Novel Technological Solutions for Assessment, Treatment, and Assistance in Mild Cognitive Impairment

Technology for activity participation in older people with mild cognitive impairment or dementia: expert perspectives and a scoping review

PURPOSE

This two-phased study aimed to collate, summarize and characterize - through the lens of an occupation-based, person-centred framework - ongoing research and practice featuring activity participation-supportive digital health technology (DHT) for direct use by older persons with mild cognitive impairment or Alzheimer's disease and related dementias (PwMCI/ADRD).

MATERIALS AND METHODS

Phase 1: Using scoping review procedures, PubMed, MEDLINE and PsycInfo were searched to identify primary research studies. Phase 2: Semi-structured interviews were completed with MCI/ADRD expert stakeholders identified through publicly available biographies and snowball referral. Thematic analysis was used to identify, synthesize and cross-compare emergent themes from both data sources that were subsequently organized into core facets of the Human Activity Assistive Technology (HAAT) model.

RESULTS

The scoping review resulted in 28 studies, which were primarily feasibility work with small sample sizes. Interviewed experts (N = 17) had 4+ years of MCI/ADRD experience, came from a variety of settings, and held myriad roles. Real world and research-based use of DHTs held some commonalities, particularly around support for social participation and instrumental activities of daily engagement. No DHT for sleep or work/volunteerism were noted in either phase. People with milder MCI/ADRD conditions were most often targeted users. Soft technology strategies facilitating implementation centred on product design (e.g., prompting software, customisability, multimedia/multisensory experiences), instructional methods and technology partner involvement.

CONCLUSIONS

This study demonstrates that although DHT supportive of activity participation is being studied and integrated into the lives of PwMCI/ADRD, there are still key opportunities for growth to meet the needs of diverse MCI/ADRD end users.Implications for rehabilitationMainstream digital health technologies (DHTs) are being utilized by persons with mild cognitive impairment and Alzheimer's disease and related dementias (PwMCI/ADRD) in everyday life, in limited capacities, to support social participation, leisure, health management and instrumental activities of daily living (IADL).Innovative research-based technologies to be used directly by PwMCI/ADRD are under development, particularly to facilitate management of ADL, social participation and IADL in persons with mild-to-moderate forms of cognitive impairment.Soft technology strategies to support technology implementation with MCI/ADRD target users include close attention to design of the technology (e.g., customisability, sensory stimulators and prompting features), instructional strategies that promote learning and motivation and involvement of technology partners to facilitate engagement with the technology.Future studies will require more robust research designs with transparent reports of participant characteristics and facilitative instructional methods to expand DHT's potential to account for and better meet the needs of diverse MCI/ADRD communities in real-world contexts.

Efficacy of ICT-based interventions in improving psychological outcomes among older adults with MCI and dementia: A systematic review and meta-analysis

The purpose of this systematic review and meta-analysis was to investigate empirical evidence about the effectiveness of Information and Communication Technology-based interventions (ICTs) on different psychological outcomes in adults aged over 60 years with Mild Cognitive Impairment (MCI) or dementia. We conducted a systematic search on Pubmed, Web of Science, Scopus, and PsycInfo with publication year between January 2010 up to April 2021. Any pre-post quantitative intervention study with at least one of the following domains examined: quality of life (QoL), psychological well-being, social interaction, engagement, mood, anxiety, stress, loneliness, self-efficacy, or self-esteem was included. The risk of bias and quality of evidence were assessed using tools based on the Cochrane Handbook for Systematic Review of Interventions criteria. Forty-eight studies with a total of 1488 participants met the selection criteria. Because of the high heterogeneity, we ran nine different random effects meta-analyses divided by outcome and type of cognitive decline which indicated that these treatments were ineffective overall, with some exceptions. Only anxiety (small effect size =-0.375 [-0.609; -0.140]) and behavioral symptoms (BS) (medium effect size =-0.585 [-1.019; -0.152]) in people with dementia (PwD) were found to change significantly. Moreover, effect sizes for QoL in dementia and for mood in people with MCI became significant when moderated by type of ICT, living situation, and experimental setting. In particular, Virtual Reality (VR) appeared to be more effective than other devices for both PwD and MCI, and nursing homes were found to be the best setting for administering these treatments. The trim and fill method found no evidence of publication bias in any of the 9 analyses. However, quality of evidence within (RoB 2, RoB 2 Crossover, ROBINS) and across (GRADE assessment) studies was low, thus these findings should be interpreted with caution. In general, ICT-based intervention can be considered a promising approach for improving anxiety and BS in PwD, and for improving QoL in PwD and mood in people with MCI, specifically when VR is used, when participants live in nursing homes, and when interventions are carried out in nursing homes.¹.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

Personalizing Care Through Robotic Assistance and Clinical Supervision

By 2030, the World Health Organization (WHO) foresees a worldwide workforce shortfall of healthcare professionals, with dramatic consequences for patients, economies, and communities. Research in assistive robotics has experienced an increasing attention during the last decade demonstrating its utility in the realization of intelligent robotic solutions for healthcare and social assistance, also to compensate for such workforce shortages. Nevertheless, a challenge for effective assistive robots is dealing with a high variety of situations and contextualizing their interactions according to living contexts and habits (or preferences) of assisted people. This study presents a novel cognitive system for assistive robots that rely on artificial intelligence (AI) representation and reasoning features/services to support decision-making processes of healthcare assistants. We proposed an original integration of AI-based features, that is, knowledge representation and reasoning and automated planning to 1) define a human-in-the-loop continuous assistance procedure that helps clinicians in evaluating and managing patients and; 2) to dynamically adapt robot behaviors to the specific needs and interaction abilities of patients. The system is deployed in a realistic assistive scenario to demonstrate its feasibility to support a clinician taking care of several patients with different conditions and needs.

