Association between walking speed and age in healthy, free-living individuals using mobile accelerometry--a cross-sectional study.
ABSTRACT Walking speed is a fundamental parameter of human motion and is increasingly considered as an important indicator of individuals' health status.
To evaluate the relationship of gait parameters, and demographic and physical characteristics in healthy men and women.
Recruitment of a subsample (n = 358) of male and female blood donors taking part in the Cambridge CardioResource study. Collection of demographic data, measurement of physical characteristics (height, weight and blood pressure) and assessment of 7-day, free-living activity parameters using accelerometry and a novel algorithm to measure walking speed. Participants were a median (interquartile range[IQR]) age of 49 (16) years; 45% women; and had a median (IQR) BMI of 26 (5.4).
In this study, the hypothesis that walking speed declines with age was generated using an initial 'open' dataset. This was subsequently validated in a separate 'closed' dataset that showed a decrease of walking speed of -0.0037 m/s per year. This is equivalent to a difference of 1.2 minutes, when walking a distance of 1 km aged 20 compared to 60 years. Associations between walking speed and other participant characteristics (i.e. gender, BMI and blood pressure) were non-significant. BMI was negatively correlated with the number of walking and running steps and longest non-stop distance.
This is the first study using accelerometry which shows an association between walking speed and age in free-living, healthy individuals. Absolute values of gait speed are comparable to published normal ranges in clinical settings. This study highlights the potential use of mobile accelerometry to assess gait parameters which may be indicative of future health outcomes in healthy individuals.
- Journal of geriatric physical therapy (2001) 01/2009; 32(2):46-9. · 1.21 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: 878 Mobility, or locomotion, could be considered a distinguishing feature of the entire animal kingdom. It is central to the ability to obtain food, escape danger and survive. Interestingly, across virtually all animal species, aging is associated with generalized slowing of movement (1-3). In humans, the capacity to move underlies many basic and community functions necessary for independence. For these reasons, a simple indicator of mobility has the potential to serve as a core indicator of health and function in aging and disease. In the current issue of JNHA, a Task Force of the International Academy on Nutrition and Aging has made an invaluable contribution to the field of mobility and aging by summarizing the literature on the predictive capacity of usual gait speed in older persons (4). Using rigorous methods to identify sources of evidence, they combined information from diverse aging populations and used high priority outcomes to assess potential validity. Gait speed was a powerful predictor of survival, disability, hospitalization or institutionalization, dementia and falls. The Task Force also considered practical issues related to feasibility in clinical settings in order to generate initialThe Journal of Nutrition Health and Aging 04/2012; 13(10):878-880. · 2.39 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Extended walking speed is a predictor of incident cardiovascular disease (CVD) in older individuals, but the ability of an objective short-distance walking speed test to stratify the severity of preclinical conditions remains unclear. This study examined whether performance in an 8-ft walking speed test is associated with metabolic risk factors and subclinical atherosclerosis. Cross-sectional. Setting Epidemiological cohort. 530 adults (aged 63 + or - 6 years, 50.3% male) from the Whitehall II cohort study with no known history or objective signs of CVD. Electron beam computed tomography and ultrasound was used to assess the presence and extent of coronary artery calcification (CAC) and carotid intima-media thickness (IMT), respectively. High levels of CAC (Agatston score >100) were detected in 24% of the sample; the mean IMT was 0.75 mm (SD 0.15). Participants with no detectable CAC completed the walking course 0.16 s (95% CI 0.04 to 0.28) faster than those with CAC > or = 400. Objectively assessed, but not self-reported, faster walking speed was associated with a lower risk of high CAC (odds ratio 0.62, 95% CI 0.40 to 0.96) and lower IMT (beta=-0.04, 95% CI -0.01 to -0.07 mm) in comparison with the slowest walkers (bottom third), after adjusting for conventional risk factors. Faster walking speed was also associated with lower adiposity, C-reactive protein and low-density lipoprotein cholesterol. Short-distance walking speed is associated with metabolic risk and subclinical atherosclerosis in older adults without overt CVD. These data suggest that a non-aerobically challenging walking test reflects the presence of underlying vascular disease.Heart (British Cardiac Society) 12/2009; 96(5):380-4. · 5.01 Impact Factor
Association between Walking Speed and Age in Healthy,
Free-Living Individuals Using Mobile Accelerometry—A
Michaela Schimpl1,2, Carmel Moore3, Christian Lederer1, Anneke Neuhaus1, Jennifer Sambrook4, John
Danesh3, Willem Ouwehand4, Martin Daumer1,2*
1Sylvia Lawry Centre for Multiple Sclerosis Research e.V. – The Human Motion Institute, Munich, Germany, 2Trium Analysis Online GmbH, Munich, Germany,
3Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, 4Department of Haematology, University of Cambridge and NHS
Blood and Transplant, Cambridge, United Kingdom
Context: Walking speed is a fundamental parameter of human motion and is increasingly considered as an important
indicator of individuals’ health status.
