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Abstract

Introduction/Objectives:

To assess the utility of the computerized cognitive function assessment tool, CogEvo, as a screening tool for mild cognitive impairment in primary care, we explored the relationship between CogEvo performance, age, and the severity of cognitive dysfunction evaluated by the Mini-Mental State Examination (MMSE).

Methods:

The observational cross-sectional study included 209 individuals’ data (mean age 79.4 ± 8.9 years). We conducted a correlation analysis between CogEvo and MMSE scores, compared the performance among the 3 cognitive function groups (MMSE ≥ 28 group; MMSE24-27 group; MMSE ≤ 23 group) using the MMSE cut-off, and evaluated CogEvo’s predictive accuracy for cognitive dysfunction through ROC analysis.

Results:

Both total CogEvo and MMSE scores significantly decreased with age. A significant positive correlation was observed between total CogEvo and MMSE scores, but a ceiling effect was detected in MMSE performance. Significant differences were observed in the total CogEvo score, including orientation and spatial cognitive function scores, among the 3 groups. CogEvo showed no educational bias. ROC analyses indicated moderate discrimination between the MMSE ≥ 28 group and the MMSE24-27 and MMSE ≤ 23 groups.

Conclusions:

The computer-administered CogEvo has the advantage of not exhibiting ceiling effects or educational bias like the MMSE, and was found to be able to detect age-related cognitive decline and impairment.

Keywords

CogEvo computerized cognitive function assessment tool screening mild cognitive impairment dementia

Introduction

With the aging of the population, the incidence of dementia is rapidly increasing, posing a significant public health concern.¹ Currently, it is estimated that there are approximately 50 million dementia patients worldwide, with cases resulting from degenerative diseases causing severe cognitive impairment, such as Alzheimer’s disease, accounting for over 50% of these cases.² Due to cognitive dysfunction resulting from dementia, various aspects of functional independence are restricted. This not only affects the societal costs of home healthcare services, day care, and the care of hospitalized patients but also increases individual costs such as pharmaceuticals and care provided by family members.³ Consequently, it leads to a decline in overall societal productivity and imposes a significant burden. Maintaining and restoring functional independence through appropriate treatment and intervention for mild cognitive impairment (MCI), a precursor state to dementia, and preventing cognitive decline in healthy older adults are central challenges worldwide. Although the progression rate from MCI to dementia is estimated to be 15% to 25% annually,⁴ indications suggest that normal cognitive function can be restored with proper treatment and intervention.⁵ Expectations for the therapeutic effects of recently developed amyloid-modifying drugs on MCI are emerging.^6,7 These underscore the necessity of public health initiatives to promote early screening for cognitive impairment in the elderly, particularly those utilizing primary care medical facilities in the community.

Despite recent advances in biomarkers related to MCI and Alzheimer’s disease (AD), neuropsychological assessments remain crucial evaluation tools to confirm the association between cognitive function and biomarkers. These assessments are indispensable for detecting the progression from the normal cognitive (NC) to MCI or from MCI to early-stage dementia and for conducting follow-up evaluations. Traditional neuropsychological tests conducted face-to-face using pen and paper, such as MMSE and MoCA, have been recognized for their ability to detect cognitive impairment, sensitively identify MCI and AD.^8
-11 However, these tests, although advantageous for their short duration (approximately 10 min),¹² are prone to ceiling effects (situation where a measurement tool reaches its upper limit of sensitivity, making it unable to differentiate effectively among high-performing individuals) due to their maximum 30-point deduction system,^9
-11 and their susceptibility to educational bias has been suggested.^10,13,14 Moreover, requiring skilled evaluators (physicians or psychologists) makes them suitable for hospital assessments but less practical for large-scale health assessments conducted in the community as part of dementia prevention policies. Considering the nature of the test items, they are not designed for reliable assessments at short intervals such as daily or weekly, and their characteristics do not support frequent evaluations (eg, patients remembering test content and answers, hindering accurate status assessment). In Japan, where both aging and declining birth rates are ongoing, the proportion of elderly individuals among healthcare professionals is expected to increase.¹⁴ Relying solely on conventional methods may become challenging for adequately assessing age-related cognitive decline, MCI, and mild dementia in the elderly.

