Wrist-worn Accelerometry for Runners:
Objective Quantification of Training Load
VICTORIA H. STILES1, MATTHEW PEARCE1, ISABEL S. MOORE2, JOSS LANGFORD3,
and ALEX V. ROWLANDS4,5,6
1Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter, UNITED KINGDOM;
2Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UNITED KINGDOM; 3GENEActiv,
Activinsights, Cambridgeshire, UNITED KINGDOM; 4Diabetes Research Centre, University of Leicester, Leicester, UNITED
KINGDOM; 5National Institute for Health Research (NIHR), Leicester Biomedical Research Centre, Leicester, UNITED
KINGDOM; and 6Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research,
Division of Health Sciences, University of South Australia, Adelaide, AUSTRALIA
ABSTRACT
STILES, V. H., M. PEARCE, I. S. MOORE, J. LANGFORD, and A. V. ROWLANDS. Wrist-worn Accelerometry for Runners:
Objective Quantification of Training Load. Med. Sci. Sports Exerc., Vol. 50, No. 11, pp. 2277–2284, 2018. Purpose: This study aimed to
apply open-source analysis code to raw habitual physical activity data from wrist-worn monitors to: 1) objectively, unobtrusively, and
accurately discriminate between ‘‘running’’ and ‘‘nonrunning’’ days; and 2) develop and compare simple accelerometer-derived metrics of
external training load with existing self-report measures. Methods: Seven-day wrist-worn accelerometer (GENEActiv; Activinsights Ltd,
Kimbolton, UK) data obtained from 35 experienced runners (age, 41.9 T 11.4 yr; height, 1.72 T 0.08 m; mass, 68.5 T 9.7 kg; body mass
index, 23.2 T 2.2 kgImj2; 19 [54%] women) every other week over 9 to 18 wk were date-matched with self-reported training log data.
Receiver operating characteristic analyses were applied to accelerometer metrics (‘‘Average Acceleration,’’ ‘‘Most Active-30mins,’’
‘‘MinsQ400 mg’’) to discriminate between ‘‘running’’ and ‘‘nonrunning’’ days and cross-validated (leave one out cross-validation). Variance
explained in training log criterion metrics (miles, duration, training load) by accelerometer metrics (MinsQ400 mg, ‘‘workload (WL) 400-4000 mg’’)
was examined using linear regression with leave one out cross-validation. Results: Most Active-30mins and MinsQ400 mg had 994% accuracy for
correctly classifying ‘‘running’’ and ‘‘nonrunning’’ days, with validation indicating robustness. Variance explained in miles, duration, and training
load by MinsQ400 mg (67%–76%) and WL400–4000 mg (55%–69%) was high, with validation indicating robustness. Conclusions: Wrist-
worn accelerometer metrics can be used to objectively, unobtrusively, and accurately identify running training days in runners, reducing the
need for training logs or user input in future prospective research or commercial activity tracking. The high percentage of variance explained
in existing self-reported measures of training load by simple, accelerometer-derived metrics of external training load supports the future use
of accelerometry for prospective, preventative, and prescriptive monitoring purposes in runners. Key Words: WORKLOAD, TRAINING
EXPOSURE, TRAINING PROGRAMS, ATHLETE MONITORING, INJURY PREVENTION, PERFORMANCE
R
unners are suggested to be particularly at risk of
developing a running-related injury (RRI) if they
have one or a combination of the following: a history
of injury, low or high running experience (high indicates that
long distances have been run for many years), a low (women)
or high (men) weekly training frequency, a low or high overall
weekly running mileage or a sudden increase in training load
(1–3). Characteristics of external training load (work done)
typically described as the distance, frequency, intensity, and
duration of running per day/week or month are therefore highly
modifiable risk factors for RRI (1–4). Optimal patterns of
training load relative to rest and sleep (recovery) are also im-
portant in the prevention of RRI and illness (5–7). However, a
single validated method enabling longitudinal training patterns
to be objectively, accurately, and unobtrusively quantified in
runners is unavailable. A more detailed understanding of the
influence of training load on RRI and performance could be
enhanced by an improved ability to objectively monitor sim-
ple, yet meaningful characteristics of external training load in
runners on a large population scale (5,8,9).
Within research and applied settings, characteristics of
external training load, such as miles and duration, are
Address for correspondence: Victoria Stiles, Ph.D., Sport and Health Sci-
ences, College of Life and Environmental Sciences, University of Exeter,
Exeter, United Kingdom; E-mail: v.h.stiles@exeter.ac.uk.
Submitted for publication February 2018.
Accepted for publication June 2018.
0195-9131/18/5011-2277/0
MEDICINE & SCIENCE IN SPORTS & EXERCISE
Copyright  2018 The Author(s). Published by Wolters Kluwer Health, Inc.
on behalf of the American College of Sports Medicine. This is an open access
article distributed under the Creative Commons Attribution License 4.0 (CCBY),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
DOI: 10.1249/MSS.0000000000001704
2277
EPIDEMIOLOGY
typically recorded using a training log (self-reported or coach-
reported), global navigational satellite system (GNSS), or
prescribed within a training program. To avoid inaccuracies
from self-reported data due to recall bias (overreported/
underreported training/activity), characteristics of external
training load can be more accurately quantified using objec-
tive measurements (9,10). For example, initial findings from
the use of pedometers in military recruits to estimate dis-
tances covered over consecutive weeks of training have
highlighted the importance of capturing evidence of previ-
ously unreported additional training and habitual physical
activity (PA) associated with stress fractures (9). In addition
to pedometers, there has been a vast increase in the use of
more sophisticated, commercial, consumer-focused wrist-
worn activity trackers that are worn 24/7 to monitor habitual
PA (11). These usually incorporate accelerometers that sam-
ple at various frequencies and/or GNSS. With or without
additional user input to improve the accuracy of identifying
training events, the external training characteristics objec-
tively recorded by the majority of these devices seem only to
replicate those captured in a training log, for example, dis-
tance and duration. Restricting objective quantification of
training characteristics to the replication of existing metrics
may limit insight into the possible effects of accelerometer-
derived metrics of external training load on performance and
injury outcomes in runners.
