Thirty to sixty per cent of older patients experience functional decline after hospitalisation, associated with an increase in dependence, readmission, nursing home placement and mortality. First step in prevention is the identification of patients at risk. The objective of this study is to develop and validate a prediction model to assess the risk of functional decline in older hospitalised patients.
Objective: To obtain insight into (a) the prevalence of nursing staff–experienced barriers regarding the promotion of functional activity among nursing home residents, and (b) the association between these barriers and nursing staff–perceived promotion of functional activity. Method: Barriers experienced by 368 nurses from 41 nursing homes in the Netherlands were measured with the MAastrIcht Nurses Activity INventory (MAINtAIN)-barriers; perceived promotion of functional activities was measured with the MAINtAIN-behaviors. Descriptive statistics and hierarchical linear regression analyses were performed. Results: Most often experienced barriers were staffing levels, capabilities of residents, and availability of resources. Barriers that were most strongly associated with the promotion of functional activity were communication within the team, (a lack of) referral to responsibilities, and care routines. Discussion: Barriers that are most often experienced among nursing staff are not necessarily the barriers that are most strongly associated with nursing staff–perceived promotion of functional activity.
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
