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Sleep Sci. 2018;11(1):37-44
37 Athlete sleep behavior questionnaire
Development of the athlete sleep behavior
questionnaire: A tool for identifying maladaptive
sleep practices in elite athletes
ORIGINAL ARTICLE
Corresponding author: Matthew W
Driller.
E-mail: mdriller@waikato.ac.nz
E-mail: matthew.driller@gmail.com
Received: November 1, 2017; Accepted:
January 30, 2018.
DOI: 10.5935/1984-0063.20180009
ABSTRACT
Introduction: Existing sleep questionnaires to assess sleep behaviors may not be sensitive in
determining the unique sleep challenges faced by elite athletes. The purpose of the current study
was to develop and validate the Athlete Sleep Behavior Questionnaire (ASBQ) to be used as a
practical tool for support staff working with elite athletes. Methods: 564 participants (242 athletes,
322 non-athletes) completed the 18-item ASBQ and three previously validated questionnaires; the
Sleep Hygiene Index (SHI), the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality
Index (PSQI). A cohort of the studied population performed the ASBQ twice in one week to
assess test-retest reliability, and also performed sleep monitoring via wrist-actigraphy. Results:
Comparison of the ASBQ with existing sleep questionnaires resulted in moderate to large correlations
(r=0.32 - 0.69). There was a signicant difference between athletes and non-athletes for the ASBQ
global score (44±6 vs. 41±6, respectively, p<0.01) and for the PSQI, but not for the SHI or the ESS.
The reliability of the ASBQ was acceptable (ICC=0.87) when re-tested within 7 days. There was
a moderate relationship between ASBQ and total sleep time (r=-0.42). Conclusion: The ASBQ is a
valid and reliable tool that can differentiate the sleep practices between athletes and non-athletes,
and offers a practical instrument for practitioners and/or researchers wanting to evaluate the sleep
behaviors of elite athletes. The ASBQ may provide information on areas where improvements to
individual athletes’ sleep habits could be made.
Keywords: Surveys and Questionnaires; Actigraphy; Polysomnography; Sleep Hygiene; Athletes.
Matthew W Driller1-4
Cheri D Mah2
Shona L Halson3
1 University of Waikato, Health, Sport
and Human Performance - Hamilton -
Waikato - New Zealand.
2 University of California, Human
Performance Center - San Francisco -
California - USA.
3 Australian Institute of Sport,
Physiology - Canberra - ACT -
Australian.
4 High Performance Sport New Zealand,
Performance Physiology - Auckland -
Auckland - New Zealand.
Article published online: 2023-10-13
38
Driller, et al.
Sleep Sci. 2018;11(1):37-44
INTRODUCTION
There is increasing recognition that sleep plays a
signicant role in aiding the recovery process in highly-trained
athletes1-3. According to Halson4 and Leeder et al.5, sleep is
reported to be the single best psycho-physiological recovery
strategy available to elite athletes. Therefore, the quantication
and measurement of sleep amongst athletic populations has
become commonplace in the sport setting6. Objective methods
of measuring sleep such as polysomnography and actigraphy
require a somewhat intrusive and expensive assessment of
sleep, coupled with the need for specialized expertise, making
them difcult to administer across large numbers of athletes.
Different questionnaires and scales used for surveying sleep
have been validated in the literature, with little focus on athlete-
specic measures. Indeed, research has shown that athletes may
display different sleeping patterns and habits compared to the
non-athlete population5,7-9, likely due to the unique physiological
and psychological demands of being an elite athlete.
It has been reported that sleep may be compromised in
elite athletes due to a number of factors, including the increase
in core temperature following exercise10, increases in muscle
tension, fatigue and pain following training and competition11,12,
frequent international travel13, disruption from light and noise
and increases in psychological stress14. Juliff et al.7 reported
from 283 elite athletes, that 64% of athletes reported sleep
disturbances due to nervousness or over-thinking before
competition and more than half of the sample suffered sleep
disturbances following a late training session or competition.
Other sleep disturbances that seem to be magnied in elite
athletes include the use of long naps in the afternoon interrupting
night-time sleep, the use of stimulants (e.g. caffeine), frequent
travel, sleeping in different environments (e.g. hotels), and either
over-hydration or dehydration prior to bed14-16. Existing surveys,
scales and questionnaires that evaluate sleep behavior in the
general population may not be specic enough to detect these
unique differences in an athlete’s sleeping patterns and habits.
