A randomized controlled trial of continuous activity, short bouts, and a 10,000 step
guideline in inactive adults
Tiana Y. Samuels, Thomas D. Raedeke⁎, Matthew T. Mahar, Kristina H. Karvinen, Katrina D. DuBose
Department of Exercise and Sport Science, East Carolina University, USA
a b s t r a c ta r t i c l ei n f o
Available online 13 December 2010
Physical Activity Recommendations
Physical activity bouts
Objective. Although several studies have examined the effect of accumulated bouts on health outcomes,
the impact ofrecommending short bouts on activity-related behavior in health promotion efforts has received
Method. During this 5-week study in 2007–2008, 43 university employees (8 male, 35 female) in the
Southeastern United States were randomly assigned to a group recommended to achieve (a) 10,000 steps
(10 K), (b) 30-minutes (30 min) of continuous physical activity, or (c) 30-minutes of activity in bouts of at
least 10 minutes (bouts).
Results and conclusions. Repeated measures ANOVA revealed that the 10 K group showed the largest
increase in step counts whereas the bouts group showed the smallest change over the intervention period,
p=0.01. Condition differences were most pronounced on days in which participants met their activity
recommendation. Accelerometer results revealed that the 10 K (d=1.1) and 30 min groups (d=0.89)
showed large increases in minutes of moderate to vigorous activity (MVPA), whereas the bouts group showed
minimal change (d=0.11). Although activity recommendations did not differentially affect self-efficacy,
participants from all conditions showed decreased self-efficacy across the intervention (p=0.02),
highlighting the need to develop strategies to increase self-efficacy in activity promotion efforts.
Published by Elsevier Inc.
Although the importance of physical activity (PA) is well
established, a majority of the population is insufficiently active.
Across national level surveillance self-report data, 25–50% of the U.S.
adult population achieves recommended PA amounts whereas 25%–
40% are physically inactive (e.g., see CDC, 2009a, b). Studies using
objectively measured PA through accelerometers find that approxi-
mately 5% of adults meet activity recommendations (e.g., Hagströmer
et al., 2007; Troiano et al., 2008). In the effort to increase PA
participation, national organizations have released recommendations
highlighting that 30 minutes of moderate intensity activity on most
(e.g., five) days per week, in either a single session or accumulated
bouts of at least 10 minutes, has substantial health benefits (CDC,
2009a, b; Haskell et al., 2007; USDHHS, 2008).
The impetus for moderate intensity activity was based in part on
research documenting a relationship between it and health outcomes
(Haskell et al., 2007; Pate et al., 1995; USDHHS, 1999). In addition, a
common belief was that moderate intensity activity, along with a
focus on accumulated bouts, offered greater flexibility and might be
more realistic for individuals to achieve (Murphy et al., 2009;
USDHHS, 1999). In a recent review, Murphy et al. (2009) noted that
although accumulated bouts are associated with health benefits,
minimal research has evaluated the impact of recommending bouts
on adherence. Coleman et al. (1999) reported that both bouts and
continuous exercise were associated with similar changes in activity
level; whereas Jakicic et al. (1995) reported that being prescribed
several short bouts resulted in higher adherence. However, both
studies involved intensive lifestyle counseling, limiting their
Although not directly equivalent to the public health recommen-
dation of accumulated bouts, a 10,000 (10 K) step guideline has been
highlighted in both the popular press and academic literatures
(Tudor-Locke and Bassett, 2004). In comparing the 10 K guideline
and 30 minute (30 min) recommendation, Hultquist et al. (2005)
found that participants instructed to accumulate 10 K steps per day
had higher step counts than participants instructed to walk
30 minutes across a four week intervention. Overall, short-term
pedometer intervention studies using a 10 K step recommendation
have shown daily step count increases of approximately 3000 (e.g.,
Bravata et al., 2007; Schneider et al., 2006; Sidman et al., 2004).
