Posts Tagged Statistics

[WEB SITE] Depression: MedlinePlus

Also called: Clinical depression, Dysthymic disorder, Major depressive disorder, Unipolar depression

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Summary

Depression is a serious medical illness. It’s more than just a feeling of being sad or “blue” for a few days. If you are one of the more than 19 million teens and adults in the United States who have depression, the feelings do not go away. They persist and interfere with your everyday life. Symptoms can include

  • Feeling sad or “empty”
  • Loss of interest in favorite activities
  • Overeating, or not wanting to eat at all
  • Not being able to sleep, or sleeping too much
  • Feeling very tired
  • Feeling hopeless, irritable, anxious, or guilty
  • Aches or pains, headaches, cramps, or digestive problems
  • Thoughts of death or suicide

Depression is a disorder of the brain. There are a variety of causes, including genetic, biological, environmental, and psychological factors. Depression can happen at any age, but it often begins in teens and young adults. It is much more common in women. Women can also get postpartum depression after the birth of a baby. Some people get seasonal affective disorder in the winter. Depression is one part of bipolar disorder.

There are effective treatments for depression, including antidepressants, talk therapy, or both.

NIH: National Institute of Mental Health

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via Depression: MedlinePlus

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[ARTICLE] What the Proportional Recovery Rule Is (and Is Not): Methodological and Statistical Considerations – Full Text

In 2008, it was proposed that the magnitude of recovery from nonsevere upper limb motor impairment over the first 3 to 6 months after stroke, measured with the Fugl-Meyer Assessment (FMA), is approximately 0.7 times the initial impairment (“proportional recovery”). In contrast to patients with nonsevere hemiparesis, about 30% of patients with an initial severe paresis do not show such recovery (“nonrecoverers”). Hence it was suggested that the proportional recovery rule (PRR) was a manifestation of a spontaneous mechanism that is present in all patients with mild-to-moderate paresis but only in some with severe paresis. Since the introduction of the PRR, it has subsequently been applied to other motor and nonmotor impairments. This more general investigation of the PRR has led to inconsistencies in its formulation and application, making it difficult to draw conclusions across studies and precipitating some cogent criticism. Here, we conduct a detailed comparison of the different studies reporting proportional recovery and, where appropriate, critique statistical methodology. On balance, we conclude that existing data in aggregate are largely consistent with the PRR as a population-level model for upper limb motor recovery; recent reports of its demise are exaggerated, as these excessively focus on the less conclusive issue of individual subject-level predictions. Moving forward, we suggest that methodological caution and new analytical approaches will be needed to confirm (or refute) a systematic character to spontaneous recovery from motor and other poststroke impairments, which can be captured by a mathematical rule either at the population or at the subject level.

It has been appreciated since Hippocrates that the strongest predictor of final motor impairment after stroke is initial impairment (Aphorisms of Hippocrates, Section 2: 42). A prominent poststroke motor impairment in humans is the intrusion of unwanted synergies, with synergy referring to a systematic pattern of either joint co-articulation or muscle co-activation. The Fugl-Meyer Assessment (FMA) was explicitly developed to track progression of recovery from such synergies. A seminal study tracking the recovery of patients using the upper extremity subscale of the Fugl-Meyer Assessment (FMA-UE) demonstrated that more severely affected patients saw greater recovery in this outcome, on average, than more mildly affected patients in the immediate poststroke recovery period1; however, the average final score of the FMA-UE among the severly affected still trailed behind the mildly affected. The authors of this study stated, “The most dramatic recovery in motor function occurred over the first 30 days, regardless of the initial severity of the stroke.” On the basis of this study and other considerations, Krakauer et al2 sought to investigate the nature of this FMA-UE change early after stroke; work that led to the formulation of the proportional recovery rule (PRR).2 The PRR states that patients recover approximately 70% of their maximal potential reduction in impairment as measured by the FMA.2

Since it was introduced, the PRR has been applied in a broad range of studies that involve recovery from stroke, both for FMA-UE and for other outcomes. Claims related to the PRR have been made for upper and lower limb impairment measured by the FMA,310 aphasia measured with the Western Aphasia Battery (WAB),11 the resting motor threshold (RMT) of the extensor carpi radialis,6 and visuospatial neglect measured with the Letter Cancellation Test (LCT),12 among others. Applications of the PRR typically distinguish between two distinct subgroups of patients, referred to as “recoverers” and “nonrecoverers”: the former subgroup is composed of patients who recover a significant amount of lost function, and the latter is composed of those who do not. The PRR is thought to usefully characterize the recovery process among recoverers only. Although the methods by which the PRR was applied and evaluated have differed substantially across publications, many authors have argued that their findings are evidence for a PRR that accurately describes an underlying biological process that arises across neurolocical domains. Recently, however, the PRR has been the subject of criticism related to the validity of the statistical methods underlying its implementation and to the degree to which data are consistent with claims in support of the PRR.13,14 Much of the critique on the PRR articulated by these articles was focused on specific statements associated with the PRR followed by a general dismissal of all findings.

