Affective and Engagement Issues in the Conception and Assessment of a Robot-Assisted Psychomotor Therapy for Persons with Dementia

ZORA robot to assist a caregiver in prospective memory tasks: A preliminary study

The objective of the present study was to evaluate the interest of an assistance robot to help caregivers manage the activities of daily living of institutionalized elderly people with Alzheimer's disease. Twenty-three institutionalized persons (60% women; average age 89; average MMSE score of 20.8) with Alzheimer Disease (AD) were recruited and invited to participate in prospective memory exercise sessions, conducted either by a caregiver or by a robot (assisted by a caregiver). They were divided into two groups equivalent in age, level of education and MMSE score. In addition, the sessions were recorded in order to compare the interaction behaviors of the 2 groups, using a validated observation grid. The results showed that: 1) prospective memory tasks are better performed when offered by the caregiver; 2) when strong help linked to the recovery index is provided to perform the tasks, the robot or caregiver no longer show significant differences; 3) participants interact more with the caregiver than with the robot. Our results confirm that the use of companion robots is a promising way to help caregivers manage the daily activities of people with Alzheimer's. However, to optimize this assistance, further investigations should be conducted to improve the fluidity of interactions between the patient and the robot.

Impact of digital assistive technologies on the quality of life for people with dementia: a scoping review

BACKGROUND

Digital assistive technologies (DATs) have emerged as promising tools to support the daily life of people with dementia (PWD). Current research tends to concentrate either on specific categories of DATs or provide a generic view. Therefore, it is of essence to provide a review of different kinds of DATs and how they contribute to improving quality of life (QOL) for PWD.

DESIGN

Scoping review using the framework proposed by Arksey and O'Malley and recommendations from Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews.

DATA SOURCES

Cochrane, Embase, PubMed, Scopus and Web of Science (January 2013 to May 2023).

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Completed scientific literature with a primary focus on DATs for PWD, perspectives of caregivers, family members or healthcare workers in relation to a PWD, people living in diverse settings and all severities of dementia.

DATA EXTRACTION AND SYNTHESIS

Screening and data extraction were conducted, followed by quantitative and qualitative analyses using thematic analysis principles and Digital Therapeutics Alliance categories for DAT grouping.

RESULTS

The literature search identified 6083 records, with 1056 duplicates. After screening, 4560 full texts were excluded, yielding 122 studies of different designs. The DATs were categorised into digital therapeutics (n=109), patient monitoring (n=30), digital diagnostics (n=2), care support (n=2) and health system clinical software (n=1). These categories were identified to impact various aspects of QOL: preserving autonomy, engagement, and social interaction, health monitoring and promotion, improving activities of daily living, improving cognition, maintaining dignity, managing behavioural and psychological symptoms of dementia and safety/surveillance.

CONCLUSIONS

Various DATs offer extensive support, elevating the QOL of PWD. Digital therapeutics are predominantly used for ageing-in-place and independent living through assistance with daily tasks. Future research should focus on less-represented digital health technology categories, such as care support, health & wellness or software solutions. Observing ongoing DAT developments and their long-term effects on QOL remains essential.

Development of a decision-support framework to support professionals and promote comfort among older hospital inpatients living with dementia

BACKGROUND

Acute hospital wards can be difficult places for many people living with dementia. Promoting comfort and wellbeing can be challenging in this environment. There is little evidence-based support for professionals working on acute care wards on how to respond to distress and maximise comfort and wellbeing among patients living with dementia.

OBJECTIVES

Our overall aim was to codesign an evidence-based easy-to-use heuristic decision-support framework, which was acceptable and practical but acknowledges the complex and acute nature of caring for patients with dementia in the hospital. This paper presents the development process and resulting framework.

METHODS

A codesign study was informed by data from (1) a literature review of the care and management of people living with dementia in acute hospitals; (2) a cohort study of comfort and discomfort in people with dementia in acute hospitals; and (3) interviews with family carers and health care professionals. We synthesised evidence from these data sources and presented to key stakeholders through codesign meetings and workshops to produce our decision-support framework.