Accelerometer-derived measures of load, if available, tend
to input accelerations to ‘‘black-box’’ on-board processors and
produce manufacturer-specific, proprietary metrics that ap-
pear difficult to interpret (5). For example, in team sports
(12–15), there has been some development with the use of
vest-/back-mounted triaxial accelerometers to provide a
proprietary measure termed PlayerLoadi (modified vector
magnitude in arbitrary units representing rates of change in
instantaneous acceleration (16)). Proprietary metrics limit
comparisons with data recorded by other devices. The wear
location and limited battery life in these devices also limits
their ability to monitor other important training or nontraining
activity outside of training sessions. These aspects, alongside
other practical issues related to access to longitudinal data,
limit the use of accelerometry in running-related research
that seeks to develop new measures of external training load
that might help reduce RRI and improve performance. The
ability to objectively, unobtrusively, and accurately quantify
external training load without user input using high-
resolution, triaxial, open source (nonproprietary) acceleration
data from a single wearable device over weeks at a time, is
therefore attractive.
Wrist-worn accelerometers are now widely used in very
large research cohorts to measure characteristics of habitual
PA (17) including sleep without the need for a sleep diary
(18). These research-grade monitors generate high-
resolution raw data, which can be processed using open-
source software, facilitating the development of metrics
most appropriate for a specific research question. For ex-
ample, outputs from these monitors have been validated
with ground reaction force data (19,20) enabling metrics
indicative of external mechanical loading relative to bone
health to be established (21). A similar approach could
therefore be developed to provide a field-based proxy mea-
sure of external mechanical load (biomechanical risk factor)
relevant to injury. Example metrics in PA and health re-
search include ‘‘Average Acceleration,’’ ‘‘Most Active-
30mins’’ or ‘‘MinsQ400 mg’’ (Table 1) which describe the
intensity of activity in different user-defined periods or time
spent above user-defined intensities of activity (e.g. 400 mg
is a validated vigorous activity threshold in adults (23)). Al-
though these wrist-worn triaxial accelerometer-derived
metrics are validated for use in large-scale population PA
research, it is not yet known whether they can be used to ac-
curately and unobtrusively measure external training load in
runners in the field. The application of these sample metrics
provides a justifiable starting point for objectively classifying
and quantifying an alternative measure of external training
load in runners. Further experimentation with the creation of a
composite metric of workload (WL400–4000 mg; Table 1)
from intensity multiplied by duration (25), may also provide a
possible accelerometer-derived alternative to Foster_s (24)
composite measure of training load (RPE  duration).
Embedding a procedure for classifying running and nonrunning
training days from accelerometer data and accurately obtaining
accelerometer-derived metrics of external training load within
existing, validated protocols for accurately monitoring habitual
activity (26,27) including those used to derive accurate mea-
sures of sleep (18), would benefit subsequent analysis of pat-
terns of training relative to rest and recovery (6,7). The benefits
of high wear compliance and increased measurement reliability
associated with the use of wrist-worn monitors (22) would also
support this future analysis.
The aims of this study are to assess whether simple PA
metrics derived from the application of open-source analysis
code to repeated week-long raw habitual PA data from wrist-
worn tri-axial accelerometers in runners can be used to 1) ob-
jectively, unobtrusively and accurately discriminate between
running training days and nonrunning days; and 2) quantify
external training load on running training days. It was hypoth-
esized that the Most Active 30mins metric (Table 1) would be
the best discriminator for classifying running and nonrunning
days as it focuses on a single continuous period of activity
rather than an average derived from the entire day. It was also
hypothesized that MinsQ400 mg and WL400–4000 mg would
demonstrate at least a moderate level of correspondence
(variance explained) with existing self-reported measures of
training load (criterion measures) from a training log.
METHODS
Participants
Forty-one runners (22 women) with 92 yr running expe-
rience who were training for an event (e.g. 10 km, half/full
marathon) were recruited. An early attrition rate (14.6%) due
http://www.acsm-msse.org
2278
Official Journal of the American College of Sports Medicine
EPIDEMIOLOGY
to injury or withdrawal resulted in 35 runners (19 women
[54%]; Table 2) monitoring their training load for at least
nine consecutive weeks (mean 12.6 T 2.3 wk) between De-
cember 2015 and June 2016, to obtain a range of training
intensities before their target event. Variations in average
weekly running mileage, duration and pace (minutes per mile)
averaged over the monitoring period indicate heterogeneous
characteristics of training in this sample of runners (Table 2).
The Sport and Health Sciences Ethics Committee at the
University of Exeter approved this study, and all participants
provided written informed consent.