In recent years, screening batteries for evaluating cognitive function using visual and auditory tasks presented on monitors, employing PCs or tablets as alternatives to traditional neuropsychological tests, have been developed worldwide and widely adopted.^1,15 The computerization of neuropsychological tests offers several advantages: (1) test instructions are not only visually presented but also audibly explained, enabling implementation without specialized examiners; (2) automated scoring allows for instantaneous recording of temporal changes, facilitating accurate status assessment; (3) cloud-based management is possible if integrated with the internet, enabling assessments of patients in remote locations online without time or location constraints. Furthermore (4) if coordinated with administrative and medical/healthcare management systems, such as dementia prevention policies, service additions, and medical record and billing systems, not only can individual cognitive function assessments and records be conducted, but a unified management system can be established, potentially leading to significant cost reduction and improved productivity.

CogEvo (CogEvo^®, Total Brain Care Co. Ltd, Kobe, Japan) is a computerized cognitive assessment tool developed in Japan in 2017, serving as a cloud service for cognitive assessment and training.^14,16,17 It can be administered on both stationary monitor screens and tablet screens. Comprising 12 tasks,¹⁷ CogEvo has undergone validation for the 1-dimensionality, structural and construct validity, and reliability of its core 5 tasks, including “Same shape,” “Orientation,” “Flash light,” “Route 99,” and “Follow the order,” as described later.¹⁴ As demonstrated in Supplemental Figure 1, these core tasks share many similarities with traditional neuropsychological tests. Participants engage with each task for approximately 10 min using simple finger manipulations, and scoring and recording are automatically conducted on a computer, eliminating the absolute necessity for a skilled examiner. Our study aimed to evaluate the convenience and effectiveness of CogEvo in identifying age-related cognitive decline and cognitive impairments. To this end, we investigated whether CogEvo eliminates the ceiling effect and educational bias of conventional paper-and-pencil tests, and whether there is a correlation with conventional neuropsychological tests.

Methods

Design and Participants

This observational cross-sectional study focused on examining cognitive dysfunction in older community-dwellers. We conducted a retrospective review of medical data from patients who visited our hospital between April 2018 and March 2022. The selection criteria for the data encompassed patients who had undergone the CogEvo test, as described below, for the first time, in addition to the neuropsychological test (MMSE), among our patient population. Data were not obtained from individuals with significant visual, auditory, or motor function impairments that would hinder test performance, as well as those with severe comorbid conditions. Data collection for this study occurred in October 2022, and only data from patients meeting the criteria were included. Ethical approval for this study was granted by the Ethics Committee of the Ryofukai Medical Corporation (Approval Number: IRB: 2022-02, approval date: September 2022). All research procedures adhered to the applicable guidelines and regulations, and written informed consent was obtained from all patients involved in the study.

Neuropsychological Screening Tests: MMSE and CogEvo

The neuropsychological tests were administered by a physician specializing in neurosurgery, neurology, and psychiatry (T.S.) with experience, along with speech-language pathologists (H.K., Y.K., Y.S., T.K., and S.S.), using the Japanese version of the MMSE.¹⁸ Cognitive function was conveniently categorized based on the total MMSE score (out of 30 points) into 3 groups: MMSE ≥ 28 group (30-28 points), MMSE24-27 group (27-24 points), and MMSE ≤ 23 group (23 points or less).

