Balance Impairments as Differential Markers of Dementia Disease Subtype

The association between dynamic balance and executive function: Which dynamic balance test has the strongest association with executive function? A systematic review and meta-analysis

AIM

The aging global population poses increasing challenges related to falls and dementia. Early identification of cognitive decline, particularly before noticeable symptoms manifest, is crucial for effective intervention. This review aims to determine the dynamic balance test most closely associated with executive function, potentially serving as a biomarker for cognitive decline.

RECENT FINDINGS

Based on recent reviews, inhibitory control, a component of executive function, holds significance in influencing balance performance. Studies suggest that the strength of the correlation between cognition and balance tends to be domain-specific and task-specific. Despite these findings, inconclusive evidence remains regarding the connection between executive function and various dynamic balance assessments. Our review identifies a significant association between all dynamic balance tests and executive function, albeit with varying strengths. Notably, a medium effect size is observed for the Timed Up and Go and Functional Reach Test, a small effect size for balance scales, and a strong effect size for postural sway. This review underscores a clear relationship between dynamic balance task performance and executive function. Dynamic posturography holds potential as a clinical biomarker for early detection of cognitive decline, with a note of caution due to observed heterogeneity and limited studies.

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

Impaired Standing Balance in Older Adults with Cognitive Impairment after a Severe Fall

INTRODUCTION

Fall-related sequelae as well as balance and gait impairments are more pronounced in older adults who are cognitively impaired (OACI) compared to older adults who are cognitively healthy (OACH). Evidence is scarce about differences in standing balance and gait in OACH and OACI after a fall, even though these are major risks for recurrent falls. Thus, the aim of this study was to investigate early impairments in gait and balance, by adding inertial measurement units (IMUs) to a functional performance test in OACH and OACI after a severe fall with a presentation to the emergency department (ED) and immediate discharge.

METHODS

The study sample was stratified into participants with and without probable cognitive impairment using the result of the Montreal Cognitive Assessment total score (maximum of 30 points). The cutoff for probable cognitive impairment was set at ≤ 24. Standing balance and gait parameters were measured using three IMUs in n = 69 OACH (72.0 ± 8.2 years) and n = 76 OACI (78.7 ± 8.1 years). Data were collected at participants' homes as part of a comprehensive geriatric assessment in the "SeFallED" study within 4 weeks after presentation to the ED after a severe fall (German Clinical Trials Register ID: 00025949). ANCOVA was used for statistical analysis, adjusted for age.

RESULTS

The data indicated significantly more sway for OACI compared to OACH during balance tasks, whereas no differences in gait behavior were found. In detail, differences in standing balance were revealed for mean velocity (m/s) during parallel stance with eyes open (ηp2 = 0.190, p < 0.001) and eyes closed on a balance cushion (ηp2 = 0.059, p = 0.029), as well as during tandem stance (ηp2 = 0.034, p = 0.044) between OACI and OACH. Further differences between the two groups were detected for path length (m/s2) during parallel stance with eyes open (ηp2 = 0.144, p < 0.001) and eyes closed (ηp2 = 0.044, p < 0.027) and for range (m/s2) during tandem (ηp2 = 0.036, p = 0.036) and parallel stance with eyes closed (ηp2 = 0.045, p = 0.032).

CONCLUSION

Even though both groups have experienced a severe fall with presentation to the ED in the preceding 4 weeks, balance control among OACI indicated a higher fall risk than among OACH. Therefore, effective secondary fall prevention efforts have to be established, particularly for OACI.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

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.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

CLINICALTRIAL

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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 extensively 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.

Reliability and Usability Analysis of an Embedded System Capable of Evaluating Balance in Elderly Populations Based on a Modified Wii Balance Board

This paper analyzes the reliability and usability of a portable electronic instrument that measures balance and balance impairment in older adults. The center of pressure (CoP) metrics are measured with a modified Wii Balance Board (mWBB) platform. In the intra- and inter-rater testing, 16 and 43 volunteers (mean 75.66 and standard deviation (SD) of 7.86 years and 72.61 (SD 7.86) years, respectively) collaborated. Five volunteer raters (5.1 (SD 3.69) years of experience) answered the System Usability Scale (SUS). The most reliable CoP index in the intra-examiner tests was the 95% power frequency in the medial-lateral displacement of the CoP with closed-eyes. It had excellent reliability with an intraclass correlation coefficient ICC = 0.948 (C.I. 0.862-0.982) and a Pearson's correlation coefficient PCC = 0.966 (p < 0.001). The best index for the inter-rater reliability was the centroidal frequency in the anterior-posterior direction closed-eyes, which had an ICC (2,1) = 0.825. The mWBB also obtained a high usability score. These results support the mWBB as a reliable complementary tool for measuring balance in older adults. Additionally, it does not have the limitations of laboratory-grade systems and clinical screening instruments.