Self-monitoring of Training Load
For the duration of the monitoring period, after each ac-
tivity, runners were required to record the following data as
soon as possible in a training log: date of session; start/end
times (session duration calculated); activity/training type (e.g.,
road/off-road/track/treadmill run or other (e.g., gym, swim-
ming, cycling, circuits or yoga)); running miles covered; an
overall session RPE after consulting a visual scale, regardless
of session type. Training logs were returned every 2 wk (mail)
for manual input into a database. A composite measure of
running training load (session RPE  session duration) in
arbitrary units was subsequently calculated (7,24). Using ac-
tivity/training type data, each day was classified as either a
‘‘running’’ (all surface types) or ‘‘nonrunning’’ day, with the
latter further classified as either ‘‘other training’’ (e.g. gym,
swimming, cycling, circuits or yoga) or a ‘‘rest’’ day. Where
running training occurred twice on 1 d, running miles and
duration were summed, a mean running RPE was calculated
and training load was recalculated. If different types of train-
ing including running occurred on the same day, that day was
labeled as a ‘‘running’’ day. Average self-reported weekly
training load characteristics for the sample of runners are
presented in Table 2.
Accelerometer Monitoring of Training Load
Runners were issued with a GENEActiv accelerometer
(100 Hz, triaxial, T8g; Activinsights Ltd, Kimbolton UK)
every other week to wear on their nondominant wrist to
collect 7 d of data. Monitoring on alternate weeks allowed
monitors to be refreshed and reduced participant burden to
TABLE 2. Summary characteristics for runners including self-reported weekly training
volume metrics.
Average
Range (Min–Max)
Age (yr)
41.9 (11.4)
23–63
Height (m)
1.72 (0.08)
1.60–1.86
Mass (kg)
68.5 (9.7)
54.1–93.8
BMI (kgImj2)
23.2 (2.2)
18.8–27.7
aMiles per week (miles)
22.1 (12.4–34.1)
1–149.5
aTotal duration per week (min)
208.0 (124–323)
10–1048
aMinutes per mile (min)
9.3 (8.2–10.6)
6.2–41.6
aRunning sessions per week
3 (1–4)
0–13
All values are means (standard deviations) unless indicated otherwise.
aMedian (inter quartile range). Ranges for training volume metrics represent minimum-
maximum volumes from individual weeks.
TABLE 1. Acceleration metrics considered* for discriminating between running and nonrunning days and used# to quantify external training load on running training days.
Acceleration Metric
Description
Rationale
Average Acceleration*
Average daily dynamic acceleration in mg
This metric (also known as ENMO) has previously been used
to quantify levels of habitual PA (17,22). The inclusion of
running activity within a day was assumed to lead to higher
average acceleration for that day.
This metric was not used to quantify external training load
on running training days as average acceleration reflects the
whole day and not just higher intensity accelerations
reflective of training.
Most Active-30mins*
Average acceleration in mg for the most active
continuous 30-min period of the day
This metric identifies the single most active 30-min period of
activity within a day and not the entire day. By looking at a
single continuous period of activity, it has the potential to
discriminate well between a day including a running training
session (regardless of length of run) compared to the most
active 30-min period on other training and rest (nonrunning) days.
This metric was not used to quantify training load on
running days as it only corresponds to 30 min of the day.
MinsQ400 mg*#
Time in minutes accumulated throughout the day at
or greater than an intensity of 400 mg
An intensity of 400 mg is a validated threshold of activity used
to estimate time spent at vigorous (six times the rate of
energy expenditure at rest; 6 METS [metabolic equivalents])
levels of habitual PA in population research (23).
All high intensity activity is summed, wherever it occurs
within a day, which means this metric may be useful for both
discrimination of days and quantification of external training load.
aWL400–4000 mg#
Time in minutes accumulated in 50 mg bins
Q400 mg was multiplied by the average intensity
of the bin (e.g. 425 mg was the average intensity
of the 400–450 mg bin) to create individual
workload (WL) bins in mg-minutes (mgmins).
WL bins between 400 and 4000 mg were summed
to create a total WL metric in mgmins.
Similar to methods presented by Foster (24) for calculating
session rating of perceived effort as a composite measure
of training load (RPE  duration), this acceleration metric multiplies
intensity by duration to give value to short but potentially
meaningful amounts of high-intensity activity that may be
particularly relevant in new models relating accelerometer-derived
metrics of external training load with RRI and performance outcomes.
The lower and upper accelerations (400 and 4000 mg) border the
zone where accelerations typical of running fall (23).
This metric was considered for quantifying external training load only.
aAs recently proposed by Hillsdon (25).
OBJECTIVELY QUANTIFYING TRAINING LOAD
Medicine & Science in Sports & Exercised
2279
EPIDEMIOLOGY
help maximize wear compliance during test weeks. Partici-
pants were requested to wear the monitor 24 hId-1. As mini-
mal differences exist in accelerometer output between
monitors worn on dominant and nondominant wrists during
higher intensity activity (28), runners were permitted to
swap the wear location of the GENEActiv to their dominant
wrist for the duration of a run if the wear location clashed
with the preferred placement of another personal wearable
device. Raw acceleration files were extracted and processed
through an open-source package (GGIR Version 1.2–8,
(26)) in R (http://cran.r-project.org) for autocalibration and
calculation of the dynamic acceleration in milligravitational
units (mg) averaged over 5-s epochs (the resultant vector
magnitude corrected for gravity, ENMO, as described pre-
viously (27)). A total of 1532 d were obtained from which
1494 (97.5%) accelerometer days with at least 10 h of wear
per waking day (29) were analyzed. Time accumulated in
bins spanning 50-mg intervals between 50 and 4000 mg
(50–99.99 mg; 100–149.99 mg; 150–199.99 mg etc) were
obtained with activity G50 mg considered non–meaningful
(21,30), and the incidence of time accumulated 94000 mg
extremely brief and rare.
