Early Detection Of Cognitive Impairment Research Articles

Monitoring reaction time to digital device in the very-old to detect early cognitive decline

Early detection of cognitive decline is essential for timely intervention and effective management of age-related impairments. We monitored repetitive reaction times to a simple task on senior-friendly tablet computers among 72 functionally independent older adults, with a mean age of 82, ranging up to 100 years, within natural settings over two years. Functional principal component analyses revealed a consistent decrease in reaction time in line with their task experience among those without subjective cognitive decline. Conversely, individuals reporting subjective cognitive decline showed no consistent trend and exhibited wide variability over time. These distinctive reaction time trajectories in very old adults suggest the potential for monitoring as a non-invasive, convenient method for early detection of cognitive impairment.

MoCA use in general practice for the early detection of cognitive impairment.

GPs can detect cognitive impairment at a very early stage, allowing early support for people and their caregivers. The early onset of cognitive impairment is between 50 and 60 years. Currently, in France, the Mini Mental State Examination remains the most used screening test, though it has a lower sensitivity and specificity than the Montreal Cognitive Assessment (MoCA) for detecting mild cognitive impairment, taking an average of 15 minutes to complete. To investigate the feasibility of the MoCA during routine consultations in general practice for the early detection of cognitive impairment and to determine prevalence of cognitive impairment in a primary care setting. A quantitative, prospective feasibility study was carried out in real-life working condition during routine consultation. GPs performed MoCA on adults aged 50 years and older, without suspected or confirmed cognitive impairment. 61 GPs performed 221 MoCA with a mean duration of 8 minutes and detected mild neurocognitive impairment in 62% of patients. The MoCA is feasible and easy to perform during routine consultations in general practice by trained and experienced physicians.

Biomarkers of sleep-wake disturbance as predictors of cognitive decline and accelerated disease progression.

In older adults, where sleep disturbances and cognitive impairment are common, mounting evidence suggests a potential connection between sleep and cognitive function, highlighting the significance of utilizing sleep as a biomarker for early detection of cognitive impairment to improve clinical outcomes in a noninvasive, cost-effective manner. This review describes the relationship between sleep and cognitive function in older adults, encompassing both subjective and objective measures of sleep quality, duration, architecture, and sleep-disordered breathing. The authors consider the directionality of the associations observed in prospective and cross-sectional studies, exploring whether sleep disturbances precede cognitive decline or vice versa. Furthermore, they discuss the potential bidirectional relationships between sleep and Alzheimer's disease (AD) risks in older adults while also examining the neurodegenerative pathways of this relationship. Routine sleep monitoring in primary care settings has the potential to bolster early detection and treatment of sleep disturbance, and by extension, reduce the risk of dementia. Improving sleep assessment tools, such as wearables, provide scalable alternatives to traditional methods like polysomnography, potentially enabling widespread monitoring of sleep characteristics. Standardized measurement and inclusive participant recruitment are needed to enhance generalizability, while longitudinal studies are essential to understand the interaction between sleep and AD pathology.

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method

BackgroundCognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of cognitive diseases such as Alzheimer’s and Lewy body dementia. Large-scale screening relies heavily on cognitive assessment scales as primary tools, with some low sensitivity and others expensive. Despite significant progress in machine learning for cognitive function assessment, its application in this particular screening domain remains underexplored, often requiring labor-intensive expert annotations. AimsThis paper introduces a semi-supervised learning algorithm based on pseudo-label with putback (SS-PP), aiming to enhance model efficiency in predicting the high risk of cognitive impairment (HR-CI) by utilizing the distribution of unlabeled samples. DataThe study involved 189 labeled samples and 215,078 unlabeled samples from real world. A semi-supervised classification algorithm was designed and evaluated by comparison with supervised methods composed by 14 traditional machine-learning methods and other advanced semi-supervised algorithms. ResultsThe optimal SS-PP model, based on GBDT, achieved an AUC of 0.947. Comparative analysis with supervised learning models and semi-supervised methods demonstrated an average AUC improvement of 8% and state-of-art performance, repectively. ConclusionThis study pioneers the exploration of utilizing limited labeled data for HR-CI predictions and evaluates the benefits of incorporating physical examination data, holding significant implications for the development of cost-effective strategies in relevant healthcare domains.

