A review of dementia screening tools based on Mobile application

The Latest Advances in the Diagnosis and Treatment of Dementia

Dementia is a debilitating neurological condition that is characterized by persistent cognitive decline. It is a global health challenge, with a rapidly increasing prevalence due to an increasing aging population. Although definitive diagnosis of various conditions of dementia is only possible by autopsy, clinical diagnosis can be performed by a specialist. The diagnostic process has evolved with recent breakthroughs in diagnostic tools, such as advanced imaging techniques and biomarkers. These tools facilitate early and accurate identification of the condition. Early diagnosis is vital, as it enables timely interventions to improve the quality of life for affected individuals. Treatment strategies for dementia encompass both pharmacological and non-pharmacological approaches. Non-pharmacological treatments include cognitive training and lifestyle modifications. Among pharmacological treatments, acetyl-cholinesterase inhibitors including donepezil, rivastigmine, and galantamine can be used in various doses based on the severity of the disease. Apart from these, N-methyl-D-aspartate receptor antagonists such as memantine can also be used. Furthermore, personalized treatments have also gained significant attention in dementia treatment. Interdisciplinary care, involving healthcare professionals, social workers, and support networks, is crucial for comprehensive and holistic dementia management.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

Evaluating the feasibility of cognitive impairment detection in Alzheimer's disease screening using a computerized visual dynamic test

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease without known cure. However, early medical treatment can help control its progression and postpone intellectual decay. Since AD is preceded by a period of cognitive deterioration, the effective assessment of cognitive capabilities is crucial to develop reliable screening procedures. For this purpose, cognitive tests are extensively used to evaluate cognitive areas such as language, attention, or memory.

METHODS

In this work, we analyzed the potential of a visual dynamics evaluation, the rapid serial visual presentation task (RSVP), for the detection of cognitive impairment in AD. We compared this evaluation with two of the most extended brief cognitive tests applied in Spain: the Clock-drawing test (CDT) and the Phototest. For this purpose, we assessed a group of patients (mild AD and mild cognitive impairment) and controls, and we evaluated the ability of the three tests for the discrimination of the two groups.

RESULTS

The preliminary results obtained suggest the RSVP performance is statistically higher for the controls than for the patients (p-value = 0.013). Furthermore, we obtained promising classification results for this test (mean accuracy of 0.91 with 95% confidence interval 0.72, 0.97).

CONCLUSIONS

Since the RSVP is a computerized, auto-scored, and potentially self-administered brief test, it could contribute to speeding-up cognitive impairment screening and to reducing the associated costs. Furthermore, this evaluation could be combined with other tests to augment the efficiency of cognitive impairment screening protocols and to potentially monitor patients under medical treatment.

Digital healthcare for dementia and cognitive impairment: A scoping review

BACKGROUND

Cognitive disorders, such as Alzheimer's disease, are a global health problem. Digital healthcare technology is an innovative management tool for delaying the progression of dementia and mild cognitive impairment. Thanks to digital technology, the possibility of safe and effective care for patients at home and in the community is increasing, even in situations that threaten the continuity of care, such as the COVID-19 pandemic. However, it is difficult to select appropriate technology and alternatives due to the lack of comprehensive reviews on the types and characteristics of digital technology for cognitive impairment, including their effects and limitations.

OBJECTIVE

This study aims to identify the types of digital healthcare technology for dementia and mild cognitive impairment and comprehensively examine how its outcome measures were constructed in line with each technology's purpose.

METHODS

According to the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews guidelines, a literature search was conducted in August 2021 using Medline (Ovid), EMBASE, and Cochrane library. The search terms were constructed based on Population-Concept-Context mnemonic: 'dementia', 'cognitive impairment', and 'cognitive decline'; digital healthcare technology, such as big data, artificial intelligence, virtual reality, robots, applications, and so on; and the outcomes of digital technology, such as accuracy of diagnosis and physical, mental, and social health. After grasping overall research trends, the literature was classified and analysed in terms of the type of service users and technology.

RESULTS

In total, 135 articles were selected. Since 2015, an increase in literature has been observed, and various digital healthcare technologies were identified. For people with mild cognitive impairment, technology for predicting and diagnosing the onset of dementia was studied, and for people with dementia, intervention technology to prevent the deterioration of health and induce significant improvement was considered. Regarding caregivers, many studies were conducted on monitoring and daily living assistive technologies that reduce the burden of care. However, problems such as data collection, storage, safety, and the digital divide persisted at different intensities for each technology type.

CONCLUSIONS

This study revealed that appropriate technology options and considerations may differ depending on the characteristics of users. It also emphasises the role of humans in designing and managing technology to apply digital healthcare technology more effectively.

Virtual Reality-based Cognitive Intervention for Enhancing Executive Functions in Community-dwelling Older Adults

