Self-assembly of block copolymer thin films

Background: Innovative interventions that empower patients in diabetes self-management (DSM) are needed to provide accessible, sustainable, cost-effective patient education and support that surpass current noninteractive interventions. Skills acquired in digital virtual environments (VEs) affect behaviors in the physical world. Some VEs are programmed as real-time three-dimensional representations of various settings via the Internet. For this research, a theoretically grounded VE that facilitates DSM was developed and pilot tested. It offered weekly synchronous DSM education classes, group meetings, and social networking in a community in which participants practiced real world skills such as grocery shopping, exercising, and dining out, allowing for interactive knowledge application. The VE was available 24/7 on the Internet, minimizing access barriers. Objective: The objective of this study was to evaluate the feasibility and efficacy of participation in a VE for DSM education and support. Methods: This study utilized a single group, pre-mid-post measure design. At 0, 3, and 6 months, we assessed participants’ perceived VE usability and usefulness, self-efficacy, diabetes self-management behaviors, perceived social support, and diabetes knowledge using validated survey measures; and we recorded metabolic indicators (HbA1c, BP, BMI). Process data were continuously collected in the VE (log-ins, voice recordings, locations visited, objects interacted with, and movement). Data analysis included descriptive statistics, t tests to evaluate changes in mediators and outcomes over time, and depiction of utilization and movement data. Results: We enrolled 20 participants (13/20, 65% white, 7/20, 35% black), with an age range of 39-72 years (mean age, 54 years) and diabetes duration from 3 months to 25 years. At baseline, 95% (18/19) and 79% (15/19) of participants rated usefulness and ease of use as high on validated surveys with no significant changes at 3 or 6 months. Participants logged into the site a mean of 2.5 hours/week over the course of 6 months. High DSM class attendance was reflected by the largest percentage of time spent in the classroom (48.6%). Self-efficacy, social support, and foot care showed significant improvement ( P <.05). There were improvement trends in clinical outcomes that were clinically meaningful but did not reach statistical significance given the small sample size. Conclusions: Because relatively little is known about usability, acceptability, and efficacy of health interventions in VEs, this study constitutes an important, innovative first step in exploring the potential of VEs for facilitating DSM. The preliminary data suggest that VEs provide a feasible and useful platform for patients and educators that affects self-management and related mediators. Flexible access to both synchronous and asynchronous diabetes education, skill building activities, and support from a home computer remove barriers to attending clinic-based meetings. This program has potential for improving DSM in an easily disseminated alternative model. [JMIR Res Protoc 2014;3(2):e23]

/pdf/feasibility-and-preliminary-effects-of-a-virtual-environment-4hms6ajla6.pdf

Feasibility and preliminary effects of a virtual environment for adults with type 2 diabetes: pilot study.

We surveyed ecologists and evolutionary biologists in American Universities to understand how they are coping with the COVID-19 pandemic. Female respondents, assistant professors, and those who care for at least one child or teenager, were significantly more dissatisfied with their work-life balance during this pandemic than others, and further expected these negative impacts to be long-lived. Online teaching support, relaxed expectations on publications, the possibility of pausing the tenure clock, and an acknowledgment of "no business as usual" by administrators were thought to be effective policies in mitigating these negative impacts. This survey serves as a manifesto to what our professional community is currently experiencing, and should be used to inform academic policies directed at improving faculty productivity and welfare.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.2265

Impacts of COVID-19 on ecology and evolutionary biology faculty in the United States.

An evaluation of urban green space in Shanghai, China, using eye tracking

Objectives: Employing new health information technologies while concurrently providing quality patient care and reducing risk is a major challenge in all health care sectors. In this study, we investigated the usability gaps in the Emergency Department Information System (EDIS) as ten nurses differentiated by two experience levels, namely six expert nurses and four novice nurses, completed two lists of nine scenario-based tasks. Methods: Standard usability tests using video analysis, including four sets of performance measures, a task completion survey, the system usability scale (SUS), and sub-task analysis were conducted in order to analyze usability gaps between the two nurse groups. Results: A varying degree of usability gaps were observed between the expert and novice nurse groups, as novice nurses completed the tasks both less efficiently, and expressed less satisfaction with the EDIS. The most interesting finding in this study was the result of ‘percent task success rate,’ the clearest performance measure, with no substantial difference observed between the two nurse groups. Geometric mean values between expert and novice nurse groups for this measure were 60% vs. 62% in scenario 1 and 66% vs. 55% in scenario 2 respectively, while there were some marginal to substantial gaps observed in other performance measures. In addition to performance measures and the SUS, sub-task analysis highlighted navigation pattern differences between users, regardless of experience level. Conclusion: This study will serve as a baseline study for a future comparative usability evaluation of EDIS in other institutions with similar clinical settings.

/pdf/a-pilot-study-on-usability-analysis-of-emergency-department-30q212bzmn.pdf

A pilot study on usability analysis of emergency department information system by nurses.