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study.

Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based "drawing and dragging" tasks. We iteratively designed "drawing and dragging" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. The "drawing and dragging" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.

A novel abbreviated version of the Luria neuropsychological diagnosis battery: reliability and convergent validity in Spanish older adults

ABSTRACT Objective To estimate the test-retest and inter-rater reliability of the new Spanish abbreviated version of the Luria Neuropsychological Diagnosis (DNA-2) battery for older adults. Method A total of thirty cognitively healthy volunteers were examined in this study. The participants completed a comprehensive standardized assessment, encompassing cognitive and functional performance. Intraclass correlation coefficients (ICC) were used to examine test-retest and inter-rater reliability. One month was allowed between administrations. Furthermore, correlations between Luria DNA-2 (total and domain subscores) and other classical cognitive measures were explored. Results The test-retest reliability on the overall Luria DNA-2 score was high (ICC= .834, 95% CI [.680, .917], p < .001). Furthermore, the inter-rater reliability for the total score demonstrated an excellent concordance between administrators (ICC= .990, 95% CI [.979, .995], p < .001). Positive and significant correlations were observed between Luria DNA-2 (both total and domain subscores) and the Addenbrooke’s Cognitive Examination (ACE-III; ρ = .857, p < .001). Conclusions This study supports the adequate reliability of the Luria DNA-2, as an abbreviated neuropsychological battery, for assessing cognitive performance in Spaniards aged 55 years and older. Future studies should continue to explore the psychometric properties of the Luria DNA-2, particularly those related to its diagnostic validity for early detection of cognitive impairment.

Cultural adaptation of the brain health assessment for early detection of cognitive impairment in Southeast Nigeria.

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.

Self-administered gerocognitive examination (SAGE) aids early detection of cognitive impairment at primary care provider visits

Self-administered gerocognitive examination (SAGE) aids early detection of cognitive impairment at primary care provider visits

Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development

Introduction: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention. Methods: This study included 2,288 participants with normal cognitive function from the Ma’anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level. Results: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773–0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults. Conclusion: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population.

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Protocol for a construct and clinical validation study of MyCog Mobile: a remote smartphone-based cognitive screener for older adults

IntroductionAnnual cognitive screening in older adults is essential for early detection of cognitive impairment, yet primary care settings face time constraints that present barriers to routine screening. A remote cognitive...

Reversal of the concreteness effect can be detected in the natural speech of older adults with amnestic, but not non-amnestic, mild cognitive impairment.

Patients with Alzheimer's disease present with difficulty in lexical retrieval and reversal of the concreteness effect in nouns. Little is known about the phenomena before the onset of symptoms. We anticipate early linguistic signs in the speech of people who suffer from amnestic mild cognitive impairment (MCI). Here, we report the results of a corpus-linguistic approach to the early detection of cognitive impairment. One hundred forty-eight English-speaking Singaporeans provided natural speech data, on topics of their choice; 74 were diagnosed with single-domain MCI (38 amnestic, 36 non-amnestic), 74 cognitively healthy. The recordings yield 267,310 words, which are tagged for parts of speech. We calculate the per-minute word counts and concreteness scores of all tagged words, nouns, and verbs in the dataset. Compared to controls, subjects with amnestic MCI produce fewer but more abstract nouns. Verbs are not affected. Slower retrieval of nouns and the reversal of the concreteness effect in nouns are manifested in natural speech and can be detected early through corpus-based analysis. Reversal of the concreteness effect is manifested in patients with Alzheimer's disease (AD) and semantic dementia.The paper reports a corpus-based analysis of natural speech by people with amnestic and non-amnestic mild cognitive impairment (MCI) and cognitively healthy controls.People with amnestic MCI produce fewer and more abstract nouns than people with non-amnestic MCI and healthy controls. Verbs appear to be unaffected.The imageability problem can be detected in natural everyday speech by people with amnestic MCI, which carries a higher risk of conversion to AD.