RESULTS

The framework consists of a series of flowcharts and operates using a three-stage process of: (1) assess comfort/discomfort; (2) consider causes of discomfort; and (3) address patient needs to manage the discomfort.

CONCLUSION

Working with key stakeholders, synthesising diverse quantitative and qualitative evidence to build a clinical framework is a feasible approach to help address the needs of patients living with dementia in an acute hospital setting. The result is a framework which is now ready for evaluation and implementation.

PATIENT AND PUBLIC CONTRIBUTION

We worked closely with people living with dementia and family carers throughout this study, including the development of the study protocol with input on study development and design, through to inclusion in stakeholder workshops and codesign of the decision support framework.

Socially Assistive Robots for People Living with Dementia in Long-Term Facilities: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND AND OBJECTIVE

The purpose of our study was to explore the immediate and long-term effects of socially assistive robots (SARs) on neuropsychiatric symptoms (NPSs), behavioral and psychological symptoms of dementia (BPSD), positive emotional experiences, and social interaction in older people living with dementia.

METHODS

We set keywords and used Boolean operators to search the CINAHL, Cochrane Library, EMBASE, IEEE Digital Library, MEDLINE, PsycINFO, PubMed, Web of Science, Scopus, and Chinese Electronic Periodical Service from inception to February 2022 for randomized controlled trials. The Cochrane Collaboration bias assessment tool was used to assess article quality, and RevMan 5.4.1 software was used to conduct the meta-analysis.

RESULTS

A total of 14 studies were included in the meta-analysis. SARs can help people living with dementia reduce their NPS of depression and anxiety, provide happiness from positive emotional experiences, and improve their social interaction through conversation. However, there was no significant improvement in agitation behavior, overall BPSD, or quality of life in people living with dementia. In follow-up, it was found that the effect of SRT was limited.

CONCLUSION

SARs can reduce depression and increase positive emotions in people living with dementia. They may also reduce the burden on healthcare workers during the COVID-19 pandemic. This research was registered on PROSPERO CRD42020169340.

Compassionate Technology: A Systematic Scoping Review of Compassion as Foundation for Blended and Digital Mental Health Interventions (Preprint)

BACKGROUND

An essential value in mental health care is compassion: awareness of suffering, tolerating difficult feelings in the face of suffering, and acting or being motivated to alleviate suffering. Currently, technologies for mental health care are on the rise and could offer several advantages, such as more options for self-management by clients and more accessible and economically viable care. However, digital mental health interventions (DMHIs) have not been widely implemented in daily practice. Developing and evaluating DMHIs around important mental health care values, such as compassion, could be key for a better integration of technology in the mental health care context.

OBJECTIVE

This systematic scoping review explored the literature for previous instances where technology for mental health care has been linked to compassion or empathy to investigate how DMHIs can support compassion in mental health care.

METHODS

Searches were conducted in the PsycINFO, PubMed, Scopus, and Web of Science databases, and screening by 2 reviewers resulted in 33 included articles. From these articles, we extracted the following data: technology types, goals, target groups, and roles of the technologies in the intervention; study designs; outcome measures; and the extent to which the technologies met a 5-step proposed definition of compassion.

RESULTS

We found 3 main ways in which technology can contribute to compassion in mental health care: by showing compassion to people, by enhancing self-compassion in people, or by facilitating compassion between people. However, none of the included technologies met all 5 elements of compassion nor were they evaluated in terms of compassion.

CONCLUSIONS

We discuss the potential of compassionate technology, its challenges, and the need to evaluate technology for mental health care on compassion. Our findings could contribute to the development of compassionate technology, in which elements of compassion are explicitly embedded in its design, use, and evaluation.

6G and Artificial Intelligence Technologies for Dementia Care: Literature Review and Practical Analysis

Background

The dementia epidemic is progressing fast. As the world’s older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients’ health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients’ health care needs and improve their quality of life.

