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Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

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Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study JOURNAL OF MEDICAL INTERNET RESEARCH Bayen et al Original Paper Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study Eleonore Bayen1,2, MD, PhD; Shirley Nickels3, BSc; Glen Xiong4, MD; Julien Jacquemot3, BSc, MSc; Raghav Subramaniam3, BSc, MSc; Pulkit Agrawal3,5, PhD; Raheema Hemraj3, BA, BSc, MSc; Alexandre Bayen6, PhD; Bruce L Miller2, MD; George Netscher3, BSc, MSc 1Department of Neuro-rehabilitation, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, Sorbonne Université, Paris, France 2Global Brain Health Institute, Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States 3SafelyYou, Inc, San Francisco, CA, United States 4Alzheimer's Disease Center, Department of Neurology, University of California, Davis, Sacramento, CA, United States 5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, CA, United States 6Electrical Engineering and Computer Science Department, University of California, Berkeley, CA, United States Corresponding Author: Eleonore Bayen, MD, PhD Department of Neuro-rehabilitation Hôpital Pitié-Salpêtrière Assistance Publique des Hôpitaux de Paris, Sorbonne Université 47 Bd de l©Hôpital Paris, 75013 France Phone: 33 142161101 Fax: 33 142161148 Email: [email protected] Abstract Background: Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fall-related injuries due to the trauma itself, prolonged immobilization on the floor results in a wide range of comorbidities and may double the risk of death in elderly. Thus, reducing the length of Time On the Ground (TOG) in fallers seems crucial in vulnerable individuals with cognitive disorders who cannot get up independently. Objective: This study aimed to examine the effect of a new technology called SafelyYou Guardian (SYG) on early post-fall care including reduction of Time Until staff Assistance (TUA) and TOG. Methods: SYG uses continuous video monitoring, artificial intelligence, secure networks, and customized computer applications to detect and notify caregivers about falls in real time while providing immediate access to video footage of falls. The present observational study was conducted in 6 California memory care facilities where SYG was installed in bedrooms of consenting residents and families. Fall events were video recorded over 10 months. During the baseline installation period (November 2017 to December 2017), SYG video captures of falls were not provided on a regular basis to facility staff review. During a second period (January 2018 to April 2018), video captures were delivered to facility staff on a regular weekly basis. During the third period (May 2018 to August 2018), real-time notification (RTN) of any fall was provided to facility staff. Two digital markers (TUA, TOG) were automatically measured and compared between the baseline period (first 2 months) and the RTN period (last 4 months). The total number of falls including those happening outside of the bedroom (such as common areas and bathrooms) was separately reported by facility staff. Results: A total of 436 falls were recorded in 66 participants suffering from Alzheimer disease or related dementias (mean age 87 years; minimum 65, maximum 104 years). Over 80% of the falls happened in bedrooms, with two-thirds occurring overnight (8 PM to 8 AM). While only 8.1% (22/272) of falls were scored as moderate or severe, fallers were not able to stand up alone in 97.6% (247/253) of the cases. Reductions of 28.3 (CI 19.6-37.1) minutes in TUA and 29.6 (CI 20.3-38.9) minutes in TOG were observed between the baseline and RTN periods. The proportion of fallers with TOG >1 hour fell from 31% (8/26; baseline) to zero events (RTN period). During the RTN period, 76.6% (108/141) of fallers received human staff assistance in less than 10 minutes, and 55.3% (78/141) of them spent less than 10 minutes on the ground. https://www.jmir.org/2021/6/e17551 J Med Internet Res 2021 | vol. 23 | iss. 6 | e17551 | p. 1 (page number not for citation purposes) XSL·FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Bayen et al Conclusions: SYG technology is capable of reducing TOG and TUA while efficiently covering the area (bedroom) and time zone (nighttime) that are at highest risk. After 6 months of SYG monitoring, TOG was reduced by a factor of 3. The drastic reduction of TOG is likely to decrease secondary comorbid complications, improve post-fall prognosis, and reduce health care costs. (J Med Internet Res 2021;23(6):e17551) doi: 10.2196/17551 KEYWORDS artificial intelligence; video monitoring; real-time video detection; fall; time on the ground; Alzheimer disease; dementia; memory care facilities may wait for a second caregiver to come and assist the resident Introduction to his or her bed. These 2 digital care markers were measured Falls are the leading cause of injuries among people aged 