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Zhou Z, Liu Z, Liu Y, Zhao Y, Wang J, Zhang B, Xia Y, Zhang X, Li S. TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method. Digit Health 2024; 10:20552076241259047. [PMID: 38840661 PMCID: PMC11151769 DOI: 10.1177/20552076241259047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/15/2024] [Indexed: 06/07/2024] Open
Abstract
Background Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.
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Affiliation(s)
- Ziyu Zhou
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Zhaoqing Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Yujie Liu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Yan Zhao
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Jiarui Wang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Bowen Zhang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Youbing Xia
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Xiao Zhang
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Shuyan Li
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
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Koszalinski R, Tappen RM, Ghoraani B, Vieira ER, Marques O, Furht B. Use of Sensors for Fall Prediction in Older Persons: A Scoping Review. Comput Inform Nurs 2023; 41:993-1015. [PMID: 37652446 DOI: 10.1097/cin.0000000000001052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
The application of technological advances and clear articulation of how they improve patient outcomes are not always well described in the literature. Our research team investigated the numerous ways to measure conditions and behaviors that precede patient events and could signal an important change in health through a scoping review. We searched for evidence of technology use in fall prediction in the population of older adults in any setting. The research question was described in the population-concept-context format: "What types of sensors are being used in the prediction of falls in older persons?" The purpose was to examine the numerous ways to obtain continuous measurement of conditions and behaviors that precede falls. This area of interest may be termed emerging knowledge . Implications for research include increased attention to human-centered design, need for robust research trials that clearly articulate study design and outcomes, larger sample sizes and randomization of subjects, consistent oversight of institutional review board processes, and elucidation of the human costs and benefits to health and science.
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Affiliation(s)
- Rebecca Koszalinski
- Author Affiliations: Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton (Drs Koszalinski and Tappen); Department of Physical Therapy, Florida International University, Miami (Dr Vieira); and Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton (Drs Ghoraani, Marques, and Furht)
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Lear R, Ellis S, Ollivierre-Harris T, Long S, Mayer EK. Video Recording Patients for Direct Care Purposes: Systematic Review and Narrative Synthesis of International Empirical Studies and UK Professional Guidance. J Med Internet Res 2023; 25:e46478. [PMID: 37585249 PMCID: PMC10468707 DOI: 10.2196/46478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Video recordings of patients may offer advantages to supplement patient assessment and clinical decision-making. However, little is known about the practice of video recording patients for direct care purposes. OBJECTIVE We aimed to synthesize empirical studies published internationally to explore the extent to which video recording patients is acceptable and effective in supporting direct care and, for the United Kingdom, to summarize the relevant guidance of professional and regulatory bodies. METHODS Five electronic databases (MEDLINE, Embase, APA PsycINFO, CENTRAL, and HMIC) were searched from 2012 to 2022. Eligible studies evaluated an intervention involving video recording of adult patients (≥18 years) to support diagnosis, care, or treatment. All study designs and countries of publication were included. Websites of UK professional and regulatory bodies were searched to identify relevant guidance. The acceptability of video recording patients was evaluated using study recruitment and retention rates and a framework synthesis of patients' and clinical staff's perspectives based on the Theoretical Framework of Acceptability by Sekhon. Clinically relevant measures of impact were extracted and tabulated according to the study design. The framework approach was used to synthesize the reported ethico-legal considerations, and recommendations of professional and regulatory bodies were extracted and tabulated. RESULTS Of the 14,221 abstracts screened, 27 studies met the inclusion criteria. Overall, 13 guidance documents were retrieved, of which 7 were retained for review. The views of patients and clinical staff (16 studies) were predominantly positive, although concerns were expressed about privacy, technical considerations, and integrating video recording into clinical workflows; some patients were anxious about their physical appearance. The mean recruitment rate was 68.2% (SD 22.5%; range 34.2%-100%; 12 studies), and the mean retention rate was 73.3% (SD 28.6%; range 16.7%-100%; 17 studies). Regarding effectiveness (10 studies), patients and clinical staff considered video recordings to be valuable in supporting assessment, care, and treatment; in promoting patient engagement; and in enhancing communication and recall of information. Observational studies (n=5) favored video recording, but randomized controlled trials (n=5) did not demonstrate that video recording was superior to the controls. UK guidelines are consistent in their recommendations around consent, privacy, and storage of recordings but lack detailed guidance on how to operationalize these recommendations in clinical practice. CONCLUSIONS Video recording patients for direct care purposes appears to be acceptable, despite concerns about privacy, technical considerations, and how to incorporate recording into clinical workflows. Methodological quality prevents firm conclusions from being drawn; therefore, pragmatic trials (particularly in older adult care and the movement disorders field) should evaluate the impact of video recording on diagnosis, treatment monitoring, patient-clinician communication, and patient safety. Professional and regulatory documents should signpost to practical guidance on the implementation of video recording in routine practice. TRIAL REGISTRATION PROSPERO CRD42022331825: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331825.
