Background: Identifying the determinants of health-related quality of life (HRQOL) in patients with systolic heart failure (CHF) is rare in primary care; studies often lack a defined sample, a comprehensive set of variables and clear HRQOL outcomes. Our aim was to explore the impact of such a set of variables on generic and disease-specific HRQOL. Methods: In a cross-sectional study, we evaluated data from 318 eligible patients. HRQOL measures used were the SF-36 (Physical/Mental Component Summary, PCS/MCS) and four domains of the KCCQ (Functional status, Quality of life, Self efficacy, Social limitation). Potential determinants (instruments) included socio-demographical variables (age, sex, socio-economic status: SES), clinical (e.g. NYHA class, LVEF, NT-proBNP levels, multimorbidity (CIRS-G)), depression (PHQ-9), behavioural (EHFScBs and prescribing) and provider (e.g. list size of and number. of GPs in practice) variables. We performed linear (mixed) regression modelling accounting for clustering. Results: Patients were predominantly male (71.4%), had a mean age of 69.0 (SD: 10.4) years, 12.9% had major depression, according to PHQ-9. Across the final regression models, eleven determinants explained 27% to 55% of variance (frequency across models, lowest/highest β): Depression (6×, -0.3/-0.7); age (4×, -0.1/-0.2); multimorbidity (4×, 0.1); list size (2×, -0.2); SES (2×, 0.1/0.2); and each of the following once: no. of GPs per practice, NYHA class, COPD, history of CABG surgery, aldosterone antagonist medication and Self-care (0.1/-0.2/-0.2/0.1/-0.1/-0.2). Conclusions: HRQOL was determined by a variety of established individual variables. Additionally the presence of multimorbidity burden, behavioural (self-care) and provider determinants may influence clinicians in tailoring care to individual patients and highlight future research priorities.
In motion capture applications using electromagnetic tracking systems the process of anatomical calibration as- sociates the technical frames of sensors attached to the skin with the human anatomy. Joint centers and axes are determined relative to these frames. A change of orientation of the sensor relative to the skin renders this calibration faulty. This sensitivity regarding sensor displacement can turn out to be a serious problem with movement recordings of several minutes duration. We propose the “dislocation distance” as a novel method to quantify sensor displacement and to detect gradual and sudden changes of sensor orientation. Furthermore a method to define a so called fixed technical frame is proposed as a robust reference frame which can adapt to a new sensor orientation on the skin. The proposed methods are applied to quantify the effects of sensor displacement of 120 upper and lower limb movement recordings of newborns revealing the need for a method to compensate for sensor displacement. The reliability of the fixed technical frame is quantified and it is shown that trend and dispersion of the dislocation distance can be signif- icantly reduced. A working example illustrates the consequences of sensor displacement on derived angle time series and how they are avoided using the fixed technical frame.