1,721,038 research outputs found
Tailored balance exercises on people with multiple sclerosis: A pilot randomized, controlled study
No full textBackground:
Altered integration of signals from visual (VIS), somatosensory (PROP) and vestibular system (VEST) lead to balance control impairments affecting the daily living activities of patients with multiple sclerosis (PwMS). As a consequence, tailored interventions could be crucial in improving efficacy of balance rehabilitation treatments.
Objective:
The objective of this paper is to assess the efficacy of tailored rehabilitation treatments for balance disorders based on visual, somatosensory and vestibular deficits versus traditional rehabilitation exercises.
Methods:
Thirty-two PwMS were assessed with the Berg Balance Scale (BBS), the composite score (CS) obtained by computerized dynamic posturography (CDP) test and the Modified Fatigue Impact Scale (MFIS). Based on CDP analysis, prevalent VIS, PROP or VEST deficits were identified and patients randomly allocated to a personalized (PRG) or traditional (TRG) rehabilitation group.
Results:
BBS score showed a significant difference between pre- and post-treatment scores of 6.3 and 2.0 points respectively for PRG and TRG. CS showed a significant difference between pre- and post-treatment scores of 16.6 and 7.6 points respectively for PRG and TRG. No interaction effect was found for MFIS score.
Conclusions:
BBS and CS showed changes in the PRG group that met clinical relevant difference, underlining that tailored rehabilitation interventions based on patient-specific sensory system impairment could improve balance and postural control in PwMS
Exploring cortical activation patterns during linear vs. curvilinear walking in people with multiple sclerosis using a walking aid
No full text
Mind wandering in people with Multiple Sclerosis: A psychometric study
Full text linkBackground: Although mind wandering (MW) is associated with various psychological aspects frequently affected in people with Multiple Sclerosis (PwMS), there is lack of validated tools to assess MW in this clinical population.Objective: This psychometric study aimed to assess structural and construct validity and reliability of a brief Italian version of Mind Wandering (MW) Scale that measures two different dimensions of MW, i.e., spontaneous (MW-S) and deliberate (MW-D).Methods: Structural validity of the MW Scale was assessed by explorative factor analysis (EFA). To investigate construct validity, mood (Hospital Anxiety Depression Scale) and personality (10-items Big Five Inventory Test) were correlated with MW constructs. Reliability was assessed by Cronbach's alpha for internal consistency and intraclass correlation coefficients.Results: EFA confirmed two distinct constructs of MW, i.e., MW-S and MW-D, also in PwMS. This tool appropriately fits the graded response model, supporting validity (about 79% of hypotheses for convergent and discriminant constructs confirmed) and internal consistency (MW-S: Cronbach's alpha = 0.84; MW-D: Cronbach's alpha = 0.88).Conclusion: MW Scale could be a useful tool to measure MW also in PwMS. As MW seems to be connected to clinical manifestations of MS, a detailed assessment of MW should be encouraged in clinical practice
Improving disease course detection in multiple sclerosis: an alternative patient-reported outcomes-based strategy
No full textSelf-assessment reliability in multiple sclerosis: the role of socio-demographic, clinical and quality of life aspects
No full textIntroduction: Several multiple sclerosis studies matching self- and physician assessment of disease course and disability show moderate and high agreement respectively. However, the role played by socio-demographic, clinical, and quality of life (QoL) factors was not much investigated. The study aims at exploring how self-/physician agreement could depend on these variables. Materials and methods: Participants were asked to report own disease course and disability according to preset categories. Kappa-value and confidence interval (CI) for disease course and two-way random interclass correlation coefficient (ICC) and CI for disability were calculated to evaluate self-/physician agreement. Χ2 was applied to examine whether other factors (gender, age, education, civil status, disease duration, fatigue, quality of life) had systematic effects. Results: Data analysis on 203 participants indicated fair agreement (Kappa-value = 0.30; 95% CI 0.23–0.38) and no dependency on the categories of each variable for disease course. Satisfactory correlation was found for disability (ICC = 0.74; 95% IC 0.67–0.80), good agreement was found for almost all variable categories, and significant differences were observed for education (better agreement for higher levels), disease duration, fatigue and QoL (better agreement for worse conditions). Discussion: Results seem to suggest that higher education and worse clinical and QoL conditions could engage the patient in developing more disease awareness and realistic self-perception and self-evaluation
Unmet care needs of people with a neurological chronic disease: A cross-sectional study in Italy on Multiple Sclerosis