Objective: To evaluate the relationship of gait parameters, and demographic and physical characteristics in healthy men
Design, Setting, and Participants: Recruitment of a subsample (n=358) of male and female blood donors taking part in the
Cambridge CardioResource study. Collection of demographic data, measurement of physical characteristics (height, weight
and blood pressure) and assessment of 7-day, free-living activity parameters using accelerometry and a novel algorithm to
measure walking speed. Participants were a median (interquartile range[IQR]) age of 49 (16) years; 45% women; and had a
median (IQR) BMI of 26 (5.4).
Main Outcome Measure: Walking speed.
Results: In this study, the hypothesis that walking speed declines with age was generated using an initial ‘open’ dataset.
This was subsequently validated in a separate ‘closed’ dataset that showed a decrease of walking speed of 20.0037 m/s per
year. This is equivalent to a difference of 1.2 minutes, when walking a distance of 1 km aged 20 compared to 60 years.
Associations between walking speed and other participant characteristics (i.e. gender, BMI and blood pressure) were non-
significant. BMI was negatively correlated with the number of walking and running steps and longest non-stop distance.
Conclusion: This is the first study using accelerometry which shows an association between walking speed and age in free-
living, healthy individuals. Absolute values of gait speed are comparable to published normal ranges in clinical settings. This
study highlights the potential use of mobile accelerometry to assess gait parameters which may be indicative of future
health outcomes in healthy individuals.
Citation: Schimpl M, Moore C, Lederer C, Neuhaus A, Sambrook J, et al. (2011) Association between Walking Speed and Age in Healthy, Free-Living Individuals
Using Mobile Accelerometry—A Cross-Sectional Study. PLoS ONE 6(8): e23299. doi:10.1371/journal.pone.0023299
Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain
Received April 11, 2011; Accepted July 12, 2011; Published August 10, 2011
Copyright: ? 2011 Schimpl et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Thisworkwaspartlysupportedby grants from theGerman Ministryfor Education andResearch(BMBF,‘‘GermanCompetenceNetwork MultipleSclerosis’’
(KKNMS), 01GI0920), EU FP7 (NETSIM (215820-2) and VPHOP (FP7-223865)). The Cambridge CardioResource study was funded by the UK Medical Research Council/
Wellcome Trust. Additional support was provided by NHS Blood and Transplant, National Institute for Health Research and British Heart Foundation. These funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Michaela Schimpl and Martin Daumer are employees
of Trium Analysis Online GmbH and contributed to all aspects of the study. No additional external funding was received for this study.
Competing Interests: Martin Daumer, Scientific Director of the Sylvia Lawry Centre for Multiple Sclerosis Research e.V. (SLC) and one of the two Managing
Directors of Trium Analysis Online GmbH, serves on the Editorial Board of MedNous and holds Patent 10 2007 044 705.3-35, German patent and trademark office
307 19 449.3/09. The Sylvia Lawry Centre received honoraria for interviews of Dr. Daumer with Propagate Pharma Limited and Deerfield Research LLC; is Medical
Advisor of the German Multiple Sclerosis Society; received honoraria for consultancy, statistical analysis and use of actibeltH technology from the following
entities: Bayer Schering, Biopartners, Biogen Idec, Biogenerix, Bo ¨hringer-Ingelheim, EISAI Limited, Heron Evidence Development Ltd, Hoffmann-La Roche, Johnson
& Johnson Pharmaceutical Research & Development LLC, Sanofi-Aventis U.S. INC., Novartis Pharma GmbH, University of Oxford, Imperial College London,
University of Southampton, Charite Berlin, University of Vienna, Greencoat Ltd, University Medical Center Hamburg-Eppendorf; serves on the advisory board for
EPOSA study and received research grants from the following governmental entities: Federal Ministry of Education and Research Grant No 01GI0904, 01GI0920,
Mayo Clinic Rochester, European Union (for SLC and Trium) Grant No 215820, European Union (for SLC) Grant No 223865, Federal Ministry of Economics and
Technology Grant No KF0564001KF7, University of Oxford, Technical University of Munich, Hertie Foundation Grant No 1.01.1/07/015, Bavarian Research
Foundation, National Multiple Sclerosis Society (NMSS), Porticus Foundation Grant No 900.50578, European Union Grant No LSHM-CT-2006-03759, University of
Rochester, European Union Grant No LSHM-CT-2004-503485. Michaela Schimpl is an employee of Trium Analysis Online GmbH. Anneke Neuhaus and Christian
Lederer are employees of the Sylvia Lawry Centre for Multiple Sclerosis Research. John Danesh is a board member of Merck Sharp & Dohme UK Atherosclerosis
Advisory Board and Novartis Cardiovascular & Metabolic Advisory Board. John Danesh has received payment for lectures including service on speakers bureaus as
well as travel/accomodation/meeting expenses (unrelated to the activities listed) from GlaxoSmithKline, Merck, Novartis, Pfizer. This does not alter the authors’
adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.
