Anxiety — Is There an App for That? Considering Technology, Psychiatry, and Internet-Assisted Cognitive Behavioral Therapy

Across Western countries, more than a third of people will have a mental health disorder over their lifetime; mood and anxiety disorders are the most common. The effectiveness of psychological interventions is well established. Cognitive Behavioural Therapy (CBT), for example, is as effective for mild and moderate anxiety as medications; combined psychopharmacology and CBT is superior to either modality alone, suggesting a synergistic effect. However, CBT requires a major investment of time and resources. Thus, in public systems, CBT has limited availability and is subject to long waiting times; primary-care physicians and psychiatrists may not offer CBT

Explanation Needs in App Reviews: Taxonomy and Automated Detection

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms (e.g., machine learning, self-adaptive systems). There is relatively little work on what situations and types of behavior should be explained. There is also a lack of support for eliciting explainability requirements. In this work, we explore the need for explanation expressed by users in app reviews. We manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of Explanation Needs. We also explore several approaches to automatically identify Explanation Needs in app reviews. Our best classifier identifies Explanation Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%. Our work contributes to a better understanding of users' Explanation Needs. Automated tools can help engineers focus on these needs and ultimately elicit valid Explanation Needs

Desirable Components for a Customized, Home-Based, Digital Care-Management App for Children and Young People With Long-Term, Chronic Conditions: A Qualitative Exploration

Background: Mobile apps for mobile phones and tablet devices are widely used by children and young people aged 0-18 years with long-term health conditions, such as chronic kidney disease (CKD), and their healthy peers for social networking or gaming. They are also poised to become a major source of health guidance. However, app development processes that are coproduced, rigorously developed, and evaluated to provide tailored, condition-specific, practical advice on day-to-day care management are seldom systematic or sufficiently described to enable replication. Furthermore, attempts to extrapolate to the real world are hampered by a poor understanding of the effects of key elements of app components. Therefore, effective and cost-effective novel, digital apps that will effectively and safely support care management are critical and timely. To inform development of such an app for children with CKD, a user requirements-gathering exercise was first needed. Objective: To explore the views of children with CKD, their parents, and health care professionals to inform future development of a child-focused, care-management app. Methods: Using age- and developmentally appropriate methods, we interviewed 36 participants: 5-10-year-olds (n=6), 11-14-year-olds (n=6), 15-18-year-olds (n=5), mothers (n=10), fathers (n=2), and health care professionals (n=7). Data were analyzed using Framework Analysis and behavior change theories. Results: Of the 27 interviews, 19 (70%) interviews were individual and 8 (30%) were joint—5 out of 8 (63%) joint interviews were with a child or young person and their parent, 1 out of 8 (13%) were with a child and both parents, and 2 out of 8 (25%) were with 2 professionals. Three key themes emerged to inform development of a software requirement specification for a future home-based, digital care-management app intervention: (1) Gaps in current online information and support, (2) Difficulties experienced by children with a long-term condition, and (3) Suggestions for a digital care-management app. Reported gaps included the fact that current online information is not usually appropriate for children as it is “dry” and “boring,” could be “scary,” and was either hard to understand or not relevant to individuals’ circumstances. For children, searching online was much less accessible than using a professional-endorsed mobile app. Children also reported difficulty explaining their condition to others, maintaining treatment adherence, coping with feeling isolated, and with trying to live a “normal” life. There was recognition that a developmentally appropriate, CKD-specific app could support the process of explaining the condition to healthy peers, reducing isolation, adhering to care-management plans, and living a “normal” life. Participants recommended a range of media and content to include in a tailored, interactive, age- and developmentally appropriate app. For example, the user would be able to enter their age and diagnosis so that only age-appropriate and condition-specific content is displayed. Conclusions: Future development of a digital app that meets the identified information and support needs and preferences of children with CKD will maximize its utility, thereby augmenting CKD caregiving and optimizing outcomes

