Amber Blount

Background: Older adults engage in increased amounts of sedentary behavior (SB), which can result in a significant decline in muscle function and overall health. An understanding of the motivational driving factors that lead older adults to engage in SB can help to create effective intervention programs. Objective: This study aimed to determine the association between prevention and promotion focus with SB in older adults, as well as compare these associations with two factors (ie, age and BMI) that are commonly known to have an association with SB among older adults. Methods: A cross-sectional analysis was conducted among 93 community-dwelling older adults with a mean age of 74.98 (SD 6.68) years. Prevention and promotion focus were both assessed using the Regulatory Focus Questionnaire. Correlation analysis was performed to determine the associations between prevention focus, promotion focus, age, and BMI with SB. Anderson-Darling tests confirmed nonnormal data distributions for all factors (except age); therefore, Spearman rank correlation was used to determine correlations between factors. Comparative analysis of significant correlations was performed using Fisher Z transformation. Results: Prevention focus had the greatest statistically significant correlation with SB (ρ=0.296; P=.004), followed by BMI (ρ=0.204; P=.049). Both age (ρ=0.116; P=.27) and promotion focus (ρ=0.002; P=.99) had statistically insignificant correlations with SB, indicating no associations. The correlation between prevention focus and SB did not significantly differ from the correlation between BMI and SB (P=.51). Conclusions: Prevention focus was found to have a weak, but significant positive association with SB in older adults. Although age and BMI have been found to have an association with SB in previous literature, age was not associated with SB in this study, while BMI had a significant but relatively weaker association with SB than that with prevention focus. However, the association found between BMI and SB did not statistically differ from the association found between prevention focus and SB. These findings suggest that older adults could be driven to engage in increased amounts of SB due to having a dominant prevention focus, which revolves around thoughts of safety and avoiding negative consequences. The recognition of this association has the potential to aid in developing intervention programs that could promote shifting from prevention to promotion focus, thereby reducing SB in older adults.

Objective: This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language. Materials and Methods: We utilized RStudio, a free development environment for the R language, as well as the functions within the “ggplot2” and “grid” packages, to develop a code that would recreate the FRA matrix for use in data visualization and analysis, as well as feedback for older adults. Results: The developed code successfully recreates the FRA matrix in R and allows researchers and clinicians to graph participant data onto the matrix itself. Discussion: The use of an R code allows for a streamlined approach to manipulating the FRA matrix for use in data visualization and feedback for older adults, which improves upon the traditional paper-pencil method that has been previously used. Conclusions: The code presented in this study recreates the FRA matrix instrument in the R language and gives researchers the ability to instantaneously add, remove, or change different aspects of the instrument to improve its readability for researchers and older adults.

Purpose: The US Centers for Disease Control and Prevention (CDC) has implemented the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative. This initiative provides an algorithm for fall risk screening. However, the algorithm has the potential to overcategorize individuals as high risk for falling upon initial screening, which may burden clinicians with the task of recategorizing individuals after follow-up testing. Therefore, this study aimed to compare the accuracy, sensitivity, and specificity of fall risk appraisal between the STEADI, Short Fall-Efficacy Scale International (FES-I), and portable balance system (BTrackS) assessments in community-dwelling older adults. Patients and Methods: This cross-sectional analysis included 122 community-dwelling older adults, comprising 94 women and 28 men. Center-of-pressure postural sway was assessed using the BTrackS, fear of falling was assessed using the Short FES-I questionnaire, and all participants completed the STEADI checklist. Each assessment categorized participants as either high or low fall risk and fall risk appraisal was compared between groups using McNemar tests. Results: The STEADI checklist (high risk: n = 62; low risk: n = 60) significantly differed in fall risk appraisal compared to the BTrackS (high risk: n = 44; low risk: n = 78; p = 0.014) and the Short FES-I (high risk: n = 42; low risk: n = 80; p = 0.002). Compared to the BTrackS, the STEADI checklist had a specificity of 62.8%, sensitivity of 70.5%, and accuracy of 65.6%. Compared to the Short FES-I, the STEADI checklist had a specificity of 67.5%, sensitivity of 81.0%, and accuracy of 72.1%. Conclusion: The STEADI checklist appears to overcategorize individuals as high fall risk more frequently than direct assessments of postural sway and fear of falling. Further research is needed to examine potential improvements in accuracy when combining the STEADI checklist with direct assessments of postural sway and/or fear of falling. Plain Language Summary: Fall risk assessments are crucial for preventative care in older adults. However, the demands of clinical practice require an accurate and time-efficient method. The U.S Centers for Disease Control and Prevention (CDC) has implemented a fall risk checklist through the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative. However, the STEADI checklist might cost clinicians more time than expected, as some patients initially classified as high risk for falling may not actually be at high risk. This leads to unnecessary follow-up assessments. In this study, we compared the STEADI checklist to direct measures of postural sway (balance) using the BTrackS system and fear of falling using the Short FES-I survey to determine how they differed in classifying community-dwelling older adults as high versus low fall risk. Our results show that the STEADI checklist classifies older adults as high risk more frequently than the BTrackS and Short FES-I. Considering that the follow-up assessments for a high-risk classification by the STEADI checklist include a balance test, we suggest that combining a balance test such as the BTrackS with a questionnaire or checklist may yield better screening outcomes and accurately identify high-risk individuals in a timely manner. Further research is needed to determine the effectiveness of this combination and to establish a true gold standard method for fall risk appraisal.