Real App Research Articles

Perceived interactivity in real estate APP increases consumers' psychological well-being: a moderated mediation model

PurposeThis study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.Design/methodology/approachThis study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.FindingsThe findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.Originality/valueThis study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

Understanding the Impact Mechanism of User Behavior Pattern toward Real Estate APP Platforms in the Post-COVID Era: A Quantitative Analysis

While technology factors are the main driver of the booming real estate APP platforms with important implications for user behavior pattern during and post-pandemic contexts, there is a lack of adequate research. In response, this study explores the user behavior pattern of real estate APP platforms to promote user mental health by taking the real estate APP platforms users as the participants based on theory of technology readiness and acceptance model. Data collected from offline surveys are analyzed using PLS-SEM. The results reveal the technology readiness index positively affects individuals’ perceived usefulness and satisfaction, ultimately positively affects individuals’ continuance intention with real estate APP platforms; satisfaction with real estate APP platforms mediated the relationship between technology readiness index, perceived ease of use, perceived usefulness, and individuals’ continuance intention with real estate APP platforms. However, the group comparison finds no significant difference in user behavior patterns by gender. The contribution of this study is to reveal the influence mechanisms of digital technology on users’ behavioral patterns toward real estate APP platforms, which can help guide the sustainable development of real estate APP platforms and promote user mental health and wellbeing in the post-COVID era.

Optimal Packet Camouflage Against Traffic Analysis

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app that the source app will resemble. We define a model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier by identifying a mutated source app as the target app. In our second obfuscation algorithm, we present an optimized scheme resulting in a trade-off between privacy and complexity overhead. For this reason, we propose a mathematical model for network obfuscation. We formulate analytically the problem of selecting the target app and the length from the target app to mutate to. Then, we propose an algorithm to solve it dynamically. Extensive evaluation of the proposed models, on real app traffic traces, shows significant obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy from 91.1% to 0.22% using the first algorithm, with 11.86% padding overhead. The same classification accuracy was reduced to 1.76% with only 0.73% overhead using the second algorithm.

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

The protection and processing of the sensitive data in recommendation system are the major concern. Existing literature, used homomorphic encryption (HE), Reversible Data Transform (RDT), differential privacy (DP) and many more schemes to protect user information. Existing RDT scheme require prior sharing of the parameters and an alternative mechanism e.g., Shamir Threshold Protocol or Diffie-hellman algorithm are used to protect the sharing parameters. In this paper, we proposed a chaotic based RDT approach for privacy-preserving data mining (PPDM) in recommendation system. Using this approach, RDT parameter values will be generated locally and because of this, prior sharing of the parameter values for the recovery process will not be necessary. This approach can be used as an alternative to the standard-RDT algorithm where bandwidth and memory are considered important factors. Our results on the Iris data set clearly show that the proposed chaotic RDT shows similar results as standard-RDT. Secondly, in this paper, we explore the usage of the RDT algorithm on real app usage records in the mobile app recommendation (MAR) domain. Thirdly, we tested the application of the RDT algorithm for the standard MovieLens dataset to ensure the validity of results because app usage dataset is publicly not available. Our results show that the proposed RDT algorithm can replace HE if an adaptive recommendation approach is used. Similarly, we can safely use the RDT approach to any data including user rating, health data or app usage frequency to ensure user privacy before delivering it to the recommender-server.

The Diabits App for Smartphone-Assisted Predictive Monitoring of Glycemia in Patients With Diabetes: Retrospective Observational Study.

BackgroundDiabetes mellitus, which causes dysregulation of blood glucose in humans, is a major public health challenge. Patients with diabetes must monitor their glycemic levels to keep them in a healthy range. This task is made easier by using continuous glucose monitoring (CGM) devices and relaying their output to smartphone apps, thus providing users with real-time information on their glycemic fluctuations and possibly predicting future trends.ObjectiveThis study aims to discuss various challenges of predictive monitoring of glycemia and examines the accuracy and blood glucose control effects of Diabits, a smartphone app that helps patients with diabetes monitor and manage their blood glucose levels in real time.MethodsUsing data from CGM devices and user input, Diabits applies machine learning techniques to create personalized patient models and predict blood glucose fluctuations up to 60 min in advance. These predictions give patients an opportunity to take pre-emptive action to maintain their blood glucose values within the reference range. In this retrospective observational cohort study, the predictive accuracy of Diabits and the correlation between daily use of the app and blood glucose control metrics were examined based on real app users’ data. Moreover, the accuracy of predictions on the 2018 Ohio T1DM (type 1 diabetes mellitus) data set was calculated and compared against other published results.ResultsOn the basis of more than 6.8 million data points, 30-min Diabits predictions evaluated using Parkes Error Grid were found to be 86.89% (5,963,930/6,864,130) clinically accurate (zone A) and 99.56% (6,833,625/6,864,130) clinically acceptable (zones A and B), whereas 60-min predictions were 70.56% (4,843,605/6,864,130) clinically accurate and 97.49% (6,692,165/6,864,130) clinically acceptable. By analyzing daily use statistics and CGM data for the 280 most long-standing users of Diabits, it was established that under free-living conditions, many common blood glucose control metrics improved with increased frequency of app use. For instance, the average blood glucose for the days these users did not interact with the app was 154.0 (SD 47.2) mg/dL, with 67.52% of the time spent in the healthy 70 to 180 mg/dL range. For days with 10 or more Diabits sessions, the average blood glucose decreased to 141.6 (SD 42.0) mg/dL (P<.001), whereas the time in euglycemic range increased to 74.28% (P<.001). On the Ohio T1DM data set of 6 patients with type 1 diabetes, 30-min predictions of the base Diabits model had an average root mean square error of 18.68 (SD 2.19) mg/dL, which is an improvement over the published state-of-the-art results for this data set.ConclusionsDiabits accurately predicts future glycemic fluctuations, potentially making it easier for patients with diabetes to maintain their blood glucose in the reference range. Furthermore, an improvement in glucose control was observed on days with more frequent Diabits use.