AnFlo: Detecting anomalous sensitive information flows in Android apps

Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for "Health" apps to share heart rate information through the Internet but is anomalous for "Travel" apps. In this paper, we propose a novel approach to detect anomalous sensitive data flows in Android apps, with improved accuracy. To achieve this objective, we first group trusted apps according to the topics inferred from their functional descriptions. We then learn sensitive information flows with respect to each group of trusted apps. For a given app under analysis, anomalies are identified by comparing sensitive information flows in the app against those flows learned from trusted apps grouped under the same topic. In the evaluation, information flow is learned from 11,796 trusted apps. We then checked for anomalies in 596 new (benign) apps and identified 2 previously-unknown vulnerable apps related to anomalous flows. We also analyzed 18 malware apps and found anomalies in 6 of them

Surgical site infection after caesarean section? There is an app for that: results from a feasibility study on costs and benefits

Surgical site infections (SSIs) are one of the most common and, yet, preventable healthcare associated infections. In Ireland, the rate of Caesarean section (CS) is increasing, while postpartum hospital stay is decreasing, adversely affecting SSI among women. There is much need to develop post-discharge surveillance which can effectively monitor, detect, and arrange treatment for affected women. The use of modern technology to survey SSI following discharge from hospital remains unexplored. We report the results of a feasibility study which investigates whether an integrated mobile application (hereafter, app) is more cost-beneficial than a stand-alone app or telephone helpline at surveying SSI following CS. We find women prefer the integrated app (47.5%; n=116/244) over the stand-alone app (8.2%; n=20/244) and telephone helpline (18.0%; 44/244), although there is no significant difference in women's valuation of these services using willingness to pay techniques. The stand-alone app is the only cost-beneficial service due to low labour costs. Future research should employ alternative measures when evaluating the benefits of the health technology. The use of a mobile app as a mechanism for postpartum care could represent a considerable advancement towards technological health care

Smartphone Apps for Food Purchase Choices: Scoping Review of Designs, Opportunities, and Challenges

Background: Smartphone apps can aid consumers in making healthier and more sustainable food purchases. However, there is still a limited understanding of the different app design approaches and their impact on food purchase choices. An overview of existing food purchase choice apps and an understanding of common challenges can help speed up effective future developments.Objective: We examined the academic literature on food purchase choice apps and provided an overview of the design characteristics, opportunities, and challenges for effective implementation. Thus, we contribute to an understanding of how technologies can effectively improve food purchase choice behavior and provide recommendations for future design efforts.Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we considered peer-reviewed literature on food purchase choice apps within IEEE Xplore, PubMed, Scopus, and ScienceDirect. We inductively coded and summarized design characteristics. Opportunities and challenges were addressed from both quantitative and qualitative perspectives. From the quantitative perspective, we coded and summarized outcomes of comparative evaluation trials. From the qualitative perspective, we performed a qualitative content analysis of commonly discussed opportunities and challenges.Results: We retrieved 55 articles, identified 46 unique apps, and grouped them into 5 distinct app types. Each app type supports a specific purchase choice stage and shares a common functional design. Most apps support the product selection stage (selection apps; 27/46, 59%), commonly by scanning the barcode and displaying a nutritional rating. In total, 73% (8/11) of the evaluation trials reported significant findings and indicated the potential of food purchase choice apps to support behavior change. However, relatively few evaluations covered the selection app type, and these studies showed mixed results. We found a common opportunity in apps contributing to learning (knowledge gain), whereas infrequent engagement presents a common challenge. The latter was associated with perceived burden of use, trust, and performance as well as with learning. In addition, there were technical challenges in establishing comprehensive product information databases or achieving performance accuracy with advanced identification methods such as image recognition.Conclusions: Our findings suggest that designs of food purchase choice apps do not encourage repeated use or long-term adoption, compromising the effectiveness of behavior change through nudging. However, we found that smartphone apps can enhance learning, which plays an important role in behavior change. Compared with nudging as a mechanism for behavior change, this mechanism is less dependent on continued use. We argue that designs that optimize for learning within each interaction have a better chance of achieving behavior change. This review concludes with design recommendations, suggesting that food purchase choice app designers anticipate the possibility of early abandonment as part of their design process and design apps that optimize the learning experience

