Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies

Dementia with Lewy bodies is the second most common type of neurodegenerative dementia, and identification at the prodromal stage$-$i.e., mild cognitive impairment due to Lewy bodies (MCI-LB)$-$is important for providing appropriate care. However, MCI-LB is often underrecognized because of its diversity in clinical manifestations and similarities with other conditions such as mild cognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we propose a machine learning-based automatic pipeline that helps identify MCI-LB by exploiting balance measures acquired with an insole sensor during a 30-s standing task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46 cognitively normal) showed that the resultant models could discriminate MCI-LB from the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8% better than the accuracy of a reference model based on demographic and clinical neuropsychological measures. Our findings may open up a new approach for timely identification of MCI-LB, enabling better care for patients

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications

Heat shock factor 2 is required for maintaining proteostasis against febrile-range thermal stress and polyglutamine aggregation

International audienceHeat shock response is characterized by the induction of heat shock proteins (HSPs), which facilitate protein folding, and non-HSP proteins with diverse functions, including protein degradation, and is regulated by heat shock factors (HSFs). HSF1 is a master regulator of HSP expression during heat shock in mammals, as is HSF3 in avians. HSF2 plays roles in development of the brain and reproductive organs. However, the fundamental roles of HSF2 in vertebrate cells have not been identified. Here we find that vertebrate HSF2 is activated during heat shock in the physiological range. HSF2 deficiency reduces threshold for chicken HSF3 or mouse HSF1 activation, resulting in increased HSP expression during mild heat shock. HSF2-null cells are more sensitive to sustained mild heat shock than wild-type cells, associated with the accumulation of ubiquitylated misfolded proteins. Furthermore, loss of HSF2 function increases the accumulation of aggregated polyglutamine protein and shortens the lifespan of R6/2 Huntington's disease mice, partly through αB-crystallin expression. These results identify HSF2 as a major regulator of proteostasis capacity against febrile-range thermal stress and suggest that HSF2 could be a promising therapeutic target for protein-misfolding diseases

Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech.

ObjectiveThe growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.DesignParticipants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.SettingIndependent living sector of a senior housing community in San Diego County.ParticipantsEighty English-speaking older adults with age range 66-94 (mean 83 years).MeasurementsInterviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.ResultsLonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).ConclusionsAI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs

Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech.

ObjectiveThe growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.DesignParticipants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.SettingIndependent living sector of a senior housing community in San Diego County.ParticipantsEighty English-speaking older adults with age range 66-94 (mean 83 years).MeasurementsInterviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.ResultsLonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).ConclusionsAI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study

BackgroundThe rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. ObjectiveThis study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. MethodsThis was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. ResultsWe obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. ConclusionsThis study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE

Predictive factors for hyperglycaemic progression in patients with schizophrenia or bipolar disorder

Background: Patients with schizophrenia or bipolar disorder have a high risk of developing type 2 diabetes. Aims: To identify predictive factors for hyperglycaemic progression in individuals with schizophrenia or bipolar disorder and to determine whether hyperglycaemic progression rates differ among antipsychotics in regular clinical practice. Method: We recruited 1166 patients who initially had normal or prediabetic glucose levels for a nationwide, multisite, l-year prospective cohort study to determine predictive factors for hyperglycaemic progression. We also examined whether hyperglycaemic progression varied among patients receiving monotherapy with the six most frequently used antipsychotics. Results: High baseline serum triglycerides and coexisting hypertension significantly predicted hyperglycaemic progression. The six most frequently used antipsychotics did not significantly differ in their associated hyperglycaemic progression rates over the 1-year observation period. Conclusions: Clinicians should carefully evaluate baseline serum triglycerides and coexisting hypertension and perform strict longitudinal monitoring irrespective of the antipsychotic used

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.

BackgroundWith the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.ObjectiveThe aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.MethodsWe collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.ResultsWe found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P&lt;.001).ConclusionsThis study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment