Multisensory Home-Monitoring in Individuals With Stable Chronic Obstructive Pulmonary Disease and Asthma: Usability Study of the CAir-Desk

Background: Research integrating multisensory home-monitoring in respiratory disease is scarce. Therefore, we created a novel multisensory home-monitoring device tailored for long-term respiratory disease management (named the CAir-Desk). We hypothesize that recent technological accomplishments can be integrated into a multisensory participant-driven platform. We also believe that this platform could improve chronic disease management and be accessible to large groups at an acceptable cost. Objective: This study aimed to report on user adherence and acceptance as well as system functionality of the CAir-Desk in a sample of participants with stable chronic obstructive pulmonary disease (COPD) or asthma. Methods: We conducted an observational usability study. Participants took part in 4 weeks of home-monitoring with the CAir-Desk. The CAir-Desk recorded data from all participants on symptom burden, physical activity, spirometry, and environmental air quality; data on sputum production, and nocturnal cough were only recorded for participants who experienced symptoms. After the study period, participants reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. We used descriptive statistics and visualizations to display results. Results: Ten participants, 5 with COPD and 5 with asthma took part in this study. They completed symptom burden questionnaires on a median of 96% (25th percentile 14%, 75th percentile 96%), spirometry recordings on 55% (20%, 94%), wrist-worn physical activity recordings on 100% (97%, 100%), arm-worn physical activity recordings on 45% (13%, 63%), nocturnal cough recordings on 34% (9%, 54%), sputum recordings on 5% (3%, 12%), and environmental air quality recordings on 100% (99%, 100%) of the study days. The participants indicated that the measurements consumed a median of 13 (10, 15) min daily, and that they preferred the wrist-worn physical activity monitor to the arm-worn physical activity monitor. Conclusions: The CAir-Desk showed favorable technical performance and was well-accepted by our sample of participants with stable COPD and asthma. The obtained insights were used in a redesign of the CAir-Desk, which is currently applied in a randomized controlled trial including an interventional program

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

Data collected from the real world tends to be biased, unbalanced, and at risk of exposing sensitive and private information. This reality has given rise to the idea of creating synthetic datasets to alleviate risk, bias, harm, and privacy concerns inherent in the real data. This concept relies on Generative AI models to produce unbiased, privacy-preserving synthetic data while being true to the real data. In this new paradigm, how can we tell if this approach delivers on its promises? We present an auditing framework that offers a holistic assessment of synthetic datasets and AI models trained on them, centered around bias and discrimination prevention, fidelity to the real data, utility, robustness, and privacy preservation. We showcase our framework by auditing multiple generative models on diverse use cases, including education, healthcare, banking, human resources, and across different modalities, from tabular, to time-series, to natural language. Our use cases demonstrate the importance of a holistic assessment in order to ensure compliance with socio-technical safeguards that regulators and policymakers are increasingly enforcing. For this purpose, we introduce the trust index that ranks multiple synthetic datasets based on their prescribed safeguards and their desired trade-offs. Moreover, we devise a trust-index-driven model selection and cross-validation procedure via auditing in the training loop that we showcase on a class of transformer models that we dub TrustFormers, across different modalities. This trust-driven model selection allows for controllable trust trade-offs in the resulting synthetic data. We instrument our auditing framework with workflows that connect different stakeholders from model development to audit and certification via a synthetic data auditing report.Comment: 49 pages; submitte

Fooling Explanations in Text Classifiers

State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in real-life scenarios. However, it has been shown in vision applications that explanation methods are susceptible to local, imperceptible perturbations that can significantly alter the explanations without changing the predicted classes. We show here that the existence of such perturbations extends to text classifiers as well. Specifically, we introduceTextExplanationFooler (TEF), a novel explanation attack algorithm that alters text input samples imperceptibly so that the outcome of widely-used explanation methods changes considerably while leaving classifier predictions unchanged. We evaluate the performance of the attribution robustness estimation performance in TEF on five sequence classification datasets, utilizing three DNN architectures and three transformer architectures for each dataset. TEF can significantly decrease the correlation between unchanged and perturbed input attributions, which shows that all models and explanation methods are susceptible to TEF perturbations. Moreover, we evaluate how the perturbations transfer to other model architectures and attribution methods, and show that TEF perturbations are also effective in scenarios where the target model and explanation method are unknown. Finally, we introduce a semi-universal attack that is able to compute fast, computationally light perturbations with no knowledge of the attacked classifier nor explanation method. Overall, our work shows that explanations in text classifiers are very fragile and users need to carefully address their robustness before relying on them in critical applications

