Reciprocal Associations Between Sleep, Mental Strain, and Training Load in Junior Endurance Athletes and the Role of Poor Subjective Sleep Quality

Abstract

The importance of adequate sleep for athletic functioning is well established. Still, the literature shows that many athletes report sleep of suboptimal quality or quantity. To date, no research has investigated how bidirectional variations in mental and physiological states influence sleep patterns. The present study, therefore, investigates reciprocal associations between sleep, mental strain, and training load by utilizing a prospective, observational design. In all, 56 junior endurance athletes were followed over 61 consecutive days. Unobtrusive, objective measurements of sleep with novel radar technology were obtained, and subjective daily reports of mental strain and training load were collected. The role of subjective sleep quality was investigated to identify whether the reciprocal associations between sleep, mental strain, and training load depended on being a good versus poor sleeper. Multilevel modeling with Bayesian estimation was used to investigate the relationships. The results show that increases in mental strain are associated with decreased total sleep time (TST, 95% CI = −0.12 to −0.03), light sleep (95% CI = −0.08 to −0.00), and sleep efficiency (95% CI = −0.95 to −0.09). Further, both mental strain and training load are associated with subsequent deceased rapid eye movement (REM, respectively, 95% CI = −0.05 to −0.00 and 95% CI = −0.06 to −0.00) sleep. Increases in TST, light, deep, and REM sleep are all associated with subsequent decreased training load (respectively, 95% CI = −0.09 to −0.03; 95% CI = −0.10 to −0.01; 95% CI = −0.22 to −0.02; 95% CI = −0.18 to −0.03). Finally, among poor sleepers, increases in sleep onset latency are associated with increases in subsequent mental strain (95% CI = 0.09-0.46), and increases in deep sleep are associated with decreases in subsequent training load (95% CI = −67.65 to 11.43). These results offer novel insight into the bidirectional associations between sleep, mental strain, and training load in athletes and demonstrate the detrimental effects of mental strain on sleep, likely caused by mental activation incompatible with sleep. An increased need for recovery, suggested by increased TST and time in different sleep stages, is associated with subsequent self-regulatory reduction of training loads by the athletes. In poor sleepers, increases in deep sleep may suggest an elevated need for physiological recovery.

Full text

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ORIGINAL RESEARCH
published: 09 October 2020
doi: 10.3389/fpsyg.2020.545581
Edited by:
Ana-Maria Cebolla,
Université libre de Bruxelles, Belgium
Reviewed by:
Arnaud Boutin,
Université Paris-Sud, France
Daniele Conte,
Lithuanian Sports University, Lithuania
*Correspondence:
Maria Hrozanova
maria.hrozanova@ntnu.no
Specialty section:
This article was submitted to
Movement Science and Sport
Psychology,
a section of the journal
Frontiers in Psychology
Received: 25 March 2020
Accepted: 31 August 2020
Published: 09 October 2020
Citation:
Hrozanova M, Klöckner CA,
Sandbakk Ø, Pallesen S and Moen F
(2020) Reciprocal Associations
Between Sleep, Mental Strain,
and Training Load in Junior
Endurance Athletes and the Role
of Poor Subjective Sleep Quality.
Front. Psychol. 11:545581.
doi: 10.3389/fpsyg.2020.545581
Reciprocal Associations Between
Sleep, Mental Strain, and Training
Load in Junior Endurance Athletes
and the Role of Poor Subjective
Sleep Quality
Maria Hrozanova1*, Christian A. Klöckner2, Øyvind Sandbakk1, Ståle Pallesen3,4,5 and
Frode Moen6
1Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University
of Science and Technology, Trondheim, Norway, 2Department of Psychology, Faculty of Social and Educational Sciences,
Norwegian University of Science and Technology, Trondheim, Norway, 3Department of Psychosocial Science, Faculty
of Psychology, University of Bergen, Bergen, Norway, 4Norwegian Competence Center for Sleep Disorders, Haukeland
University Hospital, Bergen, Norway, 5Optentia, The Vaal Triangle Campus of the North-West University, Vanderbijlpark,
South Africa, 6Department of Education and Lifelong Learning, Faculty of Social and Educational Sciences, Norwegian
University of Science and Technology, Trondheim, Norway
The importance of adequate sleep for athletic functioning is well established. Still,
the literature shows that many athletes report sleep of suboptimal quality or quantity.
To date, no research has investigated how bidirectional variations in mental and
physiological states influence sleep patterns. The present study, therefore, investigates
reciprocal associations between sleep, mental strain, and training load by utilizing a
prospective, observational design. In all, 56 junior endurance athletes were followed over
61 consecutive days. Unobtrusive, objective measurements of sleep with novel radar
technology were obtained, and subjective daily reports of mental strain and training load
were collected. The role of subjective sleep quality was investigated to identify whether
the reciprocal associations between sleep, mental strain, and training load depended
on being a good versus poor sleeper. Multilevel modeling with Bayesian estimation was
used to investigate the relationships. The results show that increases in mental strain are
associated with decreased total sleep time (TST, 95% CI = −0.12 to −0.03), light sleep
(95% CI = −0.08 to −0.00), and sleep efficiency (95% CI = −0.95 to −0.09). Further,
both mental strain and training load are associated with subsequent deceased rapid eye
movement (REM, respectively, 95% CI = −0.05 to −0.00 and 95% CI = −0.06 to −0.00)
sleep. Increases in TST, light, deep, and REM sleep are all associated with subsequent
decreased training load (respectively, 95% CI = −0.09 to −0.03; 95% CI = −0.10 to
−0.01; 95% CI = −0.22 to −0.02; 95% CI = −0.18 to −0.03). Finally, among poor
sleepers, increases in sleep onset latency are associated with increases in subsequent
mental strain (95% CI = 0.09–0.46), and increases in deep sleep are associated with
decreases in subsequent training load (95% CI = −67.65 to 11.43). These results offer
novel insight into the bidirectional associations between sleep, mental strain, and training
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Hrozanova et al. Sleep, Mental Strain, Physical Load
load in athletes and demonstrate the detrimental effects of mental strain on sleep, likely
caused by mental activation incompatible with sleep. An increased need for recovery,
suggested by increased TST and time in different sleep stages, is associated with
subsequent self-regulatory reduction of training loads by the athletes. In poor sleepers,
increases in deep sleep may suggest an elevated need for physiological recovery.
