RESEARCH ARTICLE
Number of Days Required to Estimate
Habitual Activity Using Wrist-Worn
GENEActiv Accelerometer: A Cross-Sectional
Study
Christina B. Dillon1*, Anthony P. Fitzgerald2,3, Patricia M. Kearney2, Ivan J. Perry1, Kirsten
L. Rennie4, Robert Kozarski4, Catherine M. Phillips1
1 HRB Centre for Diet and Health Research, Department of Epidemiology and Public Health, University
College Cork, Cork, Ireland, 2 Department of Epidemiology & Public Health, University College Cork,
Western Gateway Building, Western Road, Ireland, 3 Department of Statistics, University College Cork,
Western Gateway Building, Western Road, Ireland, 4 School of Life and Medical Sciences, University of
Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, United Kingdom
* c.dillon@ucc.ie
Abstract
Introduction
Objective methods like accelerometers are feasible for large studies and may quantify vari-
ability in day-to-day physical activity better than self-report. The variability between days
suggests that day of the week cannot be ignored in the design and analysis of physical
activity studies. The purpose of this paper is to investigate the optimal number of days
needed to obtain reliable estimates of weekly habitual physical activity using the wrist-worn
GENEActiv accelerometer.
Methods
Data are from a subsample of the Mitchelstown cohort; 475 (44.6% males; mean aged 59.6
±5.5 years) middle-aged Irish adults. Participants wore the wrist GENEActiv accelerometer
for 7-consecutive days. Data were collected at 100Hz and summarised into a signal magni-
tude vector using 60s epochs. Each time interval was categorised according to intensity
based on validated cut-offs. Spearman pairwise correlations determined the association
between days of the week. Repeated measures ANOVA examined differences in average
minutes across days. Intraclass correlations examined the proportion of variability between
days, and Spearman-Brown formula estimated intra-class reliability coefficient associated
with combinations of 1–7 days.
Results
Three hundred and ninety-seven adults (59.7±5.5yrs) had valid accelerometer data. Over-
all, men were most sedentary on weekends while women spent more time in sedentary
behaviour on Sunday through Tuesday. Post hoc analysis found sedentary behaviour and
PLOSONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 1 / 11
a11111
OPEN ACCESS
Citation: Dillon CB, Fitzgerald AP, Kearney PM,
Perry IJ, Rennie KL, Kozarski R, et al. (2016) Number
of Days Required to Estimate Habitual Activity Using
Wrist-Worn GENEActiv Accelerometer: A Cross-
Sectional Study. PLoS ONE 11(5): e0109913.
doi:10.1371/journal.pone.0109913
Editor: Maciej Buchowski, Vanderbilt University,
UNITED STATES
Received: March 10, 2014
Accepted: April 7, 2016
Published: May 5, 2016
Copyright: © 2016 Dillon 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.
Data Availability Statement: All relevant data are
available via Figshare (https://dx.doi.org/10.6084/m9.
figshare.3188872).
Funding: Authors acknowledge the Health Research
Board, Ireland (reference HRC/2007/13) for funding
this study. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
light activity levels on Sunday to differ to all other days in the week. Analysis revealed
greater than 1 day monitoring is necessary to achieve acceptable reliability. Monitoring
frame duration for reliable estimates varied across intensity categories, (sedentary (3 days),
light (2 days), moderate (2 days) and vigorous activity (6 days) and MVPA (2 days)).
Conclusion
These findings provide knowledge into the behavioural variability in weekly activity patterns
of middle-aged adults. Since Sunday differed from all other days in the week this suggests
that day of the week cannot be overlooked in the design and analysis of physical activity
studies and thus should be included in the study monitoring frames. Collectively our data
suggest that six days monitoring, inclusive of Saturday and Sunday, are needed to reliably
capture weekly habitual activity in all activity intensities using the wrist-worn GENEActiv
accelerometer.
Background
Accurately measuring habitual physical activity is crucial to understanding the relationship
between frequency, duration, type and amount of physical activity and health. A range of sub-
jective and objective methods to quantify physical activity and sedentary behaviour exist.
Objective measures such as accelerometers and pedometers provide information on patterns of
physical behaviour within a given day and across several days, are feasible for large studies, are
less prone to error and no recall is necessary. Thus in comparison to subjective methods, objec-
tive measures provide significantly more reliable data on habitual physical behaviour.
