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One-Year, Weekly Online Survey to Monitor Healthcare Visits and its Association With Walking-Speed in Older Adults

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Abstract

Walking speed has been highlighted as an important biomarker of healthy aging. 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. This exploratory study was 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.
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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.
Background
Methodology
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.
References
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:
https://doi.org/10.1182/blood.2019000758
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:
https://doi.org/10.1016/j.jchf.2015.12.017
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:
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-015-0470-9
Acknowledgements
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.
Results
-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:
URL: https://www.ncbi.nlm.nih.gov/pubmed/23459976
- 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/
Bibliography
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
Older Adults
Ibukun E. Fowe, MBChB, MSGH,1
OHSU-PSU School of Public Health, Portland, Oregon.2
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Background There is no criterion reference for assessing healthy ageing and this creates difficulties when conducting and comparing research on ageing across studies. A cardinal feature of ageing is loss of function which translates into wide-ranging consequences for the individual and for family, carers and society. We undertook comprehensive reviews of the literature searching for biomarkers of ageing on five ageing-related domains including physical capability and cognitive, physiological and musculoskeletal, endocrine and immune functions. Where available, we used existing systematic reviews, meta-analyses and other authoritative reports such as the recently launched NIH Toolbox for assessment of neurological and behavioural function, which includes test batteries for cognitive and motor function (the latter described here as physical capability). We invited international experts to comment on our draft recommendations. In addition, we hosted an experts workshop in Newcastle, UK, on 22–23 October 2012, aiming to help capture the state-of-the-art in this complex area and to provide an opportunity for the wider ageing research community to critique the proposed panel of biomarkers. Discussion Here we have identified important biomarkers of healthy ageing classified as subdomains of the main areas proposed. Cardiovascular and lung function, glucose metabolism and musculoskeletal function are key subdomains of physiological function. Strength, locomotion, balance and dexterity are key physical capability subdomains. Memory, processing speed and executive function emerged as key subdomains of cognitive function. Markers of the HPA-axis, sex hormones and growth hormones were important biomarkers of endocrine function. Finally, inflammatory factors were identified as important biomarkers of immune function. Summary We present recommendations for a panel of biomarkers that address these major areas of function which decline during ageing. This biomarker panel may have utility in epidemiological studies of human ageing, in health surveys of older people and as outcomes in intervention studies that aim to promote healthy ageing. Further, the inclusion of the same common panel of measures of healthy ageing in diverse study designs and populations may enhance the value of those studies by allowing the harmonisation of surrogate endpoints or outcome measures, thus facilitating less equivocal comparisons between studies and the pooling of data across studies. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0470-9) contains supplementary material, which is available to authorized users.
Article
This study aimed to evaluate if gait speed and grip strength are useful predictors of clinical outcomes among older adults with blood cancers. We prospectively recruited 448 patients aged 75 and older presenting for initial consultation at the MDS/leukemia, myeloma, or lymphoma clinics of a large tertiary hospital, who agreed to assessment of gait and grip. A subset of 314 patients followed for at least 6 months at local institutions was evaluated for unplanned hospital or emergency department (ED) use. Cox proportional hazards models calculated hazard ratios (HR) and 95% confidence intervals (95% CI) for survival, and logistic regression to calculate odds ratios (OR) for hospital or ED use. Mean age was 79.7 (+/-4.0 SD) years. After adjustment for age, sex, Charlson comorbidity index (CCI), cognition, treatment intensity, and cancer aggressiveness/type, every 0.1 m/s decrease in gait speed was associated with higher mortality (HR=1.22; 95% CI:1.15-1.30), odds of unplanned hospitalizations (OR 1.33; 95% CI 1.16-1.51) and ED visits (OR 1.34; 95% CI 1.17-1.53). Associations held among patients with good ECOG performance status (0 or 1). Every 5 kg decrease in grip strength was associated with worse survival (adjusted HR =1.24; 95% CI: 1.07-1.43), but not hospital or ED use. A model with gait speed and all covariates had comparable predictive power to comprehensive, validated frailty indexes (phenotype and cumulative deficit) and all covariates. In summary, gait speed is an easily obtained "vital sign" that accurately identifies frailty and predicts outcomes independent of performance status among older patients with blood cancers.
Article
Objectives: The aim of this study was to assess the relationship between gait speed and the risk for death and/or hospital admission in older patients with heart failure (HF). Background: Gait speed is a reliable single marker of frailty in older people and can predict falls, disability, hospital admissions, and mortality. Methods: In total, 331 community-living patients ≥70 years of age (mean age 78 ± 6 years, 43% women, mean ejection fraction 35 ± 11%, mean New York Heart Association functional class 2.7 ± 0.6) in stable condition and receiving optimized therapy for chronic HF were prospectively enrolled and followed for 1 year. Gait speed was measured at the usual pace over 4 m, and cutoffs were defined by tertiles: ≤0.65, 0.66 to 0.99, and ≥1.0 m/s. Results: There was a significant association between gait speed tertiles and 1-year mortality: 38.3%, 21.9%, and 9.1% (p < 0.001), respectively. On multivariate analysis, gait speed was associated with a lower risk for all-cause death (hazard ratio: 0.62; 95% confidence interval: 0.43 to 0.88) independently of age, ejection fraction <20%, systolic blood pressure, anemia, and absence of beta-blocker therapy. Gait speed was also associated with a lower risk for hospitalization for HF and all-cause hospitalization. When gait speed was added to the multiparametric Cardiac and Comorbid Conditions Heart Failure risk score, it improved the accuracy of risk stratification for all-cause death (net reclassification improvement 0.49; 95% confidence interval: 0.26 to 0.73, p < 0.001) and HF admissions (net reclassification improvement 0.37; 95% confidence interval: 0.15 to 0.58; p < 0.001). Conclusions: Gait speed is independently associated with death, hospitalization for HF, and all-cause hospitalization and improves risk stratification in older patients with HF evaluated using the Cardiac and Comorbid Conditions Heart Failure score. Assessment of frailty using gait speed is simple and should be part of the clinical evaluation process.