Keywords: sleep, athlete, mental strain, training load, sleep quality
INTRODUCTION
Optimal sleep is recognized for its positive contributions
to physical performance and psychological well-being in the
athletic population (Thun et al., 2015;Kroshus et al., 2019).
During sleep, learning and memory processes consolidate
and strengthen retrieval of factual information and facilitate
procedural motor skills, which contribute to athletic performance
(Walker and Stickgold, 2004). Sleep is also important for
energy reestablishment (Jung et al., 2011); emotional regulation
(Palmer and Alfano, 2017); and the functioning of immune,
cardiovascular, endocrine, and metabolic systems (Manzar et al.,
2015). In athletic populations, sleep has, consequently, been
recognized as an essential aspect of recovery (O’Donnell
et al., 2018), enabling athletic progression and performance
improvement (Kellmann et al., 2018).
Although it seems paramount that athletes obtain optimal
sleep, existing evidence suggests that this is not the case for
many athletes. For example, one study based on actigraphic
recordings finds that athletes have poorer sleep than non-
athletes in terms of sleep latency, sleep efficiency, and sleep
fragmentation (Leeder et al., 2012), and another study finds
poorer subjective sleep quality among athletes (Bender et al.,
2018). Poor sleep in athletes may reflect sport-specific demands,
such as challenging organizational, competitive (Hanton et al.,
2005), and psychophysiological stressors (Nixdorf et al., 2015;
Campbell et al., 2018). Indeed, previous research has established
that sleep of athletes may be negatively influenced by increases in
training load (Kolling et al., 2016) and early morning scheduling
of training sessions (Sargent et al., 2014) and matches (Juliff et al.,
2015) as well as psychological aspects (e.g., worry) of the stress
response (Hrozanova et al., 2019).
Still, the mechanisms causing sleep disruptions in athletes
have not been thoroughly investigated. Both physical, i.e.,
training load, as well as mental components of stress, i.e., mental
strain, may represent sleep-impairing factors. Importantly,
bidirectional associations between physical and mental stressors
and sleep may also be present. Regarding the effects of training
load and sleep, some studies find no significant associations
(Knufinke et al., 2018;O’Donnell et al., 2019;Lastella et al.,
2020), and others find that increased training load leads to
reductions in sleep duration (Kolling et al., 2016) and sleep
efficiency (Teng et al., 2011). Research investigating the effects
of sleep on training load has found that self-determined training
load is lower in partially sleep deprived elite athletes than in
teammates who obtained adequate sleep (Cook et al., 2012).
In another study, sleep restricted to 4 h for three nights led
to performance impairments, slower response time, and loss in
joint coordination (Mah et al., 2019). Overall, studies on the
bidirectional associations between training load and sleep are few
and present contrasting results.
Likewise, only a handful of studies explore the mentally
straining effects of athletic participation on sleep. During
competition, sleep onset latency is found to increase, and sleep
quality deteriorates, likely due to stress-related cognitive activity
associated with competing (Lastella et al., 2014;Juliff et al.,
2015). One large cross-sectional study identifies perceived stress
as the most important predictor of poor sleep quality (Hrozanova
et al., 2019). Regarding the effects of sleep on mental functioning
in athletes, another cross-sectional study finds associations
between poor sleep quality and confusion, depression, and fatigue
(Andrade et al., 2019). However, research based on more suitable,
longitudinal designs is lacking.
It is likely that the ambiguous results regarding the association
between sleep, training load, and mental strain in athletic
populations may be attributed to methodological challenges
with sleep monitoring. Most studies to date use subjective
sleep assessment due to the low cost and availability of such
measurements. However, subjective measures may be subject
to recall (Coughlin, 1990), common method (Podsakoff et al.,
2003), and social desirability (Grimm, 2010) biases. Several
existing studies also utilize objective measures of sleep, typically
employing actigraphy or, to a lesser extent, polysomnography
(PSG). The former has generally limited specificity (Marino et al.,
2013) and cannot differentiate between sleep stages, whereas
PSG suffers from practical drawbacks related to cost, skills,
and time, limiting its feasibility in long-term sleep assessment
(Pallesen et al., 2018).
As a solution to the methodological limitations in previous
research, the present study performed objective, repeated
measurements of sleep by employing novel technology in junior
endurance athletes. The novel sleep monitoring device is based
on an impulse radio ultra-wideband (IR-UWB) radar sensor
technique and machine learning and has been validated against
PSG with substantial agreement on both sleep/wake and sleep
staging (Toften et al., 2020). The primary aim of the present
study is to investigate the reciprocal and temporal associations
between sleep, mental strain, and training load. The secondary
aim is to investigate whether being a good versus poor sleeper
has moderating effects on the associations between sleep, mental
strain, and training load. The latter aim is important for
identifying athletes vulnerable to the effects of fluctuations in
stress and sleep. Ecological validity is ensured by collecting
data during a dynamic 2-month period representative of a
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typical preparatory phase of the season, documenting real-life
fluctuations in sleep, training load, and mental strain as they
occur without any interventions or artificial constraints.
MATERIALS AND METHODS
Participants
Participants were recruited from high schools specialized for
winter endurance sports in Norway. In these schools, training
is a part of athletes’ educational plan, and they also train after
school in their respective sports teams. From the schools, a total
of 80 students were invited to information meetings about the
research project. Athletes who showed interest were asked to sign
up for the study by contacting the researchers. Those interested
then signed and returned an informed consent form and were,
thus, included. Due to equipment constraints (sleep monitors)
the maximum number of participants that could be included
was 60. Of these 60 initially included, four dropped out: three
were excluded due to lack of willingness to commit, and one
participant did not provide a reason. Thus, 56 (93.3% of 60)
athletes completed the study, of which 37 were male and 19
female. The mean age of the sample was 17.6 (range 17–19) years.
In all, 40 athletes practiced cross-country skiing, and 16 athletes
practiced biathlon.
Ethical Considerations
All athletes gave their informed consent to participate. According
to the Norwegian Health Research Act (§17.1), the lower legal
age for providing consent to participate in health-related research
is 16 years. Hence, because all participants in this study were
16 years or older, parental consent was not necessary. The
Regional Committee for Medical and Health Research Ethics
(REC) in Central Norway approved the study (project ID
2017/2072/REK Central Norway).
