Heterogeneity in the social networks of young and older adults: Prediction of mental health and cardiovascular reactivity during acute stress.

BackgroundThe recent proliferation of self-tracking technologies has allowed individuals to generate significant quantities of data about their lifestyle. These data can be used to support health interventions and monitor outcomes. However, these data are often stored and processed by vendors who have commercial motivations, and thus, they may not be treated with the sensitivity with which other medical data are treated. As sensors and apps that enable self-tracking continue to become more sophisticated, the privacy implications become more severe in turn. However, methods for systematically identifying privacy issues in such apps are currently lacking.ObjectiveThe objective of our study was to understand how current mass-market apps perform with respect to privacy. We did this by introducing a set of heuristics for evaluating privacy characteristics of self-tracking services.MethodsUsing our heuristics, we conducted an analysis of 64 popular self-tracking services to determine the extent to which the services satisfy various dimensions of privacy. We then used descriptive statistics and statistical models to explore whether any particular categories of an app perform better than others in terms of privacy.ResultsWe found that the majority of services examined failed to provide users with full access to their own data, did not acquire sufficient consent for the use of the data, or inadequately extended controls over disclosures to third parties. Furthermore, the type of app, in terms of the category of data collected, was not a useful predictor of its privacy. However, we found that apps that collected health-related data (eg, exercise and weight) performed worse for privacy than those designed for other types of self-tracking.ConclusionsOur study draws attention to the poor performance of current self-tracking technologies in terms of privacy, motivating the need for standards that can ensure that future self-tracking apps are stronger with respect to upholding users’ privacy. Our heuristic evaluation method supports the retrospective evaluation of privacy in self-tracking apps and can be used as a prescriptive framework to achieve privacy-by-design in future apps.

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This paper sets out to investigate the critical factors of knowledge management (KM) considered to have an impact on the performance of Chinese information and communication technology (ICT) firms. An integrated KM framework is developed whereby it set to evaluate those critical factors and the role of KM on the performance of these firms. The findings from our 556 survey responses indicate that organizational culture and technology variables are found to form essential elements for knowledge management. This study confirms that the culture environment of an enterprise is central to its success in the context of China. Furthermore, it shows that a collaborated, trusted and learning environment within ICT firms will have a positive impact on their KM performance. In doing so, it provides the key to understanding KM in the Chinese context and also is recognizing the networking nature of the Chinese society which operates on the basis of "Guanxi". Although this research may seem limited to a developing country, however, the finding of this study has contributed to formulate some guidelines to develop KM strategies for the ICT firms from emerging economies.