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Browsing by Subject "Electronically Activated Recorder (EAR)"
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Item Examining Affect in Psychometric Schizotypy Using Behavioral Experience Sampling Methodology(Office of the Vice Chancellor for Research, 2015-04-17) Brown, Chase A.; Davis, Beshaun; Marggraf, Matthew P.; Luther, Lauren; Minor, Kyle S.In schizophrenia, patients often experience more negative emotions in the form of anger, sadness, and anxiety when compared to the general population. One unique way of measuring affect outside of the laboratory has been to use Experience Sampling Methods (ESM) to assess how individuals perceive current emotions in their daily life. However, these methods are still subject to self-report bias. In this study, we examined affect using traditional ESM methods while also implementing the Electronically Activated Recorder (EAR), a behaviorally-based ESM measure that provides real-world assessments of speech. To examine the EAR, we evaluated affect in schizotypy and non-schizotypy groups. Research shows that schizophrenia-like experiences, like increased negative affect, run along a continuum. Schizotypy is a category on the healthier end of the schizophrenia-spectrum; it applies to individuals who are thought to have a putative genetic liability for schizophrenia. Using the Linguistic Inquiry and Word Count (LIWC), we compared affective word usage among schizotypy and non-schizotypy groups to provide a real-world, behavioral ESM measure. When traditional ESM measures were used, we found individuals with schizotypy reported less negative emotions compared to the non-schizotypy group, but results did not reach the level of significance. We also observed that non-schizotypy individuals reported slightly higher positive emotions, and the schizotypy group reported slightly higher negative emotions. A similar pattern was observed when examining EAR data. Overall, results suggested that traditional and behavioral ESM measures of affect had significant overlap. In general, those with schizotypy demonstrated slightly more negative emotion and slightly less positive emotion than the non-schizotypy group. Findings did not reach the level of significance. This study demonstrates that the EAR provides behavioral ratings of affect that are on par with traditional ESM ratings. Future work should examine the EAR at different points on the schizophrenia-spectrum.Item Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach(JMIR, 2022-03-08) Ferrario, Andrea; Luo, Minxia; Polsinelli, Angelina J.; Moseley, Suzanne A.; Mehl, Matthias R.; Yordanova, Kristina; Martin, Mike; Demiray, Burcu; Neurology, School of MedicineBackground: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. Objective: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. Methods: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. Results: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. Conclusions: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.