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.

Purpose: Dynamic balance, an important contributor to fall risk in older adults, involves maintaining the center of pressure while in locomotive states and is. Fall risk appraisal (FRA) is defined as assessing an older adult’s awareness of their physiological and perceived fall risk. This longitudinal study aimed to evaluate how multimodal factors predict fluctuations in dynamic balance in community-dwelling low-income older adults, utilizing fear of falling (FoF), static balance, fall history, and moderate-to-vigorous physical activity (MVPA). Patients and Methods: The longitudinal study included 140 community-dwelling, low-income older adults, with 124 women and 16 men. FoF was assessed using the Short Falls Efficacy Scale International (Short FES-I) and static balance using BTracks Balance Test (BBT). Both were utilized to define FRA Distance, an integrated quantification of physiological and perceived balance deficits. MVPA was assessed using accelerometers, fall history using self-report, and dynamic balance using the Timed Up and Go (TUG) test. The study was conducted at 4 timepoints at T1 (baseline), T2 (2 months), T3 (4 months), and T4 (6 months). Results: Using mixed effects multilevel models, TUG scores were predicted by time, %MVPA, and FRA distance ratio. The effect of FRA distance ratio was primarily driven by FoF, and the effect of %MVPA varied by age. Additionally, while fall history did not show a predictive relationship with TUG scores, it did predict FRA distance. Conclusion: Dynamic balance fluctuated over time and was influenced by multimodal factors, namely MVPA and FRA, which captured the interplay between static balance and FoF. Fall history did not directly predict dynamic balance but played a role in FRA, implicating the subjective effects of fall history. These findings demonstrate how physical activity, FRA, and their interactions can predict changes in dynamic balance. Future work can utilize the results to evaluate low-cost interventions for community-dwelling older adults. Plain Language Summary: Evaluating changes in dynamic balance is critical for evaluating fall risk in older adults. This study aimed to determine what drives these fluctuations, including fear of falling, static balance, physical activity, and fall history using assessments at four timepoints across 6 months. The authors found that each of these variables except fall history directly influenced dynamic balance, with fall history influencing how older adults perceive their fall risk compared to their actual fall risk. These results confirm that changes in dynamic balance are influenced by modifiable factors, an important point for designing future interventions.

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.

Background : The ActiGraph GT9X Link wearable accelerometer has been widely utilized for physical activity (PA) measurement but is no longer in production. The performance of the ActiGraph LEAP, regarded as its successor and released in 2023, remains unevaluated in comparison to other accelerometers. This study aimed to establish concurrent validity of the ActiGraph LEAP device through PA measurements and comparison with the GT9X Link. Methods : A total of 26 young adults (mean age = 21 ± 4 years) wore the ActiGraph LEAP and GT9X Link simultaneously for 7 consecutive days on their nondominant wrist. Monitor Independent Movement Summary units were utilized for calculation of daily sedentary time, light PA, and moderate to vigorous PA across valid days of wear (≥10 hr). Nonwear time was determined using a preexisting algorithm. Intraclass correlation coefficient, mean absolute percent error, equivalence tests (with GT9X Link as reference), and Bland–Altman plots assessed agreement between daily values of sedentary time, light PA, and moderate to vigorous PA recorded by each device across all valid days recorded among participants. Results : A total of 149 days of data were compared between the two devices. Across all PA levels, acceptable-excellent agreement was found (mean absolute percent error = 3.86%–13.86% and intraclass correlation coefficient = .94–.99), and Bland–Altman plots showed that at least 90% of recorded data fell within the 95% limits of agreement. Conclusions : The findings suggest that sedentary time and PA measurements using Monitor Independent Movement Summary units from the ActiGraph LEAP compare well with the GT9X Link, indicating that the LEAP performs similarly to the GT9X Link.

Background: Depression is prevalent among older adults and is known to negatively affect balance, ultimately leading to falls. However, few studies have investigated the effect of depression on static balance metrics beyond postural sway distance and area of older adults without mild cognitive impairment (MCI). Aims: To investigate if postural sway distance, sway area, medial-lateral (ML) sway range, anterior-posterior (AP) sway range, and center-of-pressure (COP) sway speed variability differed between non-cognitively impaired older adults with minimal-to-no depression (Minimally Depressive group) and mild-to-severe depression (Mildly-to-Severely Depressive group). Methods: A total of 204 community-dwelling older adults were included. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), MCI using the Rowland Universal Dementia Assessment Scale (RUDAS), and static balance metrics using the Balance Tracking System (BTrackS). Mann-Whitney U tests determined differences between groups. Results: Sway area, AP sway range, and ML sway range were significantly greater in the Mildly-to-Severely Depressive group than the Minimally Depressive group (p = 0.010, p = 0.016, and p = 0.031, respectively). Sway distance (p = 0.445) and COP sway speed variability (p = 0.193) were not significantly different between groups. Discussion: The findings revealed greater sway area, as well as greater ranges in the AP and ML directions, in the Mildly-to-Severely Depressive group when compared to the Minimally Depressive Group. Reduced concentration and affected postural stabilization mechanisms driven by depression may have attributed to these results. Conclusions: This study highlights the need for further understanding of how static balance metrics, such as directional sway, are affected by depression, thereby creating interventions tailored to individual’s postural sway characteristics to help reduce fall risk and improve balance. Trial registration: ClinicalTrials.gov (NCT05778604).