Procedure
A prospective observational cohort study design was employed
with daily monitoring of sleep, mental strain, and training
load. In the period of data collection, no interventions were
implemented. Training load was self-regulated with the aim
of improving performance and followed up by professional
coaches. Mental strain was self-assessed based on the athletes’
own perception and understanding of strain. Athletes were
instructed to follow their normal sleep and wake patterns
and were informed that monitoring was meant to assess their
normal, daily functioning, representative of the preparatory
phase of the season.
Before the assessment was initiated, participants completed a
questionnaire assessing basic demographics and the Pittsburgh
Sleep Quality Index (PSQI, Buysse et al., 1989) on the web-
based survey service Questback. The PSQI was administered in
the beginning of the study as this was logistically most feasible.
After questionnaire completion, equipment for monitoring of
sleep (i.e., Somnofy sleep monitor), mental strain (i.e., well-being
questionnaire, WQ), and training load (i.e., training diaries)
was handed out along with instructions for use. Athletes were
instructed to complete the WQ each evening before bedtime
and to complete training diaries every day upon completion of
their training sessions to eliminate issues with past recall and
inaccurate data entries.
Instruments
Somnofy Sleep Monitor
The Somnofy sleep monitor is a novel, fully unobtrusive tool for
sleep assessment based on an IR-UWB pulse radar and Doppler
technology. It is recently shown to be an adequate measure
of sleep and wake as well as sleep stages in a healthy adult
population. When validated against the “gold standard” of sleep
measurement, PSG, epoch-by-epoch analyses of the Somnofy
sleep monitor showed that accuracy of measurements determined
by Cohen’s kappa was 0.97 for sleep, 0.72 for wake, 0.75 for light
sleep, 0.74 for deep sleep, and 0.78 for REM sleep. Therefore,
some divergence between the Somnofy and PSG exists, which
should be taken into consideration when interpreting its output.
Still, Somnofy seems to provide more accurate sleep staging than
several comparable non-obtrusive sleep assessment alternatives
(Peake et al., 2018). For a full technical overview of the sleep
monitor, including its limitations and results of its validation,
see Toften et al., 2020.
The following sleep variables, derived from the sleep monitor,
are included in the analyses: sleep onset latency; total sleep time;
time in light, deep, and REM sleep; and sleep efficiency. These
sleep variables are described in greater detail in Table 1.
App-Based Well-Being Questionnaire
The WQ, a self-report stress scale based on recommendations
for monitoring of overtraining in athletes (Hooper and
Mackinnon, 1995), was used for daily assessment of mental
strain. The WQ is utilized in multiple studies investigating
wellness and fatigue in athletes (McLean et al., 2010;Buchheit
et al., 2013a,b;Gallo et al., 2016;Conte et al., 2018;
Lukonaitiene et al., 2020) and is routinely used in various athletic
populations due to its low cost, user-friendly design, and ease
of implementation.
The original WQ includes questions about fatigue, sleep
quality, muscle soreness, stress levels, and mood rated on a visual
TABLE 1 | Descriptions of the measured sleep variables collected with the
Somnofy sleep monitor in 56 junior endurance athletes.
Sleep variable Abbreviation Description
Sleep onset latency SOL Time from lights off to sleep onset
Total sleep time TST Total sleep time achieved during the
night
Light sleep – Time in the light stages of sleep
(stage N1 and N2)
Deep sleep – Time in the deep stages of sleep
(stage N3)
Rapid eye movement sleep REM Time in REM sleep (stage R)
Sleep efficiency SE The ratio of time from lights off to
leaving bed
For more information on sleep stages, see American Academy of Sleep Medicine
(2020).
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TABLE 2 | The intensity scale used to determine the training load of endurance and strength, plyometric, and speed training in this study.
5-zone Norwegian Olympic
Federation’s Intensity Scale
Physiologically accurate
3-zone scale Reference information
Zone Lactate
(mmol/L)
Heart rate
(% of max.)
Lactate turning
point
Intensity
zone
TRIMP
weight sRPE Typical training sessions
Endurance training
1 0.8–1.5 55–72 <1st lactate
threshold
Low-intensity
training 1 0–4 Warm-up/cool down, >90 min
2 1.5–2.5 72–81 Moderate duration, 45–90 min
3 2.5–4.0 82–87 1st–2nd lactate
threshold
Moderate-intensity
training 2 5–6 Continuous sessions, 30–60 min;
intervals with 6–15 min periods
4 4.0–6.0 88–92 >2nd lactate
threshold
High-intensity
training 3 7–10
Competitions, intervals with
4–8 min periods
Competitions, intervals with
1–5 min periods
5 6.0–10.0 92–97
Strength, plyometric, and speed training
– – – – – 1.5 –
Plyometric/strength exercises,
4–30 reps; speed training, 5–15 s
periods
sRPE, session rating of perceived exertion. The lactate and heart rate values refer to typical values for the training in the different intensity zones, although these
parameters were not directly measured in this study. The training impulse (TRIMP) weight for strength, plyometric, and speed training is based on the assessment of load
by researchers and elite coaches in cross-country skiing.
Max. possible days of data with
participants that completed the study
(N=56) = 3416
Max. possible days of data with all
recruited participants (N=60) = 3660
Dropouts: N=4, equaling
244 days
Mental strain data Training load data
Missing = 468 nights of data
Technical issues,
no access to
power / internet
(N=213 nights)
Poor data quality,
filtered away
(N=259 nights)
Collected and analyzed = 2944 nights
(86.2%)
Sleep data
Missing = 670 days of data
Forgetfulness
of athletes
Phone out
of battery
Collected and analyzed = 2746 days
(80.4%)
Collected and analyzed = 2950
training sessions (86.4%)
Missing = 466 training sessions
Failed to
register data
(N=5 athletes)
Inconsistent
with data entry
(N=8 athletes)
FIGURE 1 | A breakdown of data-collection compliance with reasons for missing data. Percentages were calculated out of 3416, the maximum number of
observations in each of the daily measured variables with 56 participants.
analog scale. We slightly adapted the original WQ to fit the
purpose of the current study. The question about stress levels
was reframed as a question about worry and rumination, and
the question about mood was kept the same. This was done in
order to obtain markers of both cognitive (worry/rumination)
and affective (mood) components of the stress response. Athletes
were asked to provide daily scores on the WQ with visual
rating scales, ranging from 0 to 10 for each question, where
“0” indicates low levels of stress, and “10” indicates high
levels of stress. For the purposes of the present study, only
data regarding the two questions about worry/rumination and
mood are analyzed. The scores of the two questions were
added and then averaged to obtain a measure of mental strain
with both emotional and cognitive components. The WQ was
built into the smartphone-based Somnofy app, to which all
participants had access.