Accelerometer Metrics and Statistical Analysis
Discriminating
between
‘‘running’’
and
‘‘non-
running’’ days (aim 1). Accelerometer data were time-
matched with training log data for each calendar day in
STATA (version 15). Average Acceleration, Most Active
30-mins, and MinsQ400 mg, which are typical metrics used
to describe characteristics of habitual PA, were considered
candidates for discriminating between ‘‘running,’’ ‘‘other
training’’ and ‘‘rest’’ days (Table 1). Receiver operating
characteristic (ROC) analyses were carried out for these
metrics to derive the optimum thresholds for discrimination
between running and nonrunning training days. Perfor-
mances were summarized by calculating the area under the
ROC curves (AUROC). Similar to the methods by Evenson
et al (31), thresholds were selected that optimized the bal-
ance between sensitivity (running classified as ‘‘running’’)
and specificity (nonrunning classified as ‘‘nonrunning’’).
Optimal thresholds were applied to the data and the per-
centage of days correctly classified as ‘‘running’’ and
‘‘nonrunning’’ calculated. The percentage of days correctly
classified as ‘‘nonrunning’’ was further broken down
according to whether the day was an ‘‘other training’’ day or
a ‘‘rest’’ day. The percentage of misclassification for each
type of ‘‘other training’’ misclassified as ‘‘running’’ was
also identified. To detect a medium effect size with power of
80% and alpha of 0.05 (AUROC of 0.6 as significantly
different from an AUROC of 0.5, no association), a total
sample of at least 258 days (sample ratio of 1:1 with
129 positive days and 129 negative days) was required. The
generalizability and performance of the ROC models on
unseen data was assessed using leave-one-out-cross-validations
(LOOCV) (32).
Estimation of external training load on accelerometer-
classified ‘‘running’’ days (aim 2). From training log data,
miles, duration, and training load, which are frequently
monitored to understand the influence of training load on
performance, injury, and illness (1,3,6,7,24,33), were used to
represent external and composite criterion measures of train-
ing load (criterion measures). On running training days that
were classified using cut points from accelerometer metrics
which demonstrated the highest levels of accuracy for cor-
rectly classifying running days (see aim 1), accelerometer-
derived metrics of training load (MinsQ400 mg and
WL400–4000 mg; Table 1) were examined to see how
closely they corresponded to criterion measures. On each set
of classified days, variances explained in training log criterion
measures (miles, duration, and training load) by MinsQ400 mg
and WL400–4000 mg were examined using linear regression
analysis. The generalizability and performance of the model
on unseen data was assessed using LOOCV. Statistical anal-
yses were carried out in STATA (version 15) with an alpha
level set at 0.05.
RESULTS
Discriminating between running and nonrunning
days (aim 1). From 35 participants, a total of 1494 d with
910-h wear were analyzed, of which 694 were ‘‘running’’
days, 641 were ‘‘rest’’ days, and 159 were ‘‘other training’’
days. Each participant contributed 18 to 56 d (mean [SD] =
42.7 [8.8]). Of these, 2 to 42 (19.8 [10]) were ‘‘running’’
days, 0 to 37 (18.3 [9.0]) were ‘‘rest’’ days, and 0 to 23 (4.5
[6.0]) were ‘‘other training’’ days.
Cutpoints for identifying running days from habitual PA
using respective accelerometer metrics with area under
curve (AUC) significant at P G 0.05 are presented in Table 3.
Discrimination between ‘‘running’’ and ‘‘rest’’ days was
excellent (88%–94% agreement; Table 3). Both the Most
Active-30mins and MinsQ400 mg had 994% accuracy for
classifying running as ‘‘running’’ and nonrunning as
‘‘nonrunning’’ and were subsequently used to separately
classify running days for aim 2. ‘‘Average Acceleration’’
performed similarly for correctly classifying ‘‘nonrunning’’
days (93%), but was weaker at correctly classifying ‘‘run-
ning’’ days (88%). Irrespective of the metric, the greatest
inaccuracy was from misclassifying ‘‘other training’’ days as
‘‘running’’ days, ranging from 14% misclassification for Most
Active-30mins to 33% misclassification for Average Acceler-
ation. The LOOCV procedure indicated robustness and sta-
bility as the high performance was maintained (AUC Q0.93).
The rate of misclassification of other training activities as
running is shown in Table 4. The most frequent other
training activities undertaken were cycling (47 occurrences)
and gym/exercise classes (45 occurrences). The most likely
activities to be misclassified by the Average Acceleration
metric were field/racket sport (95%), circuit training (57%),
and then cycling (53%). A similar pattern was found when
using MinsQ400 mg, except circuit training was not
http://www.acsm-msse.org
2280
Official Journal of the American College of Sports Medicine
EPIDEMIOLOGY
misclassified. The Most Active-30mins metric performed
better for field or racket sports (25% misclassification) and
generally across the board, but still misclassified nearly a
third of cycling occurrences.