* E-mail: firstname.lastname@example.org
PLoS ONE | www.plosone.org1August 2011 | Volume 6 | Issue 8 | e23299
Walking speed is a fundamental parameter of human motion
and is increasingly considered as an important indicator of
individuals’ health status [1–3]. It is a universal yet easily
understandable outcome measure, which has been shown to be
meaningful for the prediction of future health outcomes  and
the assessment of health status in chronic disabling conditions
[5–7]. Patients who exhibit ‘‘accelerated ageing’’ in diseases such
as multiple sclerosis (MS) have reported loss of walking ability as
their greatest fear . Recently, the limitations of widely used
outcome measures for walking ability in MS (Expanded Disability
Status Scale [EDSS], Multiple Sclerosis Functional Composite
[MSFC]) have highlighted the need to find more appropriate
outcome measures [9,10]. A valid outcome measure should
combine specific expert knowledge, feasible methods of assess-
ment, ability to discriminate among healthy and diseased
individuals and, of course, have a significant impact on human
health . Walking speed has emerged as an interesting
candidate in various diseases e.g. chronic obstructive pulmonary
disease (COPD), MS, Parkinson’s disease and cardiovascular
diseases . Walking speed has been associated with fall risk,
mortality, mobility limitation and disability  and conditions
which may not be apparent from routine clinical measures, e.g.
subclinical atherosclerosis . Moreover, walking speed was found
to be the best physical performance measure for predicting the
onset of functional dependence in a Japanese older population
Short distance walking tests (e.g. 6 minute walking test, timed
25-foot walk [T25-FW]) are standard tools to evaluate walking
speed in a clinical setting. Changes in walking speed as
measured with the T25-FW have recently been accepted by
the Food and Drug Administration (FDA) as an outcome
measure in a phase III trial , but this method is far from
being widely accepted. Major drawbacks are the time and effort
needed, measurement error and high day-to-day variability .
A change of 0.1 m/sec and/or 10% is considered to be clinically
meaningful , but the variability in a clinical setting in three
repeated measurements is up to 20% . More importantly,
walking speeds in a clinical setting are unlikely to fully represent
those in a free-living environment , typifying the funda-
mental problem of applying results from clinical trials to a real-
world situation .
Accelerometry has been developed as a means to enable
observer-independent, long-term assessment of activity-related
parameters. Technical challenges in using body-worn ambulatory
assessment methods are now being overcome, opening up the
possibility for the collection of objective, prolonged physical
activity records. There are many examples of accelerometer
devices which work on generally the same principle but vary in
design, technology, costs and sensitivity to motion . One
important challenge for researchers is to extract clinically
meaningful parameters which may be used in the prediction of
future disease outcomes .
Methods for predicting walking speed range from simple
approaches which combine step length with frequency (i.e.
pedometers), to more sophisticated algorithms using complex
estimates based on biomechanical models of gait  and/or
linear and non-linear models. Using mobile accelerometry as a
tool to objectively measure and quantify walking speed and
changes in walking speed in both clinical and free-living
environments may help to determine normal ranges, critical
thresholds  and establish walking speed as a useful outcome
parameter in epidemiological studies and clinical trials .
Here we describe the use of a tri-axial, waist-mounted
accelerometer (‘‘actibelt’’ ) for the acquisition of physical
activity data and the application of a novel, validated algorithm
 to predict walking speed in free-living, healthy individuals.
