Wearable Sensor-Based Daily Life Walking Assessment of Gait for Distinguishing Individuals With Amnestic Mild Cognitive Impairment

Artificial intelligence for dementia-Applied models and digital health

INTRODUCTION

The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

METHODS

This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

RESULTS

This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.

DISCUSSION

Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Gait Domains May Be Used as an Auxiliary Diagnostic Index for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction and behavioral impairment. We aimed to use principal components factor analysis to explore the association between gait domains and AD under single and dual-task gait assessments.

METHODS

A total of 41 AD participants and 41 healthy control (HC) participants were enrolled in our study. Gait parameters were measured using the JiBuEn® gait analysis system. The principal component method was used to conduct an orthogonal maximum variance rotation factor analysis of quantitative gait parameters. Multiple logistic regression was used to adjust for potential confounding or risk factors.

RESULTS

Based on the factor analysis, three domains of gait performance were identified both in the free walk and counting backward assessments: "rhythm" domain, "pace" domain and "variability" domain. Compared with HC, we found that the pace factor was independently associated with AD in two gait assessments; the variability factor was independently associated with AD only in the counting backwards assessment; and a statistical difference still remained after adjusting for age, sex and education levels.

CONCLUSIONS

Our findings indicate that gait domains may be used as an auxiliary diagnostic index for Alzheimer's disease.

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

A scoping review of different monitoring-technology devices in caring for older adults with cognitive impairment

Various monitoring technologies are being developed to prevent potential complications among older adults with cognitive impairment and improve their cognitive function. This scoping review identified gaps in the development of monitoring-technology devices for cognitive health status and highlights areas that require further inquiry. This study used the Joanna Briggs Institute (JBI) and the PRISMA extension for the checklist for scoping reviews using the eligibility criteria recommended by Population, Concept, and Context (PCC) framework. The study population included adults aged 65 years and above, while the concept and context are monitoring-technology devices utilized in detecting and caring for an older adult with cognitive impairment. Three electronic databases (Medline, Scopus, and Web of Science) were searched, and a total of 21 articles met the selection criteria. Several innovative technology-based devices for screening, assessing, detecting, and monitoring the interventions for older adult cognitive impairment and for family caregivers to ensure the continuity of care were established. Monitoring devices are useful in promoting older adult safety, improving their quality of life by enabling them to live independently for a longer period, and improving their mental wellbeing to help reduce the burden on caregivers by providing them with information concerning the activities of older adults. Moreover, studies have shown that older adults and their caregivers can learn to use these devices effectively and comfortably with proper education and training. The results of this study provide crucial insights into innovative technologies that can be used to assess cognitive health among older adults, which could substantially improve their mental health, and this baseline information can be used for supporting public health policy and enhancing their quality of life.

A dual-task gait test detects mild cognitive impairment with a specificity of 91.2

Background

Mild cognitive impairment (MCI) is a valuable intervention window in the progress of senile dementia, but the question of how to easily and conveniently detect MCI in the community remains unanswered. Gait performance reflects cognitive function, but how to reliably detect MCI through gait testing is still being explored.

Objective

To develop a dual-task gait testing method that could reliably detect MCI in the community.

Methods

A cross-sectional diagnostic study was conducted in 111 older adults (mean age = 72.14 ± 6.90 years) from five communities in Wuhan, China. A novel dual-task gait testing method, walking while identifying animals in pictures (AniP-DT gait test), was developed. The participants were classified into MCI or cognitively intact based on their performance on the Montreal Cognitive Assessment Scale (MoCA). Gait performance was assessed using both single-task and the AniP-DT gait test. Multiple linear regression and binary logistic regression were used to model the association between gait speed and cognitive status, and receiver operating characteristic (ROC) curve analysis was used to assess the discrimination ability.