Correction: Using Goal-Directed Design to Create a Mobile Health App to Improve Patient Compliance With Hypertension Self-Management: Development and Deployment.

[This corrects the article DOI: 10.2196/14466.].

Using Goal-Directed Design to Create a Mobile Health App to Improve Patient Compliance With Hypertension Self-Management: Development and Deployment.

BackgroundHypertension is a lifestyle-induced chronic disease that threatens the lives of patients. Control of hypertension requires patients to follow self-management regimes; unfortunately, however, patient compliance with hypertension self-management is low, especially in developing countries. Improvement of patient compliance is premised on meeting patient needs. Mobile health apps are becoming increasingly popular for self-management of chronic diseases. However, few mobile apps have been designed to meet patient needs for hypertension self-management.ObjectiveThe goal of this study was to develop a mobile health app to improve patient compliance with hypertension self-management and evaluate the effectiveness of the app in terms of patient compliance.MethodsThe goal-directed design method was applied to guide study design. We divided the study into 4 stages. Stages 1 to 3 comprised the development process. To improve the applicability of the goal-directed design method to chronic disease management, we extracted elements of user models concerned with patient compliance and defined a concrete process for user modeling. In stage 1, personas of hypertensive patients were built using qualitative and quantitative methods. Clustering methods based on questionnaire responses were used to group patients. Qualitative interviews were conducted to identify the needs of different groups. In stage 2, several functional modules were designed to meet the needs of different groups based on the results from stage 1. In stage 3, prototypes of functional modules were designed and implemented as a real app. Stage 4 was the deployment process, in which we conducted a pilot study to investigate patient compliance after using the app. Patient compliance was calculated through the frequency with which they took blood pressure measurements. In addition, qualitative interviews were conducted to learn the underlying reasons for the compliance results.ResultsIn stage 1, patients were divided into 3 groups based on 82 valid questionnaire responses. Eighteen patients from the different groups (7, 5, and 6 patients) were interviewed, and the needs of the groups were summarized as follows: improve self-management ability, enhance self-management motivation, and receive self-management support. In stages 2 and 3, 6 functional modules were designed and implemented based on specified needs, and the usability of the app was improved through usability tests. In stage 4, 143 patients were recruited to use different versions of the app for 2 months. Results show that patient compliance improved as functional modules were added (P<.001) and was maintained at a high level (rate of 0.73). Interview results from 32 patients show that the design of the app met different needs; thus, patients were more compliant with it.ConclusionsThis study developed a mobile health app for hypertension self-management using the goal-directed design method. The app proved to be effective for improving patient compliance with hypertension self-management.

Biomarkers of prolonged time to treatment failure in metastatic castration-resistant prostate cancer patients treated with abiraterone or enzalutamide: A real life patients-based nomogram and web app.

202 Background: Survival outcomes for metastatic castration resistant prostate cancer (mCRPC) patients (pts) have greatly improved following the approval of Docetaxel and abiraterone(Abi)/enzalutamide(Ez). However, it is not clear who is likely to benefit more from these therapies in real life clinical practice. Methods: A retrospective cohort study was performed with mCRPC pts treated with Abi/Ez in pre-Docetaxel setting. Primary endpoint: to identify predictive biomarkers of long time to treatment failure (TTF), develop a nomogram and convert it into a web app. Secondary endpoint: to correlate biomarkers with overall survival (OS). Kaplan Meier survival estimates and Cox Regression models were used for time-to-event analyses. Statistical analysis was made with R software. All statistical tests were two-sided and statistical significance was fixed at 0.05. Results: From May2012 to October2017, 117 pts were assessed, 81 received Abi and 36 Ez. Median follow-up of 21 months (mo), estimated median TTF was 12.8mo (95%CI 9.98-15.7) for the total population. Predictive biomarkers for TTF included in the first nomogram model were time from start of androgen deprivation therapy (tADT) to start of Abi/Ez, pain at baseline, Gleason score, baseline testosterone and lower PSA nadir in castration sensitive prostate cancer (CSPC). The second nomogram model included pain and Gleason plus neutrophil counts, tADT to CRPC and baseline PSA at CRPC. C-index: 0.726 and 0.731 respectively, providing a reliable prediction of long-term benefit with Abi/Ez in this setting. All these characteristics were significantly associated with OS. Conclusions: The two nomogram achieved a good internal validation and can work as a tool to the decision-making process of the best strategy to first-line therapy in mCRPC pts.[Table: see text]