A plethora of sleep questionnaires have been evaluated
in the research literature. Some of these include the Pittsburgh
Sleep Quality Index17, the Sleep Hygiene Index18, and the
Epworth Sleepiness Scale19. While these questionnaires and
scales may be appropriate for general or clinical populations,
they lack specic questions that are tailored towards the sleep
challenges faced by elite athletes.
To our knowledge, the only athlete-specic sleep
questionnaire in the literature is the Athlete Sleep Screening
Questionnaire (ASSQ)20. The ASSQ was designed to provide
clinical screening with cut-off scores associated with the
specic clinical interventions to manage sleep disorders. While
initial reports of the ASSQ have shown that it is a valid tool in
screening athletes for sleep disturbances, there remains a need for
an instrument that can provide useful information on the sleep
behavior practices of elite athletes, allowing for individualized
feedback and behavioral modications based on their responses.
Indeed, recent research has shown that improvements in sleep
can be achieved in elite athletes, through simple sleep-behavior
education and subsequent changes in maladaptive habits21.
Therefore, the purpose of the current study was to
develop an athlete-specic sleep questionnaire and validate
it against both objective (wrist-actigraphy) and subjective
(validated questionnaires) sleep measures in both athletes and
non-athletes. A further aim of the study was to determine the
test-retest reliability of the questionnaire.
METHODS
Participants
The survey was completed by a convenience sample of
564 participants (282 male/282 female, mean±SD, age; 25±7 y)
across 9 countries (Australia, Canada, England, India, Malaysia,
New Zealand, Portugal, Sweden, USA). The study population
was divided into athletes (n=242) and non-athletes (n=322) for
analysis (Table 1). All participants for both groups were aged
between 18-45 y at the time of taking part in the study. New
parents (children <2 y) and individuals with diagnosed sleep
disorders were excluded from taking part in the study.
Athletes (n=242) Non-athletes (n=322)
Age (y) 22±5 25±6
Male (n=) 87 195
Female (n=) 155 127
Team sport (n=) 128 N/A
Individual sport (n=) 114
Table 1. Participant demographics.
The criteria for the ‘athlete’ population used in the current
study was: representation of their country at either national or
international-level (semi-professional or professional) for their
chosen sport. Athletes were surveyed across 18 different sports
(team sport athletes = 128 and individual sport athletes = 114)
and completed the survey during the in-season phase of their
training (minimum of 4-weeks into their competition season).
Athletes from the following sports were surveyed: badminton
(n=8), baseball (n=9), basketball (n=15), boxing (n=5), cricket
(n=10), cycling (n=15), football/soccer (n=12), golf (n=10),
hockey (n=18), netball (n=19), rowing (n=17), rugby league
(n=14), rugby union (n=24), swimming (n=14), track and eld
(n=26), tennis (n=6), triathlon (n=13) and water-polo (n=7).
The criteria for the ‘non-athlete’ population included
participants that; a) were not members of any regional or
national-level sporting team, and b) were performing ≤3 planned
exercise training sessions per week. The ‘non-athlete’ population
was a random selection of participants also surveyed from the
9 aforementioned countries. All participants were recruited via
National Sporting Organisations, various social media channels
and word-of-mouth advertising. The questionnaire was not
translated into any other languages, and therefore it was a
requirement that all participants were uent English speakers.
The study was approved by the Institutions Human Research
Ethics Committee (Ethics number: FEDU066/16) and as
Sleep Sci. 2018;11(1):37-44
39 Athlete sleep behavior questionnaire
outlined to participants, by completing the survey, informed
consent to take part in the study was given.
Instruments
The following four sleep questionnaires were
administered to all participants via an electronic online
survey (Survey Monkey, Palo Alto Inc. CA, USA). All four
questionnaires were lled out in a single sitting and average time
to complete the questionnaires was 8.5 minutes. On average, the
ASBQ took 1.5 minutes to complete. All questionnaires asked
participants to answer the questions relating to their normal
sleeping patterns over the previous month.
