A framework for understanding and predicting the take up and use of social networking tools in a collaborative envionment

Online collaborative environments, such as social networking environments, enable users to work together to create, modify, and share media collaboratively. However, as users can be autonomous in their actions the ability to create and form a shared understanding of the people, purpose, and process of the collaborative effort can be complex. This complexity is compounded by the natural implicit social and collaborative structure of these environments, a structure that can be modified by users dynamically and asynchronously. Some have tried to make this implicitness explicit through data mining, and allocation of user roles. However such methods can fail to adapt to the changing nature of an environment's structure relating to habits of users and their social connectedness. As a result, existing methods generally provide only a snapshot of the environment at a point in time. In addition, existing methods focus on whole user bases and the underlying social context of the environment. This makes them unsuitable for situations where the context of collaboration can change rapidly, for example the tools and widgets available for collaborative action and the users available for collaborative interactions. There is a pre-existing model for understanding the dynamic structure of these environments called the “Group Socialisation Model". This model has been used to understand how social group roles form and change over time as they go through a life cycle. This model also contains a concept of characteristic behaviours or descriptors of behaviour that an individual can use to make judgement about another individual and to create an understanding of a role or social norm that may or may not be explicit. Although studies have used components of this model to provide a means of role identification or role composition within online collaborative environments, they have not managed to provide a higher level method or framework that can replicate the entire life cycle continuously over time within these environments. Using the constructive research methodology this thesis presents a research construct in the form of a framework for replicating the social group role life cycle within online collaborative environments. The framework uses an artificial neural network with a unique capability of taking snapshots of its network structure. In conjunction with fuzzy logic inference, collaborative role signatures composed of characteristic behaviours can then be determined. In this work, three characteristic behaviours were identified from the literature for characterisation of stereotypical online behaviour to be used within a role signature: these were publisher, annotator, and lurker. The use of the framework was demonstrated on three case studies. Two of the case studies were custom built mobile applications specifically for this study, and one was the Walk 2.0 website from a National Health and Medical Research Council project. All three case studies allowed for collaborative actions where users could interact with each other to create an dynamic and diverse environment. For the use of these case studies, ethics was approved by the Western Sydney University Human Research Ethic Committee and consistent strategies for recruitment were carried out. The framework was thereby demonstrated to be capable of successfully determining role signatures composed of the above characteristic behaviours, for a range of contexts and individual users. Also, comparison of participant usage of case studies was carried out and it was established that the role signatures determined by the framework matched usage. In addition, the top contributors within the case studies were analysed to demonstrate the framework's capability of handling the dynamic and continual changing structure of an online collaborative environment. The major contribution of this thesis is a framework construct developed to propose and demonstrate a new framework approach to successfully automate and carry out the social group role model life cycle within online collaborative environments. This is a significant component of foundational work towards providing designers of online collaborative environments with the capacity of understanding the various implicit roles and their characteristic behaviours for individual users. Such a capability could enable more specific individual personalisation or resource allocation, which could in turn improve the suitability of environments developed for collaboration online

Effectiveness of a web- and mobile phone-based intervention to promote physical activity and healthy eating in middle-Aged males: Randomized controlled trial of the manup study