We investigated whether differences in several gait parameters
existed between different age and BMI groups.
All participants gave full informed consent to take part in the
study and ethical approval was obtained from the Cambridge 1
Local Research Ethics Committee (REC 09/H0304/42).
A subsample of participants from the Cambridge CardioR-
esource study was recruited for the assessment of physical activity
using the actibelt. Cambridge CardioResource was a pilot study to
determine the feasibility and acceptability of integrating research
protocols within the framework of the UK’s national blood service
(NHS Blood and Transplant, NHSBT) (http://ceu.phpc.cam.ac.
uk/research/cardioresource/). Blood donors were recruited,
between February and September 2010, at community venues
within a radius of approximately 60 miles from the Cambridge
Blood Centre at Addenbrooke’s Hospital (UK).
Inclusion criteria for routine blood donation are: age 17–65 y
for first-time donors (repeat donors .65 y can continue to give
blood provided they remain in good health), a minimum weight of
50 kg and general good health. Exclusion criteria for blood
donation typically aim to protect either the donor or the patient
receiving the donation from any harm. The full list of exclusion
criteria can be found on the website of the UK National Blood
Donors were considered eligible to take part in the study if they
were fit to donate and fulfilled the normal criteria for donation.
Donors who either chose to provide extra samples for other
purposes, or who were first-time donors were excluded from
Collection of blood samples and physical measurements
In addition to the routine 470 ml blood donation, an additional
(15 ml) blood sample was collected from consenting donors for the
purposes of research. After the blood had been collected and
participants had rested, measurements of height, weight and blood
pressure were made by trained research staff. Height and weight
were measured with participants wearing indoor clothing and
shoes. Height was measured to the nearest 1 cm using a SECA
Leicester portable height scale and weight to the nearest 0.1 kg
using SECA 877 digital scales (SECA, Birmingham, UK). One
reading of blood pressure and heart rate was taken using an
OMRON M10IT (OMRON, Milton Keynes, Bucks, UK); the
measurement was taken in the ‘non-donation’ arm and once the
participant was seated comfortably and rested. Participants were
also asked to complete a questionnaire, at home, covering medical
history and lifestyle.
Physical activity measurement protocol
At 12 sessions during 5 non-consecutive weeks between
February and March 2010 donors were asked, at the end of their
donation session, if they were willing to take home and wear the
actibelt over a period of 7 days. Those willing to participate were
given a short one-to-one tutorial on how to use the accelerometer
(plus, supplementary information to take home) and instructed to
start wearing the device the following morning. During the
Association of Walking Speed and Age
PLoS ONE | www.plosone.org2August 2011 | Volume 6 | Issue 8 | e23299
instruction phase, the accelerometer was switched on by a trained
study researcher and donors were advised not to switch it off until
the last day of their measurement. Additionally donors were
provided with an activity diary and asked to record times when the
device was removed and any activities conducted on each of the
seven recording days. Subjects were asked to remove the
accelerometer only while showering, taking a bath or swimming
and (if they preferred to do so) during the night. Participants were
advised to remove the device on the morning of the eighth day,
switch it off and return it, in the post, to the study centre in
Cambridge. If any problems were experienced using the actibelt,
participants were able to contact the CardioResource team via the
study telephone or e-mail help lines.
The actibelt is a tri-axial accelerometer with 100Hz sampling
frequency; it has 512 megabytes of memory corresponding to 10
days continuous recording and a battery life .20 days [23,25].
The accelerometer is placed inside a belt buckle which the wearer
fixes around the waist by either a leather or elasticated belt. With
this design, the device is discreet and unobtrusive and is located
close to the subject’s centre of mass. It can either be used for long-
term monitoring in a free-living environment  (‘‘week-in-a-
box’’) or activity assessment in a clinical setting (‘‘rapid tests’’).
The following parameters were extracted from the raw
accelerometry data collected on each day of wearing the device:
adherence (i.e. amount of time during which the device was worn),
the number of minutes spent in active and exercise mode, activity
temperature (i.e. a measure for overall physical activity), coherence
length (i.e. average length of uninterrupted periodic walking
intervals), the number of walking and running steps, step
frequency, walking speed based on both running and walking
steps  and further parameters deduced from walking speed, i.e.
the total distance travelled, the longest non-stop distance travelled
and step length.
