Browsing by Author "Almeida, Rute"
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- Adult asthma scores—development and validation of multivariable scores to identify asthma in surveysPublication . Sá-Sousa, Ana; Pereira, Ana Margarida; Almeida, Rute; Araújo, Luís; Couto, Mariana; Jacinto, Tiago; Freitas, Alberto; Bousquet, Jean; Fonseca, João A.One of the questions in epidemiology is the identification of adult asthma in studies. To develop and validate multivariable scores for adult asthma identification in epidemiological studies and to explore cutoffs to rule in/rule out asthma, compared with asthma diagnosed by a physician after clinical examination and diagnostic tests, blinded to the self-administered questions. We analyzed data (n = 711 adults) from a nationwide population-based study. The predictors were self-administered questions identified in a literature review (the Adult Asthma Epidemiological Score [A2 score]) and from the Global Allergy and Asthma Network of Excellence (GA2LEN) questionnaire (the GA2LEN Asthma Epidemiological Score [GA2LEN score]). Scores were developed using exploratory factor analysis. Internal consistency, discriminative power, and diagnostic accuracy were assessed. The A2 score comprises 8 questions (including “Did a physician confirm you had asthma?”) and the GA2LEN score comprises 6 questions (including “Have you ever had asthma?”). Both had high Cronbach α (0.89 and 0.85, respectively, for the A2 score and the GA2LEN score) and good area under the receiver-operating characteristic curve (90.4% and 89.0%). The scoring is the sum of positive answers. Asthma is present (rule in) for scores of 4 or more (specificity, 99.2%; PPV, 93.3% and 91.7%; accuracy, 89.4% and 87.4%, respectively, for the A2 score and the GA2LEN score). Asthma is excluded (rule out) for A2 scores of 0 to 1 and a GA2LEN score of 0 (sensitivity, 93.1%; NPV, 98.2% and 98.0%; accuracy 89.4% and 82.8%, respectively, for the A2 score and the GA2LEN score). These practical scores can be used to rule in/rule out asthma in epidemiological studies and clinical screening/triage settings. They may help physicians in primary care or other specialties to screen patients with asthma using a simple score with a high level of discrimination and to identify the best candidates to be referred for a diagnostic workup. Moreover, their use may contribute to reducing the inconsistencies of operational definitions of asthma across studies and surveys.
- 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.
- Automatic quality assessment of a forced expiratory manoeuvre acquired with the tablet microphonePublication . Almeida, Rute; Bernardo, Pinho; Jácome, Cristina; Teixeira, João Fonseca; Amaral, Rita; Gonçalves, Ivânia; Lopes, Filipa; Pinheiro, Ana Catarina; Jacinto, Tiago; Paixão, Cátia; Pereira, Mariana; Marques, Alda; Fonseca, João AlmeidaEvaluation of lung function is central to the management of chronic obstructive respiratory diseases. It is typically evaluated with a spirometer by a specialized health professional, who ensures the correct execution of a forced expiratory manoeuvre (FEM). Audio recording of a FEM using a smart device embedded microphone can be used to self-monitor lung function between clinical visits. The challenge of microphone spirometry is to ensure the validity and reliability of the FEM, in the absence of a health professional. In particular, the absence of a mouthpiece may allow excessive mouth closure, leading to an incorrect manoeuvre. In this work, a strategy to automatically assess the correct execution of the FEM is proposed and validated. Using 498 FEM recordings, both specificity and sensitivity attained were above 90%. This method provides immediate feedback to the user, by grading the manoeuvre in a visual scale, promoting the repetition of the FEM when needed.
- 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.
- A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: latent class analysis and partitioning around medoids methodsPublication . Amaral, Rita; Jacinto, Tiago; Pereira, Ana; Almeida, Rute; Fonseca, JoãoLatent class analysis (LCA) and partitioning around medoids (PAM) are popular data-driven methods for partitioning objects based on dichotomous data, remaining not clear which is better for large epidemiological datasets. Hence, we compared these methods in the identification of clusters of subjects with airways symptoms, using a large population-based data from the U.S. National Health and Nutrition Examination Surveys (NHANES).
- Data-driven prescription patterns in patients under maintenance treatment for respiratory diseases from the Portuguese prescription databasePublication . Sá-Sousa, Ana; Amaral, Rita; Almeida, Rute; Freitas, Alberto; Fonseca, João AWe aimed to identify prescription patterns in respiratory patients using an unsupervised (data-driven) method, in a random sample of patients aged >14 years (n=8799), retrieved from the Portuguese Electronic Medical Prescription database. Respiratory patients were defined if >2 packs of maintenance treatment for respiratory diseases were prescribed in 2016. We analysed all the prescriptions (n=39810) for respiratory diseases and exacerbations by medication type. Two-step clustering was based on the presence of ICS, LABA, LTRA, LAMA, LABA, SABA, SAMA and on the speciality of prescriber.