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t1 t1
AIV DV
s
s
Sleep
t2Bt1 IV DV
s
sMental strain
Training load
Mental strain
Training load
Sleep
FIGURE 2 | A visual representation of the primary aims in the current study,
with investigation of the effects of mental strain and training load on sleep (A),
and of sleep on next day mental strain and next day training load (B). Filled,
unmarked circles represent random intercepts. Filled circles marked ‘s’
represent random slopes. The figure specifies temporal relationships between
the variables, as well as dependent and independent variables.
Training diaries
Participants recorded their daily training sessions in digital
diaries. The platforms used included the Norwegian Olympic
Federation training diary1, the Bestr training diary2, or a
custom Excel training diary provided by the Norwegian Ski
Federation. The type of training recorded in these diaries was
the same, independent of diary, and the information extracted
from athletes’ training diaries was used to calculate training
load. For that purpose, endurance and strength as well as
plyometric and speed training were included. Other forms of
training, such as mobility, stretching, and biathletes’ shooting
practice were excluded.
For endurance training, athletes self-reported the time spent
in different aerobic intensity zones in line with a five-zone
intensity scale developed by the Norwegian Top Sport Centre
(Olympiatoppen). The scale is frequently used in endurance
sports in Norway, and junior athletes are systematically taught to
independently self-report time spent in these different intensity
zones as a part of their education in the high schools specialized
for elite sports. When self-reporting, athletes do not use the
heart rate and blood lactate information (provided as reference
values for the five different zones) and rely on their perception of
intensity as this is regularly calibrated toward these physiological
measures. Previously, this self-report method was validated
against heart rate and blood lactate levels and showed that
endurance athletes self-report their training data accurately as
both training duration and intensity distribution closely match
the heart rate and lactate data (Sylta et al., 2014).
However, the 5-zone intensity scale is not clearly anchored
in underlying physiological events (Boulay et al., 1997). In
order to match the physiological parameters with the zones, we
recalculated the 5-zone scale into a 3-zone scale. In the 3-zone
scale, the zones correspond to the first and second lactate turning
1https://olt-dagbok.nif.no/
2https://info.bestr.no/
Mental strain
Sleep
Good vs. poor sleeper
Training load
s
s
A
t1 IV t1 DV
Good vs. poor sleeper
Sleep
t1
B
VD2tVI
Mental strain
Training load
s
s
Good vs. poor sleeper
Good vs. poor sleeper
FIGURE 3 | A visual representation of the secondary aim in the current study,
with moderating effects of being a good vs. a poor sleeper on (A) the effect of
mental strain on sleep (blue arrows), and the effect of training load on sleep
(orange arrows), and (B) the effect of sleep on next day mental strain (green
arrows), and the effect of sleep on next day training load (yellow arrows).
Filled, unmarked circles on the response variables represent the random
intercepts, while filled circles marked with ‘s’ represent the random slopes.
The figure specifies temporal relationships between the variables, as well as
dependent and independent variables.
points (Boulay et al., 1997;Seiler and Kjerland, 2006). The 3-
zone scale differentiates between low-intensity training (LIT),
which encompasses all training below the first lactate threshold
[zones 1 and 2; <2 mM blood lactate, 60–81% of maximal heart
rate (HRmax)]; moderate-intensity training (MIT), corresponding
to the intensity between the first and second lactate threshold
(zone 3; 2–4 mM blood lactate, 82–87% of HRmax); and high-
intensity training (HIT), corresponding to intensity above the
second lactate threshold (zones 4 and 5; >4 mM blood lactate,
>87% of HRmax) (Seiler and Kjerland, 2006). Therefore, from the
original scale reported, we merged zones 1 and 2 into LIT, zone 3
represented MIT, and zones 4 and 5 were merged into HIT. This
approach has been successfully applied in previous studies (e.g.,
Solli et al., 2017).
Based on the training intensities reported (LIT, MIT, HIT),
training impulse (TRIMP) scores were calculated in order to
quantify training load (Pyne and Martin, 2011). Endurance
TRIMP scores were calculated by multiplying the total duration
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TABLE 3 | Descriptive statistics for objectively measured sleep patterns, mental
strain, training load, and subjective sleep quality, reporting means and SD, in 56
junior endurance athletes.
M SD
Objectively measured SOL (h) 00:35 00:31
sleep patterns TST (h) 07:33 01:12
Light (h/%) 04:16/56.3 00:52/7.2
Deep (h/%) 01:24/18.7 00:23/5.1
REM (h/%) 01:53/24.7 00:31/5.2
SE (%) 79.0 10.2
Mental strain (au) 3.5 1.6
Training load Training sessions pp (N) 52.3 23.1
Training missed due to resting
pp (N)
9.4 6.0
Training missed due to sickness
pp (N)
6.6 5.8
TTL (TRIMP) 103.8 83.8
Subjective sleep quality PSQI composite score 3.5 2.1
SOL, sleep onset latency; TST, total sleep time; REM, rapid eye movement sleep;
SE, sleep efficiency; TTL, total training load; pp, per participant; PSQI, Pittsburgh
Sleep Quality Inventory.
(min) in each endurance intensity zone by a constant for each
intensity zone (1 for LIT, 2 for MIT, 3 for HIT). TRIMP scores
for strength, plyometric, and speed training were calculated by
multiplying the total duration (min) in this training mode by
a constant of 1.5. See Table 2 for the breakdown of the 5-zone
intensity scale with typical heart rate and blood lactate values and
subsequent utilization of the 3-zone scale anchored in the first
and second lactate turning point. Also, see Table 2 for the TRIMP
weights, how reference information on the zones corresponds
to the different session ratings of perceived exertion (Foster,
1998), and examples of typical training sessions for each category
(Seiler and Kjerland, 2006).
The resulting TRIMP scores were then employed as markers
of total training load (TTL), the primary measure of training
load in this study. TTL was calculated by adding the accumulated
endurance TRIMP scores with the strength, plyometric, and
speed training TRIMP scores for each training day (e.g., Solli
et al., 2017, 2019). In case athletes trained more than once per
day, all scores for one particular day were summed, resulting in
one score per day.