Subjects were excluded from the data analysis either if no
demographic data was available or the belt was worn upside-down
during any part of the recording period. Only days with an
adherence of at least 6 hours were considered complete for analysis
in order to reduce potential bias due to periods when the device
was not worn. For statistical tests and linear models, parameters
were averaged over the whole 7-day recording period.
In order to minimize the risk of publishing false positive results,
the dataset was split into two separate and independent datasets:
one for hypotheses generation (open dataset) and the second to test
and validate the previously generated hypotheses (closed dataset)
. Basic exploratory analyses including boxplots and Spearman
rank correlations were performed using the open dataset in order
to identify the main hypotheses for validation. Subsequently,
several gait parameters showing an association with age and BMI
were selected for validation. Comparisons between the open and
closed dataset were based on the Wilcoxon test for continuous
variables and Chi-squared test for categorical variables. Addition-
ally a linear model was fitted on the open dataset to further
investigate the primary outcome. The primary outcome was
considered validated if the slope of the closed linear model was
between the upper and lower bounds of the 95% confidence
interval of the open linear model (20.0055 and 20.0016 m/s per
year). Secondary outcomes were considered confirmed if the
resulting p-value of the Spearman rank correlation test for the
closed dataset was below 0.00625 (0.05/8 due to adjustment for
multiple testing ). Pooling the open and closed dataset in order
to obtain a higher number of cases after completion of the
validation procedure was deliberately avoided.
Data were analysed using R software for statistical computing
and graphics .
Figure 1 depicts the flow of participants from assessment of
eligibility to the split between the open and closed datasets. The
rate of uptake, response (i.e. return of actibelts) and successful
download of data were 90%, 98% and 98% respectively.
Table 1 summarizes participant characteristics in the open and
closed datasets for whom demographic information was available.
Although the open and closed dataset show statistical differences in
the distribution between the sexes and median age, the
populations are considered equivalent to answer the questions at
Hypothesis generation and validation
In the open dataset, three gait parameters showed significant
correlations with age (Table 2) and four with BMI (Table 3). Of
these associations, six were also significantly correlated in the
closed dataset and confirmed by the validation process. The
relationship between walking speed and age was selected as the
primary outcome owing to existing evidence for the clinical
relevance of walking speed both for the prediction of future health
outcomes and as a novel, patient-oriented outcome measure, for
the assessment of health status in chronic disabling conditions.
An additional linear model with age as the predictor and
walking speed as the response variable was fitted on the open
dataset in order to describe the relationship between those two
variables in more detail. Intercept and slope in the open dataset
were 1.4531 and -0.0037 m/s, respectively; in the closed dataset
1.3739 and 20.0020 m/s. Since the slope of the model when
fitted on the closed dataset remained within the upper and lower
bound of the 95% confidence interval (20.0055 and -0.0016 m/s
per year), the model was validated.
Gait parameters and age
Daily walking speed across all participants ranged from 1.03 m/
s up to 2.07 m/s with a median walking speed of 1.25 m/s in the
open dataset. Figure 2 clearly shows the significant decline of
walking speed with increasing age in the open dataset.
A post-hoc analysis was performed to further investigate
whether reduction in gait speed with age was the result of fewer
running steps with increasing age. However the relationship
remained significant when running steps were not included in the
calculation of gait speed (p-value: 0.0021).
Participants had a median (IQR) number of running steps of 65
(249) which were also shown to be negatively associated with age.
The large number of running steps shown by one individual (i.e.
9953) was confirmed by reference to their completed physical
activity diary in which the participant reported running for at least
30 minutes every day.
The association between age and total distance travelled
(median 6099 m, IQR 3612 m) was only found in the open
dataset and could not be validated in the closed dataset.
A Wilcoxon rank sum test (p-value: 0.13) did not suggest a
significant difference in walking speed for male and female
subjects. The effects of BMI and blood pressure, on walking speed
were non-significant (p-values: 0.64 and 0.65, respectively).
For future applications of the methodology (e.g. in the context
of clinical trials) it is important to also quantify the day-to-day
Association of Walking Speed and Age
PLoS ONE | www.plosone.org3 August 2011 | Volume 6 | Issue 8 | e23299
variability of walking speed to determine the necessary number of
observations per subject. A mixed-effects model with random
intercept per subject was calculated to determine the day-to-day
variability; this was found to be close to the effect size. The
resulting standard deviation for day-to-day fluctuations is
0.14 m/s. Hence averaging weekly recordings would result in a
variability of 0.053 m/s, about half the clinically relevant
threshold of 0.1 m/s [5,30].
