Abstract
Background
Environmental hazards (e.g., pedestrian traffic) cause falls and testing environment impacts gait in older adults. However, most fall risk evaluations do not assess real-world moving hazard avoidance.
Aims
This study examined the effect of fall history in older adults on acceleration profiles before, during, and after a near collision with a moving hazard, in laboratory and real-world settings.
Methods
Older adults with (n = 14) and without a fall history (n = 15) performed a collision avoidance walking task with a sudden moving hazard in real-world and laboratory settings. Gait acceleration and video data of participants’ first-person views were recorded. Four mixed effects multilevel models analyzed the magnitude and variability of mean and peak anteroposterior and mediolateral acceleration while walking before, during, and after the moving hazard in both environments.
Results
In the real-world environment, older adults without a fall history increased their mean anteroposterior acceleration after the moving hazard (p = 0.046), but those with a fall history did not (p > 0.05). Older adults without a fall history exhibited more intersubject variability than those with a fall history in mean (p < 0.001) and peak anteroposterior (p = 0.015) acceleration across environments and epochs. Older adults without a fall history exhibited a slower peak mediolateral reaction during the moving hazard (p = 0.014) than those with a fall history.
Conclusions
These results suggest that compared to older adults with a fall history, older adults without a fall history are more adaptable and able to respond last-minute to unexpected hazards. Older adults with a fall history exhibited more homogenous responses.
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Data availability
The data sets generated or analyzed during the current study are not publicly available, because they contain information that could compromise the privacy of the research participants but are available from the corresponding author upon request.
References
Rubenstein LZ, Josephson KR (2002) The epidemiology of falls and syncope. Clin Geriatr Med 18:141–158
Oxley J, O’Hern S, Burtt D et al (2018) Falling while walking: A hidden contributor to pedestrian injury. Accid Anal Prev 114:77–82. https://doi.org/10.1016/j.aap.2017.01.010
Valipoor S, Pati D, Kazem-Zadeh M et al (2020) Falls in older adults: a systematic review of literature on interior-scale elements of the built environment. J Aging Environ 34:351–374. https://doi.org/10.1080/02763893.2019.1683672
Rapos V, Cinelli ME, Grunberg R et al (2021) Collision avoidance behaviours between older adult and young adult walkers. Gait Posture 88:210–215. https://doi.org/10.1016/j.gaitpost.2021.05.033
Woollacott M, Inglin B, Manchester D (1988) Response preparation and posture control neuromuscular changes in the older adult. Ann N Y Acad Sci 515:42–53. https://doi.org/10.1111/j.1749-6632.1988.tb32964.x
Patla AE, Prentice SD, Gobbi LT (1996) visual control of obstacle avoidance during locomotion: strategies in young children, young and older adults. In: Advances in psychology. Elsevier, Amsterdam, pp 257–277
Mirelman A, Maidan I, Bernad-Elazari H et al (2017) Effects of aging on prefrontal brain activation during challenging walking conditions. Brain Cogn 115:41–46. https://doi.org/10.1016/j.bandc.2017.04.002
Hilliard MJ, Martinez KM, Janssen I et al (2008) Lateral balance factors predict future falls in community-living older adults. Arch Phys Med Rehabil 89:1708–1713. https://doi.org/10.1016/j.apmr.2008.01.023
Sturnieks DL, Menant J, Delbaere K et al (2013) Force-controlled balance perturbations associated with falls in older people: a prospective cohort study. PLoS ONE 8:e70981. https://doi.org/10.1371/journal.pone.0070981
Renggli D, Graf C, Tachatos N et al (2020) Wearable inertial measurement units for assessing gait in real-world environments. Front Physiol 11:90. https://doi.org/10.3389/fphys.2020.00090
Mc Ardle R, Del Din S, Donaghy P et al (2021) The impact of environment on gait assessment: considerations from real-world gait analysis in dementia subtypes. Sensors 21:813. https://doi.org/10.3390/s21030813
Del Din S, Godfrey A, Mazzà C et al (2016) Free-living monitoring of Parkinson’s disease: Lessons from the field: wearable technology for Parkinson’s disease. Mov Disord 31:1293–1313. https://doi.org/10.1002/mds.26718
Howcroft J, Lemaire E, Kofman J et al (2018) Dual-task elderly gait of prospective fallers and non-fallers: a wearable-sensor based analysis. Sensors 18:1275. https://doi.org/10.3390/s18041275
Craig JJ, Bruetsch AP, Huisinga JM (2019) Coordination of trunk and foot acceleration during gait is affected by walking velocity and fall history in elderly adults. Aging Clin Exp Res 31:943–950. https://doi.org/10.1007/s40520-018-1036-4
Lamb SE, Jørstad-Stein EC, Hauer K et al (2005) Development of a common outcome data set for fall injury prevention trials: the prevention of falls network Europe consensus. J Am Geriatr Soc 53:1618–1622. https://doi.org/10.1111/j.1532-5415.2005.53455.x
Tucker-Drob EM, Johnson KE, Jones RN (2009) The cognitive reserve hypothesis: a longitudinal examination of age-associated declines in reasoning and processing speed. Dev Psychol 45:431–446. https://doi.org/10.1037/a0014012
