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Mois G, Lydon EA, Mathias VF, Jones SE, Mudar RA, Rogers WA. Best practices for implementing a technology-based intervention protocol: Participant and researcher considerations. Arch Gerontol Geriatr 2024; 122:105373. [PMID: 38460265 DOI: 10.1016/j.archger.2024.105373] [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: 03/10/2023] [Revised: 11/17/2023] [Accepted: 02/18/2024] [Indexed: 03/11/2024]
Abstract
Technology-based interventions present a promising approach to support health and wellness for older adults with a range of cognitive abilities. Technology can enhance access to interventions and support scaling of programs to reach more people. However, the use of technology for intervention delivery requires particular attention to users' needs and preferences and ensuring the materials are adaptable and supportive of a diverse range of technology proficiency levels. We share best practices based on lessons learned from the deployment of a randomized controlled trial (RCT) wherein we delivered an 8-week social engagement intervention through a video technology platform called OneClick for older adults with varying cognitive abilities. We developed a set of best practices and guidelines informed by the lessons learned through this RCT implementation. Technology-based interventions require attention to the application (e.g., video calls), system requirements (e.g., system memory, broadband internet), training (e.g., adaptability based on user competency), and support (e.g., handouts, live contact). These best practices relate to user needs; training design; personnel responsibility; structuring delivery and content; and evaluating success. These research-based best practices can guide the design, development, and implementation of technology-based interventions to support older adults with varying cognitive abilities.
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Affiliation(s)
- George Mois
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States.
| | - Elizabeth A Lydon
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States
| | - Vincent F Mathias
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States
| | - Sarah E Jones
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States
| | - Raksha A Mudar
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States
| | - Wendy A Rogers
- College of Applied Health Sciences, University of Illinois Urbana-Champaign, 1206 S Fourth St., Champaign, IL 61820, United States
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Young E, Hung L, Wong J, Wong KLY, Yee A, Mann J, Vasarhelyi K. The perceptions of university students on technological and ethical risks of using robots in long-term care homes. Front Robot AI 2023; 10:1268386. [PMID: 38187477 PMCID: PMC10768051 DOI: 10.3389/frobt.2023.1268386] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/06/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction: The COVID-19 pandemic has disproportionately impacted long-term care (LTC) residents and exacerbated residents' risks of social isolation and loneliness. The unmet emotional needs of residents in LTC have driven researchers and decision-makers to consider novel technologies to improve care and quality of life for residents. Ageist stereotypes have contributed to the underuse of technologies by the older population. Telepresence robots have been found to be easy to use and do not require older adults to learn how to operate the robot but are remotely controlled by family members. The study aimed to understand the perspectives of multidisciplinary university students, including healthcare students, on using telepresence robots in LTC homes. The study would contribute to the future planning, implementation, and design of robotics in LTC. Methods: Between December 2021 and March 2022, our team conducted interviews with 15 multidisciplinary students. We employed a qualitative descriptive (QD) approach with semi-structured interview methods. Our study aimed to understand the perspectives of university students (under the age of 40) on using telepresence robots in LTC homes. Participants were invited to spend 15 min remotely driving a telepresence robot prior to the interview. A diverse team of young researchers and older adults (patient and family partners) conducted reflexive thematic analysis. Results: Six themes were identified: Robots as supplementary interaction; privacy, confidentiality, and physical harm; increased mental well-being and opportunities for interactions; intergenerational perspectives add values; staffing capacity; environmental and cultural factors influence acceptance. Conclusion: We identified a diverse range of perspectives regarding risk and privacy among participants regarding the implementation of telepresence robots in long-term care. Participants shared the importance of the voice of the resident and their own for creating more equitable decision-making and advocating for including this type of technology within LTC. Our study would contribute to the future planning, implementation, and design of robotics in LTC.
