The limited allocation of resources to rural and regional communities is a major contributor to healthcare inequities in Australia. Distribution of health service resources between metropolitan and rural communities commonly sees highly populated areas prioritised over more sparsely populated and geographically vast areas. As such, challenges impacting dementia diagnosis, management, and care in metropolitan areas are experienced more acutely in rural areas. This study aimed to examine equity of access to dementia diagnosis, management, and care services amongst people who experienced the process of dementia diagnosis as a patient or significant other (partner/spouse, adult children, siblings, and friends) throughout rural and metropolitan Australia.

This exploratory qualitative study consisted of thirty-three online semi-structured interviews with thirty-seven people with experience of the dementia diagnosis process as a patient and/or significant other. Interviews explored symptoms of dementia, health professionals consulted, tests conducted, and challenges faced throughout the diagnosis and post-diagnosis process. Rurality was defined by the Australian Statistical Geography Standard Remoteness Areas (ASGS-RA) and the Modified Monash Model (MMM). Thematic analysis was conducted, with Russell's (2013) Dimensions of Access framework (geography, affordability, availability, acceptability, accommodation, awareness, and timeliness) guiding data analysis.

Participants were distributed across various regions of Australia: seven interviews from inner regional Australia, five interviews from outer regional Australia, and twenty-one interviews from metropolitan areas. Disparities in access between metropolitan and rural areas emerged in five key dimensions: 1) geography impeding ability to access services; 2) affordability of travel expenses; 3) availability of healthcare and support services; 4) acceptability of available health professionals and services; and 5) awareness of local services and resources. The dimensions of accommodation and timeliness of care were experienced as challenges irrespective of location, with lengthy appointment wait times and difficulty navigating complex systems. However, rurality often compounded the challenges in dementia diagnosis, management, and care.

Significant health inequities persist between rural and metropolitan communities that must be prioritised in endeavours to promote equitable dementia diagnosis, management, and care. Targeted action to address disparities is vital to mitigate the impact of rurality, particularly as clinical practice evolves with research advancements.

Over the past decade, research using virtual reality and serious game-based instruments for assessing spatial navigation and spatial memory in at-risk and AD populations has risen. We systematically reviewed the literature since 2012 to identify and evaluate the methodological quality and risk of bias in the analyses of the psychometric properties of VRSG-based instruments. The search was conducted primarily in July-December 2022 and updated in November 2023 in eight major databases. The quality of instrument development and study design were analyzed in all studies. Measurement properties were defined and analyzed according to COSMIN guidelines. A total of 1078 unique records were screened, and following selection criteria, thirty-seven studies were analyzed. From these studies, 30 instruments were identified. Construct and criterion validity were the most reported measurement properties, while structural validity and internal consistency evidence were the least reported. Nineteen studies were deemed very good in construct validity, whereas 11 studies reporting diagnostic accuracy were deemed very good in quality. Limitations regarding theoretical framework and research design requirements were found in most of the studies. VRSG-based instruments are valuable additions to the current diagnostic toolkit for AD. Further research is required to establish the psychometric performance and clinical utility of VRSG-based instruments, particularly the instrument development, content validity, and diagnostic accuracy for preclinical AD screening scenarios. This review provides a straightforward synthesis of the state of the art of VRSG-based instruments and suggests future directions for research.

Dementia is characterized by impairments in an individual's cognitive and functional abilities. Digital cognitive assessments have been shown to be effective in detecting mild cognitive impairment and dementia, but whether they can stage the disease remains to be studied.

In this study, we examined (1) the correlation between scores obtained from BrainCheck standard battery of cognitive assessments (BC-Assess), a digital cognitive assessment, and scores obtained from the Dementia Severity Rating Scale (DSRS), and (2) the accuracy of using the BC-Assess score to predict dementia stage delineated by the DSRS score. We also explored whether BC-Assess can be combined with information from the Katz Index of Independence in activities of daily living (ADL) to obtain enhanced accuracy.

Retrospective analysis was performed on a BrainCheck dataset containing 1751 patients with dementia with different cognitive and functional assessments completed for cognitive care planning, including the DSRS, the ADL, and the BC-Assess. The patients were staged according to their DSRS total score (DSRS-TS): 982 mild (DSRS-TS 10-18), 656 moderate (DSRS-TS 19-26), and 113 severe (DSRS-TS 37-54) patients. Pearson correlation was used to assess the associations between BC-Assess overall score (BC-OS), ADL total score (ADL-TS), and DSRS-TS. Logistic regression was used to evaluate the possibility of using patients' BC-OS and ADL-TS to predict their stage.

We found moderate Pearson correlations between DSRS-TS and BC-OS (r=-0.53), between DSRS-TS and ADL-TS (r=-0.55), and a weak correlation between BC-OS and ADL-TS (r=0.37). Both BC-OS and ADL-TS significantly decreased with increasing severity. BC-OS demonstrated to be a good predictor of dementia stages, with an area under the receiver operating characteristic curve (ROC-AUC) of classification using logistic regression ranging from .733 to .917. When BC-Assess was combined with ADL, higher prediction accuracies were achieved, with an ROC-AUC ranging from 0.786 to 0.961.

Our results suggest that BC-Assess could serve as an effective alternative tool to DSRS for grading dementia severity, particularly in cases where DSRS, or other global assessments, may be challenging to obtain due to logistical and time constraints.

