Characterization of the fine motor problems in patients with cognitive dysfunction - A computerized handwriting analysis

Deficits in reaching movements under visual interference as a novel diagnostic marker for mild cognitive impairment

Patients with Mild Cognitive Impairment (MCI) may exhibit poorer performance in visuomotor tasks than healthy individuals, particularly under conditions with high cognitive load. Few studies have examined reaching movements in MCI and did so without assessing susceptibility to distractor interference. This proof-of-concept study analyzed the kinematics of visually guided reaching movements towards a target dot placed along the participants' midsagittal/reaching axis. Movements were performed with and without a visual distractor (flanker) at various distances from the reaching axis. Participants were instructed to avoid "touching" the flanker during movement execution. The whole sample included 11 patients with MCI due to Alzheimer's disease, 10 healthy older adults, and 12 healthy young adults, all right-handed. Patients with MCI performed reaching movements whose trajectories deviated significantly away from the flanker, especially when it was 1 mm away, with less consistent trajectories than controls. Also, our results suggest that trajectory curvature may discriminate between patients with MCI and healthy older adults. The analysis of reaching movements under conditions of visual interference may enhance the diagnosis of MCI, underscoring the need for multidimensional assessments incorporating both cognitive and motor domains.

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques

PURPOSE

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from.

METHODS

A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly.

RESULTS

The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application.

CONCLUSION

The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

Classification of Mild Cognitive Impairment and Alzheimer's Disease Using Manual Motor Measures

INTRODUCTION

Manual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD.

METHODS

Fifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid β, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning.

RESULTS

Amnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid β deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD.

CONCLUSION

Grip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid β deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.

Visual-motor integration in children with unilateral cerebral palsy: application of the computer-aided measure of visual-motor integration

BACKGROUND

Children with unilateral cerebral palsy (UCP) are encouraged to participate in the regular school curriculum. However, even when using the less-affected hand for handwriting, children with UCP still experience handwriting difficulties. Visual-motor integration (VMI) is a predictor of handwriting quality. Investigating VMI in children with UCP is important but still lacking. Conventional paper-based VMI assessments is subjective and use all-or-nothing scoring procedures, which may compromise the fidelity of VMI assessments. Moreover, identifying important shapes that are predictive of VMI performance might benefit clinical decision-making because different geometric shapes represent different developmental stepping stones of VMI. Therefore, a new computer-aided measure of VMI (the CAM-VMI) was developed to investigate VMI performance in children with UCP and to identify shapes important for predicting their VMI performance.

METHODS

Twenty-eight children with UCP and 28 typically-developing (TD) children were recruited. All participants were instructed to complete the CAM-VMI and Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery-VMI). The test items of the CAM-VMI consisted of nine simple geometric shapes related to writing readiness. Two scores of the CAM-VMI, namely, Error and Effort, were obtained by image registration technique. The performances on the Beery-VMI and the CAM-VMI of children with UCP and TD children were compared by independent t-test. A series of stepwise regression analyses were used to identify shapes important for predicting VMI performance in children with UCP.

RESULTS

Significant group differences were found in both the CAM-VMI and the Beery-VMI results. Furthermore, Error was identified as a significant aspect for predicting VMI performance in children with UCP. Specifically, the square item was the only significant predictor of VMI performance in children with UCP.

CONCLUSIONS

This study was a large-scale study that provided direct evidence of impaired VMI in school-aged children with UCP. Even when using the less-affected hand, children with UCP could not copy the geometric shapes as well as TD children did. The copied products of children with UCP demonstrated poor constructional accuracy and inappropriate alignment. Furthermore, the predictive model suggested that the constructional accuracy of a copied square is an important predictor of VMI performance in children with UCP.

Function of the hand as a predictor of early diagnosis and progression of Alzheimer's dementia: A systematic review

BACKGROUND

The dominant feature of Alzheimer's dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity.

OBJECTIVE

This review analyzed reports on hand function in AD patients to determine the possibility of using it for an early diagnosis and for monitoring the disease progression of AD.

METHODS

PubMed, Web of Science, EMBASE, and Cochrane library were searched systematically (search dates: 2000-2022), and relevant articles were cross-checked for related and relevant publications.

