Mobile health apps use among Jordanian outpatients: A descriptive study

Our purpose in this descriptive cross-sectional study was to examine the prevalence of mobile health (mHealth) apps use, factors associated with downloading mHealth apps, and to describe characteristics of mHealth apps use among Jordanian patients in government-sponsored outpatient clinics. A total of 182 (41.6%) of the 438 outpatients who completed questionnaires downloaded mHealth apps. Common reasons for downloading mHealth apps included tracking physical activity, losing weight, learning exercises, as well as monitoring, and controlling diet. More than two thirds of the users (70%) stopped using the apps they downloaded due to loss of interest, lack of anticipated support, too time consuming, or better apps available. The most common personal reasons for never downloading mHealth apps were lack of interest, in good health, and the most common technical reasons included a limited data plan, lack of trust, cost, and complexity of the apps. We also found that gender, age, weight, and educational level influenced the decision whether to download mHealth apps or not. We have shown the potential in mHealth apps use among Jordanian patients is promising, and health care systems must adopt this technology as well as work through population needs and preferences to supply it

Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.Comment: Preprint, forthcoming at Information Technology and Managemen