Background: The high number of adult males engaging in low levels of physical activity and poor dietary practices, and the health risks posed by these behaviours, necessitate broad-reaching intervention strategies. IT-based (web and mobile phone) interventions can be accessed by large numbers of people, yet there are few reported IT-based interventions targeting males’ physical activity and dietary practices. Objective: This study examines the effectiveness of a 9-month IT-based intervention to improve the physical activity, dietary behaviours and health literacy in middle-aged males compared to a print-based intervention. Methods: Participants, recruited offline (e.g. newspaper ads), were randomized into either an IT-based or print-based intervention arm on a 2:1 basis in favour of the fully automated IT-based arm. Participants were adult males aged 35-54 years living in two regional cities in Queensland Australia who could access the internet, owned a mobile phone and were able to increase their activity level. The intervention, ManUp, was informed by social cognitive and self regulation theories and was specifically designed to target males. Educational materials were provided and self-monitoring of physical activity and nutrition behaviours was promoted. Intervention content was the same in both intervention arms, only the delivery mode differed, and content could be accessed throughout the 9-month study period. Participants’ physical activity, dietary behaviours, and health literacy were measured using online surveys at baseline, 3 months and 9 months. Results: A total of 301 participants completed baseline assessments, 205 in the ITbased arm and 96 in the print-based arm. A total of 124 participants completed all three assessments. There were no significant between group differences in physical 5 activity and dietary behaviours (p ≥0.05). Participants reported an increased number of minutes and sessions of physical activity at 3 months (b(exp)=1.45, 95% CI=1.09-1.95; b(exp)=1.61, 95% CI=1.17-2.22) and 9 months (b(exp)=1.55, 95% CI=1.14-2.10; b(exp)=1.51, 95% CI=1.15-2.00). Overall dietary behaviours improved at 3 months (b(exp)=1.07, 95% CI=1.03-1.11) and 9 months (b(exp)=1.10, 95% CI=1.05-1.13). The proportion of participants in both groups eating higher-fibre bread and low-fat milk increased at 3 months (b(exp) = 2.25, 95% CI = 1.29-3.92; b(exp)=1.65, 95% CI = 1.07-2.55). Participants in the IT-based arm were less likely to report that 30 minutes of physical activity per day improves health (b(exp)=0.48, 95% CI=0.26-0.90) and more likely to report that vigorous intensity physical activity 3 times per week is essential (b(exp)=1.70, 95% CI=1.02-2.82). The average number of logins to the IT-platform at 3 and 9 months was 6.99 (SE=0.86) and 9.22 (SE=1.47), respectively. The average number of self-monitoring entries at 3 and 9 months was 16.69 (SE=2.38) and 22.51 (SE=3.79), respectively. Conclusions: The ManUp intervention was effective in improving physical activity and dietary behaviours in middle aged males with no significant differences between IT- and print-based delivery modes

Effectiveness of a Web 2.0 Intervention to Increase Physical Activity in Real-World Settings: Randomized Ecological Trial

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.Background: The translation of Web-based physical activity intervention research into the real world is lacking and becoming increasingly important. Objective: To compare usage and effectiveness, in real-world settings, of a traditional Web 1.0 Web-based physical activity intervention, providing limited interactivity, to a Web 2.0 Web-based physical activity intervention that includes interactive features, such as social networking (ie, status updates, online “friends,” and personalized profile pages), blogs, and Google Maps mash-ups. Methods: Adults spontaneously signing up for the freely available 10,000 Steps website were randomized to the 10,000 Steps website (Web 1.0) or the newly developed WALK 2.0 website (Web 2.0). Physical activity (Active Australia Survey), quality of life (RAND 36), and body mass index (BMI) were assessed at baseline, 3 months, and 12 months. Website usage was measured continuously. Analyses of covariance were used to assess change over time in continuous outcome measures. Multiple imputation was used to deal with missing data. Results: A total of 1328 participants completed baseline assessments. Only 3-month outcomes (224 completers) were analyzed due to high attrition at 12 months (77 completers). Web 2.0 group participants increased physical activity by 92.8 minutes per week more than those in the Web 1.0 group (95% CI 28.8-156.8; P=.005); their BMI values also decreased more (–1.03 kg/m2, 95% CI –1.65 to -0.41; P=.001). For quality of life, only the physical functioning domain score significantly improved more in the Web 2.0 group (3.6, 95% CI 1.7-5.5; P<.001). The time between the first and last visit to the website (3.57 vs 2.22 weeks; P<.001) and the mean number of days the website was visited (9.02 vs 5.71 days; P=.002) were significantly greater in the Web 2.0 group compared to the Web 1.0 group. The difference in time-to-nonusage attrition was not statistically significant between groups (Hazard Ratio=0.97, 95% CI 0.86-1.09; P=.59). Only 21.99% (292/1328) of participants (n=292 summed for both groups) were still using either website after 2 weeks and 6.55% (87/1328) were using either website after 10 weeks. Conclusions: The website that provided more interactive and social features was more effective in improving physical activity in real-world conditions. While the Web 2.0 website was visited significantly more, both groups nevertheless displayed high nonusage attrition and low intervention engagement. More research is needed to examine the external validity and generalizability of Web-based physical activity interventions

Interactive Visualisation of Time-Based Vital Signs

Vital signs interpretation is an important element of patient monitoring. The use of visualisation could enhance this interpretation. We present a model for time-based visual analytics visualisation of vital signs. Our model allows for multiple vital signs to be charted along a time-based axis. The patient management care team then can apply lenses to alter the view being presented of the data, allowing the viewer to understand the meaning and improve the interpretation. The lenses applied will allow for generalisation, refinement, and traversing

