This study aimed to investigate the association between a virtual reality (VR) intervention program and cognitive, brain and physical functions in high-risk older adults. In a randomized controlled trial, we enrolled 68 individuals with mild cognitive impairment (MCI). The MCI diagnosis was based on medical evaluations through a clinical interview conducted by a dementia specialist. Cognitive assessments were performed by neuropsychologists according to standardized methods, including the Mini-Mental State Examination (MMSE) and frontal cognitive function: trail making test (TMT) A & B, and symbol digit substitute test (SDST). Resting state electroencephalogram (EEG) was measured in eyes open and eyes closed conditions for 5 minutes each, with a 19-channel wireless EEG device. The VR intervention program (3 times/week, 100 min each session) comprised four types of VR game-based content to improve the attention, memory and processing speed. Analysis of the subjects for group–time interactions revealed that the intervention group exhibited a significantly improved executive function and brain function at the resting state. Additionally, gait speed and mobility were also significantly improved between and after the follow-up. The VR-based training program improved cognitive and physical function in patients with MCI relative to controls. Encouraging patients to perform VR and game-based training may be beneficial to prevent cognitive decline.

Our study examined the association between chronotype, daily physical activity, and the estimated risk of dementia in 170 community-dwelling older adults. Chronotype was assessed with the Horne–Östberg Morningness–Eveningness Questionnaire (MEQ). Daily physical activity (of over 3 METs) was measured with a tri-axial accelerometer. The Korean version of the Mini-Mental State Examination (K-MMSE) was used to measure the estimated risk of dementia. The evening chronotype, low daily physical activity, and dementia were positively associated with each other. The participants with low physical activity alongside evening preference had 3.05 to 3.67 times higher estimated risk of developing dementia, and participants with low physical activity and morning preference had 1.95 to 2.26 times higher estimated risk than those with high physical activity and morning preference. Our study design does not infer causation. Nevertheless, our findings suggest that chronotype and daily physical activity are predictors of the risk of having dementia in older adults aged 70 years and above.

Current step-count estimation techniques use either an accelerometer or gyroscope sensors to calculate the number of steps. However, because of smartphones unfixed placement and direction, their accuracy is insufficient. It is necessary to consider the impact of the carrying position on the accuracy of the pedometer algorithm, because of people carry their smartphones in various positions. Therefore, this study proposes a carrying-position independent ensemble step-counting algorithm suitable for unconstrained smartphones in different carrying positions. The proposed ensemble algorithm comprises a classification algorithm that identifies the carrying position of the smartphone, and a regression algorithm that considers the identified carrying position and calculates the number of steps. Furthermore, a data acquisition system that collects (i) label data in the form of the number of steps estimated from the Force Sensitive Resistor (FSR) sensors, and (ii) input data in the form of the three-axis acceleration data obtained from the smartphones is also proposed. The obtained data were used to allow the machine learning algorithms to fit the signal features of the different carrying positions. The reliability of the proposed ensemble algorithms, comprising a random forest classifier and a regression model, was comparatively evaluated with a commercial pedometer application. The results indicated that the proposed ensemble algorithm provides higher accuracy, ranging from 98.1% to 98.8%, at self-paced walking speed than the commercial pedometer application, and the machine learning-based ensemble algorithms can effectively and accurately predict step counts under different smart phone carrying positions.

We investigated the effectiveness of virtual-reality-based cognitive training (VRCT) and exercise on the brain, cognitive, physical and activity of older adults with mild cognitive impairment (MCI). Methods: This study included 99 participants (70.8 ± 5.4) with MCI in the VRCT, exercise, and control groups. The VRCT consisted of a series of games targeting different brain functions such as executive function, memory, and attention. Twenty-four sessions of VRCT (three days/week) were performed, and each session was 100 min long. Exercise intervention consisted of aerobic and resistance trainings performed in 24 sessions for 60 min (2 times/week for 12 weeks). Global cognitive function was measured using the Mini-Mental State Examination (MMSE) test. Resting-state electroencephalography (EEG) of the neural oscillatory activity in different frequency bands was performed. Physical function was measured using handgrip strength (HGS) and gait speed. Results: After the intervention period, VRCT significantly improved the MMSE scores (p < 0.05), and the exercise group had significantly improved HGS and MMSE scores (p < 0.05) compared to baseline. One-way analysis of variance (ANOVA) of resting-state EEG showed a decreased theta/beta power ratio (TBR) (p < 0.05) in the central region of the brain in the exercise group compared to the control group. Although not statistically significant, the VRCT group also showed a decreased TBR compared to the control group. The analysis of covariance (ANCOVA) test showed a significant decrease in theta band power in the VRCT group compared to the exercise group and a decrease in delta/alpha ratio in the exercise group compared to the VRCT group. Conclusion: Our findings suggest that VRCT and exercise training enhances brain, cognitive, and physical health in older adults with MCI. Further studies with a larger population sample to identify the effect of VRCT in combination with exercise training are required to yield peak benefits for patients with MCI.

Alzheimer's disease (AD) is highly prevalent in dementia, with no specifically effective treatment having yet been discovered. Amyloid plaques are one of the key hallmarks of AD. Transgenic mouse models exhibiting Alzheimer's disease-like pathology have been widely used to study the pathophysiology of Alzheimer's disease. In this study, we showed an age-dependent correlation between cognitive function, pathological findings, and [F-18] Florbetaben (FBB) PET images. Nineteen transgenic mice (12 with AD, 7 with controls) were used for this study. We observed an increase in β-Amyloid deposition (Aβ) in brain tissue and [F-18] FBB amyloid PET imaging in the AD group. The [F-18] FBB data showed a mildly negative trend with cognitive function. Pathological findings were negatively correlated with cognitive functions. These finding suggests that amyloid beta deposition can be well-monitored with [F-18] FBB PET and a decline in cognitive function is related to the increase in amyloid plaque burden.