Enhancing Geriatric Care: Insights from a Mixed Method Study on In-Bed Real-Time Motion Monitoring

Abstract

Purpose The global rise in emergency hospital admissions among older individuals will likely continue due to ongoing demographic changes. Currently, about two-thirds of hospital patients are elderly, with up to 50% experiencing some form of cognitive impairment, including dementia (George et al., 2013; Jackson et al., 2016; Mudge et al., 2021), which can lead e.g., to falls or accidental removal of intravenous accesses (Choi et al., 2020). There's ongoing debate about using technological innovations to support nurses and caregivers (Astell et al., 2019; Husebo et al., 2020). Bed exit systems, like those studied by Komagata et al. (2019) and Oh-Park et al. (2021), are known in this context. In a previous study, nurses at a university medical center tested a bed exit system placed under patients' mattresses that provided notification when patients moved toward the edge of the bed or left unassisted, which was easy to use but limited in application due to symptom complexity (Walzer et al.). To address this, the research team decided to test a technology with more functionality. Nurses in the geriatric unit of a university hospital piloted a more complex bed sensor system alongside standard care. This system not only provides bed edge and exit information but also real-time monitoring of patient movement patterns in bed. Previous evaluations (Ziegler et al., 2023) found potential benefits for nightly monitoring of patients in general wards and particularly for those with delirium. Method This study employed a parallel mixed methods triangulation design, using a quantitative survey, qualitative focus groups, an interview, and participatory observations (Ingham-Broomfield, 2016). Conducted in a geriatric ward at a university medical center, the study lasted 24 weeks divided into two phases. Phase 0 lasted 6 weeks, following the center's standard care and daily prevalence surveys. Phase 1 lasted 18 weeks, during which the ward was equipped with the system. Nurses used it as an aid in patient care, assessing cognitive status and bed movement tendencies. Nurses had autonomy to select patients, and control over real-time monitoring and bed edge/exit information settings. Results and Discussion Overall, the evaluation of the system was positive across all methods. Nurses benefited mainly from motion monitoring, particularly at night, which increased their sense of security and reduced psychological stress, consistent with Mileski et al.'s systematic review (2019). While the technology's effectiveness in preventing falls couldn't be proven, it reduced nurses' fear of falls and enhanced their ability to respond adequately to such events. However, the focus on preventing falls did not emerge as significantly as other benefits, possibly due to the limited ability to respond in constant circumstances and the general lack of fall events during the study period. Literature suggests that the information system alone isn't effective (Barker et al., 2017; Chu, 2017; Hamm et al., 2016), hence its primary use at night when nurses have more attention for monitoring. Integration of technology into comprehensive care plans is crucial for optimal benefits (Mileski et al., 2019). Ambiguities in the assessment may be related to debates about nursing professionalism in Germany (Roth et al., 2022), with some participants believing that technology isn't necessary for good care but could enhance the profession in general. The findings indicating a lack of serious consideration of patients' concerns by nurses may be related to their diminishing fears of surveillance or invasion of privacy. Integration with documentation standards could alleviate nurses' workload, enhancing the technology's advantages, reinforcing self-determined, knowledge-based nursing practice and professional standing.

References

Astell, A. J., Bouranis, N., Hoey, J., Lindauer, A., Mihailidis, A., Nugent, C., & Robillard, J. M. (2019). Technology and dementia: The future is now. Dementia and Geriatric Cognitive Disorders, 47(3), 131–139.

Barker, A. L., Morello, R. T., Ayton, D. R., Hill, K. D., Brand, C. A., Livingston, P. M., & Botti, M. (2017). Acceptability of the 6-PACK falls prevention program: A pre-implementation study in hospitals participating in a cluster randomized controlled trial. PloS One, 12(2), e0172005.

Chu, R. Z. (2017). Preventing in-patient falls: The nurse's pivotal role. Nursing2022, 47(3), 24–30.

Hamm, J., Money, A. G., Atwal, A., & Paraskevopoulos, I. (2016). Fall prevention intervention technologies: A conceptual framework and survey of the state of the art. Journal of Biomedical Informatics, 59, 319–345.

Husebo, B. S., Heintz, H. L., Berge, L. I., Owoyemi, P., Rahman, A. T., & Vahia, I. V. (2020). Sensing technology to monitor behavioral and psychological symptoms and to assess treatment response in people with dementia. A systematic review. Frontiers in Pharmacology, 10, 1699.

George, J., Long, S., & Vincent, C. (2013). How can we keep patients with dementia safe in our acute hospitals? A review of challenges and solutions. Journal of the Royal Society of Medicine, 106(9), 355–361.

Ingham-Broomfield, R. (2016). A nurses' guide to mixed methods research. Australian Journal of Advanced Nursing, the, 33(4), 46–52.

Jackson, T. A., MacLullich, A. M. J., Gladman, J. R. F., Lord, J. M., & Sheehan, B. (2016). Undiagnosed long-term cognitive impairment in acutely hospitalised older medical patients with delirium: a prospective cohort study. Age and Ageing, 45(4), 493–499.

Komagata, H., Hattori, T., Ohshima, R., Kakinuma, E., Ishikawa, M., Shinoda, K., & Kobayashi, N. Development of human behavior monitoring system around bed using infrared depth sensor. In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) (pp. 117–118). IEEE.

Mileski, M., Brooks, M., Topinka, J. B., Hamilton, G., Land, C., Mitchell, T., Mosley, B., & McClay, R. (2019). Alarming and/or alerting device effectiveness in reducing falls in long-term care (LTC) facilities? A systematic review. In Healthcare (p. 51). MDPI.

Mudge, A. M., Lee-Steere, K., Treleaven, E., Cahill, M., Finnigan, S., & McRae, P. (2021). Cognitive impairment in older hospital inpatients: prevalence, care needs and carer perceptions. Australian Health Review, 46(2), 244–250.

Oh-Park, M., Doan, T., Dohle, C., Vermiglio-Kohn, V., & Abdou, A. (2021). Technology utilization in fall prevention. American Journal of Physical Medicine & Rehabilitation, 100(1), 92–99.

Roth, C., Wensing, M., Breckner, A., Mahler, C., Krug, K., & Berger, S. (2022). Keeping nurses in nursing: a qualitative study of German nurses’ perceptions of push and pull factors to leave or stay in the profession. BMC Nursing, 21(1), 48.

Walzer, S., Schoen, I., Pfeil, J., Schepputat, S., Ziegler, S., Schgmoor, C., & Kunze, C. Nurses' Perspectives and Experiences of Using a Bed Exit Information System in an Acute Hospital Setting: A Mixed Methods Study: Submitted to NursingOpen for publication.

Ziegler, S., Schmoor, C., Schöler, L. M., Schepputat, S., Takem, E., Grotejohann, B., Steinbrenner, I., & Feuchtinger, J. (2023). Potential for reducing immobility times of a mobility monitor in-bed sensor system–a stepped-wedge cluster-randomised trial. BMC Nursing, 22(1), 478

Keywords: geriatric patients, cognitive impairment, technology, fall prevention, hospital

Affiliation: Care and Technology Lab, Furtwangen University of Applied Sciences, Furtwangen, Germany

Corresponding Author Email: stefan.walzer@hfu.eu

Acknowledgement This study was conducted at the Center of Implementing Nursing Care Innovations, Freiburg, Germany. This joint project on the development and research of new care technologies in Germany is funded by the Federal Ministry of Education and Research.