Results

Compared to the cognitively intact group, the gait speed of the MCI group was lower in both single-task and the AniP-DT gait tests. The gait speed of the AniP-DT gait test was significantly associated with MoCA scores after adjusting the covariates and exhibited good discrimination ability in MCI detection (AUC = 0.814), with a specificity of 91.2%. ROC analysis of the logistic models revealed better discrimination ability of dual-task gait velocity when adjusted with age and years of education (AUC = 0.862).

Conclusion

The evidence in this study suggested that the AniP-DT gait test could be an easy and reliable screening tool for MCI in community older adults.

Capturing Measures That Matter: The Potential Value of Digital Measures of Physical Behavior for Alzheimer's Disease Drug Development

Alzheimer's disease (AD) is a devastating neurodegenerative disease and the primary cause of dementia worldwide. Despite the magnitude of AD's impact on patients, caregivers, and society, nearly all AD clinical trials fail. A potential contributor to this high rate of failure is that established clinical outcome assessments fail to capture subtle clinical changes, entail high burden for patients and their caregivers, and ineffectively address the aspects of health deemed important by patients and their caregivers. AD progression is associated with widespread changes in physical behavior that have impacts on the ability to function independently, which is a meaningful aspect of health for patients with AD and important for diagnosis. However, established assessments of functional independence remain underutilized in AD clinical trials and are limited by subjective biases and ceiling effects. Digital measures of real-world physical behavior assessed passively, continuously, and remotely using digital health technologies have the potential to address some of these limitations and to capture aspects of functional independence in patients with AD. In particular, measures of real-world gait, physical activity, and life-space mobility captured with wearable sensors may offer value. Additional research is needed to understand the validity, feasibility, and acceptability of these measures in AD clinical research.

Gait in cerebral small vessel disease, pre-dementia, and dementia: A systematic review

BACKGROUND

The interrelationships between gait, cerebral small vessel disease (CSVD), and cognitive impairments in aging are not well-understood-despite their common co-occurrence.

OBJECTIVE

To systematically review studies of gait impairment in CSVD, pre-dementia, and dementia, and to identify key gaps for future research and novel pathways toward intervention.

METHODS

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search strategy was implemented in PubMed to identify relevant studies. Potential articles (n = 263) published prior to 1 December 2021 were screened by two reviewers. Studies with sample sizes >20 and including some adults over > 65 years (n = 202) were included.

RESULTS

The key findings were that (1) adverse gait and cognitive outcomes were associated with several (rather than select) CSVD pathologies distributed across the brain, and (2) poor gait and CSVD pathologies were more strongly associated with dementia with a vascular, rather than an Alzheimer's disease-related, cause.

DISCUSSION

A better understanding of the interrelationships between gait performance in CSVD, pre-dementia, and dementia requires studies examining (1) comprehensive patterns in the clinical manifestations of CSVD, (2) racially/ethnically diverse samples, (3) samples followed for extended periods of time or across the adult life span, (4) non-traditional CSVD neuroimaging markers (e.g. resting-state functional magnetic resonance imaging (fMRI)), and (5) continuous (e.g. wearable sensors) and complex (e.g. dual-task) walking performance.

Relationship between Cognitive Decline and Daily Life Gait among Elderly People Living in the Community: A Preliminary Report

Introduction

Early detection and intervention are important to prevent dementia. Gait parameters have been recognized as a potentially easy screening tool for mild cognitive impairment (MCI); however, differences in gait parameters between cognitive healthy individuals (CHI) and MCI are small. Daily life gait change may be used to detect cognitive decline earlier. In the present study, we aimed to clarify the relationship between cognitive decline and daily life gait.

Methods

We performed 5-Cog function tests and daily life and laboratory-based gait assessments on 155 community-dwelling elderly people (75.5 ± 5.4 years old). Daily life gait was measured for 6 days using an iPod-touch with an accelerometer. Laboratory-based 10-m gait (fast pace) was measured using an electronic portable walkway.

