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Goerss D, Köhler S, Rong E, Temp AG, Kilimann I, Bieber G, Teipel S. Smartwatch-Based Interventions for People With Dementia: User-Centered Design Approach. JMIR Aging 2024; 7:e50107. [PMID: 38848116 PMCID: PMC11193079 DOI: 10.2196/50107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 01/30/2024] [Accepted: 02/27/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Assistive technologies can help people living with dementia maintain their everyday activities. Nevertheless, there is a gap between the potential and use of these materials. Involving future users may help close this gap, but the impact on people with dementia is unclear. OBJECTIVE We aimed to determine if user-centered development of smartwatch-based interventions together with people with dementia is feasible. In addition, we evaluated the extent to which user feedback is plausible and therefore helpful for technological improvements. METHODS We examined the interactions between smartwatches and people with dementia or people with mild cognitive impairment. All participants were prompted to complete 2 tasks (drinking water and a specific cognitive task). Prompts were triggered using a smartphone as a remote control and were repeated up to 3 times if participants failed to complete a task. Overall, 50% (20/40) of the participants received regular prompts, and 50% (20/40) received intensive audiovisual prompts to perform everyday tasks. Participants' reactions were observed remotely via cameras. User feedback was captured via questionnaires, which included topics like usability, design, usefulness, and concerns. The internal consistency of the subscales was calculated. Plausibility was also checked using qualitative approaches. RESULTS Participants noted their preferences for particular functions and improvements. Patients struggled with rating using the Likert scale; therefore, we assisted them with completing the questionnaire. Usability (mean 78 out of 100, SD 15.22) and usefulness (mean 9 out of 12) were rated high. The smartwatch design was appealing to most participants (31/40, 76%). Only a few participants (6/40, 15%) were concerned about using the watch. Better usability was associated with better cognition. The observed success and self-rated task comprehension were in agreement for most participants (32/40, 80%). In different qualitative analyses, participants' responses were, in most cases, plausible. Only 8% (3/40) of the participants were completely unaware of their irregular task performance. CONCLUSIONS People with dementia can have positive experiences with smartwatches. Most people with dementia provided valuable information. Developing assistive technologies together with people with dementia can help to prioritize the future development of functional and nonfunctional features.
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Affiliation(s)
- Doreen Goerss
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen Rostock/Greifswald, Rostock, Germany
| | - Stefanie Köhler
- Deutsches Zentrum für Neurodegenerative Erkrankungen Rostock/Greifswald, Rostock, Germany
| | - Eleonora Rong
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Anna Gesine Temp
- Neurozentrum, Berufsgenossenschaftliches Klinikum Hamburg, Hamburg, Germany
| | - Ingo Kilimann
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen Rostock/Greifswald, Rostock, Germany
| | - Gerald Bieber
- Fraunhofer Institut für Graphische Datenverarbeitung, Rostock, Germany
| | - Stefan Teipel
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen Rostock/Greifswald, Rostock, Germany
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Fernandes S, von Gunten A, Verloo H. Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review. JMIR Nurs 2024; 7:e54496. [PMID: 38805252 PMCID: PMC11167323 DOI: 10.2196/54496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 04/15/2024] [Accepted: 04/26/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early. OBJECTIVE The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early. METHODS A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach. RESULTS A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD. CONCLUSIONS Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.
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Affiliation(s)
- Sofia Fernandes
- School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland
- Les Maisons de la Providence Nursing Home, Le Châble, Switzerland
- Faculty of Biology and Medicine, Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
| | - Armin von Gunten
- Service of Old Age Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Henk Verloo
- School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland
- Service of Old Age Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Pruthviraja D, Nagaraju SC, Mudligiriyappa N, Raisinghani MS, Khan SB, Alkhaldi NA, Malibari AA. Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm. Diagnostics (Basel) 2023; 13:2687. [PMID: 37627946 PMCID: PMC10453097 DOI: 10.3390/diagnostics13162687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.
