Influenza is associated with severe illness, death, and economic burden. Sentinel surveillance systems have a central role in the community since they support public health interventions. This study aimed to describe and compare the influenza-coded primary care consultations with the reference index of influenza activity used in Portugal, General Practitioners Sentinel Network, from 2012 to 2017. An ecological time-series study was conducted using weekly R80-coded primary care consultations (according to the International Classification of Primary Care-2), weekly influenza-like illness (ILI) incidence rates from the General Practitioners Sentinel Network and Goldstein Index (GI). Good accordance between these three indicators was observed in the characterization of influenza activity regarding to start and length of the epidemic period, intensity of influenza activity, and influenza peak. A high correlation (>0.75) was obtained between weekly ILI incidence rates and weekly number of R80-coded primary care consultations during all five studied seasons. In 3 out of 5 seasons this correlation increased when weekly ILI incidence rates were multiplied for the percentage of influenza positive cases. A cross-correlation between weekly ILI incidence rates and the weekly number of R80-coded primary care consultations revealed that there was no lag between the rate curves of influenza incidence and the number of consultations in the 2012/ 13 and 2013/14 seasons. In the last three seasons, the weekly influenza incidence rates detected the influenza epidemic peak for about a week earlier. In the last season, the GI anticipated the detection of influenza peak for about a two-week period. Sentinel networks are fundamental elements in influenza surveillance that integrate clinical and virological data but often lack representativeness and are not able to provide regional and age groups estimates. Given the good correlation between weekly ILI incidence rate and weekly number of R80 consultations, primary care consultation coding system may be used to complement influenza surveillance data, namely, to monitor regional influenza activity. In the future, it would be interesting to analyse concurrent implementation of both surveillance systems with the integration of all available information.

Influenza is associated with severe illness, death, and economic burden. Sentinel surveillance systems have a central role in the community since they support public health interventions. This study aimed to describe and compare the influenza-coded primary care consultations with the reference index of influenza activity used in Portugal, General Practitioners Sentinel Network, from 2012 to 2017. An ecological time-series study was conducted using weekly R80-coded primary care consultations (according to the International Classification of Primary Care-2), weekly influenza-like illness (ILI) incidence rates from the General Practitioners Sentinel Network and Goldstein Index (GI). Good accordance between these three indicators was observed in the characterization of influenza activity regarding to start and length of the epidemic period, intensity of influenza activity, and influenza peak. A high correlation (>0.75) was obtained between weekly ILI incidence rates and weekly number of R80-coded primary care consultations during all five studied seasons. In 3 out of 5 seasons this correlation increased when weekly ILI incidence rates were multiplied for the percentage of influenza positive cases. A cross-correlation between weekly ILI incidence rates and the weekly number of R80-coded primary care consultations revealed that there was no lag between the rate curves of influenza incidence and the number of consultations in the 2012/13 and 2013/14 seasons. In the last three seasons, the weekly influenza incidence rates detected the influenza epidemic peak for about a week earlier. In the last season, the GI anticipated the detection of influenza peak for about a two-week period. Sentinel networks are fundamental elements in influenza surveillance that integrate clinical and virological data but often lack representativeness and are not able to provide regional and age groups estimates. Given the good correlation between weekly ILI incidence rate and weekly number of R80 consultations, primary care consultation coding system may be used to complement influenza surveillance data, namely, to monitor regional influenza activity. In the future, it would be interesting to analyse concurrent implementation of both surveillance systems with the integration of all available information.

Background Previous studies have demonstrated the feasibility of using an asthma app to support medication management and adherence but failed to compare with other measures currently used in clinical practice. However, in a clinical setting, any additional adherence measurement must be evaluated in the context of both the patient and physician perspectives so that it can also help improve the process of shared decision making. Thus, we aimed to compare different measures of adherence to asthma control inhalers in clinical practice, namely through an app, patient self-report and physician assessment. Methods This study is a secondary analysis of three prospective multicentre observational studies with patients (≥13 years old) with persistent asthma recruited from 61 primary and secondary care centres in Portugal. Patients were invited to use the InspirerMundi app and register their inhaled medication. Adherence was measured by the app as the number of doses taken divided by the number of doses scheduled each day and two time points were considered for analysis: 1-week and 1-month. At baseline, patients and physicians independently assessed adherence to asthma control inhalers during the previous week using a Visual Analogue Scale (VAS 0–100). Results A total of 193 patients (72% female; median [P25–P75] age 28 [19–41] years old) were included in the analysis. Adherence measured by the app was lower (1 week: 31 [0–71]%; 1 month: 18 [0–48]%) than patient self-report (80 [60–95]) and physician assessment (82 [51–94]) (p < 0.001). A negligible non-significant correlation was found between the app and subjective measurements (ρ 0.118–0.156, p > 0.05). There was a moderate correlation between patient self-report and physician assessment (ρ = 0.596, p < 0.001). Conclusions Adherence measured by the app was lower than that reported by the patient or the physician. This was expected as objective measurements are commonly lower than subjective evaluations, which tend to overestimate adherence. Nevertheless, the low adherence measured by the app may also be influenced by the use of the app itself and this needs to be considered in future studies.

