Browsing by Author "Vieira-Marques, Pedro"
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- AIRDOC: Smart mobile application for individualized support and monitoring of respiratory function and sounds of patients with chronic osbtructive diseasePublication . Almeida, Rute; Amaral, Rita; Jácome, Cristina; Martinho, Diogo; Vieira-Marques, Pedro; Jacinto, Tiago; Ferreira, Ana; Almeida, Ana; Martins, Constantino; Pereira, Mariana; Pereira, Ana; Valente, José; Almeida, Rafael; Vieira, Ana; Amaral, Rita; Sá-Sousa, Ana; Gonçalves, Ivânia; Rodrigues, Pedro; Alves-Correia, Magna; Freitas, Alberto; Marreiros, GoretiCurrent tools for self-management of chronic obstructive respiratory diseases (CORD) are difficult to use, not individualized and requiring laborious analysis by health professionals, discouraging their use in healthcare. There is an opportunity for cost-effective and easy-to-disseminate advanced technological solutions directed to patients and attractive to different stakeholders. The strategy of AIRDOC is to develop and integrate self-monitoring and self-managing tools, making use of the smartphone's presence in everyday life. AIRDOC intends to innovate on: i) technologies for remote monitoring of respiratory function and computerized lung auscultation; ii) coaching solutions, integrating psychoeducation, gamification and disease management support systems; and iii) management of personal health data, focusing on security, privacy and interoperability. It is expected that AIRDOC results will contribute for the innovation in CORD healthcare, with increased patient involvement and empowerment while providing quality prospective information for better clinical decisions, allowing more efficient and sustainable healthcare delivery.
- Combined image-based approach for monitoring the adherence to inhaled medicationsPublication . Vieira-Marques, Pedro; Teixeira, João Fonseca; Valente, José; Pinho, Bernardo; Guedes, Rui; Almeida, Rute; Jácome, Cristina; Pereira, Ana; Jacinto, Tiago; Amaral, Rita; Gonçalves, Ivânia; Sousa, Ana Sá; Couto, Mariana; Magalhães, Manuel; Bordalo, Diana; Silva, Luís Nogueira; Fonseca, J. AlmeidaThe adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.
- Engagement and usage patterns of a mobile application to monitor disease and treatment adherence in patients with asthmaPublication . Almeida, Rute; Jácome, Cristina; Vieira-Marques, Pedro; Pereira, Ana; Sá-Sousa, Ana; Amaral, Rita; Valente, José; Fonseca, João Almeida; Amaral, RitaInhaled therapies are the cornerstone of effective asthma treatment and adequate inhaled medication adherence (IMA) is critical. mHealth technologies have shown to be promise for asthma self-management, including IMA improvement. InspirerMundi is a mobile application designed to support self-management of patients with asthma. It aims to transform the adherence to treatment into a positive experience through gamification and social interaction while allowing for ubiquitous verified IMA monitoring. The app includes an image-based inhaler usage detection tool, tools for reporting symptoms and burden of the asthma (disease monitoring) and Game & Peer support features. Still, the effectiveness of these tools depends on a regular app use and a real-life assessment of patient engagement and pattern of use is needed. This work evaluates the patient engagement, usage, and acceptance of InspirerMundi app during a real-world multicentre feasibility study. The app use was recommended for a 4-months period to 77 participants with persistent asthma. From those, 72 installed the app, with 67% of them beginning to use both the inhaler usage detection tool and the disease monitoring components within the first week. Over 95% used it more than once, with the period of usage (from first use to last registered monitoring in 2018) being over 30 days for almost 70% of the users, and over 90 days for around 35%. Nevertheless, the usage rate (ratio of the number of days with app usage and the period of use) had a median value of 0.6 and was above 75% for only 35% of the users, revealing room for improvement. In general, the users started to use the app features right after installation and the usage patterns and retention rates indicate that InspirerMundi is well accepted among patients with asthma.
- Multidisciplinary development and initial validation of a clinical knowledge base on chronic respiratory diseases for mHealth decision support systemsPublication . Pereira, Ana Margarida; Jácome, Cristina; Jacinto, Tiago; Amaral, Rita; Pereira, Mariana; Sá-Sousa, Ana; Couto, Mariana; Vieira-Marques, Pedro; Martinho, Diogo; Vieira, Ana; Almeida, Ana; Martins, Constantino; Marreiros, Goreti; Freitas, Alberto; Almeida, Rute; Fonseca, João A.Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.