The Athlete Sleep Behavior Questionnaire (ASBQ)
The ASBQ is the survey that has been specically
designed for evaluation in the current study. A combination of
the Sleep Hygiene Index18, the International Classication of
Sleep Disorders22, and previous research describing the most
common sleep issues in elite athletes7,23 and recommended tips
and strategies to address these issues10,15,21 was used to develop
the ASBQ. The ASBQ is an 18-item survey that includes
questions on sleeping behavior and habits thought to be
common areas of concern for elite athletes (Table 2) and was
designed as a practical tool to identify areas where improvements
in sleep behavior could be made, rather than a clinical screening
tool. The survey asks participants how frequently they engage
in specic behaviors (never, rarely, sometimes, frequently,
always). Weightings for each response (1 = never, 2 = rarely, 3 =
sometimes, 4 = frequently, 5 = always) were summed to provide
an ASBQ global score. A higher global score is indicative of
poor sleep behaviors.
The Sleep Hygiene Index (SHI)
The SHI is a 13-item self-administered index intended
to assess the presence of behaviors thought to comprise sleep
hygiene. Participants are asked to indicate how frequently they
engage in specic behaviors (always, frequently, sometimes,
rarely, never). Item scores were then summed providing a global
score for sleep hygiene. Higher scores are indicative of more
maladaptive sleep hygiene status. The SHI has been shown to be
both valid and reliable in a healthy population18.
Epworth Sleepiness Scale (ESS)
The Epworth Sleepiness Scale (ESS) is a self-reported
8-item questionnaire that produces a global score from 0-24.
Scores greater than 10 suggest signicant daytime sleepiness19.
The ESS is commonly used to differentiate between individuals
with and without sleep disorders and has also shown to correlate
with objective measures of sleepiness24.
The Pittsburgh Sleep Quality Index (PSQI)
The PSQI is a self-rated 19-item instrument intended
to assess sleep quality and sleep disturbance over a 1-month
period in clinical and nonclinical populations25. Global scores
range from 0 to 21 with higher scores indicating poorer overall
sleep quality. The PSQI has been demonstrated to have good
internal reliability, validity and is perhaps the most commonly-
used subjective sleep measure not only in the research literature,
but also in the sleep community25.
Reliability
The test-retest reliability and sleep-monitoring
component of the study was completed by 50 participants (27
male/23 female, 19 team sport athletes/31 individual athletes,
mean ± SD; age: 23±5 y) from the athlete cohort. Athletes were
randomly selected and the following sports were included in the
reliability (and actigraphy) analysis: cycling (n=7), football (n=3),
netball (n=9), rugby league (n=7), rowing (n=10), swimming
(n=4), track and eld (n=10). All participants completed the
ASBQ two times separated by exactly 7 days. The test was
performed at the same time of day on both occasions and
took place during an in-season, non-competitive week. The day
that the ASBQ was lled out on both occasions was preceded
by a rest day, where no athletic training or competition was
performed. The reliability component of the current study was
assessed concurrently with the measurement of sleep through
wrist-actigraphy.
Actigraphy
A total of 50 athletes from the current study (same
cohort as described in the reliability component above) wore
a wrist activity monitor to evaluate their sleeping patterns.
Participants were required to wear the activity monitor (SBV2
Readiband™, Fatigue Science, Honolulu, USA), continuously
over a 7-day period with the exception of time spent in water,
bathing or showering. Participants were instructed to maintain
their usual sleep habits and general daily activity patterns during
the monitoring period.
Sleep indices used for comparison to the ASBQ global
score were: total time in bed, total sleep time, sleep efciency
and sleep latency. Each morning during the monitoring period,
athletes were also asked to rate their perceived sleep quality on a
scale from 1-5 (1 = very poor, 5 = excellent). Participants were
also asked to record their sleep and wake times in a diary, to
allow for cross-checking and corrections with the actigraphy
data. The accuracy and inter-device reliability of the Readiband
device has been deemed acceptable, as described elsewhere26,27.