With the rapid growth of the older population globally, it is anticipated that age-related cognitive decline in the prodromal phase and more severe pathological decline will increase. Moreover, currently, no effective treatment options for the disease exist. Thus, early and timely prevention actions are promising and prior strategies to preserve cognitive functions by preventing symptomatology from increasing the age-related deterioration of the functions in healthy older adults. This study aims to develop the virtual reality-based cognitive intervention for enhancing executive functions (EFs) and examine the EFs after training with the virtual reality-based cognitive intervention in community-dwelling older adults. Following inclusion/exclusion criteria, 60 community-dwelling older adults aged 60-69 years were involved in the study and randomly divided into passive control and experimental groups. Eight 60 min virtual reality-based cognitive intervention sessions were held twice a week and lasted for 1 month. The EFs (i.e., inhibition, updating, and shifting) of the participants were assessed by using standardized computerized tasks, i.e., Go/NoGo, forward and backward digit span, and Berg's card sorting tasks. Additionally, a repeated-measure ANCOVA and effect sizes were applied to investigate the effects of the developed intervention. The virtual reality-based intervention significantly improved the EFs of older adults in the experimental group. Specifically, the magnitudes of enhancement were observed for inhibitory as indexed by the response time, F(1) = 6.95, p < .05, η_p² = .11, updating as represented by the memory span, F(1) = 12.09, p < .01, η_p² = .18, and the response time, F(1) = 4.46, p = .04, η_p² = .07, and shifting abilities as indexed by the percentage of correct responses, F(1) = 5.30, p = .03, η_p² = .09, respectively. The results indicated that the simultaneous combined cognitive-motor control as embedded in the virtual-based intervention is safe and effective in enhancing EFs in older adults without cognitive impairment. Nevertheless, further studies are required to investigate the benefits of these enhancements to motor functions and emotional aspects relating to daily living and the well-being of older populations in communities.

Digital Health Applications in the Context of Dementia: A Questionnaire Study to Assess the Likelihood of Use among Physicians (Preprint)

Background

Age-related diseases such as dementia are playing an increasingly important role in global population development. Thus, prevention, diagnostics, and interventions require more accessibility, which can be realized through digital health apps. With the app on prescription, Germany made history by being the first country worldwide to offer physicians the possibility to prescribe and reimburse digital health apps as of the end of the year 2020.

Objective

Considering the lack of knowledge about correlations with the likelihood of use among physicians, this study aimed to address the question of what makes the use of a digital health app by physicians more likely.

Methods

We developed and validated a novel measurement tool—the Digital Health Compliance Questionnaire (DHCQ)—in an interdisciplinary collaboration of experts to assess the role of proposed factors in the likelihood of using a health app. Therefore, a web-based survey was conducted to evaluate the likelihood of using a digital app called DemPredict to screen for Alzheimer dementia. Within this survey, 5 latent dimensions (acceptance, attitude toward technology, technology experience, payment for time of use, and effort of collection), the dependent variable likelihood of use, and answers to exploratory questions were recorded and tested within directed correlations. Following a non–probability-sampling strategy, the study was completed by 331 physicians from Germany in the German language, of whom 301 (90.9%) fulfilled the study criteria (eg, being in regular contact with patients with dementia). These data were analyzed using a range of statistical methods to validate the dimensions of the DHCQ.

Results

The DHCQ revealed good test theoretical measures—it showed excellent fit indexes (Tucker-Lewis index=0.98; comparative fit index=0.982; standardized root mean square residual=0.073; root mean square error of approximation=0.037), good internal consistency (Cronbach α=.83), and signs of moderate to large correlations between the DHCQ dimensions and the dependent variable. The correlations between the variables acceptance, attitude toward technology, technology experience, and payment for the time of use and the dependent variable likelihood of use ranged from 0.29 to 0.79, and the correlation between effort of the collection and likelihood of use was −0.80. In addition, we found high levels of skepticism regarding data protection, and the age of the participants was found to be negatively related to their technical experience and attitude toward technology.

Conclusions

In the context of the results, increased communication between the medical and technology sectors and significantly more awareness raising are recommended to make the use of digital health apps more attractive to physicians as they can be adjusted to their everyday needs. Further research could explore the connection between areas such as adherence on the patient side and its impact on the likelihood of use by physicians.

Reliability and Validity of Alzheimer's Disease Screening With a Semi-automated Smartphone Application Using Verbal Fluency

Introduction: This study aimed to examine the reliability and validity of Alzheimer's disease (AD) screening with a self-administered categorical verbal fluency test using a semi-automated Android application (app; tCVFT). Furthermore, its diagnostic accuracy concerning AD was compared with both that of a conventional categorical verbal fluency test (cCVFT) administered by a health professional and the Mini-Mental State Examination (MMSE).

Materials and Methods: Participants included 100 community-dwelling patients with early AD, whose Clinical Dementia Rating was either 0.5 or 1, and a further 100 sex-matched cognitively normal controls. The internal consistency and test-retest reliability of the tCVFT weighted sum score (tCVFT-WS) was examined using Cronbach's alpha and Pearson's correlation analyses (adjusted for age and education), respectively. The concurrent validity of the tCVFT-WS was examined by testing its correlations with the cCVFT weighted sum score (cCVFT-WS) and MMSE using Pearson's correlation tests. The diagnostic accuracies for early AD of the tCVFT-WS, cCVFT-WS, and MMSE were estimated and compared using receiver operating characteristic (ROC) analyses.

Results: The tCVFT-WS exhibited strong internal consistency (Cronbach's alpha = 0.79). However, its test-retest reliability was moderate (r = 0.54) owing to the low test-retest reliability of the second-half responses. The patient group exhibited a higher tCVFT-WS than the control group (p < 0.001). Correlations between the tCVFT-WS, cCVFT-WS, and MMSE were significant. The tCVFT-WS's area under the ROC curve for AD was 0.861. At its optimal cutoff, the sensitivity and specificity for AD were 0.78 and 0.77, respectively.

Conclusions: The self-administered tCVFT-WS, using an Android app, proved valid and reliable at distinguishing people with early AD from cognitively normal controls.

Dementia medical screening using mobile applications: A systematic review with a new mapping model

Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.