Pittsburgh Sleep Quality Index
The PSQI (Buysse et al., 1989) is a self-report measure of
subjective sleep quality. The index, adapted into Norwegian
by Pallesen et al. (2005), includes 19 questions that investigate
factors associated with sleep quality over the past month. In all,
4 questions had open entry answers, and the rest were scored on
a 4-point (0–3) Likert scale. The responses were then added to
obtain a global composite score, ranging from 0 to 21. Low scores
signify good sleep quality. A cutoff of 5 was used to categorize
respondents into good sleepers (≤5) and poor sleepers (>5). The
Norwegian version of the PSQI has been shown to have good
psychometric properties (Pallesen et al., 2005).
TABLE 4 | Two-level random intercept and random slope models investigating the effect of mental strain and training load (IVs) on sleep variables (DVs) and whether
these effects vary between individuals in 56 junior endurance athletes.
Means Variances
DV Coefficients Est. P. SD Lower
2.5% of
95% CI
Upper
2.5% of
95% CI
Sig. Est. P. SD Lower
2.5% of
95% CI
Upper
2.5% of
95% CI
Sig. R2
Sleep onset latency (h) Intercept 0.56 0.03 0.49 0.62 <0.001*0.05 0.01 0.03 0.07 <0.001*2.0%
Slope * MS 0.00 0.01 −0.02 0.01 0.480 0.00 0.00 0.00 0.00 <0.001*
Slope * TTL −0.01 0.02 −0.03 0.02 0.360 0.00 0.00 0.00 0.01 <0.001*
Total sleep time (h) Intercept 7.61 0.06 7.49 7.73 <0.001*0.18 0.05 0.12 0.32 <0.001*1.8%
Slope * MS −0.07 0.02 −0.12 −0.03 <0.001*0.01 0.01 0.00 0.02 <0.001*
Slope * TTL −0.02 0.03 −0.09 0.04 0.280 0.01 0.01 0.00 0.03 <0.001*
Light sleep (h) Intercept 4.32 0.05 4.24 4.41 <0.001*0.11 0.03 0.07 0.19 <0.001*1.9%
Slope * MS −0.04 0.02 −0.08 −0.00 0.005*0.01 0.00 0.00 0.02 <0.001*
Slope * TTL 0.02 0.02 −0.03 0.05 0.245 0.00 0.00 0.00 0.01 <0.001*
Deep sleep (h) Intercept 1.40 0.02 1.36 1.44 <0.001*0.03 0.01 0.02 0.05 <0.001*2.4%
Slope * MS −0.01 0.01 −0.03 0.01 0.120 0.00 0.00 0.00 0.00 <0.001*
Slope * TTL 0.00 0.01 −0.02 0.03 0.420 0.00 0.00 0.00 0.01 <0.001*
REM sleep (h) Intercept 1.89 0.03 1.84 1.94 <0.001*0.04 0.01 0.02 0.06 <0.001*2.0%
Slope * MS −0.03 0.01 −0.05 −0.00 0.005*0.00 0.00 0.00 0.00 <0.001*
Slope * TTL −0.03 0.02 −0.06 −0.00 0.015*0.00 0.00 0.00 0.01 <0.001*
Sleep efficiency (%) Intercept 79.53 0.70 78.22 80.94 <0.001*5.26 6.21 16.76 42.10 <0.001*1.8%
Slope * MS −0.50 0.22 −0.95 −0.09 0.005*0.67 0.55 0.08 2.04 <0.001*
Slope * TTL 0.08 0.24 −0.46 0.46 0.330 0.36 0.36 0.04 1.50 <0.001*
Values refer to the between-level. Significant results are based on Bayesian estimation, and are marked with an *. R2values refer to within-level explained variance averaged
across athletes. Abbreviations: P. SD, Posterior standard deviation; CI, credibility interval; DV, dependent variable; ND, next day; TRIMP, training impulse; au, arbitrary
units; SOL, sleep onset latency; TST, total sleep time; LS, light sleep; SWS, deep sleep; REM, rapid eye movement sleep; SE, sleep efficiency.
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3.9
4
4.1
4.2
4.3
4.4
4.5
4.6
−3 −2 −1 0 1 2 3 4 5
Mental strain (a.u.) | Grand Mean centered
Light sleep (hs)
1.6
1.7
1.8
1.9
2
2.1
−3 −2 −1 0 1 2 3 4 5
Mental strain (a.u.) | Grand Mean centered
Rapid eye movement sleep (hs)
74
75
76
77
78
79
80
81
82
83
−3 −2 −1 0 1 2 3 4 5
Mental strain (a.u.) | Grand Mean centered
Sleep efficiency (%)
1.6
1.7
1.8
1.9
2
−1 0 1 2 3
Training load (TRIMP) | Grand Mean centered
Rapid eye movement sleep (hs)
6.9
7.1
7.3
7.5
7.7
7.9
8.1
−3 −2 −1 0 1 2 3 4 5
Mental strain (a.u.) | Grand Mean centered
Total sleep time (hs)
BA
DC
E
FIGURE 4 | Significant findings identified when testing the same-day effects of mental strain and training load on sleep in 56 junior endurance athletes: (A) effect of
mental strain on total sleep time, (B) effect of mental strain on light sleep, (C) effect of mental strain on rapid eye movement sleep, (D) effect of mental strain on sleep
efficiency, and (E) effect of training load on rapid eye movement sleep. The bold line represents the average intercept and slope identified in the given models. The
shaded area above the line represents the upper 2.5% of 95% credibility interval, while the shaded area below the line represents the lower 2.5% of 95% credibility
interval. Value 0 on the x-axis refers to the average value on the measured outcome parameter.
Data Collection Compliance
Due to the demanding day-to-day nature of the data collection,
technical issues and challenges with participants’ inconsistent
reporting were present, leading to missing data. Figure 1 presents
a breakdown of collected and missing data with reasons for the
lack of compliance.
Statistical Analyses
The collected data created a nested data structure, in which up
to 61 repeated measurements were nested within 56 individual
athletes. In order to allow for dependence among the responses
and to avoid excessive Type I errors and biased parameter
estimates, multilevel modeling in Mplus, version 8.3 (Muthén
and Muthén, 2017), was utilized to carry out the statistical
analyses. The use of Mplus allowed for the investigation of
both within (repeated measurement units, level 1) and between
level (individual athletes, level 2) relations between sleep, mental
strain, and training load.
