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Mobile Computing for Smart Health
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Mobile Computing for Smart Health

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (25 December 2023) | Viewed by 4546

Special Issue Editor

Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: computational intelligence; Internet of Things; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile devices, especially smartphones, are playing an essential role in people’s lives. Such devices embedded many sensors and could be used to collect information regarding the activities of daily living. Mobile devices have been proven to offer a great means to collect important information, which can help in understanding the physical and mental health status of patients and enacting intervention for patients. Mobile devices constitute an indispensable vehicle to conduct telemedicine, which is especially relevant during the prolonged pandemic. This line of research is often referred to as mHealth. The research and development in the field of mHealth is highly active with over 1000 papers published annually over the last 5 years according to Web of Science using mHealth as the only keyword. In addition to the many opportunities of applying mHealth in the understanding, diagnosis, and intervention for various physical and mental diseases, there are some fundamental gaps that deserve to be investigated. For example, it is well known that the abandon rate is high among patients who use mobile apps for intervention. Designing mobile apps that are engaging could advance the state of the art in human–computer interaction, as well as improve the efficacies of mhealth. mHealth could also incorporate other disrupt technologies, such as blockchain, to increase transparency, accountability, and integrity for clinical trials using mobile apps.

Mobile devices embedded many traditional sensors (such as an accelerometer, a gyroscope, and light sensors) as well as non-traditional sensors (such as Bluetooth LE, cameras, and microphones) to collect various information of the users of the devices. Tracking of app usage patterns could also be regarded as a form of sensing. As such, this Special Issue is highly relevant to the mission of the journal.  

Prof. Dr. Wenbing Zhao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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15 pages, 1231 KiB  
Article
App-Based Mindfulness Training Predicts Reductions in Smoking Behavior by Engaging Reinforcement Learning Mechanisms: A Preliminary Naturalistic Single-Arm Study
by Veronique A. Taylor, Ryan Smith and Judson A. Brewer
Sensors 2022, 22(14), 5131; https://doi.org/10.3390/s22145131 - 08 Jul 2022
Viewed by 1703
Abstract
Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for [...] Read more.
Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for smoking cessation, and whether this relationship would be mediated by the attenuating impact of MT on expected reward values of smoking. Our craving tool embedded in our MT app-based smoking cessation program was used by 108 participants upon the experience of cigarette cravings in real-world contexts. Each use of the tool involved mindful awareness to the experience of cigarette craving, a decision as to whether the participant wanted to smoke or ride out their craving with a mindfulness exercise, and paying mindful attention to the choice behavior and its outcome (contentment levels felt from engaging in the behavior). Expected reward values were computed using contentment levels experienced from the choice behavior as the reward signal in a Rescorla–Wagner reinforcement learning model. Multi-level mediation analysis revealed a significant decreasing trajectory of smoking frequency across MT craving tool uses and that this relationship was mediated by the negative relationship between MT and expected reward values (all ps < 0.001). After controlling for the mediator, the predictive relationship between MT and smoking was no longer significant (p < 0.001 before and p = 0.357 after controlling for the mediator). Results indicate that the use of our app-based MT craving tool is associated with negative slope trajectories of smoking behavior across uses, mediated by reward learning mechanisms. This single-arm naturalistic study provides preliminary support for further RCT studies examining the involvement of reward learning mechanisms underlying app-based mindfulness training for smoking cessation. Full article
(This article belongs to the Special Issue Mobile Computing for Smart Health)
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Review

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29 pages, 532 KiB  
Review
A Survey on Autism Care, Diagnosis, and Intervention Based on Mobile Apps: Focusing on Usability and Software Design
by Xiongyi Liu, Wenbing Zhao, Quan Qi and Xiong Luo
Sensors 2023, 23(14), 6260; https://doi.org/10.3390/s23146260 - 09 Jul 2023
Cited by 1 | Viewed by 2260
Abstract
This article presents a systematic review on autism care, diagnosis, and intervention based on mobile apps running on smartphones and tablets. Here, the term “intervention” means a carefully planned set of activities with the objective of improving autism symptoms. We guide our review [...] Read more.
This article presents a systematic review on autism care, diagnosis, and intervention based on mobile apps running on smartphones and tablets. Here, the term “intervention” means a carefully planned set of activities with the objective of improving autism symptoms. We guide our review on related studies using five research questions. First, who benefits the most from these mobile apps? Second, what are the primary purposes of these mobile apps? Third, what mechanisms have been incorporated in these mobiles apps to improve usability? Fourth, what guidelines have been used in the design and implementation of these mobile apps? Fifth, what theories and frameworks have been used as the foundation for these mobile apps to ensure the intervention effectiveness? As can be seen from these research questions, we focus on the usability and software development of the mobile apps. Informed by the findings of these research questions, we propose a taxonomy for the mobile apps and their users. The mobile apps can be categorized into autism support apps, educational apps, teacher training apps, parental support apps, and data collection apps. The individuals with autism spectrum disorder (ASD) are the primary users of the first two categories of apps. Teachers of children with ASD are the primary users of the teacher training apps. Parents are the primary users of the parental support apps, while individuals with ASD are usually the primary users of the data collection apps and clinicians and autism researchers are the beneficiaries. Gamification, virtual reality, and autism-specific mechanisms have been used to improve the usability of the apps. User-centered design is the most popular approach for mobile app development. Augmentative and alternative communication, video modeling, and various behavior change practices have been used as the theoretical foundation for intervention efficacy. Full article
(This article belongs to the Special Issue Mobile Computing for Smart Health)
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