Publications
7 results found
Kirby P, Lai H, Horrocks S, et al., 2024, Patient and public involvement in technology-related dementia research: a scoping review, JMIR Aging, Vol: 7, ISSN: 2561-7605
Background:Technology-related research for people with dementia and their carers often aims to enable people to remain living at home for longer and to prevent unnecessary hospital admissions. To develop research that is person-centred, effective and ethical, patient and public involvement (PPI) is necessary, though may be perceived as more difficult with this cohort. With recent and rapid expansions in health and care related technology, this review explores how, and with what impact, collaborations between researchers and stakeholders such as people with dementia have taken place.Objective:To describe approaches to PPI used to date in technology-related dementia research, along with the barriers and facilitators and impact of PPI in this area.Methods:A scoping review of literature relating to dementia, technology and patient and public involvement was conducted using Medline, PsycINFO, EMBASE and CINAHL. Papers were screened for inclusion by two authors. Data was then extracted using a pre-designed data extraction table by the same two authors; a third author supported resolution of any conflicts at each stage. Barriers and facilitators of undertaking PPI were then examined and themed.Results:Thirty-one papers were included for analysis. The majority (21/31) did not make clear distinctions between activities undertaken as PPI and activities undertaken by research participants, and as such their involvement did not fit easily into the NIHR definition of PPI. Most of this mixed involvement focused on the reviewing or evaluating of technology prototypes. A range of approaches was described, most typically using focus groups or co-design workshops. Nine studies described involvement at multiple stages through the research cycle, sometimes with evidence of sharing of decision-making power. Some studies commented on barriers or facilitators to effective PPI. Challenges identified were often around issues of working with people with significant cognitive impairments, and
Raposo de Lima M, Horrocks S, Daniels S, et al., 2023, The role of conversational AI in ageing and dementia care at home: a participatory study, 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, ISSN: 1944-9437
Conversational artificial intelligence (AI) technologies hold significant promise to support the independence,well-being and safety of older adults living with frailty ordementia at home. However, further studies are needed toidentify: 1) valuable scenarios of support, 2) desired interactivefeatures, and 3) key challenges preventing long-term adoptionand utility in dementia care. In this paper, we explore therole of conversational technology in ageing and dementia careat home. Using a community-based participatory approach, weengaged 20 stakeholders, including people with lived experienceof dementia and frailty, to understand preferences, perceivedbenefits and concerns about integrating conversational AIinto daily routines at home. We uncovered key roles of thetechnology, including support of daily functions, health monitoring, risk mitigation, and cognitive stimulation. We emphasizethe need for adapting interactions to different levels of userfamiliarity and progression of cognitive decline. We addressthe importance of the communication style and suggest carefuluse of open-ended questions with target populations. We furtherdiscuss feasibility considerations to overcome current barriersto adoption. Overall, this work proposes design guidelines toshape the future conceptualization and development of naturallanguage interactions to support dementia care at home.
Del Giovane M, Trender WR, Bălăeţ M, et al., 2023, Computerised cognitive assessment in patients with traumatic brain injury: an observational study of feasibility and sensitivity relative to established clinical scales, EClinicalMedicine, Vol: 59, ISSN: 2589-5370
Background:Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity.Methods:This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary’s Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George’s Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients’ performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary’s Hospital TBI clinic in London (UK).Findings:The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.9
Wairagkar M, De Lima MR, Harrison M, et al., 2021, Conversational artificial intelligence and affective social robot for monitoring health and well-being of people with dementia., Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e053276-e053276, ISSN: 1552-5260
BACKGROUND: Social robots are anthropomorphised platforms developed to interact with humans, using natural language, offering an accessible and intuitive interface suited to diverse cognitive abilities. Social robots can be used to support people with dementia (PwD) and carers in their homes managing medication, hydration, appointments, and evaluating mood, wellbeing, and potentially cognitive decline. Such robots have potential to reduce care burden and prolong independent living, yet translation into PwD use remains insignificant. METHOD: We have developed two social robots - a conversational robot and a digital social robot for mobile devices capable of communicating through natural language (powered by Amazon Alexa) and facial expressions that ask PwD daily questions about their health and wellbeing and also provide digital assistant functionality. We record data comprising of PwD's responses to daily questions, audio speech and text of conversations with Alexa to automatically monitor their health and wellbeing using machine learning. We followed user-centric development processes by conducting focus groups with 13 carers, 2 PwD and 5 clinicians to iterate the design. We are testing social robot with 3 PwD in their homes for ten weeks. RESULT: We received positive feedback on social robot from focus group participants. Ease of use, low maintenance, accessibility, assistance with medication, supporting with health and wellbeing were identified as the key opportunities for social robots. Based on responses to a daily questionnaire, our robots generate a report detailing PwD wellbeing that is automatically sent via email to family members or carers. This information is also stored systematically in a database that can help clinicians monitor their patients remotely. We use natural language processing to analyse conversations and identify topics of interest to PwD such that robot behaviour could be adapted. We process speech using signal processing and machine lear
