According to Person-Centred Care, as far as possible, people with dementia should be cared for in a way that takes into account their personality, life experiences and preferences. Personalisation is hence the core of Person-Centred Care, yet the approaches, recommendations and tools are lacking for this purpose. Therefore, the author investigated how this personalisation could be facilitated by design. Specifically, the author explored how to personalise the care for Behavioural and Psychosocial Symptoms of Dementia (BPSD). This is because BPSD contributes to the most stressful, complex, and costly aspects of dementia care. Non-pharmacological interventions for BPSD care have been developed, which offers ample room for personalisation. From the field of healthcare, the author drew on Person-Centred Care, and from there, she looked at BPSD from the lens of the Need-driven Dementia-compromised Behaviour (NDB) model, where BPSD is interpreted as a way for people with dementia to express their unmet needs. Factors contributing to BPSD have been categorised by this model, which could be unique for each person with dementia. From the field of design, she approached the challenge from the lens of Human-Centered Design and explored three design approaches that are most relevant in designing for personalised BPSD care, namely, Ergonomics in Ageing, Co-design and Data-enabled Design. The author hypothesised that a combination of these three design approaches could reveal insights into the factors contributing to BPSD, as mentioned in the NDB model, for each person with dementia exhibiting BPSD symptoms. She further hypothesised that gaining insights about these factors could facilitate the design of personalised dementia care. The author implemented a series of steps in evaluating these hypotheses from the literature and from the field. The learnings gained throughout the literature and field research enabled the integration of the three design approaches into Knowme, a toolkit for designing for personalised dementia care. The author concludes with a summary of research findings, a reflection on the research approach, and ends with recommendations for future work.@en

Personalisation is a crucial element in providing person-centred care for people with de-mentia. This paper presents the development and evaluation of a design toolkit to facilitate the work of designers and healthcare professionals in personalising dementia care. This toolkit, named “Know-me”, was grounded in the findings of Ergonomics in Aging, Co-design, and Data-enabled Design, derived from literature review and from the field during a four-year doctorate project. “Know-me” was designed to be easily accessible, flexible, and engaging, providing concrete and hands-on guidance for designers and healthcare professionals to use in designing for personalised dementia care. A proof-of-concept evaluation of the “Know-me” toolkit was conducted via student projects on design for dementia care. During this process, we found that “Know-me” could be adapted flexibly so that the care team could use some of the tools by themselves. A feature-by-feature comparison of the “Know-me” toolkit with similar state-of-the-art toolkits was conducted, and based upon this, the strengths and weaknesses of the “Know-me” toolkit are discussed. This preliminary study indicates that the “Know-me” toolkit is a helpful addition to the current pool of toolkits on designing for dementia care.

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Background: Care personalization is key to the well-being of people with dementia according to person-centered care. With the development of the internet of things, a large quantity of personal data can be collected securely and reliably, which has the potential to facilitate care personalization for people with dementia. Yet, there are limited assistive technologies developed for this purpose, and the user acceptance of assistive technologies is low in nursing homes. Therefore, through a data-enabled design approach, a digital platform was developed for helping the care team in a nursing home to personalize dementia care, specifically in the management of behavioral and psychological dementia symptoms. Objective: This study aimed to evaluate the digital platform in a real-life context with potential users from the following two aspects: (1) to explore if the digital platform could help with generating insights on the current state of each person with dementia and (2) to gather feedback on the digital platform from the care team. Methods: The digital platform was deployed in the nursing home for 7 weeks and the data collected were visualized and presented to the care team via the digital platform. The visualizations were analyzed by the researchers for pattern detection. Meanwhile, the care team was asked to examine the visualizations and were interviewed for the following: (1) if any insights and actions were generated from the examination, (2) the usefulness of the digital platform, and (3) the improvements they would like to see. Results: The data collected on the digital platform demonstrated its potential for pattern detection. Insights were generated by the care team and categorized into “client level,” “ward level,” and “team level.” The corresponding actions taken by the care team were classified into “investigation” and “implementation.” User acceptance varied across the care team, and three aspects of improvement for the digital platform were identified. Conclusions: By evaluating the digital platform, this study gained insights on applying data-enabled design for personalizing dementia care; besides, it offers future researchers some recommendations on how to integrate assistive technologies in the nursing home context.