The emergence of disease-modifying treatment options for Alzheimer disease is creating a paradigm shift in strategies to identify patients with mild symptoms in primary care settings. Systematic reviews on digital cognitive tests reported that most showed diagnostic performance comparable with that of paper-and-pencil tests for mild cognitive impairment and dementia. However, most studies have small sample sizes, with fewer than 100 individuals, and are based on case-control or cross-sectional designs.

This study aimed to examine the predictive validity of the Japanese Cognitive Function Test (J-Cog), a new computerized cognitive battery test, for dementia development.

We randomly assigned 2520 older adults (average age 72.7, SD 6.7 years) to derivation and validation groups to determine and validate cutoff points for the onset of dementia. The Mini-Mental State Examination (MMSE) was used for comparison purposes. The J-Cog consists of 12 tasks that assess orientation, designation, attention and calculation, mental rotation, verbal fluency, sentence completion, working memory, logical reasoning, attention, common knowledge, word memory recall, and episodic memory recall. The onset of dementia was monitored for 60 months. In the derivation group, receiver operating characteristic curves were plotted to determine the MMSE and J-Cog cutoff points that best discriminated between the groups with and without dementia. In the validation group, Cox proportional regression models were developed to predict the associations of the group classified using the cutoff points of the J-Cog or MMSE with dementia incidence. Harrell C-statistic was estimated to summarize how well a predicted risk score described an observed sequence of events. The Akaike information criterion was calculated for relative goodness of fit, where lower absolute values indicate a better model fit.

Significant hazard ratios (HRs) for dementia incidence were found using the MMSE cutoff between 23 and 24 point (HR 1.93, 95% CI 1.13-3.27) and the J-Cog cutoff between 43 and 44 points (HR 2.42, 95% CI 1.50-3.93). In the total validation group, the C-statistic was above 0.8 for all cutoff points. Akaike information criterion with MMSE cutoff between 23 and 24 points as a reference showed a poor fit for MMSE cutoff between 28 and 29 points, and a good fit for the J-Cog cutoff between 43 and 44 points.

The J-Cog has higher accuracy in predicting the development of dementia than the MMSE and has advantages for use in the community as a test of cognitive function, which can be administered by nonprofessionals.

With recent technical advances, many cognitive and sensory tasks have been adapted for smartphone testing. This study aimed to assess the criterion validity of a subset of self-administered, open-source app-based cognitive and sensory tasks by comparing test performance to lab-based alternatives. An in-person baseline was completed by 43 participants (aged 21 to 82) from the larger Labs without Walls project (Brady et al., 2023) to compare the self-administered, app-based tasks with researcher-administered equivalents. 4 preset tasks sourced from Apple's ResearchKit (Spatial Memory, Trail Making Test, Stroop Test, and dBHL Tone Audiometry) and 1 custom-built task (Ishihara Color Deficiency Test) were compared. All tasks except the Spatial Memory task demonstrated high comparability to the researcher-administered version. Specifically, the Trail Making Tests were strongly correlated (.77 and .78 for parts A and B, respectively), Stroop correlations ranged from .77 to .89 and the Ishihara tasks were moderately correlated (r = .69). ICCs for the Audiometry task ranged from .56 to .96 (Moderate to Excellent) with 83% sensitivity and 100% specificity. Bland-Altman plots revealed a mean bias between -5.35 to 9.67 dB for each ear and frequency with an overall bias of 3.02 and 1.98 for the left and right ears, respectively, within the minimum testing interval. Furthermore, all app-based tasks were significantly correlated with age. These results offer preliminary evidence of the validity of four open-source cognitive and sensory tasks with implications for effective remote testing in non-lab settings.

Dementia is a widespread syndrome that currently affects more than 55 million people worldwide. Digital screening instruments are one way to increase diagnosis rates. Developing an app for older adults presents several challenges, both technical and social. In order to make the app user-friendly, feedback from potential future end users is crucial during this development process.

This study aimed to establish a user-centered design process for the development of digiDEM-SCREEN, a user-friendly app to support early identification of persons with slight symptoms of dementia.

This research used qualitative and quantitative methods and involved 3 key stakeholder groups: the digiDEM research team, the software development team, and the target user group (older adults ≥65 years with and without cognitive impairments). The development of the screening app was based on an already existing and scientifically analyzed screening test (Self-Administered Tasks Uncovering Risk of Neurodegeneration; SATURN). An initial prototype was developed based on the recommendations for mobile health apps and the teams' experiences. The prototype was tested in several iterations by various end users and continuously improved. The app's usability was evaluated using the System Usability Scale (SUS), and verbal feedback by the end users was obtained using the think-aloud method.

The translation process during test development took linguistic and cultural aspects into account. The texts were also adapted to the German-speaking context. Additional instructions were developed and supplemented. The test was administered using different randomization options to minimize learning effects. digiDEM-SCREEN was developed as a tablet and smartphone app. In the first focus group discussion, the developers identified and corrected the most significant criticism in the next version. Based on the iterative improvement process, only minor issues needed to be addressed after the final focus group discussion. The SUS score increased with each version (score of 72.5 for V1 vs 82.4 for V2), while the verbal feedback from end users also improved.

The development of digiDEM-SCREEN serves as an excellent example of the importance of involving experts and potential end users in the design and development process of health apps. Close collaboration with end users leads to products that not only meet current standards but also address the actual needs and expectations of users. This is also a crucial step toward promoting broader adoption of such digital tools. This research highlights the significance of a user-centered design approach, allowing content, text, and design to be optimally tailored to the needs of the target audience. From these findings, it can be concluded that future projects in the field of health apps would also benefit from a similar approach.