Objective

This study aims to investigate ways in which 6G and AI technologies could elevate dementia care to address this study gap.

Methods

A literature review was conducted in databases such as PubMed, Scopus, and PsycINFO. The search focused on three themes: dementia, 6G, and AI technologies. The initial search was conducted on April 25, 2021, complemented by relevant articles identified via a follow-up search on November 11, 2021, and Google Scholar alerts.

Results

The findings of the study were analyzed in terms of the interplay between people with dementia’s unique health challenges and the promising capabilities of health technologies, with in-depth and comprehensive analyses of advanced technology-based solutions that could address key dementia care needs, ranging from impairments in memory (eg, Egocentric Live 4D Perception), speech (eg, Project Relate), motor (eg, Avatar Robot Café), cognitive (eg, Affectiva), to social interactions (eg, social robots).

Conclusions

To live is to grow old. Yet dementia is neither a proper way to live nor a natural aging process. By identifying advanced health solutions powered by 6G and AI opportunities, our study sheds light on the imperative of leveraging the potential of advanced technologies to elevate dementia patients’ will to live, enrich their daily activities, and help them engage in societies across shapes and forms.

A Robot-Mediated Activity Using the Nao Robot to Promote COVID-19 Precautionary Measures among Older Adults in Geriatric Facilities

Precautionary measures (e.g., social distancing, mask wearing, washing hands regularly) to limit the transmission of the Coronavirus 19 (COVID-19) have been put in place worldwide. However, a limited understanding of precautionary measures and low compliance with them has been observed in older adults with neurocognitive disorders, persons with intellectual disability, or mental illness. The objective of this study is to create and evaluate a robot-mediated activity to deliver information on COVID-19 precautionary measures in an accessible and engaging way using the humanoid robot Nao. An interactive scenario explaining and demonstrating COVID-19 precautionary measures is created using the verbal and non-verbal behaviours of the robot. The scenario (≈5 min) is presented to 124 users of a geriatric hospital, including the following: older patients (n = 45), accompanying persons (n = 39), and health professionals (n = 40). The data regarding perceived usefulness, acceptability, and accessibility are collected using a questionnaire. A video analysis of the participants' behaviour during the interaction with the robot is performed to examine the quality of engagement in the activity. The results show a good acceptance, satisfaction, and perceived usefulness of the robot-mediated activity. These findings suggest that robot-mediated interventions using humanoid robots can be an effective tool for the delivery of health promotion information.

Social Robots for the Care of Persons with Dementia

Intelligent assistive robots can enhance the quality of life of people with dementia and their caregivers. They can increase the independence of older adults, reduce tensions between a person with dementia and their caregiver, and increase social engagement. This article provides a review of assistive robots designed for and evaluated by persons with dementia. Assistive robots that only increased mobility or brain-computer interfaces were excluded. Google Scholar, IEEE Digital Library, PubMed, and ACM Digital Library were searched. A final set of 53 articles covering research in 16 different countries are reviewed. Assistive robots are categorized into five different applications and evaluated for their effectiveness, as well as the robots’ social and emotional capabilities. Our findings show that robots used in the context of therapy or for increasing engagement received the most attention in the literature, whereas the robots that assist by providing health guidance or help with an activity of daily living received relatively limited attention. PARO was the most commonly used robot in dementia care studies. The effectiveness of each assistive robot and the outcome of the studies are discussed, and particularly, the social/emotional capabilities of each assistive robot are summarized. Gaps in the research literature are identified and we provide directions for future work.

Therapeutic engagement in robot-assisted psychological interventions: A systematic review

PURPOSE

Therapeutic engagement is a key component of psychological interventions. Robot-assisted psychological interventions appear to have therapeutic benefits for service users that are challenging to engage. However, engagement with robots in robot-assisted psychological interventions is not well understood. The aim of this systematic review is to evaluate the quality of therapeutic engagement in robot-assisted psychological interventions (PROSPERO: 122437).

