Tag: nevin manimala

Biom J. 2022 Sep 27. doi: 10.1002/bimj.202200073. Online ahead of print.

ABSTRACT

Common count distributions, such as the Poisson (binomial) distribution for unbounded (bounded) counts considered here, can be characterized by appropriate Stein identities. These identities, in turn, might be utilized to define a corresponding goodness-of-fit (GoF) test, the test statistic of which involves the computation of weighted means for a user-selected weight function f. Here, the choice of f should be done with respect to the relevant alternative scenario, as it will have great impact on the GoF-test’s performance. We derive the asymptotics of both the Poisson and binomial Stein-type GoF-statistic for general count distributions (we also briefly consider the negative-binomial case), such that the asymptotic power is easily computed for arbitrary alternatives. This allows for an efficient implementation of optimal Stein tests, that is, which are most powerful within a given class $F$ of weight functions. The performance and application of the optimal Stein-type GoF-tests is investigated by simulations and several medical data examples.

PMID:36166681 | DOI:10.1002/bimj.202200073

Am J Phys Med Rehabil. 2022 Sep 23. doi: 10.1097/PHM.0000000000002111. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the effectiveness of a rehabilitation program with electromyographic biofeedback (EMG-BF) compared to the control group on patients with massive rotator cuff (RC) tear.

DESIGN: Forty-six adults with massive RC tears, randomly assigned to two groups (23 EMG-BF group vs. 23 Control group). The EMG-BF group (experimental group) performed the exercises under the guidance of EMG-BF, unlike the control group. All patients underwent a 45-minute training session a day, three times a week over a 6-week duration, and followed up until 1-year. The outcome measures were American Shoulder and Elbow (ASES) score, shoulder flexion strength, shoulder range of motion (ROM), numeric pain rating scale (NPRS), and Global Rating of Change Scale (GRCS).

RESULTS: Compared with the control group, the EMG-BF group demonstrated a significant change in shoulder flexion strength and patient satisfaction from baseline to 6 weeks (post-training) and from baseline to 12-month follow-up (F = 4.671, P = 0.005). There were significant improvements in within groups statistics for ASES score, shoulder flexion strength, shoulder ROM, and NPRS in both groups (p < 0.05).

CONCLUSION: The results demonstrate that deltoid focused structured rehabilitation program combined with EMG-BF can be used to increase shoulder flexion strength and patient satisfaction in conservative treatment of massive RC tear.

PMID:36166658 | DOI:10.1097/PHM.0000000000002111

JMIR Form Res. 2022 Sep 7. doi: 10.2196/41410. Online ahead of print.

ABSTRACT

BACKGROUND: There are around 1.3million people in the UK living with the devastating psychological, physical and cognitive consequences of Long COVID. UK guidelines recommend that Long COVID symptoms are managed pragmatically with holistic support for patients’ biopsychosocial needs, including psychological, emotional, and physical health. Self-management strategies such as pacing, prioritisation, and goal setting are vital for the self-management of many Long COVID symptoms. This paper describes the co-development and initial testing of a digital intervention combining peer support with positive psychology approaches for self-managing the physical, emotional, psychological, and cognitive challenges associated with Long COVID.

OBJECTIVE: The objectives of this study were to: i) co-design an intervention with and for people living with Long COVID; ii) test the intervention and study methods; iii) measure changes in participant wellbeing, self-efficacy, fatigue and loneliness; iv) gain understand the types of self-management goals and strategies used by people living with Long COVID.

METHODS: The study employed a pre-post, mixed methods, pragmatic, uncontrolled design. Digital intervention content was co-developed with a lived experience group to meet the needs uncovered during the intervention development and logic mapping phase. The resulting 8-week digital intervention – Hope Program for Long COVID – was attended by 47 participants, who completed pre- and post-program measures of wellbeing, self-efficacy, fatigue and loneliness. Goal-setting data was extracted from the digital platform at the end of the intervention.

RESULTS: The recruitment rate (83.9%) and follow up rate of (59.6%) were encouraging. Positive mental wellbeing (mean difference 6.5, p<.001) and self-efficacy (mean difference 1.1, p=.009) improved from baseline to post-course. All goals set by participants mapped onto the five goal-directed everyday self-management strategies in the TEDSS taxonomy. The most frequent type of goals related to activities strategies, followed by health behaviour and internal strategies.

