Ecological Assessment of Autonomy in Instrumental Activities of Daily Living in Dementia Patients by the Means of an Automatic Video Monitoring System

Use of artificial intelligence to support quality of life of people with dementia: A scoping review

BACKGROUND

Dementia has an impact on the quality of life (QoL) of people with dementia. Tailored services are crucial for improving their QoL. Advances in artificial intelligence (AI) offer opportunities for personalised care, potentially delaying institutionalisation and enhancing QoL. However, AI's specific role in approaches to support QoL for people with dementia remains unclear. This scoping review aims to synthesise the scientific evidence and grey literature on how AI can support the QoL of people with dementia.

METHOD

Following Joanna Briggs Institute guidelines, we searched PubMed, Scopus, ACM Digital Library, and Google Scholar in January 2024. Studies on AI, QoL (using Lawton's four-domain QoL definition), and people with dementia across various care settings were included. Two reviewers conducted a two-stage screening, and a narrative synthesis identified common themes arising from the individual studies to address the research question.

RESULTS

The search yielded 5.467 studies, after screening, thirty studies were included. Three AI categories were identified: monitoring systems, social robots, and AI approaches for performing activities of daily living. Most studies were feasibility studies, with little active involvement of people with dementia during the research process. Most AI-based approaches were monitoring systems targeting Lawton's behavioural competence (capacity for independent functioning) domain.

CONCLUSION

This review highlights that AI applications for enhancing QoL in people with dementia are still in early development, with research largely limited to small-scale feasibility studies rather than demonstrating clinical effectiveness. While AI holds promise, further exploration and rigorous real-world validation are needed before AI can meaningfully impact the daily lives of people with dementia.

The relation between cognitive functioning and activities of daily living in normal aging, mild cognitive impairment, and dementia: a meta-analysis

Literature suggests that dementia and, more generally, cognitive impairment affect the capacity to carry out activities of daily living (ADL) in aging. However, it is important to decipher the weight of specific cognitive domains and neurodegenerative profiles mainly related to ADL difficulties. A meta-analysis was conducted to investigate the nature and strength of the association between cognitive functioning and ADL in healthy older adults, mild cognitive impairment (MCI), and dementia. A comprehensive search of the PubMed, PsycINFO (PROQUEST), and Scopus databases for cross-sectional or longitudinal studies up until December 2022. Our meta-analytic results revealed that: overall, instrumental ADL (IADL) showed a significant association with executive functioning, in particular, abstraction ability/concept formation, set-shifting, and processing speed/complex attention/working memory, regardless of type of participants (i.e., healthy older adults, MCI, and dementia); whereas ADL (both basic ADL, BADL, and IADL) significantly correlated with global cognitive functioning and long-term verbal memory, with a moderator effect of clinical condition (e.g., increasing ES based on the level of cognitive impairment). Moreover, visuospatial and language abilities significantly correlated with ADL, mainly when performance-based tasks were used for ADL assessment. These findings emphasize the importance of neuropsychological assessment in aging to early identify people most at risk of functional decline and shed light on the need to consider specific cognitive abilities in rehabilitation programs.

A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Performance-based IADL evaluation of older adults with cognitive impairment within a smart home: A feasibility study

INTRODUCTION

Mild cognitive impairment (MCI) is characterized by subtle deficits that functional assessment via informant-report measures may not detect. Sensors can potentially detect deficits in everyday functioning in MCI. This study aims to establish feasibility and acceptability of using sensors in a smart home for performance-based assessments of two instrumental activities of daily living (IADLs).

METHODS

Thirty-five older adults (>65 years) performed two IADL tasks in a smart home laboratory equipped with sensors and a web camera. Participants' cognitive states were determined using published criteria including measures of global cognition and comprehensive neuropsychological test batteries. Selected subtasks of the IADL assessment were autonomously captured by the sensors. Total time taken for each task and subtask were computed. A point scoring system captured accuracy and number of attempts. Acceptability of the smart home setup was assessed.

RESULTS

Participants with MCI (n = 21) took longer to complete both tasks than participants with healthy cognition (HC; n = 14), with significant time differences observed only in "Cost calculation." Completion time for IADL tasks and scores correlated in the expected direction with global cognition. Over 95% of the participants found the smart home assessment acceptable and a positive experience.

DISCUSSION

We demonstrated the feasibility and acceptability of the use of unobtrusive commercially available sensors in a smart home for facilitating parts of the objective assessment of IADL in older adults. Future studies need to identify more IADLs that are suitable for semi-automated or automated assessments through the use of simple, low-cost sensors.

A French-Greek Cross-Site Comparison Study of the Use of Automatic Video Analyses for the Assessment of Autonomy in Dementia Patients

Background: At present, the assessment of autonomy in daily living activities, one of the key symptoms in Alzheimer’s disease (AD), involves clinical rating scales. Methods: In total, 109 participants were included. In particular, 11 participants during a pre-test in Nice, France, and 98 participants (27 AD, 38 mild cognitive impairment—MCI—and 33 healthy controls—HC) in Thessaloniki, Greece, carried out a standardized scenario consisting of several instrumental activities of daily living (IADLs), such as making a phone call or preparing a pillbox while being recorded. Data were processed by a platform of video signal analysis in order to extract kinematic parameters, detecting activities undertaken by the participant. Results: The video analysis data can be used to assess IADL task quality and provide clinicians with objective measurements of the patients’ performance. Furthermore, it reveals that the HC statistically significantly outperformed the MCI, which had better performance compared to the AD participants. Conclusions: Accurate activity recognition data for the analyses of the performance on IADL activities were obtained.