METHODS

Scopus, Web of Science, PsycInfo and Medline were searched until 15 January 2021 for studies which quantitatively evaluated therapeutic engagement in robot-assisted psychological interventions. The Effective Public Health Practice Project (EPHPP) quality assessment tool was used to assess methodological dimensions of studies.

RESULTS

3647 studies were identified through database searching. Thirty studies (N = 1462), published between 2004 and 2020, and from 14 countries, were included. Robots were typically toy animals or humanoids and were used to provide support and improve wellbeing through social interaction. Studies primarily tested robots on older adults with dementia and children with autism and indicated positive therapeutic engagement. Twelve studies included a control group. EPHPP ratings were 'strong' (N = 1), 'moderate' (N = 10) and 'weak' (N = 19).

CONCLUSIONS

Therapeutic engagement between service users and robots is generally positive. Methodological limitations of studies, such as small sample sizes, and lack of control groups and longitudinal data, mean that the field is in early stages of its development and conclusions should be drawn with caution. There are important practical and ethical implications for policymakers to consider, such as responsible clinical practice and how service users may understand the therapeutic relationship with robots.

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies.

Can a humanoid social robot stimulate the interactivity of cognitively impaired elderly? A thorough study based on computer vision methods

Social Assistive Robotics is increasingly being used in care settings to provide psychosocial support and interventions for the elderly with cognitive impairments. Most of these social robots have provided timely stimuli to the elderly at home and in care centres, including keeping them active and boosting their mood. However, previous investigations have registered shortcomings in these robots, particularly in their ability to satisfy an essential human need: the need for companionship. Reports show that the elderly tend to lose interests in these social robots after the initial excitement as the novelty wears out and the monotonous familiarity becomes all too familiar. This paper presents our research facilitating conversations between a social humanoid robot, Nadine, and cognitively impaired elderly at a nursing home. We analysed the effectiveness of human-humanoid interactions between our robot and 14 elderly over 29 sessions. We used both objective tools (based on computer vision methods) and subjective tools (based on observational scales) to evaluate the recorded videos. Our findings showed that our subjects engaged positively with Nadine, suggesting that their interaction with the robot could improve their well-being by compensating for some of their emotional, cognitive, and psychosocial deficiencies. We detected emotions associated with cognitively impaired elderly during these interactions. This study could help understand the expectations of the elderly and the current limitations of Social Assistive Robots. Our research is aligned with all the ethical recommendations by the NTU Institutional Review Board.

Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement

COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural 'human-like' conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot's facial representation of emotions, such that the robot adapts its emotional response to users' speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions.

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Can Use of Digital Technologies by People with Dementia Improve Self-Management and Social Participation? A Systematic Review of Effect Studies

There is increasing interest in the use of technology to support social health in dementia. The primary objective of this systematic review was to synthesize evidence of effectiveness of digital technologies used by people with dementia to improve self-management and social participation. Records published from 1 January 2007 to 9 April 2020 were identified from Pubmed, PsycInfo, Web of Science, CINAHL, and the Cochrane Central Register of Controlled Trials. Controlled interventional studies evaluating interventions based on any digital technology were included if: primary users of the technology had dementia or mild cognitive impairment (MCI); and the study reported outcomes relevant to self-management or social participation. Studies were clustered by population, intervention, and outcomes, and narrative synthesis was undertaken. Of 1394 records identified, nine met the inclusion criteria: two were deemed to be of poor methodological quality, six of fair quality, and one of good quality. Three clusters of technologies were identified: virtual reality, wearables, and software applications. We identified weak evidence that digital technologies may provide less benefit to people with dementia than people with MCI. Future research should address the methodological limitations and narrow scope of existing work. In the absence of strong evidence, clinicians and caregivers must use their judgement to appraise available technologies on a case-by-case basis.

