Examining Relationships between Multiple Self-Reported Sleep Measures and Gait Domains in Cognitively Healthy Older Adults

Longitudinal association of sleep quality with physical performance measures: SABE cohort study, Brazil

OBJECTIVES

The aim of this study was to investigate the longitudinal association of sleep with physical performance in a representative sample of non-institutionalised older adults residing in the municipality of São Paulo, Brazil.

STUDY DESIGN

Prospective cohort study.

METHODS

The current longitudinal study used data extracted from the Health, Well-being, and Aging Study (Estudo Saúde Bem-Estar e Envelhecimento [SABE]). The study population consisted of individuals aged ≥60 years who participated in the study in 2010 or 2015. Dependent variables included the Short Physical Performance Battery (SPPB) and gait speed. Independent variables of interest were self-reported sleep difficulty, daytime sleepiness and sleep quality. The longitudinal association between sleep variables and the outcomes was evaluated using Generalised Estimating Equations (GEE) Models adjusted for covariates. All the variables, except age, sex and schooling, were assessed at baseline and follow-up visits (2010 and 2015).

RESULTS

The analyses included 2205 observations from 1559 individuals. The population mean age was 72 years in 2010 and 71 years in 2015, with a higher prevalence of women in both years. Between 2010 and 2015, there was a decline in the SPPB score and gait speed. Daytime sleepiness was negatively associated with the SPPB score [Coef.: -0.38 (95% confidence interval {CI}: -0.56, -0.21)] and gait speed [Coef.: -0.03 (95% CI: -0.05, -0.01)]. Poor sleep quality was negatively associated with the SPPB score [Coef.: -0.29 (95% CI: -0.57, -0.01)] and gait speed [Coef.: -0.03 (95% CI: -0.06, -0.00)].

CONCLUSIONS

Daytime sleepiness and poor sleep quality are associated with compromised physical performance in non-institutionalised older adults, and this association remained consistent over time.

Association between Self-reported Sleep Quality and Single-task Gait in Young Adults: A Study Using Machine Learning

Objective  The objective of the present study was to find biomechanical correlates of single-task gait and self-reported sleep quality in a healthy, young population by replicating a recently published study. Materials and Methods  Young adults ( n  = 123) were recruited and were asked to complete the Pittsburgh Sleep Quality Inventory to assess sleep quality. Gait variables ( n  = 53) were recorded using a wearable inertial measurement sensor system on an indoor track. The data were split into training and test sets and then different machine learning models were applied. A post-hoc analysis of covariance (ANCOVA) was used to find statistically significant differences in gait variables between good and poor sleepers. Results  AdaBoost models reported the highest correlation coefficient (0.77), with Support-Vector classifiers reporting the highest accuracy (62%). The most important features associated with poor sleep quality related to pelvic tilt and gait initiation. This indicates that overall poor sleepers have decreased pelvic tilt angle changes, specifically when initiating gait coming out of turns (first step pelvic tilt angle) and demonstrate difficulty maintaining gait speed. Discussion  The results of the present study indicate that when using traditional gait variables, single-task gait has poor accuracy prediction for subjective sleep quality in young adults. Although the associations in the study are not as strong as those previously reported, they do provide insight into how gait varies in individuals who report poor sleep hygiene. Future studies should use larger samples to determine whether single task-gait may help predict objective measures of sleep quality especially in a repeated measures or longitudinal or intervention framework.

Effects of Brain Pathologies on Spatiotemporal Gait Parameters in Patients with Mild Cognitive Impairment

BACKGROUND

Impaired gait can precede dementia. The associations between gait parameters and brain pathologies are therefore of interest.

OBJECTIVE

To explore how different brain pathologies (i.e., vascular and Alzheimer's) are associated with specific gait parameters from various gait components in persons with mild cognitive impairment (MCI), who have an increased risk of developing dementia.

METHODS

This cross-sectional study included 96 patients with MCI (mean 72, ±7.5 years; 52% women). Gait was evaluated by using an electronic walkway, GAITRite®. Four gait parameters (step velocity variability; step length; step time; stance time asymmetry) were used as dependent variables in multivariable linear regression analyses. Independent variables included Alzheimer's disease pathologies (amyloid-β and tau) by using PET imaging and white matter hyperintensities (WMH) by using MRI. Covariates included age, sex, comorbidities (and intracranial volume in analyses that includedWMH).

RESULTS

Increased tau-PET (Braak I-IV region of interest [ROI]) was associated with step velocity variability (standardized regression coefficient, β= 0.383, p < 0.001) and step length (β= 0.336, p < 0.001), which remained significant when using different Braak ROIs (I-II, III-IV, V-VI). The associations remained significant when adjusting for WMH (p < 0.001). When also controlling for gait speed, tau was no longer significantly (p = 0.168) associated with an increased step length. No significant associations between gait and Aβ-PET load or WMH were identified.

CONCLUSIONS

The results indicate that one should pay specific attention to assess step velocity variability when targeting single task gait in patients with MCI. Future studies should address additional gait variability measures and dual tasking in larger cohorts.

Sleep Quality and Aging: A Systematic Review on Healthy Older People, Mild Cognitive Impairment and Alzheimer’s Disease

Aging is characterized by changes in the structure and quality of sleep. When the alterations in sleep become substantial, they can generate or accelerate cognitive decline, even in the absence of overt pathology. In fact, impaired sleep represents one of the earliest symptoms of Alzheimer’s disease (AD). This systematic review aimed to analyze the studies on sleep quality in aging, also considering mild cognitive impairment (MCI) and AD. The review process was conducted according to the PRISMA statement. A total of 71 studies were included, and the whole sample had a mean age that ranged from 58.3 to 93.7 years (62.8–93.7 healthy participants and 61.8–86.7 pathological populations). Of these selected studies, 33 adopt subjective measurements, 31 adopt objective measures, and 10 studies used both. Pathological aging showed a worse impoverishment of sleep than older adults, in both subjective and objective measurements. The most common aspect compromised in AD and MCI were REM sleep, sleep efficiency, sleep latency, and sleep duration. These results underline that sleep alterations are associated with cognitive impairment. In conclusion, the frequency and severity of sleep disturbance appear to follow the evolution of cognitive impairment. The overall results of objective measures seem more consistent than those highlighted by subjective measurements.