The CogEvo test was conducted using a fixed touch panel monitor and a desktop PC located in an exam room, with staff present throughout the exam. Participants completed 5 subcategories of CogEvo tasks used in the previous study (Same shape, Orientation, Flashlight, Route 99, and Follow the order; Supplemental Figure 1).^14,16,17 After the test, the computer automatically calculated results based on the number of correct answers and response time using the development company’s proprietary formula. If all 5 tasks have high scores, it is determined that the functions and abilities reflected by each task are high. Following previous research on the validity and reliability of CogEvo,¹⁴ in this study, in addition to the total score of the 5 tasks, we also evaluated the Orientation/spatial cognitive function factor consisting of the Same shape and Orientation tasks, and the Flash light, Route 99, and Follow the order. A total score based on the attention/executive function factor consisting of tasks was also used.

Statistical Analysis

Initially, we collected age, sex, educational level, MMSE scores, and CogEvo scores (including 5 subcategories score) from last 4 years of medical records. Age, sex, and educational level were utilized as basic demographic information of the study participants and as adjustment variables in the analysis. Sex was categorized as a nominal scale, with a value of 0 assigned to females and 1 assigned to males. The educational level was classified as an ordinal scale, where 1 represented elementary and junior high school, 2 represented high school graduates, 3 represented junior college and vocational school graduates, and 4 represented university graduates or individuals with higher qualifications.

Associations between demographic data, MMSE score, and CogEvo score were examined using Pearson’s and Spearman’s correlation analysis. Additionally, the total CogEvo and MMSE scores were compared among the 3 cognitive function groups (MMSE ≥ 28, MMSE24-27, and MMSE ≤ 23 groups) using 1-way analyses of variance (ANOVA), 1-way analyses of covariance (ANCOVA), and Kruskal-Wallis test. In addition, ANOVA, χ² test, and Kruskal-Wallis test were used to compare intergroup comparison of demographic data (age, sex, and educational level). Multiplicity of significance tests in multiple comparisons and correlation analyses were addressed with Bonferroni corrections. Receiver operating characteristic (ROC) analyses were conducted to assess the discrimination accuracy between 3 cognitive function groups (MMSE ≤ 23 groups vs MMSE ≥ 28, MMSE ≤ 23 vs MMSE24-27, and MMSE24-27 vs MMSE ≥ 28) by CogEvo scores. DeLong’s test was used to compare the area under the curve (AUC) among CogEvo scores. Statistical analysis was performed using IBM SPSS Statistics 25 (IBM Corp., Tokyo, Japan), JASP ver.0.16 (https://jasp-stats.org), and EZR ver.1.11 (https://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html). Statistical significance level was set at P < .05 (2-tailed test).

Results

Demographic Data of Participants

Based on selection criteria, a total of 209 sample data were collected in this study, and there were no missing data. The average age ± standard deviation was 79.4 ± 8.9 years, the male-to-female ratio was 85:124, and the participants had diverse educational backgrounds: 66 (31.6%) completed elementary and junior high school, 116 (55.5%) were high school graduates, 12 (5.7%) had completed junior college or vocational school, and 15 (7.2%) were university graduates or had higher qualifications.

Correlation Between MMSE and CogEvo Scores

Correlation analyses showed significant positive associations between the MMSE and CogEvo scores (total score: ρ = .54, Orientation/spatial cognitive function: ρ = .56, and Attention/executive function: ρ = .32, P < .001, respectively; Figure 1 and Table 1). The histogram of the total MMSE score revealed a ceiling effect (uneven distribution of scores caused by the upper limit restriction) compared to that of the total CogEvo score (Figure 1a). Similarly, a ceiling effect was noted in the comparison of total MMSE score versus Orientation/spatial cognitive function (Figure 1b), as well as in the comparison of total MMSE score versus Attention/executive function (Figure 1c). Also, MMSE and CogEvo scores were negatively associated with age (MMSE: ρ = −.25, P < .001, CogEvo: r = −.35, P < .001), while not with sex (MMSE: ρ = .09, n.s., CogEvo: ρ = .08, n.s.). Educational level was significantly associated with MMSE score (ρ = .21, P = .031), while not with CogEvo score (ρ = .16, n.s.).