Estimation of external training load on accelerometer-
classified running days (aim 2). On running days classi-
fied using MinsQ400 mg or using Most Active-30mins
(accelerometer metrics most successful at classifying run-
ning days from aim 1), the accelerometer-derived training load
metric MinsQ400 mg explained approximately 75% to 76%
and 74% of the variance in miles and duration, respectively,
and 67% and 71% of the variance in training load (Table 5).
The variance explained by WL400–4000 mg in miles, dura-
tion, and training load was slightly lower at 63% to 69% on
running days classified using either MinsQ400 mg or Most
Active-30mins, except for training load when running days
were classified using MinsQ400 mg, which was much lower
(55%). The LOOCV procedure indicated robustness and
stability as the high performance was maintained in all cases.
DISCUSSION
Raw acceleration data from wrist-worn accelerometers
widely used in research can be used to objectively, unob-
trusively, and accurately identify running training days and
quantify external training load in runners. Importantly, the
accelerometer metrics used are embedded within existing,
validated open-source software for processing and analyzing
accelerometer data for accurate quantification of habitual PA
(26,27). As a field-based proxy measure of external me-
chanical load (19,20), use of these accelerometer-derived
metrics will enhance future research that seeks to further
understand the influence of objectively measured modifiable
patterns of external training load relative to rest and sleep on
RRI and performance outcomes (1–8).
Discriminating between running and nonrunning
days. The high degree of accuracy for correctly classifying
running days and days with no training indicates that wrist-
worn accelerometer metrics can be used to objectively and
unobtrusively discriminate between running and nonrunning
days. While each accelerometer metric was able to discrimi-
nate between these days, the mean acceleration recorded
during ‘‘Most Active-30mins’’ was the best discriminator. As
running is characterized by high accelerations (23), which
incorporate an impact peak (19,20), high accelerations for
the most active continuous 30 min of the day likely reflect the
deliberate inclusion of a running session. The length of the
training session may not match 30 min, but the elevation of
the acceleration alone is sufficient to simply differentiate
between running and nonrunning days. In contrast, metrics
that sum time spent at high accelerations across the day
(MinsQ400 mg), or the average accelerations across the day,
can be elevated due to short activity bursts spread across the
day which may or may not be part of a training session.
Even for the Most Active-30mins, a degree of mis-
classification in ‘‘field or racket sports’’ and ‘‘circuits’’ is likely
due to these activities, including aspects of running or lunging
and jumping, which could elevate average acceleration to
exceed magnitudes typically found during running (19,20).
For cycling, road or track vibration also has the potential to
elevate this average acceleration to similar levels found when
running. Post hoc analysis of demographic and training data
indicated that very short runs may be a potential source of
misclassification. However, we are also cognizant that vali-
dation of accelerometer data in the field is complicated by the
use of potentially inaccurate self-reported training log or
TABLE 3. Optimum accelerometer cutpoints for differentiation between running and nonrunning days (includes rest days [no training] and other-training days [days with a different type
of training]).
Accelerometer Metrics
Average Acceleration
MinsQ400 mg
Most Active-30mins
Cutpoint
40.9 mg
22.4 min
525.3 mg
AUC (95% CI)
a0.93 (0.92–0.95)
a0.95 (0.94–0.96)
a0.97 (0.96–0.98)
Agreement (%)
88
92
94
‘‘Running’’ correctly classified as ‘‘running’’ (%)
88
94
94
‘‘Rest’’ correctly classified as ‘‘nonrunning’’ (%)
93
95
95
‘‘Other training’’ correctly classified as ‘‘nonrunning’’ (%)
67
72
86
LOOCV AUC (95% CI)
0.93 (0.92–0.95)
0.95 (0.94–0.96)
0.97(0.96–0.98)
aSignificantly different (P G 0.05) to the null hypothesis of an AUC of 0.5.
CI, confidence interval.
TABLE 4. Percentage of ‘‘other training’’ activities misclassified as ‘‘running’’ when using ‘‘Average Acceleration,’’ ‘‘MinsQ400 mg’’, and ‘‘Most Active-30mins’’ to discriminate between ‘‘running’’
and ‘‘nonrunning’’ days.
% Misclassified as ‘‘running’’ by Accelerometer Metrics
Other Training
Actual Number of Occurrences
Average Acceleration
MinsQ400 mg
Most Active-30mins
Field or racket sport
20
95.0
90.0
25.0
Circuit training
7
57.1
0
14.3
Cycling
47
53.2
53.2
31.9
Walk
6
16.7
0
0
Gym/exercise class
45
4.5
4.4
2.2
Swimming
32
0
0
0
Sailing
2
0
0
0
OBJECTIVELY QUANTIFYING TRAINING LOAD
Medicine & Science in Sports & Exercised
2281
EPIDEMIOLOGY
training program information (underreported and overreported
training activity) (9,10). In using this simple metric to identify
running training days in future studies, any accepted level of
misclassification will depend on the nature of the activity
misclassified relative to the research question.