Statistical Analysis
Descriptive statistics are shown as means ± SD unless
stated otherwise. Statistical analysis was performed using SPSS
V22.2 (IBM Corporation; Chicago, IL, USA). Comparison of
athletes to non-athletes were performed for each questionnaire
and each item of the ASBQ using independent samples t-tests,
with statistical signicance set at p<0.05. There were no outliers
in the data, as assessed by inspection of a boxplot. Global scores
for each questionnaire and each item of the ASBQ were normally
distributed, as assessed by Shapiro-Wilk’s test (p>0.05), and there
was homogeneity of variances between groups, as assessed by
Levene’s test for equality of variances (p>0.05). Cohen’s effect
40
Driller, et al.
Sleep Sci. 2018;11(1):37-44
No. In recent times (over the last month)… Never Rarely Sometimes Frequently Always
1 I take afternoon naps lasting two or more hours
2 I use stimulants when I train/compete (e.g caffeine)
3 I exercise (train or compete) late at night (after 7pm)
4 I consume alcohol within 4 hours of going to bed
5I go to bed at different times each night
(more than ±1 hour variation)
6 I go to bed feeling thirsty
7 I go to bed with sore muscles
8I use light-emitting technology in the hour leading up to
bedtime (e.g laptop, phone, television, video games)
9I think, plan and worry about my sporting performance
when I am in bed
10 I think, plan and worry about issues not related to my
sport when I am in bed
11 I use sleeping pills/tablets to help me sleep
12 I wake to go to the bathroom more than once per night
13 I wake myself and/or my bed partner with my snoring
14 I wake myself and/or my bed partner with my muscle
twitching
15 I get up at different times each morning
(more than ±1 hour variation)
16 At home, I sleep in a less than ideal environment (e.g too
light, too noisy, uncomfortable bed/pillow, too hot/cold)
17 I sleep in foreign environments (e.g hotel rooms)
18 Travel gets in the way of building a consistent sleep-wake
routine
Table 2. The Athlete Sleep Behavior Questionnaire (ASBQ).
Scoring:
Never = 1, Rarely = 2, Sometimes = 3, Frequently = 4, Always = 5 Total Global Score: _________
sizes (d) were calculated between athletes and non-athletes for
each questionnaire and interpreted using thresholds of 0.2, 0.5,
0.8 for small, moderate and large, respectively28. Comparison of the
previously validated sleep questionnaire global scores and the
ASBQ global score was achieved with Pearson product-moment
correlation analysis for the entire sample (n=564).
Correlation between the ASBQ and measured sleep
variables were also assessed in a cohort of the study population
(n=50). The magnitude of correlation between the ASBQ and
the other questionnaires/sleep measures was assessed using the
following thresholds: <0.1, trivial; 0.1-0.3, small; 0.3-0.5, moderate;
0.5-0.7, large; 0.7-0.9, very large; and 0.9-1.0, almost perfect. Test-
retest reliability of the ASBQ were analyzed using an Excel
spreadsheet for reliability29 with data shown as intra-class
correlation coefcients (ICC), Pearson correlations (r), typical
error of measurement (TEM) and coefcient of variation
percentage (CV%).
Internal reliability/consistency of the ASBQ was de-
termined using Cronbach’s α. A principal component analysis
(PCA) was run on the 18-item questionnaire and the suitability
of the PCA was assessed prior to analysis via the Kaiser-Meyer-
Olkin measure and the Bartlett’s test of sphericity30. Explorato-
ry factor analysis using PCA with a varimax rotation was used to
extract three underlying dimensions of the questionnaire. PCA
revealed that the three factors that had eigenvalues greater than
one and visual inspection of the scree plot conrmed that three
components should be retained31. Interpretation of these three
components was consistent with themes of routine/environ-
mental related factors for factor 1, behavioral factors for factor
2 and sport-related factors for factor 3 (Table 6).
RESULTS
There were no signicant differences between male and
female participants for the ASBQ global score within either
athlete (p=0.20) or non-athlete groups (p=0.21), nor were there
differences for team vs. individual sport athletes (p=0.69),
therefore, both the athlete group and non-athlete groups were
pooled for comparison with each other.
There was a signicant difference between athlete and
non-athlete groups for the ASBQ global score (43.5 and 40.6,
respectively, p<0.01, d=0.47, Table 3), which included a signicant
difference between groups in 10 of the 18 items in the questionnaire
(Figure 1). There were no signicant differences between groups for
the SHI or the ESS and both associated with trivial effect sizes (Table
3). The PSQI global score was signicantly higher in the non-athlete
group (p<0.01, d=0.36, Table 3).