Physical behaviour is influenced by a range of factors including demographic characteristics,
emotional influences and behavioural attributes [1]. As a result patterns of physical behaviour
show substantial intra- and inter-individual variation, the extent of which plays a major role on
data quality and reliability [2]. Methodological issues such as duration of monitoring-frame,
position of wear, accelerometer type and wear-time compliance may also affect data quality.
Modern devices are fully waterproof and can be worn on the wrist, resulting in improved wear-
time compliance as the device can be worn all day and does not need to be removed for water
based activities [3, 4]. Minimising the number of days monitoring will likely have important
implications on wear-time compliance. Extended monitoring periods can be a burden to par-
ticipants and financially costly, leading to the removal of the device, reduced wear-time, and
subsequently reduced data quality. Thus a current challenge is determining an appropriate
monitoring frame for researchers who want to minimise participant burden and maximise
wear-time compliance.
Several studies have examined a suitable monitoring frame to accurately measure physical
behaviour in adults [5–8]. These studies have varied in terms of statistical analysis, position of
wear, type of accelerometer and time-frame of interest, producing variable monitoring frames
of 7 days, 5 days, 5–6 days and 3–5 days, respectively [5–8]. In addition, some examined the
appropriate monitoring frames to reliably estimate habitual physical behaviour intensities indi-
vidually [5, 6]. Matthews et al. (2002) concluded that 3–4 days monitoring were required to
correctly measure moderate-to-vigorous physical activity (MVPA), and that 7 days were
needed to reliably estimate physical inactivity [5]. Findings by Scheers et al. (2012) recom-
mended overall that both Saturday and Sunday in addition to at least 3 weekdays were needed
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 2 / 11
to obtain reliable estimates of habitual physical activity [6]. Hart et al. (2011) recommended 5,
3 and 2 days monitoring for sedentary behaviour, light activity, moderate and vigorous activity
respectively [8]. Such conflicting recommendations highlight the need to determine an appro-
priate monitoring frame to reliably measure both habitual physical activity and sedentary
behaviour for each accelerometer and activity intensity. Importantly no studies to date have
sought to determine a suitable monitoring frame to accurately measure physical behaviour
using the wrist-worn GENEActiv accelerometer. Only this wear location was examined. Com-
pliance will vary across wear locations. The GENEActiv accelerometer is a waterproof device
which can be worn 24 hours a day for long durations (up to 30 days), on multiple different
bodily positions (hip, thigh, waist and wrist). The wrist-worn position has been found to pro-
vide equally accurate data to hip and waist mounted devices and are associated with better
compliance to wear protocols [3, 4, 9]. Furthermore, the 24 hour wear protocol implemented
in this paper may have important implications on calculation of daily physical behaviour
estimates as errors associated with missing data imputation (due to device removal) will be
avoided and additionally waterproofing will allow water-based activities of higher intensity
activities to be captured. The GENEActiv accelerometer is a relatively new device in the field of
habitual physical activity research. Unlike many other accelerometers, data is collected and
stored as raw acceleration in g units (m/s2) for offline analysis thereby allowing a range of data
processing techniques to be applied post data-collection.
Thus the aim of this study is to examine the intra- and inter-individual variability across
days, and thus identify an appropriate monitoring frame for capturing weekly habitual physical
behaviour in middle-aged adults using the wrist-worn GENEActiv accelerometer.
Methods
Subjects
A population representative random sample (Mitchelstown cohort) was recruited from a large
primary care centre in Mitchelstown, County Cork, Ireland [10]. The primary care centre
includes 8 general practitioners and the practice serves a catchment area of approximately 20,
000 with a mix of urban and rural residents. Participants were randomly selected from all regis-
tered attending patients in the 50–69 year age group. In total 3, 807 potential participants were
selected from the practice list. Following exclusion of duplicates, deaths and ineligibles, 3, 043
were invited to participate in the study and of these 2, 047 (49.2% male) completed the ques-
tionnaire and physical examination components of the baseline assessment (response rate
67%) during the study period (April 2010 and May 2011). Accelerometers were introduced
into the study in January 2011. Of the 745 cohort participants seen between January and May
of 2011, 475 (44.6% males; mean aged 59.6±5.5 years) subjects agreed to participate (response
rate 64%) and of these 397 (46.1% males; mean 59.6±5.5years) had valid accelerometer data.
Ethics Statement
The study was approved by the Clinical Research Ethics Committee of University College
Cork. Written informed consent to participate was obtained from all participants.