Dementia is one of the most relevant widespread diseases, with a prevalence of currently 55 million people with dementia worldwide. However, about 60-75% of people with dementia have not yet received a formal diagnosis. Asymptomatic screening of cognitive impairments using neuropsychiatric tests has been proven to efficiently enhance diagnosis rates. Digital screening tools, in particular, provide the advantage of being accessible without spatial or time restrictions. The study aims to validate a digital cognitive screening test (digiDEM-SCREEN) as an app in the German language.

This is a multicentre study in Bavaria. Participants are people with mild cognitive impairment, people with dementia in an early stage and cognitively healthy people. Recruitment will take place in specialised diagnostic facilities (memory outpatient clinics). 135 participants are aimed based on a power analysis. Sociodemographic data, diagnosis and results of neuropsychiatric tests (Consortium to Establish a Registry for Alzheimer's Disease, Montreal Cognitive Assessment, digiDEM-SCREEN) will be collected at one point per person via electronic data capturing. The sensitivity, specificity and corresponding cut-off values will be determined based on receiver-operating-characteristic curves. The correlation of the digiDEM-SCREEN test with existing cognitive screening/testing procedures will be analysed.

The study obtained ethical approval from the Ethics Committee of the Julius-Maximilians-Universität of Würzburg (JMU) (application number: 177/23-sc). The test will give feedback about the current cognitive status and possible cognitive impairments that should lead to the users seeking further diagnostic measures by medical professionals. It will be accessible free of charge in established app stores. The results of the validation study will be published in peer-reviewed journals.

A mobile cognition scale for community screening in cognitive impairment with rigorous validation is in paucity. We aimed to develop a digital scale that overcame low education for community screening for mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and AD.

A mobile cognitive self-assessment scale (CogSAS) was designed through the Delphi process, which is feasible for the older population with low education. In Phase 1, 518 clinically diagnosed participants were subjected to optimise the items. In Phase 2, the scale was validated in 358 participants with cognitively unimpaired and 396 participants of clinically diagnosed MCI and dementia for reliability, validity and diagnostic accuracy. In Phase 3, specificity and sensitivity were tested for biologically diagnosed participants of 38 with cognitively unimpaired and 45 with MCI and dementia due to AD according to the amyloid, tau, neurodegeneration classification system.

The CogSAS was a three-task mobile scale testing memory and executive function. In Phase 2, the internal consistency was 0.81, and the test-retest reliability was 0.82. The construct validity was 0.74, and the criterion validity was 0.77. The sensitivity and specificity for discriminating clinically diagnosed participants with MCI and dementia from cognitively unimpaired were 0.90 and 0.67, respectively. For discriminating biologically diagnosed MCI and dementia due to AD from cognitively unimpaired, the sensitivity and specificity were 1.00 and 0.78, respectively.

The CogSAS has good reliability, validity and feasibility, showing a high sensitivity and specificity both in the community and the clinic, identifying biologically diagnosed MCI and dementia due to AD.

Choline, an essential nutrient, plays a critical role in cognition, and may help prevent dementia and mild cognitive impairment. However, studies on dietary choline and its derivatives for preventing these conditions are limited and inconsistent.

The objective of this study was to explore the associations between dietary choline intake and the incidence of dementia, Alzheimer disease (AD), mild cognitive impairment (MCI), and current cognitive performance in the United Kingdom Biobank cohort.

Dietary choline intake was categorized into quartiles of consumption based on 24-h dietary recalls, with units expressed as milligrams per day. Diagnoses of dementia, AD, and MCI were identified using the International Classification of Diseases (ICD-9/10) codes. Current cognitive performance was assessed via the computerized touchscreen interface. After adjusting for sociodemographic factors, dietary and lifestyle behaviors, and comorbid conditions, Cox proportional hazards regression, logistic regression, and restricted cubic splines were used to analyze the association between choline intake and dementia or cognitive performance.

Among 125,594 participants (55.8% female), with a mean age of 56.1 y (range: 40-70 years) at baseline and a median follow-up of 11.8 y, 1103 cases of dementia (including 385 AD and 87 cases of MCI) were recorded. U-shaped associations were observed between choline intake and dementia and AD. Participants in the 2nd quartile of total choline intake had lower risks than those in the lowest quartile, with HR of 0.80 (95% CI: 0.67, 0.96) for dementia and 0.76 (95% CI: 0.58, 1.00) for AD. Moderate intake of choline derivative, including free choline (HR, 0.77; 95%CI, 0.65, 0.92), phosphatidylcholine (HR 0.82; 95% CI: 0.68, 0.98), sphingomyelin (HR 0.82; 95% CI: 0.69, 0.98) and glycerophosphocholine (HR 0.83; 95% CI: 0.70, 1.00), were associated with a 17%-23% lower odds of dementia. Additionally, moderate total choline intake was associated with an 8%-13% lower odds of poor cognitive performance in visual attention (OR: 0.92; 95% CI: 0.86, 0.99), fluid intelligence (OR: 0.87; 95% CI: 0.82, 0.92), and complex processing speed (OR: 0.90; 95% CI: 0.84, 0.95).

In conclusion, our findings suggest that moderate dietary choline intake, ranging from 332.89 mg/d to 353.93 mg/d, is associated with lower odds of dementia and better cognitive performance.