Design and development of a social assistive robot for music and game activities: a case study in a residential facility for disabled people

Cognitive disability strongly reduces people's autonomy in performing desired as well as daily activities. The use of Social Assistive Robots (SARs) for cognitive rehabilitation therapy for disabled people could be a valuable gateway for the residential facility of the future. In this work, we design and develop a SAR that can be used for cognitive therapy proposing music and game activities. The results confirm that participants were positively engaged during the proposed activities and satisfied by the robot, despite the low perception of its usability. Professional caregivers noticed and confirmed the high level of engagement and the positive acceptance of the robot within the session, suggesting future tasks for SAR. Clinical Relevance- The results suggest the potential use of SAR also with disabled people proposing cognitive games as a part of the cognitive rehabilitation program.

Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations

Socially assistive robots have the potential to augment and enhance therapist's effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots' behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist's expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients' performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist's preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human-human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.

Can assistive technology support social services during Covid-19 emergency? Barriers and opportunities

During the COVID-19 emergency, most domiciliary social services were suspended to avoid the risk of contagion, leaving older people at a greater risk of social isolation. Assistive technology has the potential to support the work of social professionals in promoting social inclusion and assistance of the older people. In this context, this paper aims to investigate the expectations of social operators toward assistive technology before and during the COVID-19 emergency. It also explores how the said emergency could guide us to implement social services in the future, including a discussion on the barriers to the adoption of assistive technologies. A total of 72 social professionals participated in this study comprising of three phases: two online questionnaires and one semi structured interview. In the first two phases, the two online questionnaires were administered before and during the COVID-19 emergency to 62 social professionals. In the third phase, 10 social workers were interviewed to discuss the results of the previous questionnaires to gain an in-depth understanding. The results highlight that the COVID-19 emergency is responsible for an increased perceived need of services involving telepresence, proposing a hybrid paradigm of assistance with both remote and in-presence assistance. Furthermore, the identified barriers to technology adoption are lack of organizational structure and ready-to-use technology. As for the facilitators for the technology adoption, social workers suggested investing in education and training of social professionals to reduce skepticism towards the usefulness of technology. The social professionals involved in this study highlight a generally positive view of technology in supporting their work. Finally, the lessons learned is also presented as a guideline for researchers in this field.

Identifying Mild Cognitive Impairment by Using Human-Robot Interactions

BACKGROUND

Mild cognitive impairment (MCI), which is common in older adults, is a risk factor for dementia. Rapidly growing health care demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults.

OBJECTIVE

To overcome methodological drawbacks of previous studies (e.g., use of potentially imprecise screening tools that fail to include patients with MCI), this study investigated the feasibility of assessing multiple cognitive functions in older adults with and without MCI by using a social robot.

METHODS

This study included 33 older adults with or without MCI and 33 healthy young adults. We examined the utility of five robotic cognitive tests focused on language, episodic memory, prospective memory, and aspects of executive function to classify age-associated cognitive changes versus MCI. Standardized neuropsychological tests were collected to validate robotic test performance.

RESULTS

The assessment was well received by all participants. Robotic tests assessing delayed episodic memory, prospective memory, and aspects of executive function were optimal for differentiating between older adults with and without MCI, whereas the global cognitive test (i.e., Mini-Mental State Examination) failed to capture such subtle cognitive differences among older adults. Furthermore, robot-administered tests demonstrated sound ability to predict the results of standardized cognitive tests, even after adjustment for demographic variables and global cognitive status.

CONCLUSION

Overall, our results suggest the human-robot interaction approach is feasible for MCI identification. Incorporating additional cognitive test measures might improve the stability and reliability of such robot-assisted MCI diagnoses.

Smart Textiles for Improved Quality of Life and Cognitive Assessment

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs.

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

Feasibility Study on the Role of Personality, Emotion, and Engagement in Socially Assistive Robotics: A Cognitive Assessment Scenario

This study aims to investigate the role of several aspects that may influence human–robot interaction in assistive scenarios. Among all, we focused on semi-permanent qualities (i.e., personality and cognitive state) and temporal traits (i.e., emotion and engagement) of the user profile. To this end, we organized an experimental session with 11 elderly users who performed a cognitive assessment with the non-humanoid ASTRO robot. ASTRO robot administered the Mini Mental State Examination test in Wizard of Oz setup. Temporal and long-term qualities of each user profile were assessed by self-report questionnaires and by behavioral features extrapolated by the recorded videos. Results highlighted that the quality of the interaction did not depend on the cognitive state of the participants. On the contrary, the cognitive assessment with the robot significantly reduced the anxiety of the users, by enhancing the trust in the robotic entity. It suggests that the personality and the affect traits of the interacting user have a fundamental influence on the quality of the interaction, also in the socially assistive context.

