Vocal expression of emotions discriminates dementia with Lewy bodies from Alzheimer's disease.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), the two most common neurodegenerative dementias, both exhibit altered emotional processing. However, how vocal emotional expressions alter in and differ between DLB and AD remains uninvestigated. We collected voice data during story reading from 152 older adults comprising DLB, AD, and cognitively unimpaired (CU) groups and compared their emotional prosody in terms of valence and arousal dimensions. Compared with matched AD and CU participants, DLB patients showed reduced overall emotional expressiveness, as well as lower valence (more negative) and lower arousal (calmer), the extent of which was associated with cognitive impairment and insular atrophy. Classification models using vocal features discriminated DLB from AD and CU with an AUC of 0.83 and 0.78, respectively. Our findings may aid in discriminating DLB patients from AD and CU individuals, serving as a surrogate marker for clinical and neuropathological changes in DLB. Highlights: DLB showed distinctive reduction in vocal expression of emotions.Cognitive impairment was associated with reduced vocal emotional expression in DLB.Insular atrophy was associated with reduced vocal emotional expression in DLB.Emotional expression measures successfully differentiated DLB from AD or controls.

Distinct eye movement patterns to complex scenes in Alzheimer's disease and Lewy body disease.

Background: Alzheimer's disease (AD) and Lewy body disease (LBD), the two most common causes of neurodegenerative dementia with similar clinical manifestations, both show impaired visual attention and altered eye movements. However, prior studies have used structured tasks or restricted stimuli, limiting the insights into how eye movements alter and differ between AD and LBD in daily life. Objective: We aimed to comprehensively characterize eye movements of AD and LBD patients on naturalistic complex scenes with broad categories of objects, which would provide a context closer to real-world free viewing, and to identify disease-specific patterns of altered eye movements. Methods: We collected spontaneous viewing behaviors to 200 naturalistic complex scenes from patients with AD or LBD at the prodromal or dementia stage, as well as matched control participants. We then investigated eye movement patterns using a computational visual attention model with high-level image features of object properties and semantic information. Results: Compared with matched controls, we identified two disease-specific altered patterns of eye movements: diminished visual exploration, which differentially correlates with cognitive impairment in AD and with motor impairment in LBD; and reduced gaze allocation to objects, attributed to a weaker attention bias toward high-level image features in AD and attributed to a greater image-center bias in LBD. Conclusion: Our findings may help differentiate AD and LBD patients and comprehend their real-world visual behaviors to mitigate the widespread impact of impaired visual attention on daily activities.

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

BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: This 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. RESULTS: We 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. CONCLUSIONS: This 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.

Association between behavioural and psychological symptoms of dementia and residence status in patients with Alzheimer's disease.

BACKGROUND: Examining the relationship between the behavioural and psychological symptoms of dementia (BPSD) and residence status is crucial to improving BPSD and reducing the burden on caregivers. However, studies on how BPSD differ between individuals living at home and those in institutional settings are lacking. We conducted a questionnaire survey among healthcare providers (HCPs) involved in dementia care and nursing to clarify the characteristics of BPSD by residence status in patients with Alzheimer's disease (AD) living at home or in facilities. METHODS: We sent questionnaires to HCPs and asked them to answer questions on up to five cases that needed treatment for BPSD and who received long-term care insurance services from 1 April 2016 to 31 March 2017. Responses were received for 371 cases, of which 130 diagnosed with AD were analyzed. The patients were divided into two groups: patients with AD living at home (home care group) and patients with AD living in facilities (facility care group). A Chi-square test was used to identify differences between the two groups. A binomial logistic regression analysis was also conducted to clarify the association between residence status and BPSD. RESULTS: Of the 130 patients, 72 lived at home (home care group) and 58 resided in facilities (facility care group). None of the background factors was significantly different between the two groups. The Chi-square test indicated that sleep disturbance was significantly more common in the facility care group (60.3% in the facility care group vs. 33.3% in the home care group, P = 0.003), while the logistic regression analysis indicated that sleep disturbance was significantly associated with residence status (odds ratio: 2.529, P = 0.038). CONCLUSIONS: Sleep disturbances were more frequently observed among patients with AD living in institutions than among those living in their homes.

Structural Cerebral Network Differences in Prodromal Alzheimer's Disease and Prodromal Dementia with Lewy Bodies.