Background: Health 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. Objective: This study aimed to evaluate the use of general health and fitness apps by patients with asthma and to identify determinants of usage. Methods: A secondary analysis of the INSPIRERS observational studies was conducted using data from face-to-face visits. Patients with a diagnosis of asthma were included between November 2017 and August 2020. Individual-level data were collected, including age, gender, marital status, educational level, health status, presence of anxiety and depression, postcode, socioeconomic level, digital literacy, use of health services, and use of health and fitness apps. Multivariate logistic regression was used to model the probability of being a health and fitness app user. Statistical analysis was performed in R. Results: A total of 526 patients attended a face-to-face visit in the 49 recruiting centers and 514 had complete data. Most participants were ≤40 years old (66.4%), had at least 10 years of education (57.4%), and were in the 3 higher quintiles of the socioeconomic deprivation index (70.1%). The majority reported an overall good health status (visual analogue scale [VAS] score>70 in 93.1%) and the prevalence of anxiety and depression was 34.3% and 11.9%, respectively. The proportion of participants who reported using health and fitness mobile apps was 41.1% (n=211). Multivariate models revealed that single individuals and those with more than 10 years of education are more likely to use health and fitness mobile apps (adjusted odds ratio [aOR] 2.22, 95%CI 1.05-4.75 and aOR 1.95, 95%CI 1.12-3.45, respectively). Higher digital literacy scores were also associated with higher odds of being a user of health and fitness apps, with participants in the second, third, and fourth quartiles reporting aORs of 6.74 (95%CI 2.90-17.40), 10.30 (95%CI 4.28-27.56), and 11.52 (95%CI 4.78-30.87), respectively. Participants with depression symptoms had lower odds of using health and fitness apps (aOR 0.32, 95%CI 0.12-0.83). Conclusions: A better understanding of the barriers and enhancers of app use among patients with lower education, lower digital literacy, or depressive symptoms is key to design tailored interventions to ensure a sustained and equitable use of these technologies. Future studies should also assess users’ general health-seeking behavior and their interest and concerns specifically about digital tools. These factors may impact both initial engagement and sustained use.

Health 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.

 Anxiety and depression are relevant comorbidities in asthma, but, in Portugal and Spain, data on this topic are scarce. We assessed, in patients with asthma, the frequency of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS) and the European Quality of Life Five Dimension Questionnaire (EQ-5D); the level of agreement between these questionnaires, and the factors associated with these symptoms. This is a secondary analysis of the INSPIRERS studies. A total of 614 adolescents and adults with persistent asthma (32.6±16.9 years, 64.7% female) were recruited from 30 primary care centres and 32 allergy, pulmonology and paediatric clinics. Demographic and clinical characteristics, HADS and EQ-5D were collected. A score ≥8 on Hospital Anxiety and Depression Scale-Anxiety/Hospital Anxiety and Depression Scale-Depression or a positive answer to EQ-5D item 5 indicated the presence of these symptoms. Agreement was determined by Cohen’s kappa. Two multivariable logistic regressions were built. According to HADS, 36% of the participants had symptoms of anxiety and 12% of depression. According to EQ-5D, 36% of the participants had anxiety/depression. The agreement between questionnaires in identifying anxiety/depression was moderate (k=0.55, 95% CI 0.48 to 0.62). Late asthma diagnosis, comorbidities and female gender were predictors of anxiety/depression, while better asthma control, health-related quality of life and perception of health were associated with lower odds for anxiety/depression. At least 1/3 of the patients with persistent asthma experience symptoms of anxiety/depression, showing the relevance of screening these disorders in patients with asthma. EQ-5D and HADS questionnaires showed a moderate agreement in the identification of anxiety/depression symptoms. The identified associated factors need to be further investigated in long-term studies.