Background: Both the Short Physical Performance Battery (SPPB) and daily life gait quality and quantity obtained from wearable sensors are used to measure functional status in older adults. It is generally assumed that they are interrelated and exchangeable, but this has not yet been established. Interchangeability of these measures would pave the way for remote monitoring of functional status.Research Question: Are the SPPB and daily life gait quality and quantity measures correlated in community-dwelling older adults?Methods: The SPPB and gait quality and quantity data of 229 community-dwelling adults of 65 years or older were collected. The SPPB is a combined score of the Three Stage Balance test, Four Meter Walk test, and Five Times Sit to Stand test and ranges from 0 to 12. Participants wore a tri-axial inertial sensor for one week to assess gait quality (e.g. gait stability and smoothness) and quantity (e.g. number of strides). Correlation coefficients between SPPB scores and gait quality and quantity measures were assessed using Spearman's correlation.Results: The median age of the study population was 76.2 years (IQR 72.6-81.0), and 76 % were women (n=175). The median SPPB score was 10 (IQR 8-11). Spearman's correlation coefficients between the SPPB and gait quality and quantity measures were all below 0.3.Significance: A possible explanation for the observed weak correlations is that the SPPB reflects one's maximal capacity, while gait quality and quantity reflect the submaximal performance in daily life. The SPPB and gait quality and quantity seem therefore distinct constructs with complementary value, rather than interchangeable. A more comprehensive understanding of functional status might be achieved by combining the SPPB assessment of standardized activities with the evaluation of inertial sensor measurements obtained during daily life activities.
Objectives: To investigate immediate changes in walking performance associated with three implicit motor learning strategies and to explore patient experiences of each strategy. Design: Participants were randomly allocated to one of three implicit motor learning strategies. Within-group comparisons of spatiotemporal parameters at baseline and post strategy were performed. Setting: Laboratory setting. Subjects: A total of 56 community-dwelling post-stroke individuals. Interventions: Implicit learning strategies were analogy instructions, environmental constraints and action observation. Different analogy instructions and environmental constraints were used to facilitate specific gait parameters. Within action observation, only videotaped gait was shown. Main measures: Spatiotemporal measures (speed, step length, step width, step height) were recorded using Vicon 3D motion analysis. Patient experiences were assessed by questionnaire. Results: At a group level, three of the four analogy instructions (n=19) led to small but significant changes in speed (d=0.088m/s), step height (affected side d=0.006m) and step width (d=–0.019m), and one environmental constraint (n=17) led to significant changes in step width (d=–0.040m). At an individual level, results showed wide variation in the magnitude of changes. Within action observation (n=20), no significant changes were found. Overall, participants found it easy to use the different strategies and experienced some changes in their walking performance. Conclusion: Analogy instructions and environmental constraints can lead to specific, immediate changes in the walking performance and were in general experienced as feasible by the participants. However, the response of an individual patient may vary quite considerably.
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.
