Control charts for chronic disease surveillance: testing algorithm sensitivity to changes in data coding.

BACKGROUND: Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts. METHODS: Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar's test with Holm-Bonferroni adjustment. RESULTS: The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar's test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence. CONCLUSIONS: Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.

Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada.

OBJECTIVE: This study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify the average number of data years needed for classification (ie, average trend needed). DESIGN: Retrospective cohort study design. PARTICIPANTS: 608 MS cases and 59 620 MS non-cases. SETTING: Data from 1 April 2004 to 31 March 2022 were obtained from the Manitoba Population Research Data Repository. MS case status was ascertained from homecare records and linked to health data. Trend-based case definitions were constructed using multivariate generalised linear mixed models applied to annual numbers of general and specialist physician visits, hospitalisations and MS healthcare contacts or medication dispensations. Dynamic classification, which ascertains cases and non-cases annually, was used to estimate mean classification time. Classification accuracy performance measures, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), proportion correctly classified (PCC) and F1-scores, were compared for trend-based case definitions and a deterministic case definition of 3+MS healthcare contacts or medication dispensations. RESULTS: When applied to the full study period, classification accuracy performance measure estimates for all case definitions exceeded 0.90, except sensitivity and PPV for the trend-based dynamic case definition (0.88, 0.64, respectively). PCC was high for all case definitions (0.94-0.99); F1-scores were lower for the trend-based case definitions compared with the deterministic case definition (0.74-0.93 vs 0.96). Dynamic classification identified 5 years as the average trend needed. When applied to the average trend windows, accuracy estimates for trend-based case definitions were lower than the estimates from the full study period (sensitivity: 0.77-0.89; specificity: 0.90-0.97; PPV: 0.54-0.81; NPV: 0.97-0.99; F1-score: 0.64-0.84). Accuracy estimates for the deterministic case definition remained high, except sensitivity (0.42-0.80). F1-score was variable (0.59-0.89). CONCLUSIONS: Trend-based and deterministic case definitions classifications were similar to a population-based clinician assessment reference standard for multiple measures of classification accuracy. However, accuracy estimates for both trend-based and deterministic case definitions varied as the years of data used for classification were reduced. Dynamic classification appears to be a viable option for identifying the average trend needed for trend-based case definitions.

Databiting: Lightweight, Transient, and Insight Rich Exploration of Personal Data.

As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values. We then discuss five research considerations that we deem important for enabling databiting: contextual factors, interaction modalities, the relationship between databiting and other forms of exploration, personalization, and evaluation challenges. We envision this line of work in databiting could enable people to easily gain meaningful personal insight from their data anytime and anywhere.

HPUI: Hand Proximate User Interfaces for One-Handed Interactions on Head Mounted Displays.

We explore the design of Hand Proximate User Interfaces (HPUIs) for head-mounted displays (HMDs) to facilitate near-body interactions with the display directly projected on, or around the user's hand. We focus on single-handed input, while taking into consideration the hand anatomy which distorts naturally when the user interacts with the display. Through two user studies, we explore the potential for discrete as well as continuous input. For discrete input, HPUIs favor targets that are directly on the fingers (as opposed to off-finger) as they offer tactile feedback. We demonstrate that continuous interaction is also possible, and is as effective on the fingers as in the off-finger space between the index finger and thumb. We also find that with continuous input, content is more easily controlled when the interaction occurs in the vertical or horizontal axes, and less with diagonal movements. We conclude with applications and recommendations for the design of future HPUIs.

The Use of Head-Worn Augmented Reality Displays in Health Communications.

The health industry is always seeking innovative ways to use technology to create or improve the experiences of their professionals. Such improvements are seen in a variety of areas including the analysis of relevant health data and the establishment of new ways of communicating medical education and training. Advancements in head-worn augmented reality displays (HWDs), such as the Microsoft Hololens, present a unique opportunity to leverage technology in the ongoing challenge of creating meaningful and novel educational experiences. This paper will review contemporary HWD technologies, how these technologies are being used to enhance the work-training environment, and how these technologies might enhance the communication of health professionals.

Challenges in Displaying Health Data on Small Smartwatch Screens.

Using smartwatches for self-tracking purposes has become increasingly common. This tracking is possible as a result of the many sensors embedded in modern smartwatches including GPS, heart rate monitor, accelerometer, and gyroscope. The ability to obtain personal health-related data is one of the most compelling reason to purchase such devices. However, form factor limitations create numerous challenges for users hoping to access and interpret the data available. Typically, users rely on a secondary device, such as a smartphone to view health data. The aim of our research is to identify methods to improve user consumption of health-related data directly on a smartwatch. To study and apply novel visualization approaches, several key challenges need to be addressed. We present these here along with their corollary methods of circumvention.