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Affiliation(s)
- Rachael Lear
- Imperial Clinical Analytics, Research & Evaluation (iCARE), London, United Kingdom
- National Institute for Health and Care Research North West London Patient Safety Research Collaborative, Institute of Global Health Innovation, Imperial College London - St Mary's Hospital Campus, London, United Kingdom
| | - Sophia Ellis
- Imperial College Healthcare NHS Trust, London, United Kingdom
- Hillingdon NHS Foundation Trust, London, United Kingdom
| | | | - Susannah Long
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Erik K Mayer
- Imperial Clinical Analytics, Research & Evaluation (iCARE), Digital Collaboration Space, London, United Kingdom
- National Institute for Health and Care Research North West London Patient Safety Research Collaborative, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
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Manchanda N, Aggarwal A, Setya S, Talegaonkar S. Digital Intervention For The Management Of Alzheimer's Disease. Curr Alzheimer Res 2023; 19:CAR-EPUB-129308. [PMID: 36744687 DOI: 10.2174/1567205020666230206124155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/08/2023] [Accepted: 01/12/2023] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) is a progressive, multifactorial, chronic, neurodegenerative disease with high prevalence and limited therapeutic options, making it a global health crisis. Being the most common cause of dementia, AD erodes the cognitive, functional, and social abilities of the individual and causes escalating medical and psychosocial needs. As yet, this disorder has no cure and current treatment options are palliative in nature. There is an urgent need for novel therapy to address this pressing challenge. Digital therapeutics (Dtx) is one such novel therapy that is gaining popularity globally. Dtx provides evidence based therapeutic interventions driven by internet and software, employing tools such as mobile devices, computers, videogames, apps, sensors, virtual reality aiding in the prevention, management, and treatment of ailments like neurological abnormalities and chronic diseases. Dtx acts as a supportive tool for the optimization of patient care, individualized treatment and improved health outcomes. Dtx uses visual, sound and other non-invasive approaches for instance-consistent therapy, reminiscence therapy, computerised cognitive training, semantic and phonological assistance devices, wearables and computer-assisted rehabilitation environment to find applications in Alzheimer's disease for improving memory, cognition, functional abilities and managing motor symptom. A few of the Dtx-based tools employed in AD include "Memory Matters", "AlzSense", "Alzheimer Assistant", "smart robotic dog", "Immersive virtual reality (iVR)" and the most current gamma stimulation. The purpose of this review is to summarize the current trends in digital health in AD and explore the benefits, challenges, and impediments of using Dtx as an adjunctive therapy for the management of AD.
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Affiliation(s)
- Namish Manchanda
- School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
| | - Akanksha Aggarwal
- Delhi Institute of Pharmaceutical Sciences And Research, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
| | - Sonal Setya
- Department of Pharmacy Practice, SGT College of Pharmacy, SGT University, Gurugram, Haryana-122505, India
| | - Sushama Talegaonkar
- School of Pharmaceutical Sciences, Delhi Pharmaceutical Sciences & Research University, Govt. of NCT of Delhi, New Delhi-110017, India
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Behera CK, Condell J, Dora S, Gibson DS, Leavey G. State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:4688. [PMID: 34300428 PMCID: PMC8309480 DOI: 10.3390/s21144688] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/31/2021] [Accepted: 07/02/2021] [Indexed: 01/10/2023]
Abstract
In the last decade, there has been a significant increase in the number of people diagnosed with dementia. With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. However, existing devices may not fully meet these needs due to fears and uncertainties about their use, educational support, and finances. Further challenges have been created by COVID-19 and the need for improved safety and security. We have performed a systematic review by exploring several databases describing assistive technologies for dementia and identifying relevant publications for this review. We found there is significant need for appropriate user testing of such devices and have highlighted certifying bodies for this purpose. Given the safety measures imposed by the COVID-19 pandemic, this review identifies the benefits and challenges of existing assistive technologies for people living with dementia and their caregivers. It also provides suggestions for future research in these areas.