However, pedometers do not include a measure of intensity and thus
preclude determination of whether individuals meet public health
activity recommendations. A stronger evaluation of the impact of a
10 K step or time-based activity recommendations could be made by
assessing activity through accelerometers.
Preventive Medicine 52 (2011) 120–125
⁎ Corresponding author. 172 Minges Coliseum, Department of Exercise and Sport
Science, East Carolina University, Greenville, NC 27858, USA. Fax: +1 252 328 4654.
E-mail address: email@example.com (T.D. Raedeke).
0091-7435/$ – see front matter. Published by Elsevier Inc.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/ypmed
Additional insights on the impact of activity recommendations
might be gained by examining psychosocial constructs theoretically
related to PA behavior, such as self-efficacy (i.e., belief in one's ability
to successfully perform a specified behavior). The USDHHS (1999)
suggested that activity promotion efforts emphasizing accumulated
bouts may positively influence self-efficacy as short bouts may seem
less daunting than inserting a long-bout of activity into one's daily
schedule. However, research has not examined the impact of
continuous versus accumulated bouts of activity on self-efficacy.
Therefore, this study examined the impact of three PA guidelines
including being instructed to (a) take 10,000 steps per day (10 K), (b)
engage in 30-minutes of moderate intensity daily activity (30 min),
and (c) accumulate 30-minutes of moderateintensity PA in bouts of at
least 10-minutes or longer on a daily basis (bouts) on PA levels
assessed by pedometers/accelerometers and on self-efficacy. Out-
come measures included the number of days per week activity
recommendations were met, daily step count averages including both
days recommendations were met and unmet, minutes of moderate to
vigorous intensity activity (MVPA), and self-efficacy.
After Institutional Review Board approval, participants were recruited
through the university email system, followed by phone calls providing more
information to those expressing interest. The sample consisted of 43 faculty
and staff volunteers from a large university in the Southeastern United States
ranging in age from 27 to 68 years (mean ((standard deviation, sd)=48.7
(9.1)). A priori power calculations using GPower (Faul et al., 2007) based on
an alpha of 0.05, a moderate correlation among repeated measures (i.e., 0.60)
and the expectation of a medium effect size (0.25) for ANOVA results based
on Cohen (1988) suggestthis sample size had sufficient power. Specifically, to
achieve statistical power of 0.80, a sample size of at least 24 is needed based
on three conditions with 5 repeated measures and a sample size of 36 for two
Of the volunteers, 35 were female and eight were male who on average
were overweight with a mean body mass index of 29. A majority were
Caucasian (n=36) with the remaining being African American (n=6) and
Hispanic (n=1). Inclusion criteria included being inactive based on public
health guidelines but expressing interest in becoming active.1Individuals
with medical contraindications to increasing their PA level were excluded as
were those with travel obligations during the study period.
This study used a randomized design with three conditions: a 10 K,
30 min, and bouts group with an approximately equal number assigned to
each condition. The lead author was involved in all phases of the study
including participant enrollment and weekly meetings with participants
while the second author initiated participant recruitment and oversaw
After signing an informed consent, participants received a sealed
pedometer and an accelerometer with instruction on how to wear them to
collect a one-week baseline. During the baseline period, participants were
instructed to follow typical daily activities and avoid modifications to their
After the initial visit, participants were stratified by gender, age, and
body composition and were randomly assigned to one of the three
intervention groups. Following the baseline period, participants were
informed of their group's activity guideline. In doing so, participants were
blind to the other study conditions. The intervention period lasted four
weeks during which participants met with the principal investigator on a
weekly basis to have their pedometer data recorded and to drop off their
activity log. During the meeting, the principal investigator did not provide
PA counseling or advice on how to achieve activity recommendations.