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Affiliation(s)
- Dayananda Pruthviraja
- Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sowmyarani C. Nagaraju
- Department of Computer Science and Engineering, R V College of Engineering, Bengaluru 560059, India
| | - Niranjanamurthy Mudligiriyappa
- Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Bengaluru 560064, India
| | | | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M54WT, UK
| | - Nora A. Alkhaldi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Areej A. Malibari
- Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Itoh S, Tan HP, Kudo K, Ogata Y. Comparison of the Mental Burden on Nursing Care Providers With and Without Mat-Type Sleep State Sensors at a Nursing Home in Tokyo, Japan: Quasi-Experimental Study. JMIR Aging 2022; 5:e19641. [PMID: 35319474 PMCID: PMC8987956 DOI: 10.2196/19641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 09/16/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background Increasing need for nursing care has led to the increased burden on formal caregivers, with those in nursing homes having to deal with exhausting labor. Although research activities on the use of internet of things devices to support nursing care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes. Objective This study aims to investigate whether mat-type sleep state sensors for supporting nursing care can reduce the mental burden of formal caregivers in a nursing home. Methods This was a quasi-experimental study at a nursing home in Tokyo, Japan. The study participants were formal caregivers who cared for residents in private rooms on the fourth and fifth floors of the nursing home. In the intervention group, formal caregivers took care of residents who used sleep state sensors on the fourth floor of the nursing home. The sleep state sensors were mat types and designed to detect body motion such as the frequency of toss and turning and to measure heartbeat and respiration. One sensor was placed on a bed in a private room. When body motion is detected, the information is instantly displayed on a monitor at a staff station. In addition, the mental condition of the formal caregivers was measured using a validated self-reported outcome measure—the Profile of Mood States (POMS), Short-Form, 2nd edition. Formal caregivers in both groups received the POMS at baseline, midpoint (week 4), and endpoint (week 8) to identify changes in these domains. The primary outcome was the difference in total mood disturbance (TMD) of the POMS at baseline and week 8. Results Of the 22 eligible formal caregivers, 12 (intervention group) utilized sleep state sensors for 8 weeks. The remaining 10 formal caregivers (control group) provided nursing care as usual. As for the primary outcome of the difference between TMD at baseline and week 8, TMD in the intervention group improved by –3.67 versus 4.70 in the control group, resulting in a mean difference of –8.37 (95% CI –32.02 to 15.29; P=.48) in favor of the intervention. Conclusions The present 8-week study showed that sleep state sensing for elderly residents might not be associated with reduced mental burdens on formal caregivers in nursing homes.
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Affiliation(s)
- Sakiko Itoh
- Department of Gerontological Nursing and Healthcare Systems Management, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan.,Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan.,Department of Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Hwee-Pink Tan
- School of Information Systems, Singapore Management University, Singapore, Singapore.,Policy, Research and Surveillance Group, Health Promotion Board, Singapore, Singapore
| | - Kenichi Kudo
- Research Innovation Initiatives Organization, Hirosaki University, Aomori, Japan
| | - Yasuko Ogata
- Department of Gerontological Nursing and Healthcare Systems Management, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Cheung JCW, So BPH, Ho KHM, Wong DWC, Lam AHF, Cheung DSK. Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review. Front Psychiatry 2022; 13:913213. [PMID: 36186887 PMCID: PMC9523077 DOI: 10.3389/fpsyt.2022.913213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.
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Affiliation(s)
- James Chung-Wai Cheung
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.,Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Bryan Pak-Hei So
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Ken Hok Man Ho
- The Nethersole School of Nursing, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Duo Wai-Chi Wong
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Alan Hiu-Fung Lam
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Daphne Sze Ki Cheung
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.,School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Iaboni A, Spasojevic S, Newman K, Schindel Martin L, Wang A, Ye B, Mihailidis A, Khan SS. Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12305. [PMID: 35496371 PMCID: PMC9043905 DOI: 10.1002/dad2.12305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/24/2022] [Accepted: 02/27/2022] [Indexed: 11/15/2022]
Abstract
Introduction Behavioral and psychological symptoms of dementia (BPSD) signal distress or unmet needs and present a risk to people with dementia and their caregivers. Variability in the expression of these symptoms is a barrier to the performance of digital biomarkers. The aim of this study was to use wearable multimodal sensors to develop personalized machine learning models capable of detecting individual patterns of BPSD. Methods Older adults with dementia and BPSD (n = 17) on a dementia care unit wore a wristband during waking hours for up to 8 weeks. The wristband captured motion (accelerometer) and physiological indicators (blood volume pulse, electrodermal activity, and skin temperature). Agitation or aggression events were tracked, and research staff reviewed videos to precisely annotate the sensor data. Personalized machine learning models were developed using 1‐minute intervals and classifying the presence of behavioral symptoms, and behavioral symptoms by type (motor agitation, verbal aggression, or physical aggression). Results Behavioral events were rare, representing 3.4% of the total data. Personalized models classified behavioral symptoms with a median area under the receiver operating curve (AUC) of 0.87 (range 0.64–0.95). The relative importance of the different sensor features to the predictive models varied both by individual and behavior type. Discussion Patterns of sensor data associated with BPSD are highly individualized, and future studies of the digital phenotyping of these behaviors would benefit from personalization.
