Regression-Based Normative Data for Independent and Cognitively Active Spanish Older Adults: Free and Cued Selective Reminding Test, Rey-Osterrieth Complex Figure Test and Judgement of Line Orientation

Normative scores for attentional tests used by the Spanish consortium for ageing normative data (SCAND) study: Trail Making Test, Digit Symbol and Letter Cancellation

Objective: This paper reports normative data for different attentional tests obtained from a sample of middle-aged and older native Spanish adults and considering effects of age, educational level and sex. Method: 2,597 cognitively intact participants, aged from 50 to 98 years old, participated voluntarily in the SCAND consortium studies. The statistical procedure included conversion of percentile ranges into scaled scores. The effects of age, education and sex were taken into account. Linear regressions were used to calculate adjusted scaled scores. Results: Scaled scores and percentiles corresponding to the TMT, Digit Symbol and Letter Cancellation Task are shown. Additional tables show the values to be added to or subtracted from the scaled scores, for age and education in the case of the TMT and Letter Cancellation Task measures, and for education in the case of the Digit Symbol subtest. Conclusions: The current norms provide clinically useful data for evaluating Spanish people aged 50 to 98 years old and contribute to improving detection of initial symptoms of cognitive impairment.

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

BACKGROUND

Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method.

OBJECTIVE

This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices.

METHODS

This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).

RESULTS

We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.

CONCLUSIONS

The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.

Regression-Based Normative Data for Independent and Cognitively Active Spanish Older Adults: Verbal Fluency Tests and Boston Naming Test

An increased cognitive reserve is associated with changes in the pattern of cognitive decline during aging. Thus, normative data adapted to the characteristics of the target population are needed to reduce the possibility of false diagnoses. The aim of this work was to develop normative data for the Phonemic Verbal Fluency test, the Semantic Verbal Fluency test and the Boston Naming Test (BNT).

METHOD

Regression-based normative data were calculated from a sample of 118 non-depressed, cognitively active, independent community-dwelling adults aged 55 or older (64.4% women) from SABIEX (University for Seniors at the Universidad Miguel Hernández de Elche). Raw scores were regressed on age, sex, and education.

RESULTS

The effects of age and education varied across neuropsychological measures. No effect of sex was found in any of the tests assessed. Statistically significant differences were found in the proportion of low scores using SABIEX or population-based normative datasets. The level of agreement identifying individuals labeled as showing one or more low scores was only fair-to-good.

CONCLUSIONS

Normative data obtained from the general population might not be sensitive to identify low scores in cognitively active older adults, increasing the risk of misdiagnoses. A friendly calculator is available for neuropsychological assessment.