Self-administered assessments and hybrid care models for hearing rehabilitation

Remote Care, Computational Audiology, and ICU noise reduction

Remote Care and Computational Audiology

As the demand for audiological care continues to grow, there is an increasing need for service models that are both clinically robust and accessible beyond the hospital setting. This research line focuses on the development and validation of remote and self-administered assessment tools that support hearing diagnostics and rehabilitation in real-world environments.

Our work particularly emphasizes the ecological validity of remote measurements and their integration into routine clinical workflows. In earlier work, we reviewed the emerging literature on digital technologies in hearing healthcare and introduced the concept of Computational Audiology, describing the use of computational tools, automated testing, and data-driven approaches to improve hearing diagnostics and care delivery Computational Audiology (Wasmann et al., 2021).

A practical example is the development and evaluation of remote hearing assessments for cochlear implant users, including at-home testing of hearing thresholds and speech perception in noise. These approaches aim to complement traditional clinical assessments and enable more continuous monitoring of auditory performance outside the clinic (Wasmann et al., 2024).

Remote care

Environmental Sound and Noise Reduction

A related line of research focuses on the measurement and reduction of environmental noise in healthcare settings. Excessive sound levels in clinical environments, particularly in critical care units, can negatively affect both patients and healthcare professionals.

Our work in this area includes:

  • A systematic review of interventions aimed at reducing sound levels in healthcare environments (Vreman et al., 2023).
  • Empirical evaluation of noise-reduction strategies implemented in intensive care units (Vreman et al., 2024).
  • A comprehensive assessment of a multicomponent noise-reduction bundle, examining its impact on measured sound levels as well as perceived distraction and workload among ICU nursing staff (Vreman et al., 2026).

Together, these studies aim to improve the acoustic environment in healthcare settings and to better understand how sound influences staff performance and patient well-being.

Key references

Journal Articles

  1. Vreman, J., Lanting, C., Frenzel, T., van der Hoeven, J. G., Lemson, J., & van den Boogaard, M. (2026). The Impact of a Noise Reduction Bundle on ICU Staff: A Stepped-Wedge Cluster Randomized Clinical Trial. Intensive and Critical Care Nursing, 93, 104306. https://doi.org/10.1016/j.iccn.2025.104306
  2. Wasmann, J.-W. A., Huinck, W. J., & Lanting, C. P. (2024). Remote Cochlear Implant Assessments: Validity and Stability in Self-Administered Smartphone-Based Testing. Ear and Hearing, 45(1), 239–249. https://doi.org/10.1097/AUD.0000000000001422
  3. Vreman, J., Cris, L., Frenzel, T., van der Hoeven, J., Lemson, J., & van den Boogaard, M. (2024). Reduction of Sound Levels in the Intermediate Care Unit; a Quasi-Experimental Time-Series Design Study. Intensive and Critical Care Nursing, 85, 103810. https://doi.org/10.1016/j.iccn.2024.103810
  4. Vreman, J., Lemson, J., Lanting, C., van der Hoeven, J., & van den Boogaard, M. (2023). The Effectiveness of the Interventions to Reduce Sound Levels in the ICU: A Systematic Review. Critical Care Explorations, 5(4), e0885. https://doi.org/10.1097/CCE.0000000000000885
  5. Wasmann, J.-W. A., Lanting, C. P., Cris P. Lanting, Huinck, W. J., Mylanus, E. A. M., van der Laak, J. W. M., Govaerts, P., De Wet Swanepoel, Moore, D. R., & Barbour, D. L. (2021). Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age. Ear and Hearing. https://doi.org/10.1097/aud.0000000000001041

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