"Fall Risk Scoring" in Outpatient Gait Analysis: Validation of a New Fall Risk Assessment for Nursing Home Residents

Abstract

Falls in senior home residents are common. Individual preventive training can lower the fall risk. To detect the need for training, a systematic assessment of the individual fall risk is needed. The aim of this study was thus to assess whether a fall risk score based on free field insole measurements can distinguish between an at-risk group of senior home residents and a healthy young control group. A published fall risk score was used in senior home residents over the age of 75 and a young (< 40 years) control group to determine the individual fall risk. In addition, the fall events over 12 months were assessed. Statistical analysis including ROC analysis was performed to determine the ability of the score to detect participants at heightened fall risk. In total, 18 nursing home residents and 9 young control participants were included. Of the nursing home residents, 15 had at least one fall, with a total of 37 falls recorded over 12 months. In the control group, no falls were recorded. The fall risk score was significantly different between nursing home residents and the control group (9.2 + 3.2 vs. 5.7 ± 2.2). Furthermore, the score significantly differentiated fallers from non-fallers (10.3 ± 1.8 vs. 5.2 ± 2.5), with a cut-off > 7.5 (AUC: 0.95) and a sensitivity of 86.7% (specificity 83.3%). The fall risk score is able to detect the difference between senior nursing home residents and young, healthy controls, as well as between fallers and non-fallers. Its main proof of concept is demonstrated, as based on movement data outside special gait labs, and it can simplify the risk of fall determination in geriatric nursing home residents and can now be used in further, prospective studies.