Figure 1.

Results of correlation analyses between MMSE and CogEvo scores: (a) correlation between MMSE total score and CogEvo total score, (b) correlation between MMSE total score and Orientation/spatial cognitive function score, and (c) correlation between MMSE total score and Attention/executive function score.

Table 1.

Correlation Analyses Among CogEvo, MMSE, and Demographic Data.

	MMSE total	CogEvo total
CogEvo total	ρ = .54, P < .001	—
Orientation/spatial cognitive function	ρ = .56, P < .001	r = .81, P < .001
Attention/executive function	ρ = .32, P < .001	r = .83, P < .001
Age	ρ = −.25, P < .001	r = −.35, P < .001
Sex	ρ = .09, n.s.	ρ = 0.08, n.s.
Educational level (range: 1-4)	ρ = .21, P = .031	ρ = 0.16, n.s.

Abbreviations: MMSE, mini-mental state examination; ρ, Spearman’s correlation efficient; r, Pearson’ s correlation efficient.

Significant Difference among 3 Cognitive Function Groups

In the 3 cognitive function groups, significant main effects were observed for age (F_{2, 206} = 5.6, P = .004, partial η² = .05), educational level (H₂ = 9.9, P = .007) and total MMSE score (H₂ = 186.3, P < .001), while sex difference was not significant (χ²₂ = 1.2, P = .548, Cramer’s V = 0.05) (Table 2). ANCOVAs adjusting for age and educational level indicated significant differences in the total CogEvo score (F_{2, 204} = 25.8, P < .001, partial η² = .20), Same shape score (F_{2, 204} = 9.6, P < .001, partial η² = .09), Orientation score (F_{2, 204} = 35.8, P < .001, partial η² = .26), Follow the order score (F_{2, 204} = 11.1, P < .001, partial η² = .10), Orientation/spatial cognitive function (F_{2, 204} = 32.7, P < .001, partial η² = .24), and Attention/executive function (F_{2, 204} = 6.0, P = .003, partial η² = .06). There were no significant differences in Flash light score (F_{2, 204} = 2.0, P = .143, n.s.) and Route 99 score (F_{2, 204} = 2.0, P = .140, n.s.).

Table 2.

Comparison of CogEvo and MMSE Performance by 3 Cognitive Function Groups.

	Total	A: MMSE ≥ 28	B: MMSE24-27	C: MMSE ≤ 23	Group comparison
Sample	209	68	75	66	—
Age (years)	79.4 ± 8.9	76.9 ± 8.6	79.5 ± 8.8	81.9 ± 8.6	F_2,206 = 5.6* A < C*
Number of females	124	37	45	42	χ²₂ = 1.2n.s.
Educational level	2	2	2	2	H₂ = 9.9* A > C*
MMSE total	24.7 ± 5.0	29.1 ± 0.8	25.9 ± 1.1	18.7 ± 4.2	—
CogEvo total	1019.2 ± 276.7	1173.9 ± 239.4	1039.6 ± 250.3	836.7 ± 235.1	F_2,204 = 25.8* All pairs*
Same shape	244.3 ± 94.4	278.4 ± 82.5	250.1 ± 96.4	202.7 ± 89.1	F_2,204 = 9.6* A > C, B > C
Orientation	235.6 ± 103.8	292.0 ± 70.9	252.4 ± 90.4	158.6 ± 101.6	F_2,204 = 35.8* All pairs*
Flash light	177.5 ± 124.1	208.3 ± 131.7	175.1 ± 118.6	148.5 ± 116.4	F_2,204 = 2.0n.s.
Route 99	167.5 ± 49.9	178.3 ± 55.3	169.9 ± 43.8	153.7 ± 48.1	F_2,204 = 2.0n.s.
Follow the order	194.2 ± 51.9	217.0 ± 56.7	192.1 ± 43.5	173.2 ± 46.2	F_2,204 = 11.1* A > B, A > C
Orientation/spatial cognitive function	480.0 ± 164.8	570.4 ± 123.3	502.4 ± 152.9	361.3 ± 145.7	F_2,204 = 32.7* All pairs*
Attention/executive function	539.2 ± 173.1	603.5 ± 188.1	537.1 ± 151.1	475.4 ± 157.9	F_2,204 = 6.0* A > C*