Mirelman A, Herman T, Brozgol M et al (2012) Executive function and falls in older adults: new findings from a five-year prospective study link fall risk to cognition. PLoS ONE 7:e40297. https://doi.org/10.1371/journal.pone.0040297
Gale CR, Cooper C, Aihie Sayer A (2016) Prevalence and risk factors for falls in older men and women: the English Longitudinal Study of Ageing. Age Ageing 45:789–794. https://doi.org/10.1093/ageing/afw129
Zukowski LA, Iyigün G, Giuliani CA et al (2020) Effect of the environment on gait and gaze behavior in older adult fallers compared to older adult non-fallers. PLOS ONE 15:e0230479. https://doi.org/10.1371/journal.pone.0230479
Zukowski LA, Tennant JE, Iyigun G et al (2021) Dual-tasking impacts gait, cognitive performance, and gaze behavior during walking in a real-world environment in older adult fallers and non-fallers. Exp Gerontol. https://doi.org/10.1016/j.exger.2021.111342
Nasreddine ZS, Phillips NA, Bédirian V et al (2005) The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53:695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x
Wechsler D (1997) Wechsler adult intelligence scale, 3rd edn. Psychological Corporation, San Antonio
Reynolds CR (2002) Comprehensive trail making test (CTMT). PRO-ED Inc, Austin
Podsiadlo D, Richardson S (1991) The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148
Dite W, Temple VA (2002) A clinical test of stepping and change of direction to identify multiple falling older adults. Arch Phys Med Rehabil 83:1566–1571
Herman T, Inbar-Borovsky N, Brozgol M et al (2009) The Dynamic Gait Index in healthy older adults: the role of stair climbing, fear of falling and gender. Gait Posture 29:237–241. https://doi.org/10.1016/j.gaitpost.2008.08.013
Haymes SA, Chen J (2004) Reliability and validity of the melbourne edge test and high/low contrast visual acuity chart. Optom Vis Sci Off Publ Am Acad Optom 81:308–316
Washburn RA, McAuley E, Katula J et al (1999) The physical activity scale for the elderly (PASE): evidence for validity. J Clin Epidemiol 52:643–651
Powell LE, Myers AM (1995) The activities-specific balance confidence (ABC) scale. J Gerontol A Biol Sci Med Sci 50A:M28-34
Lindemann U, Najafi B, Zijlstra W et al (2008) Distance to achieve steady state walking speed in frail elderly persons. Gait Posture 27:91–96. https://doi.org/10.1016/j.gaitpost.2007.02.005
R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing
RStudio Team (2021) RStudio: integrated development environment for R
Bates D, Maechler M, Bolker B, et al (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01
Lohse KR, Shen J, Kozlowski AJ (2020) Modeling longitudinal outcomes: a contrast of two methods. J Mot Learn Dev 8:145–165. https://doi.org/10.1123/jmld.2019-0007
Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. J Wildl Manag 67:655. https://doi.org/10.2307/3802723
Gerards MHG, Meijer K, Karamanidis K et al (2021) Adaptability to balance perturbations during walking as a potential marker of falls history in older adults. Front Sports Act Living 3:682861. https://doi.org/10.3389/fspor.2021.682861
dos Santos LO, Carvalho de Abreu DC, Moraes R (2018) Performance of faller and nonfaller older adults on a motor–motor interference task. J Mot Behav 50:293–306. https://doi.org/10.1080/00222895.2017.1341380
Falk J, Strandkvist V, Vikman I et al (2021) What explains successful or unsuccessful postural adaptations to repeated surface perturbations among older adults? Int J Environ Res Public Health 18:12069. https://doi.org/10.3390/ijerph182212069
Bauby CE, Kuo AD (2000) Active control of lateral balance in human walking. J Biomech 33:1433–1440. https://doi.org/10.1016/S0021-9290(00)00101-9
Skiadopoulos A, Stergiou N (2021) Risk-of-falling related outcomes improved in community-dwelling older adults after a 6-week sideways walking intervention: a feasibility and pilot study. BMC Geriatr 21:60. https://doi.org/10.1186/s12877-021-02010-6
Acknowledgements
The authors would like to thank Alyssa Kappert for her assistance in the acquisition and post-processing of the data.
Funding
This research was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR001111 to Lisa A. Zukowski: https://ncats.nih.gov/. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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All authors contributed to the study conception and design. Funding was acquired by LAZ, CAG, and PP. Material preparation and data collection were performed by LAZ, GI, CG, and PP. Data analysis was performed by SAB and JAR. The first draft of the manuscript was written by LAZ and SAB, and all authors revised it critically for important intellectual content. All authors read and approved the final manuscript.
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Zukowski, L.A., Brinkerhoff, S.A., Iyigun, G. et al. Fall history in older adults impacts acceleration profiles after a near collision with a moving pedestrian hazard. Aging Clin Exp Res 35, 621–631 (2023). https://doi.org/10.1007/s40520-023-02345-7
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DOI: https://doi.org/10.1007/s40520-023-02345-7