The development and validation of the impact of multiple sclerosis scale and the symptoms of multiple sclerosis scale

Objective

To develop and validate the Impact of Multiple Sclerosis Scale (IMSS) and the Symptoms of Multiple Sclerosis Scale (SMSS) using the Extended Disability Status Scale (EDSS) for construct validity.
Design

Panel design involving test-retest over 4 months.
Setting

A mailed survey.
Participants

Volunteers with a diagnosis of multiple sclerosis (MS) recruited from an MS support service in Australia: 193 people (mean age, 39y) and 150 people participated at time 1 and time 2, respectively.
Interventions

Not applicable.
Main Outcome Measures

Principal components analyses, the Cronbach α, and descriptive statistics for the 2 scales; correlations for construct validity with the EDSS and retest; and confirmatory factor analysis to test the stability of IMSS and SMSS components over time.
Results

The IMSS yielded 5 independent and reliable components; the SMSS yielded 3 components; both component structures were stable over time. These scales showed convergent validity with the EDSS.
Conclusions

The IMSS and SMSS are psychometrically sound scales suitable for clinical and research purposes to assess the symptoms and impact of MS.

Modelling semantics across multiple time series and its applications

Analysis based on the holistic multiple time series system has been a practical and crucial topic. In this paper, we mainly study a new problem that how the data is produced underneath the multiple time series system, which means how to model time series data generating and evolving rules (here denoted as semantics). We assume that there exist a set of latent states, which are the system basis and make the system run: data generating and evolving. Thus, there are several challenges on the problem: (1) How to detect the latent states; (2) How to learn the rules based on the states; (3) What the semantics can be used for. Hence, a novel correlation field-based semantics learning method is proposed to learn the semantics. In the method, we first detect latent state assignment by comprehensively considering kinds of multiple time series characteristics, which contain tick-by-tick data, temporal ordering, relationship among multiple time series and so on. Then, the semantics are learnt by Bayesian Markov characteristic. Actually, the learned semantics could be applied into various applications, such as prediction or anomaly detection for further analysis. Thus, we propose two algorithms based on the semantics knowledge, which are applied to make next-n step prediction and detect anomalies respectively. Some experiments on real world data sets were conducted to show the efficiency of our proposed method.

The effects of a simplified tai-chi exercise program (STEP) on the physical health of older adults living in long-term care facilities : a single group design with multiple time points

Background
Studies support the positive effects that Tai Chi has on the physical health of older adults. However, many older adults residing in long-term care facilities feel too weak to practice traditional Tai Chi, and a more simplified style is preferred.
Objective
To test the effects of a newly-developed, Simplified Tai-Chi Exercise Program (STEP) on the physical health of older adults who resided in long-term care facilities.
Design
A single group design with multiple time points: three pre-tests, one month apart; four post-tests at one month, two months, three months, and six months after intervention started.
Settings
Two 300–400 bed veteran homes in Taiwan.
Participants
The 51 male older adults were recruited through convenience sampling, and 41 of them completed six-month study. Inclusion criteria included: (1) aged 65 and over; (2) no previous training in Tai Chi; (3) cognitively alert and had a score of at least eight on the Short Portable Mental Status Questionnaire; (4) able to walk without assistance; and (5) had a Barthel Index score of 61 or higher. Participants who had dementia, were wheel-chair bound, or had severe or acute cardiovascular, musculoskeletal, or pulmonary illnesses were excluded.
Methods
The STEP was implemented three times a week, 50 min per session for six months. The outcome measures included cardio-respiratory function, blood pressure, balance, hand-grip strength, lower body flexibility, and physical health actualization.
Results
A drop in systolic blood pressure (p=.017) and diastolic blood pressure (p<.001) was detected six months after intervention started. Increase in hand-grip strength from pre to post intervention was found (left hand: p<.001; right hand: p=.035). Participants also had better lower body flexibility after practicing STEP (p=.038).
Conclusions
Findings suggest that the STEP be incorporated as a floor activity in long-term care facilities to promote physical health of older adults.

Improvement on delay dependent absolute stability of Lurie control systems with multiple time delays

Absolute stability of Lurie control systems with multiple time-delays is studied in this paper. By using extended Lyapunov functionals, we avoid the use of the stability assumption on the main operator and derive improved stability criteria, which are strictly less conservative than the criteria in [2,3].

Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study

In this paper, we present a novel anomaly detection framework for multiple heterogeneous yet correlated time series, such as the medical surveillance series data. In our framework, we propose an anomaly detection algorithm from the viewpoint of trend and correlation analysis. Moreover, to efficiently process huge amount of observed time series, a new clustering-based compression method is proposed. Experimental results indicate that our framework is more effective and efficient than its peers. © 2012 Springer-Verlag Berlin Heidelberg.

Minimum-order filter design for partial state estimation of linear systems with multiple time-varying state delays and unknown input delays

Designing delay-dependent functional observers for LTI systems with multiple known time-varying state delays and unknown time-varying input delays is studied. The input delays are arbitrary, but the state delays should be upper-bounded. In addition, two scenarios of slow-varying and fast-varying state delays are investigated. The results of the paper can also be considered as one of the first contributions considering unknown-input functional observer design for linear systems with multiple time-varying state delays. Based on the Lyapunov Krasovskii approach, delay-dependent sufficient conditions of the exponential stability of the observer in each scenario are established in terms of linear matrix inequalities. Because of using effective techniques, such as the descriptor transformation and an advanced weighted integral inequality, the proposed stability criteria can result in larger stability regions compared with the other papers that study functional observers for time-varying delay systems. Furthermore, to help with the design procedure, a genetic algorithm-based scheme is proposed to adjust a weighting matrix in the established linear matrix inequalities. Two numerical examples illustrate the design procedure and demonstrate the efficacy of the proposed observer in each scenario.

