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Applied Sciences
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Digital Therapeutics Applications for Chronic Disease Management
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Digital Therapeutics Applications for Chronic Disease Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 6813

Special Issue Editors

Dr. Giacomo Cappon

E-Mail Website
Guest Editor
Department of Information Engineering (DEI), University of Padova, via Gradenigo 6B, 35131 Padova, Italy
Interests: machine learning; Bayesian methods; signal processing; decision support systems; wearable sensors; digital health and therapeutics; telemedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Martina Vettoretti

E-Mail Website
Guest Editor
Department of Information Engineering (DEI), University of Padova, via Gradenigo 6B, 35131 Padova, Italy
Interests: artificial intelligence; machine learning; signal processing; modeling and simulation techniques; wearable sensors; predictive models of health risks in various applications regarding the prevention and treatment of chronic diseases (e.g., diabetes, respiratory diseases and neurodegenerative diseases)
Special Issues, Collections and Topics in MDPI journals
Dr. Manuel Ottaviano

E-Mail Website
Guest Editor
Life Supporting Technologies, Universidad Politecnica de Madrid, avenida Complutense 30, 28040 Madrid, Spain
Interests: human factors in technology; digital health interventions; mobile health; distributed architectures for e-health; clinical decision support systems; persuasive technologies

Special Issue Information

Dear Colleagues,

The digital revolution that healthcare has been experiencing in the last several decades has been greatly accelerated by the COVID-19 pandemic, which has fostered the development of new decentralized technologies, implementing innovative infrastructures able to provide remote healthcare to the population in a cost-efficient manner.

In this context, digital therapeutics (DT) technologies played a key role. DT consists of software applications, products, and services which implement evidence-based treatments through digital means to increase the fruition and effectiveness of modern healthcare. Moreover, DT unlocks the ability to define standardized data models to harmonize health data at the global level; based on those data, researchers all over the world can develop and discover new methodologies and strategies to further improve healthcare.

Specifically, new DT approaches targeting chronic diseases such as diabetes, multiple sclerosis, and asthma, among others, could be revolutionary, integrating conventional therapies with new cutting-edge tools able to not only increase the impact of treatment on patients’ health but also to improve their quality of life. Indeed, DT facilitates the implementation of patient-centric approaches where therapies are personalized at the individual level and automatic algorithms are deployed in appealing and familiar devices such as smartphones and smartwatches, further increasing patient adherence.

We are pleased to announce the Special Issue entitled “Digital Therapeutics Applications for Chronic Disease Management: Latest Advances and Prospects”, the aim of which is to collect original research papers and reviews about the use of DT approaches to facilitate the management of chronic diseases. Topics of interest include, but are not limited to, the use of DT for:

  • Decision support systems;
  • Remote patient monitoring and telemedicine systems;
  • Therapy optimization and personalization;
  • Precision medicine;
  • Digital intervention to foster self- management;
  • Persuasive technology and behavior change support systems to foster healthy habits and adherence to therapy;
  • Prevention of disease progression or complications;
  • Big data analytics;
  • Artificial intelligence and machine learning model deployment;
  • Mobile apps in healthcare;
  • Advanced wearable sensors development;
  • AI-assisted systems for telerehabilitation and the remote assessment of patients.

Dr. Giacomo Cappon
Dr. Martina Vettoretti
Dr. Manuel Ottaviano
Guest Editors

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. Applied Sciences 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 2400 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.

Keywords

  • digital therapeutics
  • digital health
  • personalized therapy
  • proactive medicine
  • chronic diseases
  • wearable sensors
  • advanced healthcare methods
  • telemedicine
  • telemonitoring
  • healthcare platform

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Published Papers (2 papers)