Rather, she was generally supportive and encouraged participants to focus
on meeting their assigned recommendation. During the intervention period,
participants in the 10 K group wore unsealed pedometers so that they could
view and record their daily step count. The 30 min and bouts groups wore
sealed pedometers to prevent them from receiving feedback on the step
counts that may have prompted them to strive to increase their step count
in addition to or instead of their prescribed activity recommendation. All
participants were asked to maintain an activity log throughout the
intervention period to ensure all groups received feedback from self-
monitoring. Participants also wore the accelerometer during the final week
of the intervention. The self-efficacy measure was administered at the end
of baseline, week 1, and week 4. Self-efficacy was assessed after the first
week of the intervention in addition to the baseline period because self-
efficacy estimates are theoretically more accurate if individuals have task
Measures and instrumentation
Consistent with the measurement approach recommended by Bandura
(1997) and McAuley and Mihalko (1998), a 15-item PA self-efficacy scale
evaluated individuals' belief in their ability to complete progressively more
challenging PA defined in terms of frequency of activity recommendation
completion. Specifically, participants rated their confidence to meet their
activity recommendation on one day per week through seven days per week
as well as over a time period ranging from one to eight weeks on a scale of 0%
to 100%. A self-efficacy score was derived by averaging the items to result in a
score that ranges from 0% to 100% confident. The self-efficacy questionnaire
exhibited an alpha reliability of at least 0.98 for each measurement time
One potential reason why the 10 K guideline may impact PA levels is
because pedometers provide instant feedback and encourage self-monitor-
ing. To control for this, participants were supplied with an activity log that
corresponded to their activity recommendation. To make the log easy to
complete and the feedback visual, participants in the 30 min and bouts
conditions shaded in circles that corresponded to how much activity they
achieved each day. The 10 K log was fill-in the blank where participants
recorded their daily step count.
New Lifestyles (NL)-800 model pedometer was used. The NL-800 uses the
same mechanical engineering (i.e., piezoelectric strain gauge and algorithm
rather than hairspring or spring lever mechanics) as the NL-2000 which has
been shown to have acceptable accuracy at all walking speeds (Crouter et al.,
2003) and has a seven day memory capacity. Thus, the NL-800 should also
provide reliable and valid estimates of step count2. Average daily step count
each week was determined by summing the daily step counts each day and
dividing by the number of days the pedometer was worn.
Although pedometers estimate activity through step counts, they do not
assess PA intensity. Consequently, accelerometers were used as a second
measure of activity as they can be used to calculate the amount of time spent
in MVPA (Freedson et al., 1998; Sirard et al., 2000; Welk, 2002). For this study,
the Actigraph (model number 7164) accelerometer was used which has been
shown to provide reliable and valid assessments of PA (Welk et al., 2004).
Data were recorded in one-minute epochs and time spent in MVPA was based
on application of count thresholds (Freedson et al., 1998).
Height and weight
Height was assessed with a wall-mounted stadiometer (Perspective
Enterprises, Portage, MI). Weight was assessed using a Healthometer
physician's scale (Healthometer, Brideview, IL.).
1Participants who reported participating in MVPA on three or more days per week
for a duration of at least 20 minutes were excluded based on responses to an interview
and a self-report activity questionnaire (Godin and Shephard, 1985).
2The NL-2000 was not used because it also provides feedback on caloric
expenditure which might have encouraged those in the 10 K group to set additional
non-step related goals.
T.Y. Samuels et al. / Preventive Medicine 52 (2011) 120–125
If a person wore the pedometer less than three days during a given week,
then the two surrounding weeks step counts were averaged and that value
was substituted for the missing week (this was done on three occasions). The
week three daily step count average was carried forward in the event of
missing information during the final week (two occasions). No data were
recorded by the accelerometer for six participants during the pretest and
seven participants during the post-test. To adopt a conservative approach and
maximize the sample size, we used a last observation carried forward
approach in the event of post-test accelerometer malfunction. Preliminary
analyses revealed that replacing the missing data did not impact mean scores.
and (e) self-efficacy.Effectsizes (Cohen'sd) werecalculatedasmeandifference
divided by pooled standard deviation. Frequency data were evaluated to
determine how many participants from each condition increased their step
counts during the course of the intervention. Finally, correlations between step
counts and self-efficacy were conducted.