Time-based interactive visualisation of vital signs

Physiological vital signs are important factors for clinical processes like tele-assessment, tele-monitoring and tele-treatment of patients. Records of patient vital signs over time are complex to understand without proper visualisation and data analysis aids. We present a model for interactively visualising vital signs in patient data histories, as used for remote chronic disease management. Our model allows simultaneous access to all vital signs records for the patient along a time-based axis. Clinicians and other health carers can then apply generalisation and refinement 'lenses' in a multi-layering visualisation mechanism to examine records further over single or multiple time sessions. The model also includes rule-based decisions for issuing notifications, when expected limits are exceeded

Classifying collaborative behavior in the form of behavioral stereotypes in collaborative mobile applications

Online Social networks empower users to collaborate with other users through complex interfaces. They enable users to take on various behaviors to achieve an objective or goal together. However, with the rise of smart devices and their small view ports these interfaces have been restricted. This results in the user having to wait until they have access to a desktop version before they can interact with these complex interfaces again. This paper presents a framework for classifying collaborative behavior in the form of Behavioral Stereotypes. In addition it presents initial results of a implementation of the framework in a collaborative mobile application to demonstrate its ability to help understand user behavior and how it changes from social ties users establish

Adaptive web framework for online collaborative environments

Online social networks are online collaborative environments where users can interact with other users to create, annotate, communicate, modify and delete media together. However, online social networks do not provide adaptive interfaces, by which one user might be presented with tools which are relative to their collaborative behaviour with another. This work presents behavioural stereotypes derived from the literature to help understand and predict user behaviour in an online social network underpinned by an adaptive interface framework. A user's behaviour stereotype will be determined by a proposed classication framework which processes usage data in real time

Assessing user engagement in a health promotion website using social networking

Remote provision of supportive mechanisms for preventive health is a fast-growing area in eHealth. Web-based interventions have been suggested as an effective way to increase adoption and maintenance of healthy lifestyle behaviours. This paper describes results obtained in the “Walk 2.0” trial to promote physical activity through a self-managed walking programme, using a social networking website that provided an online collaborative environment. Engagement of participants with the website was assessed by monitoring usage ofthe individual social networking functions (e.g. status post). The results demonstrate that users generally preferred contributing non-interactive public posts of information concerned with their individual physical activity levels, and more occasionally communicating privately to friends. Further analysis of topics within posts was done by classifying word usage frequencies. Results indicated that the dominant topics are well aligned with the social environment within which physical activity takes place. Topics centred around four main areas: description of the activity, timing of the activity, affective response to the activity, and context within which the activity occurs. These findings suggest that strong levels of user awareness and communication occur in the social networking setting, indicative of beneficial self-image and self-actualisation effects

Associations between quality of life and duration and frequency of physical activity and sedentary behaviour: Baseline findings from the WALK 2.0 randomised controlled trial

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.While physical and mental health benefits of regular physical activity are well known, increasing evidence suggests that limiting sedentary behaviour is also important for health. Evidence shows associations of physical activity and sedentary behaviour with health-related quality of life (HRQoL), however, these findings are based predominantly on duration measures of physical activity and sedentary behaviour (e.g., minutes/week), with less attention on frequency measures (e.g., number of bouts). We examined the association of HRQoL with physical activity and sedentary behaviour, using both continuous duration (average daily minutes) and frequency (average daily bouts≥10 min) measures. Baseline data from the WALK 2.0 trial were analysed. WALK 2.0 is a randomised controlled trial investigating the effects of Web 2.0 applications on engagement, retention, and subsequent physical activity change. Daily physical activity and sedentary behaviour (duration = average minutes, frequency = average number of bouts ≥10 minutes) were measured (ActiGraph GT3X) across one week, and HRQoL was assessed with the ‘general health’ subscale of the RAND 36-Item Health Survey. Structural equation modelling was used to evaluate associations. Participants (N = 504) were 50.8±13.1 (mean±SD) years old with a BMI of 29.3±6.0. The 465 participants with valid accelerometer data engaged in an average of 24.0±18.3 minutes and 0.64±0.74 bouts of moderate-vigorous physical activity per day, 535.2±83.8 minutes and 17.0±3.4 bouts of sedentary behaviour per day, and reported moderate-high general HRQoL (64.5±20.0). After adjusting for covariates, the duration measures of physical activity (path correlation = 0.294, p<0.05) and sedentary behaviour were related to general HRQoL (path coefficient = -0.217, p<0.05). The frequency measure of physical activity was also significant (path coefficient = -0.226, p<0.05) but the frequency of sedentary behaviour was not significantly associated with general HRQoL. Higher duration levels of physical activity in fewer bouts, and lower duration of sedentary behaviour are associated with better general HRQoL. Further prospective studies are required to investigate these associations in different population groups over time