Gait parameters and BMI
There was a negative association between BMI and the number
of walking and running steps and also the total and longest non-
stop distance (Table 3). No differences in coherence length or step
frequency were found between the different age and BMI groups.
To our knowledge this is the first study using accelerometry
which shows an association between walking speed and age in free-
living, healthy individuals. To date this has previously only been
shown in a clinical setting . Absolute values of gait speed are
comparable to published normal ranges in clinical settings .
Walking speed has been shown to decline with increased risks
for heart disease. In a group of ‘healthy’ participants, walking
speed was shown to decrease with subclinical atherosclerosis .
While it is not possible to draw conclusions on the directionality of
this association, one hypothesis suggested by the authors was that
subclinical vascular factors may play a role in motor function as a
result of increased cerebral white matter hyperintensities. If this
hypothesis were true, then an association would be expected
between blood pressure and walking speed, as hypertension is a
main risk factor for white matter hyperintensities. However,
Hamer et al.  and the authors of this study found no evidence of
such an association.
The negative relationship between BMI and walking parame-
ters (i.e., amount and distance) is consistent with the findings of
Levine et al. . In this study, free-living walking was measured
using a system of multiple sensors integrated in specially designed
body suit that recorded data on body posture and movement in
Figure 1. Flow diagram of participant involvement and division of data into ‘open’ and‘closed’ datasets.
Table 1. Participant characteristics (median [IQR]).
Women, No. (%) 72 (51)64 (40)0.04
Age (y) 52 (15)48 (19)0.03
Height (cm)173 (12)175 (12)0.21
Weight (kg)76.7 (19)79.4 (21)0.35
BMI (kg/m2) 26 (6)26 (5) 0.91
Association of Walking Speed and Age
PLoS ONE | www.plosone.org4 August 2011 | Volume 6 | Issue 8 | e23299
lean and obese participants over a 10-day period of weight
maintenance feeding and a 10-day period of overfeeding. It was
found that lean individuals walked 3.5 miles/day more than obese
individuals due to differences in the distance travelled, rather
than number of walking bouts; this association may be
attributed to the higher level of energy needed to walk a given
distance with increasing BMI. With overfeeding walking
declined similarly in both lean and obese participants; reduced
levels of walking with increased weight gain may in turn lead to
further weight gain due to concurrent reductions in energy
expenditure and this may be reinforced through a continuous
A decrease in distances walked with advancing age has been
self-reported  and demonstrated using the 6-minute walking
test . However, in this study, although a similar finding was
shown in the open dataset, this was not replicated in the closed
A potential limitation of this study is the involvement of
participants not drawn from the general population but from a
select group of individuals who volunteer and meet the eligibility
criteria for donating blood. However blood donors represent a
diverse population group in terms of age, physical characteristics
and lifestyle, which should allow extrapolation to a larger
An additional consideration is that, for logistical reasons, the
actibelt was worn for 7-days post-donation and the effect of blood
loss on usual physical activity levels is unknown. There is no
evidence that supports advice to avoid usual activities more than a
Table 2. Median (IQR) of gait parameters associated with age in open and closed datasets.
, ,30 30 - 3940 - 49 50 - 59
Open dataset 141121633 54 26
Closed dataset 162 1631544021
Walking speed (m/s)
Open dataset1.25 (0.12) 1.34 (0.12)1.26 (0.12) 1.26 (0.12) 1.23 (0.11)1.21 (0.11)
Closed dataset1.27 (0.14) 1.27 (0.14)1.27 (0.19)1.29 (0.11)1.28 (0.08)1.20 (0.07)0.004
No. of running steps
Open dataset65 (249)534 (676) 112 (247)157 (433) 47 (113) 26 (46)
Closed dataset 103 (280)122 (205) 176 (651)164 (324)87 (288) 24 (39)
Total distance (m)
Open dataset 6099 (3612)8297 (1853)7655 (2803)6284 (4479)5876 (3676)5534 (2154) 0.001
Closed dataset 6226 (3777) 5425 (2818)6624 (3291)6004 (3769)7621 (4633)5622 (2860) 0.90
Table 3. Median (IQR) of gait parameters associated with BMI in open and closed datasets.