This study was conducted in 2007–2008 and concluded when the
desired sample size was achieved. Participant flow through the study
is shown in Fig. 1. Participants from the three groups did not differ on
demographic characteristics such as BMI or age (pN0.05; see Table 1).
On average, participants from all three groups wore the pedometer on
at least 5 days per week. Overall, those in the 10 K group met their
walking goal on mean(sd)=3.5(2.1) days per week, whereas those in
the 30-min and bouts conditions met their goal on mean(sd)=2.8
(1.8) and mean(sd)=2.3(1.3), respectively (see Table 2). Although
these differences were not statistically significant (pN0.05), the effect
size for the difference between the 10 K group and the other two
groups was moderate (d=0.41 and 0.70 for the 30-min and bouts
A three (group)×five (week) repeated measures ANOVA was
performed with average daily step counts each week as the de-
pendent measure. Significant group, p=0.03, and week, pb0.0001
main effects existed. However, the main effects were superseded by a
significant group by week interaction, p=0.01 (see Fig. 2). Tukey's
post-hoc comparisons revealed that the three groups did not
significantly differ on step counts during the baseline or week 4, but
did differ in the remaining weeks. Overall, the 10 K group showed the
largest increase in step counts and the bouts group experienced a
lower increase in step count than the other two groups over the
course of the study.
Participants were compared on their baseline step counts as well
as their daily average over all the intervention weeks using a 3
(group)×2(baseline versus intervention) repeated measures ANOVA.
70 Respondents Contacted
n = 10 did not meet inclusion criteria
n = 10 no longer interested in
Randomized n = 50
10k n = 18 30min n = 17Bouts n = 15
Discontinued n = 4
n = 2 no longer interested
n = 2 did not attend appointments
Discontinued n = 0Discontinued n = 3
n = 2 did not attend appointments
n = 1 no longer interested
n = 14 completed study
n = 17 completed studyn = 12 completed study
n =14 step count
n = 12 MVPA
n = 17 step count
n = 15 MVPA
n = 12 step count
n = 10 MVPA
Fig. 1. Flow of participants through the study from a large university in the Southeastern United States, 2007–2008.
Descriptive characteristics of study participants from a large university in the
Southeastern United States, 2007–2008.
8 M / 35 F
4 M / 10 F
2 M / 15 F
2 M / 10 F
10 K=10,000 steps per day, 30-min=30-minutes of moderate intensity activity,
bouts=accumulate 30-minutes of moderate intensity physical activity in bouts of at
least 10-minutes, sd=standard deviation, BMI=body mass index, cm=centimeter,
kg=kilogram, M=Male, F=Female.
T.Y. Samuels et al. / Preventive Medicine 52 (2011) 120–125
This analysis revealed a significant interaction, p=0.002 as well as a
time main effect, pb0.0001. Follow-upanalyses revealed that both the
10 K and 30 min group significantly increased their step counts
(pb0.05) during the intervention weeks compared to baseline
whereas the bouts group did not. The 10 K group participants
increased by 2,721 steps per day from baseline to intervention
(d=2.0) and the 30 min group increased by 1,875 steps per day
(d=1.3). In contrast, the bouts group increased by 733 steps per day
(d=0.61, see Table 2).
ANOVA comparing the three groups on days in which they met
their goal revealed significant step count differences, pb0.0001. Both
the 10 K and 30-min groups had significantly higher step counts than
the bouts condition with the differences being large in magnitude
(d=2.3 for 10 K and 1.1 for 30 min condition versus the bouts
condition). The step count difference between the 10 K and 30 min
condition was also large (d=1.4). The 10 K guideline demonstrated
an increase of 5,583 steps compared to baseline. The 30-min group
averaged 9,583 steps on days in which they met their activity
recommendation for an increase of 3,319 steps over baseline. The
bouts group showed the smallest change with an increase of 1,234
steps per day.