Results

The subjects consisted of 98 CHI (63.2%) and 57 cognitive decline individuals (CDI; 36.8%). Daily life maximum gait velocity in the CDI group (113.7 [97.0-128.5] cm/s) was significantly slower than that in the CHI group (121.2 [105.8-134.3] cm/s) (p = 0.032). In the laboratory-based gait, the stride length variability in the CDI group (2.6 [1.8-4.1]) was significantly higher than that in the CHI group (1.8 [1.2-2.7]) (p < 0.001). The maximum gait velocity in daily life gait was weakly but significantly correlated with stride length variability in laboratory-based gait (ρ = -0.260, p = 0.001).

Conclusion

We found an association between cognitive decline and slower daily life gait velocity among community-dwelling elderly people.

Diagnostic accuracy of multi-component spatial-temporal gait parameters in older adults with amnestic mild cognitive impairment

Objective

This study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI).

Method

In this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic accuracy of kinematic parameters in discriminating aMCI from healthy subjects.

Results

Multivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905–0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter’s sensitivity was 0.26–0.95 and specificity was 0.21–0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94).

Conclusion

We developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan.

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.

High Fall Risk Associated With Memory Deficit and Brain Lobes Atrophy Among Elderly With Amnestic Mild Cognitive Impairment and Mild Alzheimer's Disease

Objectives

This study aimed to primarily examine the association between memory deficit and increased fall risk, second, explore the underlying neuroanatomical linkage of this association in the elderly with aMCI and mild AD.

Methods

In this cross-sectional study, a total of 103 older adults were included (55 cognitively normal, CN; 48 cognitive impairment, CI, elderly with aMCI, and mild AD). Memory was assessed by the Auditory Verbal Learning Test (AVLT). Fall risk was evaluated by the Timed Up and Go (TUG) Test, heel strike angles, and stride speed, which were collected by an inertial-sensor-based wearable instrument (the JiBuEn™ gait analysis system). Brain volumes were full-automatic segmented and quantified using AccuBrain^® v1.2 from three-dimensional T1-weighted (3D T1W) MR images. Multivariable regression analysis was used to examine the extent of the association between memory deficit and fall risk, the association of brain volumes with memory, and fall risk. Age, sex, education, BMI, and HAMD scores were adjusted. Sensitivity analysis was conducted.

Results

Compared to CN, participants with aMCI and mild AD had poorer cognitive performance (p < 0.001), longer TUG time (p = 0.018), and smaller hippocampus and medial temporal volumes (p = 0.037 and 0.029). In the CI group, compared to good short delayed memory (SDM) performance (AVLT > 5), the elderly with bad SDM performance (AVLT ≤ 3) had longer TUG time, smaller heel strike angles, and slower stride speed. Multivariable regression analysis showed that elderly with poor memory had higher fall risk than relative good memory performance among cognitive impairment elderly. The TUG time increased by 2.1 s, 95% CI, 0.54∼3.67; left heel strike angle reduced by 3.22°, 95% CI, −6.05 to −0.39; and stride speed reduced by 0.09 m/s, 95% CI, −0.19 to −0.00 for the poor memory elderly among the CI group, but not found the association in CN group. In addition, serious medial temporal atrophy (MTA), small volumes of the frontal lobe and occipital lobe were associated with long TUG time and small heel strike angles; small volumes of the temporal lobe, frontal lobe, and parietal lobe were associated with slow stride speed.

Conclusion

Our findings suggested that memory deficit was associated with increased fall risk in the elderly with aMCI and mild AD. The association might be mediated by the atrophy of medial temporal, frontal, and parietal lobes. Additionally, increased fall risk, tested by TUG time, heel stride angles, and stride speed, might be objective and convenient kinematics markers for dynamic monitoring of both memory function and fall risk.

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.

