Emerging wearable technology applications in gastroenterology: A review of the literature

Chong, Kimberly P L; Woo, Benjamin K.P.

doi:10.3748/wjg.v27.i12.1149

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…The clinical monitoring of disease activity and drug compatibility could be further enhanced by wearable devices that track physical parameters and by noninvasive biomarker monitoring (eg, c-reactive protein or interleukin-1 for IBD from sweat [ 79 ]). In their comprehensive review, Chong and Woo [ 80 ] have demonstrated that approaches for the implementation of wearable sensor systems for gastrointestinal disease already exist and could change clinical practice in the near future [ 80 ].…”

Section: Discussionmentioning

confidence: 99%

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Messner¹,

Sturm²,

Terhorst³

et al. 2022

J Med Internet Res

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Background Gastrointestinal diseases are associated with substantial cost in health care. In times of the COVID-19 pandemic and further digitalization of gastrointestinal tract health care, mobile health apps could complement routine health care. Many gastrointestinal health care apps are already available in the app stores, but the quality, data protection, and reliability often remain unclear. Objective This systematic review aimed to evaluate the quality characteristics as well as the privacy and security measures of mobile health apps for the management of gastrointestinal diseases. Methods A web crawler systematically searched for mobile health apps with a focus on gastrointestinal diseases. The identified mobile health apps were evaluated using the Mobile Application Rating Scale (MARS). Furthermore, app characteristics, data protection, and security measures were collected. Classic user star rating was correlated with overall mobile health app quality. Results The overall quality of the mobile health apps (N=109) was moderate (mean 2.90, SD 0.52; on a scale ranging from 1 to 5). The quality of the subscales ranged from low (mean 1.89, SD 0.66) to good (mean 4.08, SD 0.57). The security of data transfer was ensured only by 11 (10.1%) mobile health apps. None of the mobile health apps had an evidence base. The user star rating did not correlate with the MARS overall score or with the individual subdimensions of the MARS (all P>.05). Conclusions Mobile health apps might have a positive impact on diagnosis, therapy, and patient guidance in gastroenterology in the future. We conclude that, to date, data security and proof of efficacy are not yet given in currently available mobile health apps.

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Section: Discussionmentioning

confidence: 99%

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Messner¹,

Sturm²,

Terhorst³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…According to [42], smartwatches are unique wearables because of their shape and design, which resembles those of the agelong wrist fashion items. The new Amazon Halo, Fitbit Sense, Xiaomi Mi and Apple Watch Series are examples of fashionable wrist-worn wearables that aim to provide users with real-time health feedback [43]. Other wearables for managing health and well-being include headbands, armbands, motion trackers, activity trackers, smart clothing, smart glasses, smart jewellery, smart patches, smart tattoos, smart implants and ingestible [17,19,24].…”

Section: Wearables For Health and Well-being Managementmentioning

confidence: 99%

Wearables and Their Potential to Transform Health Management: A Step towards Sustainable Development Goal 3

Izu,

Scholtz,

Fashoro

2024

Sustainability

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In the era of rapid technological advancement, wearables have emerged as a promising tool for enhancing health and well-being. The convergence of health and technology drives an unprecedented change in the approach to health and well-being management. This paper aims to provide a comprehensive understanding of the potential role of wearables in actualising health and well-being, thereby paving the way for a healthier and more sustainable future. Using the Affordance Theory lens, this paper delves into the transformative potential of wearables in health and well-being management, thereby promoting Sustainable Development Goal 3 to ensure healthy lives and well-being for all at all ages. The thematic analysis of online reviews on wearable devices captured through web scraping was carried out to explore the potential of these devices in the management of health and well-being. The paper explored how wearables, often integrated into everyday life, can monitor vital signs, track fitness metrics, and even provide therapeutic benefits for health and well-being. The findings reveal that wearables can empower individuals to take charge of their health by leveraging real-time data and personalised feedback, promoting a proactive and preventive approach to health management and resource-effective healthcare. Furthermore, the paper highlights how wearables can contribute to long-term health outcomes for the present generation without exerting excessive strain on the resources for future generations.

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“…Ingestible sensing technology refers to the use of miniature electronic devices that are swallowed or ingested in the form of pills or capsules to monitor various physiological parameters within the gastrointestinal tract [116,117]. These devices contain sensors that can detect and transmit information about the pH levels, temperature, pressure, and other relevant indicators, and can provide valuable insights into digestive processes, medication effectiveness, and disease progression [116]. Ingestible sensing technology relies on a variety of sensors to measure physiological parameters within the body.…”

Section: Ingestible Sensing Technologymentioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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Emerging wearable technology applications in gastroenterology: A review of the literature

Cited by 10 publications

References 60 publications

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Mobile Apps for the Management of Gastrointestinal Diseases: Systematic Search and Evaluation Within App Stores

Wearables and Their Potential to Transform Health Management: A Step towards Sustainable Development Goal 3

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

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