ORCID Profile
0000-0002-0233-830X
Current Organisations
Faculty of Medicine, University of Porto
,
Cardiac and Respiratory Physiologist
,
Uppsala University
,
Porto Health School, Polytechnic Institute of Porto
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Publisher: Wiley
Date: 30-06-2023
DOI: 10.1111/ALL.15740
Abstract: Biomarkers for the diagnosis, treatment and follow‐up of patients with rhinitis and/or asthma are urgently needed. Although some biologic biomarkers exist in specialist care for asthma, they cannot be largely used in primary care. There are no validated biomarkers in rhinitis or allergen immunotherapy (AIT) that can be used in clinical practice. The digital transformation of health and health care (including mHealth) places the patient at the center of the health system and is likely to optimize the practice of allergy. Allergic Rhinitis and its Impact on Asthma (ARIA) and EAACI (European Academy of Allergy and Clinical Immunology) developed a Task Force aimed at proposing patient‐reported outcome measures (PROMs) as digital biomarkers that can be easily used for different purposes in rhinitis and asthma. It first defined control digital biomarkers that should make a bridge between clinical practice, randomized controlled trials, observational real‐life studies and allergen challenges. Using the MASK‐air app as a model, a daily electronic combined symptom‐medication score for allergic diseases (CSMS) or for asthma (e‐DASTHMA), combined with a monthly control questionnaire, was embedded in a strategy similar to the diabetes approach for disease control. To mimic real‐life, it secondly proposed quality‐of‐life digital biomarkers including daily EQ‐5D visual analogue scales and the bi‐weekly RhinAsthma Patient Perspective (RAAP). The potential implications for the management of allergic respiratory diseases were proposed.
Publisher: Elsevier BV
Date: 04-2023
Publisher: Wiley
Date: 20-11-2023
DOI: 10.1111/ALL.15574
Abstract: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK‐air®, these data have only been analyzed cross‐sectionally, without considering the changes of symptoms over time. We analyzed data from MASK‐air® longitudinally, clustering weeks according to reported rhinitis symptoms. We analyzed MASK‐air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k‐means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly‐variable control. Clusters with poorly‐controlled rhinitis displayed a higher frequency of rhinitis co‐medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. We identified 16 patterns of weekly rhinitis control. Co‐medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.
Publisher: Elsevier BV
Date: 04-2023
Publisher: Wiley
Date: 2023
DOI: 10.1002/CLT2.12215
Abstract: MASK‐air ® , a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK‐air ® is a Good Practice of DG Santé on digitally‐enabled, patient‐centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co‐operation and Development). MASK‐air ® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK‐air ® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as‐needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom‐medication score (ARIA‐EAACI‐CSMS) has been validated for clinical practice and trials. The implications of the novel MASK‐air ® results should lead to change management in rhinitis and asthma.
Publisher: Elsevier BV
Date: 11-2022
DOI: 10.1016/J.JAIP.2022.08.015
Abstract: Several studies have suggested an impact of allergic rhinitis on academic productivity. However, large studies with real-world data (RWD) are not available. To use RWD to assess the impact of allergic rhinitis on academic performance (measured through a visual analog scale [VAS] education and the Work Productivity and Activity Impairment Questionnaire plus Classroom Impairment Questions: Allergy Specific [WPAI+CIQ:AS] questionnaire), and to identify factors associated with the impact of allergic rhinitis on academic performance. We assessed data from the MASK-air mHealth app of users aged 13 to 29 years with allergic rhinitis. We assessed the correlation between variables measuring the impact of allergies on academic performance (VAS education, WPAI+CIQ:AS impact of allergy symptoms on academic performance, and WPAI+CIQ:AS percentage of education hours lost due to allergies) and other variables. In addition, we identified factors associated with the impact of allergic symptoms on academic productivity through multivariable mixed models. A total of 13,454 days (from 1970 patients) were studied. VAS education was strongly correlated with the WPAI+CIQ:AS impact of allergy symptoms on academic productivity (Spearman correlation coefficient = 0.71 [95% confidence interval (CI) = 0.58 0.80]), VAS global allergy symptoms (0.70 [95% CI = 0.68 0.71]), and VAS nose (0.66 [95% CI = 0.65 0.68]). In multivariable regression models, immunotherapy showed a strong negative association with VAS education (regression coefficient = -2.32 [95% CI = -4.04 -0.59]). Poor rhinitis control, measured by the combined symptom-medication score, was associated with worse VAS education (regression coefficient = 0.88 [95% CI = 0.88 0.92]), higher impact on academic productivity (regression coefficient = 0.69 [95% CI = 0.49 0.90]), and higher percentage of missed education hours due to allergy (regression coefficient = 0.44 [95% CI = 0.25 0.63]). Allergy symptoms and worse rhinitis control are associated with worse academic productivity, whereas immunotherapy is associated with higher productivity.
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.PULMOE.2022.10.005
Abstract: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale - "VAS Asthma") at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users) (ii) treated and partly-controlled asthma (6.3-9.7%) (iii) treated and controlled asthma (4.6-5.5%) (iv) untreated uncontrolled asthma (18.2-20.5%) (v) untreated partly-controlled asthma (10.1-10.7%) (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma.
Publisher: Wiley
Date: 08-2017
Publisher: Elsevier BV
Date: 2022
Publisher: Wiley
Date: 13-06-2022
DOI: 10.1111/ALL.15371
Abstract: Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., “real‐world data”). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co‐medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT. We assessed the MASK‐air® app data (May 2015–December 2020) by users self‐reporting AR (16–90 years). We compared eight AR medication schemes on reported VAS of allergy symptoms, clustering data by the patient and controlling for confounding factors. We compared (i) allergy symptoms between patients with and without AIT and (ii) different drug classes used in co‐medication. We analysed 269,837 days from 10,860 users. Most days (52.7%) involved medication use. Median VAS levels were significantly higher in co‐medication than in monotherapy (including the fixed combination azelastine‐fluticasone) schemes. In adjusted models, azelastine‐fluticasone was associated with lower average VAS global allergy symptoms than all other medication schemes, while the contrary was observed for oral corticosteroids. AIT was associated with a decrease in allergy symptoms in some medication schemes. A difference larger than the MID compared to no treatment was observed for oral steroids. Azelastine‐fluticasone was the drug class with the lowest chance of being used in co‐medication (adjusted OR = 0.75 95% CI = 0.71–0.80). Median VAS levels were higher in co‐medication than in monotherapy. Patients with more severe symptoms report a higher treatment, which is currently not reflected in guidelines.
Publisher: Wiley
Date: 11-2022
DOI: 10.1002/CLT2.12208
Abstract: Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real‐time, real‐world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK‐air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.
Location: Portugal
Location: Portugal
No related grants have been discovered for Rita Amaral.