A Telemonitoring and Hybrid Virtual Coaching Solution “CAir” for Patients with Chronic Obstructive Pulmonary Disease: Protocol for a Randomized Controlled Trial

Background: Chronic obstructive pulmonary disease (COPD) is one of the most common disorders in the world. COPD is characterized by airflow obstruction, which is not fully reversible. Patients usually experience breathing-related symptoms with periods of acute worsening and a substantial decrease in the health-related quality-of-life. Active and comprehensive disease management can slow down the progressive course of the disease and improve patients’ disabilities. Technological progress and digitalization of medicine have the potential to make elaborate interventions easily accessible and applicable to a broad spectrum of patients with COPD without increasing the costs of the intervention. Objective: This study aims to develop a comprehensive telemonitoring and hybrid virtual coaching solution and to investigate its effects on the health-related quality of life of patients with COPD. Methods: A monocentric, assessor-blind, two-arm (intervention/control) randomized controlled trial will be performed. Participants randomized to the control group will receive usual care and a CAir Desk (custom-built home disease-monitoring device to telemonitor disease-relevant parameters) for 12 weeks, without feedback or scores of the telemonitoring efforts and virtual coaching. Participants randomized to the intervention group will receive a CAir Desk and a hybrid digital coaching intervention for 12 weeks. As a primary outcome, we will measure the delta in the health-related quality of life, which we will assess with the St. George Respiratory Questionnaire, from baseline to week 12 (the end of the intervention). Results: The development of the CAir Desk and virtual coach has been completed. Recruitment to the trial started in September 2020. We expect to start data collection by December 2020 and expect it to last for approximately 18 months, as we follow a multiwave approach. We expect to complete data collection by mid-2022 and plan the dissemination of the results subsequently. Conclusions: To our knowledge, this is the first study investigating a combination of telemonitoring and hybrid virtual coaching in patients with COPD. We will investigate the effectiveness, efficacy, and usability of the proposed intervention and provide evidence to further develop app-based and chatbot-based disease monitoring and interventions in COPD

Multisensory Home-Monitoring in Individuals With Stable Chronic Obstructive Pulmonary Disease and Asthma: Usability Study of the CAir-Desk

Background: Research integrating multisensory home-monitoring in respiratory disease is scarce. Therefore, we created a novel multisensory home-monitoring device tailored for long-term respiratory disease management (named the CAir-Desk). We hypothesize that recent technological accomplishments can be integrated into a multisensory participant-driven platform. We also believe that this platform could improve chronic disease management and be accessible to large groups at an acceptable cost. Objective: This study aimed to report on user adherence and acceptance as well as system functionality of the CAir-Desk in a sample of participants with stable chronic obstructive pulmonary disease (COPD) or asthma. Methods: We conducted an observational usability study. Participants took part in 4 weeks of home-monitoring with the CAir-Desk. The CAir-Desk recorded data from all participants on symptom burden, physical activity, spirometry, and environmental air quality; data on sputum production, and nocturnal cough were only recorded for participants who experienced symptoms. After the study period, participants reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. We used descriptive statistics and visualizations to display results. Results: Ten participants, 5 with COPD and 5 with asthma took part in this study. They completed symptom burden questionnaires on a median of 96% (25th percentile 14%, 75th percentile 96%), spirometry recordings on 55% (20%, 94%), wrist-worn physical activity recordings on 100% (97%, 100%), arm-worn physical activity recordings on 45% (13%, 63%), nocturnal cough recordings on 34% (9%, 54%), sputum recordings on 5% (3%, 12%), and environmental air quality recordings on 100% (99%, 100%) of the study days. The participants indicated that the measurements consumed a median of 13 (10, 15) min daily, and that they preferred the wrist-worn physical activity monitor to the arm-worn physical activity monitor. Conclusions: The CAir-Desk showed favorable technical performance and was well-accepted by our sample of participants with stable COPD and asthma. The obtained insights were used in a redesign of the CAir-Desk, which is currently applied in a randomized controlled trial including an interventional program.ISSN:2292-949