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Research

13 pages, 902 KiB  
Article
Glycemic Management by a Digital Therapeutic Platform across Racial/Ethnic Groups: A Retrospective Cohort Study
by Tamar Gershoni, Marilyn D. Ritholz, David L. Horwitz, Omar Manejwala, Trisha Donaldson-Pitter and Yifat Fundoiano-Hershcovitz
Appl. Sci. 2023, 13(1), 431; https://doi.org/10.3390/app13010431 - 29 Dec 2022
Cited by 2 | Viewed by 3138
Abstract
Significant racial/ethnic disparities in healthcare and diabetes technology use have been observed in Type 2 diabetes mellitus (T2DM), which are associated with nonengagement in diabetes self-management and out-of-range glycemia. This study aimed to assess whether there were differences in the blood glucose levels [...] Read more.
Significant racial/ethnic disparities in healthcare and diabetes technology use have been observed in Type 2 diabetes mellitus (T2DM), which are associated with nonengagement in diabetes self-management and out-of-range glycemia. This study aimed to assess whether there were differences in the blood glucose levels achieved by several racial/ethnic groups using the same digital tool. Study objectives were to determine whether engagement with the digital tool and blood glucose levels differ among ethnic groups, and to determine whether any differences in the in-target-glycemia are related to engagement levels. The retrospective real-world analysis followed a group of 1000 people with Type 2 diabetes who used the DarioTM digital therapeutic platform over 12 months. Participants included in the study had a blood glucose average > 180 mg/dL (hyperglycemia, high-risk) in their first month. The differences between/within the groups’ average blood glucose level (Avg.bg) and glycemic variability were evaluated. Furthermore, three general linear models were constructed to predict the Avg.bg by the number of blood glucose measurements (Bgm) in Model 1 (with the moderator White persons (WP)/people from racial and ethnic minority groups (REM)) and by the frequency of measurements by months (F.m) within REM and WP in Model 2 and Model 3, respectively. The Avg.bg was significantly reduced in each group over a year with no differences between REM/WP users. Blood glucose measurements in Model 1 and frequency of measurements by months in Model 2 and Model 3 predicted the Avg.bg (β1 = −0.20, p = 0.045; β2 = −4.38, p = 0.009; β3= −3.77, p < 0.001, respectively). Findings indicate a positive association between digital engagement and glycemia, with no differences between REM and WP participants. Full article
(This article belongs to the Special Issue Digital Therapeutics Applications for Chronic Disease Management)
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18 pages, 2941 KiB  
Article
Accounting for Patient Engagement in Randomized Controlled Trials Evaluating Digital Cognitive Behavioral Therapies
by Oleksandr Sverdlov and Yevgen Ryeznik
Appl. Sci. 2022, 12(10), 4952; https://doi.org/10.3390/app12104952 - 13 May 2022
Viewed by 1443
Abstract
Background: Cognitive behavioral therapy (CBT) can be a useful treatment option for various mental health disorders. Modern advances in information technology and mobile communication enable delivery of state-of-the-art CBT programs via smartphones, either as stand-alone or as an adjunct treatment augmenting traditional sessions [...] Read more.
Background: Cognitive behavioral therapy (CBT) can be a useful treatment option for various mental health disorders. Modern advances in information technology and mobile communication enable delivery of state-of-the-art CBT programs via smartphones, either as stand-alone or as an adjunct treatment augmenting traditional sessions with a therapist. Experimental CBTs require careful assessment in randomized clinical trials (RCTs). Methods: We investigate some statistical issues for an RCT comparing efficacy of an experimental CBT intervention for a mental health disorder against the control. Assuming a linear model for the clinical outcome and patient engagement as an influential covariate, we investigate two common statistical approaches to inference—analysis of covariance (ANCOVA) and a two-sample t-test. We also study sample size requirements for the described experimental setting. Results: Both ANCOVA and a two-sample t-test are appropriate for the inference on treatment difference at the average observed level of engagement. However, ANCOVA produces estimates with lower variance and may be more powerful. Furthermore, unlike the t-test, ANCOVA allows one to perform treatment comparison at the levels of engagement other than the average level observed in the study. Larger sample sizes may be required to ensure experiments are sufficiently powered if one is interested in comparing treatment effects for different levels of engagement. Conclusions: ANCOVA with proper adjustment for engagement should be used for the for the described experimental setting. Uncertainty on engagement patterns should be taken into account at the study design stage. Full article
(This article belongs to the Special Issue Digital Therapeutics Applications for Chronic Disease Management)
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