Consensus Paper: Ataxic Gait

The aim of this consensus paper is to discuss the roles of the cerebellum in human gait, as well as its assessment and therapy. Cerebellar vermis is critical for postural control. The cerebellum ensures the mapping of sensory information into temporally relevant motor commands. Mental imagery of gait involves intrinsically connected fronto-parietal networks comprising the cerebellum. Muscular activities in cerebellar patients show impaired timing of discharges, affecting the patterning of the synergies subserving locomotion. Ataxia of stance/gait is amongst the first cerebellar deficits in cerebellar disorders such as degenerative ataxias and is a disabling symptom with a high risk of falls. Prolonged discharges and increased muscle coactivation may be related to compensatory mechanisms and enhanced body sway, respectively. Essential tremor is frequently associated with mild gait ataxia. There is growing evidence for an important role of the cerebellar cortex in the pathogenesis of essential tremor. In multiple sclerosis, balance and gait are affected due to cerebellar and spinal cord involvement, as a result of disseminated demyelination and neurodegeneration impairing proprioception. In orthostatic tremor, patients often show mild-to-moderate limb and gait ataxia. The tremor generator is likely located in the posterior fossa. Tandem gait is impaired in the early stages of cerebellar disorders and may be particularly useful in the evaluation of pre-ataxic stages of progressive ataxias. Impaired inter-joint coordination and enhanced variability of gait temporal and kinetic parameters can be grasped by wearable devices such as accelerometers. Kinect is a promising low cost technology to obtain reliable measurements and remote assessments of gait. Deep learning methods are being developed in order to help clinicians in the diagnosis and decision-making process. Locomotor adaptation is impaired in cerebellar patients. Coordinative training aims to improve the coordinative strategy and foot placements across strides, cerebellar patients benefiting from intense rehabilitation therapies. Robotic training is a promising approach to complement conventional rehabilitation and neuromodulation of the cerebellum. Wearable dynamic orthoses represent a potential aid to assist gait. The panel of experts agree that the understanding of the cerebellar contribution to gait control will lead to a better management of cerebellar ataxias in general and will likely contribute to use gait parameters as robust biomarkers of future clinical trials.

Mild Cognitive Impairment Subtypes Are Associated With Peculiar Gait Patterns in Parkinson's Disease

Background

Mild cognitive impairment (MCI) is frequent in Parkinson's disease (PD) and represents a risk factor for the development of dementia associated with PD (PDD). Since PDD has been associated with disability, caregiver burden, and an increase in health-related costs, early detection of MCI associated with PD (PD-MCI) and its biomarkers is crucial.

Objective

Given that gait is considered a surrogate marker for cognitive decline in PD, the aim of this study was to compare gait patterns in PD-MCI subtypes in order to verify the existence of an association between specific gait features and particular MCI subtypes.

Methods

A total of 67 patients with PD were consecutively enrolled and assessed by an extensive clinical and cognitive examination. Based on the neuropsychological examination, patients were diagnosed as patients with MCI (PD-MCI) and without MCI (no-PD-MCI) and categorized in MCI subtypes. All patients were evaluated using a motion capture system of a BTS Bioengineering equipped with six IR digital cameras. Gait of the patients was assessed in the ON-state under three different tasks (a single task and two dual tasks). Statistical analysis included the t-test, the Kruskal-Wallis test with post hoc analysis, and the exploratory correlation analysis.

Results

Gait pattern was poorer in PD-MCI vs. no-PD-MCI in all tasks. Among PD-MCI subtypes, multiple-domain PD-MCI and amnestic PD-MCI were coupled with worse gait patterns, notably in the dual task.

Conclusion

Both the magnitude of cognitive impairment and the presence of memory dysfunction are associated with increased measures of dynamic unbalance, especially in dual-task conditions, likely mirroring the progressive involvement of posterior cortical networks.