CONCLUSIONS: The bespoke self-management intervention -Hope Program for Long COVID – was well-attended and follow up was encouraging. The sample characteristics largely mirrored those of the wider UK population living with Long COVID. Although not powered to detect statistically significant changes, the preliminary data shows improvements in self-efficacy and positive mental wellbeing. Our next trial (ISRCTN: 11868601) will employ a non-randomised waitlist control design to further examine intervention efficacy.

PMID:36166651 | DOI:10.2196/41410

J Nurs Adm. 2022 Oct 1;52(10):519-524. doi: 10.1097/NNA.0000000000001193.

ABSTRACT

AIM: The aim of this study was to investigate the effect of career plateau as a mediating factor on nurses’ job satisfaction and promotability.

BACKGROUND: A nurse’s career, as well as other professionals, may arrive at a point where further hierarchical development is limited. Nurses may remain longer than expected in the same position within an organization and may be plateaued, resulting in career dissatisfaction, job dissatisfaction, and turnover.

METHODS: This is a descriptive correlational study. Two hundred twenty-one nurses were recruited from 1 university hospital in Egypt. Respondents completed the self-administered, printed questionnaires. Measures included career plateau, job satisfaction, and promotability questionnaires. Findings were investigated via descriptive and inferential statistics as well as structured equation modeling to examine the mediating effect of career plateauing on job satisfaction and promotability.

RESULTS: The mean scores of job satisfaction, career plateauing, and promotability were 3.09 ± 0.71, 3.75 ± 0.43, and 3.70 ± 0.53, respectively. Data revealed that nurses’ career plateauing accounted for 34% and 18% of the variance of their job satisfaction and promotability, respectively.

CONCLUSION: Career plateauing is a significant determinant of nurses’ job satisfaction and promotability.

PMID:36166630 | DOI:10.1097/NNA.0000000000001193

Orthop Nurs. 2022 Sep-Oct 01;41(5):318-323. doi: 10.1097/NOR.0000000000000878.

ABSTRACT

Cast immobilization remains the standard of care in managing pediatric fractures. Cast complications often result in emergency department visits, office calls and visits, or lasting patient morbidities that burden the healthcare institution from a time and economic standpoint. The purpose of this quality improvement project was to create a multimodal cast care education protocol with an aim of decreasing cast complications over a 6-week period. Qualified patients (0-18) placed in cast immobilization received a quick response (QR) code sticker on their casts linked to a custom cast care website with text, pictures, and video instructions. Incidence of cast complications, complication type, effect(s) on workflow, and patient demographics were recorded. The complication rate declined 7.6%, but it was not statistically significant. Continuous access to clinic-specific cast instructions demonstrates decreased cast complications in pediatric populations, and this approach to patient education can be easily utilized across all medical specialties.

PMID:36166606 | DOI:10.1097/NOR.0000000000000878

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209383. Online ahead of print.

ABSTRACT

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.

PMID:36166551 | DOI:10.1109/TVCG.2022.3209383

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209405. Online ahead of print.

ABSTRACT

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one’s belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process. Through two crowdsourced experiments, we demonstrate that supposedly objective assessments of the strength of a correlational relationship can be influenced by how strongly a viewer believes in the existence of that relationship. Participants viewed scatterplots depicting a relationship between meaningful variable pairs (e.g., number of environmental regulations and air quality) and estimated their correlations. They also estimated the correlation of the same scatterplots labeled instead with generic ‘X’ and ‘Y’ axes. In a separate section, they also reported how strongly they believed there to be a correlation between the meaningful variable pairs. Participants estimated correlations more accurately when they viewed scatterplots labeled with generic axes compared to scatterplots labeled with meaningful variable pairs. Furthermore, when viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1. While data visualizations are typically thought to present objective truths to the viewer, these results suggest that existing personal beliefs can bias even objective statistical values people extract from data.

PMID:36166548 | DOI:10.1109/TVCG.2022.3209405

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209481. Online ahead of print.

ABSTRACT

The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human’s expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover’s Distance (EMD) and Entropy Difference (ED), which are closer to human perception of color distributions.

PMID:36166543 | DOI:10.1109/TVCG.2022.3209481

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209473. Online ahead of print.

ABSTRACT

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.

PMID:36166541 | DOI:10.1109/TVCG.2022.3209473

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209373. Online ahead of print.

ABSTRACT

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team’s offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

PMID:36166529 | DOI:10.1109/TVCG.2022.3209373