Applying a Pneumatic Interface to Intervene with Rapid Eating Behaviour.

Higher eating rates are positively correlate with obesity. In this paper, we propose the design of a new eating utensil that can reduce eating rate by interfering with eater's ability to eat quickly. This utensil can change its rigidity and shape by deflating itself to interfere with eating. In this study, a low fidelity proof-of-concept prototype device has been designed to provide physical resistance in order to help people reduce their eating rate. The proposed prototype could be used to demonstrate the feasibility of applying a pneumatically actuated shape-changing interface to embed physical resistance into an eating utensil.

An OpenMP-based tool for finding longest common subsequence in bioinformatics.

OBJECTIVE: Finding the longest common subsequence (LCS) among sequences is NP-hard. This is an important problem in bioinformatics for DNA sequence alignment and pattern discovery. In this research, we propose new CPU-based parallel implementations that can provide significant advantages in terms of execution times, monetary cost, and pervasiveness in finding LCS of DNA sequences in an environment where Graphics Processing Units are not available. For general purpose use, we also make the OpenMP-based tool publicly available to end users. RESULT: In this study, we develop three novel parallel versions of the LCS algorithm on: (i) distributed memory machine using message passing interface (MPI); (ii) shared memory machine using OpenMP, and (iii) hybrid platform that utilizes both distributed and shared memory using MPI-OpenMP. The experimental results with both simulated and real DNA sequence data show that the shared memory OpenMP implementation provides at least two-times absolute speedup than the best sequential version of the algorithm and a relative speedup of almost 7. We provide a detailed comparison of the execution times among the implementations on different platforms with different versions of the algorithm. We also show that removing branch conditions negatively affects the performance of the CPU-based parallel algorithm on OpenMP platform.

Smart Home Interactions for People with Reduced Hand Mobility Using Subtle EMG-Signal Gestures.

Smart home technology is receiving significant attention. This is largely in response to an increase in the size of demographic those who require assistance due to reduced mobility, in particular, older adults. Smart home technology enables the assistance individuals with limited mobility need for their daily routines: these limitations can be addressed using modern ambient assisted living technologies. In particular we discuss the benefits of using electromyography (EMG) sensors to capture gestural input that would normally be difficult to sense in the absence of such sensors. With EMG, we can provide user control of a smart environment through the use of gestures based on muscle activity of the hands. This paper will focus on presenting the benefits of EMG technologies that can potentially assist individuals with hand mobility issues. We will describe the current state of EMG sensory technologies and their role in shaping gesture-based interaction techniques. We present our approach using such EMG signals and demonstrate their value in a smart home scenario. Finally we introduce the concept of subtle EMG gestures and build a better understanding of how we might improve accessibility for those with limited upper limb motion.

Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos.

Self-tracking feedback with engaging and persuasive visualizations not only helps convey data but can also affect people's attitudes and behaviors. We investigate persuasive self-tracking feedback by augmenting data videos (DVs)-novel, engaging storytelling media. We introduce a new class of DVs, called Persuasive Data Videos (PDVs), by incorporating four persuasive elements-primary task, dialogue, system credibility, and social supports-drawn from the Persuasive System Design Model. We describe the iterative design of PDVs and a within-subjects preliminary validation to check their persuasive potential. We then assess PDVs' feasibility using the Persuasive Potential Questionnaire in a between-subjects study comparing a PDV against a conventional DV on Amazon Mechanical Turk (N = 252). Our results indicate the feasibility of using PDVs in providing individuals' self-tracking feedback to convey persuasive health messages, based on which we discuss opportunities for designing persuasive behavioral feedback in an engaging way.

Visualizing causal semantics using animations.

Michotte's theory of ampliation suggests that causal relationships are perceived by objects animated under appropriate spatiotemporal conditions. We extend the theory of ampliation and propose that the immediate perception of complex causal relations is also dependent on a set of structural and temporal rules. We designed animated representations, based on Michotte's rules, for showing complex causal relationships or causal semantics. In this paper we describe a set of animations for showing semantics such as causal amplification, causal strength, causal dampening, and causal multiplicity. In a two part study we compared the effectiveness of both the static and animated representations. The first study (N=44) asked participants to recall passages that were previously displayed using both types of representations. Participants were 8% more accurate in recalling causal semantics when they were presented using animations instead of static graphs. In the second study (N=112) we evaluated the intuitiveness of the representations. Our results showed that while users were as accurate with the static graphs as with the animations, they were 9% faster in matching the correct causal statements in the animated condition. Overall our results show that animated diagrams that are designed based on perceptual rules such as those proposed by Michotte have the potential to facilitate comprehension of complex causal relations.

Spatial Analytic Interfaces: Spatial User Interfaces for In Situ Visual Analytics.