An LMI-based functional estimation scheme of large-scale time-delay systems with strong interconnections

In this paper, the problem of distributed functional state observer design for a class of large-scale interconnected systems in the presence of heterogeneous time-varying delays in the interconnections and the local state vectors is considered. The resulting observer scheme is suitable for strongly coupled subsystems with multiple time-varying delays, and is shown to give better results for systems with very strong interconnections while only some mild existence conditions are imposed. A set of existence conditions are derived along with a computationally simple observer constructive procedure. Based on the Lyapunov-Krasovskii functional method (LKF) in the framework of linear matrix inequalities (LMIs), delay-dependent conditions are derived to obtain the observer parameters ensuring the exponential convergence of the observer error dynamics. The effectiveness of the obtained results is illustrated and tested through a numerical example of a three-area interconnected system.

Discovering semantics from multiple correlated time series stream

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method. © Springer-Verlag 2013.

Constraint-based conversion of fiction text to a time-based graphical representation

This paper presents a method for converting unrestricted fiction text into a time-based graphical form. Key concepts extracted from the text are used to formulate constraints describing the interaction of entities in a scene. The solution of these constraints over their respective time intervals provides the trajectories for these entities in a graphical representation.

Three types of entity are extracted from fiction books to describe the scene, namely Avatars, Areas and Objects. We present a novel method for modeling the temporal aspect of a fiction story using multiple time-line representations after which the information extracted regarding entities and time-lines is used to formulate constraints. A constraint solving technique based on interval arithmetic is used to ensure that the behaviour of the entities satisfies the constraints over multiple universally quantified time intervals. This approach is demonstrated by finding solutions to multiple time-based constraints, and represents a new contribution to the field of Text-to-Scene conversion. An example of the automatically produced graphical output is provided in support of our constraint-based conversion scheme.

Multitasking, working memory and remembering intentions

Multitasking refers to the performance of a range of tasks that have to be completed within a limited time period. it differs from dual task paradigms in that tasks are performed not in parallel, but by interleaving, switching from one to the other. it differs also from task switching paradigms in that the time scale is very much longer, multiple different tasks are involved, and most tasks have a clear end point. Multitasking has been studied extensively with particular sets of experts such as in aviation and in the military, and impairments of multitasking performance have been studied in patients with frontal lobe lesions. Much less is known as to how multitasking is achieved in healthy adults who have not had specific training in the necessary skills. This paper will provide a brief review of research on everyday multitasking, and summarise the results of some recent experiments on simulated everyday tasks chosen to require advance and on-line planning, retrospective memory, prospective memory, and visual, spatial and verbal short-term memory.

Television viewing time and 13-year mortality in adults with cardiovascular disease: data from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab)

BACKGROUND: In the general population, excessive sedentary behaviour is associated with increased all-cause mortality. Few studies have examined this relationship in people with cardiovascular disease (CVD). Using a sample of people with CVD who were excluded from an analysis of the Australian Diabetes, Obesity and Lifestyle (AusDiab) study, we examined the relationship between sedentary behaviour and 13-year all-cause mortality.

METHODS: In the original AusDiab study, television viewing time was used as a marker of sedentary behaviour in 609 adults (≥45 years of age) with CVD. During 6,291 person-years of follow-up (median follow-up 13 years), there were 294 deaths (48% of sample). Using the time scale of attained age, the Cox proportional hazards model predicting all-cause mortality adjusted for sex, self-rated general health, leisure-time physical activity, smoking status, education, household income, body mass index, lipid levels, blood pressure, and diabetes mellitus was used.

RESULTS: Compared with a TV viewing time of <2hours per day, the fully adjusted hazard ratios for all-cause mortality were 1.18 (95% CI, 0.88 to 1.57) for ≥2 to <4hours per day and 1.52 (95% CI, 1.09 to 2.13) for >4hours per day.

CONCLUSIONS: Sedentary behaviour was associated with increased risk of all-cause mortality in people with CVD, independent of physical activity and other confounders. In addition to the promotion of regular physical activity, cardiac rehabilitation efforts which also focus on reducing sedentary behaviour may be beneficial.

Predictors of outcomes of psychological treatments for disordered gambling : a systematic review

This systematic review aimed to synthesise the evidence relating to pre-treatment predictors of gambling outcomes following psychological treatment for disordered gambling across multiple time-points (i.e., post-treatment, short-term, medium-term, and long-term). A systematic search from 1990 to 2016 identified 50 articles, from which 11 socio-demographic, 16 gambling-related, 21 psychological/psychosocial, 12 treatment, and no therapist-related variables, were identified. Male gender and low depression levels were the most consistent predictors of successful treatment outcomes across multiple time-points. Likely predictors of successful treatment outcomes also included older age, lower gambling symptom severity, lower levels of gambling behaviours and alcohol use, and higher treatment session attendance. Significant associations, at a minimum of one time-point, were identified between successful treatment outcomes and being employed, ethnicity, no gambling debt, personality traits and being in the action stage of change. Mixed results were identified for treatment goal, while education, income, preferred gambling activity, problem gambling duration, anxiety, any psychiatric comorbidity, psychological distress, substance use, prior gambling treatment and medication use were not significantly associated with treatment outcomes at any time-point. Further research involving consistent treatment outcome frameworks, examination of treatment and therapist predictor variables, and evaluation of predictors across long-term follow-ups is warranted to advance this developing field of research.