Diagnostic Accuracy of Digital Solutions for Screening for Cognitive Impairment: A Systematic Review and Meta-Analysis

The early detection of cognitive impairment is essential in order to initiate interventions and guarantee access to healthcare services. Digital solutions are emerging in the literature as an alternative approach to cognitive screening. Our primary goal is to synthesize the evidence on digital solutions’ diagnostic ability to screen for cognitive impairment and their accuracy. A secondary goal is to distinguish whether the ability to screen for cognitive impairment varies as a function of the type of digital solution: paper-based or innovative digital solutions. A systematic review and meta-analysis of digital solutions’ diagnostic accuracy were conducted, including 25 studies. Digital solutions presented a variable diagnostic accuracy range. Innovative digital solutions offered at least 0.78 of sensitivity but showed lower specificity levels than the other subgroup. Paper-based digital solutions revealed at least 0.72 of specificity, but sensitivity started at 0.49. Most digital solutions do not demand the presence of a trained professional and include an automatic digital screening system and scoring, which can enhance cognitive screening and monitoring. Digital solutions can potentially be used for cognitive screening in the community and clinical practice, but more investigation is needed for an evidence-based decision. A careful assessment of the accuracy levels and quality of evidence of each digital solution is recommended.

Visual Cognitive Assessment Test: Utility of the brief cognitive battery for early screening of cognitive impairment in Chongqing, China.

Early detection of cognitive impairment is essential for timely intervention. Currently, most widely used cognitive screening tests are influenced by language and cultural differences; therefore, there is a need for the development of a language-neutral, visual-based cognitive assessment tool. The Visual Cognitive Assessment Test (VCAT), a 30-point test that assesses memory, executive function, visuospatial function, attention, and language, has demonstrated its utility in a multilingual population. In this study, we evaluated the reliability, validity, and diagnostic performance of the VCAT for screening early cognitive impairment in Chongqing, China METHODS: A total of 134 individuals (49 healthy controls (HCs), 52 with mild cognitive impairment (MCI), and 33 with mild dementia) completed the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), VCAT, and domain-specific neuropsychological assessments. The diagnostic performances of MMSE, MoCA, and VCAT were evaluated using the area under the curve (AUC), sensitivity, and specificity. Construct validity of the VCAT was assessed with well-established domain-specific cognitive assessments. Reliability was measured using Cronbach's alpha. The VCAT and its subdomains demonstrated both good construct validity and internal consistency (α=0.577). The performance of VCAT was comparable to that of MoCA and MMSE in differentiating mild dementia from nondemented groups (AUC: 0.940vs. 0.902 and 0.977, respectively; p=.098 and .053) and in distinguishing cognitive impairment (CI) from HC (AUC: 0.929vs. 0.899 and 0.891, respectively; p=.239 and .161), adjusted for education level. The optimal score range for VCAT in determining dementia, MCI, and HC was 0-14, 15-19, and 20-30, respectively. The VCAT proves to be a reliable screening test for early cognitive impairment within our cohort. Being both language and cultural neutral, the VCAT has the potential to be utilized among a wider population within China.

Biological and Physical Performance Markers for Early Detection of Cognitive Impairment in Older Adults

Dementia is a major cause of poor quality of life, disability, and mortality in old age. According to the geroscience paradigm, the mechanisms that drive the aging process are also involved in the pathogenesis of chronic degenerative diseases, including dementia. The dissection of such mechanisms is therefore instrumental in providing biological targets for interventions and new sources for biomarkers. Within the geroscience paradigm, several biomarkers have been discovered that can be measured in blood and that allow early identification of individuals at risk of cognitive impairment. Examples of such markers include inflammatory biomolecules, markers of neuroaxonal damage, extracellular vesicles, and DNA methylation. Furthermore, gait speed, measured at a usual and fast pace and as part of a dual task, has been shown to detect individuals at risk of future dementia. Here, we provide an overview of available biomarkers that may be used to gauge the risk of cognitive impairment in apparently healthy older adults. Further research should establish which combination of biomarkers possesses the highest predictive accuracy toward incident dementia. The implementation of currently available markers may allow the identification of a large share of at-risk individuals in whom preventive interventions should be implemented to maintain or increase cognitive reserves, thereby reducing the risk of progression to dementia.

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study.

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.

