Gait Kinematic and Kinetic Characteristics of Older Adults With Mild Cognitive Impairment and Subjective Cognitive Decline: A Cross-Sectional Study

Biomechanics of sit-to-stand with dual tasks in older adults with and without mild cognitive impairment

BACKGROUND

The decline in cognitive function in older adults with mild cognitive impairment (MCI) may contribute to a change in movement pattern during sit-to-stand transitions (STS). However, when comparing older adults with MCI to older adults without MCI, there is a lack of evidence of kinematic and kinetic data during STS. Furthermore, while significant cognitive dual-task interference has been demonstrated in older adults with MCI, studies on the effects of dual motor tasks in MCI, particularly during STS, have not been reported.

RESEARCH QUESTION

Are there any differences in the movement time, joint angles, and maximum joint moments while performing STS under single- and dual-task conditions in older adults with and without MCI?

METHODS

In a cross-sectional study, 70 participants were divided into two groups: older adults with MCI and without MCI. Motion analysis and a force plate system were used to collect and analyze the STS movement. All participants were asked to do the STS movement alone and the STS with a dual motor task with the self-selected pattern on an adjustable bench.

RESULTS

Older adults with MCI had greater maximum trunk flexion during STS with a dual task than older adults without MCI and greater than STS alone. Furthermore, older adults with MCI had a greater ankle plantar flexion moment during STS with a dual task than during STS alone.

SIGNIFICANCE

Even though the STS task is one of the simplest functional activities, different strategies to achieve the STS action with dual tasks were found among older adults with and without MCI in terms of joint angle and joint moments.

Neural correlates of impaired learning and recognition of novel faces in mild cognitive impairment

OBJECTIVE

Face memory impairment significantly affects social interactions and daily functioning in individuals with mild cognitive impairment (MCI). While deficits in recognizing familiar faces among individuals with MCI have been reported, their ability to learn and recognize unfamiliar faces remains unclear. This study examined the behavioral performance and event-related potentials (ERPs) of unfamiliar face memorization and recognition in MCI.

METHODS

Fifteen individuals with MCI and 15 healthy controls learned and recognized 90 unfamiliar neutral faces. Their performance accuracy and cortical ERPs were compared between the two groups across the learning and recognition phases.

RESULTS

Individuals with MCI had lower accuracy in identifying newly learned faces than healthy controls. Moreover, individuals with MCI had reduced occipitotemporal N170 and central vertex positive potential responses during both the learning and recognition phases, suggesting impaired initial face processing and attentional resources allocation. Also, individuals with MCI had reduced central N200 and frontal P300 responses during the recognition phase, suggesting impaired later-stage face recognition and attention engagement.

CONCLUSION

These findings provide neurobehavioral evidence for impaired learning and recognition of unfamiliar faces in individuals with MCI.

SIGNIFICANCE

Individuals with MCI may have face memory deficits in both early-stage face processing and later-stage recognition .

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors.

Effects of repetitive transcranial magnetic stimulation on episodic memory in patients with subjective cognitive decline: study protocol for a randomized clinical trial

Introduction

Early decline of episodic memory is detectable in subjective cognitive decline (SCD). The left dorsolateral prefrontal cortex (DLPFC) is associated with encoding episodic memories. Repetitive transcranial magnetic stimulation (rTMS) is a novel and viable tool to improve cognitive function in Alzheimer's disease (AD) and mild cognitive impairment, but the treatment effect in SCD has not been studied. We aim to investigate the efficacy of rTMS on episodic memory in individuals with SCD, and to explore the potential mechanisms of neural plasticity.

Methods

In our randomized, sham-controlled trial, patients (n = 60) with SCD will receive 20 sessions (5 consecutive days per week for 4 weeks) of real rTMS (n = 30) or sham rTMS (n = 30) over the left DLPFC. The primary outcome is the Auditory Verbal Learning Test-Huashan version (AVLT-H). Other neuropsychological examinations and the long-term potentiation (LTP)-like cortical plasticity evaluation serve as the secondary outcomes. These outcomes will be assessed before and at the end of the intervention.

Discussion

If the episodic memory of SCD improve after the intervention, the study will confirm that rTMS is a promising intervention for cognitive function improvement on the early stage of dementia. This study will also provide important clinical evidence for early intervention in AD and emphasizes the significance that impaired LTP-like cortical plasticity may be a potential biomarker of AD prognosis by demonstrating the predictive role of LTP on cognitive improvement in SCD.

Ethics and dissemination

The study was approved by the Human Research Ethics Committee of the hospital (No. 2023-002-01). The results will be published in peer-review publications.