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

Alzheimer's disease, and dementia, represent a common cause of disability and one of the most relevant challenges in the health world. In addition, these conditions do not have, at moment, a pharmacological treatment that can stop the pathological progress. Mild cognitive impairment (MCI), defined as the borderline between normal aging and early dementia, represents a meaningful field of study because, in the transition to dementia, clinicians have defined a useful therapeutic window. Additionally, due to the lack of effective pharmacological interventions, recent years have seen an increase in research into new technological solutions to assess, stimulate, and assist patients afflicted with Alzheimer's disease. This review aims to outline the use of information and communication technologies in the field studying MCI. Particularly, the goal is to depict the framework and describe the most worthwhile research efforts, in order to display the current technologies available, describe the research objectives, and delineate prospective future researches. Regarding data sources, the research was conducted within three databases, PubMed Central, Web of Science, and Scopus, between January 2009 and December 2017. A total of 646 articles were found in the initial search. Accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers to use for the study. Finally, 56 papers were fully evaluated and included in this review. Three major clinical application areas have been portrayed, namely “Cognitive Assessment,” “Treatment,” and “Assistance.” These have been combined with three main technological solutions, specifically “Sensors,” “Personal Devices,” and “Robots.” Furthermore, the study of the publications time series illustrates a steadily increasing trend, characterized by the enrollment of small groups of subjects, and particularly oriented to the subjects assistance using robots companion. In conclusion, despite the new technological solutions for people with MCI have received much interest, particularly regarding robots for assistance, nowadays it still owns vast room for improvement.

Technology and Dementia: The Future is Now

BACKGROUND

Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place.

OBJECTIVES

To summarise key areas of technology development in dementia and identify future directions and implications.

METHOD

Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia.

RESULTS

The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management.

CONCLUSIONS

The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.

Step by Step: Kinematics of the Reciprocal Trail Making Task Predict Slowness of Activities of Daily Living Performance in Alzheimer's Disease

Dementia impairs the ability to perform everyday activities. Reduced motor capacity and executive functions as well as loss of memory function and forms of apraxia and action disorganization syndrome can be reasons for such impairments. In this study, an analysis of the hand trajectories during the sequential movements in an adapted version of the trail making task, the reciprocal trail making task (RTMT), was used to predict performance in activities of daily living (ADL) of patients suffering from mild cognitive impairment and dementia. 1 patient with dementia of the Alzheimer’s type and 15 healthy, age-matched adults were tested in the standardized ADL of tea making and document filing. The characteristics of the kinematic performance in the RTMT were assessed, and models of multiple linear regression were computed to predict the durations of the ADL. Patients showed increased trial durations (TDs) in the ADL (Cohen’s d: tea making 1.64, document filing 1.25). Parameters and explained variability differed across patients and control as well as between different activities. The models for the patient sample were stronger and particularly high for the document filing task for which kinematics explained 71% of the variance (Radjusted2: tea making 0.62, document filing 0.71; both tasks combined patients 0.55, controls 0.25). The most relevant factors for the models were the TD and a parameter characterizing movement fluency and variability (“movement harmonicity”) in the RTMT. The models of multiple linear regression suggested that the patients’ activity of daily living performance was limited by cognitive demands, namely, identifying the varying targets during sequencing and the healthy controls’ performance by their motor capacity. Such models could be used to estimate the severity of ADL impairments in patients.

Objective measurement of gait parameters in healthy and cognitively impaired elderly using the dual-task paradigm

Objectives

The present study explores the differences in gait parameters in elderly subjects with or without cognitive impairment measured by means of ambulatory actigraphy while performing a single and a dual task.

Methods

Sixty-nine participants of which 23 individuals were diagnosed with Alzheimer’s disease (AD), 24 individuals with mild cognitive impairment (MCI), and 22 healthy controls performed a single and dual walking task while wearing a wrist-worn accelerometer. Objective measures of gait features such as walking speed, cadence (i.e., number of steps per minute), and step variance (i.e., variance in time between two consecutive steps) were derived and analyzed.

Results

While differences in several gait parameters, namely walking speed, were found between MCI and AD patients, no differences between healthy elderly and MCI patients were found.

Conclusion

Walking speed seems to be a gait-related feature that differs significantly between MCI and AD patients and thus could be used as an additional measurement in clinical assessment. However, differences in gait may not be salient enough in the early stages of dementia to be detected by actigraphy. More research comparing different methods to measure gait in early stages of dementia under different dual task conditions is neccessary.

Recommendations for the Use of ICT in Elderly Populations with Affective Disorders

Objective: Affective disorders are frequently encountered among elderly populations, and the use of information and communication technologies (ICT) could provide an added value for their recognition and assessment in addition to current clinical methods. The diversity and lack of consensus in the emerging field of ICTs is however a strong limitation for their global use in daily practice. The aim of the present article is to provide recommendations for the use of ICTs for the assessment and management of affective disorders among elderly populations with or without dementia.

Methods: A Delphi panel was organized to gather recommendations from experts in the domain. A set of initial general questions for the use of ICT in affective disorders was used to guide the discussion of the expert panel and to analyze the Strengths, Weaknesses, Opportunities, and Threats (SWOT) of employing ICT in elderly populations with affective disorders. Based on the results collected from this first round, a web survey was sent to local general practitioners (GPs) and to all interns in psychiatry in France.

Results: The results of the first round revealed that ICT may offer very useful tools for practitioners involved in the diagnosis and management of affective disorders. However, the results of the web survey showed the interest to explain better to current and upcoming practitioners the utility of ICT especially for people living with dementia.