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Affiliation(s)
- Chandan Kumar Behera
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - Joan Condell
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - Shirin Dora
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - David S. Gibson
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, University of Ulster, Altnagelvin Area Hospital, C-TRIC Building, Glenshane Road, Londonderry BT47 6SB, UK;
| | - Gerard Leavey
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
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6
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Bayen E, Nickels S, Xiong G, Jacquemot J, Subramaniam R, Agrawal P, Hemraj R, Bayen A, Miller BL, Netscher G. Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study. J Med Internet Res 2021; 23:e17551. [PMID: 34137723 PMCID: PMC8277400 DOI: 10.2196/17551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 10/15/2020] [Accepted: 03/11/2021] [Indexed: 12/03/2022] Open
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. 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.
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Affiliation(s)
- Eleonore Bayen
- Department of Neuro-rehabilitation, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, Sorbonne Université, Paris, France.,Global Brain Health Institute, Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States
| | | | - Glen Xiong
- Alzheimer's Disease Center, Department of Neurology, University of California, Davis, Sacramento, CA, United States
| | | | | | - Pulkit Agrawal
- SafelyYou, Inc, San Francisco, CA, United States.,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, CA, United States
| | | | - Alexandre Bayen
- Electrical Engineering and Computer Science Department, University of California, Berkeley, CA, United States
| | - Bruce L Miller
- Global Brain Health Institute, Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States
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Lazarou I, Stavropoulos TG, Mpaltadoros L, Nikolopoulos S, Koumanakos G, Tsolaki M, Kompatsiaris IY. Human Factors and Requirements of People with Cognitive Impairment, Their Caregivers, and Healthcare Professionals for mHealth Apps Including Reminders, Games, and Geolocation Tracking: A Survey-Questionnaire Study. J Alzheimers Dis Rep 2021; 5:497-513. [PMID: 34368634 PMCID: PMC8293665 DOI: 10.3233/adr-201001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2021] [Indexed: 11/15/2022] Open
Abstract
Background: Mobile Health (mHealth) apps can delay the cognitive decline of people with dementia (PwD), by providing both objective assessment and cognitive enhancement. Objective: This patient involvement survey aims to explore human factors, needs and requirements of PwD, their caregivers, and Healthcare Professionals (HCPs) with respect to supportive and interactive mHealth apps, such as brain games, medication reminders, and geolocation trackers through a constructive questionnaire. Methods: Following the principles of user-centered design to involve end-users in design we constructed a questionnaire, containing both open-ended and closed-ended questions as well as multiple choice and Likert scale, in order to investigate the specific requirements and preferences for mHealth apps. We recruited 48 participants including people with cognitive impairment (n = 15), caregivers (n = 16), and HCPs (n = 17) and administered the questionnaire. Results: All participants are likely to use mHealth apps, with the primary desired features being the improvement of memory and cognition, assistance on medication treatment, and perceived ease to use. HCPs, caregivers, and PwD consider brain games as an important technology-based, non-pharmaceutical intervention. Both caregivers and patients are willing to use a medication reminder app frequently. Finally, caregivers are worried about the patient wandering. Therefore, global positioning system tracking would be particularly important to them. On the other hand, patients are concerned about their privacy, but are still willing to use a geolocation app for cases of emergency. Conclusion: This research contributes to mHealth app design and potential adoption. All three groups agree that mHealth services could facilitate care and ameliorate behavioral and cognitive disturbances of patients.
