1,720,974 research outputs found
Algorithms and Digital Health Solutions for Wearable Sensor Data Acquisition, Processing and Interpretation in Chronic Diseases
No full textWearable devices have revolutionized healthcare by enabling real-time, continuous monitoring of patients in clinical settings and everyday environments. These devices provide valuable data for monitoring chronic conditions, predicting disease onset, and personalizing healthcare interventions. Despite their growing availability, challenges remain in data collection, processing, and exploitation. One main issue in data collection is supporting digital health solutions that seamlessly gather and manage data without increasing the burden on the wearer with manual tasks.
The collected data faces challenges in processing and preparation. Ensuring data quality and reliability is crucial, as manual entry errors or improper device use can lead to inaccuracies that affect clinical assessments. Analyzing these data streams requires complex algorithms to filter out noise and extract meaningful insights. While several solutions have been proposed, most focus on non-consumer devices, highlighting the need for robust algorithms tailored to consumer wearables like smartwatches.
The challenges of wearable device data collection and processing significantly impact their effectiveness in healthcare. Despite their efficacy, issues such as data interpretability and actionability hinder the clinical implementation of wearable devices. Additionally, while the collected data is often used for calculating statistics and trends, its true potential lies in personalized care through therapy tuning and optimization.
This thesis addresses the challenges related to data acquisition, processing, and utilization by developing algorithms and digital health solutions specifically designed for chronic conditions such as Type 1 Diabetes (T1D), post-bariatric surgery hypoglycemia (PBH), amyotrophic lateral sclerosis, and multiple sclerosis. The thesis is divided into three parts, each aimed at describing the solutions developed to address these challenges.
Part 1 describes the digital health solutions developed for two clinical studies. Both solutions adapt IMPACT, our core mobile platform for conducting clinical trials, detailed in Chapter 1. Chapters 2 and 3 present the solutions for the first study, which aimed to develop a non-invasive continuous glucose monitoring sensor (CGM), and the second, focusing on individuals with post-bariatric surgery hypoglycemia.
Part 2 focuses on developing two solutions for wearable device signal processing. Chapter 5 describes the European Horizon 2020 BRAINTEASER project, during which we developed processing pipelines to prepare signals from a smartwatch for subsequent use. Chapter 6 details the developed pipeline, emphasizing clinical interactions at various development steps. Chapter 7 presents a Bayesian-based algorithm designed to improve the signal-to-noise ratio of the heart rate signal collected from the smartwatch using an adaptive methodology.
Part 3 describes three solutions that use wearable device data in a clinical setting. Chapter 10 introduces a novel real-time algorithm to identify mealtimes, augmenting information from glucose CGM sensors with heart rate data for prompt detection. Chapter 11 discusses integrating a real-time glucose prediction algorithm into the mobile application from Chapter 3, allowing patients to receive alerts of imminent hypoglycemic events. Finally, Chapter 12 shifts focus to clinicians, detailing the development of ad-hoc visualizations and statistics to adapt the standardized reporting tool for T1D, the Ambulatory Glucose Profile, for the PBH population.
Lastly, Part 4 is dedicated to conclusions and future perspectives. In Chapter 14, we summarize the issues related to implementing wearable devices in clinical settings, highlight the impact of this thesis, and discuss the main outcomes. We also outline possible future work directions, indicating pathways for research to improve the adoption of wearable sensors in healthcare
Automated pipeline for denoising, missing data processing, and feature extraction for signals acquired via wearable devices in multiple sclerosis and amyotrophic lateral sclerosis applications
Full text linkIntroduction The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices.Methods The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis.Results The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.Discussion Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios
Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices
Full text linkThe use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The algorithm is able to adapt to changes in the signal-to-noise ratio (SNR) over time, improving performance through windowed analysis and Bayesian criterion-based smoothing. By evaluating the algorithm on heart-rate (HR) data collected from Garmin Vivoactive 4 smartwatches worn by individuals with amyotrophic lateral sclerosis and multiple sclerosis, it is demonstrated that BF provides superior SNR tracking and smoothing compared with non-adaptive methods. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings
Design and Usability Assessment of a User-Centered, Modular Platform for Real-World Data Acquisition in Clinical Trials involving Post-bariatric Surgery Patients
No full textBackground: Clinical trials often face challenges in efficient data collection and participant monitoring. To address these issues, we developed the IMPACT platform, comprising a real-time mobile application for data collection and a web-based dashboard for remote monitoring and management. Methods: This article presents the design, development, and usability assessment of the IMPACT platform customized for patients with post-bariatric surgery hypoglycemia (PBH). We focus on adapting key IMPACT components, including continuous glucose monitoring (CGM), symptom tracking, and meal logging, as crucial elements for user-friendly and efficient PBH monitoring. Results: The adapted IMPACT platform demonstrated effectiveness in data collection and remote participant monitoring. The mobile application allowed patients to easily track their data, while the clinician dashboard provided a comprehensive overview of enrolled patients, featuring filtering options and alert mechanisms for identifying data collection issues. The platform incorporated various visual representations, including time plots and category-based visualizations, which greatly facilitated data interpretation and analysis. The System Usability Scale questionnaire results indicated a high level of usability for the web dashboard, with an average score of 86.3 out of 100. The active involvement of clinicians throughout the development process ensured that the platform allowed for the collection and visualization of clinically meaningful data. Conclusions: By leveraging IMPACT's existing features and infrastructure, the adapted version streamlined data collection, analysis, and trial customization for PBH research. The platform's high usability underscores its alignment with the requirements for conducting research using continuous real-world data in PBH patients and other populations of interest
Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection
Full text link: In recent years, the integration of game-like elements into non-gaming contexts has shown promise in enhancing user engagement and motivation. This study assesses the impact of gamification elements on data collection efficacy in m-health applications. An ad-hoc mobile application was developed and used in a randomized two-arm pilot study. Participants interacted either with the gamified meal-logging application or with its non-gamified version for ten days. The results from this study emphasize the benefits of incorporating gamification techniques into health applications embedded in digital platforms. While both versions were well-received, reaching high System Usability Scale (SUS) scores (91 and 93.5) and generally positive feedback, the gamified app demonstrated a distinct advantage in promoting user engagement and consistent data logging. This highlights the importance of gamification in health research, suggesting its potential to ensure thorough and consistent data collection, which is essential for producing reliable research outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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