Estimation of external training load on running
days. When running days were classified using Most
Active-30mins, approximately 71% to 76% of the variance
in Miles, Duration or Training Load was explained by the
accelerometer-derived training load metric MinsQ400 mg,
which was approximately 7%–13% more than the variance
explained by the composite workload metric WL400–4000 mg
on these days. When running days were classified using
MinsQ400 mg, similarly high levels of variance in miles and
duration were explained by the accelerometer-derived training
load metrics MinsQ400 mg and WL400–4000 mg compared
with when days were classified using Most Active-30mins
but 4% and 9% less variance was explained in training load
by respective metrics on these days. Despite differences, these
accelerometer-derived metrics correspond highly with crite-
rion measures, especially miles and duration, which suggests
a high degree of convergent validity with existing training log
methods for quantifying external training load. MinsQ400 mg
in particular appears to be a good measure of external
training load and can be easily obtained from longitudinal
monitoring of habitual PA. For comparison, fast walking at
5 kmIhj1 in adults yields approximately 170 T 56 mg from a
wrist-worn accelerometer, whereas running at 8 kmIhj1
yields approximately 760 T 200 mg (23). A threshold of
400 mg, which is also validated to quantify vigorous activity
equivalent to 6 METs (23) (Table 1), therefore, provides
sufficient margins to avoid capturing lower-intensity walking-
type activity while capturing lower accelerations introduced
by large variability when running at 8 kmIhj1 and lower ac-
celerations from slower speed running. For comparison, ad-
ditional analysis of accelerometer-derived metrics of external
training load on days classified using training log informa-
tion, indicated that MinsQ400 mg (75%, 75%, and 72%, re-
spectively) and WL400–4000 mg (65%, 60%, and 62%,
respectively) explained similar variation in miles, duration
and training load when days were classified using either
MinsQ400 mg or Most Active-30mins. An ability to use a
single simple accelerometer-derived metric (e.g., MinsQ400 mg)
to accurately classify running days and provide a valid measure
of external training load, lays the foundation for overcoming
challenges, such as ease of use and data interpretation described
by Bourdon and colleagues (5) for accelerometry to be used in
training program prescription. Further, it would be possible to
use the regression analyses to predict outcomes familiar to
runners (e.g., miles), but for analytical purposes, we believe it
preferable to use the directly measured metrics.
Accelerometer metrics are also highly correlated with
laboratory-based measures of ground reaction force (19,20),
which suggests that accelerometer-derived metrics of exter-
nal training load may add more value to models of RRI and
performance than existing training log-based measures.
Further research to determine whether MinsQ400 mg and/or
WL400–4000 mg translate into meaningful measures of
external training load in relation to injury and performance
would be beneficial.
Implications of this study. The ability to obtain ac-
curate, objective training records without the need for user
input removes the reliance on the creation of a subjective
training log, reduces participant burden, avoids bias, and
other reporting inaccuracies associated with logging or
marking data on paper or a device, (9,10) and facilitates the
accurate monitoring of runners_ training behavior in future
prospective studies. It also removes the need to match training
log data, sometimes with multiple entries, with accelerometer
data across days. A high monitor wear compliance (90% of
days 916 h; 76% of days 922 h) in this population also supports
its inclusion in the future analysis of patterns of training relative
to rest and sleep. In contrast to GNSS devices, which are reliant
on tracking a physical change in position in an outdoor envi-
ronment (5), accelerometers also have the advantage of being
able to be used anywhere, even to monitor external load when
running on the spot. Developing accelerometer-derived mea-
sures of external training load provides a natural extension of
an accelerometer_s existing ability to accurately measure all
aspects of habitual PA including rest and sleep longitudinally.
Further
developments. Streamlining
methods
for
collecting, generating, and visualizing simple accelerometer-
derived training load metrics (5) could facilitate their inclu-
sion in commercial activity trackers and healthcare monitors
for training program monitoring, prescription, and injury
prevention purposes. It would also be beneficial to examine
time spent at higher intensities of acceleration (e.g. 91000 mg
approximating 10 kmIhj1; 28) to separately analyze higher
and lower-intensity running. In an effort to avoid bias from
self-reported measures of miles and duration (10), further
comparison of accelerometer-derived metrics with objectively
measured criterion measures (e.g., GPS) might be beneficial,
TABLE 5. Percentage of the variance explained in miles, duration and training load when using ‘‘MinsQ400 mg’’ and ‘‘WL400–4000 mg’’ to quantify external training load on ‘‘running’’
days classified using MinsQ400 mg and Most Active-30mins.
Miles
Duration
Training Load
R 2
LOOCV R 2
R 2
LOOCV R 2
R 2
LOOCV R 2
Training load on ‘‘running’’ days classified using ‘‘Mins Q 400 mg’’
MinsQ400 mg
74.8
74.6
73.7
73.4
66.9
70.0
WL400–4000 mg
69.2
68.9
63.5
63.2
54.8
64.2
Training load on ‘‘running’’ days classified using ‘‘Most Active-30mins’’
MinsQ400 mg
76.2
76.0
74.6
74.4
70.8
70.4
WL400–4000 mg
69.0
68.7
62.9
62.5
64.0
63.5
R2, coefficient of determination from linear regression; LOOCV R2, coefficient of determination for LOOCV.
http://www.acsm-msse.org
2282
Official Journal of the American College of Sports Medicine
EPIDEMIOLOGY
however it would be important to avoid over-burdening the
runner with a requirement to wear multiple monitoring de-
vices. To improve the classification of running from other
training activities, alternative analysis methods such as those
used in the sedentary sphere (34,35), which consider the
orientation of the monitor due to wrist position to estimate
upright, sitting, and lying down postures, could also be ex-
plored to distinguish running from other activities, such as
cycling. Analysis of the frequency compositions of the raw
acceleration signal from different activities may also allow the
selection of suitable filters to improve classification perfor-
mance or enable metrics to be developed that allow cycling
and racket sports to be identified explicitly. Although the high
performance from the LOOCV carried out in this study
demonstrate the robustness of using accelerometer metrics to
classify running days, further validation of the method in an
independent sample would also be beneficial.