Inferring User Needs & Tasks from App Usage Interactions

Mobile devices have become an increasingly ubiquitous part of our everyday life, which are not only used for basic communication. Nowadays, the need for mobile services arises from a broad range of requirements including both single app usage (e.g., check on the weather) and complex task completion (such as planning vacation) which may lead to lengthy operations within distinct apps. Understanding how users interact with apps could provide us with great signals for profiling users and help service providers/app developers/smartphone manufacturers to improve user experience and retention. Therefore, in this thesis, we present work towards inferring user needs and tasks from their app usage interactions. Firstly, we aim to better understand users' behaviour on using one particular app under different contexts. There have been many researchers proposed models for recommending the app user would use next proactively. However, less work has been conducted to enhance the app usage prediction when a new user comes whose information is insufficient for learning. Additionally, besides predicting which app users would use, we aim to further investigate if the app dwell time could also be modelled based on various user characteristics and contextual information. By conducting the comprehensive analysis and experiments, we demonstrate that users' next app and the time spent could be effectively predicted at the same time. Other than effectively serving the individual apps that correspond to users' simple needs, we aim to further understand the high-level tasks within users' minds while engaging with different apps. We focus on identifying and characterizing tasks from app usage behaviour and then leveraging the extracted task information for improving mobile services. We first present an automatic method that accurately determines mobile tasks from users' app usage logs based on a set of features. Given the extracted tasks, we further investigate if there are common patterns that exist among all the complex mobile tasks. Finally, we demonstrate that the extracted task information could benefit user profiling in demographics prediction and next task prediction, especially when compared to the traditional app-based methods. To summarize, in this thesis, we conduct a more comprehensive study on modelling users app usage behaviour. Additionally, we propose to set the stage for evaluating mobile apps usage, not on a per-app basis, but on the basis of users' tasks. Finally, we provide the initial steps in shaping future research on investigating whether and how the extracted tasks could be applied for improving mobile services

On Constructor Rewrite Systems and the Lambda Calculus

We prove that orthogonal constructor term rewrite systems and lambda-calculus with weak (i.e., no reduction is allowed under the scope of a lambda-abstraction) call-by-value reduction can simulate each other with a linear overhead. In particular, weak call-by- value beta-reduction can be simulated by an orthogonal constructor term rewrite system in the same number of reduction steps. Conversely, each reduction in a term rewrite system can be simulated by a constant number of beta-reduction steps. This is relevant to implicit computational complexity, because the number of beta steps to normal form is polynomially related to the actual cost (that is, as performed on a Turing machine) of normalization, under weak call-by-value reduction. Orthogonal constructor term rewrite systems and lambda-calculus are thus both polynomially related to Turing machines, taking as notion of cost their natural parameters.Comment: 27 pages. arXiv admin note: substantial text overlap with arXiv:0904.412

Relational Graph Models at Work

We study the relational graph models that constitute a natural subclass of relational models of lambda-calculus. We prove that among the lambda-theories induced by such models there exists a minimal one, and that the corresponding relational graph model is very natural and easy to construct. We then study relational graph models that are fully abstract, in the sense that they capture some observational equivalence between lambda-terms. We focus on the two main observational equivalences in the lambda-calculus, the theory H+ generated by taking as observables the beta-normal forms, and H* generated by considering as observables the head normal forms. On the one hand we introduce a notion of lambda-K\"onig model and prove that a relational graph model is fully abstract for H+ if and only if it is extensional and lambda-K\"onig. On the other hand we show that the dual notion of hyperimmune model, together with extensionality, captures the full abstraction for H*