MMSE ≥ 28, MMSE24-27, and MMSE ≤ 23. The educational level in the table shows the median; the other values show the mean ± standard deviation. All pairs: Significant differences on multiple comparisons with Bonferroni correction were observed in all group combinations. The confounder-adjusted estimates and 95% confidence intervals are presented in Supplemental Table 1. The results of sensitivity analysis using non-parametric tests were nearly identical.

Abbreviation: MMSE, mini-mental state examination.

P < .05.

Post-hoc multiple comparisons showed that the total CogEvo, the Orientation and Orientation/spatial cognitive function scores significantly decreased in the order of MMSE ≥ 28, MMSE24-27, and MMSE ≤ 23 groups (respectively, P < .001, except for the 3 comparisons of MMSE ≥ 28 and MMSE24-27 groups, in CogEvo: P = .013, in Orientation: P = .041, in Orientation/spatial cognitive function: P = .029). The Same shape score was significantly lower in the impaired group compared to the unimpaired and mildly impaired groups (MMSE ≤ 23 vs MMSE ≥ 28 groups: P < .001, MMSE ≤ 23 vs MMSE24-27 groups: P = .012). In the Follow the order score, a significant decrease was observed in the MMSE24-27 and MMSE ≤ 23 groups compared to the MMSE ≥ 28 group (MMSE24-27 vs MMSE ≥ 28 groups: P = .015, MMSE ≤ 23 vs MMSE ≥ 28 groups: P < .001). The MMSE ≤ 23 group had significantly lower performance than the unimpaired group in the Attention/executive function (P = .002).

Discrimination of MMSE ≤ 23 and MMSE ≥ 28 Groups

The ROC curves illustrating the discrimination of the total CogEvo score, Orientation/spatial cognitive function and Attention/executive function derived from CogEvo were presented in Figure 2. For discriminating between the MMSE ≤ 23 and MMSE ≥ 28 groups (Figure 2a), the AUC values were as follows: total CogEvo score = 0.85 (sensitivity 79%, specificity 77%, cut-off 996 points), Orientation/spatial cognitive function = 0.86 (sensitivity 84%, specificity 76%, cut-off 449 points), and Attention/executive function = 0.70 (sensitivity 54%, specificity 85%, cut-off 592 points). There was a significant difference between the AUC values of the total CogEvo score and the Attention/executive function (DeLong’s P < .001), as well as between the AUC values of the Orientation/spatial cognitive function (DeLong’s P = .002). However, there was no significant difference between the AUC values of the total CogEvo score and Orientation/spatial cognitive function.

Figure 2.

Results of ROC analyses: (a) discrimination of MMSE ≤ 23 and MMSE ≥ 28 groups, (b) discrimination of MMSE ≤ 23 and MMSE24-27 groups, and (c) discrimination of MMSE24-27 and MMSE ≥ 28 Groups.

Discrimination of MMSE ≤ 23 and MMSE24-27 Groups

In terms of discriminating between the MMSE ≤ 23 and MMSE24-27 groups (Figure 2b), the AUC values were as follows: total CogEvo score = 0.72 (sensitivity 75%, specificity 62%, cut-off 898 points) and Orientation/spatial cognitive function = 0.74 (sensitivity 63%, specificity 74%, cut-off 444 points). However, the discrimination accuracy for the Attention/executive function was low at 0.61 (sensitivity 59%, specificity 59%, cut-off 496 points). DeLong’s test revealed that the total CogEvo score and Orientation/spatial cognitive function were more discriminative than the Attention/executive function (respectively, P < .001 and P = .017). There was no significant difference between the AUC values of the total CogEvo score and Orientation/spatial cognitive function.