Accelerometer protocol
The GENEActiv accelerometer was introduced in the latter half of the study. Objective physical
activity levels were assessed using a tri-axial, GENEActiv accelerometer. The accelerometer
(ActivInsights Ltd, Kimbolton, Cambridgeshire, United Kingdom) comprised a tri-axial
STMicroelectronics accelerometer with a dynamic range of +/-8 g (1 g = 9.81 m/s2), where g
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 3 / 11
represents gravitational unit, and was attached to the participants’ preferred wrist with a strap.
The technical reliability and validity of this accelerometer has been reported elsewhere [11].
For the current study, acceleration was sampled at 100Hz and the accelerometer worn for
7-consecutive days. Following return of the accelerometer to the co-ordination centre, the data
was extracted using GENEActiv software and then collapsed using the following, sum of the
vector magnitude, equation (
X
j ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix2 þ y2 þ z2p  g j ) [11]. This equation is used to calculate
the sum (∑) of the signal magnitude vector
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
x2 þ y2 þ z2p with gravity subtracted (-g). The
sum is calculated for a specific time interval (epoch) e.g. 60 second epoch. Each time interval,
from the daytime wear-time (6am-12am) periods, was categorised based on validated cut-off
points for dominant and non-dominant wrist wear. Cut-point values were created from a con-
venience sample of 56 volunteers aged between 18 and 65 years, free from injury and in good
health. Data was sampled at a frequency of 30Hz and collapsed into 15-second epochs. Oxygen
consumption (VO2) was measured using a portable metabolic unit (Cosmed K4B2, Rome,
Italy) [12]. Activities (sitting, standing, dish washing, floor sweeping, slow walking, fast walking
and jogging) were completed in ascending intensity. Participants were asked to complete each
activity at a pace that was comfortable to them, but within each speed range: slow walking
(2.5–4.5 km/hour), brisk walking (4.5–6.5 km/hour) and light jogging (6.5–8.5 km/hour).
Intensity cut-points (sum of the vector magnitude counts) created for dominant and non-dom-
inant wrist-wear based on all activities, with the exception of dish washing, are presented in
Table 1.
Wear and non-wear time was identified by the procedure outlined by Van Hees et al.,
(2011) [13]. Non-wear time was calculated for each accelerometer axis on the basis of the stan-
dard deviation (SD) and the value range. The procedure was carried out on successive blocks
of 30 minutes. A block was categorised as non-wear time if the SD was less than 3.0 mg (1
mg = 0.00981 m.s-2) or if the value range was less than 50 mg, for at least two out of the three
axes. Four-hundred and seventy-five subjects wore the accelerometer. One-hundred and sixty-
six participants wore the GENEActiv accelerometer on their non-dominant wrist, 210 wore the
device on their dominant hand while 21 did not have this data recorded. Choice of wrist was
selected by the participant to ensure comfort and wear compliance, this decreased the likeli-
hood of the participant removing the device and thus increased the quality and quantity of
Table 1. Sensitivity, specificity, AUC and GENEActiv cut-points based on 3 and 6 METS.
Intensity* Sensitivity Specificity Area under the curve (95%CI) GENEActiv cut-points (SVMgs
(15s epoch))
Dominant wrist 30Hz 100Hz
Sedentary 91.6 92.4 0.97 <57.5 <191.8
Light NA NA NA 57.5–84.3 191.8–281.5
Moderate 67.9 67.9 0.694 84.4–178.4 281.6–595
Vigorous 97.1 97.0 0.994 >178.4 >595
Non-dominant wrist
Sedentary 92.9 92.4 0.98 <47.5 <158.5
Light NA NA NA 47.5–78.3 158.5–261.8
Moderate 70.5 68.6 0.716 78.4–148.5 261.9–495
Vigorous 97.1 97.0 0.992 >148.5 >495
* sedentary (<1.5 METS), light (1.5–2.99 METS), moderate (3.00–5.99 METS), vigorous (>6 METS)
NA; not applicable as sedentary and moderate intensity cut-points provide the margins for light intensity
doi:10.1371/journal.pone.0109913.t001
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 4 / 11
data for analysis as fewer participants were deemed invalid (<7 days wear) for analysis. Follow-
ing exclusion of 16 participants with missing data due to technical issues and 62 participants
with less than 10 hours of wear time activity during daytime hours on any day, the remaining
397 subjects were eligible for further analysis. The number of participants with various num-
bers of valid days (days in which the participant recorded>10 hours of wear time data) of data
are presented in Table 2.
