Background: Inadvertent postoperative hypothermia (IPH) is known to be associated with various adverse effects. The aim of this study was to evaluate the incidence, predictors and outcome of core inadvertent hypothermia on admission in the post-anesthesia care unit. Methods: Observational, prospective study in a Post-Anesthesia Care Unit. The study population consisted of adult patients after non-cardiac and non-neurologic surgery. Patients' demographics, intraoperative and postoperative data were collected. Descriptive analysis of variables was used to summarize data and the Mann-Whitney U test, Fisher's exact test or Chi-square test was used. Univariate and multivariate analyses were done with logistic binary regression with calculation of an Odds Ratio (OR) and its 95% Confidence Interval. Results: The incidence of IPH on admission was 32%. In univariate analysis: age, body mass index (BMI), high risk surgery, revised cardiac risk index (RCRI), type of anesthesia, use of forced-air warming, amount of intravenous crystalloids administrated, duration of anesthesia, duration of surgery and admission visual analogue scale (VAS) for pain > 3 were considered predictors of hypothermia. In multiple logistic regression analysis, age (OR 1.7, P = 0.045, for age > 65 years), RCRI (OR 3.18, P = 0.041, for RCRI > 2), duration of anesthesia (OR 1.52, P < 0.001) and admission VAS for pain (OR 2.05, P = 0.007) were considered independent predictors of IPH. Patients with IPH at PACU admission stay longer in the PACU. Conclusions: IPH was associated with a longer stay in the PACU. Age, comorbidities duration of anesthesia and pain at PACU admission were considered independent predictors for IPH.

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 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.

ObjectiveTo assess patients’ preferred roles in healthcare-related decision-making in a representative sample of the Portuguese population.DesignPopulation-based nationwide cross-sectional study.Setting and participantsA sample of Portuguese people 20 years or older were interviewed face-to-face using a questionnaire with the Problem-Solving Decision-Making scale.OutcomesThe primary outcome was patients’ preferred role for each vignette of the problem-solving decision-making scale. Sociodemographic factors associated with the preferred roles were the secondary outcomes.Results599 participants (20–99 years, 53.8% women) were interviewed. Three vignettes of the Problem-Solving Decision-Making scale were compared: morbidity, mortality and quality of life. Most patients preferred a passive role for both the problem-solving and decision-making components of the scale, particularly for the mortality vignette (66.1% in the analysis of the three vignettes), although comparatively more opted to share decision in the decision-making component. For the quality of life vignette, a higher percentage of patients wanted a shared role (44.3%) than with the other two vignettes. In the problem-solving component, preferences were significantly associated with area of residence (p<0.001) and educational level (p=0.013), while in the decision-making, component preferences were associated with age (p=0.020), educational level (p=0.015) and profession (p<0.001).ConclusionsIn this representative sample of the Portuguese mainland population, most patients preferred a practitioner-controlling role for both the problem-solving and decision-making components. In a life-threatening situation, patients were more willing to let the doctor decide. In contrast, in a less serious situation, there is a greater willingness to participate in decision-making. We have found that shared decision-making is more acceptable to better-educated patients in the problem-solving component and to people who are younger, higher educated and employed, in the decision-making component.