- Determinants of the use of health and fitness mobile apps by patients with asthma: secondary analysis of observational studiesPublication . Neves, Ana Luísa; Jácome, Cristina; Taveira-Gomes, Tiago; Pereira, Ana Margarida; Almeida, Rute; Amaral, Rita; Alves-Correia, Magna; Mendes, Sandra; Chaves-Loureiro, Cláudia; Valério, Margarida; Lopes, Cristina; Carvalho, Joana; Mendes, Ana; Ribeiro, Carmelita; Prates, Sara; Ferreira, José Alberto; Teixeira, Maria Fernanda; Branco, Joana; Santalha, Marta; Vasconcelos, Maria João; Lozoya, Carlos; Santos, Natacha; Cardia, Francisca; Moreira, Ana Sofia; Taborda-Barata, Luís; Pinto, Cláudia Sofia; Ferreira, Rosário; Silva, Pedro Morais; Ferreira, Tânia Monteiro; Câmara, Raquel; Lobo, Rui; Bordalo, Diana; Guimarães, Cristina; Santo, Maria Espírito; Oliveira, José Ferraz de; Augusto, Maria José Cálix; Gomes, Ricardo; Vieira, Inês; Silva, Sofia da; Marques, Maria; Cardoso, João; Morete, Ana; Aroso, Margarida; Cruz, Ana Margarida; Nunes, Carlos; Câmara, Rita; Rodrigues, Natalina; Abreu, Carmo; Albuquerque, Ana Luísa; Vieira, Claúdia; Santos, Carlos; Páscoa, Rosália; Chaves-Loureiro, Carla; Alves, Adelaide; Neves, Ângela; Marques, José Varanda; Reis, Bruno; Ferreira-Magalhães , Manuel; Fonseca, João AlmeidaHealth and fitness apps have potential benefits to improve self-management and disease control among patients with asthma. However, inconsistent use rates have been reported across studies, regions, and health systems. A better understanding of the characteristics of users and nonusers is critical to design solutions that are effectively integrated in patients’ daily lives, and to ensure that these equitably reach out to different groups of patients, thus improving rather than entrenching health inequities.
- Development and validation of a digital image processing-based pill detection tool for an oral medication self-monitoring systemPublication . Holtkötter, Jannis; Amaral, Rita; Almeida, Rute; Jácome, Cristina; Cardoso, Ricardo; Pereira, Ana; Pereira, Mariana; Chon, Ki H.; Fonseca, João AlmeidaLong-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.
- Development of a mobile health app for the management of hypertension, including treatment adherence assessment,using image detection technology - inspirers-HTNPublication . Nogueira-Silva, Luís; Viera-Marques, Pedro; Valente, José; Holtkötter, Jannis; Amaral, Rita; Jácome, Cristina; Ferreira, Ana; Almeida, Rute; Almeida Fonseca, JoãoWe aim to develop a mHealth smartphone app with a novel strategy to support the management of hypertension, including the measurement of adherence to treatment, taking advantage of the widespread use of smartphones and using only their embedded sensors. We have designed and developed a cross-platform, multi-language app which allows to register a pharmacological treatment and to customize alerts for the patient to take his/her medication and to measure his/her blood pressure (BP). Moreover, the app will be able to identify the number of pills in a blister and to capture the BP values from the screen of BP measuring devices, using the smartphone's camera. Thus, the app will quantify adherence to therapy and generate automatic BP reports. Blister photos and BP values collected by the users are enhanced using standard image processing methods for contrast increase, gap filling and relevant elements location. Classification strategies allow to count the pills present in the blisters, while the Google MLKit text mining API is employed for BP values recognition. Health Level Seven International (HL7) Fast Health Interoperability Resources (FHIR) standard is used for health care data modeling and exchange, promoting interoperability while guaranteeing data quality and security. Evaluation of the app's performance by real users will be presented, regarding its usability and the offline validity of the data acquisition. The user interface concept of the app has been defined and the mockups have been produced. Regarding pill counting, blisters with diverse materials and textures were considered. Different processing strategies are used depending on blisters’ characteristics, which has allowed an accuracy above 95% for most of the tested blisters. Extracting the BP measures from smartphone acquired images using the MLKIT app seems to be feasible. Further improvements and evaluations are ongoing. We will show preliminary results regarding usability and offline validity. We propose a new smartphone app that will support the management of hypertension, including an innovative image detection tool that will allow to objectively measure adherence to therapy and will facilitate the capture of BP values.
- Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypesPublication . Amaral, Rita; Bousquet, Jean; Pereira, Ana M.; Araújo, Luís M.; Sá‐Sousa, Ana; Jacinto, Tiago; Almeida, Rute; Delgado, Luís; Fonseca, João A.Background Refined phenotyping of allergic diseases may unravel novel phenotypes. Conjunctivitis as an independent disorder has never been approached. Aim To identify distinct classes of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class using classification and regression tree (CART) analysis. Methods Seven hundred and twenty‐eight adults from the Portuguese general population study ICAR had a structured medical interview combined with blood collection, skin prick tests, spirometry with bronchodilation, and exhaled nitric oxide. LCA was applied to 19 variables. The CART algorithm selected the most likely variables distinguishing LCA‐classes. Results A six‐class model was obtained. Class 1 (25%): nonallergic participants without bronchial or ocular symptoms. Classes 2 (22%) and 3 (11%): nasal and ocular (low levels) symptoms without nasal impairment, monosensitized (Class 2) or polysensitized (Class 3). Class 4 (13%): polysensitized participants with high levels of nasal and ocular symptoms, and nasal impairment. Classes 5 (16%) and 6 (14%): high level of nasal, bronchial and ocular symptoms with nasal impairment (non‐allergic or polysensitized, respectively). Participants in classes 5 and 6 had more bronchial exacerbations and unscheduled medical visits (P < 0.001). Ocular symptoms were significantly higher in classes with nasal impairment, compared to those without impairment (P < 0.001) or no nasal symptom (P < 0.001). CART highlighted ocular symptoms as the most relevant variable in distinguishing LCA‐classes. Conclusion Novel severe phenotypes of participants with co‐occurrence of ocular, nasal and bronchial symptoms, and exacerbation‐prone were identified. The tree algorithm showed the importance of the ocular symptoms in the expression of allergic diseases phenotypes.