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

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  • Ulrike Baum

    (Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
    These authors contributed equally to this work.)

  • Frauke Kühn

    (Institute for Sensory and Innovation Research (ISI GmbH), Ascherberg 2, 37124 Rosdorf, Germany
    These authors contributed equally to this work.)

  • Marcel Lichters

    (Chair of Marketing and Retailing, Faculty of Economics and Business Administration, Chemnitz University of Technology, Reichenhainer Straße 39, 09126 Chemnitz, Germany)

  • Anne-Katrin Baum

    (Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany)

  • Renate Deike

    (Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany)

  • Hermann Hinrichs

    (Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
    Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118 Magdeburg, Germany
    Center for Behavioral Brain Sciences (CBBS), Universitätsplatz 2, 39106 Magdeburg, Germany)

  • Thomas Neumann

    (Department of Neurology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, 39120 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)

Abstract

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.

Suggested Citation

  • Ulrike Baum & Frauke Kühn & Marcel Lichters & Anne-Katrin Baum & Renate Deike & Hermann Hinrichs & Thomas Neumann, 2022. "Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring—An Analysis Based on the UTAUT Model," IJERPH, MDPI, vol. 19(20), pages 1-22, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13202-:d:941590
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    References listed on IDEAS

    as
    1. Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
    2. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
    3. Heiko Sorg & Jan P. Ehlers & Christian G. G. Sorg, 2022. "Digitalization in Medicine: Are German Medical Students Well Prepared for the Future?," IJERPH, MDPI, vol. 19(14), pages 1-15, July.
    4. Shmueli, Galit & Ray, Soumya & Velasquez Estrada, Juan Manuel & Chatla, Suneel Babu, 2016. "The elephant in the room: Predictive performance of PLS models," Journal of Business Research, Elsevier, vol. 69(10), pages 4552-4564.
    5. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Duarte, Paulo & Pinho, José Carlos, 2019. "A mixed methods UTAUT2-based approach to assess mobile health adoption," Journal of Business Research, Elsevier, vol. 102(C), pages 140-150.
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