The analyses were carried out in two temporal directions.
First, we investigated associations between mental strain and
training load at time 1 and subsequent sleep at time 1. In
these analyses, the mental strain and training load reported
on a particular day (e.g., Monday) were tested for associations
with sleep that started on the evening of the same day that
mental strain and training load were reported (e.g., sleep onset
on Monday evening). In these analyses, mental strain and
training load, thus, were the independent variables, and the
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TABLE 5 | Two-level random intercept and random slope models investigating the effect of sleep variables (IVs) on mental strain and training load (DVs) and whether
these effects vary between individuals in 56 junior endurance athletes.
Means Variances
DV Coefficients Est. P. SD Lower
2.5% of
95% CI
Upper
2.5% of
95% CI
Sig. Est. P. SD Lower
2.5% of
95% CI
Upper
2.5% of
95% CI
Sig. R2
ND mental strain (au) Intercept 3.55 0.16 3.24 3.87 <0.001*1.45 0.32 1.04 2.24 <0.001*0.3%
Slope * SOL −0.02 0.07 −0.17 0.11 0.407 0.01 0.02 0.00 0.07 <0.001*
ND training load (TRIMP) Intercept 1.04 0.04 0.95 1.12 <0.001*0.08 0.02 0.05 0.13 <0.001*0.4%
Slope * SOL 0.01 0.04 −0.08 0.09 0.408 0.01 0.01 0.00 0.05 <0.001*
ND mental strain (au) Intercept 3.57 0.17 3.25 3.88 <0.001*1.49 0.30 1.04 2.14 <0.001*1.9%
Slope * TST 0.01 0.03 −0.06 0.05 0.425 0.02 0.01 0.01 0.04 <0.001*
ND training load (TRIMP) Intercept 1.04 0.05 0.95 1.13 <0.001*0.08 0.02 0.05 0.13 <0.001*1.3%
Slope * TST −0.06 0.02 −0.09 −0.03 <0.001*0.00 0.00 0.00 0.01 <0.001*
ND mental strain (au) Intercept 3.56 0.17 3.24 3.88 <0.001*1.48 0.29 1.04 2.13 <0.001*2.4%
Slope * LS 0.00 0.04 −0.10 0.07 0.500 0.05 0.02 0.03 0.10 <0.001*
ND training load (TRIMP) Intercept 1.04 0.05 0.95 1.13 <0.001*0.09 0.02 0.05 0.13 <0.001*0.9%
Slope * LS −0.05 0.02 −0.10 −0.01 0.010*0.01 0.01 0.00 0.02 <0.001*
ND mental strain (au) Intercept 3.55 0.16 3.26 3.89 <0.001*1.45 0.32 0.94 2.31 <0.001*1.7%
Slope * SWS 0.06 0.09 −0.10 0.27 0.250 0.19 0.08 0.09 0.41 <0.001*
ND training load (TRIMP) Intercept 1.04 0.04 0.95 1.12 <0.001*0.08 0.02 0.05 0.15 <0.001*1.3%
Slope * SWS −0.13 0.05 −0.22 −0.02 0.020*0.03 0.02 0.01 0.09 <0.001*
ND mental strain (au) Intercept 3.57 0.16 3.25 3.89 <0.001*1.47 0.29 1.03 2.13 <0.001*1.2%
Slope * REM −0.02 0.06 −0.14 0.06 0.365 0.07 0.04 0.02 0.16 <0.001*
ND training load (TRIMP) Intercept 1.03 0.05 0.94 1.12 <0.001*0.08 0.02 0.05 0.13 <0.001*1.3%
Slope * REM −0.10 0.04 −0.18 −0.03 0.010*0.03 0.02 0.01 0.08 <0.001*
ND mental strain (au) Intercept 3.54 0.16 3.25 3.89 <0.001*1.49 0.33 0.97 2.38 <0.001*3.4%
Slope * SE −0.00 0.01 −0.01 0.01 0.260 0.00 0.00 0.00 0.00 <0.001*
ND training load (TRIMP) Intercept 1.04 0.04 0.94 1.11 <0.001*0.08 0.02 0.05 0.13 <0.001*4.4%
Slope * SE 0.01 0.00 −0.00 0.02 0.050 0.00 0.00 0.00 0.00 <0.001*
Values refer to the between level. Significant results are based on Bayesian estimation, and are marked with an *. R2values refer to within-level explained variance
averaged across athletes. P. SD, Posterior standard deviation; CI, redibility interval; DV, dependent variable; ND, next day; TRIMP, training impulse; au, arbitrary units;
SOL, sleep onset latency; TST, total sleep time; LS, light sleep; SWS, deep sleep; REM, rapid eye movement sleep; SE, sleep efficiency.
different sleep variables represented the dependent variables.
Second, we investigated associations between sleep and next-day
(subsequent) mental strain and training load. In these analyses,
sleep that started on a particular evening (e.g., Monday night)
was tested for associations with mental strain and training load
experienced the following day (e.g., Tuesday). In these analyses,
the different sleep variables comprised the independent variables,
and next-day mental strain and training load represented the
dependent variables.
Two-level random intercept and random slope models, which
assume that variation between individuals is at their intercept
(random intercept) and that the effects of the explanatory
variables vary between individuals (random slope), were used
to investigate the research questions in both aim 1 (Figure 2)
and aim 2 (Figure 3). For aim 1 analyses, training load scores
were divided by 100 to scale down the values in order to
communicate the effects more clearly. Bayesian estimation was
utilized in all random slope models investigated in this study
as the models were computationally too complex for maximum
likelihood estimation. In Bayesian analysis, prior distributions for
parameters are combined with data likelihood to form posterior
distributions for parameter estimates. Bayesian estimation is
based on posterior probability distributions and uses the
credibility interval for significance testing (Muthén, 2010). Grand
mean centering of predictor variables was implemented to
reduce multicollinearity and to establish a meaningful zero
point: the intercept became an average across the whole sample.
On the between level, the results show the estimated means
and variances of the predictor variables across athletes with
average values on the measured outcome parameters. For all
multilevel models, R2-values, stating the explained variance were
reported. P-values were set at <0.05 for all models. For readers
interested in the specific effect of sport (cross-country skiing and
biathlon) in the models assessing reciprocal associations between
sleep, mental strain, and training load, results are available in
Supplementary Materials.