Identifying Mobile Inter-App Communication Risks

Malware collusion is a technique utilized by attackers to evade standard detection. It is a new threat where two or more applications, appearing benign, communicate to perform a malicious task. Most proposed approaches aim at detecting stand-alone malicious applications. We point out the need for analyzing data flows across multiple Android apps, a problem referred to as end-to-end flow analysis. In this work, we present a flow analysis for app pairs that computes the risk level associated with their potential communications. Our approach statically analyzes the sensitivity and context of each inter-app flow based on inter-component communication (ICC) between communicating apps, and defines fine-grained security policies for inter-app ICC risk classification. We perform an empirical study on 7,251 apps from the Google Play store to identify the apps that communicate with each other via ICC channels. Our results report four times fewer warnings on our dataset of 197 real app pairs communicating via explicit external ICCs than the state-of-the-art permission-based collusion detection.

Leveraging app usage contexts for app recommendation: a neural approach

The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods.

Reusable Software Usability Specifications for mHealth Applications.

One of the key factors for the adoption of mobile technologies, and in particular of mobile health applications, is usability. A usable application will be easier to use and understand by users, and will improve user's interaction with it. This paper proposes a software requirements catalog for usable mobile health applications, which can be used for the development of new applications, or the evaluation of existing ones. The catalog is based on the main identified sources in literature on usability and mobile health applications. Our catalog was organized according to the ISO/IEC/IEEE 29148:2011 standard and follows the SIREN methodology to create reusable catalogs. The applicability of the catalog was verified by the creation of an audit method, which was used to perform the evaluation of a real app, S Health, application created by Samsung Electronics Co. The usability requirements catalog, along with the audit method, identified several usability flaws on the evaluated app, which scored 83%. Some flaws were detected in the app related to the navigation pattern. Some more issues related to the startup experience, empty screens or writing style were also found. The way a user navigates through an application improves or deteriorates user's experience with the application. We proposed a reusable usability catalog and an audit method. This proposal was used to evaluate a mobile health application. An audit report was created with the usability issues identified on the evaluated application.

부동산 시장 O2O(Online to Offline) 앱서비스의 만족도 및 재이용에 관한 연구

본 연구는 최근 급성장하는 O2O 비즈니스 모델 중 부동산 모바일 중개업체의 사업모델을 분석하고, 사용경험이 있는 사용자를 대상으로 한 재이용의사를 조사하여 사용자의 만족도와 재이용과의 관계를 분석하였다. 연구결과 부동산중개 앱서비스 사용의 용이성과 유용성은 고객만족에 양의 영향을 주며, 이는 재이용에 긍정적인 영향을 미치고 있는 것으로 분석되었다. 또한 부동산중개 앱서비스에서 연결한 오프라인 중개사무실 사용의 용이성과 유용성도 고객만족에 양의 영향을 주고 있으며, 이 역시 재이용에 긍정적인 영향을 미치는 것으로 나타났다. 본 연구에서는 앱서비스 및 중개업소이용 모두에서 용이성보다 유용성이 고객만족에 더 큰 영향력을 가지는 것으로 분석되었는데, 이는 이용자특성이 주택임차라는 이용목적이 뚜렷한 점에서 유용성의 가치가 이용성보다 더 크다는 것을 보여준다. 따라서 부동산 중개서비스 O2O 시장의 지속적인 성장을 위해서는 이용자의 유용성을 높이는 전략이 필요하다.

Towards Better Understanding of App Functions

Apps are attracting more and more attention from both mobile and web platforms. Due to the self-organized nature of the current app marketplaces, the descriptions of apps are not formally written and contain a lot of noisy words and sentences. Thus, for most of the apps, the functions of them are not well documented and thus cannot be captured by app search engines easily. In this paper, we study the problem of inferring the real functions of an app by identifying the most informative words in its description. In order to utilize and integrate the diverse information of the app corpus in a proper way, we propose a probabilistic topic model to discover the latent data structure of the app corpus. The outputs of the topic model are further used to identify the function of an app and its most informative words. We verify the effectiveness of the proposed methods through extensive experiments on two real app datasets crawled from Google Play and Windows Phone Store, respectively.

Modeling and Optimization of Aggregate Production Planning by Time Varying Acceleration Coefficients PSO

The aim of this paper is to apply Time-varying Acceleration Coefficients PSO (TVACPSO) for modeling and optimization of the aggregate production planning problem. TVACPSO is a modified and updated form of Particle Swarm Optimization (PSO). Most practical decisions made to solve APP problems usually consider total costs; we have eliminated other objective functions of APP in this case. There was several variables problem with constraints which was solved in MATLAB. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, and overtime, subcontracting and backordering levels. An industrial case is presented to demonstrate the feasibility of applying the proposed method to real APP problems. Consequently, the proposed TVACPSO approach yields an efficient APP compromise solution.