ABSTRACT
Background: Cardiac rehabilitation (CR) reduces recurrent cardiac events and mortality in patients with cardiovascular diseases (CVD). Innovative eHealth methods can facilitate CR uptake and effectiveness by addressing barriers associated with clinic-based rehabilitation. Tailoring eHealth-based CR to patient preferences is needed to further enhance CR. Purpose: To identify preferred behavior change techniques (BCTs) as well as barriers and facilitators for the different health behaviors targeted in eHealth-based CR among patients who have been referred to CR. Methods: Thirty-nine patients were interviewed in nine focus groups in The Netherlands, Germany, and Spain. A thematic analysis, using a combined deductive and inductive approach to coding, was conducted to identify BCTs and barriers and facilitators to behavior change. Behaviors under investigation included physical activity, medication adherence, eating a cardiac healthy-diet, stress reduction and smoking cessation. Results: The perceived helpfulness of BCTs depended on the specific behavior targeted. Common barriers were negative emotional state and physical limitations. A desire to feel physically or mentally well and having experienced a cardiac life event were the most common facilitators across health behaviors. Specific BCTs, barriers and facilitators were found for each of the health behavior. Conclusions: Behavior change techniques that patients preferred for each health behavior targeted in eHealth-based CR were identified. A negative emotional state, experiencing a life event, and improving physical functioning are important barriers and facilitators in multiple behaviors targeted in eHealth-based CR programs. Additional tailoring of interventions to patient preferences for BCTs and patient-specific barriers and facilitators per health behavior could lead to further improvement of eHealth-based CR.
ABSTRACT
BACKGROUND: Healthcare systems are under prominent stress due to the COVID-19 pandemic. A fast and simple triage is mandatory to screen patients who will benefit from early hospitalization, from those that can be managed as outpatients. There is a lack of all-comers scores, and no score has been proposed for western-world population. AIMS: To develop a fast-track risk score valid for every COVID-19 patient at diagnosis. METHODS: Single-center, retrospective study based on all the inhabitants of a healthcare area. Logistic regression was used to identify simple and wide-available risk factors for adverse events (death, intensive care admission, invasive mechanical ventilation, bleeding > BARC3, acute renal injury, respiratory insufficiency, myocardial infarction, acute heart failure, pulmonary emboli, or stroke). RESULTS: Of the total healthcare area population, 447.979 inhabitants, 965 patients (0.22%), were diagnosed with COVID-19. A total of 124 patients (12.85%) experienced adverse events. The novel SODA score (based on sex, peripheral O2 saturation, presence of diabetes, and age) demonstrated good accuracy for adverse events prediction (area under ROC curve 0.858, CI: 0.82-0.98). A cut-off value of ≤2 points identifies patients with low risk (positive predictive value [PPV] for absence of events: 98.9%) and a cut-off of ≥5 points, high-risk patients (PPV 58.8% for adverse events). CONCLUSIONS: This quick and easy score allows fast-track triage at the moment of diagnosis for COVID-19 using four simple variables: age, sex, SpO2, and diabetes. SODA score could improve preventive measures taken at diagnosis in high-risk patients and also relieve resources by identifying very low-risk patients.