Our goal in this work is to provide a critical reexamination of the literature pertaining to the PRR. We focus first on the interpretation and implementation of PRR as a statistical model, and on data-driven concerns about the use of the PRR in studies of recovery. We then reexamine data reported in the literature and the extent to which past studies provide evidence for the PRR with these considerations in mind. Our hope is that this will serve as an instructive overview of issues that can arise in the application of the PRR to studies of recovery, aiming to improve future investigations into the PRR. Although our primary purpose is not to provide direct response to recent critiques,13,14 we are mindful of the concerns raised and address these directly in the Discussion section.

The breadth of work on the PRR introduces a commensurate range of methodological concerns one might consider. We attempt to be complete in our discussion but prefer to focus on overarching concerns regarding the statistical validity of the PRR instead of point-by-point inspections of the existing literature. Two themes we will revisit while pursuing the main goals of this paper are the identification of recoverers and the distinction between describing biological mechanisms and making patient-level predictions. The manner in which nonrecoverers are identified is a point of legitimate concern, as some statistical approaches can artifactually create evidence for the PRR. The PRR was originally intended to describe biological mechanisms at the population level, although implicitly it is expected that the PRR may be useful for predicting recovery of individual patients. Both of these are related to recent concerns regarding the PRR.

The next section provides an overview of the statistical formulation of the PRR and introduces three simulated datasets to illustrate scenarios over which the PRR shows varying degrees of validity. Subsequent sections conduct a selective review of the literature, reevaluating specific articles in the light of the three scenarios, comment on recent criticisms of the PRR, and end with our current view on the veracity of the PRR.

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Continue —>  What the Proportional Recovery Rule Is (and Is Not): Methodological and Statistical Considerations – Robinson Kundert, Jeff Goldsmith, Janne M. Veerbeek, John W. Krakauer, Andreas R. Luft,

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[Abstract] Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation – IEEE Conference Publication

Abstract

The objective of this paper was to compare the incidence of a rehabilitation game in motor ability with dynamic difficulty adjustment (ADD) in comparison to a manual configuration. To achieve that, a virtual tool called “Bug catcher” was developed, which is focused in upper limb rehabilitation. This tool uses a dynamic difficulty adjustment based in fuzzy logic. The population involved for the present study were made by 2 users, a 18-year-old patient with a hemiparesis that limits her motor ability in her left upper limb, and a 37-year-old patient with motor monoparesis in his right upper limb. This tool was used in both users, each one with a different configuration (automatic or manual), and the motor ability from both participants was objectively measured using Box and Blocks Test, applied before, during and after each session; additionally, a performance index (percentage of success) was defined in order to determine the progress of the participants in the virtual tool. As a result, it was obtained that user number one using the game with ADD, managed to obtain not only a better performance in the sessions but also an important advance in her motor skill in comparison to the user 2 with the manual configuration.

via Dynamic Difficulty Adjustment in Virtual Reality Applications for Upper Limb Rehabilitation – IEEE Conference Publication

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[WEB SITE] Rehabilitation Measures | Shirley Ryan AbilityLab – Formerly RIC

 

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With over 400 measures and supported by some of the worlds best doctors, clinicians, therapists and Physical Medicine and Rehabilitation researchers, the Rehabilitation Measure Database (RMD) is the go-to resource for benchmarks and outcomes.

 

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Browse our database of instruments to find the right treatment for your patients or research projects. Questions? Please contact us at rehabmeasures@sralab.org

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Rehabilitation Measurement Statistics

For those visiting our site for the first time or even if you just need a refresher, we’re happy to provide some of the common terms & acronyms and their definitions.

For more visit —>  Rehabilitation Measures | Shirley Ryan AbilityLab – Formerly RIC

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[Abstract] Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation

Abstract

In the modern world, due to an increased aging population, hand disability is becoming increasingly common. The prevalence of conditions such as stroke is placing an ever-growing burden on the limited fiscal resources of health care providers and the capacity of their physical therapy staff. As a solution, this paper presents a novel design for a wearable and adaptive glove for patients so that they can practice rehabilitative activities at home, reducing the workload for therapists and increasing the patient’s independence. As an initial evaluation of the design’s feasibility the prototype was subjected to motion analysis to compare its performance with the hand in an assessment of grasping patterns of a selection of blocks and spheres. The outcomes of this paper suggest that the theory of design has validity and may lead to a system that could be successful in the treatment of stroke patients to guide them through finger flexion and extension, which could enable them to gain more control and confidence in interacting with the world around them.

I. Introduction

In the modern world an extended life expectancy coupled with a sedentary lifestyle raises concerns over long term health in the population. This is highlighted by the increasing incidence of disability stemming from multiple sources, for example medical conditions such as cancer or stroke [1]. While avoiding the lifestyle factors that have a high association with these diseases would be the preferred solutions of health services the world over, as populations get progressively older and more sedentary, this becomes increasingly more difficult [1], [2]. The treatment of these conditions is often complex; in stroke for example, the initial incident is a constriction of blood flow in the brain which in turn damages the nervous system’s ability to communicate with the rest of the body. This damage will occur in one hemisphere of the body but can impact both the upper and lower limbs, as well as impairing functional processes such as speech and cognitive thinking.

 

via Design and Evaluation of a Soft and Wearable Robotic Glove for Hand Rehabilitation – IEEE Journals & Magazine

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