Social Robot Interventions for People with Dementia: A Systematic Review on Effects and Quality of Reporting

Background:

Using non-pharmacological interventions is a current approach in dementia care to manage responsive behaviors, to maintain functional capacity, and to reduce emotional stress. Novel technologies such as social robot interventions might be useful to engage people with dementia in activities and interactions as well as to improve their cognitive, emotional, and physical status.

Objective:

Assessing the effects and the quality of reporting of social robot interventions for people with dementia.

Methods:

In our systematic review, we included quasi-experimental and experimental studies published in English, French, or German, irrespective of publication year. Searching CINAHL, Cochrane Library, MEDLINE, PsycINFO, and Web of Science Core Collection was supplemented by citation tracking and free web searching. To assess the methodological quality of included studies, we used tools provided by the Joanna Briggs Institute. To assess the reporting of the interventions, we applied CReDECI 2 and TIDieR.

Results:

We identified sixteen studies published between 2012 and 2018, including two to 415 participants with mostly non-defined type of dementia. Eight studies had an experimental design. The predominant robot types were pet robots (i.e., PARO). Most studies addressed behavioral, emotion-related, and functional outcomes with beneficial, non-beneficial, and mixed results. Predominantly, cognitive outcomes were not improved. Overall, studies were of moderate methodological quality.

Conclusion:

Heterogeneous populations, intervention characteristics, and measured outcomes make it difficult to generalize the results with regard to clinical practice. The impact of social robot interventions on behavioral, emotion-related, and functional outcomes should therefore be assessed considering the severity of dementia and intervention characteristics.

The Perceptions of People with Dementia and Key Stakeholders Regarding the Use and Impact of the Social Robot MARIO

People with dementia often experience loneliness and social isolation. This can result in increased cognitive decline which, in turn, has a negative impact on quality of life. This paper explores the use of the social robot, MARIO, with older people living with dementia as a way of addressing these issues. A descriptive qualitative study was conducted to explore the perceptions and experiences of the use and impact of MARIO. The research took place in the UK, Italy and Ireland. Semi-structured interviews were held in each location with people with dementia (n = 38), relatives/carers (n = 28), formal carers (n = 28) and managers (n = 13). The data was analyzed using qualitative content analysis. The findings revealed that despite challenges in relation to voice recognition and the practicalities of conducting research involving robots in real-life settings, most participants were positive about MARIO. Through the robot's user-led design and personalized applications, MARIO provided a point of interest, social activities, and cognitive engagement increased. However, some formal carers and managers voiced concern that robots might replace care staff.

Robotic assistance for people with dementia: a viable option for the future?

INTRODUCTION

Demographic changes in society and fewer personnel working in healthcare services have resulted in an increase in the speed of development of safe, reliable robotic assistance technologies for patients with neurological diseases. This paper aims to advocate for the frailty of patients in light of the economic need for robotic assistance, discuss potential hazards, and outline related factors that influence positive outcomes.

AREAS COVERED

This article reviews the state of the art and perspectives regarding the use of robotics in older adults with dementia. We focus on current trends in the development of robotic technologies for these patients and discuss the potential hazards associated with the implementation of such cutting-edge technology in daily practice.

EXPERT OPINION

We envisage a gradual increase in the usage of robot-based devices for the management and support of patients with cognitive deficits. In particular, the introduction of artificial intelligence will enhance the functionality of these technologies, but also increase potential hazards resulting from human-robot interactions. The development of such technology must consider whether neurological syndromes are static or progressive. Progressive syndromes pose the biggest challenge since the functionality of robotic devices must adapt to patients changing cognitive and motor performance profiles.

The effectiveness of interventions to improve the care and management of people with dementia in general hospitals: A systematic review

BACKGROUND

People with dementia are at greater risk of being admitted to hospital where care may not be tailored to their needs. Interventions improving care and management are vital.

AIM

Assess the effectiveness of interventions designed to improve the care and management of people with dementia in hospital.