Components of gait in people with and without mild cognitive impairment

BACKGROUND

Several objective gait parameters are associated with cognitive impairment, but there is limited knowledge of gait models in people with mild cognitive impairment (MCI).

RESEARCH QUESTION

How can 18 objective gait characteristics be used to define different components of gait in people with MCI (with suspected incipient neurocognitive disorder) and cognitively unimpaired people (CU), respectively?

METHODS

Spatiotemporal gait data were collected by using an electronic walkway (GAITRite®), i.e. assessments in comfortable gait speed. Using cross-sectional gait data, two principal component analyses (PCA) were performed (varimax rotation) to define different components of gait in people with MCI (n = 114) and CU (n = 219), respectively, from the BioFINDER-2 study.

RESULTS

Both PCAs produced four components, here called Variability, Pace/Stability, Rhythm and Asymmetry. Total variance explained was 81.0% (MCI) versus 80.3% (CU). The Variability component explained the largest amount of variance (about 25%) in both groups. The highest loading gait parameter was the same for both groups in three out of four components, i.e. step velocity variability (Variability), mean step length (Pace/Stability) and mean step time (Rhythm). In the asymmetry component, stance time asymmetry (MCI) and swing time asymmetry (CU) loaded the highest.

SIGNIFICANCE

The gait components seem similar in people with and without MCI, although there were some differences. This study may aid the identification of gait variables that represent different components of gait. Gait parameters such as step velocity variability, mean step length, mean step time as well as swing and stance time asymmetry could serve as interesting core variables of different gait components in future research in people with MCI (with suspected incipient neurocognitive disorder) and CU. However, the selection of gait variables depends on the purpose. It needs to be noted that assessment of variability measures requires more advanced technology than is usually used in the clinic.

Why Do They Fall? The Impact of Insomnia on Gait of Older Adults: A Case-Control Study

STUDY OBJECTIVES

To compare gait and cognitive performance conducted separately as a single- (ST) and simultaneously as a dual-task (DT), ie, when a cognitive task was added, among community-dwelling older adults with and without insomnia.

METHODS

Participants included: 39 (28 females) community-dwelling older adults with insomnia, 34 (21 females) controls without insomnia. Subject groups were matched for age, gender, and education. Sleep quality was evaluated based on two-week actigraphy. Gait speed and cognition were assessed as ST and DT performance. DT costs (DTCs) were calculated for both tasks. Outcomes were compared via independent samples t-tests or Mann-Whitney U-tests.

RESULTS

Older adults with insomnia demonstrated significantly slower gait speed during ST (1 ± 0.29 vs 1.27 ± 0.17 m/s, p<0.001) and DT (0.77 ± 0.26 vs 1.14 ± 0.20 m/s, p<0.001) and fewer correct responses in the cognitive task during ST (21 ± 7 vs 27 ± 11, p=0.009) and DT (19 ± 7 vs 23 ± 9, p=0.015) compared to control group. DTC for the gait task was higher among older adults with insomnia (18.32%, IQR: 9.48-30.93 vs 7.81% IQR: 4.43-14.82, p<0.001). However, no significant difference was observed in DTC for the cognitive task (14.71%, IQR: -0.89-38.84 vs 15%, IQR: -0.89-38.84%, p=0.599).

CONCLUSION

Older adults with insomnia have lower gait speed and poorer cognitive performance during ST and DT and an inefficient pattern of task prioritization during walking, compared to counterparts without insomnia. These findings may explain the higher risk of falls among older adults with insomnia. Geriatric professionals should be aware of potential interrelationships between sleep and gait.

IS POOR QUALITY SLEEP PRESENT IN OLDER ADULTS WITH WORSE SOCIAL AND HEALTH STATUS?

ABSTRACT Objective: to analyze the relationship among sleep and sociodemographic aspects, health, frailty, performance in activities of daily living, cognitive performance and depressive symptoms of older residents in the community. Method: a cross-sectional, quantitative study was conducted with 81 older adults residents in the area covered by a Family Health Unit in the city of São Carlos (SP), Brazil. Data collection occurred in 2019, through the application of the following instruments: questionnaire for socioeconomic and health characterization of the older adult, Pittsburgh Sleep Quality Index, Frailty Phenotype proposed by Linda Fried, Mini Mental State Examination, Geriatric Depression Scale, Katz Index and Lawton Scale. Participants were divided into comparative groups according to sleep quality scores. Fisher's exact and Pearson's χ2 were used. A significance level of 5% was adopted. Results: 50.6% of the older adults had poor quality sleep (n=41), followed by 33.3% of older adults with good quality sleep (n=27) and 16.1% had sleep disorders (n=13). There was a relationship between sleep quality and sex (p=0.008), work status (p=0.001), self-assessment of health (p=0.013), falls (p=0.034), pain (p=0.012), frailty level (p=0.026) and the slow gait criterion (p<0.001). Conclusion: there was a higher prevalence of poor quality sleep and sleep disorders in older patients, who do not work outside the home, who evaluated their health as regular or poor, who suffered falls in the last year and who complained of pain, frailty and slow gait.