Discrimination of MMSE24-27 and MMSE ≥ 28 Groups

Regarding the discrimination accuracy between the MMSE24-27 and MMSE ≥ 28 groups (Figure 2c), the AUC values were low for all measures: total CogEvo score = 0.66 (sensitivity 65%, specificity 64%, cut-off 1085 points), Orientation/spatial cognitive function = 0.65 (sensitivity 63%, specificity 65%, cut-off 556 points), and Attention/executive function = 0.61 (sensitivity 53%, specificity 72%, cut-off 604 points). There were no significant differences on the Delong’s test between the AUC values of the total CogEvo score, Orientation/spatial cognitive function, and Attention/executive function.

Discussion

The MMSE is a widely used global cognitive dysfunction screening test that assesses orientation, memory, attention, language, and visuospatial cognition, with a total score of 30 points. The Japanese version, validated by Sugishita et al,¹⁸ suggests optimal cutoff values of 28/27 points for distinguishing between NC and MCI, and 24/23 points for differentiating MCI from AD. However, the MMSE often exhibits a ceiling effect, reducing its sensitivity in detecting mild cognitive dysfunction.^{9
-11,14,16,17,19} Furthermore, the MMSE lacks effectiveness in evaluating executive functions such as inference, planning, problem-solving, and set-shifting, which are often impaired in early cognitive dysfunction. Moreover, its results can be influenced by educational bias, rendering it more sensitive to variations in education levels than to the degree of cognitive dysfunction.^7,13,15

CogEvo is a computerized cognitive function assessment tool where participants interact with a touchscreen monitor and complete tasks in 5 subcategories: spatial recognition, orientation to time, attention, planning, and executive function. CogEvo scores are automatically computed based on reaction time and accuracy.^14,16,17 The CogEvo total score ranges from 0 to 2000 points, with a peak at around 1000 points, displaying a normal distribution without a ceiling effect—unlike MMSE and MoCA.

This renders CogEvo capable of detecting mild cognitive dysfunction and age-related cognitive decline. It’s also quantitative, objective, and reproducible, as it doesn’t require psychological specialists for administration and assessment. CogEvo’s independence from educational level is a notable advantage. Furthermore, CogEvo serves not only as a cognitive screening tool but also as a cognitive training tool, offering repeated assessments.

In this study, we conducted a comprehensive comparison and analysis of the correlation between CogEvo and MMSE performance across 3 cognitive function groups. Significant variations were observed in the total CogEvo and MMSE scores within these groups. Among the 5 subcategories of CogEvo, “Same shape,” “Orientation,” and “Follow the order” exhibited noteworthy differences, whereas “Flashlight” and “Route 99” did not show significant distinctions. Remarkably, the “Orientation/spatial cognitive function,” consisting of “Same shape” and “Orientation,” displayed significant differences across the 3 groups. Conversely, the “Attention/executive function,” which includes “Flash light,” and “Route 99” showed relatively little significant difference. This discrepancy may be attributed to the broad scoring of the orientation items on the MMSE, which strongly correlates with performance on the “Orientation” subtask or the “Orientation/spatial cognitive function” within CogEvo.