RESULTS

Seventeen studies assessed the association of the handgrip strength or dexterity with cognitive performance. The hand dexterity was strongly correlated with the cognitive function in all studies. In the hand dexterity test using the pegboard, there was little difference in the degree of decline in hand function between the healthy elderly (HE) group and the mild cognitive impairment (MCI) group. On the other hand, there was a difference in the hand function between the HE group and the AD group. In addition, the decline in hand dexterity is likely to develop from moderate to severe dementia. In complex hand movements, movement speed variations were greater in the AD than in the HE group, and the automaticity, regularity, and rhythm were reduced.

CONCLUSIONS

HE and AD can be identified by a simple hand motion test using a pegboard. The data can be used to predict dementia progression from moderate dementia to severe dementia. An evaluation of complex hand movements can help predict the transition from MCI to AD and the progression from moderate to severe dementia.

A Study of Assisted Screening for Alzheimer's Disease Based on Handwriting and Gait Analysis

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD.

Objective

This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification.

Methods

We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared.

Results

The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features.

Conclusions

The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects.

The "Little Circles Test" (LCT): a dusted-off tool for assessing fine visuomotor function

BACKGROUND

The fine visuomotor function is commonly impaired in several neurological conditions. However, there is a scarcity of reliable neuropsychological tools to assess such a critical domain.

AIMS

The aim of this study is to explore the psychometric properties and provide normative data for the Visual-Motor Speed and Precision Test (VMSPT).

RESULTS

Our normative sample included 220 participants (130 females) aged 18-86 years (mean education = 15.24 years, SD = 3.98). Results showed that raw VMSPT scores were affected by higher age and lower education. No effect of sex or handedness was shown. Age- and education-based norms were provided. VMSPT exhibited weak-to-strong correlations with well-known neuropsychological tests, encompassing a wide range of cognitive domains of clinical relevance. By gradually intensifying the cognitive demands, the test becomes an indirect, performance-oriented measure of executive functioning. Finally, VMSPT seems proficient in capturing the speed-accuracy trade-off typically observed in the aging population.

CONCLUSIONS

This study proposes the initial standardization of a versatile, time-efficient, and cost-effective neuropsychological tool for assessing fine visuomotor coordination. We propose renaming the VMSPT as the more approachable "Little Circles Test" (LCT).

A study of auxiliary screening for Alzheimer’s disease based on handwriting characteristics

Background and objectives

Alzheimer’s disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool.

Materials and methods

Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student’s t-test and Mann–Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic.

Results

Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p < 0.05, p < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994.

Conclusion

This study’s results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis.

Temporal Appearance of Enhanced Innate Anxiety in Alzheimer Model Mice

The prevalence of Alzheimer's disorder (AD) is increasing worldwide, and the co-morbid anxiety is an important, albeit often neglected problem, which might appear early during disease development. Animal models can be used to study this question. Mice, as prey animals, show an innate defensive response against a predator odor, providing a valuable tool for anxiety research. Our aim was to test whether the triple-transgenic mice model of AD shows signs of innate anxiety, with specific focus on the temporal appearance of the symptoms. We compared 3xTg-AD mice bearing human mutations of amyloid precursor protein, presenilin 1, and tau with age-matched controls. First, separate age-groups (between 2 and 18 months) were tested for the avoidance of 2-methyl-2-thiazoline, a fox odor component. To test whether hypolocomotion is a general sign of innate anxiety, open-field behavior was subsequently followed monthly in both sexes. The 3xTg-AD mice showed more immobility, approached the fox odor container less often, and spent more time in the avoidance zone. This effect was detectable already in two-month-old animals irrespective of sex, not visible around six months of age, and was more pronounced in aged females than males. The 3xTg-AD animals moved generally less. They also spent less time in the center of the open-field, which was detectable mainly in females older than five months. In contrast to controls, the aged 3xTg-AD was not able to habituate to the arena during a 30-min observation period irrespective of their sex. Amyloid beta and phospho-Tau accumulated gradually in the hippocampus, amygdala, olfactory bulb, and piriform cortex. In conclusion, the early appearance of predator odor- and open space-induced innate anxiety detected already in two-month-old 3xTg-AD mice make this genetically predisposed strain a good model for testing anxiety both before the onset of AD-related symptoms as well as during the later phase. Synaptic dysfunction by protein deposits might contribute to these disturbances.