The three groups did not significantly differ in terms of step counts
on days in which they did not meet their activity recommendations,
p=0.44. Although the difference was not significant, effect size
calculations showeda moderatedifferencebetweenthe stepcounts of
both the 10 K (d=0.57) and 30 min group (d=0.37) versus the bouts
groups. The difference between the 10 K and 30 min group was small
(d=0.07) on days that recommendations were not met.
In evaluating the effect of activity recommendations on MVPA, a 3
(group) × 2 (baseline/posttest)repeated measures ANOVA revealed a
significant time main effect, p=0.001. No significant group main
effect, p=0.46 or group by time interaction, p=0.15 existed. Effect
size analysis of each group's change revealed a large increase in MVPA
for the 10 K (d=1.1) and 30 min groups (d=0.89). The bouts group
experienced a small increase in MVPA (d=0.11) (Table 2).
We also evaluated the number of participants from each condition
that increased their step counts (Table 3). More participants from the
10 K condition increased step counts by 3,000–4,000 steps compared
to the other conditions, whereas a greater number of participants in
the bouts condition showed either no change or a small decrease in
To examine the impact of activity guidelines on self-efficacy, a 3
(group) × 3(time) repeated measures ANOVA revealed a significant
time main effect, p=0.01. Trend analysis revealed a significant linear
decrease across time, p=0.02. Self-efficacy decreased over time for all
groups with the effect size between intervention weeks (i.e., week 1
and post-intervention) and baseline being moderate in magnitude
(i.e., d=0.40 in both cases). There were moderate, yet significant
correlations between week four step count averages and self-efficacy
measured at all three time-points (Table 4).
Although research has shown that both continuous and accumu-
lated bouts of PA are associated with health benefits, it is unknown
whether highlighting the value of accumulated bouts in activity
promotion efforts is beneficial. This is one of the first studies to
evaluatetheeffect ofPArecommendations onPArelatedbehaviorand
self-efficacy in physically inactive adults.
Overall, the 10 K guideline resulted in the greatest increase in PA
in the initial stages of activity adoption based on step count and
MVPA. On average, pedometer-based interventions incorporating a
10 K guideline show a step count increase of approximately 3,000
steps per day over baseline (Bravata et al., 2007), which is similar to
the increase shown by the 10 K group in this study. Similar to
Hultquist et al.'s findings (2005), the 10 K guideline appeared to
have had the most pronounced effect on step count on days that
activity recommendations were met with individuals increasing by
5,583 steps per day compared to baseline. Assuming a moderate
intensity walking pace of three to four miles per hour, individuals
typically accumulated 3,000–4,000 steps in a 30 minute walk.
Although not as large of an increase as the 10 K group, participants
in the 30 min condition also showed increased step count and MVPA
overthe courseof thestudy. The 30 min groupaveragednearly10,000
Daily step counts and MVPA from participants employed at a large university in the Southeastern United States, 2007–2008.
count (week 1–4)
Days goals not
Min of MVPA-baselineMin of MVPA-post
10 k=10,000 steps, 30-min=30 minutes of continuous moderate intensity activity, Bouts=accumulate 30-minutes of moderate intensity physical activity in bouts of at least 10-
minutes, MVPA=moderate to vigorous physical activity, sd=standard deviation, min=minutes.
BaseWk 1 Wk 2 Wk 3
Daily Step Count Average
Fig. 2. Changes in step count across the baseline and intervention weeks for participants
from a large university in the Southeastern United States, 2007–2008. 10 k=10,000
steps, 30-min=30 minutes of continuous moderate intensity activity, Bouts=accu-
mulate 30-minutes of moderate intensity physical activity in bouts of 10-minutes or
longer, MVPA=moderate to vigorous physical activity, Base=Baseline, Wk=week.