Several major challenges, including an ageing population and declining workforce and the implementation of recent breakthrough therapies for Alzheimer disease, are prompting a necessary rethink of how people with neurodegenerative dementias are diagnosed and medically managed. Digital health technologies could play a pivotal part in this transformation, with new advances enabling the collection of millions of data points from a single individual. Possible applications include unobtrusive monitoring that aids early detection of disease and artificial intelligence-based health advice. To translate these advances to meaningful benefits for people living with a disease, technologies must be implemented within a system that retains the physician expert as a central figure in decision-making. This Perspective presents a new framework, termed the Digitized Memory Clinic, for the diagnostic pathway of neurodegenerative dementias that incorporates digital health technologies with currently available assessment tools, such as fluid and imaging biomarkers, in an interplay with the physician. The Digitized Memory Clinic will manage people across the entire disease spectrum, from the detection of risk factors for cognitive decline and the earliest symptoms to dementia, and will replace the present paradigm of a pure 'brick-and-mortar' memory clinic. Important ethical, legal and societal barriers associated with the implementation of digital health technologies in memory clinics need to be addressed. The envisioned Digitized Memory Clinic aims to improve diagnostics and enable precise disease-tracking prognostication for individuals with memory disorders and to open new possibilities, such as precision medicine for prevention and treatment.

Recent methodological developments have contributed to a significant advance in computerised neuropsychological instruments and procedures, including those accessible from remote. In this paper we present Auto-GEMS, a newly developed, web-based, self-administered screening test allowing to quickly estimate an individual's cognitive state also considering their cognitive reserve. Auto-GEMS measures cognitive functioning on eleven items similarly to the in-person paper-and-pencil version (GEMS) and to the remote (phone or video call) version (Tele-GEMS) of the same screening. We collected normative data on a sample of 1308 Italian-speaking participants (age range 18-93) to verify its psychometric properties and computed regression models on demographic variables to establish clinical cut-offs. The psychometric properties of Auto-GEMS have shown good internal consistency, test-retest reliability and convergent validity. This short and user-friendly tool has a number of potential applications. For instance, it can be useful in clinical practice to monitor the cognitive profile of patients or vulnerable individuals, or even administered in a face-to-face, standard clinical setting. It can also be used in research studies to screen participants. The testing materials and the collected data are freely available in a digital archive along with a web App to visualise the test outcome with reference to its normative data.

This study aimed to establish normative data for the Self-Administered Tasks Uncovering Risk of Neurodegeneration (SATURN), a brief computer-based test for global cognitive assessment through accuracy and response times on tasks related to memory, attention, temporal orientation, visuo-constructional abilities, math (calculation), executive functions, and reading speed.

A sample of 323 Italian individuals with Montreal Cognitive Assessment (MoCA) equivalent score ≥1 (180 females; average age: 61.33 years; average education: 11.32 years), stratified by age, education, and sex, completed SATURN using PsychoPy, and a paper-and-pencil protocol consisting of Mini-Mental State Examination (MMSE) and MoCA. Data analyses included: (i) correlations between the total accuracy scores of SATURN and those of MMSE and MoCA; (ii) multiple regressions to determine the impact of sex, age, and education, along with the computation of adjusted scores; (iii) the calculation of inner and outer tolerance limits, equivalent scores, and the development of correction grids.

The mean total time on tasks was 6.72 ± 3.24 min. Age and education significantly influence the SATURN total accuracy, while sex influences the total time on tasks. Specific sociodemographic characteristics influence subdomain accuracies and times on task differently. For the adjusted SATURN total score, the outer limit corresponds to 16.56 out of 29.00 (cut-off), while the inner limit is 18.57. SATURN significantly correlates with MMSE and MoCA.

In conclusion, SATURN is the first open-source digital tool for initial cognitive assessment in Italy, showing potential for self-administration in primary care, and remote administration. Future studies need to assess its sensitivity and specificity in detecting pathological cognitive decline.

This article provides the test-retest reliability and Reliable Change Indices (RCIs) of the Philips IntelliSpace Cognition (ISC) platform, which contains digitized versions of well-established neuropsychological tests.

147 participants (ages 19 to 88) completed a digital cognitive test battery on the ISC platform or paper-pencil versions of the same test battery during two separate visits. Intraclass correlation coefficients (ICC) were calculated separately for the ISC and analog test versions to compare reliabilities between administration modalities. RCIs were calculated for the digital tests using the practice-adjusted RCI and standardized regression-based (SRB) method.

Test-retest reliabilities for the ISC tests ranged from moderate to excellent and were comparable to the test-retest reliabilities for the paper-pencil tests. Baseline test performance, retest interval, age, and education predicted test performance at visit 2 with baseline test performance being the strongest predictor for all outcome measures. For most outcome measures, both methods for the calculation of RCIs show agreement on whether or not a reliable change was observed.

RCIs for the digital tests enable clinicians to determine whether a measured change between assessments is due to real improvement or decline. Together, this contributes to the growing evidence for the clinical utility of the ISC platform.

We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K).

We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales.

The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively).

Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.

As society ages, the incidence of Alzheimer's disease and other dementias has surged, highlighting the importance of early dementia diagnosis. The Seoul Cognitive Status Test (SCST), a digital neuropsychological test, is designed for the early detection of cognitive impairment and has been standardized to establish reliability and validity. This study aims to verify whether the SCST effectively discriminates between groups based on three cognitive statuses (subjective cognitive decline [SCD], mild cognitive impairment [MCI], Dementia) in a large sample. We also seek to determine whether the SCST discriminates between individuals with three different cognitive statuses as defined by the Cognitive Dementia Rating (CDR).