INTRODUCTION: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have long prodromal phases without dementia. However, the patterns of cerebral network alteration in this early stage of the disease remain to be clarified. METHOD: Participants were 48 patients with mild cognitive impairment (MCI) due to AD (MCI-AD), 18 patients with MCI with DLB (MCI with Lewy bodies: MCI-LB), and 23 healthy controls who underwent a 1.5-Tesla magnetic resonance imaging scan. Cerebral networks were extracted from individual T1-weighted images based on the intracortical similarity, and we estimated the differences of network metrics among the three diagnostic groups. RESULTS: Whole-brain analyses for degree, betweenness centrality, and clustering coefficient images were performed using SPM8 software. The patients with MCI-LB showed significant reduction of degree in right putamen, compared with healthy subjects. The MCI-AD patients showed significant lower degree in left insula and bilateral posterior cingulate cortices compared with healthy subjects. There were no significant differences in small-world properties and in regional gray matter volume among the three groups. CONCLUSIONS: We found the change of degree in the patients with MCI-AD and with MCI-LB, compared with healthy controls. These findings were consistent with the past single-photon emission computed tomography studies focusing on AD and DLB. The disease-related difference in the cerebral neural network might provide an adjunct biomarker for the early detection of AD and DLB.

Speech and language characteristics differentiate Alzheimer's disease and dementia with Lewy bodies.

Introduction: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated. Methods: We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features. Results: The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine-learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB. Discussion: Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

Characteristics of Drawing Process Differentiate Alzheimer's Disease and Dementia with Lewy Bodies.

BACKGROUND: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been studied. OBJECTIVE: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. METHODS: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. RESULTS: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. CONCLUSION: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks.

BACKGROUND: Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored. OBJECTIVE: We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability. METHODS: We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the associations of the features with the participants' diagnostic statuses and cognitive measures. RESULTS: The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model. CONCLUSION: Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated, reliable detection of AD and MCI.

Effects of a multicomponent day-care program on cerebral blood flow in patients with mild cognitive impairment.

BACKGROUND: Mild cognitive impairment (MCI) is a prodromal phase of dementia and is considered an important period for intervention to prevent conversion to dementia. It has been well established that multicomponent day-care programs including exercise training, cognitive intervention and music therapy have beneficial effects on cognition, but the effects on cerebral blood flow (CBF) in MCI remain unknown. This study examined whether a multicomponent day-care program would have beneficial effects on the longitudinal changes of CBF in MCI patients. METHODS: Participants were 24 patients with MCI attending a day-care program; they underwent two 99 mTc-ethyl cysteinate dimer single photon emission computed tomography scans during the study period. We evaluated the association between the changes of regional cerebral blood flow and the attendance rate. RESULTS: There was a significant negative correlation between the reduction of regional CBF in the right parietal region and the attendance rate. We found no significant relation between the baseline CBF images and the attendance rate. CONCLUSIONS: Our results suggest that continuous participation in a multicomponent day-care program might prevent reduction in brain activity in patients with MCI.

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

BACKGROUND: With 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: We 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<.001). CONCLUSIONS: This 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.

The Multicomponent Day-Care Program Prevents Volume Reduction in a Memory-Related Brain Area in Patients with Mild Cognitive Impairment.

INTRODUCTION: Mild cognitive impairment (MCI) is considered an important period for interventions to prevent progression to dementia. Nonpharmacological interventions for MCI include exercise training, cognitive intervention, and music therapy. These play an important role in improving cognitive function, but their effects on brain plasticity in individuals with MCI are largely unknown. We investigated the effects of a multicomponent day-care program provided by the University of Tsukuba Hospital on the longitudinal brain volume changes in MCI patients. METHODS: MCI patients who participated in the multicomponent day-care program and underwent whole-brain magnetic resonance imaging (MRI) twice during their participation (n = 14), were included. We divided them into two groups according to their attendance rate and conducted a between-group analysis of longitudinal volume changes in the whole cerebral cortex. Regional brain volumes derived from the patients' MRI were calculated with Freesurfer 6.0.0. RESULTS: The neuroimaging analysis demonstrated that the left rostral anterior cingulate cortex volume was significantly preserved in the high-attendance group compared to that of the low-attendance group. CONCLUSION: Our results suggest that continuous participation in a multicomponent day-care program could help prevent a volume reduction in memory-related brain areas in patients with MCI.

Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions.

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R 2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.

Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions.

Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.

BACKGROUND: Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. OBJECTIVE: We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. METHODS: Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. RESULTS: Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. CONCLUSION: Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study.

BACKGROUND: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. OBJECTIVE: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. METHODS: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data-neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)-from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. RESULTS: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. CONCLUSIONS: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.

A Novel Exercise for Enhancing Visuospatial Ability in Older Adults with Frailty: Development, Feasibility, and Effectiveness.