Tiersen F, Batey P, Harrison MJC, et al., 2021, Smart home sensing and monitoring in households with dementia: user-centered design approach, JMIR Aging, Vol: 4, Pages: 1-20, ISSN: 2561-7605
Background:As life expectancy grows, so do the challenges of caring for an ageing population. Older adults, including people with dementia, want to live independently and feel in control of their lives for as long as possible. Assistive technologies powered by Artificial Intelligence and Internet of Things devices are being proposed to provide living environments that support the users’ safety, psychological, and medical needs through remote monitoring and interventions.Objective:This study investigates the functional, psychosocial, and environmental needs of people living with dementia, their caregivers, clinicians, and health and social care service providers towards the design and implementation of smart home systems.Methods:We used an iterative user-centered design approach comprising nine sub-studies. First, semi-structured interviews (N = 9 people with dementia, 9 caregivers, 10 academic and clinical staff), ethnographic observations in clinics (N = 10 people with dementia, 10 caregivers, 3 clinical monitoring team members), and workshops (N = 35 pairs of people with dementia and caregivers, 12 health and social care clinicians) were conducted to define the needs of people with dementia, home caregivers and professional stakeholders in both daily activities and technology-specific interactions. Then, the spectrum of needs identified was represented via patient-caregiver personas and discussed with stakeholders in a workshop (N = 14 occupational therapists, 4 National Health Service pathway directors, 6 researchers in occupational therapy, neuropsychiatry and engineering) and two focus groups with managers of healthcare services (N = 8), eliciting opportunities for innovative care technologies and public health strategies. Finally, these opportunities were discussed in semi-structured interviews with participants of a smart home trial involving environmental sensors, physiological measurement devices, smart watches, and tablet-based chatbots and cognitive
Tiersen F, Batey P, Harrison MJC, et al., 2021, Smart Home Sensing and Monitoring in Households With Dementia: User-Centered Design Approach (Preprint)
<sec> <title>BACKGROUND</title> <p>As life expectancy grows, so do the challenges of caring for an aging population. Older adults, including people with dementia, want to live independently and feel in control of their lives for as long as possible. Assistive technologies powered by artificial intelligence and internet of things devices are being proposed to provide living environments that support the users’ safety, psychological, and medical needs through remote monitoring and interventions.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study investigates the functional, psychosocial, and environmental needs of people living with dementia, their caregivers, clinicians, and health and social care service providers toward the design and implementation of smart home systems.</p> </sec> <sec> <title>METHODS</title> <p>We used an iterative user-centered design approach comprising 9 substudies. First, semistructured interviews (9 people with dementia, 9 caregivers, and 10 academic and clinical staff) and workshops (35 pairs of people with dementia and caregivers, and 12 health and social care clinicians) were conducted to define the needs of people with dementia, home caregivers, and professional stakeholders in both daily activities and technology-specific interactions. Then, the spectrum of needs identified was represented via patient–caregiver personas and discussed with stakeholders in a workshop (14 occupational therapists; 4 National Health Service pathway directors; and 6 researchers in occupational therapy, neuropsychiatry, and engineering) and 2 focus groups with managers of health care services (n=8), eliciting opportunities for innovat
Warren L, Harrison M, Arora S, et al., 2019, Working with patients and the public to design an electronic health record interface: A qualitative mixed-methods study, BMC Medical Informatics and Decision Making, Vol: 19, ISSN: 1472-6947
BackgroundEnabling patients to be active users of their own medical records may promote the delivery of safe, efficient care across settings. Patients are rarely involved in designing digital health record systems which may make them unsuitable for patient use. We aimed to develop an evidence-based electronic health record (EHR) interface and participatory design process by involving patients and the public.MethodsParticipants were recruited to multi-step workshops involving individual and group design activities. A mixture of quantitative and qualitative questionnaires and observational methods were used to collect participant perspectives on interface design and feedback on the workshop design process.Results48 recruited participants identified several design principles and components of a patient-centred electronic medical record interface. Most participants indicated that an interactive timeline would be an appropriate way to depict a medical history. Several key principles and design components, including the use of specific colours and shapes for clinical events, were identified. Participants found the workshop design process utilised to be useful, interesting, enjoyable and beneficial to their understanding of the challenges of information exchange in healthcare.ConclusionPatients and the public should be involved in EHR interface design if these systems are to be suitable for use by patient-users. Workshops, as used in this study, can provide an engaging format for patient design input. Design principles and components highlighted in this study should be considered when patient-facing EHR design interfaces are being developed.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.