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Affiliation(s)
- Andrea Iaboni
- KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada
- Department of Psychiatry University of Toronto Toronto Ontario Canada
| | - Sofija Spasojevic
- KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada
- Department of Occupational Science and Occupational Therapy University of Toronto Toronto Ontario Canada
| | - Kristine Newman
- Daphne Cockwell School of Nursing, Ryerson University Toronto Ontario Canada
| | | | - Angel Wang
- Daphne Cockwell School of Nursing, Ryerson University Toronto Ontario Canada
| | - Bing Ye
- KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada
- Department of Occupational Science and Occupational Therapy University of Toronto Toronto Ontario Canada
| | - Alex Mihailidis
- KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada
- Department of Occupational Science and Occupational Therapy University of Toronto Toronto Ontario Canada
| | - Shehroz S. Khan
- KITE Research Institute Toronto Rehabilitation Institute University Health Network Toronto Ontario Canada
- Institute of Biomaterials & Biomedical Engineering University of Toronto Toronto Ontario Canada
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Zolfaghari S, Khodabandehloo E, Riboni D. TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes. Cognit Comput 2021; 14:1549-1570. [PMID: 33552305 PMCID: PMC7851509 DOI: 10.1007/s12559-020-09816-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/29/2020] [Indexed: 11/29/2022]
Abstract
The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-\documentclass[12pt]{minimal}
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\begin{document}$$F_1$$\end{document}F1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.
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Affiliation(s)
- Samaneh Zolfaghari
- Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
| | - Elham Khodabandehloo
- Department of Geo-spatial Information Systems, K. N. Toosi University of Technology, Tehran, Iran
| | - Daniele Riboni
- Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy
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Bringas S, Salomón S, Duque R, Lage C, Montaña JL. Alzheimer's Disease stage identification using deep learning models. J Biomed Inform 2020; 109:103514. [PMID: 32711124 DOI: 10.1016/j.jbi.2020.103514] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in order to provide the optimal treatment and the prevention of complications. MATERIALS AND METHODS We employed data from 35 patients with AD collected by smartphones for a week in a daycare center. The data sequences of each patient recorded the accelerometer changes while daily activities were performed and they were labeled with the stage of the disease (early, middle or late). Our methodology processes these time series and uses a Convolutional Neural Network (CNN) model to recognize the patterns that identify each stage. RESULTS The CNN-based method achieved a 90.91% accuracy and an F1-score of 0.897, greatly improving the results obtained by the traditional feature-based classifiers. DISCUSSION AND CONCLUSION In our research, we show that mobility data can be a valuable resource for the treatment of patients with AD as well as to study the progress of the disease. The use of our CNN-based method improves the accuracy of the identification of AD stages in comparison to common supervised learning models.
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Affiliation(s)
- Santos Bringas
- Fundación Centro Tecnológico de Componentes CTC, 39011 Santander, Spain.
| | | | - Rafael Duque
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain.
| | - Carmen Lage
- Cognitive Disorders Unit, Department of Neurology, Marqués de Valdecilla University Hospital (HUMV), Valdecilla Biomedical Research Institute (IDIVAL), 39008 Santander, Spain.
| | - José Luis Montaña
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain.
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Smart Environments and Social Robots for Age-Friendly Integrated Care Services. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17113801. [PMID: 32471108 PMCID: PMC7312538 DOI: 10.3390/ijerph17113801] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/25/2020] [Accepted: 05/26/2020] [Indexed: 12/13/2022]
Abstract
The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.
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Powell KR, Deroche CB, Carnahan EJ, Alexander GL. Exploring Resident Care Information Technology Use and Nursing Home Quality. J Gerontol Nurs 2020; 46:15-20. [PMID: 32219453 DOI: 10.3928/00989134-20200303-02] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
A wide array of sophisticated information technology (IT) systems are being used in nursing home (NH) resident care to improve quality. The purpose of the current study was to explore differences in NH IT sophistication, a comprehensive measure of adoption, used in resident care processes based on facility characteristics over 4 consecutive years and to examine the impact on select long-stay NH quality measures. Results indicate IT systems used in resident care are becoming increasingly sophisticated. NH bed size, type of ownership, and location were significant predictors of IT score in areas related to resident care. Results also suggest that as electronic clinical processes and documents increase (e.g., incident reporting, nursing flowsheets, care planning) in resident care, more falls with injury are detected. Continued assessments of NH IT sophistication are important as the impact of technology on quality continues to be evaluated. [Journal of Gerontological Nursing, 46(4), 15-20.].
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Behavioral Interventions for Alzheimer’s Management Using Technology: Home-Based Monitoring. CURRENT GERIATRICS REPORTS 2020. [DOI: 10.1007/s13670-020-00312-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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