Statistical analysis
Analysis was performed separately for each intensity category. Individual median and 25th
and 75th percentiles for minutes spent in each activity category were calculated for each day,
data non-normally distributed. Data are reported as median and 25th and 75th percentiles
unless otherwise stated. Kruskal-Wallis p-values assessed whether activity levels varied signif-
icantly across days of the week. Spearman pairwise correlations determined the association
between days of the week. Number of days required to reliably estimate habitual physical
activity was assessed using repeated measures analysis of variance (ANOVA), intra-class cor-
relations (ICC), and modified Spearman-Brown formula [14]. Repeated measures ANOVA
established whether minutes spent in activity differed across days. In the case of the violation
of the assumption of sphericity, the Greenhouse-Geisser adjusted F was interpreted. If an
overall significant F level was shown, post hoc tests (Tukey HSD pairwise comparisons) were
used to assess differences between days. Effect size was assessed to determine the amount of
variation in the criterion (total weekly minutes) that was accounted for by various days of
monitoring. Coefficient of variation ((SD/mean)100) was calculated to explain intra-individ-
ual and inter-individual variability. Intra-individual variability was calculated for each indi-
vidual using weekly days of data while inter-individual variability was analysed as the group
mean and SD for weekly minutes. ICCs were calculated to determine the reliability of using
any single day of activity to estimate daily activity using 7 days of data. An ICC of 0.80 is con-
sidered standard to designated acceptable reliability [2]. A modified version of the Spearman-
Brown calculation determined the intraclass reliability coefficient associated with 1, 2, 3, 4, 5,
6, and 7 days of activity [5, 14, 15]. The intraclass reliability coefficient was estimated as the
proportion of total variance attributable to between-subject variance as follows: [(between-
subject variation) 2 / ((between-subject variation)c 2 + ((within-subject variation) 2 /n))],
where n is the number of days monitoring. All statistical analyses were conducted using Stata
(version 12, Stata Corp, College Station, Texas, USA), except coefficient of variation and
Spearman-Brown formula which were performed by hand. An alpha level of 0.05 was set to
evaluate significance.
Table 2. Number of participants with valid days (>10 hours of wear time) of data.
Number of valid days wear Number of participants
7 days 397
6 days 27
5 days 12
4 days 4
3 days 3
2 days 4
1 days 6
0 days 6
doi:10.1371/journal.pone.0109913.t002
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 5 / 11
Results
Descriptive analysis
Median time (minutes) spent in each activity type across days of the week was calculated sepa-
rately for men and women (Table 3). Overall, differences in median activity levels across days
were significant (P<0.05), with the exception of vigorous activity (P>0.05). Among all subjects
time spent in sedentary activity was greatest on Sunday (946 minutes). Among men, sedentary
activity was higher on Sunday (956 minutes) compared to all other weekdays (889–912 min-
utes), while women were most sedentary on Sunday through Tuesday (930–940 minutes). Both
men and women were more physically active on weekdays. Time spent in vigorous activity was
similarly low for men and women throughout the week.
Pairwise comparisons
All Spearman pairwise correlations between days of the week were significant (P<0.001). The
range of pairwise correlations varied across days of the week and intensity type; sedentary
(0.59–0.79), light (0.59–0.77), moderate (0.59–0.77), vigorous (0.37–0.60) and MVPA (0.58–
0.78), (S1 Table). The mean pairwise correlations across days of the week were 0.72, 0.68, 0.69
and 0.41 for sedentary, light, moderate and vigorous activity respectively.
Variance analysis
There were significant differences between days for sedentary (P<0.01), light activity
(P<0.01), moderate activity (P<0.01) and MVPA (P<0.01), whereas vigorous activity (P =
0.15) was not significantly different between days. In relation to sedentary and light activity,
Sunday differed from all other days of the week (P<0.05). The differences in mean minutes
between days was small; sedentary (0.7%) moderate (0.4%), vigorous (0.2%) and MVPA
(0.4%), except for light activity (1.6%). The mean intra-individual variability for each activity
type were; sedentary (30.3%), light activity (45.4%), moderate activity (60.8%), vigorous activity
(73.7%) and MVPA (61.6%), while inter-individual variability was 1.8–178% of total variance;
sedentary (3.3%), light activity (2.3%), moderate activity (1.8%), vigorous activity (107%) and
MVPA (178%).
