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Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

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  • Mohamed-Amine Choukou

    (Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
    Riverview Health Centre, Winnipeg, MB R3L 2P4, Canada
    Centre on Aging, University of Manitoba, Winnipeg, MB R3T 2N2, Canada)

  • Sophia Mbabaali

    (Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada)

  • Ryan East

    (Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada)

Abstract

The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.

Suggested Citation

  • Mohamed-Amine Choukou & Sophia Mbabaali & Ryan East, 2021. "Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes," IJERPH, MDPI, vol. 18(5), pages 1-16, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2720-:d:512704
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    References listed on IDEAS

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    1. Albert Haque & Arnold Milstein & Li Fei-Fei, 2020. "Illuminating the dark spaces of healthcare with ambient intelligence," Nature, Nature, vol. 585(7824), pages 193-202, September.
    2. Melissa L. O'Connor & Jerri D. Edwards & Virginia G. Wadley & Michael Crowe, 2010. "Changes in Mobility Among Older Adults with Psychometrically Defined Mild Cognitive Impairment," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 65(3), pages 306-316.
    3. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
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