2 results
78 Preliminary Exploration of a Novel Speech Analysis Algorithm to Detect Cognitive Impairment in a Spanish Population
- Alyssa N Kaser, Jeff Schaffert, Munro Cullum, Javier Jimenez-Raboso, Pablo de la Guardia, Peru Gabirondo, Alberto J Coca, Laura Lacritz
-
- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 482-483
-
- Article
-
- You have access Access
- Export citation
-
Objective:
Early detection of mild cognitive impairment (MCI) and dementia is crucial for initiation of treatment and access to appropriate care. While comprehensive neuropsychological assessment is often an intrinsic part of the diagnostic process, access to services may be limited and cannot be utilized effectively on a large scale. For these reasons, cognitive screening instruments are used as brief and cost-effective methods to identify individuals who require further evaluation. Novel technologies and automated software systems to screen for cognitive changes in older individuals are evolving as new avenues for early detection. The present study presents preliminary data on a new technology that uses automated linguistic analysis software to screen for MCI and dementia.
Participants and Methods:Data were collected from 148 Spanish-speaking individuals recruited in Spain (MAge=74.4, MEducation=12.93, 56.7% females) of whom 78 were diagnosed as cognitively normal [CN; Mmmse = 28.51 (1.39)], 49 as MCI [MMMSE = 25.65 (2.94)], and 21 as all-cause dementia [MMMSE = 22.52 (2.06)]. Participants were recorded performing various verbal tasks [Animal fluency, phonemic (F) fluency, Cookie Theft Description, and CERAD list learning task]. Recordings were processed via text-transcription and sound signal processing techniques to capture neuropsychological variables and audio characteristics. Features from each task were used in the development of an algorithm (for that task) to compute a score between 0 or 1 (healthy to more impairment), and a fifth algorithm was constructed using audio characteristics from all tasks. These five classifiers were combined algorithmically to provide the final algorithm. Receiver Operating Characteristic (ROC) analysis was conducted to determine sensitivity and specificity of predicted algorithm performance [CN vs. impaired (MCI or dementia)] against clinical diagnoses, and additional general linear modeling was used to test whether age, sex, education, and multilingualism significantly predicted logistically transformed weighted algorithm scores.
Results:Scores were transformed to logit scores, with significant differences in mean logit scores between all groups (p <.001). Logit-inverse transformation of mean logit scores (possible range 0 -1) resulted in values of 0.06 for CN, 0.90 for MCI, and 0.99 for all-cause dementia groups. ROC curve analyses revealed the algorithm obtained a total area under the curve of 0.92, with an overall accuracy of 86.8%, a sensitivity of 0.92, and specificity of 0.82. Age was identified as a significant predictor (beta = 0.22; p <0.01) of algorithm output, whereas years of education (beta = -0.04; p = 0.64), sex (beta = 0.38; p = 0.02, did not survive correction for type-1 error), and multilingualism (beta = -0.24; p = 0.22) were non-significant.
Conclusions:These findings provide initial support for the utility of an automated speech analysis algorithm to detect cognitive impairment quickly and efficiently in a Spanish-speaking population. Although sociodemographic variables were not included in the algorithm, age significantly predicted algorithm output, and should be further explored to determine if age-adjusted formulas would improve algorithm accuracy for younger versus older individuals. Additional research is needed to validate this novel methodology in other languages, as this may represent a promising cross-cultural screening method for MCI and dementia detection.
Attitudes towards Alzheimer's disease as a risk factor for caregiver burden
- L. Zawadzki, K. Mondon, N. Peru, C. Hommet, T. Constans, Ph. Gaillard, V. Camus
-
- Journal:
- International Psychogeriatrics / Volume 23 / Issue 9 / November 2011
- Published online by Cambridge University Press:
- 04 May 2011, pp. 1451-1461
-
- Article
- Export citation
-
Background: There is abundant literature on the determinants of caregiver burden in Alzheimer's disease (AD), but little is known about the possible implication of specific patterns of a caregiver's attitudes towards the disease that could increase their risk of – or protect them from – emotional distress and burden. The aim of this study was to test the hypothesis that negative attitudes towards AD are associated with an increased level of burden experienced by caregivers of AD patients.
Methods: Family caregivers of 51 patients with AD were asked to complete a questionnaire regarding their attitudes towards AD. In addition, we assessed the level of their quality of life, anxiety and depression as well as their perceived level of burden. In parallel, we documented the patients’ characteristics: global cognitive efficiency (Mini-Mental State Examination), behavioral and affective symptoms (Neuropsychiatric Inventory) and functional level (Instrumental Activities of Daily Living).
Results: The score of caregiver burden was positively correlated with negative attitudes such as authoritarianism (r = 0.41, p < 0.01) and social restrictiveness (r = 0.49, p < 0.001) as well as emotional reactions of anxiety (r = 0.44, p < 0.01) and aggressiveness (r = 0.47, p < 0.001). In addition, scores of social restrictiveness, rejection and anxiety were significantly higher in women than in men.
Conclusion: These results may have implications in terms of the prevention of caregiver burden. In particular, educational and support programs for caregivers should not be limited to developing their knowledge and skills but should also target attitudes towards the disease.