Expected Findings
RS, MCI, depressive mood
We aimed to reveal the psychometric properties of EOmciSS, a newly designed, rapid, digital cognitive test for screening of individuals with MCI in community settings. The ability of EOmciSS to differentiate individuals with MCI risks from those with NCF was found to be higher than that of other online systems in this research field. Essential content domains for the MCI classification versus the NCF classification were mainly in the Acc of the performances in the order of visuospatial attention, planning, and executive function (in CC and CD); cognitive flexibility and working memory (in TM); and verbal memory (in DR). In the TM subtest, the connection from the third-to-fourth alternative symbols contributed the most to the classification model. Besides, the speed of performances in terms of RT (in RS) and recall of memory items (in DR) significantly predicted membership of the MCI group or the NCF group. Incorporation of a “depressive mood” measure in the first section of EOmciSS is a new design for a cognitive screening test. Evidence suggests that individuals with a depressive mood are likely to have low motivation and delays in executing actions, which would pose threats to the internal validity of cognitive test performances (eg, lower Acc or longer RT). For EOmciSS, the impacts would be on the measures of Acc and RT. The identification of users of EOmciSS with the GDS-15 before them entering into the cognitive test section would safeguard the validity of the cognitive screening for MCI.The test construct of EOmciSS was substantiated with results from the confirmatory factor analysis. The 20 item factors were clustered satisfactorily into 3 latent Acc factors with respect to the test content of visual attention and mental flexibility (TM), visuospatial and executive function (CD and CC), memory (DR), and cognitive processing speed (RS). The Acc of the 3 CD items and 1 CC item was grouped under 1 single latent factor. For CD, the 3 Acc item factors were “placing the 12 numbers (ie, “1” to “12”) on the face of a clock” (#CD-Acc1), “orienting the hour hand” (#CD-Acc2), and “orienting the minute hand” (#CD-Acc3). The CD item had 1 Acc item factor: “drawing the cube.” Within CD items, the “orienting the minute hand” Acc item factor was the most difficult (mean 0.85), but it was less difficult than the “draw the cube” item factor (mean 0.63). CD and CC are tests for visuospatial, planning, and executive functions, which have been reported to be effective for screening for individuals with MCI [We emphasized the Acc and speed of participants’ performances when designing EOmciSS. EOmciSS has made possible the capture of both performance parameters at a response level rather than at an item level. For instance, CD has 3 Acc factors and 1 RT factor. The definitions for the 4 item factors are different from those in the Digital Clock Drawing study, which quantifies the size, angle, and spacing between the clock elements, and their latencies [For the CC item, Oonuma et al [The TM items, despite their marginal significance in the prediction model, appeared to have a compatibly important role as those of CD and CC. Interestingly, among the 6 factors, the “third-to-fourth symbol connection” Acc item factor (#TM-Acc4) was the best discriminative index. The “third-to-fourth symbol connection” was the only TM item to be included in the MCI versus NCF prediction model. It yielded the largest β coefficient and, therefore, was assigned with the heaviest weight contributing to the EOmciSS score. The design of the TM item is comparable with the conventional Trail Making Test [About 90% (745/827) of participants completed EOmciSS in 10 minutes. This short time is a promising factor for maximizing the number of community-dwelling older adults who complete the test. Compared with other computerized tests, the CoCoSc system used by Wong et al [Our study had 3 main limitations. First, participants were older adults who had been educated for more than 6 years. Thus, our results may not be generalizable to those who have only a few years of education or who are illiterate. We should explore possible adaptations of test content and a user interface that is appropriate for these groups. Second, compared with other computerized screening tests, EOmciSS could be used to differentiate individuals with MCI from individuals with NCF. However, the content representativeness of EOmciSS was limited. Therefore, EOmciSS data should be interpreted as indicating the risks, but not the diagnosis, of MCI. Additional neuropsychological and medical tests are recommended for reaching a clinical diagnosis. Third, we used a cross-sectional method to gather evidence, which hampers the repeatability and stability of test results. We must investigate the robustness of the test scores and classification results by using a longitudinal design and analytic methods, as described by Lei and colleagues [