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Affiliation(s)
- Ioulietta Lazarou
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece.,Medical School, Neuroscience Department, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Thanos G Stavropoulos
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece
| | - Lampros Mpaltadoros
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece
| | - Spiros Nikolopoulos
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece
| | | | - Magda Tsolaki
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders (GAADRD-Alzheimer Hellas), Thessaloniki, Greece.,Medical School, Neuroscience Department, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
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Choukou MA, Shortly T, Leclerc N, Freier D, Lessard G, Demers L, Auger C. Evaluating the acceptance of ambient assisted living technology (AALT) in rehabilitation: A scoping review. Int J Med Inform 2021; 150:104461. [PMID: 33892446 DOI: 10.1016/j.ijmedinf.2021.104461] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 03/05/2021] [Accepted: 04/06/2021] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Ambient assisted living technologies (AALTs) are being used to help community-dwelling older adults (OAs) age in place. Although many AALT are available, their acceptance (perceived usefulness, ease of use, intention to use and actual usage) is needed to improve their design and impact. This study aims to 1) identify AALTs that underwent an acceptance evaluation in rehabilitation contexts, 2) identify methodological tools and approaches to measure acceptance in ambient assisted living (AAL) in rehabilitation research, and 3) summarize AALT acceptance results in existing rehabilitation literature with a focus on peer-reviewed scientific articles. METHODS A scoping review was conducted in the following databases: Medline, Embase, Cinahl, and PsycInfo, following the Arksey and O'Malley framework (2009). Four acceptance attributes were extracted: 'user acceptance', 'perceived usefulness', 'ease of use', and 'intention to use'. Data regarding AALT, participants, acceptance evaluation methods and results were extracted. RESULTS A total of 21 articles were included among 634 studies retrieved from the literature. We identified 51 AALTs dedicated to various rehabilitation contexts, most of which focused on monitoring OAs' activities and environmental changes. Acceptance of AALT was evaluated using interviews, questionnaires, focus groups, informal feedback, observation, card sort tasks, and surveys. Although OAs intend to use - or can perceive the usefulness of - AALTs, they are hesitant to accept the technology and have concerns about its adoption. DISCUSSION AND CONCLUSIONS The assessment of AALT acceptance in contexts of rehabilitation requires more comprehensive and standardized methodologies. The use of mixed-methods research is encouraged to cover the needs of particular studies. The timing of acceptance assessment should be considered throughout technology development phases to maximize AALT implementation.
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Affiliation(s)
- Mohamed-Amine Choukou
- Department of Occupational Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0T6, Canada; Centre on Aging, University of Manitoba, Winnipeg, Manitoba, R3E 0T6, Canada.
| | - Taylor Shortly
- Department of Occupational Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0T6, Canada
| | - Nicole Leclerc
- Department of Occupational Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0T6, Canada
| | - Derek Freier
- Department of Occupational Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0T6, Canada
| | - Genevieve Lessard
- Centre for Interdisciplinary Research in Rehabilitation of the Greater Montreal (CRIR), Institut universitaire sur la réadaptation en déficience physique de Montréal (IURDPM), 6363 Hudson Road, Montreal, Quebec, H3S 1M9, Canada
| | - Louise Demers
- School of Rehabilitation, Faculty of Medicine, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada; Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Centre Intégré universitaire de santé et de services sociaux du Centre-Sud-de-l'Île-de-Montréal, 4565 Queen Mary Road, Montreal, Quebec, H3W 1W5, Canada
| | - Claudine Auger
- Centre for Interdisciplinary Research in Rehabilitation of the Greater Montreal (CRIR), Institut universitaire sur la réadaptation en déficience physique de Montréal (IURDPM), 6363 Hudson Road, Montreal, Quebec, H3S 1M9, Canada; School of Rehabilitation, Faculty of Medicine, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada
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9
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Choukou MA, Mbabaali S, East R. Healthcare Professionals' Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:2720. [PMID: 33800257 PMCID: PMC7967440 DOI: 10.3390/ijerph18052720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
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.
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Affiliation(s)
- Mohamed-Amine Choukou
- Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada; (S.M.); (R.E.)
- 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; (S.M.); (R.E.)
| | - Ryan East
- Department of Occupational Therapy, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada; (S.M.); (R.E.)