Clinical trial registration

https://www.chictr.org.cn/, identifier ChiCTR2300075517.

A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait

Vision-based methods using RGB inputs for human pose estimation have grown in recent years but have undergone limited testing in clinical and biomechanics research areas like gait analysis. The purpose of the present study was to compare lower extremity kinematics during overground gait between a traditional marker-based approach and a commercial multi-view markerless system in a sample of subjects including young adults, older adults, and adults diagnosed with Parkinson's disease. A convenience sample of 35 adults between the age of 18-85 years were included in this study, yielding a total of 114 trials and 228 gait cycles that were compared between systems. A total of 30 time normalized waveforms, including three-dimensional joint centers, segment angles, and joint angles were compared between systems using root mean-squared error (RMSE), range of motion difference (ΔROM), Pearson correlation coefficients (r), and interclass correlation coefficients (ICC). RMSEs for joint center positions were less than 28 mm in all joints with correlations indicating good to excellent agreement. RMSEs for segment and joint angles were in range of previous results, with highest agreement between systems in the sagittal plane. ΔROM differences were within reference values that characterize clinical groups like Parkinson's disease, stroke, or knee osteoarthritis. Further improvements in pelvis tracking, markerless keypoint model definitions, and standardization of comparison study protocols are needed. Nevertheless, markerless solutions seem promising toward unrestricted motion analysis in biomechanics research and clinical settings.

Gait variability-based classification of the stages of the cognitive decline using partial least squares-discriminant analysis

The purpose of the present study was to examine the differences in gait variability in terms of spatiotemporal, sub-gait cycle, ground reaction force, and the joint profiles of kinematics and kinetics between older individuals with and without risk of potential cognitive impairment, and to derive the crucial features to discriminating the older adults with future risk cognitive decline by using partial least squares-discriminant analysis. A total of 90 community-dwelling older adults aged over 65 years underwent cognitive function assessment and were divided into three groups depending on cognitive assessment score. The participants' level-walking was analyzed by using three-dimensional instrumented gait analysis. The coefficient of variation was extracted and then comparatively analyzed depending on the stages of the cognitive decline. To identify the most important contributor when differentiating the older adults with a risk of future cognitive decline, partial least squares-discriminant analysis was applied, and the discriminative power of the coefficients confirmed as features of great importance were investigated via the receiver operating characteristic area under the curve. The differences in gait variability were found mainly between the suspected dementia groups and other groups, especially in joint dynamics variables. Through the partial least squares-discriminant analysis, the discriminative features were found as follows: the mid-stance, the moments, and the power in the hip, knee, and ankle joints. In addition, the discrimination model was found to differentiate well between the three groups. The classification accuracy of intact cognition, diminished cognition, and suspected dementia was 0.857, 0.710, and 0.857, respectively. These findings mean that gait variability changes according to continuous cognitive decline, especially in sub-gait cycles and joint biomechanics, and suggest that measures of variation can be used as predictors to identify older individuals with a risk of potential cognitive impairment.

A novel dual-task paradigm with story recall shows significant differences in the gait kinematics in older adults with cognitive impairment: A cross-sectional study

Objective

Cognitive and motor dysfunctions in older people become more evident while dual-tasking. Several dual-task paradigms have been used to identify older individuals at the risk of developing Alzheimer's disease and dementia. This study evaluated gait kinematic parameters for dual-task (DT) conditions in older adults with mild cognitive impairment (MCI), subjective cognitive decline (SCD), and normal cognition (NC).

Method

This is a cross-sectional, clinical-based study carried out at the Zhongshan Rehabilitation Branch of First Affiliated Hospital of Nanjing Medical University, China.

Participants

We recruited 83 community-dwelling participants and sorted them into MCI (n = 24), SCD (n = 33), and NC (n = 26) groups based on neuropsychological tests. Their mean age was 72.0 (5.55) years, and male-female ratio was 42/41 (p = 0.112). Each participant performed one single-task walk and four DT walks: DT calculation with subtracting serial sevens; DT naming animals; DT story recall; and DT words recall.

Outcome and measures

Kinematic gait parameters of speed, knee peak extension angle, and dual-task cost (DTC) were obtained using the Vicon Nexus motion capture system and calculated by Visual 3D software. A mixed-effect linear regression model was used to analyze the data.

Results

The difference in gait speed under DT story recall and DT calculation was -0.099 m/s and - 0.119 m/s (p = 0.04, p = 0.013) between MCI and SCD, respectively. Knee peak extension angle under DT story recall, words recall, and single task was bigger in the MCI group compared to the NC group, respectively (p = 0.001, p = 0.001, p = 0.004). DTC was higher in the DT story recall test than all other DT conditions (p < 0.001).

