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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Pradeep Kumar, Danya Toosizadeh, Nima Mohler, Jane Ehsani, Hossein Mannier, Cassidy Laksari, Kaveh |
| Abstract | Background Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty. Trial registration The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229. |
| Related Links | https://bmcgeriatr.biomedcentral.com/counter/pdf/10.1186/s12877-020-01572-1.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712318 |
| DOI | 10.1186/s12877-020-01572-1 |
| Journal | BMC Geriatrics |
| Issue Number | 1 |
| Volume Number | 20 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2020-05-06 |
| Access Restriction | Open |
| Subject Keyword | Geriatrics Gerontology Aging Rehabilitation Frailty Daily physical activity Wearable sensors Continuous walking Performance parameters Geriatrics/Gerontology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Geriatrics and Gerontology |
| Journal Impact Factor | 3.4/2023 |
| 5-Year Journal Impact Factor | 4.1/2023 |
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