Full text linkBackground: Community-based studies are required to accurately describe the supportive services needed by people with multiple sclerosis (MS). Methods: A total of 1205 people with MS participated in a cross-sectional study evaluating their unmet health and social care needs through a questionnaire collecting information used in the study. It was specifically developed by a multi-disciplinary team. Results: Overall, 79% of the responders declared at least one health or social care needs. The most prevalent health care need was the psychological support (27.5%), whereas the transport was the social care need more frequent (over 41%) in our sample. The multivariate analysis highlighted that unmet health care needs depended mainly on clinical factors such as disease stage, influenced by disease duration, and disability degree, whereas the social care needs were related to both clinical and socio-demographic factors. Conclusion: These findings suggest that MS needs significantly change over time during the disease development and to find the best way to personalize PwMS management is crucial. Moreover, more public funding directed at improving the health-related quality of life of people with MS is needed. For this reason, we think that these results will provide important information and baseline data on how to build the national service strategies thereby making healthcare planning more efficient
Predicting multiple sclerosis disease course with patient centred outcomes (PCOs): a machine learning approach
No full textBackground: Achieving an accurate clinical course description in Multiple Sclerosis (MS) is a very hard task even for clinical experts, but it is crucial for communication, prognosis, treatment decision-making, design and recruitment of clinical trials. In this context, meaningful data being “hidden” into Patient Centered Outcomes (PCOs), could provide, through Advanced Machine learning (ML) approaches, a new perspective in predicting MS disease course.
Aims: This work aims at using PRO, CS and anthropometric measures to build a statistical model for the detection of MS courses by means of machine learning techniques. The analysis has been conducted on the dataset of the ongoing Italian MS Foundation (FISM) initiative “A New Functional Profile to Monitor the Progression Of Disability In Multiple Sclerosis - PROMOPRO-MS”.
Methods: The dataset is composed of 778 patients with MS, that were enrolled in the study without any inclusion/exclusion criteria unless MS diagnosis. The variables identified in the study were based on functions sufficient to encompass the patient"s disability and to represent whole-person behaviours. The set of PCOs selected were related mainly to mobility, fatigue, cognitive performances, emotional status, bladder continence, quality of life. Both unsupervised and supervised machine learning methods were taken into account. The first goal was to assess whether the collected features could discriminate any of the different disease courses by using unsupervised learning techniques looking for a meaningful data structure, then to apply a supervised approach, inferred in the previous step, in order to learn a classifier based only on a subset of the available features.
Results: The applied machine learning techniques showed that patients with MS (PwMS) diagnosed as relapsing-remitting (RR) could be isolated from other clinical courses (ALL). In particular, nine “top” questions were selected by the "Features Selection" supervised (FS) algorithm: three questions from Life Satisfaction Index, three items from Functional Independence Measure; two from Modified Fatigue Impact Scale and one from Hospital Anxiety and Depression Scale.
Conclusions: To the very best of our knowledge this is the first study which predicted MS course taking only into account a small subset of anthropometric and questionnaires variables, which could be proposed as a novel questionnaire, tailored for RR detection
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Profile and burden of the family caregiver: the caring experience in multiple sclerosis. An observational study
Full text linkBackground The broad implications of caring for a family member with a chronic medical condition, such as MS, can lead caregivers to experience a high burden of care. The aim of the study was to describe profile of MS caregivers and their burden and to explore potential factors influencing this burden. Methods 200 family caregivers of a person with MS completed survey questionnaires across a cross-sectional study. Many information were collected: caregiver socio-demographic and health-related data, caregiving activities, knowledge of MS, coping strategies, mood, social support received and care recipient information. Caregiving burden was measured by the ZBI (Zarit Burden Interview). The extent to which the variables explained caregiver burden was analyzed using a hierarchical approach. Results 68% of the caregivers reported a perceived burden of care (ZBI score > 20). Our results show that physical and mental related-health variables are important predictive factors of the care burden, explaining much of the observed variance (40.9%). Conclusion Family caregivers in MS continue to make up the shortfall produce by national health and welfare systems. We highlighted the importance of good physical and mental health in decreasing perceived burden. Working to alleviate psychological distress through mechanisms focus on reducing worries and perceived burden may be a valid approach
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