Mobile devices, specifically, smartphones proved easy and quick access to data visualisations throughout various tracking apps. Mobile health (mHealth) apps have given non-expert users access to data visualisation to track their activities and health-related issues such as heart tracking and medication. However, no work is done on user experience or perception of data visualisations in mHealth apps. App reviews offer an indirect anchor for researchers to examine how non-expert users perceive and interact with data visualisations and identify the key challenges and recommendations. This paper introduces an analysis of app reviews on data visualisations reported on a dataset of 250 mHealth apps on the Google Play Store. We identified 8,406 comments related to data visualisations. 919 neutral comments, 1,557 negative comments and 5,930 positive comments. From analysing the user reviews, functional requirements turned out to be the most common problem across these app reviews, followed by the look and feel and then data problems. A complete set of data visualisations seem to be the most well-received capability of mHealth apps. We used these comments to develop classification and data visualisation guidelines when developing mobile data visualisations.
Obesity is known to lower the quality of life substantially. It is often associated with increased chances of non-communicable diseases such as diabetes, cardiovascular problems, various cancers, etc. Evidence suggests that diet-related mobile applications play a vital role in assisting individuals in making healthier choices and keeping track of food intake. However, due to an abundance of similar applications, it becomes pertinent to evaluate each of them in terms of functionality, usability, and possible design issues to truly determine state-of-the-art solutions for the future. Since these…
Baidu Research（百度研究院）；University of Virginia（弗吉尼亚大学）；Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms…
Main objective of this study is to introduce an expert system-based mHealth application that takes Artificial Intelligence support by considering previously introduced solutions from the literature and employing possible requirements for a better solution. Thanks to that research study, a mobile software system having Artificial Intelligence support and providing dynamic support against the common health problems in daily life was designed-developed and it was evaluated via survey and diagnosis-based evaluation tasks. Evaluation tasks indicated positive outcomes for the mHealth system.
Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified. App description contents and featur…
Personal data visualisations are becoming a critical contributor toward the successful adoption of mobile health (m-health) apps. Thus, understanding user needs and challenges when using mobile personal data visualisation is essential to ensuring the adoption of these apps. This paper presents the results of a user survey to understand users' demographics, tasks, needs, and challenges of using mobile personal data visualisations. We had 56 complete responses. The survey's key findings are: 1) 51\% of the users use multiple health tracking apps to achieve their goals/needs; 2) bar charts and pi…
Dark patterns in mobile apps take advantage of cognitive biases of end-users and can have detrimental effects on people's lives. Despite growing research in identifying remedies for dark patterns and established solutions for desktop browsers, there exists no established methodology to reduce dark patterns in mobile apps. Our work introduces GreaseDroid, a community-driven app modification framework enabling non-expert users to disable dark patterns in apps selectively.
Recent research has identified a few design flaws in popular mobile health (mHealth) applications for promoting healthy eating lifestyle, such as mobile food journals. These include tediousness of manual food logging, inadequate food database coverage, and a lack of healthy dietary goal setting. To address these issues, we present Foodbot, a chatbot-based mHealth application for goal-oriented just-in-time (JIT) healthy eating interventions. Powered by a large-scale food knowledge graph, Foodbot utilizes automatic speech recognition and mobile messaging interface to record food intake. Moreover…
The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a user's perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users' preference and their actual usage of a mental well-being app. Furthermore, we explored users' preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interesting…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity predict…
The majority of blindness is preventable, and is located in developing countries. While mHealth applications for retinal imaging in combination with affordable smartphone lens adaptors are a step towards better eye care access, the expert knowledge and additional hardware needed are often unavailable in developing countries. Eye screening apps without lens adaptors exist, but we do not know much about the experience of guiding users to take medical eye images. Additionally, when an AI based diagnosis is provided, trust plays an important role in ensuring in the adoption. This work addresses fa…
