Developing and validating an accelerometerbased algorithm with machine learning to classify physical activity after acquired brain injury. Brain Injury, 35(4), 460-467.

BACKGROUND: Clinicians are often required to provide a qualified guess on the probability of decannulation in estimating patients' rehabilitation potential and relaying information about prognosis to patients and next of kin. The objective of this study was to use routinely gathered clinical data to develop a prognostic model of time to decannulation in subjects with acquired brain injury, for direct implementation in clinical practice. METHODS: Data from a large cohort including 574 tracheostomized subjects admitted for neurorehabilitation were analyzed using discrete time-to-event analysis with logit-link. Within this model, a reference hazard function was modeled using restricted cubic splines, and estimates were presented using odds ratios (95% CIs). RESULTS: A total of 411 subjects (72%) were decannulated within a median of 27 d (interquartile range 16-49) at the rehabilitation hospital. The prognostic model for decannulation included age, diagnosis, days from injury until admission for rehabilitation, swallowing, and overall functional level measured with the Early Functional Abilities score. Among these, the strongest predictors for decannulation were age and a combination of overall functional abilities combined with swallowing ability. CONCLUSIONS: A prognostic model for decannulation was developed using routinely gathered clinical data. Based on the model, an online graphical user interface was applied, in which the probability of decannulation within x days is calculated along with the statistical uncertainty of the probability. Furthermore, a layman's interpretation is provided. The online tool was directly implemented in clinical practice at the rehabilitation hospital, and is available through this link: (

BACKGROUND: Development of clinical practice at a Danish neurorehabilitation centre was delegated to a group of health professional developers. Their job function lacked conceptual foundation, and it was unclear how their working tasks contributed to evidence-based practice. OBJECTIVE: Conceptual clarification of the job function and pattern analysis of activity distributions for health professional developers. METHODS: Health professional developers kept continuous time geographical diaries for two weeks. Meaningful categories were subtracted through content analysis. Patterns were analysed within activity distributions with regards to evidence-based practice. RESULTS: A total of 213 diaries were collected from 21 health professional developers of three professions (physiotherapists, occupational therapists and nurses). Each participant reported 6–13 workdays (median 10 days). Eleven main categories of work tasks emerged with 42 subcategories. Overall, 7% of total time reported was spent on external knowledge, with minimal variation between professions and contractual time allocation. CONCLUSION: Conceptual clarification of work tasks was established for health professional developers. Their work activity distributions contributed mainly to maintenance of existing level of professional knowledge rather than to implementation of new knowledge, which did not fulfil the intended responsibility for development of evidence-based practice. Educational competence boost and data-driven change of organisation structure was recommended.

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