scholarly journals Detection of Real-World Trips in At-Fall Risk Community Dwelling Older Adults Using Wearable Sensors

2020 ◽  
Vol 7 ◽  
Author(s):  
Shirley Handelzalts ◽  
Neil B. Alexander ◽  
Nicholas Mastruserio ◽  
Linda V. Nyquist ◽  
Debra M. Strasburg ◽  
...  
Author(s):  
Grainne Vavasour ◽  
Oonagh M. Giggins ◽  
Julie Doyle ◽  
Daniel Kelly

Abstract Background Globally the population of older adults is increasing. It is estimated that by 2050 the number of adults over the age of 60 will represent over 21% of the world’s population. Frailty is a clinical condition associated with ageing resulting in an increase in adverse outcomes. It is considered the greatest challenge facing an ageing population affecting an estimated 16% of community-dwelling populations worldwide. Aim The aim of this systematic review is to explore how wearable sensors have been used to assess frailty in older adults. Method Electronic databases Medline, Science Direct, Scopus, and CINAHL were systematically searched March 2020 and November 2020. A search constraint of articles published in English, between January 2010 and November 2020 was applied. Papers included were primary observational studies involving; older adults aged > 60 years, used a wearable sensor to provide quantitative measurements of physical activity (PA) or mobility and a measure of frailty. Studies were excluded if they used non-wearable sensors for outcome measurement or outlined an algorithm or application development exclusively. The methodological quality of the selected studies was assessed using the Appraisal Tool for Cross-sectional Studies (AXIS). Results Twenty-nine studies examining the use of wearable sensors to assess and discriminate between stages of frailty in older adults were included. Thirteen different body-worn sensors were used in eight different body-locations. Participants were community-dwelling older adults. Studies were performed in home, laboratory or hospital settings. Postural transitions, number of steps, percentage of time in PA and intensity of PA together were the most frequently measured parameters followed closely by gait speed. All but one study demonstrated an association between PA and level of frailty. All reports of gait speed indicate correlation with frailty. Conclusions Wearable sensors have been successfully used to evaluate frailty in older adults. Further research is needed to identify a feasible, user-friendly device and body-location that can be used to identify signs of pre-frailty in community-dwelling older adults. This would facilitate early identification and targeted intervention to reduce the burden of frailty in an ageing population.


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 ◽  
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 ◽  
Author(s):  
Takuya Ataka ◽  
Noriyuki Kimura ◽  
Atsuko Eguchi ◽  
Etsuro Matsubara

Abstract Background: In this manuscript, we aimed at investigating whether objectively measured lifestyle factors, including walking steps, sedentary time, amount of unforced physical activity, level of slight and energetic physical activity, conversation time, and sleep parameters altered before and during the COVID-19 pandemic among community-dwelling older adults.Methods: Data were obtained from a prospective cohort study conducted from 2015 to 2019 and a subsequent dementia prevention study undertaken in September 2020. Community-dwelling adults aged ≥65 years wore wearable sensors before and during the pandemic.Results: A total of 56 adults were enrolled in this study. The mean age was 74.2±3.9 years, and 58.9% (n=33) of the participants were female. The moderate and vigorous physical activity time significantly decreased and sedentary time significantly increased during the pandemic. Conclusions: This is the first study to demonstrate differences in objectively assessed lifestyle factors before and during the COVID-19 pandemic among community-dwelling older adults. The findings show that the pandemic has adversely affected physical activity among older adults living on their own in Japan.


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

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 622 ◽  
Author(s):  
Thomas Gerhardy ◽  
Katharina Gordt ◽  
Carl-Philipp Jansen ◽  
Michael Schwenk

Background: Decreasing performance of the sensory systems’ for balance control, including the visual, somatosensory and vestibular system, is associated with increased fall risk in older adults. A smartphone-based version of the Timed Up-and-Go (mTUG) may allow screening sensory balance impairments through mTUG subphases. The association between mTUG subphases and sensory system performance is examined. Methods: Functional mobility of forty-one community-dwelling older adults (>55 years) was measured using a validated mTUG. Duration of mTUG and its subphases ‘sit-to-walk’, ‘walking’, ‘turning’, ‘turn-to-sit’ and ‘sit-down’ were extracted. Sensory systems’ performance was quantified by validated posturography during standing (30 s) under different conditions. Visual, somatosensory and vestibular control ratios (CR) were calculated from posturography and correlated with mTUG subphases. Results: Vestibular CR correlated with mTUG total time (r = 0.54; p < 0.01), subphases ‘walking’ (r = 0.56; p < 0.01), and ‘turning’ (r = 0.43; p = 0.01). Somatosensory CR correlated with mTUG total time (r = 0.52; p = 0.01), subphases ‘walking’ (r = 0.52; p < 0.01) and ‘turning’ (r = 0.44; p < 0.01). Conclusions: Supporting the proposed approach, results indicate an association between specific mTUG subphases and sensory system performance. mTUG subphases ‘walking’ and ‘turning’ may allow screening for sensory system deterioration. This is a first step towards an objective, detailed and expeditious balance control assessment, however needing validation in a larger study.


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