Sleep Sci. 2018;11(1):37-44
41 Athlete sleep behavior questionnaire
Athletes
(mean ± SD)
Non-Athletes
(mean ± SD)
Raw Difference
(Non-Athlete - Athlete) p-value Effect-Size
d
ASBQ 43.5±5.8 40.6±6.1 -2.9 <0.01 0.47
Small
SHI 32.3±6.1 32.4±6.4 0.1 0.81 0.02
Trivial
ESS 5.7±3.4 5.2±3.3 -0.6 0.06 0.18
Trivial
PSQI 5.1±2.5 6.1±2.9 1.0 <0.01 0.36
Small
ASBQ = Athlete Sleep Behavior Questionnaire; SHI = Sleep Hygiene Index; ESS = Epworth Sleepiness Scale; PSQI = Pittsburgh Sleep Quality Index.
Table 3. Global scores for the four sleep questionnaires between athletes and non-athletes including p-values and effect-size comparisons between groups.
Data shown as means ± SD.
Figure 1. Legenda: Mean scores (out of 5) for Non-athletes (n=322, black bar) and Athletes (n=242, white bar) for each item of the 18-question Athlete Sleep Behavior
Questionnaire (ASBQ). * Indicates signicant difference between groups (p<0.05).
The ASBQ was shown to have moderate to large
correlations with the existing validated sleep questionnaires
(r=0.38 – 0.69, Table 4). The correlation between the ASBQ and
objective sleep indices resulted in a small relationship for total
time in bed and sleep efciency (r=-0.18, -0.16, respectively), a
moderate relationship for total sleep time and sleep quality (r=-
0.42, -0.39, respectively) and a trivial correlation for sleep latency
(r=0.07, Table 4).
The ASBQ resulted in acceptable levels of reliability
(ICC=0.87, r=0.88, TEM = 2.3 AU, CV = 6.4%) when tested
twice in one week (Table 5). The mean difference between
test one and two was just 0.1±3.2 AU (Table 5). The internal
consistency of the ASBQ resulted in a Cronbach’s α of 0.63.
The PCA factoring for the three-factor structure was performed
with varimax rotation, which collectively accounted for 69.6%
of the variance. The factor matrix showed that every item-factor
loading was above the criterion of 0.45. Item loadings ranged
from 0.45 to 0.61 (Table 6).
The sleep monitoring period in a cohort of the athlete
population (n=50) used for correlation to the ASBQ resulted
in the following mean ± SD values: total time in bed = 552±61
mins, total sleep time = 441±38 mins, sleep efciency =
85±8%, sleep latency = 38±20 mins and subjective sleep quality
= 3.7±0.6.
DISCUSSION
The results from the current study would support the use
of the proposed 18-item Athlete Sleep Behavior Questionnaire
for use as a practical tool for identifying maladaptive sleep
practices in elite athletes. The ASBQ was a valid measurement tool
42
Driller, et al.
Sleep Sci. 2018;11(1):37-44
SHI ESS PSQI Total Time in Bed
(mins)
Total Sleep Time
(mins)
Sleep Efciency
%
Sleep Latency
(mins)
Sleep Quality
(1 - 5 AU)
ASBQ 0.69
Large
0.32
Moderate
0.38
Moderate
-0.18
Small
-0.42
Moderate
-0.16
Small
0.07
Trivial
-0.39
Moderate
Table 4. Pearson’s correlation coefcient (r) between the ASBQ global score and the three other questionnaires (n=564 participants) and between the
ASBQ and sleep indices as measured by wrist-actigraphy (n=50 participants).
ASBQ = Athlete Sleep Behavior Questionnaire; SHI = Sleep Hygiene Index; ESS = Epworth Sleepiness Scale; PSQI = Pittsburgh Sleep Quality Index; AU = Arbitrary Units.
Test 1
(mean±SD)
Test 2
(mean±SD)
Raw Difference
(mean±SD)
r
(90% CI)
ICC
(90% CI)
TEM
(90% CI)
CV%
(90% CI)
ASBQ Global
Score 38.6±6.6 38.7±5.6 0.1±3.2 0.88
(0.81-0.92)
0.87
(0.80-0.92)
2.3
(2.0-2.7)
6.4
(5.4-7.7)
Table 5. Test-retest reliability of the Athlete Sleep Behavior Questionnaire (n=50) when performed twice over 7-days. Mean data shown along with intra-
class correlation coefcients (ICC), coefcient of variation % (CV%) and typical error of measurement (TEM), with 90% condence intervals (90% CI).