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Researchers have been exploring how to manage Behavioural and Psychological Symptoms of Dementia (BPSD) in a personalised way, meanwhile, assistive technologies have been developed to collect a variety of personal data. This urges more research in investigating the combination of: data collected by the care team, which are mainly qualitative; and data collected by assistive technologies, the majority of which are quantitative. Previous studies, however, have yet to explore if and how a combination of quantitative and qualitative data could facilitate the care team to better understand each resident with dementia in the nursing home context for personalised BPSD management. Guided by a Research through Design approach, a prototype for collecting and visualising the quantitative and qualitative data towards personalised BPSD management was developed together with the care team. Via developing this prototype, knowledge was gained in what types of data could be combined for personalised BPSD management in nursing homes, what are their values, how to collect and present them, and how to introduce them in the working routine of the care team for analysis. The main findings suggest that the types of data to be collected could be unique for each resident with dementia; the quantitative and qualitative data are of value to each other during data collection and analysis; data collection should be quick and standardised yet flexible for the care team; the overview page is vital for data presentation; and user scenarios could be created to nudge the care team to analyse the data at certain points of their working routine. In general, a combination of qualitative data and quantitative data could help the care team to discover more insights about each resident with dementia and thus improve the current practice of personalised BPSD management.

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This project aims to understand Behavioral and Psychological Symptoms of Dementia (BPSD), specifically, identify location-related factors influencing BPSD at the individual level in the nursing home setting by means of a mixed methods approach facilitated by Indoor Positioning System (IPS). The use of a mixed methods approach fits with the “pragmatism” research paradigm in ergonomics. Previous research has identified some factors influencing BPSD by observations, interviews and multidisciplinary meetings; however, the data supporting the previous findings are mainly subjective. IPS will keep track of the location data of each People with Dementia (PwD) and the caregivers over time in this project. Therefore, we could generate objective data for location-related factors, which could influence BPSD for each PwD in the nursing home setting. The caregivers will analyze the data collected by IPS together with the researcher, by reflecting on their own experiences. As such, both quantitative and qualitative data will be generated and analyzed together to identify location-related factors influencing BPSD for each PwD participant. This is the first project to collect objective data for understanding BPSD. This research protocol discusses the rationale behind this project; the mixed methods approach to be applied in this project, and the impact of this project’s outcomes.

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Background: Since subjective cognitive decline (SCD) was standardized in 2014, many studies have investigated its features. However, the risk of SCD (plus) progressing to AD is much higher, and yet there have been few studies reporting the risk factors and neuropsychological assessment characteristics of SCD (plus). Objective: To characterize SCD (plus) by comparing it with normal control (NC), amnesic mild cognitive impairment (aMCI), and Alzheimer Disease (AD) regarding their demographics, lifestyle, family history of dementia, multimorbidity and the neuropsychological assessments. Methods: A total of 135 participants were recruited, including 23 NC, 30 SCD (plus), 45 aMCI and 37 AD. Descriptive statistics were provided. A logistic regression model was used to analyze the affecting factors of SCD (plus), and finally the Receiver Operating Characteristic (ROC) analysis was applied to distinguish between SCD (plus) and NC. Results: (1) SCD (plus) group was younger than both the aMCI group and AD group. It consisted of more participants with mental work and higher body mass index (BMI) than the AD group. (2) Scores of Auditory Verbal Learning Test - Immediate recall (AVLT-IR) and AVLT-Long delayed recall (AVLT-LR) decreased in the following order: NC→SCD (plus)→aMCI→AD. (3) The Area Under Curve (AUC) for discriminating SCD (plus) and NC group was from 0.673 to 0.838. Conclusion: Aging is an important risk factor of both NC progressing to SCD (plus), and SCD (plus) progressing to aMCI or AD. In addition to aging, lower education level and lower BMI were significantly associated with greater odds of SCD (plus) progressing to aMCI or AD patients, whereas mental work was a protective factor of SCD (plus) progressing to AD. Finally, AVLT is a sensitive indicator of the cognitive decline and impairment in SCD (plus) in relative to normal controls.