The analysis of AUC curves for CogEvo revealed that the MMSE ≥ 28 group could be distinguished from the MMSE24-27 and MMSE ≤ 23 groups with AUC values of 0.66 (sensitivity: 065%/ specificity: 64%) and 0.85 (sensitivity: 79%/ specificity: 77%), respectively. The corresponding cut-off values were found to be 1085 points and 996 points. This implies that differentiation between the MMSE ≥ 28 group and the MMSE24-27 and MMSE ≤ 23 groups is feasible using a cut-off value within the range of 1000 to 1100 points. In a prior study by Ichii et al,¹⁶ a reported cut-off value of 809 points, with a sensitivity of 70% and specificity of 60%, was identified for distinguishing the dementia group (MMSE ≤ 23) from the MCI group (MMSE24-26). Additionally, for distinguishing the MCI group (MMSE24-26) from the Cognitively unimpaired group (MMSE ≥ 27), a cut-off value of 995 points was reported, with a sensitivity of 78% and specificity of 54%.¹⁶ The Orientation/spatial cognitive function demonstrated a moderate discriminative ability between the MMSE ≥ 28 and MMSE ≤ 23 groups, achieving an AUC of 0.86 (sensitivity: 84%/ specificity: 76%) with a cut-off value of 449 points. Similarly, it displayed moderate discrimination between the MMSE ≥ 28 and MMSE24-27 groups, yielding an AUC of 0.65 (sensitivity: 63%/specificity: 65%) with a cut-off value of 556 points. Conversely, the Attention/executive function successfully distinguished the MMSE ≥ 28 group from the MMSE ≤ 23 group, with an AUC (sensitivity: 54%/ specificity: 85%) of 0.70 and a cut-off value of 592 points. It also exhibited the capacity to differentiate the MMSE ≥ 28 group from the MMSE24-27 group, achieving an AUC of 0.61 (sensitivity: 53%/specificity: 72%) with a cut-off value of 604 points. According to Pinto et al’s⁹ review, the AUC (sensitivity/specificity) for distinguishing between MCI and healthy individuals using MMSE ranged from 0.43 to 0.94 (18%-88%/52.2%-100%). In current study, the AUC and sensitivity/specificity for the identification using CogEvo between the MMSE 24 to 27 group, considered equivalent to MCI, and the MMSE > 27 group, considered equivalent to NC, were 0.66 and 65/64%, respectively. While not high, these results suggest moderate discriminability and sensitivity/specificity, or even higher, for CogEvo. In a study by Takechi and Yoshino,¹⁷ which included subjects diagnosed with MCI, the AUC (sensitivity/specificity) for CogEvo was reported as 0.75 (81.8%/57.9%). Taking these findings into consideration, CogEvo, which evaluates cognitive function across 5 subcategories, is believed to be capable of detecting mild cognitive impairment without a ceiling effect, particularly when considering age, compared to MMSE.

AD, the most prevalent cause of dementia, is recognized to initiate neuropathological changes approximately 15 to 20 years before the onset of symptoms, with cognitive dysfunction gradually worsening over an extended period.²⁰ By repeatedly using CogEvo to assess the long-term trajectory of cognitive function, it may be possible to track the transient and dynamic changes in cognitive impairment over time. This approach might allow for the detection of the onset of MCI, early AD, or other forms of dementia. Moreover, due to minimal ceiling effects and educational biases, assessing cognitive abilities in individuals with MMSE scores > 27 may provide valuable insights into early-stage illnesses or mild symptoms. On the other hand, compared to conventional paper-and-pencil tests, automated scoring and evaluation using a computer can be conducted, eliminating the need for the presence of psychological professionals and facilitating management and operation. This could potentially address labor shortages in Japan and lead to increased productivity.

In Japan, where the aging population trend is advancing, there is an increasing need for efficient and user-friendly tools to detect not only MCI and AD but also age-related cognitive decline/disorders in primary care settings and public health administration, where labor shortages are expected to worsen in the future. From the results of this study, the computerized screening tool CogEvo has been suggested to detect age-related cognitive decline and mild cognitive impairment without ceiling effects or educational biases, and without the need for skilled examiners. However, there are limitations to the findings of this study: (1) the analysis was conducted only on data from subjects capable of completing both MMSE and CogEvo, (2) the use of CogEvo requires the maintenance of visual, auditory, and manual dexterity functions, (3) there was no precise diagnosis of dementia such as MCI or AD, and (4) since most of the data were obtained during the COVID-19 pandemic, there is a possibility of some impact on performance.