Keywords

fall risk - free field gait analysis - geriatric trauma

Publikationsverlauf

Eingereicht: 19. Mai 2022

Angenommen nach Revision: 27. Juli 2023

Artikel online veröffentlicht:
09. Oktober 2023

• References

• 1 Bergen G, Stevens MR, Burns ER. Falls and Fall Injuries Among Adults Aged ≥65 Years — United States, 2014. MMWR Morb Mortal Wkly Rep 2016; 65: 993-998
• 2 Houry D, Florence C, Baldwin G. et al. The CDC Injury Center’s Response to the Growing Public Health Problem of Falls Among Older Adults. Am J Lifestyle Med 2016; 10: 74-77
• 3 Burns ER, Stevens JA, Lee R. The direct costs of fatal and non-fatal falls among older adults — United States. J Safety Res 2016; 58: 99-103
• 4 Saß AC, Varnaccia G, Rommel A. Robert Koch-Institut. Sturzunfälle in Deutschland. 2016 Zugriff am 14. August 2023 unter: https://edoc.rki.de/handle/176904/3064
• 5 Rubenstein LZ. Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 2006; 35 (02) ii37-ii41
• 6 Morri M, Ambrosi E, Chiari P. et al. One-year mortality after hip fracture surgery and prognostic factors: a prospective cohort study. Sci Rep 2019; 9: 18718
• 7 Civinini R, Paoli T, Cianferotti L. et al. Functional outcomes and mortality in geriatric and fragility hip fractures—results of an integrated, multidisciplinary model experienced by the “Florence hip fracture unit”. Int Orthop 2019; 43: 187-192
• 8 Gillespie LD, Robertson MC, Gillespie WJ. et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 2012; (09) CD007146
• 9 Bet P, Castro PC, Ponti MA. Foreseeing future falls with accelerometer features in active community-dwelling older persons with no recent history of falls. Exp Gerontol 2021; 143: 111139
• 10 Park SH. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res 2018; 30: 1-16
• 11 Bet P, Castro PC, Ponti MA. Fall detection and fall risk assessment in older person using wearable sensors: A systematic review. Int J Med Inform 2019; 130: 103946
• 12 Tinetti ME, Williams TF, Mayewski R. Fall risk index for elderly patients based on number of chronic disabilities. Am J Med 1986; 80: 429-434
• 13 Senden R, Savelberg HHCM, Grimm B. et al. Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling. Gait Posture 2012; 36: 296-300
• 14 Bezold J, Krell-Roesch J, Eckert T. et al. Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review. Eur Rev Aging Phys Act 2021; 18: 15
• 15 Di Rosa M, Hausdorff JM, Stara V. et al. Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study. Gait Posture 2017; 55: 6-11
• 16 Czech MD, Psaltos D, Zhang H. et al. Age and environment-related differences in gait in healthy adults using wearables. NPJ Digit Med 2020; 3: 127
• 17 Del Din S, Galna B, Godfrey A. et al. Analysis of Free-Living Gait in Older Adults With and Without Parkinson’s Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics. J Gerontol A Biol Sci Med Sci 2019; 74: 500-506
• 18 van Schooten KS, Pijnappels M, Rispens SM. et al. Ambulatory Fall-Risk Assessment: Amount and Quality of Daily-Life Gait Predict Falls in Older Adults. J Gerontol A Biol Sci Med Sci 2015; 70: 608-615
• 19 Unger EW, Histing T, Rollmann MF. et al. Development of a dynamic fall risk profile in elderly nursing home residents: A free field gait analysis based study. Arch Gerontol Geriatr 2021; 93: 104294
• 20 Montero-Odasso M, Sarquis-Adamson Y, Song HY. et al. Polypharmacy, Gait Performance, and Falls in Community-Dwelling Older Adults. Results from the Gait and Brain Study. J Am Geriatr Soc 2019; 67: 1182-1188
• 21 Ambrose AF, Paul G, Hausdorff JM. Risk factors for falls among older adults: A review of the literature. Maturitas 2013; 75: 51-61
• 22 Weiss A, Herman T, Giladi N. et al. Objective Assessment of Fall Risk in Parkinson’s Disease Using a Body-Fixed Sensor Worn for 3 Days. PLoS One 2014; 9: e96675
• 23 Rivolta MW, Aktaruzzaman Md, Rizzo G. et al. Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis. Artif Intell Med 2019; 95: 38-47
• 24 Nithman RW, Vincenzo JL. How steady is the STEADI? Inferential analysis of the CDC fall risk toolkit. Arch Gerontol Geriatr 2019; 83: 185-194
• 25 Johansson J, Nordström A, Gustafson Y. et al. Increased postural sway during quiet stance as a risk factor for prospective falls in community-dwelling elderly individuals. Age Ageing 2017; 46: 964-970
• 26 Lycke C, Bork H, Feindt B. et al. Evaluation of the Fall Risk of Orthopedic Trauma Surgery Patients by Establishing a Fall Risk Score and a Procedure Instruction in Clinical Routine. Z Orthop Unfall 2019; 157: 440-444
• 27 Bautmans I, Jansen B, Van Keymolen B. et al. Reliability and clinical correlates of 3D-accelerometry based gait analysis outcomes according to age and fall-risk. Gait Posture 2011; 33: 366-372
• 28 Ostrosky KM, VanSwearingen JM, Burdett RG. et al. A Comparison of Gait Characteristics in Young and Old Subjects. Phys Ther 1994; 74: 637-644
• 29 Howcroft J, Kofman J, Lemaire ED. et al. Analysis of dual-task elderly gait in fallers and non-fallers using wearable sensors. J Biomech 2016; 49: 992-1001
• 30 Howcroft J, Kofman J, Lemaire ED. Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors. IEEE Trans Neural Syst Rehabil Eng 2017; 25: 1812-1820
• 31 Kiprijanovska I, Gjoreski H, Gams M. Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning. Sensors 2020; 20: 5373
• 32 Howcroft J, Lemaire ED, Kofman J. Wearable-Sensor-Based Classification Models of Faller Status in Older Adults. PLoS ONE 2016; 11: e0153240
• 33 Sun R, Sosnoff JJ. Novel sensing technology in fall risk assessment in older adults: a systematic review. BMC Geriatrics 2018; 18: 14
• 34 Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016; 1: 112