Conclusion

Kinematic gait parameters of knee peak extension angle for the DT story recall were found to be sensitive enough to discriminate MCI individuals from NC group. DTC under DT story recall was higher than the other DT conditions.

Kinematic strategies for obstacle-crossing in older adults with mild cognitive impairment

Introduction

Mild cognitive impairment (MCI) is considered a transitional stage between soundness of mind and dementia, often involving problems with memory, which may lead to abnormal postural control and altered end-point control when dealing with neuromechanical challenges during obstacle-crossing. The study aimed to identify the end-point control and angular kinematics of the pelvis-leg apparatus while crossing obstacles for both leading and trailing limbs.

Methods

12 patients with MCI (age: 66.7 ± 4.2 y/o; height: 161.3 ± 7.3 cm; mass: 62.0 ± 13.6 kg) and 12 healthy adults (age: 67.7 ± 2.9 y/o; height: 159.3 ± 6.1 cm; mass: 61.2 ± 12.0 kg) each walked and crossed obstacles of three different heights (10, 20, and 30% of leg length). Angular motions of the pelvis and lower limbs and toe-obstacle clearances during leading- and trailing-limb crossings were calculated. Two-way analyses of variance were used to study between-subject (group) and within-subject (obstacle height) effects on the variables. Whenever a height effect was found, a polynomial test was used to determine the trend. A significance level of α = 0.05 was set for all tests.

Results

Patients with MCI significantly increased pelvic anterior tilt, hip abduction, and knee adduction in the swing limb during leading-limb crossing when compared to controls (p < 0.05). During trailing-limb crossing, the MCI group showed significantly decreased pelvic posterior tilt, as well as ankle dorsiflexion in the trailing swing limb (p < 0.05).

Conclusion

Patients with MCI adopt altered kinematic strategies for successful obstacle-crossing. The patients were able to maintain normal leading and trailing toe-obstacle clearances for all tested obstacle heights with a specific kinematic strategy, namely increased pelvic anterior tilt, swing hip abduction, and knee adduction during leading-limb crossing, and decreased pelvic posterior tilt and swing ankle dorsiflexion during trailing-limb crossing. The current results suggest that regular monitoring of obstacle-crossing kinematics for reduced toe-obstacle clearance or any signs of changes in crossing strategy may be helpful for early detection of compromised obstacle-crossing ability in patients with single-domain amnestic MCI. Further studies using a motor/cognitive dual-task approach on the kinematic strategies adopted by multiple-domain MCI will be needed for a complete picture of the functional adaptations in such a patient group.

Machine learning-based muscle mass estimation using gait parameters in community-dwelling older adults: A cross-sectional study

BACKGROUND

Loss of skeletal muscle mass is associated with numerous factors such as metabolic diseases, lack of independence, and mortality in older adults. Therefore, developing simple, safe, and reliable tools for assessing skeletal muscle mass is needed. Some studies recently reported that the risks of the incidence of geriatric conditions could be estimated by analyzing older adults' gait; however, no studies have assessed the association between gait parameters and skeletal muscle loss in older adults. In this study, we applied machine learning approach to the gait parameters derived from three-dimensional skeletal models to distinguish older adults' low skeletal muscle mass. We also identified the most important gait parameters for detecting low muscle mass.

METHODS

Sixty-six community-dwelling older adults were recruited. Thirty-two gait parameters were created using a three-dimensional skeletal model involving 10-meter comfortable walking. After skeletal muscle mass measurement using a bioimpedance analyzer, low muscle mass was judged in accordance with the guideline of the Asia Working Group for Sarcopenia. The eXtreme gradient boosting (XGBoost) model was applied to discriminate between low and high skeletal muscle mass.

RESULTS

Eleven subjects had a low muscle mass. The c-statistics, sensitivity, specificity, precision of the final model were 0.7, 59.5%, 81.4%, and 70.5%, respectively. The top three dominant gait parameters were, in order of strongest effect, stride length, hip dynamic range of motion, and trunk rotation variability.

CONCLUSION

Machine learning-based gait analysis is a useful approach to determine the low skeletal muscle mass of community-dwelling older adults.

Use of electroencephalogram, gait, and their combined signals for classifying cognitive impairment and normal cognition

Background

Early identification of people at risk for cognitive decline is an important step in delaying the occurrence of cognitive impairment. This study investigated whether multimodal signals assessed using electroencephalogram (EEG) and gait kinematic parameters could be used to identify individuals at risk of cognitive impairment.