Relationships between Gait Regularity and Cognitive Function, including Cognitive Domains and Mild Cognitive Impairment, in Community-Dwelling Older People

The aim of this cross-sectional study was to examine the correlations between gait regularity, cognitive functions including cognitive domains, and the mild cognitive impairment (MCI) in community-dwelling older people. This study included 463 older adults (63.4% women, mean age: 74.1), and their step and stride regularity along the three-axis components was estimated from trunk acceleration, which was measured by inertial measurement units during a comfortable gait. Four aspects of cognitive function were assessed using a tablet computer: attention, executive function, processing speed, and memory, and participants were classified into those with or without MCI. The vertical component of stride and step regularity was associated with attention and executive function (r = -0.176--0.109, p ≤ 0.019), and processing speed (r = 0.152, p < 0.001), after it was adjusted for age and gait speed. The low vertical component of step regularity was related to the MCI after it was adjusted for covariates (OR 0.019; p = 0.016). The results revealed that cognitive function could affect gait regularity, and the vertical component of gait regularity, as measured by a wearable sensor, could play an important role in investigating cognitive decline in older people.

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

Background

Older adults with mild cognitive impairment (MCI) have slower gait speed and poor gait performance under dual-task conditions. However, gait kinematic and kinetic characteristics in older adults with MCI or subjective cognitive decline (SCD) remain unknown. This study was designed to explore the difference in gait kinematics and kinetics during level walking among older people with MCI, SCD, and normal cognition (NC).

Methods

This cross-sectional study recruited 181 participants from July to December 2019; only 82 met the inclusion criteria and consented to participate and only 79 completed gait analysis. Kinematic and kinetic data were obtained using three-dimensional motion capture system during level walking, and joint movements of the lower limbs in the sagittal plane were analyzed by Visual 3D software. Differences in gait kinematics and kinetics among the groups were analyzed using multivariate analysis of covariance (MANCOVA) with Bonferroni post-hoc analysis. After adjusting for multiple comparisons, the significance level was p < 0.002 for MANCOVA and p < 0.0008 for post-hoc analysis.

Results

Twenty-two participants were MCI [mean ± standard deviation (SD) age, 71.23 ± 6.65 years], 33 were SCD (age, 72.73 ± 5.25 years), and 24 were NC (age, 71.96 ± 5.30 years). MANCOVA adjusted for age, gender, body mass index (BMI), gait speed, years of education, diabetes mellitus, and Geriatric Depression Scale (GDS) revealed a significant multivariate effect of group in knee peak extension angle (F = 8.77, p < 0.0001) and knee heel strike angle (F = 8.07, p = 0.001) on the right side. Post-hoc comparisons with Bonferroni correction showed a significant increase of 5.91° in knee peak extension angle (p < 0.0001) and a noticeable decrease of 6.21°in knee heel strike angle (p = 0.001) in MCI compared with NC on the right side. However, no significant intergroup difference was found in gait kinetics, including dorsiflexion, plantar flexion, knee flexion, knee extension, hip flexion, and hip extension(p > 0.002).

Conclusion

An increase of right knee peak extension angle and a decrease of right knee heel strike angle during level walking were found among older adults with MCI compared to those with NC.

Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

Background

Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.

Objective

We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD.

Methods

Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system.

Results

High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F_1,103=14.1, P<.001) and stride time variability (F_1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores.

Conclusions

A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.

Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder

Background: Orthostatic hypotension (OH) caused by autonomic dysfunction is a common symptom in older people and patients with idiopathic rapid eye movement sleep behavior disorder (iRBD). The orthostatic challenge test is a standard autonomic function test that measures a decrease of blood pressure during a postural change from supine to standing positions. Although previous studies have reported that changes in heart rate variability (HRV) are associated with autonomic dysfunction, no study has investigated the relationship between HRV before standing and the occurrence of OH in an orthostatic challenge test. This study aims to examine the connection between HRV in the supine position and the occurrence of OH in an orthostatic challenge test. Methods: We measured the electrocardiograms of patients with iRBD and healthy older people during an orthostatic challenge test, in which the supine and standing positions were held for 15 min, respectively. The subjects were divided into three groups: healthy controls (HC), OH-negative iRBD [OH (-) iRBD], and OH-positive iRBD [OH (+) iRBD]. HRV measured in the supine position during the test were calculated by time-domain analysis and Poincaré plots to evaluate the autonomic dysfunction. Results: Forty-two HC, 12 OH (-) iRBD, and nine OH (+) iRBD subjects were included. HRV indices in the OH (-) and the OH (+) iRBD groups were significantly smaller than those in the HC group. The multivariate logistic regression analysis for OH identification for the iRBD groups showed the model whose inputs were the HRV indices, i.e., standard deviation 2 (SD2) and the percentage of adjacent intervals that varied by more than 50 ms (pNN50), had a receiver operating characteristic curve with area under the curve of 0.840, the sensitivity to OH (+) of 1.000, and the specificity to OH (-) of 0.583 (p = 0.023). Conclusions: This study showed the possibility that short-term HRV indices in the supine position would predict subsequent OH in iRBD patients. Our results are of clinical importance in terms of showing the possibility that OH can be predicted using only HRV in the supine position without an orthostatic challenge test, which would improve the efficiency and safety of testing.