Classification of mild cognitive impairment based on handwriting dynamics and qEEG

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Computerized handwriting evaluation and statistical reports for children in the age of primary school

This study proposed a novel computational method for evaluating logographic handwriting. It can precisely evaluate both the handwriting product and the process. The measures included handwriting performance as well as the temporospatial, kinematics, and kinetics features. For examining the psychometrics of this comprehensive evaluation system, typical development children aged 6 to 9 years old (grade 1 to grade 3) (n = 641) were involved in the study of factor analysis. From twelve measuring variables, the exploratory factor analysis extracted five factors (handwriting performance, motor control, speed and automation, halt and exertion, and “in air” events). The test reliability was confirmed by further recruitment of typically developing children (n = 242). The internal consistency mostly demonstrated good to excellent results for every measure. This study further recruited children with handwriting difficulties (n = 33) for testing the discriminative validity of the evaluation system. A series of two-way ANOVA tests was conducted to test the significance of the main effects of the groups (typical development and handwriting deficit) and grades (1, 2, and 3) and their interaction effects on the handwriting measures. All the measures showed significant differences between the two groups, indicating the discriminative validity for identifying handwriting deficits. Seven of twelve measures showed significant interaction effects, indicating the different trends across the grades between the two groups. Typically-developing children demonstrated ongoing progress from grade 1 to grade 3, suggesting a developmental trend during their early school age. Implications for motor development and clinical evaluation are discussed herein in relation to the five dimensions.

Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review

The dementia population is increasing as the world's population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle-Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.

Neural Mechanisms of Motor Dysfunction in Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review

Background:

Despite the prevalence of motor symptoms in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), their underlying neural mechanisms have not been thoroughly studied.

Objective:

This review summarizes the neural underpinnings of motor deficits in MCI and AD.

Methods:

We searched PubMed up until August of 2021 and identified 37 articles on neuroimaging of motor function in MCI and AD. Study bias was evaluated based on sample size, availability of control samples, and definition of the study population in terms of diagnosis.

Results:

The majority of studies investigated gait, showing that slower gait was associated with smaller hippocampal volume and prefrontal deactivation. Less prefrontal activation was also observed during cognitive-motor dual tasking, while more activation in cerebellar, cingulate, cuneal, somatosensory, and fusiform brain regions was observed when performing a hand squeezing task. Excessive subcortical white matter lesions in AD were associated with more signs of parkinsonism, poorer performance during a cognitive and motor dual task, and poorer functional mobility. Gait and cognitive dual-tasking was furthermore associated with cortical thickness of temporal lobe regions. Most non-gait motor measures were only reported in one study in relation to neural measures.

Conclusion:

Cross-sectional designs, lack of control groups, mixing amnestic- and non-amnestic MCI, disregard of sex differences, and small sample sizes limited the interpretation of several studies, which needs to be addressed in future research to progress the field.

Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions

BACKGROUND & OBJECTIVE

With populations aging, the number of people with dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. We urgently need, cost effective, objective biomarkers to detect AD, and other dementias, at an early stage. Risk factor modification could prevent 40% of cases and drug trials would have greater chances of success if participants are recruited at an earlier stage. Currently, detection of dementia is largely by pen and paper cognitive tests but these are time consuming and insensitive to the pre-clinical phase. Specialist brain scans and body fluid biomarkers can detect the earliest stages of dementia but are too invasive or expensive for widespread use. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. This scoping review aims to summarise the current capabilities of AI-aided digital biomarkers to aid in early detection of dementia, and also discusses potential future research directions.

METHODS & MATERIALS

In this scoping review, we used PubMed and IEEE Xplore to identify relevant papers. The resulting records were further filtered to retrieve articles published within five years and written in English. Duplicates were removed, titles and abstracts were screened and full texts were reviewed.

RESULTS

After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication. A further 771 records were excluded after screening titles and abstracts, and 496 were excluded after full text review. The final yield was 177 studies. Records were grouped into different artificial intelligence based tests: (a) computerized cognitive tests (b) movement tests (c) speech, conversion, and language tests and (d) computer-assisted interpretation of brain scans.

CONCLUSIONS

In general, AI techniques enhance the performance of dementia screening tests because more features can be retrieved from a single test, there are less errors due to subjective judgements and AI shifts the automation of dementia screening to a higher level. Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%. In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%. Deep learning techniques applied in brain scan analysis achieves around 92% accuracy. Movement tests and setting smart environments to capture daily life behaviours are two potential future directions that may help discriminate dementia from normal aging. AI-based smart environments and multi-modal tests are promising future directions to improve detection of dementia in the earliest stages.

