Applications of random forest feature selection for fine-scale genetic population assignment.

Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.

Finding and Exploring Health Information with a Slider-Based User Interface.

Despite the fact that search engines are the primary channel to access online health information, there are better ways to find and explore health information on the web. Search engines are prone to problems when they are used to find health information. For instance, users have difficulties in expressing health scenarios with appropriate search keywords, search results are not optimised for medical queries, and the search process does not account for users' literacy levels and reading preferences. In this paper, we describe our approach to addressing these problems by introducing a novel design using a slider-based user interface for discovering health information without the need for precise search keywords. The user evaluation suggests that the interface is easy to use and able to assist users in the process of discovering new information. This study demonstrates the potential value of adopting slider controls in the user interface of health websites for navigation and information discovery.

Designing Health Websites Based on Users' Web-Based Information-Seeking Behaviors: A Mixed-Method Observational Study.

BACKGROUND: Laypeople increasingly use the Internet as a source of health information, but finding and discovering the right information remains problematic. These issues are partially due to the mismatch between the design of consumer health websites and the needs of health information seekers, particularly the lack of support for "exploring" health information. OBJECTIVE: The aim of this research was to create a design for consumer health websites by supporting different health information-seeking behaviors. We created a website called Better Health Explorer with the new design. Through the evaluation of this new design, we derive design implications for future implementations. METHODS: Better Health Explorer was designed using a user-centered approach. The design was implemented and assessed through a laboratory-based observational study. Participants tried to use Better Health Explorer and another live health website. Both websites contained the same content. A mixed-method approach was adopted to analyze multiple types of data collected in the experiment, including screen recordings, activity logs, Web browsing histories, and audiotaped interviews. RESULTS: Overall, 31 participants took part in the observational study. Our new design showed a positive result for improving the experience of health information seeking, by providing a wide range of information and an engaging environment. The results showed better knowledge acquisition, a higher number of page reads, and more query reformulations in both focused and exploratory search tasks. In addition, participants spent more time to discover health information with our design in exploratory search tasks, indicating higher engagement with the website. Finally, we identify 4 design considerations for designing consumer health websites and health information-seeking apps: (1) providing a dynamic information scope; (2) supporting serendipity; (3) considering trust implications; and (4) enhancing interactivity. CONCLUSIONS: Better Health Explorer provides strong support for the heterogeneous and shifting behaviors of health information seekers and eases the health information-seeking process. Our findings show the importance of understanding different health information-seeking behaviors and highlight the implications for designers of consumer health websites and health information-seeking apps.

A mobile app offering distractions and tips to cope with cigarette craving: a qualitative study.

BACKGROUND: Despite considerable effort, most smokers relapse within a few months after quitting due to cigarette craving. The widespread adoption of mobile phones presents new opportunities to provide support during attempts to quit. OBJECTIVE: To design and pilot a mobile app "DistractMe" to enable quitters to access and share distractions and tips to cope with cigarette cravings. METHODS: A qualitative study with 14 smokers who used DistractMe on their mobiles during the first weeks of their quit attempt. Based on interviews, diaries, and log data, we examined how the app supported quitting strategies. RESULTS: Three distinct techniques of coping when using DistractMe were identified: diversion, avoidance, and displacement. We further identified three forms of engagement with tips for coping: preparation, fortification, and confrontation. Overall, strategies to prevent cravings and their effects (avoidance, displacement, preparation, and fortification) were more common than immediate coping strategies (diversion and confrontation). Tips for coping were more commonly used than distractions to cope with cravings, because they helped to fortify the quit attempt and provided opportunities to connect with other users of the application. However, distractions were important to attract new users and to facilitate content sharing. CONCLUSIONS: Based on the qualitative results, we recommend that mobile phone-based interventions focus on tips shared by peers and frequent content updates. Apps also require testing with larger groups of users to assess whether they can be self-sustaining.