Intra-class reliability coefficients
The ICC for any single day for sedentary, light, moderate activity, vigorous activity and MVPA
was calculated, 0.66, 0.69, 0.69, 0.42 and 0.68 respectively. These results demonstrate that
between 42–69% variance was accounted for using any single day of data collection to repre-
sent 7-day habitual activity. When ICC was calculated by gender, ICC did not alter, with the
exception of vigorous activity (32% and 46% for men and women respectively). Spearman-
Brown Formula calculated reliability coefficients for combination of days (Fig 1). These results
indicate that between 59–82%, 68–87%, 74–90% and 78–92% of the variance was accounted
for using 2 days, 3 days, 4 days and 5 days monitoring to represent 7 day habitual activity. The
appropriate monitoring frames for each intensity of activity are 3 days, 2 days and 6 days for
sedentary behaviour, light and moderate activity and MVPA and vigorous activity respectively.
All remaining combinations were higher than 0.80.
Discussion
Our results indicate that the number of monitoring days required to estimate weekly habitual
activity vary according to physical behaviour intensity. Based on our findings, we recommend
that data collection periods should be based on activity intensity; sedentary (3 days), light
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 6 / 11
activity (2 days), moderate activity (2 days), vigorous activity (6 days) and MVPA (2 days).
Because variability between activity intensities across days of the week was small any combina-
tion of days appears to be sufficient to acquire a stable weekly estimate of physical activity and
sedentary behaviour. Our findings support current guidelines recommending inclusion of both
weekend and week days in physical behaviour monitoring frames. Many large studies (e.g.
NHANES and Biobank) using similar protocols may apply our findings to reduce monitoring
time-frames and increase device turnover in the field. Additionally our result could influence
the analysis of these studies, i.e. if moderate activity is the exposure of interest a minimum
wear period of 2 days (inclusive of one weekend day) can be implemented in turn decreasing
the number of days and or person excluded from analysis and thus increasing the power to
finding significant associations with health outcomes.
While this is the first study to examine the required number of monitoring days needed to
accurately measure physical behaviour in adults using the wrist-worn GENEActiv accelerome-
ter, other accelerometers have been examined in this context [5–8]. Overall these studies vary
Table 3. Daily duration (minutes) of sedentary, light, moderate and vigorous activity.
Total (n = 397)
Sedentary Light Moderate Vigorous MVPA
Monday 926 (833, 984) 94 (64, 140) 50 (24, 93) 1 (0, 5) 56 (25, 100)
Tuesday 921 (837, 981) 98 (64, 135) 48 (24, 91) 1 (0, 6) 52 (25, 101)
Wednesday 911 (829, 976) 100 (68, 143) 55 (25, 95) 1 (0, 5) 59 (27, 100)
Thursday 908 (842, 977) 106 (66, 140) 56 (26, 93) 1 (0, 5) 58 (26, 100)
Friday 903 (826, 977) 106 (68, 148) 57 (25, 100) 1 (0, 5) 62 (25, 106)
Saturday 910 (839, 989) 100 (65, 142) 51 (23, 98) 1 (0, 5) 56 (23, 103)
Sunday 946 (872, 1004) 77 (48, 117) 42 (17, 82) 0 (0, 3) 46 (18, 91)
p-value <0.001 <0.001 <0.001 0.81 <0.001
Men (n = 183)
Sedentary Light Moderate Vigorous MVPA
Monday 909 (798, 972) 98 (66, 154) 60 (29, 116) 1 (0, 7) 69 (29, 126)
Tuesday 906 (813, 979) 103 (62, 146) 61 (25, 112) 1 (0, 7) 66 (27, 120)
Wednesday 903 (802, 970) 99 (67, 156) 66 (32, 118) 1 (0, 6) 76 (33, 122)
Thursday 901 (815, 978) 104 (66, 143) 66 (31, 107) 1 (0, 5) 71 (31, 116)
Friday 889 (802, 977) 100 (66, 156) 65 (29, 111) 1 (0, 6) 74 (30, 121)
Saturday 912 (840, 987) 99 (65, 135) 58 (25, 99) 1 (0, 6) 64 (25, 103)
Sunday 956 (878, 1009) 70 (43, 108) 45 (18, 85) 1 (0, 4) 47 (19, 85)
p-value <0.001 <0.001 0.009 0.77 0.01
Women (n = 214)
Sedentary Light Moderate Vigorous MVPA
Monday 931 (869, 990) 92 (58, 133) 40 (22, 76) 1 (0, 3) 46 (22, 82)
Tuesday 930 (867, 985) 97 (66, 130) 43 (21, 75) 1 (0, 5) 47 (22, 84)
Wednesday 918 (850, 980) 100 (68, 135) 49 (23, 83) 1 (0, 4) 52 (25, 87)
Thursday 915 (854, 974) 107 (68, 138) 48 (23, 83) 1 (0, 4) 51 (24, 88)
Friday 910 (844, 979) 110 (68, 146) 48 (23, 84) 1 (0, 4) 52 (25, 89)
Saturday 909 (834, 990) 103 (67, 148) 46 (21, 94) 1 (0, 4) 51 (22, 104)
Sunday 940 (862, 1002) 89 (51, 124) 38 (16, 80) 0 (0, 3) 43 (16, 85)
p-value <0.001 <0.001 <0.001 0.84 <0.001
Data is presented as median (25th, 75th percentile). P-values presented as Kruskal-Wallis, tests the difference in median activity levels across days of the
week.