We enrolled 254 participants from a dementia clinic who underwent a comprehensive neuropsychological battery (Seoul Neuropsychological Screening Battery-II) during the dementia evaluation by experienced neurologists (55 with SCD, 126 with MCI, 73 with dementia). In addition, the degree of cognitive decline in participants was classified by CDR level (186 with CDR 0.5, 52 with CDR 1, 15 with CDR 2). One-way analysis of variance was used to compare SCST scores according to each of the three cognitive status groups and CDR levels.

The SCST total score, cognitive domain scores (attention, language, visuospatial function, memory, executive function), and most of the subtest scores decreased significantly in the order of SCD, MCI and dementia. Likewise, the differences in SCST scores between CDR levels were significant, particularly in distinguishing between CDR 0.5 and CDR 1.

This study reaffirmed that the SCST can significantly discriminate between groups of individuals with SCD, MCI, and dementia based on a large sample. Furthermore, differences in SCT scores were found across the levels of CDR, confirming the clinical utility of the SCST. These findings suggest that the SCST is an efficient and useful neuropsychological test for the sensitive detection of early cognitive impairment.

The need for cognitive testing is increasing with the aging population and the advent of new Alzheimer disease therapies. To respond to the increased demand, the XpressO was developed as a self-administered digital cognitive prescreening tool that will help distinguish between populations of subjective and objective cognitive impairment according to the Montreal Cognitive Assessment (MoCA).

This is a prospective validation study. XpressO is composed of tasks that assess memory and executive functions. It is validated compared to the digital MoCA as a gold standard. Out of 118 participants screened from the MoCA Clinic and a family practice clinic, 88 met inclusion criteria, two participants had missing data due to incomplete tasks, 86 participants were included in the analysis; the mean age was 70.34 years. A logistic regression model was built, and its accuracy was evaluated by the sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic.

Analysis showed strong correlation between (1) XpressO memory tasks scores and the MoCA Memory Index Score (p-values < 0.001), and between (2) XpressO sub-test scores and MoCA total score (p-values < 0.005). The AUC for predicting MoCA performance is 0.845. To classify individuals with normal and abnormal MoCA scores, two threshold values were introduced for the total XpressO scores: sensitivity of 91% at a cutoff of 72, specificity of 90% at a cutoff of 42, and an undetermined range in between.

XpressO demonstrated high AUC, high sensitivity and specificity to predict cognitive performance compared to the digital MoCA. It may provide efficient cognitive prescreening by identifying individuals who would benefit from further clinical assessments, potentially reducing waiting times and high burden on healthcare clinics.

This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression.

Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD.

Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.

The aging population in developing countries demands parallel improvements in brain health assessment services to mitigate stigma, promote healthy aging, and diagnose cognitive impairments including dementia in primary health care (PHC) facilities. The lack of culturally appropriate cognitive assessment tools in PHC facilities delays early detection. This study aims to culturally adapt a brief digital cognitive assessment tool for PHC professionals in Southeast Nigeria.

A total of 30 participants (15 healthcare workers HCW and 15 community members) were selected to be culturally representative of the community. We completed focus groups and pilot testing to evaluate and refine the Brain Health Assessment (BHA) a subset of tools from the Tablet-based Cognitive Assessment Tool (TabCAT) known to be sensitive to cognitive impairment in other settings. We examined BHA subtests across local languages (Pidgin and Igbo) spoken at two geriatric clinics in Anambra State Southeast Nigeria.

Following structured approaches in focus groups, adaptations were made to the Favorites (memory) and Line Length (visuospatial) subtests based on their input. Participants found the new adaptations to have good construct validity for the region.

The BHA subtests showed content validity for future work needed to validate the tool for detecting early cognitive changes associated with dementia and Alzheimer's disease in PHC settings. The use of culturally adapted and concise digital cognitive assessment tools relevant to healthcare professionals in Southeast Nigeria's PHCs is advocated.

With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions.

The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen.

A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale-10 to assess general anxiety levels at each cognitive screening. The SCREEN's scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments.

The study found that the SCREEN's longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing.

The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population.

RR2-10.2196/25520.

Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer's disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and FA. Prior analyses of MemTrax CRT data show no effects of sex but an effect of age on performance. The number of HITs corresponds to faster RT-HITs more closely than TC, and CRs do not relate to RT-HITs. RT-HITs show a typical skewed distribution, and cumulative RT-HITs fit a negative survival curve (RevEx). Thus, this study aimed to define precisely the effects of sex and age on HITS, CRs, RT-HITs, and the dynamics of RTs in an engaged population.

MemTrax CRT online data on 18,255 individuals was analyzed for sex, age, and distributions of HITs, CRs, MISSes, FAs, TC, and relationships to both RT-HITs and RT-FAs.

HITs corresponded more closely to RT-HITs than did TC because CRs did not relate to RT-HITs. RT-FAs had a broader distribution than RT-HITs and were faster than RT-HITs in about half of the sample, slower in the other half. Performance metrics for men and women were the same. HITs declined with age as RT-HITs increased. CRs also decreased with age and RT-FAs increased, but with no correlation. The group over aged 50 years had RT-HITs distributions slower than under 50 years. For both age ranges, the RevEx model explained more than 99% of the variance in RT-HITs.