METHOD

Six medical and trial registry, and grey literature databases were searched (1999-1998/2018). Search terms included "Dementia," "Hospital," and "Intervention" and limited to experimental designs. Interventions designed to improve the care and management of people with dementia in the general hospital setting were examined. Outcomes included behavioural and psychological symptoms of dementia (BPSD), psychosocial, clinical, staff knowledge, and length of hospital stay. The CASP tools, Cochrane risk of bias tool, and GRADE system assessed methodological quality and certainty of evidence.

RESULTS

9003 unique citations were identified; 24 studies were included. Studies were limited in study design and their conduct was at a risk of bias. There is very low-quality evidence that multisensory behaviour therapy reduces BPSD. There is low-quality evidence that a multidisciplinary programme reduces postoperative complications and that robot-assisted therapy, music therapy, multimodal-comprehensive care, person-centred care, and family-centred function-focused care interventions improved staff knowledge, competence, efficacy, and communication. No studies reported reduced length of stay.

CONCLUSIONS

Whilst we found that these interventions improved the care and management of people with dementia in hospital, it was low- to very low-quality evidence. New clinical recommendations cannot be made based on current evidence, and robust trial designs are necessary to inform evidence-based care.

Using telepresence for social connection: views of older people with dementia, families, and health professionals from a mixed methods pilot study

Objectives: To explore the acceptability of telepresence robots in dementia care from the perspectives of people with dementia, family carers, and health professionals/trainees, and investigate the utility of a social presence assessment tool, the Modified-Temple Presence Inventory (Modified-TPI), for people with dementia. Method: A mixed-methods pilot study conducted in a social robotics laboratory. Three participant groups (n = 22) - dyads of people with dementia and their carers (n = 5 respectively), and health professionals/trainees (n = 12) - participated in individual one-off intervention sessions with the telepresence robot, Giraff, with follow-up interviews. Sessions covered how to use Giraff, followed by interactive practice in making a video-call involving conversation and manoeuvring of Giraff. Participants with dementia experienced receiving a call made by their carer; healthcare professionals/trainees experienced making and receiving a call. Outcomes of interest were sense of presence [Modified-TPI], affective response (International Positive and Negative Affect Schedule [I-PANAS-S]; Observable Displays of Affect Scale [ODAS]), and attitudes and reactions to Giraff (semi-structured interviews). Results: Participants reported a sense of authenticity and social connection through the experience. They indicated positive social presence through Giraff, and significantly higher positive (mean score 18.77; ±4.00) than negative affect (mean score 8.05; ±1.76) on the I-PANAS-SF, and on the facial display subscale of the ODAS (positive - mean score 15.50; ±3.51 versus negative - mean score 4.00; ±0.00). Conclusion: Telepresence has potential use in situations where people with dementia require social connection. Studies with larger sample sizes, varied characteristics, and cost-effectiveness analysis are needed to inform the application of telepresence in healthcare practice.

Understanding Engagement in Dementia Through Behavior. The Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the Evidence-Based Model of Engagement-Related Behavior (EMODEB)

Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to identify a common structure to the behaviors in the two ethograms and unify them in a unique coding system. The inter-rater reliability (IRR) of the coding system proved to be excellent for cognitive games (kappa = 0.78) and very good for robot play (kappa = 0.74). From the scoring of the videos, we developed an evidence-based model of engagement. This was based on the most frequent patterns of body part organization (i.e., the way body parts are connected in movement) observed during activities. Each pattern was given a meaning in terms of engagement by making reference to the literature. The model was tested using structural equation modeling (SEM). It achieved an excellent goodness of fit and all the hypothesized relations between variables were significant. We called the coding system that we developed the Ethographic and Laban-Inspired Coding System of Engagement (ELICSE) and the model the Evidence-based Model of Engagement-related Behavior (EMODEB). To the best of our knowledge, the ELICSE and the EMODEB constitute the first formalization of engagement-related behavior for dementia that describes how behavior unfolds over time and what it means in terms of engagement.