RESULTS
Descriptive Statistics
Descriptive statistics for the sleep parameters, mental strain,
training load, and subjective sleep quality are reported in Table 3.
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0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
−3−2−10123
Light sleep (hs) | Grand Mean centered
Next day training load (TRIMP)
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
−3−2−10123
Total sleep time (hs) | Grand Mean centered
Next day training load (TRIMP)
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
2101−
Deep sleep (hs) | Grand Mean centered
Next day training load (TRIMP)
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
−10123
Rapid eye movement sleep (hs) | Grand Mean centered
Next day training load (TRIMP)
AB
DC
FIGURE 5 | Significant findings identified when testing the effects of sleep on mental strain and training load the next day in 56 junior endurance athletes: (A) effect
of total sleep time on next day training load, (B) effect of light sleep on next day training load, (C) effect of deep sleep on next day training load, and (D) effect of
rapid eye movement sleep on next day training load. The bold line represents the average intercept and slope identified in the given models. The shaded area above
the line represents the upper 2.5% of 95% credibility interval, while the shaded area below the line represents the lower 2.5% of 95% credibility interval. Value 0 on
the x-axis refers to the average value on the measured outcome parameter.
Associations Between Sleep, Mental
Strain, and Training Load
Same-Day Effects of Mental Strain and Training Load
on Sleep
Table 4 shows the between-level variation in the relationships
between mental strain and training load (IVs) and sleep variables
(DV) analyzed with random intercept and random slope models.
In the table, TTL values have been scaled down by 100 in
order to communicate the effects more clearly. The results show
that, with each point increase on the mental strain score, time
asleep, light sleep, REM sleep, and sleep efficiency decreased
significantly. Moreover, with each point increase in TRIMP score,
REM sleep decreased significantly. The significant findings are
illustrated in Figure 4. There were significant variances in the
strength of all tested relationships between athletes. In other
words, both the intercepts and the effect of mental strain and
training load on sleep were stronger in some athletes than others.
The explained variance of the DVs by these models is low and
ranges from 1.8 to 2.4%.
Next Day Effects of Sleep on Mental Strain and
Training Load
Table 5 shows the between-level variation in the relationships
between sleep (IVs) and next day mental strain and training load
(DV). In the table, TTL values have been scaled down by 100 in
order to communicate the effects more clearly. The results show
that with each hour increase on the sleep variables time asleep,
light sleep, deep sleep and REM sleep, TRIMP score the next day
decreased significantly (see Figure 5 for a visual representation
of the significant findings). None of the sleep variables influenced
mental strain the next day. There were significant variances in
the strength of all tested relationships between athletes. In other
words, both the intercepts, and the effect of sleep on next day
mental strain and training load differed between athletes. The
explained variance of the DVs by these models is low and ranges
from 0.3 to 4.4%.
The Effects of Subjective Sleep Quality
In all, 9 (16%) athletes were poor sleepers, and the rest (N= 47)
were good sleepers. Four different types of cross-interaction
analyses, described in section “Statistical Analyses," investigated
whether the strength of the relations explored in the previous step
varied between individuals, and whether type of sleeper (good vs.
poor) had an impact on the strength of those relations. Results of
the analyses are presented in Table 6.
The cross-level interaction analyses reveal fully moderating
effects of subjective sleep quality on the associations between
average SOL and next-day mental strain and average deep sleep
and next-day training load. For poor sleepers, the strength of
the associations are significantly different from that of good
sleepers: with each hour of SOL, poor sleepers’ next-day mental
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TABLE 6 | Cross-interaction analyses investigating the moderating effects of subjective sleep quality on the effect of explanatory variables on dependent variables (see
Figure 3 for overview of tested associations).
DV Coefficients Est. P. SD Lower 2.5% of 95% CI Upper 2.5% of 95% CI Sig. R2
Next day mental strain Intercept good sleeper 3.49 0.17 3.17 3.82 <0.001*
1Intercept poor sleeper 0.45 0.47 −0.49 1.31 0.188
Good sleeper * SOL (h) −0.08 0.05 −0.17 0.01 0.043
1Poor sleeper * SOL (h) 0.28 0.04 0.09 0.46 <0.001*39.6%
Next day training load Intercept good sleeper 105.61 4.86 96.08 115.93 <0.001*
1Intercept poor sleeper −12.43 12.26 −36.15 11.71 0.157
Good sleeper * deep sleep (h) −8.39 5.62 −18.92 2.25 0.063
1Poor sleeper * deep sleep (h) −38.75 14.39 −67.65 −11.43 0.002*21.9%
The interaction coefficients refer to the moderating effects of subjective sleep quality on the effect of the given explanatory and dependent variables. Values refer to the
between level. Significant results are based on Bayesian estimation and are marked with an *. 1represents the difference to the good sleeper’s value. Significant results
are italicized. DV, dependent variable; P. SD, Posterior standard deviation; CI, credibility interval; MS, mental strain; REM, rapid eye movement sleep; SOL, sleep onset
latency.
A
*
2
3
4
5
6
−0.500.511.522.5
Sleep Onset Latency (hs) | Grand Mean centered
Next day mental strain (a.u.)
B
*
10
35
60
85
110
135
160
−1 −0.5 0 0.5 1
Deep sleep (hs) | Grand Mean centered
Next day training load (TRIMP)
Subjective sleep quality
Poor sleepers
Good sleepers
FIGURE 6 | Significant findings identified when testing the moderating role of subjective sleep quality in 56 junior endurance athletes: (A) effect of sleep onset latency
on next day mental strain and (B) effect of deep sleep on next day training load. The red bold line represents the average intercept and slope identified for poor
sleepers, while the turquoise line represents the average intercept and slope identified for good sleepers. The shaded area above each line represents the upper
2.5% of 95% credibility interval, while the shaded area below each line represents the lower 2.5% of 95% credibility interval. Value 0 on the x-axis refers to the
average value on the measured outcome parameter. * denotes significant difference between poor and good sleepers, p<0.05.
strain increases by 0.20 points (as compared to −0.08 points
for good sleepers), and with each hour of deep sleep, poor
sleepers’ next-day training load decreases by 47.14 TRIMP points
(as compared to 8.4 points for good sleepers). See Figure 6
for a visual representation of these findings. When predicting
next-day mental strain by SOL and PSQI, the model shows
that these variables explain 39.6% of the variance in next-day
mental strain, and the model predicting next-day training load
by PSQI and deep sleep explains 21.9% of the variance in next-
day training load.