The dichotomy of HITs and CRs suggests opposing cognitive strategies: (1) less certainty about recognitions, in association with slower RT-HITs and lower HIT percentages suggests recognition difficulty, leading to more MISSes, and (2) decreased CRs (more FAs) but faster RTs to HITs and FAs, suggesting overly quick decisions leading to errors. MemTrax CRT performance provides an indication of EM (HITs and RT-HITs may relate to function of the temporal lobe), executive function (FAs may relate to function of the frontal lobe), processing speed (RTs), cognitive ability, and age-related changes. This CRT provides potential clinical screening utility for early Alzheimer's disease and other conditions affecting EM, other cognitive functions, and more accurate impairment assessment to track changes over time.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

We investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants.

Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale.

Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA).

This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening.

To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.

Detecting cognitive impairment such as Alzheimer's disease early and tracking it over time is essential for individuals at risk of cognitive decline.

This research aimed to validate the Beynex app's gamified assessment tests and the Beynex Performance Index (BPI) score, which monitor cognitive performance across seven categories, considering age and education data.

Beynex test cut-off scores of participants (n = 91) were derived from the optimization function and compared to the Montreal Cognitive Assessment (MoCA) test. Validation and reliability analyses were carried out with data collected from an additional 214 participants.

Beynex categorization scores showed a moderate agreement with MoCA ratings (weighted Cohen's Kappa = 0.48; 95% CI: 0.38-0.60). Calculated Cronbach's Alpha indicates good internal consistency. Test-retest reliability analysis using a linear regression line fitted to results yielded R∧2 of 0.65 with a 95% CI: 0.58, 0.71.

Beynex's ability to reliably detect and track cognitive impairment could significantly impact public health, early intervention strategies and improve patient outcomes.

Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.

We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation.

The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.

Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Delayed neurocognitive recovery and neurocognitive disorder are common postoperative complications among older adults. The assessment of these complications traditionally relies on analog neurocognitive tests, predominantly using the test battery from the ISPOCD-study as the standard approach. However, analog tests are time-consuming and necessitate trained staff which poses limitations. The potential availability of a digital neurocognitive test as an alternative to the ISPOCD remains unknown. We conducted a comparative study between the analog test battery from ISPOCD and the self-administrated digital test battery developed by Mindmore.

We conducted a crossover study with 50 cognitively healthy older adults ≥ 60 years of age recruited in Stockholm Sweden, between February and April 2022. The primary outcome focused on measuring comparability between the two test batteries. Our secondary outcomes included assessing participants' perceptions and attitudes about the tests with qualitative interviews and their usability experiences.

Fifty older adults, mean age 76, female 56%, with a university or college degree 48% participated in the study. The sub tests in two test batteries demonstrated a medium-large correlation (r = 0.3-0.5), except for one measure. For four out of six measures, significant differences were found with medium to large effect sizes, ranging from 0.57-1.43. Two categories were recognized in the qualitative analysis: self-competing in a safe environment, and experience with technology. Participants expressed feeling safe and at ease during the assessment, with some preferring the digital test over the analog. Participants reported a high level of usability with the digital test and a majority participants (n = 47) reported they would undergo the digital test for a potential future surgery.

The digital test battery developed by Mindmore offers several advantages, including rapid access to test results, easy comprehension, and use for participants, thereby increased accessibility of cognitive screening.

NCT05253612; ClinicalTrials.gov, 24/02/2022.

Memory complaints are the early symptoms of cognitive impairment, and they usually bring anxiety about cognitive deterioration among the elderly population. Musical interventions were demonstrated to relieve dementia symptoms. This pilot study investigated the potential benefits of rhythmic musical intervention, African drumming, on cognitive function and mood status with traditional and digital assessments for elderly participants.

Participants were recruited through social media. The musical intervention was arranged by drumming instructors certified by the Hong Kong Association of African Drumming. Participants joined regular training classes with eight lessons, which covered rhythmic clapping and drumming, power control, and overall performance with songs. The inclusion criteria included the following: (1) age over 50; (2) self-reported complaints of memory loss; (3) the ability to use digital devices, such as a smartphone; and (4) can understand the content of questionnaires and follow the intervention schedules. Those with hearing impairment, failure to use Chinese, and active psychosis or dementia were excluded. Cognitive function was measured by the Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA) and a digital platform, ScreenMat. Anxiety and depression levels were assessed by the State-Trait Anxiety Inventory (STAI) and Geriatric Depression Scale (GDS-15). All assessments were performed before and after the drumming classes. The outcomes were compared using the Wilcoxon signed rank test with 0.05 as the significance level.

Twenty-two participants joined this study with an attendance rate of 90%. The overall cognitive function of the participants was good with an average score of 27 for HK-MoCA. After eight sessions of African drum intervention, the cognitive function did not show a significant improvement, but the response time of answering the digital cognitive questions was significantly faster than before the intervention (-39.9 s, p = 0.03). The response time for the short-term memory function was most significantly reduced (-13.5 s, p = 0.017). The anxiety and depression scores (i.e. STAI and GDS) also significantly improved (p < 0.001) after the intervention.

Rhythmic musical intervention is not only effective in improving emotional status, but also potentially good for improving cognitive symptoms, including the response time of the memory test. Digital behavioral analysis may bring new insights for future research on cognitive assessment.

The global health pandemic has affected the increasing older adult population, especially those with mental illnesses. It is necessary to prevent cases of cognitive impairment in adults early on, and this requires the support of information and communication technologies for evaluating and training cognitive functions. This can be achieved through computer applications designed for cognitive assessment.

