Clinical and biomechanical factors associated with falls and rheumatoid arthritis: Baseline cohort with longitudinal nested case-control study

OBJECTIVE: To identify the clinical and biomechanical characteristics associated with falls in people with RA. METHODS: A total of 436 people ≥60 years of age with RA completed a 1 year prospective survey of falls in the UK. At baseline, questionnaires recorded data including personal and medical history, pain and fatigue scores, health-related quality of life (HRQoL), physical activity and medication history. The occurrence of falls wasmonitored prospectively over 12 months by monthly self-reporting. A nested sample of 30 fallers (defined as the report of one or more falls in 12 months) and 30 non-fallers was evaluated to assess joint range of motion (ROM), muscle strength and gait parameters. Multivariate regression analyses were undertaken to determine variables associated with falling. RESULTS: Compared with non-fallers (n = 236), fallers (n = 200) were older (P = 0.05), less likely to be married (P = 0.03), had higher pain scores (P < 0.01), experienced more frequent dizziness (P < 0.01), were frequently taking psychotropic medications (P = 0.02) and reported lower HRQoL (P = 0.02). Among those who underwent gait laboratory assessments, compared with non-fallers, fallers showed a greater anteroposterior (AP; P = 0.03) and medial-lateral (ML) sway range (P = 0.02) and reduced isokinetic peak torque and isometric strength at 60° knee flexion (P = 0.03). Fallers also showed shorter stride length (P = 0.04), shorter double support time (P = 0.04) and reduced percentage time in swing phase (P = 0.02) and in knee range of motion through the gait cycle (P < 0.01). CONCLUSION: People with RA have distinct clinical and biomechanical characteristics that place them at increased risk of falling. Assessment for these factors may be important to offer more targeted rehabilitation interventions

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Understanding the impact of organizational culture on new product development

The underlying issue explored in this paper is that an appropriate organisational culture can provide positive NPD performance, to investigate which organisational cultures best fit this paradigm, this paper uses data from an international survey of new product development (NPD) in Europe and Australia. The study found that business culture is a determinant of product development mix and influences both an organisation’s R&D activity focus and the intensity of it’s R&D activities. Organisations whose business culture is an adhocracy were found to have a greater focus on, and achieve higher results from, their NPD activities

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants