 2014, 47, 154–160. [Google Scholar] [CrossRef]
  62. Rigdon, E.E. Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods. Long Range Plan. 2012, 45, 341–358. [Google Scholar] [CrossRef]
  63. Ringle, C.M.; Wende, S.; Becker, J.M. SmartPLS 3 [Computer Software]; SmartPLS GmbH: Boenningstedt, Germany, 2015. [Google Scholar]
  64. Sarstedt, M.; Henseler, J.; Ringle, C.M. Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results. In Measurement and Research Methods in International Marketing: Advances in International Marketing Vol 22; Schwaiger, M., Taylor, C.R., Sarstedt, M., Eds.; Emerald Group Publishing Ltd.: Bradford, UK, 2011; pp. 195–218. ISBN 978-1-78052-094-0. [Google Scholar]
  65. Hock, C.; Ringle, C.M.; Sarstedt, M. Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. IJSTM 2010, 14, 188. [Google Scholar] [CrossRef]
  66. Shmueli, G.; Ray, S.; Velasquez Estrada, J.M.; Chatla, S.B. The elephant in the room: Predictive performance of PLS models. J. Bus. Res. 2016, 69, 4552–4564. [Google Scholar] [CrossRef]
  67. Shmueli, G.; Sarstedt, M.; Hair, J.F.; Cheah, J.-H.; Ting, H.; Vaithilingam, S.; Ringle, C.M. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. EJM 2019, 53, 2322–2347. [Google Scholar] [CrossRef]
  68. Danks, N.P. The Piggy in the Middle. SIGMIS Database 2021, 52, 24–42. [Google Scholar] [CrossRef]
  69. Ray, S.; Danks, N.P.; Valdez, A.C. R Package Seminr: Domain-Specific Language for Building and Estimating Structural Equation Models; Elsevier: Amsterdam, the Netherlands, 2022. [Google Scholar]
  70. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R.; Springer International Publishing: Cham, Switzerland, 2021; ISBN 978-3-030-80518-0. [Google Scholar]
  71. Kijsanayotin, B.; Pannarunothai, S.; Speedie, S.M. Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. Int. J. Med. Inform. 2009, 78, 404–416. [Google Scholar] [CrossRef]
  72. Calvin, K.L.; Ben-Tzion, K. The Patient Technology Acceptance Model (PTAM) for Homecare Patients with Chronic Illness; Sage CA: Los Angeles, CA, USA, 2006. [Google Scholar]
  73. Rho, M.J.; Kim, H.S.; Chung, K.; Choi, I.Y. Factors influencing the acceptance of telemedicine for diabetes management. Clust. Comput 2015, 18, 321–331. [Google Scholar] [CrossRef]
  74. Lewis, W.; Agarwal, R.; Sambamurthy, V. Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers. MIS Q. 2003, 27, 657–678. [Google Scholar] [CrossRef][Green Version]
  75. Baum, U.; Baum, A.-K.; Deike, R.; Feistner, H.; Scholz, M.; Markgraf, B.; Robra, B.-P.; Neumann, T. Das EEG-Home-Monitoring als alternatives Versorgungskonzept [Meeting Abstract]. 2020. Available online: (accessed on 20 August 2022).
  76. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  77. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
Figure 1. Estimated path model with direct effects (t-values/ p-values).
Figure 1. Estimated path model with direct effects (t-values/ p-values).
Ijerph 19 13202 g001
Figure 2. Estimated importance-performance map for the endogenous construct BI.
Figure 2. Estimated importance-performance map for the endogenous construct BI.
Ijerph 19 13202 g002
Table 1. Participants’ characteristics ( N = 421 ).
Table 1. Participants’ characteristics ( N = 421 ).
(n = 15)
No-Video (n = 25)
Non-Patient/Video (n = 200)Non-Patient/No-Video (n = 181)Full
Sample (n = 421)
age (F(3, 417) = 0.923; p = 0.430)
mean (SD)44.33 (13.75)51.76 (16.96)48.73 (13.87)49.61 (15.16)49.13 (14.62)
gender (Fisher’s exact p = 0.080)
male9 (60.0%)17 (68.0%)103 (51.5%)102 (56.4%)231 (54.9%)
female6 (40%)7 (28.0%)97 (48.5%)79 (43.6%)189 (44.9%)
divers0 (0.0%)1 (4.0%)0 (0.0%)0 (0.0%)1 (0.2%)
Graduation * ( X 2 (15) = 15.91; p = 0.388)
main school1 (6.7%)1 (4.2%)23 (11.5%)19 (10.5%)44 (10.5%)
secondary school1 (6.7%)7 (29.2%)23 (11.5%)17 (9.4%)48 (11.4%)
middle school3 (20.0%)2 (8.3%)58 (29.0%)53 (29.3%)116 (27.6%)
university10 (66.7%)14 (58.3%)93 (46.5%)90 (49.7%)207 (49.3%)
none0 (0.0%)0 (0.0%)1 (0.5%)0 (0.0%)1 (0.2%)
prefer not to say0 (0.0%)0 (0.0%)2 (1.0%)2 (1.1%)4 (1.0%)
Vocational qualification ( X 2 (27) = 32.25; p = 0.223)
apprenticeship7 (46.7%)8 (32.0%)90 (45.0%)83 (45.9%)188 (44.7%)