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BACKGROUND: Ultrasound guidance of cryotherapy could be improved if changes of tissue characterization parameters during freezing could be used to monitor freezing profiles and estimate frostbite distributions. OBJECTIVE: To investigate if B-mode ultrasonic imaging can provide sufficient information to define the damaged zone during cryotherapy by quantifying the differences of texture features between fresh and frozen-thawed porcine liver tissue. MATERIALS AND METHODS: The B-mode ultrasonic images were obtained from ten porcine liver tissue samples before and after frozen-thawing treatment, then 20 texture parameters of the grey level histogram (GLH) and the grey level co-occurrence matrix (GLCM) from each image were extracted and analyzed statistically. RESULTS: It was found that the differences in two texture parameters between the fresh and frozen-thawed tissue are significant enough for diagnostic purpose (variance: 317.0%, contrast: 144.4%). CONCLUSION: Texture feature analysis of B-mode ultrasonic images can effectively differentiate the fresh and frozen-thawed porcine liver tissues, which offers a prospect for the future practice of evaluating the effectiveness of cryotherapy using Ultrasound.

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Non-pharmacological interventions have been applied to manage Behavioural and Psychological Symptoms of Dementia (BPSD). However, these interventions have not been assessed from an ergonomics perspective. Ergonomics has investigated the age-related capability changes in terms of sensory, cognition and movement aspects. This study aims to review the existing non-pharmacological interventions for BPSD targeting nursing home residents and generate design recommendations based on the domain of ergonomics in ageing. The electronic databases MEDLINE, EMBASE, PsycINFO were searched for studies which applied non-pharmacological interventions for treating BPSD in nursing home residents. A total of 67 studies met the inclusion criteria; from which 16 types of interventions were identified. Within these intervention types, the main capabilities required from the interventions for People with Dementia (PwD) were identified. The interventions were then categorized into sensory-, cognition-, and movement-oriented according to the main capabilities. Design recommendations were then generated for the interventions with knowledge from the domain of ergonomics in ageing.

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Co-designing with people with dementia (PwD) can uncover their needs and preferences, which have been often overlooked. It is difficult for PwD to understand designers and express themselves in a conventional co-design session. This study aims to evaluate the effects of involving PwD in design research on both PwD and the design process; to identify the trends of involving PwD in design research; to extract tools, recommendations, and limitations of involving PwD from reviewed studies to update the recommendations on how to co-design with PwD. A scoping review was carried out within the electronic databases PubMed and Scopus, and eight research questions were proposed, in order to gain specific knowledge on the involvement of PwD in design research. Twenty-six studies met the inclusion criteria, and 32 sessions were evaluated. Beneficial effects on both PwD and the design process were reported. The number of studies involving PwD in the moderate and severe stages of dementia has increased. Based on the review, an update of the existing tools and recommendations for co-designing with PwD is provided and a list of limitations of involving PwD is presented. The review shows that involving PwD in design research is beneficial for both the PwD and the design process, and there is a shift towards involving people who are in the moderate and severe stages of dementia. The authors propose that multidisciplinary meetings and case studies should be carried out to evaluate and refine the list of tools and recommendations as well as the list of limitations generated in this review.

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Background:
Non-pharmacological interventions for Behavioral and Psychological Symptoms of Dementia (BPSD) have been developed; however, a systematic review on the effectiveness of this type of intervention from a perspective of ergonomics is lacking. According to ergonomics, the capabilities of Persons with Dementia (PwD) should be considered in the interventions for the outcomes to be reliable. We aimed to systematically review the non-pharmacological interventions for BPSD in nursing home residents with an additional assessment criterion based on ergonomics, specifically, capability consideration.

Methods:
The electronic databases MEDLINE, EMBASE, and PsycINFO were searched for non-pharmacological interventions treating BPSD in nursing homes. The interventions were categorized according to the capabilities of PwD required to participate. Study quality was assessed by National Health and Medical Research Council (NHMRC) evidence hierarchy and the capability consideration.

Results:
Sixty-four clinical trials met the inclusion criteria; 41 trials reported a significant reduction in at least one BPSD symptom; 20 trials reported no significant reduction in BPSD symptoms; three trials reported adverse effects after the intervention. Interventions were categorized into sensory-, cognition-, and movement-oriented. Capabilities of PwD were not considered in 28 trials, especially for sensory capabilities.

Conclusions:
The majority of the clinical trials reported a significant reduction in BPSD. The quality of evidence for nonpharmacological interventions in these trials is low due to the lack of capability consideration, data inhomogeneity, and inadequate study design and reporting. Future studies should focus on improving the quality of evidence by including capability consideration and examining if a relationship between capability consideration and effectiveness of non-pharmacological interventions exists.
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