In this review, we aimed to assess the state of the art of the current platforms and digital test applications for cognitive evaluation, with a focus on older adults.

A systematic literature search was conducted on 3 databases (Web of Science, PubMed, and Scopus) to retrieve recent articles on the applications of digital tests for cognitive assessment and analyze them based on the methodology used. Four research questions were considered. Through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, following the application of inclusion and exclusion criteria, a total of 20 articles were finally reviewed.

Some gaps and trends were identified regarding the types of digital applications and technologies used, the evaluated effects on cognitive domains, and the psychometric parameters and personal characteristics considered for validation.

Computerized tests (similar to paper-and-pencil tests) and test batteries (on computers, tablets, or web platforms) were the predominant types of assessments. Initial studies with simulators, virtual environments, and daily-life activity games were also conducted. Diverse validation methods and psychometric properties were observed; however, there was a lack of evaluations that involved specific populations with diverse education levels, cultures, and degrees of technology acceptance. In addition, these evaluations should consider emotional and usability aspects.

Oral health could influence cognitive function by stimulating brain activity and blood flow. The quantified oral status from oral inflammation, frailty and masticatory performance were rarely applied to the cognitive function screening. We aimed to adopt non-invasive digital biomarkers to quantify oral health and employ machine learning algorithms to detect cognitive decline in the community.

We conducted a prospective case-control study to recruit 196 participants between 50 and 80 years old from Puzi Hospital (Chiayi County, Taiwan) between December 01, 2021, and December 31, 2022, including 163 with normal cognitive function and 33 with cognitive decline. Demographics, daily interactions, electronically stored medical records, masticatory ability, plaque index, oral diadochokinesis (ODK), periodontal status, and digital oral health indicators were collected. Cognitive function was classified, and confirmed mild cognitive impairment diagnoses were used for sensitivity analysis.

The cognitive decline group significantly differed in ODK rate (P = 0.003) and acidity from SILL-Ha (P = 0.04). Younger age, increased social interactions, fewer cariogenic bacteria, high leukocytes, and high buffering capacity led to lower risk of cognitive decline. Patients with slow ODK, high plaque index, variance of hue (VOH) from bicolor chewing gum, and acidity had increased risk of cognitive decline. The prediction model area under the curve was 0.86 and was 0.99 for the sensitivity analysis.

A digital oral health biomarker approach is feasible for tracing cognitive function. When maintaining oral hygiene and oral health, cognitive status can be assessed simultaneously and early monitoring of cognitive status can prevent disease burden in the future.

Widespread cognitive test screening as part of tele-public health initiatives necessitates a test that is self-administered online and automatically scored, with no clinician effort. The feasibility of unsupervised cognitive screening is unclear. We adapted the Self-Administered Tasks Uncovering Risk of Neurodegeneration (SATURN) to make it suitable for self-administration and automatic scoring. A sample of 364 healthy older adults completed SATURN via a web browser, in a fully independent manner. SATURN's overall score was not modulated by gender, education, reading speed, the time of day at which the test was taken, or an individual's familiarity with technology. SATURN proved extremely portable across operating systems. Importantly, comments from participants reported satisfaction with the experience and the clarity of the instructions. SATURN represents a fast and easy screening tool that can be used for a first assessment, during a routine test or clinical evaluation, or during periodic health monitoring, in person or remotely.

Alzheimer's disease (AD) is a major healthcare challenge with no curative treatment at present. To address this challenge, we need a paradigm shift, where we focus on pre-dementia stages of AD. In this Perspective, we outline a strategy to move towards a future with personalized medicine for AD by preparing for and investing in effective and patient-orchestrated diagnosis, prediction and prevention of the dementia stage. While focusing on AD, this Perspective also discusses studies that do not specify the cause of dementia. Future personalized prevention strategies encompass multiple components, including tailored combinations of disease-modifying interventions and lifestyle. By empowering the public and patients to be more actively engaged in the management of their health and disease and by developing improved strategies for diagnosis, prediction and prevention, we can pave the way for a future with personalized medicine, in which AD pathology is stopped to prevent or delay the onset of dementia.

Early and appropriate diagnosis of amnestic mild cognitive impairment (aMCI) is clinically important because aMCI is considered the prodromal stage of dementia caused by Alzheimer's disease (AD). aMCI is assessed using the comprehensive neuropsychological (NP) battery, but it is rater-dependent and does not provide quick results. Thus, we investigated the performance of the computerized cognitive screening test (Inbrain Cognitive Screening Test; Inbrain CST) in the diagnosis of aMCI and compared its performance to that of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test (CERAD-K), a comprehensive and pencil-and-paper NP test.

A total of 166 participants were included in this cross-sectional study. The participants were recruited as part of a prospective, community-based cohort study for MCI (PREcision medicine platform for mild cognitive impairment on multi-omics, imaging, evidence-based R&BD; PREMIER). All participants were assessed using the CERAD-K and the Inbrain CST. The Inbrain CST comprised seven subtests that assessed the following five cognitive domains: attention, language, visuospatial, memory, and executive functions. Seventy-six participants underwent brain magnetic resonance imaging and [18F]-flutemetamol positron emission tomography (PET). We evaluated the diagnostic performance of the Inbrain CST for the identification of aMCI by comparing the findings with those of CERAD-K. We also determined the characteristics of aMCI patients as defined by the CERAD-K and Inbrain CST.