professional school degree1 (6.7%)2 (8.0%)30 (15.0%)27 (14.9%)60 (14.3%)
professional school degree (incl. administrative and engineer college degree)1 (6.7%)1 (4.0%)9 (4.5%)9 (5.0%)20 (4.8%)
college degree3 (30.0%)2 (8.0%)13 (6.5%)7 (3.9%)25 (5.9%)
Bachelor’s degree0 (0.0%)0 (0.0%)17 (8.5%)8 (4.4%)25 (5.9%)
Master’s degree0 (0.0%)1 (4.0%)10 (5.0%)9 (5.0%)20 (4.8%)
diploma1 (6.7%)6 (24.0%)15 (7.5%)18 (9.9%)40 (9.5%)
promotion0 (0.0%)0 (0.0%)3 (1.5%)3 (1.7%)6 (1.4%)
none0 (0.0%)3 (12.0%)5 (2.5%)10 (5.5%)18 (4.3%)
prefer not to say2 (13.3%)2 (8.0%)8 (4.0%)7 (3.9%)19 (4.5%)
* One observation missing within the group patient/no-video.
Table 2. Bootstrapping results of the UTAUT model.
Table 2. Bootstrapping results of the UTAUT model.
PathTotal Effect
(t-Value/p-Value/[95% CI])
Direct Effect
(t-Value/p-Value/[95% CI])
Indirect Effect
(t-Value/p-Value/[95% CI])
DC → BI0.065 (3.016/0.003/[0.028; 0.110])-0.065 (3.016/0.003/[0.028; 0.110])
CA → BI0.397 (8.533/0.000/[0.308; 0.489])-0.397 (8.533/0.000/[0.308; 0.489])
PS → BI0.142 (3.632/0.000/[0.066; 0.217])0.047 (1.176/0.239/
[−0.031; 0.125])
0.095 (3.872/0.000/[0.050; 0.147])
via EE
[−0.001; 0.039])
via PE
(3.278/0.001/[0.030; 0.109])
PE → BI0.348 (5.381/0.000/[0.223; 0.472])0.348 (5.381/0.000/[0.223; 0.472])-
EE → BI0.508 (9.549/0.000/[0.403; 0.611])0.312 (5.091/0.000/[0.189; 0.431])0.196 (4.921/0.000/[0.122; 0.276])
FC → BI0.184 (3.802/0.000/[0.090; 0.278])0.184 (3.802/0.000/[0.090; 0.278])-
SI → BI0.057 (1.126/0.260/
[−0.041; 0.160])
0.057 (1.126/0.260/
[−0.041; 0.160])
EE → PE0.562 (11.563/0.000/[0.461; 0.655])0.562 (11.563/0.000/[0.461; 0.655])-
CA → EE0.781 (24.808/0.000/[0.714; 0.837])0.781 (24.808/0.000/[0.714; 0.837])-
PS → EE0.056 (1.859/0.063/
[−0.004; 0.116])
0.056 (1.859/0.063/
[−0.004; 0.116])
PS → PE0.221 (5.296/0.000/
[0.140; 0.304])
0.189 (4.429/0.000/
[0.106; 0.273])
0.032 (1.800/0.072/
[−0.002; 0.067])
DC → PE0.184 (4.103/0.000/[0.100; 0.272])0.184 (4.103/0.000/[0.100; 0.272])-
CA → PE0.440 (9.884/0.000/[0.351; 0.525])-0.440 (9.884/0.000/[0.351; 0.525])
BI indicates behavioral intention to use; CA, computer anxiety; DC, doctor’s opinion; EE, effort expectancy; FC, facilitating conditions; PE, performance expectancy; PS, perceived security; and SI, social influence.
Table 3. PLSpredict RMSE results of the UTAUT model.
Table 3. PLSpredict RMSE results of the UTAUT model.
RMSE indicates the root mean squared error of prediction.
Table 4. Regression analysis among home monitoring preferences and various predictors.
Table 4. Regression analysis among home monitoring preferences and various predictors.
Dependent Variable = Home Monitoring PreferenceModel 1Model 2Model 3Model 4
b (t/p)b (t/p)b (t/p)b (t/p)
Main variableBehavioral intention score0.973
HealthPhysical functioning −0.002
Physical health 0.004
(0.929 /0.354)
Emotional problems 0.004
Energy/fatigue −0.003
Emotional well-being −0.008
Social functioning −0.010
Pain 0.002
General health 0.009
Health change 0.003
DemographicsAge −0.009
(0 = male,
1 = female)
ConditionsParticipant type 0.331
(0 = no,
1 = yes)
R 2 0.2360.2590.2640.278
Adjusted R 2 0.2340.2400.2420.253
Note. Model 3 and Model 4 exclude the one participant that identifies as “divers”. Higher values on the dependent variable represent a higher relative preference for home monitoring compared to inpatient monitoring.
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Baum, U.; Kühn, F.; Lichters, M.; Baum, A.-K.; Deike, R.; Hinrichs, H.; Neumann, T. Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model. Int. J. Environ. Res. Public Health 2022, 19, 13202.

AMA Style

Baum U, Kühn F, Lichters M, Baum A-K, Deike R, Hinrichs H, Neumann T. Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model. International Journal of Environmental Research and Public Health. 2022; 19(20):13202.

Chicago/Turabian Style

Baum, Ulrike, Frauke Kühn, Marcel Lichters, Anne-Katrin Baum, Renate Deike, Hermann Hinrichs, and Thomas Neumann. 2022. "Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model" International Journal of Environmental Research and Public Health 19, no. 20: 13202.

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