Methods

The survey was conducted at the Veterans Medical Research Institute in the Veterans Health Service Medical Center. A total of 220 individuals volunteered for this study and provided informed consent at enrollment. A cap-type wireless EEG device was used for EEG recording, with a linked-ear references based on a standard international 10/20 system. Three-dimensional motion capture equipment was used to collect kinematic gait parameters. Mild cognitive impairment (MCI) was evaluated by Seoul Neuropsychological Screening Battery-Core (SNSB-C).

Results

The mean age of the study participants was 73.5 years, and 54.7% were male. We found that specific EEG and gait parameters were significantly associated with cognitive status. Individuals with decreases in high-frequency EEG activity in high beta (25-30 Hz) and gamma (30-40 Hz) bands increased the odds ratio of MCI. There was an association between the pelvic obliquity angle and cognitive status, assessed by MCI or SNSB-C scores. Results from the ROC analysis revealed that multimodal signals combining high beta or gamma and pelvic obliquity improved the ability to discriminate MCI individuals from normal controls.

Conclusion

These findings support prior work on the association between cognitive status and EEG or gait, and offer new insights into the applicability of multimodal signals to distinguish cognitive impairment.

Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia

Objective

Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.

Methods

Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.

Results

Subjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.

Conclusion

Our methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.

Addressing Mild Cognitive Impairment and Boosting Wellness for the Elderly through Personalized Remote Monitoring

Mild cognitive impairment (MCI) may occur with old age and is associated with increased cognitive deterioration compared to what is normal. This may affect the person’s quality of life, health, and independence. In this ageing worldwide context, early diagnosis and personalized assistance for MCI therefore become crucial. This paper makes two important contributions: (1) a system (RO-SmartAgeing) to address MCI, which was developed for Romania; and (2) a set of criteria for evaluating its impact on remote health monitoring. The system aims to provide customized non-invasive remote monitoring, health assessment, and assistance for the elderly within a smart environment set up in their homes. Moreover, it includes multivariate AI-based predictive models that can detect the onset of MCI and its development towards dementia. It was built iteratively, following literature reviews and consultations with health specialists, and it is currently being tested in a simulated home environment. While its main strength is the potential to detect MCI early and follow its evolution, RO-SmartAgeing also supports elderly people in living independently, and it is safe, comfortable, low cost, and privacy protected. Moreover, it can be used by healthcare institutions to continuously monitor a patient’s vital signs, position, and activities, and to deliver reminders and alarms.

The Added Value of Combined Timed Up and Go Test, Walking Speed, and Grip Strength on Predicting Recurrent Falls in Chinese Community-dwelling Elderly

Purpose

To determine whether combined performance-based models could exert better predictive values toward discriminating community-dwelling elderly with high risk of any-falls or recurrent-falls.

Participants and Methods

This prospective cohort study included a total of 875 elderly participants (mean age: 67.10±5.94 years) with 513 females and 362 males, recruited from Hangu suburb area of Tianjin, China. All participants completed comprehensive assessments.

Methods

We documented information about sociodemographic information, behavioral characteristics and medical conditions. Three functional tests—timed up and go test (TUGT), walking speed (WS), and grip strength (GS) were used to create combined models. New onsets of any-falls and recurrent-falls were ascertained at one-year follow-up appointment.

Results

In total 200 individuals experienced falls over a one-year period, in which 66 individuals belonged to the recurrent-falls group (33%). According to the receiver operating characteristic curve (ROC), the cutoff points of TUGT, WS, and GS toward recurrent-falls were 10.31 s, 0.9467 m/s and 0.3742 kg/kg respectively. We evaluated good performance as “+” while poor performance as “–”. After multivariate adjustment, we found “TUGT >10.31 s” showed a strong correlation with both any-falls (adjusted odds ratio (OR)=2.025; 95% confidence interval (CI)=1.425–2.877) and recurrent-falls (adjusted OR=2.150; 95%CI=1.169–3.954). Among combined functional models, “TUGT >10.31 s, GS <0.3742 kg/kg, WS >0.9467 m/s” showed strongest correlation with both any-falls (adjusted OR=5.499; 95%CI=2.982–10.140) and recurrent-falls (adjusted OR=8.260; 95%CI=3.880–17.585). And this combined functional model significantly increased discriminating abilities on screening recurrent-fallers than a single test (C-statistics=0.815, 95%CI=0.782–0.884, P<0.001), while not better than a single test in predicting any-fallers (P=0.083).

Conclusion

Elderly people with poor TUGT performance, weaker GS but quicker WS need to be given high priority toward fall prevention strategies for higher risks and frequencies. Meanwhile, the combined “TUGT–, GS–, WS+” model presents increased discriminating ability and could be used as a conventional tool to discriminate recurrent-fallers in clinical practice.