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Nilsson MY, Andersson S, Magnusson L, Hanson E. Ambient assisted living technology-mediated interventions for older people and their informal carers in the context of healthy ageing: A scoping review. Health Sci Rep 2021; 4:e225. [PMID: 33392394 PMCID: PMC7770427 DOI: 10.1002/hsr2.225] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND AIMS There is a growing demand for health and social care services to provide technology-mediated interventions that promote the health and well-being of older people with health or care needs and of their informal carers. The objectives of this study were to scope and review the nature and extent of prior intervention studies involving ambient assisted living technology-mediated interventions for older people and their informal carers, and how and in what ways (if any) the goals and aims of these interventions reflected the domains of the World Health Organization framework for healthy ageing. METHODS We conducted a scoping review. Data were collected between June and October 2018 with an updated search in October 2020. A total of 85 articles were eligible for inclusion. RESULTS Nine categories described the aims and content of the included studies. The healthy ageing domain "Ability to meet basic needs" was mirrored in four categories, whereas "Ability to contribute to society" was not addressed at all. CONCLUSION The ways in which domains of healthy ageing are mirrored suggest that there is an emphasis on individual factors and individual responsibility, and a lack of attention given to broader, environmental factors affecting healthy ageing. Only a few of the studies used a dyadic approach when assessing health outcomes concerning older people and their informal carers.
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Affiliation(s)
- Maria Y. Nilsson
- Department of Health and Caring SciencesSwedish Family Care Competence Centre, Linnaeus UniversityKalmarSweden
| | - Stefan Andersson
- Department of Health and Caring SciencesSwedish Family Care Competence Centre, Linnaeus UniversityKalmarSweden
| | - Lennart Magnusson
- Department of Health and Caring SciencesSwedish Family Care Competence Centre, Linnaeus UniversityKalmarSweden
| | - Elizabeth Hanson
- Department of Health and Caring SciencesSwedish Family Care Competence Centre, Linnaeus UniversityKalmarSweden
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11
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Palmdorf S, Stark AL, Nadolny S, Eliaß G, Karlheim C, Kreisel SH, Gruschka T, Trompetter E, Dockweiler C. Technology-Assisted Home Care for People With Dementia and Their Relatives: Scoping Review. JMIR Aging 2021; 4:e25307. [PMID: 33470935 PMCID: PMC7857954 DOI: 10.2196/25307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/09/2020] [Accepted: 12/12/2020] [Indexed: 12/30/2022] Open
Abstract
Background Assistive technologies for people with dementia and their relatives have the potential to ensure, improve, and facilitate home care and thereby enhance the health of the people caring or being cared for. The number and diversity of technologies and research have continuously increased over the past few decades. As a result, the research field has become complex. Objective The goal of this scoping review was to provide an overview of the research on technology-assisted home care for people with dementia and their relatives in order to guide further research and technology development. Methods A scoping review was conducted following a published framework and by searching 4 databases (MEDLINE, CINAHL, PsycInfo, and CENTRAL) for studies published between 2013 and 2018. We included qualitative and quantitative studies in English or German focusing on technologies that support people with dementia or their informal carers in the home care setting. Studies that targeted exclusively people with mild cognitive impairment, delirium, or health professionals were excluded as well as studies that solely consisted of assessments without implication for the people with dementia or their relatives and prototype developments. We mapped the research field regarding study design, study aim, setting, sample size, technology type, and technology aim, and we report relative and absolute frequencies. Results From an initial 5328 records, we included 175 studies. We identified a variety of technology types including computers, telephones, smartphones, televisions, gaming consoles, monitoring devices, ambient assisted living, and robots. Assistive technologies were most commonly used by people with dementia (77/175, 44.0%), followed by relatives (68/175, 38.9%), and both target groups (30/175, 17.1%). Their most frequent goals were to enable or improve care, provide therapy, or positively influence symptoms of people with dementia (eg, disorientation). The greatest proportions of studies were case studies and case series (72/175, 41.1%) and randomized controlled trials (44/175, 25.1%). The majority of studies reported small sample sizes of between 1 and 50 participants (122/175, 69.7%). Furthermore, most of the studies analyzed the effectiveness (85/233, 36.5%) of the technology, while others targeted feasibility or usability or were explorative. Conclusions This review demonstrated the variety of technologies that support people with dementia and their relatives in the home care setting. Whereas this diversity provides the opportunity for needs-oriented technical solutions that fit individual care arrangements, it complicates the choice of the right technology. Therefore, research on the users’ informational needs is required. Moreover, there is a need for larger studies on the technologies’ effectiveness that could contribute to a higher acceptance and thus to a transition of technologies from research into the daily lives of people with dementia and their relatives.