doi:10.1371/journal.pone.0109913.t003
Number of Days Required to Estimate Habitual Activity
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in terms of statistical analysis, position of wear, type of accelerometer and time-frame of inter-
est, resulting in variable monitoring frames of 7 days, 5 days, 5–6 days and 3–5 days, respec-
tively [5–8]. All studies utilised different accelerometers; Compute Science Applications (CSA)
accelerometer [5], SenseWear Armband [6], Caltrac accelerometer [7] and the ActiGraph [8],
and positioned the device on multiple body positions; the hip [5], arm [6] and waist [7, 8]. Sev-
eral assessed the appropriate monitoring frames to reliably estimate habitual physical behav-
iour intensities independently [5, 6, 8]. Matthews et al. (2002) determined that 3–4 days
monitoring were required to accurately measure MVPA [5]. Scheers et al. (2012) suggested
that both Saturday and Sunday and at least 3 weekdays were needed to obtain reliable estimates
of habitual physical activity, and only 3 days data collection was needed to capture light activity
[6]. Hart et al. (2011) proposed monitoring frames individually for sedentary behaviour, light
activity, moderate and vigorous activity; 5, 3 and 2 days monitoring respectively [8]. These
inconsistent recommendations emphasise the need to establish an appropriate monitoring
frame to reliably capture habitual physical behaviour for each accelerometer, activity intensity
and position of wear.
Our results add to the current literature by reporting the number of monitoring days needed
to reliably estimate habitual physical behaviour using GENEActiv accelerometers. The number
of days needed to reliably estimate habitual physical behaviour vary according to activity inten-
sity and statistical tests used [2]. Tudor-Locke et al. (2005) contend that no single statistical test
is considered adequate to fully understand the issues underlying the calculation of an appropri-
ate monitoring frame [16]. As recommended by Tudor-Locke et al. (2005), which employed a
wide range of statistical techniques to determine number of days needed for an appropriate
monitoring frame, Spearman-Brown prophecy formula has been used in the majority of studies
Fig 1. Reliability coefficient for number of daysmonitoring. Fig 1 illustrates the reliability coefficient
associated with different length monitoring frames. The results propose that between 59–82%, 68–87%, 74–
90% and 78–92% of variance was explained for by using 2 days, 3 days, 4 days and 5 days monitoring to
represent 7 days of habitual activity.
doi:10.1371/journal.pone.0109913.g001
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 8 / 11
investigating appropriate monitoring frames [5, 14, 17]. Results of Spearman-Brown calcula-
tions and ICC for a single day identified consistent monitoring frames for all activity intensities
(>1 day monitoring).