ASBQ items Factor loading
Factor 1 - Routine/environmental factors
Q1. I take afternoon naps lasting two or more hours 0.52
Q5. I go to bed at different times each night (more than ±1 hour variation) 0.45
Q15. I get up at different times each morning (more than ±1 hour variation) 0.48
Q16. At home, I sleep in a less than ideal environment (e.g too light, too noisy, uncomfortable bed/pillow, too hot/cold) 0.51
Q17. I sleep in foreign environments (e.g hotel rooms) 0.43
Q18. Travel gets in the way of building a consistent sleep-wake routine 0.55
Factor 2 - Behavioral factors
Q2. I use stimulants when I train/compete (e.g caffeine) 0.58
Q4. I consume alcohol within 4 hours of going to bed 0.48
Q8. I use light-emitting technology in the hour leading up to bedtime (e.g laptop, phone, television, video games) 0.47
Q10. I think, plan and worry about issues not related to my sport when I am in bed 0.61
Q11. I use sleeping pills/tablets to help me sleep 0.56
Q12. I wake to go to the bathroom more than once per night 0.56
Q13. I wake myself and/or my bed partner with my snoring 0.48
Factor 3 - Sport-related factors
Q3. I exercise (train or compete) late at night (after 7pm) 0.49
Q6. I go to bed feeling thirsty 0.57
Q7. I go to bed with sore muscles 0.45
Q9. I think, plan and worry about my sporting performance when I am in bed 0.53
Q14. I wake myself and/or my bed partner with my muscle twitching 0.45
Table 6. Factor loadings for the Athlete Sleep Behavior Questionnaire as determined via Principal Component Analysis with a varimax rotation method.
when compared to three other established sleep questionnaires
and was sensitive enough to determine the difference in sleep
behavior scores in athletes when compared to non-athletes. The
ASBQ was shown to have high levels of test-retest reliability,
further supporting its use in both research and practical settings.
When compared to sleep monitoring via wrist-actigraphy, in a
cohort of the studied population, the ASBQ displayed a moderate
relationship with one of the key sleep measures, total sleep time.
We would suggest that the ASBQ is a useful tool to identify the
sleep behaviors of elite athletes.
Perhaps one of the pertinent issues with the existing
sleep questionnaires, is their inability to adequately differentiate
the unique sleep problems faced by elite athletes. Indeed, the
current study would support this, as evidenced through the non-
signicant differences and trivial effect sizes for athletes vs. non-
athletes in the SHI and ESS global scores (p>0.05, Table 3).
While there was a signicant difference between groups for the
PSQI, this was actually in favor of the athlete group, suggesting
that sleep quality may be higher in athletes vs. non-athletes,
which is in direct contrast to previous literature5,23,32.
Even though both groups can be classied as “poor
sleepers” according to the PSQI threshold of >5, it is still
important to speculate why non-athletes had a higher global
PSQI score. This may be explained by evaluating the individual
components of the PSQI, where there was a signicant difference
for athletes compared to non-athletes for one component of the
questionnaire. Component #4 refers to sleep efciency (time
spent sleeping divided by time spent in bed). While total sleep
time between groups was similar, non-athletes had lower sleep
efciency, due to longer time spent in bed (531±96 minutes)
when compared to the athlete group (519±104 minutes). When
comparing the ASBQ between the athlete and non-athlete
Sleep Sci. 2018;11(1):37-44
43 Athlete sleep behavior questionnaire
populations, results showed that the scores for 10 out of the 18
items/questions were signicantly greater in the athlete group,
indicating poorer sleep behaviors (Figure 1).