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Affiliation(s)
- Sarah Palmdorf
- Institute for Educational and Health-care Research in the Health Sector, Bielefeld University of Applied Sciences, Bielefeld, Germany
| | - Anna Lea Stark
- Centre for ePublic Health Research, School of Public Health, Bielefeld University, Bielefeld, Germany
| | - Stephan Nadolny
- Institute for History and Ethics of Medicine, Interdisciplinary Center for Health Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany.,Nursing Science Staff Unit, Franziskus-Hospital Harderberg, Niels-Stensen-Kliniken, Georgsmarienhütte, Germany
| | - Gerrit Eliaß
- Innovation & Research, Executive Department, Evangelisches Klinikum Bethel, University Hospital OWL - Campus Bielefeld-Bethel, Bielefeld, Germany
| | - Christoph Karlheim
- Innovation & Research, Executive Department, Evangelisches Klinikum Bethel, University Hospital OWL - Campus Bielefeld-Bethel, Bielefeld, Germany
| | - Stefan H Kreisel
- Division of Geriatric Psychiatry, Department of Psychiatry and Psychotherapy, Evangelisches Klinikum Bethel, University Hospital OWL - Campus Bielefeld-Bethel, Bielefeld, Germany
| | - Tristan Gruschka
- Faculty of Social Studies, Bielefeld University of Applied Sciences, Bielefeld, Germany
| | - Eva Trompetter
- Division of Geriatric Psychiatry, Department of Psychiatry and Psychotherapy, Evangelisches Klinikum Bethel, University Hospital OWL - Campus Bielefeld-Bethel, Bielefeld, Germany
| | - Christoph Dockweiler
- Centre for ePublic Health Research, School of Public Health, Bielefeld University, Bielefeld, Germany
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12
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Oh-Park M, Doan T, Dohle C, Vermiglio-Kohn V, Abdou A. Technology Utilization in Fall Prevention. Am J Phys Med Rehabil 2021; 100:92-99. [PMID: 32740053 DOI: 10.1097/phm.0000000000001554] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Falls, defined as unplanned descents to the floor with or without injury to an individual, remain to be one of the most challenging health conditions. Fall rate is a key quality metric of acute care hospitals, rehabilitation settings, and long-term care facilities. Fall prevention policies with proper implementation have been the focus of surveys by regulatory bodies, including The Joint Commission and the Centers for Medicare and Medicaid Services, for all healthcare settings. Since October 2008, the Centers for Medicare and Medicaid Services has stopped reimbursing hospitals for the costs related to patient falls, shifting the accountability for fall prevention to the healthcare providers. Research shows that almost one-third of falls can be prevented and extensive fall prevention interventions exist. Recently, technology-based applications have been introduced in healthcare to obtain superior patient care outcomes and experience via efficiency, access, and reliability. Several areas in fall prevention deploy technology, including predictive and prescriptive analytics using big data, video monitoring and alarm technology, wearable sensors, exergame and virtual reality, robotics in home environment assessment, and personal coaching. This review discusses an overview of these technology-based applications in various settings, focusing on the outcomes of fall reductions, cost, and other benefits.
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Affiliation(s)
- Mooyeon Oh-Park
- From the Burke Rehabilitation Hospital, White Plains, New York (MO-P, TD, CD, VV-K, AA); and Department of Rehabilitation Medicine, Montefiore Health System, Albert Einstein College of Medicine, New York, New York (MO-P, CD, AA)
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13
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Wilmink G, Dupey K, Alkire S, Grote J, Zobel G, Fillit HM, Movva S. Artificial Intelligence-Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study. JMIR Aging 2020; 3:e19554. [PMID: 32723711 PMCID: PMC7516685 DOI: 10.2196/19554] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/02/2020] [Accepted: 07/28/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Wearables and artificial intelligence (AI)-powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior's change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. METHODS Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (-CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. RESULTS The residents of the +CP and -CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the -CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. CONCLUSIONS The AI-powered digital health platform provides the community staff with actionable information regarding each resident's activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.