As wearable devices gain acceptance, we need to ask, What will user interfaces look like in a post-smartphone world? Will these future interfaces support sophisticated interactions in a mobile context? The authors draw from visual analytics concepts to address the growing need for individuals to manage information on personal devices. Spatial analytic interfaces (SAIs) can leverage the benefits of spatial interaction to enable everyday visual analytics tasks to be performed in-situ, at the most beneficial place and time. They explore the possibilities for such interfaces using head-worn display technology and discuss current developments and future research goals for the successful development of SAIs.

Authoring Data-Driven Videos with DataClips.

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

GyroWand: An Approach to IMU-Based Raycasting for Augmented Reality.

Optical see-through head-mounted displays enable augmented reality (AR) applications that display virtual objects overlaid on the real world. At the core of this new generation of devices are low-cost tracking technologies that allow us to interpret users' motion in the real world in relation to the virtual content for the purposes of navigation and interaction. The advantages of pervasive tracking come at the cost of limiting interaction possibilities, however. To address these challenges the authors introduce GyroWand, a raycasting technique for AR HMDs using inertial measurement unit (IMU) rotational data from a handheld controller.

The Impact of Interactivity on Comprehending 2D and 3D Visualizations of Movement Data.

GPS, RFID, and other technologies have made it increasingly common to track the positions of people and objects over time as they move through two-dimensional spaces. Visualizing such spatio-temporal movement data is challenging because each person or object involves three variables (two spatial variables as a function of the time variable), and simply plotting the data on a 2D geographic map can result in overplotting and occlusion that hides details. This also makes it difficult to understand correlations between space and time. Software such as GeoTime can display such data with a three-dimensional visualization, where the 3rd dimension is used for time. This allows for the disambiguation of spatially overlapping trajectories, and in theory, should make the data clearer. However, previous experimental comparisons of 2D and 3D visualizations have so far found little advantage in 3D visualizations, possibly due to the increased complexity of navigating and understanding a 3D view. We present a new controlled experimental comparison of 2D and 3D visualizations, involving commonly performed tasks that have not been tested before, and find advantages in 3D visualizations for more complex tasks. In particular, we tease out the effects of various basic interactions and find that the 2D view relies significantly on "scrubbing" the timeline, whereas the 3D view relies mainly on 3D camera navigation. Our work helps to improve understanding of 2D and 3D visualizations of spatio-temporal data, particularly with respect to interactivity.

SmartColor: Real-Time Color and Contrast Correction for Optical See-Through Head-Mounted Displays.

Users of optical see-through head-mounted displays (OHMD) perceive color as a blend of the display color and the background. Color-blending is a major usability challenge as it leads to loss of color encodings and poor text legibility. Color correction aims at mitigating color blending by producing an alternative color which, when blended with the background, more closely approximates the color originally intended. In this paper we present an end-to-end approach to the color blending problem addressing the distortions introduced by the transparent material of the display efficiently and in real time. We also present a user evaluation of correction efficiency. Finally, we present a graphics library called SmartColor showcasing the use of color correction for different types of display content. SmartColor uses color correction to provide three management strategies: correction, contrast, and show-up-on-contrast. Correction determines the alternate color which best preserves the original color. Contrast determines the color which best supports text legibility while preserving as much of the original hue. Show-up-on-contrast makes a component visible when a related component does not have enough contrast to be legible. We describe SmartColor's architecture and illustrate the color strategies for various types of display content.

Interactive exploration of surveillance video through action shot summarization and trajectory visualization.

We propose a novel video visual analytics system for interactive exploration of surveillance video data. Our approach consists of providing analysts with various views of information related to moving objects in a video. To do this we first extract each object's movement path. We visualize each movement by (a) creating a single action shot image (a still image that coalesces multiple frames), (b) plotting its trajectory in a space-time cube and (c) displaying an overall timeline view of all the movements. The action shots provide a still view of the moving object while the path view presents movement properties such as speed and location. We also provide tools for spatial and temporal filtering based on regions of interest. This allows analysts to filter out large amounts of movement activities while the action shot representation summarizes the content of each movement. We incorporated this multi-part visual representation of moving objects in sViSIT, a tool to facilitate browsing through the video content by interactive querying and retrieval of data. Based on our interaction with security personnel who routinely interact with surveillance video data, we identified some of the most common tasks performed. This resulted in designing a user study to measure time-to-completion of the various tasks. These generally required searching for specific events of interest (targets) in videos. Fourteen different tasks were designed and a total of 120 min of surveillance video were recorded (indoor and outdoor locations recording movements of people and vehicles). The time-to-completion of these tasks were compared against a manual fast forward video browsing guided with movement detection. We demonstrate how our system can facilitate lengthy video exploration and significantly reduce browsing time to find events of interest. Reports from expert users identify positive aspects of our approach which we summarize in our recommendations for future video visual analytics systems.