Effect of alcohol-dependence on cognitive performance in middle-aged men: Preliminary results

Objectives: The understanding of the relationship between alcohol-related neuropathology, cognitive impairment, and various factors such as alcohol consumption, thiamine levels, and age vulnerability is still poorly understood. Therefore, this study aims to examine the effect of alcohol dependence on cognitive performance in middle-aged men with psycho-biochemical evidence. Materials and Methods: A cross-sectional pilot study with a comparison group including 82 right-handed participants with and without alcohol dependence (n = 41 each). Alcohol dependence was diagnosed clinically by the International Classification of Disease Tenth Edition along with the use of alcohol use disorder identification test (AUDIT) and cognitive screening tests, that is, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). The serum levels of thiamine (Vitamin B1) were determined using an enzyme-linked immunosorbent assay. Results: The MoCA scores, MMSE scores, and serum thiamine levels were significantly low for alcohol-dependent men (1509.43 ± 898.63 pmol/L) versus non-alcohol-dependent men (1862.81 ± 741.30 pmol/L; P = 0.021). The cognitive sub-domains including orientation, execution, calculation, visuoconstructional skills, and recall functions were also significantly (P &lt; 0.05) affected for the alcohol-dependent patients when compared to non-alcohol-dependent men. Serum thiamine levels showed a positive (P &lt; 0.05) correlation with MoCA scores whereas serum thiamine levels showed a significant (P &lt; 0.05) negative correlation with AUDIT scores. Conclusion: Based on the significant positive association between serum thiamine levels with MoCA scores; therefore, both may be used as a screening tool for the early detection of cognitive impairment in patients with alcohol dependence.

Effects of healthy aging and mnemonic strategies on verbal memory performance across the adult lifespan: Mediating role of posterior hippocampus.

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.

EARLY DETECTION OF DEMENTIA AND MILD COGNITIVE IMPAIRMENT WITH BRAINCHECK

Abstract Dementia is projected to affect 14 million people in the United States and 152 million people globally in the coming decades. Early detection of dementia, which involves diagnosis of mild cognitive impairment (MCI), a possible prestage of dementia, is critical for intervention and care planning but remains a challenge with current clinical protocols. While brief screening tools are narrow in scope, comprehensive neuropsychological tests are time-consuming and require specialized training and expertise to conduct and interpret results. BrainCheck, a computerized cognitive assessment tool, provides an accessible and promising solution to address these current challenges. The aim of this study was to evaluate BrainCheck for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. A total of 99 participants clinically diagnosed with dementia (DEM; n=42), MCI (n=22), or normal cognition (NC; n=35) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. BrainCheck battery performance showed significant differences between the three groups, achieving 88% or higher sensitivity and specificity for separating DEM from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and DEM groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. These results show that BrainCheck was able to distinguish between diagnoses of DEM, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

Relationship between reaction time variability on go/no go tasks and neuropsychological functioning in younger and older adults

ABSTRACT Introduction Early detection of cognitive impairment in older adults is important for the prevention of dementia. Intra-individual variability in reaction time (IIV-RT) during go/no-go tasks can be used for the early detection of cognitive impairment in older adults living in the community. This study aimed to determine the relationship between IIV-RT and cognitive function during go/no-go tasks and the cutoff values for determining the risk of cognitive impairment in community-dwelling older adults. Methods This study included 31 older adults without cognitive impairment, 15 community-dwelling older adults with cognitive impairment, and 34 healthy young adults. All participants performed a go/no-go task to assess the IIV-RT. Additionally, older adults underwent neuropsychological testing. Based on the results of the Japanese version of the Montreal Test of Cognitive Abilities (MoCA-J), older adults were divided into those with normal cognition and those with cognitive impairment. Results There were significant differences in the IIV-RT among groups, including a higher IIV in the cognitively impaired group than in young adults and cognitively normal older adults. Moreover, the IIV-RT was correlated with the MoCA-J (r = −0.531, p < 0.001), Trail Making Test Part A (r = 0.571, p < 0.001), and Verbal Fluency Test scores (r = −0.442, p = 0.002). Receiver operating curve analysis showed that the area under the curve for IIV-RT was 0.935, and the cutoff value at which the IIV-RT identified cognitive impairment was 25.37%. Conclusions These findings indicate that the IIV-RT during go/no-go tasks is a useful early indicator of cognitive impairment in community-dwelling older adults.