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Affiliation(s)
| | | | - Schon Alkire
- Lifewell Senior Living Corporation, Houston, TX, United States
| | | | | | - Howard M Fillit
- Department of Geriatric Medicine and Palliative Care, Icahn School of Medicine, Mount Sinai, New York, NY, United States.,Alzheimer's Drug Discovery Foundation, New York, NY, United States
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14
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Barbosa LDM, Noronha K, Camargos MCS, Machado CJ. [Social integration profiles among non-frail elderly institutionalized individuals in Natal, State of Rio Grande do Norte, Brazil]. CIENCIA & SAUDE COLETIVA 2020; 25:2017-2030. [PMID: 32520250 DOI: 10.1590/1413-81232020256.19652018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 09/10/2018] [Indexed: 11/21/2022] Open
Abstract
The scope of this study was to profile non-frail elderly individuals in Long-Stay Care Institutions in Natal, emphasizing social integration and stratification in philanthropic and private institutions in 2012. The instrument used was the Brazil Old Age Schedule (BOAS). Descriptive analysis was carried out and sociodemographic and health profiles of the elderly were estimated using the Grade of Membership (GoM) scale to obtain social integration typologies. The results indicated that 68 elderly were eligible; 63.2% were female, and 51.5% were 80 years or older; 43% reported poor or extremely poor health. The application of the GoM method yielded three profiles. The first is characterized by the elderly with higher presence of sociable/integrated men living in philanthropic institutions (22% of the elderly); the second mainly encompasses women in philanthropic institutions, with vulnerable health conditions and depression (34.9%); the third is the profile with higher levels of integration/sociability in private institutions, but also characterized by elderly persons with functional disability (34.9%). This study is important since integration and independence must be a part of social life of the elderly irrespective of the place where they live.
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Affiliation(s)
- Lara de Melo Barbosa
- Departamento de Ciências Atmosféricas e Climáticas, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte. Campus Universitário Lagoa Nova, Lagoa Nova. 59078-970, Natal, RN, Brasil.
| | - Kenya Noronha
- Faculdade de Ciências Econômicas, Departamento de Economia, Centro de Desenvolvimento e Planejamento Regional, Universidade Federal de Minas Gerais (UFMG). Belo Horizonte, MG, Brasil
| | | | - Carla Jorge Machado
- Departamento de Medicina Preventiva e Social, Faculdade de Medicina, UFMG. Belo Horizonte, MG, Brasil
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15
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Suzuki M, Yamamoto R, Ishiguro Y, Sasaki H, Kotaki H. Deep learning prediction of falls among nursing home residents with Alzheimer's disease. Geriatr Gerontol Int 2020; 20:589-594. [PMID: 32267067 DOI: 10.1111/ggi.13920] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/29/2020] [Accepted: 03/13/2020] [Indexed: 11/30/2022]
Abstract
AIM This study aimed to use a convolutional neural network (CNN) to investigate the associations between the time of falling and multiple complicating factors, including age, dementia severity, lower extremity strength and physical function, among nursing home residents with Alzheimer's disease. METHODS A total of 42 people with Alzheimer's disease were enrolled. We evaluated falling events from nursing home admission (baseline) to 300 days later. We assessed the knee extension strength and Functional Independence Measure locomotion item and carried out the Mini-Mental State Examination at baseline. To predict falling, participants were categorized into three classes: those who fell within the first 150 (or 300) days from baseline or those who did not experience a fall within the study period. For each class, 1000 bootstrap datasets were generated using 42 actual sample datasets, and were used to propose a CNN algorithm and cross-validate the algorithm. RESULTS Eight (19.0%), 11 (26.2%) and 31 participants (73.8%) fell within 150 or 300 days after the baseline assessment or did not fall until 300 days or later, respectively. The highest accuracy rate of the CNN classification was 0.647 in the factor combination extracted from the Mini-Mental State Examination score, knee extension strength and Functional Independence Measure locomotion item score. CONCLUSIONS A CNN based on multiple complicating factors could predict the time of falling in nursing home residents with Alzheimer's disease. Geriatr Gerontol Int 2020; ••: ••-••.