Interdisciplinary Research in Alzheimer's Disease and the Roles International Societies Can Play

An ever-increasing ageing population has elevated Alzheimer's disease to be one of the biggest challenges in modern medicine. Alzheimer's disease is highly complex, and we are still no closer to understanding the causes, let alone an effective treatment. The lack of good experimental models and lack of critical understanding has led to high failure rates of clinical trials with high associated costs, as well as difficulties in implementing treatments. The multifaceted nature of this disease highlights the need for an interdisciplinary approach to address these concerns. In this essay, we suggest how collaborative work can be useful in addressing some of the above issues. We then propose that international organisations and publishers need to support interdisciplinary research by creating platforms, lobbying funders, and pushing for interdisciplinary publications. We further highlight some of the issues involved in implementing these suggestions and argue that willpower of the research community, together with a re-evaluation of evaluation metrics and incentive systems, are needed in order to foster interdisciplinary research. Overall, we emphasise the need for interdisciplinary research in Alzheimer's disease and suggest that international societies should play a huge role in this endeavour.

Less Is More-Cyclists-Triathlete's 30 min Cycling Time-Trial Performance Is Impaired With Multiple Feedback Compared to a Single Feedback

Purpose: The purpose of this article was to (i) compare different modes of feedback (multiple vs. single) on 30 min cycling time-trial performance in non-cyclist’s and cyclists-triathletes, and (ii) investigate cyclists-triathlete’s information acquisition.

Methods: 20 participants (10 non-cyclists, 10 cyclists-triathletes) performed two 30 min self-paced cycling time-trials (TT, ∼5–7 days apart) with either a single feedback (elapsed time) or multiple feedback (power output, elapsed distance, elapsed time, cadence, speed, and heart rate). Cyclists-triathlete’s information acquisition was also monitored during the multiple feedback trial via an eye tracker. Perceptual measurements of task motivation, ratings of perceived exertion (RPE) and affect were collected every 5 min. Performance variables (power output, cadence, distance, speed) and heart rate were recorded continuously.

Results: Cyclists-triathletes average power output was greater compared to non-cyclists with both multiple feedback (227.99 ± 42.02 W; 137.27 ± 27.63 W; P < 0.05) and single feedback (287.9 ± 60.07 W; 131.13 ± 25.53 W). Non-cyclist’s performance did not differ between multiple and single feedback (p > 0.05). Whereas, cyclists-triathletes 30 min cycling time-trial performance was impaired with multiple feedback (227.99 ± 42.02 W) compared to single feedback (287.9 ± 60.07 W; p < 0.05), despite adopting and reporting a similar pacing strategy and perceptual responses (p > 0.05). Cyclists-triathlete’s primary and secondary objects of regard were power (64.95 s) and elapsed time (64.46 s). However, total glance time during multiple feedback decreased from the first 5 min (75.67 s) to the last 5 min (22.34 s).

Conclusion: Cyclists-triathletes indoor 30 min cycling TT performance was impaired with multiple feedback compared to single feedback. Whereas non-cyclist’s performance did not differ between multiple and single feedback. Cyclists-triathletes glanced at power and time which corresponds with the wireless sensor networks they use during training. However, total glance time during multiple feedback decreased over time, and therefore, overloading athletes with feedback may decrease performance in cyclists-triathletes.

Improving wellbeing of community-dwelling people with mild cognitive impairment: the SENIOR (SystEm of Nudge theory based ICT applications for OldeR citizens) project

Population aging with longer life expectancy represents one of the most relevant challenges of the next future, also because of a significant proportion of older adult people may present with dementia. Motivating senior citizens with mild cognitive impairment to maintain their independence and functional abilities, improve health status and quality of life as well as social interactions, constitutes the main target of preventive medicine. According to a nudge theoretical approach, the SENIOR (SystEm of Nudge theory based ICT applications for OldeR citizens) project- developed thanks to the collaboration among Catholic University of the Sacred Heart, Bicocca University and IRCCS Auxiologico Institute in Milan (Italy) - has been designed to adopt an advanced information and communication technology coaching system able to collect and integrate physiological, psychological and behavioral data, with the final aim of interacting with community-dwelling elderly people suffering from mild cognitive impairment and of providing them personalized feedback on lifestyle management. The SENIOR project proposes to use a smart-watch app for alerting family doctors, sharing information with family members in specific cases and monitoring patients at higher risk in hospital Units, in order to ameliorate health of senior citizens with mild cognitive impairment.

The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community

Introduction

Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities.

Methods

CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants' homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing.

Results

The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160-169 min), physical mobility (e.g., mean daily transitions between rooms = 96-155), sleep (e.g., mean nightly sleep duration = 6.3-7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15-45%) were collected across cohorts.

Conclusion

The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.