eDECIDE a web-based problem-solving interventions for diabetes self-management: Protocol for a pilot clinical trial.

Background: In the US, diabetes affects 13.2% of African Americans, compared to 7.6% of Caucasians. Behavioral factors, such as poor diet, low physical activity, and general lack of good self-management skills and self-care knowledge are associated with poor glucose control among African Americans. African Americans are 77% more likely to develop diabetes and its associated health complications compared to non-Hispanic whites. A higher disease burden and lower adherence to self-management among this populations calls for innovative approaches to self-management training. Problem solving is a reliable tool for the behavior change necessary to improve self-management. The American Association of Diabetes Educators identifies problem-solving as one of seven core diabetes self-management behaviors. Methods: We are using a randomized control trial design. Participants are randomized to either traditional DECIDE or eDECIDE intervention. Both interventions run bi-weekly over 18 weeks. Participant recruitment will take place through community health clinics, University health system registry, and through private clinics. The eDECIDE is an 18-week intervention designed to deliver problem-solving skills, goal setting, and education on the link between diabetes and cardiovascular disease. Conclusion: This study will provide feasibility and acceptability of the eDECIDE intervention in community populations. This pilot trial will help inform a powered full-scale study using the eDECIDE design.

Typing on a Smartwatch While Mobile: A Comparison of Input Methods.

OBJECTIVE: The user experience of typing on a smartwatch was evaluated with three unique input methods (tap, trace, and handwriting) while standing and while walking. BACKGROUND: Despite widespread development within the technology industry, smartwatches have had a relatively slow adoption worldwide compared to smartphones. One limiting factor of smartwatches has been the lack of an efficient means of text entry. The 2017 release of Android Wear addressed this issue by providing support for native text entry (i.e., tap, trace, and handwriting). Determining how user performance and subjective ratings compare across these input methods is essential to understanding their contribution to smartwatch user experience. METHOD: Twenty college-age individuals typed phrases using tap, trace, and handwriting input on a smartwatch in three different mobility scenarios (standing, walking a simple course, walking a complex course). RESULTS: Participants typed faster with trace (30 words per minute; WPM) than with tap (20 WPM) and handwriting (18 WPM), regardless of mobility. Trace also outperformed tap and handwriting across all subjective metrics, regardless of mobility. CONCLUSION: Trace input appears to be especially well suited for typing on a smartwatch as it was found to be objectively and subjectively superior to tap and handwriting regardless of user mobility. Objectively, typing speeds with trace are shown to be nearly two times faster than most alternative input methods described in the literature. APPLICATION: Results suggest smartwatch manufacturers should include QWERTY keyboards with trace input as a standard feature in order to provide the best overall typing experience for their users.

Exploring website gist through rapid serial visual presentation.

BACKGROUND: Users can make judgments about web pages in a glance. Little research has explored what semantic information can be extracted from a web page within a single fixation or what mental representations users have of web pages, but the scene perception literature provides a framework for understanding how viewers can extract and represent diverse semantic information from scenes in a glance. The purpose of this research was (1) to explore whether semantic information about a web page could be extracted within a single fixation and (2) to explore the effects of size and resolution on extracting this information. Using a rapid serial visual presentation (RSVP) paradigm, Experiment 1 explored whether certain semantic categories of websites (i.e., news, search, shopping, and social networks/blogs) could be detected within a RSVP stream of web page stimuli. Natural scenes, which have been shown to be detectable within a single fixation in the literature, served as a baseline for comparison. Experiment 2 examined the effects of stimulus size and resolution on observers' ability to detect the presence of website categories using similar methods. RESULTS: Findings from this research demonstrate that users have conceptual models of websites that allow detection of web pages from a fixation's worth of stimulus exposure, when provided additional time for processing. For website categories other than search, detection performance decreased significantly when web elements were no longer discernible due to decreases in size and/or resolution. The implications of this research are that website conceptual models rely more on page elements and less on the spatial relationship between these elements. CONCLUSIONS: Participants can detect websites accurately when they were displayed for less than a fixation and when the participants were allowed additional processing time. Subjective comments and stimulus onset asynchrony data suggested that participants likely relied on local features for the detection of website targets for several website categories. This notion was supported when the size and/or resolution of stimuli were decreased to the extent that web elements were indistinguishable. This demonstrates that schemas or conceptualizations of websites provided information sufficient to detect websites from approximately 140 ms of stimulus exposure.

Safeuristics! Do Heuristic Evaluation Violation Severity Ratings Correlate with Patient Safety Severity Ratings for a Native Electronic Health Record Mobile Application?