STRENGTHS AND LIMITATIONS
We used longitudinal methods for objectively monitoring
habitual PA in runners every other week and obtained
written self-reported training logs every week over at least a
9-wk period. The incorporation of runners leading up to the
events of differing lengths ensured a wide range of training
patterns for testing. The results were robust across this range.
During this period, runners remained motivated to complete
training logs and were familiar with wearable devices yielding
a large number of matched training log and accelerometer
days with the added benefit of high accelerometer wear com-
pliance. A rich bank of data was therefore obtained, allowing
robust statistical methods to be used with cross-validations to
address each research question. However, the nature of the
sample does limit the generalizability of the results. All par-
ticipants were self-identified runners who were training for an
event. Most did undertake some form of cross-training, but the
degree of engagement in other activities may be greater in
people who do not identify as runners, or runners when they
are not leading up to an event. Further research should in-
vestigate the degree of misclassification of ‘‘other training’’ as
‘‘running’’ in other populations.
CONCLUSIONS
Wrist-worn accelerometer metrics can be used to objec-
tively, unobtrusively, and accurately identify running train-
ing days in runners, reducing the need for training logs or
user input in future prospective research or commercial ac-
tivity tracking. A high percentage of the variance explained
in existing metrics by new, simple, accelerometer-derived
metrics of external training load supports the development
and future use of accelerometry for prospective, preventa-
tive, and prescriptive monitoring purposes in runners.
This project was supported by Medical Research Council Prox-
imity to Discover funding (Reference: MC_PC_14127) in collabora-
tion with Activinsights Ltd, UK.
The authors would like to thank the runners who volunteered their
time and committed to having their training load monitored over
multiple weeks.
A. R. is with the National Institute for Health Research (NIHR) Bio-
medical Research Centre based at University Hospitals of Leicester
and Loughborough University, the National Institute for Health Re-
search Collaboration for Leadership in Applied Health Research and
Care – East Midlands (NIHR CLAHRC-EM) and the Leicester Clinical
Trials Unit. The views expressed are those of the authors and not
necessarily those of the NHS, the NIHR or the Department of Health.
Conflicts of Interest: As a collaborative study with industry
supported by MRC Proximity to Discover funding, the industry partner
may potentially benefit from the outcomes from the research. However,
the open-source analysis procedures employed in the current study
impose no restriction for other members of the activity monitoring in-
dustry to also benefit. There are no other competing interests. The
results of the study are presented clearly, honestly, and without fabri-
cation, falsification, or inappropriate data manipulation and do not
constitute endorsement by ACSM.
REFERENCES
1. van Gent RN, Siem D, van Middelkoop M, et al. Incidence and
determinants of lower extremity running injuries in long distance
runners: a systematic review. Br J Sports Med. 2007;41(8):469–80.
2. Nielsen RO, Buist I, SLrensen H, Lind M, Rasmussen S. Training
errors and running related injuries: A systematic review. Int J
Sports Phys Ther. 2012;7(1):58–75.
3. van der Worp MP, ten Haaf DS, van Cingel R, de Wijer A,
Nijhuis-van der Sanden MW, Staal JB. Injuries in runners; a sys-
tematic review on risk factors and sex differences. PLoS One.
2015;10(2):e0114937.
4. Hreljac A. Etiology, prevention and early intervention of overuse
injuries in runners: a biomechanical perspective. Phys Med Rehabil
Clin N Am. 2005;16(3):651–67.
5. Bourdon PC, Cardinale M, Murray A, et al. Monitoring athlete
training loads: consensus statement. Int J Sports Physiol Perform.
2017;12(2 Suppl):S2161–70.
6. Schwellnus M, Soligard T, Alonso J, et al. How much is too much?
(Part 2) International Olympic Committee consensus statement on
load in sport and risk of illness. Br J Sports Med. 2016;50(17):
1043–52.
7. Soligard T, Schwellnus M, Alonso J, et al. How much is too much?
(Part 1) International Olympic Committee consensus statement on load
in sport and risk of injury. Br J Sports Med. 2016;50(17):1030–41.
8. Willy RW. Innovations and pitfalls in the use of wearable devices
in the prevention and rehabilitation of running related injuries.
Phys Ther Sport. 2018;29:26–33.
9. Moran DS, Evans R, Arbel Y, et al. Physical and psychological
stressors linked with stress fractures in recruit training. Scand J
Med Sci Sports. 2013;23(4):443–50.
10. Nielsen RO, Nohr EA, Rasmussen S, SLrensen H. Classifying
running-related injuries based upon etiology, with emphasis on
volume and pace. Int J Sports Phys Ther. 2013;8(2):172–9.
11. Geib RW, Swink PJ, Vorel AJ, Shepard CS, Gurovich AN, Waite GN.
The bioengineering of changing lifestyle and wearable technology:
a mini review. Biomed Sci Instrum. 2015;51:69–76.