65 over 10 months for consenting residents living in 6 memory and older, with estimated yearly direct medical costs of US care facilities. We hypothesized that introducing SYG would $637.2 million for fatal falls and US $31.3 billion for nonfatal reduce TUA and TOG. Comparison of TUA and TOG markers falls in the United States [1]. Older adults with cognitive between the baseline period and RTN period were computed to impairment have an increased risk of falling [2], and those with assess the effect of SYG technology on early post-fall care. dementia living in nursing care facilities fall 4.1 times per year on average versus 2.3 times for other residents [3]. Additionally, Methods individuals with dementia are the least likely to call for assistance when they cannot rise alone from the ground, and Design of the Study and Technology Equipment cognitive impairment is found to be the most significant factor This study is part of a larger project involving multilevel that predicts lying on the floor for a long time after a fall [4]. collaborations between engineers, researchers, clinicians, and care providers. SafelyYou develops computer vision algorithms Reducing the length of time on the floor after a fall is crucial (a subfield of AI focused on visual understanding) for because fall-related injuries include not only traumatic injuries automated, real-life, real-time detection and notification of falls associated with the acute fall but also comorbid complications in memory care facilities [5]. This observational study was related to prolonged post-fall immobilization. Artificial carried out during 10 months in 6 memory care facilities in intelligence (AI) in the field of computer vision; innovative California, and a progressive introduction of SYG technology health technology using secure, network-attached storage; and was carried out so that facility staff could get familiar with the a customized computer application together offer the potential technology. In each of the 6 facilities, wall-mounted cameras to detect falls rapidly without the need for wearable devices and were installed in residents' bedrooms of consenting elderly and to support care in dementia care facilities [5]. In previous work families (at the exclusion of residents' personal bathrooms, using SafelyYou Guardian (SYG) technology [5], we showed where no camera was placed) and in accordance with the privacy that video monitoring of falls along with regular fall review by and ethical guidelines discussed below. There was no facility staff resulted in more accurate identification of falls, requirement about where the camera was placed, but cameras fall mechanisms, and the injuries related to the trauma itself were generally placed in order to capture as much of the room (such as traumatic brain injury, for instance) [6]. More recently, as possible since the camera would not detect a fall that it could we compared nonbeneficiaries to beneficiaries of SYG and not see. The field of view of the camera was greater than 90 found that review of fall footage enabled a significant reduction degrees such that an entire room could be captured if the camera of visits to the emergency department, by 72% on average, due was placed in an upper corner of the room. A fall was defined to better triaging of fall-related injuries [7]. In this article, we as an ªunexpected event in which the participant comes to rest focus on subsequent length of time spent on the floor after on the ground, floor, or lower levelº [8]. A fall incident was falling as automatically measured by SYG technology in detected by the AI-enabled camera system when a resident was beneficiaries of SYG. identified on the floor, whatever his or her position (sitting or The objective here was to investigate the effect of the SYG lying). This detection is independent of camera position, the real-time notification (RTN) system on early provision of number of individuals in the room, and which individual is on post-fall care. We analyzed 2 lengths of time associated with a the floor. ªOn the floorº was defined to be when one body part fall event: (1) The length of Time Until staff Assistance arrives other than the feet touched the floor, such as a knee, forearm, (TUA) reflects the first support provided by professional or the posterior. Falls were video captured 24 hours a day, 7 caregivers, and (2) the length of Time On the Ground (TOG) days a week, and video data were securely transmitted using a in fallers is a prognostic marker reflecting risks for secondary local network storage device. A secure, customized computer post-fall complications in residents. Discrepancies between application gave facility staff access to the video footage and TUA and TOG are common. For example, a professional fall information of fall incidents (Figure 1). caregiver who arrives in a resident's room shortly after a fall https://www.jmir.org/2021/6/e17551 J Med Internet Res 2021 | vol.
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