We aimed to develop a novel exercise to improve visuospatial ability and evaluate its feasibility and effectiveness in older adults with frailty. A non-randomized preliminary trial was conducted between June 2014 and March 2015. We recruited 35 adults with frailty (24 women), aged 66-92 years. Participants were assigned to either locomotive- or visuospatial-exercise groups. All participants exercised under the supervision of physiotherapists for 90 min/week for 12 weeks. The visuospatial exercise participants used cubes with six colored patterns and were instructed to "reproduce the same colored pattern as shown in the photo", using the cubes. In the locomotive exercise group, lower extremity functional training was provided. Rates of retention and attendance measured feasibility. Most participants completed the intervention (77.3%, locomotive; 84.6%, visuospatial) and had good attendance (83.8%, locomotive; 90.7%, visuospatial). Mini-mental state examination (MMSE), clock drawing test (CDT), and seven physical performance tests were conducted before and after interventions. The improvement in the MMSE score, qualitative analysis of CDT, grip strength, and sit and reach assessments were significantly greater in the visuospatial exercise group than in the locomotive exercise group. The cube exercise might be a feasible exercise program to potentially improve visuospatial ability and global cognition in older adults with frailty.

Handwriting Features of Multiple Drawing Tests for Early Detection of Alzheimer's Disease: A Preliminary Result.

Early detection of Alzheimer's disease (AD) has become increasingly important. Healthy monitoring technology focusing on behavioral changes is a promising approach in this vein. Among such technologies, handwriting features measured by digital tablet devices have attracted attention as potential indicators for detecting AD and mild cognitive impairment (MCI). However, previous studies have mainly investigated features in single tasks, and it remains unclear whether combining the features of multiple tasks could improve the performance of detecting AD and MCI. In this study, we investigated features in five representative tasks used in neuropsychological tests collected from 71 seniors including some diagnosed with MCI and AD. We found that our three-class classification model improved diagnosis accuracy by up to 11.3% by combining features of multiple tasks, for a final accuracy of 74.6%. We also suggested that drawing behaviors during multiple tasks might be useful for estimating disease progression simply by utilizing the labels of disease groups.

Multimodal Behavior Analysis Towards Detecting Mild Cognitive Impairment: Preliminary Results on Gait and Speech.

Behavioral analysis for identifying changes in cognitive and physical functioning is expected to help detect dementia such as mild cognitive impairment (MCI) at an early stage. Speech and gait features have been especially recognized as behavioral biomarkers for dementia that possibly occur early in its course, including MCI. However, there are no studies investigating whether exploiting the combination of multimodal behavioral data could improve detection accuracy. In this study, we collected speech and gait behavioral data from Japanese seniors consisting of cognitively healthy adults and patients with MCI. Comparing the models using single modality behavioral data, we showed that the model using multimodal behavioral data could improve detection by up to 5.9%, achieving 82.4% accuracy (chance 55.9%). Our results suggest that the combination of multimodal behavioral features capturing different functional changes resulting from dementia might improve accuracy and help timely diagnosis at an early stage.

Using Tablet-Based Assessment to Characterize Speech for Individuals with Dementia and Mild Cognitive Impairment: Preliminary Results.

Early detection of dementia as well as improvement in diagnosis coverage has been increasingly important. Previous studies involved extracting speech features during neuropsychological assessments by humans, such as medical pro- fessionals, and succeeded in detecting patients with dementia and mild cognitive impairment (MCI). Enabling such assessment in an automated fashion by using computer devices would extend the range of application. In this study, we developed a tablet-based application for neuropsychological assessments and collected speech data from 44 Japanese native speakers including healthy controls (HCs) and those with MCI and dementia. We first extracted acoustic and phonetic features and showed that several features exhibited significant difference between HC vs. MCI and HC vs. dementia. We then constructed classification models by using these features and demonstrated that these models could differentiate MCI and dementia from HC with up to 82.4 and 92.6% accuracy, respectively.

Longitudinal association between habitual walking and fall occurrences among community-dwelling older adults: analyzing the different risks of falling.

The purpose of this longitudinal study was to examine the association between habitual walking and multiple or injurious falls (falls) among community-dwelling older adults, by considering the relative risk of falling. A cohort of Japanese community-dwelling older adults (n=535) aged 60-91 years (73.1±6.6 year, 157 men and 378 women) who underwent community-based health check-ups from 2008 to 2012 were followed until 2013. Incidence rate of falls between walkers and non-walkers was compared separately by the number of risk factors (Groups R0, R1, R2, R3 and R4+). The Cox proportional hazard model was used to assess the association between habitual walking and falls separately by lower- (R<2) and higher- (R≥2) risk groups. In Groups R0 and R1, the incidence of falls was lower in walkers than non-walkers; however, in Groups R2, R3, and R4+, the incidence of falls was higher in walkers. The Cox proportional hazard model showed that habitual walking was not significantly associated with falls (hazard ratio (HR): 0.88, 95% confidence interval (CI): 0.48-1.62) among the lower risk group but that it was significantly associated with increased falls (HR: 1.89, 95% CI: 1.04-3.43) among the higher risk group. The significant interaction between habitual walking and higher risk of falling was found (P<0.05). When individuals have two or more risk factors for falling, caution is needed when recommending walking because walking can actually increase their risk of experiencing multiple or injurious falls.