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Affiliation(s)
- Makoto Suzuki
- Faculty of Health Sciences, Tokyo Kasei University, Saitama, Japan
| | - Ryosuke Yamamoto
- Department of Health Support, Setagaya Municipal Kitazawa En, Setagaya, Japan
| | - Yuko Ishiguro
- Department of Health Support, Setagaya Municipal Kitazawa En, Setagaya, Japan
| | - Hironori Sasaki
- Department of Rehabilitation, Hatsutomi Hoken Hospital, Chiba, Japan
| | - Harumi Kotaki
- Department of Rehabilitation, Hatsutomi Hoken Hospital, Chiba, Japan
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16
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Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth 2019; 7:e11966. [PMID: 31376272 PMCID: PMC6696854 DOI: 10.2196/11966] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/14/2019] [Accepted: 06/12/2019] [Indexed: 01/10/2023] Open
Abstract
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.
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Affiliation(s)
- Igbe Tobore
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China.,Graduate University, Chinese Academy of Sciences, Beijing, China
| | - Jingzhen Li
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liu Yuhang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yousef Al-Handarish
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Abhishek Kandwal
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zedong Nie
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Wang
- Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
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Yousaf K, Mehmood Z, Awan IA, Saba T, Alharbey R, Qadah T, Alrige MA. A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD). Health Care Manag Sci 2019; 23:287-309. [DOI: 10.1007/s10729-019-09486-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 06/05/2019] [Indexed: 02/01/2023]
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18
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Yousaf K, Mehmood Z, Saba T, Rehman A, Munshi AM, Alharbey R, Rashid M. Mobile-Health Applications for the Efficient Delivery of Health Care Facility to People with Dementia (PwD) and Support to Their Carers: A Survey. BIOMED RESEARCH INTERNATIONAL 2019; 2019:7151475. [PMID: 31032361 PMCID: PMC6457307 DOI: 10.1155/2019/7151475] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 03/05/2019] [Indexed: 11/26/2022]
Abstract
Dementia directly influences the quality of life of a person suffering from this chronic illness. The caregivers or carers of dementia people provide critical support to them but are subject to negative health outcomes because of burden and stress. The intervention of mobile health (mHealth) has become a fast-growing assistive technology (AT) in therapeutic treatment of individuals with chronic illness. The purpose of this comprehensive study is to identify, appraise, and synthesize the existing evidence on the use of mHealth applications (apps) as a healthcare resource for people with dementia and their caregivers. A review of both peer-reviewed and full-text literature was undertaken across five (05) electronic databases for checking the articles published during the last five years (between 2014 and 2018). Out of 6195 searches yielded articles, 17 were quantified according to inclusion and exclusion criteria. The included studies distinguish between five categories, viz., (1) cognitive training and daily living, (2) screening, (3) health and safety monitoring, (4) leisure and socialization, and (5) navigation. Furthermore, two most popular commercial app stores, i.e., Google Play Store and Apple App Store, were searched for finding mHealth based dementia apps for PwD and their caregivers. Initial search generated 356 apps with thirty-five (35) meeting the defined inclusion and exclusion criteria. After shortlisting of mobile applications, it is observed that these existing apps generally addressed different dementia specific aspects overlying with the identified categories in research articles. The comprehensive study concluded that mobile health apps appear as feasible AT intervention for PwD and their carers irrespective of limited available research, but these apps have potential to provide different resources and strategies to help this community.
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Affiliation(s)
- Kanwal Yousaf
- Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Zahid Mehmood
- Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Amjad Rehman
- College of Business Administration, Al-Yamamah University, Riyadh 11512, Saudi Arabia
| | - Asmaa Mahdi Munshi
- College of Computer Science and Engineering, University of Jeddah, Jeddah 21577, Saudi Arabia
| | - Riad Alharbey
- College of Computer Science and Engineering, University of Jeddah, Jeddah 21577, Saudi Arabia
| | - Muhammad Rashid
- Department of Computer Engineering, Umm Al-Qura University, Makkah 21421, Saudi Arabia
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