Aims and objectives To gain insight into the perceived added value of a decision support App for district nurses and case managers intended to support a problem assessment and the provision of advices on possible solutions to facilitate ageing in place of people with dementia, and to investigate how they would implement the App in daily practice. Background District nurses and case managers play an important role in facilitating ageing in place of people with dementia (PwD). Detecting practical problems preventing PwD from living at home and advising on possible solutions is complex and challenging tasks for nurses and case managers. To support them with these tasks, a decision support App was developed. Methods A qualitative study using semi‐structured interviews was conducted. A photo‐elicitation method and an interview guide were used to structure the interviews. The data were analysed according to the principles of content analysis. Results In five interviews with seven district nurses and case managers, the added value was described in terms of five themes: (a) providing a broader/better overview of possible solutions; (b) providing a guideline/checklist for problem assessment and advice on solutions; (c) supporting an in‐depth problem assessment; (d) being a support tool for unexperienced case managers/district nurses; and (e) providing up‐to‐date information. The participants regarded the App as complementary to their current work procedure, which they would use in a flexible manner at different stages in the care continuum. Conclusions The participants valued both parts, the problem assessment and the overview of possible solutions. An important requisite for the usage would be that the content is continuously updated. Before implementation of the App can be recommended, an evaluation of its effectiveness regarding decision‐making should be conducted. Relevance to clinical practice This study underpins the need of nurses and case managers for decision support with regard to problem assessment and providing advices on possible solutions to facilitate ageing in place of PwD. There results also show the importance of listening to users experience and their perceived added value of decision support tools as this helps to explain the lack of statistically significant effects on quantitative outcome measure in contrast to a high willingness to use the App in a previous study.
STUDY DESIGN: Cross-sectional study.OBJECTIVES: This study: (1) investigated the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness relative to dual-energy X-ray absorptiometry (DXA) in the assessment of body composition in people with spinal cord injury (SCI), and whether sex and lesion characteristics affect the accuracy, (2) developed new prediction equations to estimate fat free mass (FFM) and percentage fat mass (FM%) in a general SCI population using BIA and skinfolds outcomes.SETTING: University, the Netherlands.METHODS: Fifty participants with SCI (19 females; median time since injury: 15 years) were tested by DXA, single-frequency BIA (SF-BIA), segmental multi-frequency BIA (segmental MF-BIA), and anthropometry (height, body mass, calf circumference, and skinfold thickness) during a visit. Personal and lesion characteristics were registered.RESULTS: Compared to DXA, SF-BIA showed the smallest mean difference in estimating FM%, but with large limits of agreement (mean difference = -2.2%; limits of agreement: -12.8 to 8.3%). BIA and skinfold thickness tended to show a better estimation of FM% in females, participants with tetraplegia, or with motor incomplete injury. New equations for predicting FFM and FM% were developed with good explained variances (FFM: R2 = 0.94; FM%: R2 = 0.66).CONCLUSIONS: None of the measurement techniques accurately estimated FM% because of the wide individual variation and, therefore, should be used with caution. The accuracy of the techniques differed in different subgroups. The newly developed equations for predicting FFM and FM% should be cross-validated in future studies.
It is important for caregivers and patients to know which wounds are at risk of prolonged wound healing to enable timely communication and treatment. Available prognostic models predict wound healing in chronic ulcers, but not in acute wounds, that is, originating after trauma or surgery. We developed a model to detect which factors can predict (prolonged) healing of complex acute wounds in patients treated in a large wound expertise centre (WEC). Using Cox and linear regression analyses, we determined which patient- and wound-related characteristics best predict time to complete wound healing and derived a prediction formula to estimate how long this may take. We selected 563 patients with acute wounds, documented in the WEC registry between 2007 and 2012. Wounds had existed for a median of 19 days (range 6-46 days). The majority of these were located on the leg (52%). Five significant independent predictors of prolonged wound healing were identified: wound location on the trunk [hazard ratio (HR) 0·565, 95% confidence interval (CI) 0·405-0·788; P = 0·001], wound infection (HR 0·728, 95% CI 0·534-0·991; P = 0·044), wound size (HR 0·993, 95% CI 0·988-0·997; P = 0·001), wound duration (HR 0·998, 95% CI 0·996-0·999; P = 0·005) and patient's age (HR 1·009, 95% CI 1·001-1·018; P = 0·020), but not diabetes. Awareness of the five factors predicting the healing of complex acute wounds, particularly wound infection and location on the trunk, may help caregivers to predict wound healing time and to detect, refer and focus on patients who need additional attention.