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Affiliation(s)
- Erika Young
- UBC IDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada
| | - Lillian Hung
- UBC IDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada
| | - Joey Wong
- UBC IDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada
| | - Karen Lok Yi Wong
- UBC IDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada
| | - Amanda Yee
- McGill University, Faculty of Medicine and Health Sciences, Montreal, QC, Canada
| | - Jim Mann
- UBC IDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada
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Sawik B, Tobis S, Baum E, Suwalska A, Kropińska S, Stachnik K, Pérez-Bernabeu E, Cildoz M, Agustin A, Wieczorowska-Tobis K. Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study. Healthcare (Basel) 2023; 11:healthcare11091286. [PMID: 37174828 PMCID: PMC10178192 DOI: 10.3390/healthcare11091286] [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: 03/01/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
This paper focuses on three areas: the first is a review of current knowledge about social and service robots for elderly care. The second is an optimization conceptual model aimed at maximizing the efficiency of assigning robots to serve the elderly. The proposed multi-criteria optimization model is the first one proposed in the area of optimization for robot assignment for the elderly with robot utilization level and caregiver stress level. The third is the findings of studies on the needs, requirements, and adoption of technology in elderly care. We consider the use of robots as a part of the ENRICHME project for long-term interaction and monitoring of older persons with mild cognitive impairment, to optimize their independence. Additionally, we performed focus group discussions (FGD) to collect opinions about robot-related requirements of the elderly and their caregivers. Four FDGs of six persons were organized: two comprising older adults, and two of the other formal and informal caregivers, based on a detailed script. The statements of older participants and their caregivers were consistent in several areas. The analysis revealed user characteristics, robot-related issues, functionality, and barriers to overcome before the deployment of the robot. An introduction of the robot must be thoroughly planned, include comprehensive pre-training, and take the ethical and practical issues into account. The involvement of future users in the customization of the robot is essential.
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Affiliation(s)
- Bartosz Sawik
- Department of Business Informatics and Engineering Management, AGH University of Science and Technology, 30-059 Krakow, Poland
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
- Haas School of Business, University of California at Berkeley, Berkeley, CA 94720, USA
| | - Sławomir Tobis
- Occupational Therapy Unit, Chair of Geriatric Medicine and Gerontology, Poznan University of Medical Sciences, ul. Swiecickiego 6, 60-781 Poznan, Poland
| | - Ewa Baum
- Department of Social Sciences and the Humanities, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Aleksandra Suwalska
- Department of Mental Health, Chair of Psychiatry, Poznan University of Medical Sciences, ul. Szpitalna 27/33, 60-572 Poznan, Poland
| | - Sylwia Kropińska
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
| | - Katarzyna Stachnik
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
| | - Elena Pérez-Bernabeu
- Department of Applied Statistics and Operations Research, Universitat Politècnica de València, Plaza Ferrandiz y Carbonell, sn, 03801 Alcoy, Spain
| | - Marta Cildoz
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
| | - Alba Agustin
- Institute of Smart Cities, Department of Statistics, Computer Science and Mathematics, Public University of Navarre, 31006 Pamplona, Spain
| | - Katarzyna Wieczorowska-Tobis
- Geriatrics Unit, Chair of Palliative Medicine, Poznan University of Medical Sciences, os. Rusa 55, 61-245 Poznan, Poland
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McLaren JE, Shin MH, Moo LR. Poor insight and future thinking in early dementia limit patient projections of potential utility of technological innovations and advanced care planning. Front Med (Lausanne) 2023; 10:1123331. [PMID: 36993808 PMCID: PMC10040527 DOI: 10.3389/fmed.2023.1123331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/13/2023] [Indexed: 03/14/2023] Open
Abstract
IntroductionCognitive psychology posits that thinking about the future relies on memory such that those with memory impairment may have trouble imaging their future technology and other needs.MethodsWe conducted a content analysis of qualitative data from interviews with six patients with MCI or early dementia regarding potential adaptations to a mobile telepresence robot. Using a matrix analysis approach, we explored perceptions of (1) what technology could help with day-to-day functioning in the present and future and (2) what technology may help people with memory problems or dementia stay home alone safely.ResultsVery few participants could identify any technology to assist themselves or other people with memory problems and could not provide suggestions on what technology may help them stay home alone safely. Most perceived that they would never need robotic assistance.DiscussionThese findings suggest individuals with MCI or early dementia have limited perspectives on their own functional abilities now and in the future. Consideration of the individuals’ diminished understanding of their own future illness trajectory is crucial when engaging in research or considering novel technological management solutions and may have implications for other aspects of advanced care planning.
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Affiliation(s)
- Jaye E. McLaren
- New England Geriatric Research Education and Clinical Center, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States
- *Correspondence: Jaye E. McLaren,
| | - Marlena H. Shin
- Center for Healthcare Organization and Implementation Research, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Lauren R. Moo
- New England Geriatric Research Education and Clinical Center, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States
- Center for Healthcare Organization and Implementation Research, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States
- Harvard Medical School, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
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