Number of participants from a university in the Southeastern United States
demonstrating step count changes across the intervention weeks compared to baseline,
Average daily step count change from baseline10 K30-minBouts
10 k=10,000 steps, 30-min=30 minutes of continuous moderate intensity activity,
Bouts=accumulate 30-minutes of moderate intensity physical activity in bouts of at
T.Y. Samuels et al. / Preventive Medicine 52 (2011) 120–125
steps on days in which they met the activity recommendation for an
increase of nearly 3,500 steps per day over baseline.
Both continuous and accumulated bouts of PA have been found to
provide similar health benefits (Murphy et al., 2009). However, this
study's results call to question the effectiveness of the accumulated
bouts concept in activity promotion efforts. The bouts group did not
increase PA overall compared to the other two conditions. Anecdot-
ally, some individuals mentionedthatit was moredifficult to schedule
three short PA bouts compared to one longer bout. Others mentioned
that the bouts concept was less attractive due to sweating while
wearing work clothing. These comments were raised despite the fact
that individuals in the bouts group were recommended to achieve 30
minutes of activity in either short bouts or one longer physical activity
session. It is plausible that recommending short bouts might be
effective in health promotion efforts when combined with PA
counseling (e.g., Jakicic et al., 1995) in contrast to results from the
current study which did not include activity counseling.
Further insights on the impact of activity recommendations on PA
related behavior can be gleaned from self-efficacy responses. Self-
efficacy has been found to be a consistent predictor of both the
adoption and maintenance of exercise (McAuley et al., 2001).
Similarly, results revealed that self-efficacy was moderately correlat-
ed with PA levels by week 4.
Although self-efficacy was related to PA levels, individuals from all
three groups showed a decrease in self-efficacy. Simply providing
education on PA recommendations and encouraging individuals to
become active can actually lower self-efficacy. These findings
reinforce the importance of targeting self-efficacy in PA promotion
efforts. Interventions that are effective in raising self-efficacy such as
by providing mastery experiences through gradually increasing
activity levels or by developing self-regulatory skills through active
problem solving approaches to overcoming barriers, might result in
greater behavioral change than interventions that do not explicitly
Although the 10,000 step guideline resulted in the greatest
increase in PA, it is not apparent from this study whether it would
have a positivemotivational effect over a longer duration comparedto
time-based recommendations. Given that a majority of pedometer
studies are short-term (Bravata et al., 2007), it is possible that
pedometers result in a temporary increase in activity due to the
novelty of wearing a pedometer and striving to achieve 10,000 steps.
In fact, results from this study suggest its effectiveness might be short-
term in as the differences between the 10 K and time-based
recommendations may have began to dissipate by the end of the
intervention. Future research should examine the impact of these
varying PA recommendations on adherence over a longer duration as
this study was limited to the early stages of PA adoption.
It is also unclear whether males and females responded differen-
tially to information on activity recommendations as only 8 males
participated in the study precluding the ability to evaluate sex
differences. Fewer males than females expressing interest in study
participation are consistent with past pedometer-based intervention
studies (Bravata et al., 2007). Future researchers may wish to more
fully evaluate sex differences to facilitate optimally tailored
Given that the 10 K guideline is not a public health recommen-
dation, the motivational impact of using pedometers as a tool to help
individuals meet public health recommendations for moderate
intensity PA, such as by focusing on approximately 100 steps per
minute rather than a 10 K goal, could be evaluated (Marshall et al.,
2009; Rowe et al., 2010). Future research might also examine the
impact of varying combinations of time and step count based
recommendations in activity promotion efforts, such as by initially
focusing on step counts and gradually transitioning to time-based
goals focused on MVPA.
Finally, this study was limited by a relatively small sample of
inactive university faculty and staff. Despite this limitation, results
suggest that accumulated bouts were ineffective in promoting short-
term increases in PA. Although the bouts concept was ineffective in
the current sample, future research might examine under what
circumstances and for which populations PA bouts might enhance
Conflict of interest statement
The authors declare that there are no conflicts of interest.
This study stems from a master's thesis completed by Tiana Miller
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