12. Casamichana D, Castellano J, Calleja-Gonzalez J, San Roma´n J,
Castagna C. Relationship between indicators of training load in
soccer players. J Strength Cond Res. 2013;27(2):369–74.
13. Scott BR, Lockie RG, Knight TJ, Clark AC, Janse de Jonge XA. A
comparison of methods to quantify the in-season training load of
OBJECTIVELY QUANTIFYING TRAINING LOAD
Medicine & Science in Sports & Exercised
2283
EPIDEMIOLOGY
professional soccer players. Int J Sports Physiol Perform. 2013;
8(2):195–202.
14. Scanlan AT, Wen N, Tucker PS, Dalbo VJ. The relationships be-
tween internal and external training load models during basketball
training. J Strength Cond Res. 2014;28(9):2397–405.
15. Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculating
acute: chronic workload ratios using exponentially weighted moving
averages provides a more sensitive indicator of injury likelihood than
rolling averages. Br J Sports Med. 2017;51(9):749–54.
16. Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accel-
erometers for measuring physical activity in Australian Football.
Int J Sports Physiol Perform. 2011;6(3):311–21.
17. Doherty A, Jackson D, Hammerla N, et al. Large scale population
assessment of physical activity using wrist worn accelerometers: the
UK Biobank Study. PLoS One. 2017;12(2):e0169649.
18. Van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN,
Kivimaki M, Frayling TM, Pack AI, Bucan M, Mazzotti DR,
Gehrman PR, Singh-Manoux A, Weedon MN. Estimating sleep
parameters using an accelerometer without sleep diary. bioRxiv
[Internet]. 2018. [cited 2018 February 1]. Available from: https://www.
biorxiv.org/content/early/2018/02/01/257972. doi: 10.1101/257972.
19. Rowlands AV, Stiles VH. Accelerometer counts and raw acceler-
ation output in relation to mechanical loading. J Biomech. 2012;
45(3):448–54.
20. Stiles VH, Griew PJ, Rowlands AV. Use of accelerometry to
classify activity beneficial to bone in premenopausal women. Med
Sci Sports Exerc. 2013;45(12):2353–61.
21. Stiles VH, Metcalf BS, Knapp KM, Rowlands AV. A small
amount of precisely measured high-intensity habitual physical ac-
tivity predicts bone health in pre- and post-menopausal women in
UK Biobank. Int J Epidemiol. 2017;46(6):1847–56.
22. van Hees VT, RenstrO¨ m F, Wright A, et al. Estimation of daily
energy expenditure in pregnant and non-pregnant women using a
wrist-worn tri-axial accelerometer. PLoS One. 2011;6(7):e22922.
23. Hildebrand M, van Hees VT, Hansen BH, Ekelund U. Age group
comparability of raw accelerometer output from wrist- and hip-
worn monitors. Med Sci Sports Exerc. 2014;46(9):1816–24.
24. Foster C. Monitoring training in athletes with reference to
overtraining syndrome. Med Sci Sports Exerc. 1998;30(7):1164–8.
25. van der Ploeg H, Hillsdon M. Is sedentary behaviour just
physical inactivity by another name? Int J Behav Nutr Phys Act.
2017;14(1):142.
26. van Hees VT, Fang Z, Langford J, et al. Auto-calibration of ac-
celerometer data for free-living physical activity assessment
using local gravity and temperature: an evaluation on four conti-
nents. J Appl Physiol. 2014;117(7):738–44.
27. van Hees VT, Gorzelniak L, Dean Leo´n EC, et al. Separating
movement and gravity components in an acceleration signal and im-
plications for the assessment of human daily physical activity.
PLoS One. 2013;8(4):e61691.
28. Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston
RG. Validation of the GENEA accelerometer. Med Sci Sports
Exerc. 2011;43(6):1085–93.
29. Sieva¨nen H, Kujala UM. Accelerometry—simple but challenging.
Scand J Med Sci Sports. 2017;27(6):574–8.
30. Bakrania K, Yates T, Rowlands AV, et al. Intensity thresholds on
raw acceleration data: Euclidean Norm Minus One (ENMO) and
Mean Amplitude Deviation (MAD) approaches. PLoS One.
2016;11(10):e0164045.
31. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG.
Calibration of two objective measures of physical activity for
children. J Sports Sci. 2008;26(14):1557–65.
32. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. 1st ed
(Hardback). Dordrecht (Netherlands): Chapman & Hall Inc,
Springer Science+Business Media; 1994. pp. 240–1.
33. Gordon D, Wightman S, Basevitch I, et al. Physiological and
training characteristics of recreational marathon runners. Open
Access J Sports Med. 2017;8:231–41.
34. Rowlands AV, Olds TS, Hillsdon M, et al. Assessing sedentary
behavior with the GENEActiv: introducing the sedentary sphere.
Med Sci Sports Exerc. 2014;46(6):1235–47.
35. Rowlands AV, Yates T, Olds TS, Davies M, Khunti K, Edwardson CL.
Sedentary sphere: wrist-worn accelerometer-brand independent pos-
ture classification. Med Sci Sports Exerc. 2016;48(4):748–54.
http://www.acsm-msse.org
2284
Official Journal of the American College of Sports Medicine
EPIDEMIOLOGY

Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load.
Stiles, Victoria H,Pearce, Matthew,Moore, Isabel S,Langford, Joss,Rowlands, Alex V