Website physical activity interventions: preferences of potential users

Information and communication technologies (particularly websites and e-mail) have the potential to deliver health behavior change programs to large numbers of adults at low cost. Controlled trials using these new media to promote physical activity have produced mixed results. User-centered development methods can assist in understanding the preferences of potential participants for website functions and content, and may lead to more effective programs. Eight focus group discussions were conducted with 40 adults after they had accessed a previously trialed physical activity website. The discussions were audio taped, transcribed and interpreted using a themed analysis method. Four key themes emerged: structure, interactivity, environmental context and content. Preferences were expressed for websites that include simple interactive features, together with information on local community activity opportunities. Particular suggestions included online community notice boards, personalized progress charts, e-mail access to expert advice and access to information on specific local physical activity facilities and services. Website physical activity interventions could usefully include personally relevant interactive and environmentally focused features and services identified through a user-centered development process.

Engaging game characters: Informing design with player perspectives

The behavior of characters in current computer games is generally scripted and predictable. This paper discusses some issues related to creating game characters that enhance player engagement and identifies the need for a more player-centered approach to game character design. This paper reports the results of a focus group that was carried out with experienced game players to determine what game character behaviors would enhance their engagement in a game. The four general areas of concern that came out of this discussion were consistency with context, player expectations, social interactions and consistency with the environment. This paper discusses these issues and their implication for game character design with a view to creating engaging game characters. © Springer

User dyads in software testing: bypassing the need for expert observers

This paper arises out of a research study into the online help facilities provided in popular software applications such as word processors. Its particular focus is on experimental methods of evaluating the effectiveness and usability of those facilities. Focus groups, questionnaires, and online surveys had already been used in other phases of the study, but it was judged that these approaches would be unsuitable for measuring effectiveness and usability because they are susceptible to respondents' subjectivity. Direct observation of people working on set word-processing tasks was ruled out initially because of a lack of trained observers; it would have taken too long for the investigator to observe a large enough sample by himself. Automatic recording of users' actions was also rejected, as it would have demanded equipment and/or software that was not available and seemed too expensive to acquire. The approach and techniques described here were an attempt to overcome these difficulties by using observers drawn from the same population of students that provided the test subjects; as a by-product, this may also have enhanced the acceptability (and hence possibly the validity) of the experiments by reducing the exam pressure perceived by participants.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Phytotoxicity of surface waters of the Thames and Brisbane River Estuaries: A combined chemical analysis and bioassay approach for the comparison of two systems

The Thames Estuary, UK, and the Brisbane River, Australia, are comparable in size and catchment area. Both are representative of the large and growing number of the world's estuaries associated with major cities. Principle differences between the two systems relate to climate and human population pressures. In order to assess the potential phytotoxic impact of herbicide residues in the estuaries, surface waters were analysed with a PAM fluorometry-based bioassay that employs the photosynthetic efficiency (photosystem II quantum yield) of laboratory cultured microalgae, as an endpoint measure of phytotoxicity. In addition, surface waters were chemically analysed for a limited number of herbicides. Diuron atrazine and simazine were detected in both systems at comparable concentrations. In contrast, bioassay results revealed that whilst detected herbicides accounted for the observed phytotoxicity of Brisbane River extracts with great accuracy, they consistently explained only around 50% of the phytotoxicity induced by Thames Estuary extracts. Unaccounted for phytotoxicity in Thames surface waters is indicative of unidentified phytotoxins. The greatest phytotoxic response was measured at Charing Cross, Thames Estuary, and corresponded to a diuron equivalent concentration of 180 ng L-1. The study employs relative potencies (REP) of PSII impacting herbicides and demonstrates that chemical analysis alone is prone to omission of valuable information. Results of the study provide support for the incorporation of bioassays into routine monitoring programs where bioassay data may be used to predict and verify chemical contamination data, alert to unidentified compounds and provide the user with information regarding cumulative toxicity of complex mixtures. (c) 2005 Elsevier B.V. All rights reserved.

Applying a disciplined approach to the development of a context-aware communication application

Pervasive computing applications must be engineered to provide unprecedented levels of flexibility in order to reconfigure and adapt in response to changes in computing resources and user requirements. To meet these challenges, appropriate software engineering abstractions and infrastructure are required as a platform on which to build adaptive applications. In this paper, we demonstrate the use of a disciplined, model-based approach to engineer a context-aware Session Initiation Protocol (SIP) based communication application. This disciplined approach builds on our previously developed conceptual models and infrastructural components, which enable the description, acquisition, management and exploitation of arbitrary types of context and user preference information to enable adaptation to context changes

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.

Approach to site, C House, Cooparoo

Looking uphill towards house from road.