Open dataset14156 6124
No. of walking steps
Open dataset8576 (4609)9538 (4144)8239 (3697) 5738 (3396)
Closed dataset8443 (5217)9446 (4914)8380 (5567)6759 (3910)0.002
No. of running steps
Open dataset 65 (249)116 (531)47 (135)32 (133)
Closed dataset103 (280) 117 (359)124 (345)23 (111)
Total distance (m)
Open dataset6099 (3612)7126 (3562) 5586 (2905)4154 (2515)
Closed dataset 6226 (3777)6781 (3306)6317 (4000)4831 (2565)0.002
Longest n-s distance (m)
Open dataset966 (864)1376 (1453)876 (635)607 (569)
Closed dataset968 (969)1168 (1190)947 (808)573 (581)0.002
Association of Walking Speed and Age
PLoS ONE | www.plosone.org5August 2011 | Volume 6 | Issue 8 | e23299
few hours after having given blood, but it is unknown if and how
much blood donation influences e.g. walking speed and the
distribution of longest non-stop walking distance in the days after
blood donation. There is evidence that blood donation - typically
corresponding to a loss of roughly 10% of the total blood volume
needing several weeks to be replaced - is linked to a decrease in
endurance capacity, physiologically explained by a decrease of the
total number of erythrocytes that are responsible for continuous
supply of oxygen to the slow-twitch muscles fibres . We believe
that the effect is rather small leading to a potential small bias
towards lower speeds and smaller distances. This effect should be
small or negligible for normal persons with a lifestyle that is not
highly active and when focussing on changes in time. Further
studies are needed to quantify this potential source of bias for
estimating absolute values of walking speed, but the key finding
about a linear decline of walking speed in time should not be
Key strengths of this study include the high response and
acceptance rates of the actibelt protocol. Very few helpline
queries were received in relation to the use of the actibelts
indicating that they were easy to use. Furthermore, in the data
analysis, the authors have appropriately corrected for multiple
testing and taken care to minimise the risks of reporting false-
positive results by separating the exploratory analysis from the
validation analysis [27,36]. It is due to this careful validation
policy that the authors believe that the association between
walking speed and age is indeed true and not an artefact due to
bias and confounding by unobserved heterogeneities such as
social status, employment or environmental influences. The
association between walking speed and age - which does not
necessarily prove a causal relationship - showed a high degree of
correlation in the open dataset which was subsequently validated
in the independent closed dataset.
In addition, the association between walking speed and age is
robust which was further tested by raising the threshold for
minimum daily adherence from 6 to 10 hours; this did only have a
negligible effect on the overall result and the association remained
significant. Algorithms to automatically extract clinically mean-
ingful parameters have been carefully validated in independent
datasets . Furthermore, unprocessed raw data for each
participant was recorded and stored, so that future analysis with
new algorithms can be performed if necessary.
In conclusion, this is the first study using accelerometry which
shows an association between walking speed and age in free-
living, healthy individuals. This study highlights the potential use
of accelerometry, for both research and clinical purposes, to
assess gait parameters which may be indicative of future health
outcomes or changes in health status with lifestyle or medical
We thank Martina Gu ¨ntner2, Cristina Soaz Gonzalez1, Christoph Stolle1
and Ralf Strobl1for their role in the validation committee. We thank
Gerhard Aigner2, Evangelia Diamanti3, Joanne Livingstone3, Jane
McLaughlin4, Alexander Schlummer1, Cristina Soaz Gonzalez1, Matthew
Walker3and Sarah Watson3for their help with data acquisition.
1 Sylvia Lawry Centre for Multiple Sclerosis Research e.V.– The
Human Motion Institute, Munich, Germany,
2 Trium Analysis Online GmbH, Munich, Germany,
3 Department of Public Health and Primary Care, University of
Cambridge, Cambridge, United Kingdom,
Figure 2. Boxplot showing relationship between walking speed and age.
Association of Walking Speed and Age
PLoS ONE | www.plosone.org6August 2011 | Volume 6 | Issue 8 | e23299
4 Department of Haematology, University of Cambridge and NHS
Blood and Transplant, Cambridge, United Kingdom
Conceived and designed the experiments: MD JD WO CM JS. Performed
the experiments: MS MD CM JS. Analyzed the data: MS MD AN CL.
Contributed reagents/materials/analysis tools: MS MD CM JS. Wrote the
paper: MS MD CM. Critical revision of the manuscript for important
intellectual content: MS MD JD WO AN JS CL. Obtained funding: MD
JD WO. Administrative, technical or material support: MS MD AN CL.
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