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1. User-Centered Design of a Diabetes Self-Management Tool for Underserved Populations

   https://doi.org/10.1177/19322968231212220  

   Bonet-Olivencia, Samuel ; Carrillo-Leal, Jesus ; Rao, Arjun ; Sasangohar, Farzan ( November 2023 , Journal of Diabetes Science and Technology)

   User-centered design (UCD) or user experience design (UXD) methods have gained recent popularity for the design of mobile health (mHealth) interventions. However, there is a gap in application of these methods for diabetes self-management. This study aims to document the UCD process for a self-management mobile application aimed for patients with diabetes in underserved communities.

   A UCD mixed-methods approach including interviews with patients and providers, a review of literature, and a technology landscape analysis were used to define the app functional information requirements that informed the user experience/user interface design process. Usability studies with the app designers and developers, intended users, and a focus group of nurse educators and dieticians were used to test and improve the design.

   An mHealth app was developed with health-tracking features for stress, blood sugar, food, exercise, medications, weight, and blood pressure. We tackled a range of usability and user experience challenges, which encompassed addressing issues like low health literacy by employing a combination of user interface design principles, intuitive visualizations, customizable icons, seamless database integration, and automated data input features. Special attention was given to the design of educational content accounting for the intended users’ cultural background and literacy levels.

   User-centered design approach contributed to a better understanding of the intended users’ needs, limitations, mental models, and expectations, facilitating the design of a comprehensive mobile app for patients with diabetes in underserved communities that includes essential features for self-management while providing a strong educational component, addressing an important gap in the literature.

    

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2. Permission-Educator: App for Educating Users About Android Permissions

   https://doi.org/10.1007/978-3-030-98404-5_34  

   Mathur, Akshay ; Ewoldt, Ethan ; Niyaz, Quamar ; Javaid, Ahmad ; Yang, Xiaoli ( March 2022 , Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham)

   Kim, JH. ; Singh, M. ; Khan, J. ; Tiwary, U.S. ; Sur, M. ; Singh, D. (Ed.)

   Cyberattacks and malware infestation are issues that surround most operating systems (OS) these days. In smartphones, Android OS is more susceptible to malware infection. Although Android has introduced several mechanisms to avoid cyberattacks, including Google Play Protect, dynamic permissions, and sign-in control notifications, cyberattacks on Android-based phones are prevalent and continuously increasing. Most malware apps use critical permissions to access resources and data to compromise smartphone security. One of the key reasons behind this is the lack of knowledge for the usage of permissions in users. In this paper, we introduce Permission-Educator, a cloud-based service to educate users about the permissions associated with the installed apps in an Android-based smartphone. We developed an Android app as a client that allows users to categorize the installed apps on their smartphones as system or store apps. The user can learn about permissions for a specific app and identify the app as benign or malware through the interaction of the client app with the cloud service. We integrated the service with a web server that facilitates users to upload any Android application package file, i.e. apk, to extract information regarding the Android app and display it to the user. 

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3. Applying the uMars Scale to Evaluate a Mindfulness-based mHealth App

   https://doi.org/10.1109/SeGAH61285.2024.10639604  

   Panguluri, Lakshmi ; Vigi-Hayes, Morgan ; Amresh, Ashish ( August 2024 , IEEE)

   We present the design of a mHealth application aimed at improving mental health outcomes among young adults representing a Native American population. This study evaluates the application’s effectiveness and user-friendliness, allowing for a comprehensive understanding of its performance utilizing the uMARS (Mobile Application Rating Scale) protocol specifically designed to assess the quality of mobile health applications. Our findings indicate that the design meets both customers’ (young adults) and experts’ (mobile development practitioners) perceptions of the app. Our limitation is the lack of data collection from the population representing the Native American tribe. 

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4. Preventing Cardiovascular Disease Among Urban African Americans With a Mobile Health App (the MOYO App): Protocol for a Usability Study

   https://doi.org/10.2196/16699  

   Taylor Jr, Herman A ; Francis, Sherilyn ; Evans, Chad Ray ; Harvey, Marques ; Newton, Brittney A ; Jones, Camara P ; Akintobi, Tabia Henry ; Clifford, Gari ( January 2020 , JMIR Research Protocols)

   null (Ed.)

   Background Cardiovascular disease (CVD) disparities are a particularly devastating manifestation of health inequity. Despite advancements in prevention and treatment, CVD is still the leading cause of death in the United States. Additionally, research indicates that African American (AA) and other ethnic-minority populations are affected by CVD at earlier ages than white Americans. Given that AAs are the fastest-growing population of smartphone owners and users, mobile health (mHealth) technologies offer the unparalleled potential to prevent or improve self-management of chronic disease among this population. Objective To address the unmet need for culturally tailored primordial prevention CVD–focused mHealth interventions, the MOYO app was cocreated with the involvement of young people from this priority community. The overall project aims to develop and evaluate the effectiveness of a novel smartphone app designed to reduce CVD risk factors among urban-AAs, 18-29 years of age. Methods The theoretical underpinning will combine the principles of community-based participatory research and the agile software development framework. The primary outcome goals of the study will be to determine the usability, acceptability, and functionality of the MOYO app, and to build a cloud-based data collection infrastructure suitable for digital epidemiology in a disparity population. Changes in health-related parameters over a 24-week period as determined by both passive (eg, physical activity levels, sleep duration, social networking) and active (eg, use of mood measures, surveys, uploading pictures of meals and blood pressure readings) measures will be the secondary outcome. Participants will be recruited from a majority AA “large city” school district, 2 historically black colleges or universities, and 1 urban undergraduate college. Following baseline screening for inclusion (administered in person), participants will receive the beta version of the MOYO app. Participants will be monitored during a 24-week pilot period. Analyses of varying data including social network dynamics, standard metrics of activity, percentage of time away from a given radius of home, circadian rhythm metrics, and proxies for sleep will be performed. Together with external variables (eg, weather, pollution, and socioeconomic indicators such as food access), these metrics will be used to train machine-learning frameworks to regress them on the self-reported quality of life indicators. Results This 5-year study (2015-2020) is currently in the implementation phase. We believe that MOYO can build upon findings of classical epidemiology and longitudinal studies like the Jackson Heart Study by adding greater granularity to our knowledge of the exposures and behaviors that affect health and disease, and creating a channel for outreach capable of launching interventions, clinical trials, and enhancements of health literacy. Conclusions The results of this pilot will provide valuable information about community cocreation of mHealth programs, efficacious design features, and essential infrastructure for digital epidemiology among young AA adults. International Registered Report Identifier (IRRID) DERR1-10.2196/16699 

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5. Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design

   https://doi.org/10.1109/BSN56160.2022.9928521  

   Mamun, Abdullah ; Leonard, Krista S. ; Buman, Matthew P. ; Ghasemzadeh, Hassan ( January 2022 , 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN))

   Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person's physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future's activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2$ to 11.1% in mean-absolute-error. 

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