Neuropsychology of posteromedial parietal cortex and conversion factors from Mild Cognitive Impairment to Alzheimer's disease: systematic search and state-of-the-art review

In the present review, we discuss the rationale and the clinical implications of assessing visuospatial working memory (VSWM), awareness of memory deficits, and visuomotor control in patients with mild cognitive impairment (MCI). These three domains are related to neural activity in the posteromedial parietal cortex (PMC) whose hypoactivation seems to be a significant predictor of conversion from MCI to Alzheimer’s disease (AD) as indicated by recent neuroimaging evidence. A systematic literature search was performed up to May 2021. Forty-eight studies were included: 42 studies provided analytical cross-sectional data and 6 studies longitudinal data on conversion rates. Overall, these studies showed that patients with MCI performed worse than healthy controls in tasks assessing VSWM, awareness of memory deficits, and visuomotor control; in some cases, MCI patients’ performance was comparable to that of patients with overt dementia. Deficits in VSWM and metamemory appear to be significant predictors of conversion. No study explored the relationship between visuomotor control and conversion. Nevertheless, it has been speculated that the assessment of visuomotor abilities in subjects at high AD risk might be useful to discriminate patients who are likely to convert from those who are not. Being able to indirectly estimate PMC functioning through quick and easy neuropsychological tasks in outpatient settings may improve diagnostic and prognostic accuracy, and therefore, the quality of the MCI patient’s management.

Association between motor proficiency and oral health in people with intellectual disabilities

BACKGROUND

People with intellectual disabilities (IDs) may be at increased risk of developing periodontal diseases and dental caries due to poor oral hygiene. Our aim was to investigate motor proficiency factors associated with presence of visible plaque and gingival bleeding in people with IDs. We were particularly interested in the level of dependence, manual coordination and fine manual control of people with ID, as well as the level of exhaustion of the primary caregiver.

METHODS

In this cross-sectional study, 299 people with ID were evaluated for oral hygiene using the simplified Visible Plaque Index and for gum inflammation using the Gingival Bleeding Index. The Bruininks-Oseretsky Motor Proficiency Test assessed motor proficiency through fine manual control (fine motor integration and fine motor precision) and manual coordination (manual dexterity and upper limb coordination). The level of dependence was assessed by the Katz dependency index, and the caregiver was tested for exhaustion using the fatigue severity scale. Prevalence ratios [and 95% confidence intervals (CI)] were calculated using crude and adjusted Poisson regression with robust variance.

RESULTS

The exhaustion of the caregiver was associated positively to visible plaque [prevalence ratio (PR) = 1.36; 95% CI 1.06-1.65]. For gingival bleeding, people with IDs that had better fine motor integration (PR = 0.49; 95% CI 0.33-0.75) and precision (PR = 0.50; 95% CI 0.26-0.94), as well as manual dexterity (PR = 0.62, 95% CI 0.49-0.77), presented better results.

CONCLUSION

Poor oral hygiene and gum inflammation were associated with motor proficiency of people with IDs and caregivers' exhaustion. Interventions to improve the oral health of people with IDs should take into account such conditions.

A Game Changer: 'The Use of Digital Technologies in the Management of Upper Limb Rehabilitation'

Hemiparesis is a symptom of residual weakness in half of the body, including the upper extremity, which affects the majority of post stroke survivors. Upper limb function is essential for daily life and reduction in movements can lead to tremendous decline in quality of life and independence. Current treatments, such as physiotherapy, aim to improve motor functions, however due to increasing NHS pressure, growing recognition on mental health, and close scrutiny on disease spending there is an urgent need for new approaches to be developed rapidly and sufficient resources devoted to stroke disease. Fortunately, a range of digital technologies has led to revived rehabilitation techniques in captivating and stimulating environments. To gain further insight, a meta-analysis literature search was carried out using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method. Articles were categorized and pooled into the following groups; pro/anti/neutral for the use of digital technology. Additionally, most literature is rationalised by quantitative and qualitative findings. Findings displayed, the majority of the inclusive literature is supportive of the use of digital technologies in the rehabilitation of upper extremity following stroke. Overall, the review highlights a wide understanding and promise directed into introducing devices into a clinical setting. Analysis of all four categories; (1) Digital Technology, (2) Virtual Reality, (3) Robotics and (4) Leap Motion displayed varying qualities both-pro and negative across each device. Prevailing developments on use of these technologies highlights an evolutionary and revolutionary step into utilizing digital technologies for rehabilitation purposes due to the vast functional gains and engagement levels experienced by patients. The influx of more commercialised and accessible devices could alter stroke recovery further with initial recommendations for combination therapy utilizing conventional and digital resources.