In addition, the moderate to high pairwise correlations across days indicated a clear ten-
dency for activity patterns to be consistent across the days, with the exception of vigorous activ-
ity where correlations were low suggesting that vigorous activity patterns varied throughout
the week, thus explaining the longer monitoring frame. In terms of sedentary and light activity,
Sunday had the lowest correlations, suggesting the activity patterns on Sunday are less consis-
tent with other days in the week. Greater between-subject variation and lesser within-subject
variation across days results in shorter monitoring frames. Light and moderate intensity activi-
ties have the shortest monitoring frames; this could be due to higher levels of between-subject
variation and lower levels of within-subject variation across days of the week compared to sed-
entary and vigorous activity, and thus 2 days of monitoring is sufficient to capture variation in
light and moderate intensity activities. In addition, light and moderate intensity activities are
more likely to include household activities and activities such as exercise which tend to be
planned, structured and repetitive [18]. The same could be said for vigorous activity, however
due to the very low levels of vigorous activity measured in this population, variation between-
and within-subjects would be hard to capture accurately, thus resulting in a larger monitoring
frame. This is supported by the low pairwise correlations across days, which indicate inconsis-
tent activity patterns across the days.
Study strengths and limitations
Amain strength of our study is the use of a valid and reliable activity monitor which is capable
of assessing time spent in sedentary, light, moderate and vigorous activity categories [11]. In
addition, this accelerometer collects data as raw acceleration and stores the data as g units for
offline analysis thereby allowing for efficient data cleaning, management of spurious data, and
the application of various known data processing algorithms post-data collection. Further
strengths include the 24-hour study protocol, the high study participation rate and large sam-
ple size. Notwithstanding these strengths one limitation of this study is that we only examined
the required number of monitoring days needed to reliably estimate weekly habitual activity.
Further investigation could be expanded into how many days/weeks of monitoring represent a
month, a season, or a year of habitual activity using the wrist-worn GENEActiv accelerometer.
Kang et al. (2009) examined a suitable monitoring frame to capture year-round averages of
pedometer measured physical activity and found 5 consecutive days and 6 random days to be
necessary [19]. In addition, many studies have reported seasonal and monthly variations in
physical activity leading to recommendations for physical behaviour data collection to occur
during certain seasons and specific months of the year [20–22]. Generalizability of our findings
may also be limited. The Mitchelstown cohort was a random sample of middle-aged adults,
50–69 years of age, in an area which was representative of both urban and rural population in
Ireland. The sub-sample of the Mitchelstown cohort for whom accelerometer data was col-
lected, differed by gender, in that women were more likely to agree to wear the accelerometer.
In addition, participants were recruited from a primary care centre, and therefore could have
more health problems or be more health conscious. However it should be noted that there were
no statistically significant differences in age, gender, education or BMI between those included
and excluded in the final analysis.
The data for this study was collected over 7 consecutive days at a frequency of 100Hz and
collapsed into 60s epochs. Under these conditions our results demonstrate the number of mon-
itoring days required to reliably assess weekly habitual activity for each type of intensity. We
Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 9 / 11
observed marked differences between weekdays, Saturday and Sundays. If the outcome of
interest, for further studies, involves a more detailed examination of patterns of activity both
Saturday and Sunday should be included in the monitoring frame. Similarly our gender specific
findings, such as comparatively high sedentary activity in women on Monday, should be con-
sidered. This consideration may be particularly pertinent when examining overweight and
obese adults whose activity on weekend days has been shown to be particularly distinct from
normal weight subjects across week days [23, 24].
Conclusion
This study examined the number of monitoring days needed to accurately estimate habitual
physical activity and sedentary behaviour from the wrist-worn GENEActiv accelerometer in
middle-aged adults. Our data indicates 6 days monitoring are required to reliably capture
weekly activity in all activity categories. These findings may have important implications in
terms of study design and data reduction strategies. Further study protocols employing our rec-
ommendations may benefit from reduced number of data collection and processing days and
associated reductions in person-time and study cost.
Supporting Information
S1 Table. Spearman pairwise correlation coefficient of physical activity intensity by days of
week.
(PDF)
Acknowledgments
Authors acknowledge help from Dr. Alex Rowlands, University of South Australia, in data
interpretation and Prof. Alan Donnelly, Dr. Kieran Dowd and Cormac Powell, University of
Limerick, for providing GENEActiv accelerometer threshold data.
Author Contributions
Conceived and designed the experiments: CD IP PK CP. Performed the experiments: CD CP
AF IP PK KR RK. Analyzed the data: CD CP AF KR RK. Contributed reagents/materials/analy-
sis tools: CD CP AF IP PK KR RK. Wrote the paper: CD. Reviewed the manuscript: CP AF IP
PK KR RK.
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Number of Days Required to Estimate Habitual Activity
PLOS ONE | DOI:10.1371/journal.pone.0109913 May 5, 2016 11 / 11