Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as “walking” or “non-walking”. One algorithm had generic thresholds, while the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82 respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

This study aimed to identify sociodemographic and health indicators of diet quality in pre-frail community-dwelling older adults. Pre-frail older adults are those at risk of progression to clinical manifestations of frailty and are targets for preventative intervention. We previously reported that pre-frail older adults have reasonably good overall diet quality. However, further analyses found low intake of energy, protein and several micronutrients. Methods: We collected detailed dietary intake from pre-frail (FRAIL scale 1-2) older adults using NZ Intake24, an online version of 24-hour multiple pass dietary recall. Diet quality was ascertained with the Diet Quality Index-International (DQI-I). We used regression Generalised Linear Models to determine predictors of diet quality and Classification and Regression Tree (CART) analysis to examine the complex relationships of predictors and identified profiles of sub-groups of older adults that predict diet quality. Results. The median age in this sample (n=468) was 80.0 years (77.0-84.0). Living with others, high deprivation index and a higher BMI were independent predictors of poorer diet quality. With CART analysis, we found that those with a BMI >29 kg/m2, living with others and younger than 80 years were likely to have a lower diet quality. Conclusions: We found BMI, living arrangement and socioeconomic status were independent predictors of diet quality in pre-frail older adults, with BMI being the most important variable in this sample when the interacting relationships of these variables were considered. Future research is needed to determine the similarities and/or differences in the profile of subgroups of older adults with poorer diet quality.