Will We Do If We Can? Habitual Qualitative and Quantitative Physical Activity in Multi-Morbid, Older Persons with Cognitive Impairment

This study aimed to identify determinants of quantitative dimensions of physical activity (PA; duration, frequency, and intensity) in community-dwelling, multi-morbid, older persons with cognitive impairment (CI). In addition, qualitative and quantitative aspects of habitual PA have been described. Quantitative PA and qualitative gait characteristics while walking straight and while walking turns were documented by a validated, sensor-based activity monitor. Univariate and multiple linear regression analyses were performed to delineate associations of quantitative PA dimensions with qualitative characteristics of gait performance and further potential influencing factors (motor capacity measures, demographic, and health-related parameters). In 94 multi-morbid, older adults (82.3 ± 5.9 years) with CI (Mini-Mental State Examination score: 23.3 ± 2.4), analyses of quantitative and qualitative PA documented highly inactive behavior (89.6% inactivity) and a high incidence of gait deficits, respectively. The multiple regression models (adjusted R² = 0.395–0.679, all p < 0.001) identified specific qualitative gait characteristics as independent determinants for all quantitative PA dimensions, whereas motor capacity was an independent determinant only for the PA dimension duration. Demographic and health-related parameters were not identified as independent determinants. High associations between innovative, qualitative, and established, quantitative PA performances may suggest gait quality as a potential target to increase quantity of PA in multi-morbid, older persons.

Weaker Braking Force, A New Marker of Worse Gait Stability in Alzheimer Disease

Background: Braking force is a gait marker associated with gait stability. This study aimed to determine the alteration of braking force and its correlation with gait stability in Alzheimer disease (AD).

Methods: A total of 32 AD patients and 32 healthy controls (HCs) were enrolled in this study. Gait parameters (braking force, gait variability, and fall risk) in the walking tests of Free walk, Barrier, and Count backward were measured by JiBuEn^® gait analysis system. Gait variability was calculated by the coefficient of variation (COV) of stride time, stance time, and swing time.

Results: The braking force of AD was significantly weaker than HCs in three walking tests (P < 0.001, P < 0.001, P = 0.007). Gait variability of AD showed significant elevation than HCs in the walking of Count backward (COV_stride: P = 0.013; COV_swing: P = 0.006). Fall risk of AD was significantly higher than HCs in three walking tests (P = 0.001, P = 0.001, P = 0.001). Braking force was negatively associated with fall risks in three walking tests (P < 0.001, P < 0.001, P < 0.001). There were significant negative correlations between braking force and gait variability in the walking of Free walk (COV_stride: P = 0.018; COV_swing: P = 0.013) and Barrier (COV_stride: P = 0.002; COV_swing: P = 0.001), but not Count backward (COV_stride: P = 0.888; COV_swing: P = 0.555).

Conclusion: Braking force was weaker in AD compared to HCs, reflecting the worse gait stability of AD. Our study suggests that weakening of braking force may be a new gait marker to indicate cognitive and motor impairment and predict fall risk in AD.