RESEARCH POSTER PRESENTATION DESIGN © 2019
Walking speed is recognized in the literature as an indicator of health status. Lower walking speeds in the elderly
population have been associated with poorer health outcomes, lower survival rates from chronic health conditions
such as cardiovascular and hematologic conditions and higher hospitalization rates for heart failure and
hematologic cancers.1,2 Conversely, higher walking speeds in this population have been associated with better
health outcomes. Walking speed has also been highlighted as an important biomarker of healthy aging.3 Studies
have indicated the need to incorporate walking speed monitoring as part of routine care for the elderly population in
order to better predict clinical and health outcomes.1,2,3 This is an exploratory study that is focused on evaluating
the association between walking speed and health care visits in community dwelling older adults aged 60 years and
above. This is with a view to further explore the possibility of the use of walking speed as a predictive measure of
health care utilization based on the already established relationship between walking speed and health status or
health outcomes within the literature. 1,2,3.
Inferences and Conclusion
Participants with lower walking speeds may need more healthcare monitoring by health care providers to prevent
unplanned health care utilizations. Increasing walking speed with increasing planned healthcare
utilization may be indicative of improving health status due to adequate clinical care by healthcare providers.
Overall, there is an opportunity for further studies with a larger number of ER users to explore the use of walking
speed to predict health care utilization and also the exploration of the use of digitally enabled walking speed
monitors (e.g. in-home sensors and sensors in wearable devices) as potential predictors of unplanned healthcare
utilization in this population. Future research on walking speed variability and (sudden) decline can help in
understanding how walking speed can be utilized as an important biomarker for aging in different populations and
its relationship to healthcare use.
1. Liu M, DuMontier C, Murillo A, et al. Gait speed, grip strength and clinical outcomes in older patients with
hematologic malignancies. Blood 2019. [cited 2019 Aug 14] Available from: URL:
2.Pulignano G, Del Sindaco D, Di Lenarda A. Incremental Value of Gait Speed in Predicting Prognosis of Older
Adults With Heart Failure. JACC: HF Apr 2016. vol. 4 no. 4. [cited 2019 Aug 14] Available from: URL:
3.Lara J, Cooper R, Nissan. A proposed panel of biomarkers of healthy ageing. BMC Medicinevolume 13, Article
number: 222 (2015). [cited 2019 Aug 14]. Available from: URL:
This research was supported in part by the Collaborative Aging-in-place Research Using Technology (CART) initiative (National Institutes of Health U2C AG0543701; Department of Veteran Affairs Health Services Research and
Development IIR 17-144), the Oregon Clinical Translational Research Institute CTSA award (National Center for Advancing Translational Sciences UL1 TR002369), and the HomeSHARE Community Infrastructure award (National
Science Foundation 1629468). CART is funded by the Office Of The Director, National Institutes Of Health (OD), National Center For Advancing Translational Sciences (NCATS), National Institute of Biomedical Imaging And
Bioengineering (NIBIB), National Institute of Nursing Research (NINR), National Institute on Aging (NIA), National Institute of Neurological Disorders And Stroke (NINDS), National Cancer Institute (NCI) and the Departments of
Veteran Affairs Health Services Research and Development (VA HSR&D). Special acknowledgements go to Nicole Sharma BA, Nora Mattek MPH, Sarah Gothard BA, Zachary Beattie PhD, Dr Chao Yi Wu PhD, Neal Wallace MPP, PhD,
and Dr. Jeffrey Kaye MD who assisted me in the course of the study to help make it possible.
Study Design and Participants: Patient generated health data from a longitudinal cohort study which was developed
by the Oregon Center for Aging and Technology (ORCATECH) was used in this study. Study participants were
independent, able to live alone, not wheelchair bound and had no precluding medical conditions for participating in
the study. Participants were assessed to have normal cognition and normal average health status for age. Health
status in the past week including health care visits were self-reported once weekly by participants through an online
questionnaire. Participants with no filled forms in two weeks were contacted by a representative. A retrospective
analysis of planned and unplanned healthcare visits reported by 203 older adults (age > 60 years) through the
weekly on-line survey over a one-year period (June 2018 - July 2019) was compared with baseline walking-speed
of study participants.
Walking Speed Test: https://images.app.goo.gl/CXgMZHMdfVCTUXmLA
Clinical Assessment procedures: Baseline stopwatch measured walking speed of participants’ average pace was
measured at the outset of the study, measurement was based on a 15 foot out and back timed walk. Other baseline
clinical assessments included standard cognitive tests, health status evaluations, mental state examinations and the
geriatric depression scale. Care was taken to select study participants that were considered in relatively good health
conditions to avoid the selection of study participants with co-morbidities or uncontrolled health conditions that
could result in confounders in the analysis.
Statistical analysis: Spearman’s rank correlation coefficient was used to assess the strength of association between
the frequency of planned and unplanned (ER) healthcare visits and average baseline walking speed.
The median, max, and min of walking speeds for all study participants were 0.45, 0.83, and 0.17 (m/s)
respectively, while that of all participants with ER visits were 0.45, 0.63, 0.21 (m/s). Although most of the
correlations observed were not statistically significant due to limitations with the sample size, it was
observed that ER visits increased with decreasing average baseline walking speed; furthermore, one and two-
time ER visits were found to be moderately negatively correlated with walking-speed (Spearman’s r = -0.39,
p- value = 0.069), and all ER visits were observed to be moderately negatively correlated with walking speed
(Spearman’s r = -0.34, p-value = 0.063). A weak negative correlation was observed between age and walking
speed (Pearson’s r = -0.27, p-value = 7.738e-05), however, no statistically significant relationship was
observed between age and ER visits. Planned visits increased with increasing average baseline walking
speed; however, a statistically significant relationship could not be established.
-O Beauchet, G Allali, C Launay, F R Herrmann, C Annweiler. Gait variability at fast-pace walking speed: a biomarker
of mild cognitive impairment? Journ of Nutri, Hlth & Aging 2013, 17 (3): 235-9. [Cited 2019 Aug 14]. Available from:
- White DK, Neogi T, Nevitt MC, et al. Trajectories of gait speed predict mortality in well-functioning older adults: the
Health, Aging and Body Composition study. J Gerontol A Biol Sci Med Sci. 2013;68(4):456–464.
doi:10.1093/gerona/gls197. [Cited 2019 Aug 14]. Available from: URL:https://www.ncbi.nlm.nih.gov/pubmed/23051974
- Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-
wearable systems, highlighting clinical applications. Sensors (Bas). 2014;14(2):3362–3394. Published 2014 Feb 19.
[Cited 2019 Aug 14]. Available from: URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958266/
Figure I: A graphical illustration of the relationship between walking speed and ER Visits for one
and two-time ER Visits
Wireless Monitor: MIT News. https://bit.ly/2qFPG1K Digital shoe Insoles: https://bit.ly/2TyeuZo
1Master of Science in Global Health; 2PhD Student at the OHSU-PSU sch of Public Health in Portland Oregon
One-Year, Weekly Online Survey to Monitor Healthcare Visits and its Association With Walking-Speed in
Ibukun E. Fowe, MBChB, MSGH,1
OHSU-PSU School of Public Health, Portland, Oregon.2