Of the 166 participants, 93 were diagnosed with aMCI, while 73 were cognitively unimpaired. The sensitivity of the Inbrain CST for aMCI diagnosis was 81.7%, and its specificity was 84.9%. Positive and negative predictive values were 87.4% and 78.5%, respectively. The diagnostic accuracy was 83.1%, and the error rate was 16.9%. Demographic and clinical characteristics between individuals with aMCI defined by the Inbrain CST and CERAD-K were not significantly different. The frequency of positive amyloid PET scan, the hippocampal/parahippocampal volumes, and AD signature cortical thickness did not differ between the patients with aMCI defined by CERAD-K and those with aMCI defined by the Inbrain CST.

The Inbrain CST showed sufficient sensitivity, specificity, and positive and negative predictive values for diagnosing objective memory impairment in aMCI. In addition, aMCI patients identified by CERAD-K and the Inbrain CST showed comparable clinical and neuroimaging characteristics. Therefore, the Inbrain CST can be considered an alternative test to supplement the limitations of existing pencil-and-paper NP tests.

Most individuals recover quickly from a concussion; however, youth who sustain multiple concussions may be at risk for long-term cognitive impairments. This case study examines the neuropsychological performance of a 13-year-old malewith five head injuries. After his first concussion during study participation (fourth injury overall), several improvements were observed, likely due to practice effects, yet after sustaining another concussion <2 years later,declines were observed in visuoconstruction, verbal memory, and intellectual functioning. Across serial re-evaluation, his vocabulary knowledge declined, and fewer improvements were observed than anticipated when accounting for serial practice effects, highlighting the possible cumulative impact of multiple concussions.

Empowerment, an already central concept in public health, has gained additional relevance through the expansion of mobile health (mHealth). Especially direct-to-consumer self-testing app companies mobilise the term to advertise their products, which allow users to self-test for various medical conditions independent of healthcare professionals. This article first demonstrates the absence of empowerment conceptualisations in the context of self-testing apps by engaging with empowerment literature. It then contrasts the service these apps provide with two widely cited empowerment definitions by the WHO, which describe the term as a process that, broadly, leads to knowledge and control of health decisions. We conclude that self-testing apps can only partly empower their users, as they, we argue, do not provide the type of knowledge and control the WHO definitions describe. More importantly, we observe that this shortcoming stems from the fact that in the literature on mHealth and in self-testing marketing, empowerment is understood as a goal rather than a process. This characterises a shift in the meaning of empowerment in the context of self-testing and mHealth, one that reveals a lack of awareness for relational and contextual factors that contribute to empowerment. We argue that returning to a process-understanding of empowerment helps to identify these apps' deficits, and we conclude the article by briefly suggesting several strategies to increase self-testing apps' empowerment function.

In recent years, virtual reality technology has developed the potential to help in the early detection of mild cognitive impairment (MCI). However, integrative evidence of its detection performance for mild cognitive impairment is lacking, and meta-analysis or systematic reviews are required to further determine the effectiveness of virtual reality technology in screening for MCI.

Literature searches were performed for MCI screening tests in the Cochrane Library, Web of Science, PsycINFO, PubMed, EMBASE, CINAHL, and Scopus. The primary outcome was the performance of VR tests for MCI detection. A protocol for this systematic review was registered in PROSPERO (Registration number: CRD42022302139).

A total of 14 studies in 13 reports were eventually included. The combined data with the bivariate random-effects model gave a summary point of 0.89 sensitivity (95 % confidence interval [CI]: 0.82-0.94) and 0.91 specificity (95 % CI: 0.82-0.96). The SROC curve was plotted, the DOR was 79.25 (95 % CI: 22.59-277.99), and the AUC was 0.95 (95 % CI: 0.93-0.97).

Virtual reality-based tests have shown considerable detection performance in detecting MCI, and therefore, virtual reality-based tests can serve as recommended screening methods. Future studies can consider longitudinal assessment and follow-up programs to identify progressive changes.

Touchscreen cognitive tools opened new promising opportunities for the early detection of cognitive impairment; however, most research studies are conducted in English-speaking populations and high-income countries, with a gap in knowledge about their use in populations with cultural, linguistic, and educational diversity.

To review the touchscreen tools used in primary care settings for the cognitive assessment of mild cognitive impairment (MCI) and dementia, with a focus on populations of different cultures, languages, and literacy.

This systematic review was conducted following the PRISMA guidelines. Studies were identified by searching across MEDLINE, EMBASE, EBSCO, OVID, SCOPUS, SCIELO, LILACS, and by cross-referencing. All studies that provide a first-level cognitive assessment for MCI and dementia with any touchscreen tools suitable to be used in the context of primary care were included.

Forty-two studies reporting on 30 tools and batteries were identified. Substantial differences among the tools emerged, in terms of theoretical framework, clinical validity, and features related to the application in clinical practice. A small proportion of the tools are available in multiple languages. Only 7 out of the 30 tools have a multiple languages validation. Only two tools are validated in low-educated samples, e.g., IDEA and mSTS-MCI.

General practitioners can benefit from touchscreen cognitive tools. However, easy requirements of the device, low dependence on the examiner, fast administration, and adaptation to different cultures and languages are some of the main features that we need to take into consideration when implementing touchscreen cognitive tools in the culture and language of underrepresented populations.

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.

The dementia population is increasing as the world's population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle-Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.

The online version contains supplementary material available at 10.1007/s13167-022-00284-3.