Conclusion

CogEvo successfully alleviates the ceiling effect observed in MMSE, thereby enhancing its capability to detect age-related cognitive decline beyond the assessment ceiling. Furthermore, its capacity for automated evaluation and management by a computer, eliminating the requirement for the presence of psychological professionals, offers a potential solution to Japan’s labor shortages, and contributes to increased productivity. CogEvo shows significant promise in being particularly valuable for the early screening of cognitive impairment in elderly individuals within community settings, especially within primary care medical facilities.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319241239228 – Supplemental material for Assessment of Mild Cognitive Impairment Using CogEvo: A Computerized Cognitive Function Assessment Tool

Supplemental material, sj-docx-1-jpc-10.1177_21501319241239228 for Assessment of Mild Cognitive Impairment Using CogEvo: A Computerized Cognitive Function Assessment Tool by Toru Satoh, Yoichi Sawada, Hideaki Saba, Hiroshi Kitamoto, Yoshiki Kato, Yoshiko Shiozuka, Tomoko Kuwada, Sayoko Shima, Kana Murakami, Megumi Sasaki, Yudai Abe and Kaori Harano in Journal of Primary Care & Community Health

Supplemental Material

sj-docx-2-jpc-10.1177_21501319241239228 – Supplemental material for Assessment of Mild Cognitive Impairment Using CogEvo: A Computerized Cognitive Function Assessment Tool

Supplemental material, sj-docx-2-jpc-10.1177_21501319241239228 for Assessment of Mild Cognitive Impairment Using CogEvo: A Computerized Cognitive Function Assessment Tool by Toru Satoh, Yoichi Sawada, Hideaki Saba, Hiroshi Kitamoto, Yoshiki Kato, Yoshiko Shiozuka, Tomoko Kuwada, Sayoko Shima, Kana Murakami, Megumi Sasaki, Yudai Abe and Kaori Harano in Journal of Primary Care & Community Health

Footnotes

We thank the research collaborators. Permission to use the images (

) from the CogEvo has been obtained from Total Brain Care Co. Ltd.

Author Contributions

Toru Satoh (T.S.): Project administration,Supervision,Validation,Conceptualization,Methodology,Data curation,Formal analysis,Visualization,Investigation,Writing—original draft,Writing—review & editing. Yoichi Sawada (Y.S.): Validation,Conceptualization,Methodology,Data curation,Formal analysis,Writing—original draft,Writing—review & editing,Funding acquisition. Hideaki Saba (H.S.): Methodology,Data curation,Formal analysis,Investigation. Hiroshi Kitamoto (H.K.): Methodology,Data curation,Formal analysis,Investigation. Yoshiki Kato (Y.K.): Methodology,Data curation,Formal analysis,Investigation. Yoshiko Shiozuka (Y.S.): Methodology,Data curation. Tomoko Kuwada (T.K.): Methodology,Data curation. Sayoko Shima (S.S.): Methodology,Data curation. Kana Murakami (K.M.): Methodology,Data curation. Megumi Sasaki (M.S.): Methodology,Data curation. Yudai Abe (Y.A.): Methodology,Data curation. Kaori Harano (K.H.): Conceptualization,Methodology,Writing—review & editing.

Data availability

The datasets generated and/or analyzed during the current study can be obtained from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research,authorship,and/or publication of this article: The authors have declared that no competing interests exist. None of the authors received any funding from the developer of CogEvo ® (Total Brain Care Co. Ltd,Kobe,Japan).

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was primarily supported by the JSPS KAKENHI under Grant number JP18K03105.

ORCID iDs

Toru Satoh

Yoichi Sawada

Supplemental Material

Supplemental material for this article is available online.

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