In order to achieve a level of community involvement and physical independence, being able to walk is the primary aim of many stroke survivors. It is therefore one of the most important goals during rehabilitation. Falls are common in all stages after stroke. Reported fall rates in the chronic stage after stroke range from 43 to 70% during one year follow up. Moreover, stroke survivors are more likely to become repeated fallers as compared to healthy older adults. Considering the devastating effects of falls in stroke survivors, adequate fall risk assessment is of paramount importance, as it is a first step in targeted fall prevention. As the majority of all falls occur during dynamic activities such as walking, fall risk could be assessed using gait analysis. It is only recent that technology enables us to monitor gait over several consecutive days, thereby allowing us to assess quality of gait in daily life. This thesis studies a variety of gait assessments with respect to their ability to assess fall risk in ambulatory chronic stroke survivors, and explores whether stroke survivors can improve their gait stability through PBT.
Background: Follow-up of stroke survivors is important to objectify activity limitations and/or participations restrictions. Responsive measurement tools are needed with a low burden for professional and patient. Aim: To examine the concurrent validity, floor and ceiling effects and responsiveness of both domains of the Late-Life Function and Disability Index Computerized Adaptive Test (LLFDI-CAT) in first-ever stroke survivors discharged to their home setting. Design: Longitudinal study. Setting: Community. Population: First ever stroke survivors. Methods: Participants were visited within three weeks after discharge and six months later. Stroke Impact Scale (SIS 3.0) and Five-Meter Walk Test (5MWT) outcomes were used to investigate concurrent validity of both domains, activity limitations, and participation restriction, of the LLFDI-CAT. Scores at three weeks and six months were used to examine floor and ceiling effects and change scores were used for responsiveness. Responsiveness was assessed using predefined hypotheses. Hypotheses regarding the correlations with change scores of related measures, unrelated measures, and differences between groups were formulated. Results: The study included 105 participants. Concurrent validity (R) of the LLFDI-CAT activity limitations domain compared with the physical function domain of the SIS 3.0 and with the 5MWT was 0.79 and -0.46 respectively. R of the LLFDI-CAT participation restriction domain compared with the participation domain of the SIS 3.0 and with the 5MWT was 0.79 and -0.41 respectively. A ceiling effect (15%) for the participation restriction domain was found at six months. Both domains, activity limitations and participation restrictions, of the LLFDI-CAT, scored well on responsiveness: 100% (12/12) and 91% (12/11) respectively of the predefined hypotheses were confirmed. Conclusions: The LLFDI-CAT seems to be a valid instrument and both domains are able to detect change over time. Therefore, the LLFDI-CAT is a promising tool to use both in practice and in research. Clinical rehabilitation impact: The LLFDI-CAT can be used in research and clinical practice.
MULTIFILE
Background: Falls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest. Methods: This study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model. Results: We found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults. Conclusion: The interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.