While there were no signicant differences between
groups for the 8 remaining items, the authors would suggest
that these are still valuable questions for gaining specic
information on the habits of individual athletes, based on
previous recommendations15. As identied by Juliff et al.7, one
of the major challenges for athletes was problems falling asleep
due to their thoughts about competition. The current study
would support this, with one of the highest ratings by athletes
(indicative of a challenge to sleep) in question #9 - “I think, plan
and worry about my sporting performance when I am in bed”
(Figure 1). Other questions with the highest ratings by athletes
in the current study were question #7 - “I go to bed with sore
muscles” and question #2 - “I exercise late at night” (Figure 1).
The test-retest reliability of the ASBQ was very high,
with a mean difference of only 0.1 on the global score between
the two tests (Table 5). This difference was associated with an
r value of 0.88, an ICC of 0.87, a TEM of 2.3 and a CV of
6.4%. In contrast to the other scales used in the current study,
our results would suggest that the ASBQ is comparable, or even
more reliable in a test-retest setting. Authors reported an r value
of 0.71 when evaluating the SHI in a test-retest trial, with 4 weeks
between each test18. The original study to develop the PSQI
reported a test-retest correlation of r=0.8525, however, the time
duration between tests is somewhat unclear, with an average of
28.2 days reported, but the specied range was 1 - 265 days.
The ESS, when administered to 87 healthy students twice in 5
months, resulted in a test-retest r value of 0.8233. Unfortunately,
the differing range of methodologies implemented between
studies make it difcult to draw comparisons with the reliability
of the ASBQ in the current study.
A potential limitation of the current study was the
relatively short (one week) test-retest time frame for assessing
the reliability of the ASBQ. However, given the ASBQ asks for
the participants’ normal habits over the previous month, the
authors felt that if a period of one month or more was used, the
reliability of the tool may be compromised, not because of the
tool itself, but because of the change in sleep habits over longer
time frames.
Future research on the ASBQ should address the
reliability of the tool over different time frames, or different
phases of the season with athletes (e.g. pre-season vs. in-season),
and the relationship between the ASBQ and chronotype.
Indeed, it is possible that morning and evening-type individuals
may differ on some items of the ASBQ. It would also be useful
to perform sleep monitoring via wrist-actigraphy for longer
periods of time (e.g. 4 weeks) prior to lling out the ASBQ.
This would allow for a direct comparison of the monitoring
period with the surveyed time frame. It is proposed that the
aforementioned studies are incorporated into phase two of the
development of the ASBQ, as well as translating and validating
the questionnaire in different languages.
The authors acknowledge that the Cronbach’s α of
0.63 for the ASBQ is below the usually accepted threshold of
0.70, however, given this is a measure of internal consistency
for the relationship between items in a questionnaire, this was
not the aim of the practical tool being developed in the current
study. Indeed, the ASBQ was intentionally designed to measure
different aspects of sleep behavior, and therefore, it was not
critical that all items on the questionnaire are related. The
authors also acknowledge that the female athlete population
was greater than the male athlete population surveyed, however,
given there were no signicant differences between male and
female ASBQ scores, we did not see this as an issue impacting
the validity or reliability of this questionnaire.
The authors would suggest that a ASBQ global score
of ≤36 would equate to “good sleep behavior” and ≥42 =
“poor sleep behavior”. These thresholds are based on the
authors’ interpretation of the data and represent a conservative
assessment of threshold range descriptors. The lower threshold
of ≤36 would represent an average response of “rarely” for
all 18-items, while the upper threshold of ≥42 would require
more than one response of either “sometimes”, “frequently”
or “always”. However, these thresholds are suggested as a guide
only and are subject to adjustment in future studies assessing the
sensitivity and specicity of the ASBQ in athletic populations.
The ASBQ that has been proposed and developed in
phase one of the current study is an 18-item questionnaire
that is a fast (<2 mins), easy to administer, valid and reliable
tool that can help to identify the maladaptive sleep practices
and challenges faced by athletes. The ASBQ offers a practical
instrument for practitioners, coaches and/or researchers
wanting to evaluate the sleep behaviors of elite athletes. The
ASBQ is not designed to be a clinical sleep tool, but simply a
practical solution to nd out some of the key challenges faced
by athletes in terms of their sleep behaviors. The ASBQ may
also be a valuable tool for tracking changes in sleep habits over
time, or for testing the efcacy of sleep-hygiene interventions
to improve sleep. It may also be a useful tool for identifying
the differences in sleep behaviors amongst sports with vastly
different training loads and recovery needs.
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