RESUMO
Objective: 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. Method: 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. Results: 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. Conclusion: 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.
RESUMO
OBJECTIVE: The Reliable Digit Span (RDS) is an embedded measure used to assess performance validity. The purpose of this study was to determine whether the RDS can be used with Spanish speakers from Latin America. METHOD: The Digit Span subtest of the Wechsler Adult Intelligence Scale-IV was administered to 66 Spanish-speaking patients, 66 English-speaking patients, and 30 Spanish-speaking normal controls. The Spanish-speaking patients had a mean age of 65.82 (standard deviation [SD] = 12.44) and mean education of 6.62 years (SD = 4.91), whereas the controls had a mean age of 48.27 (SD = 10.97) and mean education of 8.33 (SD = 3.04). The English-speaking patients had a mean age of 56.44 (SD = 13.53) and mean education of 12.14 (SD = 2.37). RESULTS: The average RDS among the Spanish-speaking patients was 5.36 (SD = 1.27) and the average RDS among the controls was 5.90 (SD = 1.06). The mean RDS of the English-speaking patients was 7.83 (SD = 1.72). The modal RDS among Spanish-speaking patients was 5. The modal RDS among Spanish-speaking controls was also 5, though the distribution was bimodal in nature with nearly as many 7 s as 5 s. The modal RDS among the English-speaking patients was 7. Among patients with ≥9 years of education and controlling for age, there remained a significant difference between both language patient groups on the RDS (F(1, 79) = 30.85, p < .0001). CONCLUSIONS: These data suggest that the RDS may not be a valuable measure in assessing performance validity among monolingual Spanish speakers from Latin America, particularly among those with low levels of education.