Many different techniques are available for getting UX metrics from self-reported data, including rating scales such as Likert scales and semantic differential scales. Consider getting self-reported data at both a task level and at the end of the session. Task-level data can help you identify areas that need improvement. Session-level data can help you get a sense of overall usability. When testing in a lab, consider using one of the standard questionnaires for assessing subjective reactions to a system. The System Usability Scale has been shown to be robust even with relatively small numbers of participants (e.g., 8–10). When testing a live website, consider using one of the online services such as Website Analysis and Measurement Inventory (WAMMI) or the American Customer Satisfaction Index (ACSI). The major advantage they provide is the ability to show you how the results for your site compare to a large number of sites in their reference database. Be creative but also cautious in the use of other techniques in addition to simple rating scales. When possible, ask for ratings on a given topic in several different ways and average the results to get more consistent data. Construct any new rating scales carefully. Make appropriate use of open-ended questions, and consider techniques such as checking for awareness or comprehension after interacting with the product.

Chapter 6 – Self-Reported Metrics

Self-reported data gives the most important information about users' perception of the system and their interaction with it. The most efficient way to capture self-reported data in a usability test is with the rating scale. Many different techniques are available for getting usability metrics from self-reported data. The best times to collect self-reported data are at the end of each task and at the end of the entire session. Task-level data can help you identify areas that need improvement and session-level data can help you get a sense of overall usability. The main goal of ratings associated with each task is to give you some insight into which tasks the participants thought were the most difficult. One of the most common uses of self-reported metrics is as an overall measure of perceived usability that participants are asked to give after having completed their interactions with the product. The System Usability Scale (SUS) questionnaires are used in a number of usability studies that involve comparing different designs for accomplishing similar tasks. For measuring user satisfaction, using one of the online services of a live website is a good option. Appropriate use of open-ended questions and techniques like checking for awareness or comprehension after interacting with the product are suggested.

Self-Reported Metrics

Chapter 7 reviews a variety of ways to measure user behaviors and emotions. This provides potentially valuable insights into the deeper user experience that is often very easy to miss in the course of a usability test. These tools are becoming much easier to use, more accurate, more versatile and powerful, and even quite affordable. A structured approach to collecting unprompted verbal expressions during a usability test can be very helpful. Eye tracking can be a significant benefit in many kinds of usability tests. The technology continues to improve, becoming more accurate, easier to use, and less intrusive. The value is being able to compare the effectiveness of different designs, as well as calculate metrics based on areas of interest. There are three ways to measure emotions: skin conductance, facial expressions, and electroencephalography (EEG). Skin conductance measures level of arousal, facial expressions are classified and associated with six basic emotions, and EEG measures brain wave activity with unique signatures tied to specific emotional responses. There are new technologies based on each approach, and even integrate eye movement data into their applications. These are powerful new tools to gain insight into the emotional response of the user. Stress is an important component of the user experience. It is measured most often as heart rate variance. Other techniques for capturing information about the participant’s behavior, such as a mouse that registers how tightly it is being gripped, are on the horizon and may become useful additions to the battery of tools available for use in usability testing.

Chapter 7 – Behavioral and Physiological Metrics

Publisher Summary
This chapter presents a set of seven case studies. Each case study illustrates how online usability studies have been used in a wide variety of situations, including important lessons learned and impacts to the bottom line. These case studies also describe how online usability testing has been used to evaluate a wide variety of products, utilizing various tools and numbers of participants. The contributors are from industry and consulting and were selected based on their diverse set of experiences in online usability testing. The aim is to make the readers understand how online usability studies are carried out in a variety of professional settings. The products evaluated through the seven case studies are public health records (Microsoft Vault vs. Google), Lotus Notes, MTV.com, UCSF hospital Website, tax software, Familysearch.org, and authentication and identity management system for Dupont.

Chapter 9 – Case Studies

This chapter describes a variety of behavioral and physiological measures that might be helpful in usability testing as additional ways of learning about the users' experiences with a product and their reactions to it. Some of these can be detected by careful observation, and some require specialized equipment. A structured approach to collecting observational data both verbal and nonverbal during a usability test can be very helpful in subsequent analysis. Facial expressions that participants make during a usability test may give additional insight into what they are thinking and feeling beyond what they say. A trained observer can detect and categorize many of these expressions, but some are very fleeting and may require video analysis. Eye-tracking can be a significant benefit in many kinds of usability tests. Its key value can be in determining whether participants in a usability test even looked at a particular element of the interface. Most eye-tracking systems must detect the location of the participant's pupil and calculate its diameter to determine where he or she is looking. Participants' pupils tend to dilate with higher mental workload and with overall arousal. Skin conductance and heart rate can be used to detect parts of an interface that participants find particularly frustrating. But the technology readily available today for measuring these is too intrusive for normal usability testing.

Bill Albert

Papers

Chapter 6 – Self-Reported Metrics

Self-Reported Metrics

Chapter 7 – Behavioral and Physiological Metrics

Chapter 9 – Case Studies

Behavioral and Physiological Metrics