With today's technological advancements, mobile phones and wearable devices have become extensions of an increasingly diffused and smart digital infrastructure. In this paper, we examine mobile health (mHealth) platforms and their health and economic impacts on the outcomes of chronic disease patients. We partnered with a major mHealth firm that provides one of the largest mHealth apps in Asia specializing in diabetes care. We designed a randomized field experiment based on detailed patient health activities (e.g., exercises, sleep, food intake) and blood glucose values from 1,070 diabetes pat…
University of Amsterdam（阿姆斯特丹大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Università della Svizzera italiana (USI)（瑞士意大利语区大学）
Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users' lives. This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. The former is the key component of a unified mobile search system: a system that addresses the users' information needs for all the apps installed on their devices with a unified mod…
Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with …
Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user's personality. As filling out personality questionnaires is tedious, we propose the prediction of the user's personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smartphone data and utilizes psychometric personality questionnaires. With TYDR, we track a large…
Personal data cover multiple aspects of our daily life and activities, including health, finance, social, Internet, Etc. Personal data visualisations aim to improve the user experience when exploring these large amounts of personal data and potentially provide insights to assist individuals in their decision making and achieving goals. People with different backgrounds, gender and ages usually need to access their data on their mobile devices. Although there are many personal tracking apps, the user experience when using these apps and visualisations is not evaluated yet. There are publication…
Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory s…
Users need to configure default apps when they first start using their devices. The privacy configurations of the default apps do not always match what users think they have initially enabled. We first systematically evaluated the privacy configurations of default apps. We discovered serious issues with the documentation of the default apps. Based on these findings, we explored users' experiences with an interview study (N=15). Our findings from both studies show that: the instructions of setting privacy configurations of default apps are vague and lack required steps; users were unable to dis…
The COVID-19 pandemic has fueled the development of smartphone applications to assist disease management. Many "corona apps" require widespread adoption to be effective, which has sparked public debates about the privacy, security, and societal implications of government-backed health applications. We conducted a representative online study in Germany (n = 1,003), the US (n = 1,003), and China (n = 1,019) to investigate user acceptance of corona apps, using a vignette design based on the contextual integrity framework. We explored apps for contact tracing, symptom checks, quarantine enforcemen…
MIT App Inventor: Enabling Personal Mobile Computing
MIT App Inventor是一款拖放式可视化编程工具，用于设计和构建功能齐全的Android移动应用程序。App Inventor推动了个人移动计算的新时代，在这个时代，人们可以在无限独特的情况下，为日常生活设计、创建和使用个人有意义的移动技术解决方案。App Inventor直观的编程隐喻和增量开发功能使开发人员能够专注于应用程序编程的逻辑，而不是编码语言的语法，从而培养所有人的数字素养。自从它从谷歌转移到麻省理工学院以来，已经增加了一些改进，研究项目正在进行中。
In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application. Overcoming this barrier is the goal of explainable AI (XAI). However, an explanation can be perceived differently and, thus, not solve the black-box problem for everyone. The domain of Human-Centered AI deals with this problem by adapting AI to users. We present a user-centered persona concept to evaluate XAI and use it to investigate end-users preferences for various explanation styles and contents in a mobile h…
With smartphone technologies enhanced way of interacting with the world around us, it has also been paving the way for easier access to our private and personal information. This has been amplified by the existence of numerous embedded sensors utilized by millions of apps to users. While mobile apps have positively transformed many aspects of our lives with new functionalities, many of these applications are taking advantage of vast amounts of data, privacy apps, a form of Privacy Enhancing Technology can be an effective privacy management tool for smartphones. To protect against vulnerabiliti…
Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional and unconventional privacy concerns among app users. To address these concerns, the research on mobile app privacy has experienced rapid growth over the past decade. In general, this line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this survey paper, we co…
An important concern in end user development (EUD) is accidentally embedding personal information in program artifacts when sharing them. This issue is particularly important in GUI-based programming-by-demonstration (PBD) systems due to the lack of direct developer control of script contents. Prior studies reported that these privacy concerns were the main barrier to script sharing in EUD. We present a new approach that can identify and obfuscate the potential personal information in GUI-based PBD scripts based on the uniqueness of information entries with respect to the corresponding app GUI…