A Telemonitoring and Hybrid Virtual Coaching Solution "CAir" for Patients with Chronic Obstructive Pulmonary Disease: Protocol for a Randomized Controlled Trial

Background: Chronic obstructive pulmonary disease (COPD) is one of the most common disorders in the world. COPD is characterized by airflow obstruction, which is not fully reversible. Patients usually experience breathing-related symptoms with periods of acute worsening and a substantial decrease in the health-related quality-of-life. Active and comprehensive disease management can slow down the progressive course of the disease and improve patients' disabilities. Technological progress and digitalization of medicine have the potential to make elaborate interventions easily accessible and applicable to a broad spectrum of patients with COPD without increasing the costs of the intervention. Objective: This study aims to develop a comprehensive telemonitoring and hybrid virtual coaching solution and to investigate its effects on the health-related quality of life of patients with COPD. Methods: A monocentric, assessor-blind, two-arm (intervention/control) randomized controlled trial will be performed. Participants randomized to the control group will receive usual care and a CAir Desk (custom-built home disease-monitoring device to telemonitor disease-relevant parameters) for 12 weeks, without feedback or scores of the telemonitoring efforts and virtual coaching. Participants randomized to the intervention group will receive a CAir Desk and a hybrid digital coaching intervention for 12 weeks. As a primary outcome, we will measure the delta in the health-related quality of life, which we will assess with the St. George Respiratory Questionnaire, from baseline to week 12 (the end of the intervention). Results: The development of the CAir Desk and virtual coach has been completed. Recruitment to the trial started in September 2020. We expect to start data collection by December 2020 and expect it to last for approximately 18 months, as we follow a multiwave approach. We expect to complete data collection by mid-2022 and plan the dissemination of the results subsequently. Conclusions: To our knowledge, this is the first study investigating a combination of telemonitoring and hybrid virtual coaching in patients with COPD. We will investigate the effectiveness, efficacy, and usability of the proposed intervention and provide evidence to further develop app-based and chatbot-based disease monitoring and interventions in COPD

Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity

Chronic obstructive pulmonary disease (COPD) is a significant public health issue, affecting more than 100 million people worldwide. Remote patient monitoring has shown great promise in the efficient management of patients with chronic diseases. This work presents the analysis of the data from a monitoring system developed to track COPD symptoms alongside patients’ self-reports. In particular, we investigate the assessment of COPD severity using multisensory home-monitoring device data acquired from 30 patients over a period of three months. We describe a comprehensive data pre-processing and feature engineering pipeline for multimodal data from the remote home-monitoring of COPD patients. We develop and validate predictive models forecasting i) the absolute and ii) differenced COPD Assessment Test (CAT) scores based on the multisensory data. The best obtained models achieve Pearson’s correlation coefficient of 0.93 and 0.37 for absolute and differenced CAT scores. In addition, we investigate the importance of individual sensor modalities for predicting CAT scores using group sparse regularization techniques. Our results suggest that feature groups indicative of the patient’s general condition, such as static medical and physiological information, date, spirometer, and air quality, are crucial for predicting the absolute CAT score. For predicting changes in CAT scores, sleep and physical activity features are most important, alongside the previous CAT score value. Our analysis demonstrates the potential of remote patient monitoring for COPD management and investigates which sensor modalities are most indicative of COPD severity as assessed by the CAT score. Our findings contribute to the development of effective and data-driven COPD management strategies