List of Titles

An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

- Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, Adrian, Greenwood, Christopher

Authors: Linardon, Jake , Fuller-Tyszkiewicz, Matthew , Shatte, Adrian , Greenwood, Christopher
Date: 2022
Type: Text , Journal article
Relation: International Journal of Eating Disorders Vol. 55, no. 6 (2022), p. 845-850
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Description: Objective: Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms. Method: Data were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM). Results: The seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48–0.52), but adequate for symptom-level change (R2 =.15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop-out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.

Usability evaluation of a cognitive-behavioral app-based intervention for binge eating and related psychopathology : a qualitative study

- Linardon, Jake, King, Teagan, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

Authors: Linardon, Jake , King, Teagan , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
Date: 2022
Type: Text , Journal article
Relation: Behavior Modification Vol. 46, no. 5 (2022), p. 1002-1020
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Description: Despite their promise as a scalable intervention modality for binge eating and related problems, reviews show that engagement of app-based interventions is variable. Issues with usability may account for this. App developers should undertake usability testing so that any problems can be identified and fixed prior to dissemination. We conducted a qualitative usability evaluation of a newly-developed app for binge eating in 14 individuals with a diagnostic- or subthreshold-level binge eating symptoms. Participants completed a semi-structured interview and self-report measures. Qualitative data were organized into six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Qualitative and quantitative results indicated that the app demonstrated good usability. Key advantages reported were its flexible content-delivery formats, level of interactivity, easy-to-understand information, and ability to track progress. Concerns with visual aesthetics and lack of professional feedback were raised. Findings will inform the optimal design of app-based interventions for eating disorder symptoms. © The Author(s) 2021.

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Internet of vehicles for e-health applications : a potential game for optimal network capacity

- Lin, Din, Tang, Yu, Labeau, Fabrice, Yao, Yuanzhe, Imran, Muhammad, Vasilakos, Athanasios

Authors: Lin, Din , Tang, Yu , Labeau, Fabrice , Yao, Yuanzhe , Imran, Muhammad , Vasilakos, Athanasios
Date: 2017
Type: Text , Journal article
Relation: IEEE Systems Journal Vol. 11, no. 3 (2017), p. 1888-1896
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Description: Wireless technologies are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical sensors. In view of EMI on medical sensors, we propose a power control algorithm under a noncooperative game theoretic framework to schedule data transmission. Our objective is to ensure that the noncooperative game of power control can achieve a network-level objective - the optimal network capacity, although the wireless users are selfish and only interested in optimizing their own channel capacity. To obtain this objective, we show that our proposed noncooperative game is a potential game and propose the best-response-dynamics algorithm which can ensure that the game strategy of each user is induced to the optimal solution to the problem of network-level optimal capacity. Numerical results illustrate that the proposed algorithm can achieve an enhancement of 8% of network performance than the existing algorithm against the variations of mobile hospital environments. © 2007-2012 IEEE.

Historical overview and the evolution of digital health

- Ganesan, Balasankar, Tong, Raymond

Authors: Ganesan, Balasankar , Tong, Raymond
Date: 2024
Type: Text , Book chapter
Relation: Digital Technology in Public Health and Rehabilitation Care: COVID Era Chapter 1 p. 3-18
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Description: Digital health has revolutionized healthcare delivery services to a great extent. In recent decades, the healthcare industry has witnessed a remarkable transformation with the advent of digital technologies, such as mobile health apps, augmented reality, virtual reality, wearable monitoring devices, Internet of medical things, and artificial intelligence technologies, which has improved the quality of life, and health and well-being of people across the world. Overall, the integration of digital technologies into the healthcare industry has not only improved the quality of care but also has the potential to make healthcare more accessible, affordable, and patient-centered. Major digital health devices and technologies invented after the invention of telegraph, photophone, and radio. In this chapter, we discuss the history and evolution of digital health technologies and their applications in the healthcare delivery system. © 2025 Elsevier Inc. All rights reserved.

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