Relationships Between Performance on Assessments of Executive Function and Fall Risk Screening Measures in Community-Dwelling Older Adults

2016 ◽  
Vol 39 (2) ◽  
pp. 89-96 ◽  
Author(s):  
Jennifer Blackwood ◽  
Tiffany Shubert ◽  
Kieran Forgarty ◽  
Carla Chase
Author(s):  
Hazel Williams-Roberts ◽  
Catherine Arnold ◽  
Daphne Kemp ◽  
Alexander Crizzle ◽  
Shanthi Johnson

ABSTRACT Given the rising numbers of older adults in Canada experiencing falls, evidence-based identification of fall risks and plans for prevention across the continuum of care is a significant priority for health care providers. A scoping review was conducted to synthesize published international clinical practice guidelines (CPGs) and recommendations for fall risk screening and assessment in older adults (defined as 65 years of age and older). Of the 22 CPGs, 6 pertained to multiple settings, 9 pertained to community-dwelling older adults only, 2 each pertained to acute care and long-term care settings only, and 3 did not specify setting. Two criteria, prior fall history and gait and balance abnormalities, were applied either independently or sequentially in 19 CPG fall risk screening algorithms. Fall risk assessment components were more varied across CPGs but commonly included: detailed fall history; detailed evaluation of gait, balance, and/or mobility; medication review; vision; and environmental hazards assessment. Despite these similarities, more work is needed to streamline assessment approaches for heterogeneous and complex older adult populations across the care continuum. Support is also needed for sustainable implementation of CPGs in order to improve health outcomes.


2017 ◽  
Vol 1 (suppl_1) ◽  
pp. 1051-1051
Author(s):  
C. Smith ◽  
C. Bula ◽  
H. Krief ◽  
L. Seematter-Bagnoud ◽  
B. Santos-Eggimann

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Hide ◽  
Y. Ito ◽  
N. Kuroda ◽  
M. Kanda ◽  
W. Teramoto

AbstractThis study investigates how the multisensory integration in body perception changes with increasing age, and whether it is associated with older adults’ risk of falling. For this, the rubber hand illusion (RHI) and rubber foot illusion (RFI) were used. Twenty-eight community-dwelling older adults and 25 university students were recruited. They viewed a rubber hand or foot that was stimulated in synchrony or asynchrony with their own hidden hand or foot. The illusion was assessed by using a questionnaire, and measuring the proprioceptive drift and latency. The Timed Up and Go Test was used to classify the older adults into lower and higher fall-risk groups. No difference was observed in the RHI between the younger and older adults. However, several differences were observed in the RFI. Specifically, the older adults with a lower fall-risk hardly experienced the illusion, whereas those with a higher fall-risk experienced it with a shorter latency and no weaker than the younger adults. These results suggest that in older adults, the mechanism of multisensory integration for constructing body perception can change depending on the stimulated body parts, and that the risk of falling is associated with multisensory integration.


2018 ◽  
Vol 4 ◽  
pp. 233372141881149 ◽  
Author(s):  
Shunsuke Murata ◽  
Sho Nakakubo ◽  
Tsunenori Isa ◽  
Yamato Tsuboi ◽  
Kohtaroh Torizawa ◽  
...  

2018 ◽  
Author(s):  
Yang Yang ◽  
John P Hirdes ◽  
Joel A Dubin ◽  
Joon Lee

BACKGROUND  Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC). OBJECTIVE  This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data. METHODS  A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF). RESULTS  Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G0+G1} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G0 and {G1+G2+} did not result in better classification performance than that between {G0+G1} and G2+. CONCLUSIONS  Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments.


2021 ◽  
Vol 37 (3) ◽  
pp. 198-206
Author(s):  
Brenda S. Howard ◽  
Fiona Brown Jones ◽  
Aundrea Sellers Steenblock ◽  
Kiersten Ham Butler ◽  
Ellen Thomas Laub ◽  
...  

Author(s):  
Jen-Hau Chen ◽  
Tsung-Yu Kuo ◽  
Hwa-Lung Yu ◽  
Charlene Wu ◽  
Su-Ling Yeh ◽  
...  

Background: Previous studies have assessed limited cognitive domains with relatively short exposure to air pollutants, and studies in Asia are limited. Objective: This study aims to explore the association between long-term exposure to air pollutants and cognition in community-dwelling older adults. Methods: This four-year prospective cohort study recruited 605 older adults at baseline (2011–2013) and 360 participants remained at four-year follow-up. Global and domain-specific cognition were assessed biennially. Data on PM2.5 (particulate matter ≤ 2.5 μm diameter, 2005–2015), PM10 (1993–2015), and nitrogen dioxide (NO2, 1993–2015) were obtained from Taiwan Environmental Protection Administration (TEPA). Bayesian Maximum Entropy was utilized to estimate the spatiotemporal distribution of levels of these pollutants. Results: Exposure to high-level PM2.5 (>29.98 μg/m3) was associated with an increased risk of global cognitive impairment (adjusted odds ratio = 4.56; β = −0.60). High-level PMcoarse exposure (>26.50 μg/m3) was associated with poor verbal fluency (β = −0.19). High-level PM10 exposure (>51.20 μg/m3) was associated with poor executive function (β = −0.24). Medium-level NO2 exposure (>28.62 ppb) was associated with better verbal fluency (β = 0.12). Co-exposure to high concentrations of PM2.5, PMcoarse or PM10 and high concentration of NO2 were associated with poor verbal fluency (PM2.5 and NO2: β = −0.17; PMcoarse and NO2: β = −0.23; PM10 and NO2: β = −0.21) and poor executive function (PM10 and NO2: β = −0.16). These associations became more evident in women, apolipoprotein ε4 non-carriers, and those with education > 12 years. Conclusion: Long-term exposure to PM2.5 (higher than TEPA guidelines), PM10 (lower than TEPA guidelines) or co-exposure to PMx and NO2 were associated with poor global, verbal fluency, and executive function over 4 years.


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