OBJECTIVE: Usability of electronic health records (EHRs) remains challenging, and poor EHR design has patient safety implications. Heuristic evaluation detects usability issues that can be classified by severity. The National Institute of Standards and Technology provides a safety scale for EHR usability. Our objectives were to investigate the relationship between heuristic severity ratings and safety scale ratings in an effort to analyze EHR safety. MATERIALS AND METHODS: Heuristic evaluation was conducted on seven common mobile EHR tasks, revealing 58 heuristic violations and 28 unique usability issues. Each usability issue was independently scored for severity by trained hospitalists and a Human Factors researcher and for safety severity by two physician informaticists and two clinical safety professionals. RESULTS: Results demonstrated a positive correlation between heuristic severity and safety severity ratings. Regression analysis demonstrated that 49% of safety risk variability by clinical safety professionals (r = 0.70; n = 28) and 42% of safety risk variability by clinical informatics specialists (r = 0.65; n = 28) was explained by usability severity scoring of problems outlined by heuristic evaluation. Higher severity ratings of the usability issues were associated with increased perceptions of patient safety risk. DISCUSSION: This study demonstrated the use of heuristic evaluation as a technique to quickly identify usability problems in an EHR that could lead to safety issues. Detection of higher severity ratings could help prioritize failures in EHR design that more urgently require design changes. This approach is a cost-effective technique for improving usability while impacting patient safety. CONCLUSION: Results from this study demonstrate the efficacy of the heuristic evaluation technique to identify usability problems that impact safety of the EHR. Also, the use of interdisciplinary teams for evaluation should be considered for severity assessment.

The Development and Validation of the Game User Experience Satisfaction Scale (GUESS).

OBJECTIVE: The aim of this study was to develop and psychometrically validate a new instrument that comprehensively measures video game satisfaction based on key factors. BACKGROUND: Playtesting is often conducted in the video game industry to help game developers build better games by providing insight into the players' attitudes and preferences. However, quality feedback is difficult to obtain from playtesting sessions without a quality gaming assessment tool. There is a need for a psychometrically validated and comprehensive gaming scale that is appropriate for playtesting and game evaluation purposes. METHOD: The process of developing and validating this new scale followed current best practices of scale development and validation. As a result, a mixed-method design that consisted of item pool generation, expert review, questionnaire pilot study, exploratory factor analysis (N = 629), and confirmatory factor analysis (N = 729) was implemented. RESULTS: A new instrument measuring video game satisfaction, called the Game User Experience Satisfaction Scale (GUESS), with nine subscales emerged. The GUESS was demonstrated to have content validity, internal consistency, and convergent and discriminant validity. CONCLUSION: The GUESS was developed and validated based on the assessments of over 450 unique video game titles across many popular genres. Thus, it can be applied across many types of video games in the industry both as a way to assess what aspects of a game contribute to user satisfaction and as a tool to aid in debriefing users on their gaming experience. APPLICATION: The GUESS can be administered to evaluate user satisfaction of different types of video games by a variety of users.

Texting while driving using Google Glass™: Promising but not distraction-free.

Texting while driving is risky but common. This study evaluated how texting using a Head-Mounted Display, Google Glass, impacts driving performance. Experienced drivers performed a classic car-following task while using three different interfaces to text: fully manual interaction with a head-down smartphone, vocal interaction with a smartphone, and vocal interaction with Google Glass. Fully manual interaction produced worse driving performance than either of the other interaction methods, leading to more lane excursions and variable vehicle control, and higher workload. Compared to texting vocally with a smartphone, texting using Google Glass produced fewer lane excursions, more braking responses, and lower workload. All forms of texting impaired driving performance compared to undistracted driving. These results imply that the use of Google Glass for texting impairs driving, but its Head-Mounted Display configuration and speech recognition technology may be safer than texting using a smartphone.

Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults.

OBJECTIVE: User performance, perceived usability, and preference for five smartphone text input methods were compared with younger and older novice adults. BACKGROUND: Smartphones are used for a variety of functions other than phone calls, including text messaging, e-mail, and web browsing. Research comparing performance with methods of text input on smartphones reveals a high degree of variability in reported measures, procedures, and results. This study reports on a direct comparison of five of the most common input methods among a population of younger and older adults, who had no experience with any of the methods. METHOD: Fifty adults (25 younger, 18-35 years; 25 older, 60-84 years) completed a text entry task using five text input methods (physical Qwerty, onscreen Qwerty, tracing, handwriting, and voice). Entry and error rates, perceived usability, and preference were recorded. RESULTS: Both age groups input text equally fast using voice input, but older adults were slower than younger adults using all other methods. Both age groups had low error rates when using physical Qwerty and voice, but older adults committed more errors with the other three methods. Both younger and older adults preferred voice and physical Qwerty input to the remaining methods. Handwriting consistently performed the worst and was rated lowest by both groups. CONCLUSION: Voice and physical Qwerty input methods proved to be the most effective for both younger and older adults, and handwriting input was the least effective overall. APPLICATION: These findings have implications to the design of future smartphone text input methods and devices, particularly for older adults.