DISCUSSION
The present study recorded daily repeated measurements of sleep,
mental strain, and training load over a period of 61 consecutive
days in 56 junior endurance athletes in order to investigate
reciprocal associations between sleep, mental strain, and training
load as well as the moderating effects of being a good versus
a poor sleeper. The main findings are (1) increases in mental
strain are associated with decreased TST, REM sleep, and SE,
and higher training load is associated with decreased REM sleep;
(2) increases in TST, light, deep, and REM sleep are associated
with decreased training load the next day; (3) in poor sleepers,
increases in SOL are associated with increases in next-day mental
strain, and increases in deep sleep are associated with decreases
in next-day training load.
Reciprocal Associations Between Sleep,
Mental Strain, and Training Load
Increases in mental strain are associated with decreased TST
and SE. These findings are in line with previous research,
which elucidates the detrimental effect of mental strain on
sleep. For example, experimental and observational data show
that psychosocial (Kim and Dimsdale, 2007) and emotional
(Vandekerckhove et al., 2011) stressors as well as worries and
ruminations (Akerstedt et al., 2007;Van Laethem et al., 2016) and
high trait-like vulnerability to stress-related sleep disturbance in
non-insomniac individuals (Drake et al., 2004) are associated
with decreased SE. Likewise, participants exposed to emotional
stressors (Vandekerckhove et al., 2011) exhibit decreased TST.
The present study extends these findings to the athletic
population. On days with increased mental strain, the reductions
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in TST may be attributed to delayed bedtimes and the
reductions in SE to more frequent awakenings. The feasibility of
experimentally establishing causality between mental strain and
sleep in athletes remains uncertain. Such studies are nevertheless
greatly beneficial for the development of suitable interventions
aimed at resolving the negative effects of mental strain on sleep
in athletes exposed to mentally straining environments.
Increases in mental strain are also associated with decreased
light and REM sleep. These results are striking as REM sleep
is thought to play an important role in emotion regulation and
processing (Palmer and Alfano, 2017), and existing literature
shows increases in REM sleep in subjects with mood disorders
(Armitage, 2007). These effects may be attributed to processing
of negatively charged emotional and cognitive stimuli; if the
daytime regulatory processes fail to fully resolve or integrate
the stimuli, the processes may continue throughout the night,
increasing REM sleep. However, decreases in REM in association
with mentally straining stimuli are shown in healthy populations
(Kim and Dimsdale, 2007). This may point to a different
processing strategy as REM sleep may decrease as a result of
a mentally straining stimulus and may only be compensated
for after the stimulus is resolved and distress is normalized
(Vandekerckhove et al., 2011). Experimental studies should
establish whether REM sleep occurs later following the exposure
to the mentally straining stimulus when the intensity of the
stimulus has waned.
REM sleep is further reduced in association with increases in
training load. There is some limited support for these findings,
showing decreases in REM sleep as a result of acute exercise
(Youngstedt et al., 1997) or increased training load (Kubitz
et al., 1996;Brand et al., 2010). In the latter studies, decreases
in REM are accompanied by concurrent increases in deep
sleep, which may be indicative of an inhibitory influence of
deep sleep on REM sleep. Such effects are not seen in the
present study, possibly due to the circadian regulation of sleep
(Youngstedt et al., 1997). A reduction in REM could be explained
by late-evening exercise, which may reduce REM sleep if TST is
not upheld. Indeed, disruptive effects of late-night exercise on
sleep are well established (Thun et al., 2015). Unfortunately, we
were unable to investigate the effects of timing of exercise; thus,
future studies should consider the effects of exercise timing to
elucidate potential associations with sleep.
Moreover, increases in TST, light, deep, and REM sleep
are associated with decreases in training load the next day,
highlighting how daily fluctuations in sleep parameters influence
training load. Sleep-deprived elite athletes are previously shown
to decrease their training load (Cook et al., 2012), which is
a conceivable consequence of sleep loss, assumingly mediated
by changes in motivation. In the present study, however,
athletes were obtaining adequate sleep durations, which may
have influenced the subsequent training load differently. We
hypothesize that the increases in TST and sleep stages may reflect
an increased need for recovery, which may have subsequently
led to downregulation of training load. Such downregulation
of training load may not be a detrimental aspect in athletes’
training progressions. In fact, reductions of training loads in
response to increased recovery needs may reflect well-developed
self-regulatory skills. These findings should be replicated in other
studies. In addition, studies should investigate the associations
between sleep debt accumulated over specific time periods with
changes in training load to investigate how the need for recovery
influences the chosen training load.
The Moderating Role of Subjective Sleep
Quality
In poor sleepers, increases in SOL are associated with increases in
subsequent mental strain. Increased SOL, frequently attributed to
mental activity at bedtime (e.g., worrying) (Wuyts et al., 2012),
is common among poor sleepers (Paceschott et al., 1994). We
hypothesize that, in the present sample, worrying at bedtime
may have prolonged SOL. Upon awakening and if last night’s
worries remained unresolved, athletes will conceivably continue
worrying and subsequently report increased mental strain. If
such interactions between sleep and mental strain continue,
a disrupting cycle of worrying at bedtime and poor sleep may
develop (Riemann et al., 2010). Indeed, associations between
lower quality or quantity of sleep and subsequent increases in
emotional and cognitive aspects of stress in healthy populations
are previously demonstrated (Galambos et al., 2009). It is possible
that such processes are responsible for the present associations
in poor sleepers between increased SOL and increased next-
day mental strain.
Finally, in poor sleepers, increases in deep sleep are associated
with subsequent decreases in training load. This supplements
the present associations between increased TST and the sleep
stages and decreased training load. We, therefore, continue
to hypothesize that increases in deep sleep may represent
an increased need for recovery, which subsequently leads to
downregulation of training load. Considering the roles of deep
sleep in physiological recovery (Vyazovskiy and Delogu, 2014),
these results may have consequences for athletic performance
development and functioning. However, empirical studies fail to
demonstrate a consistent relationship between these variables,
showing increases in deep sleep (Shapiro et al., 1981) and
also no effects (Knufinke et al., 2018;O’Donnell et al., 2019)
following increases in physical load. It is possible that the present
results bridge the gap in existing research by showing that the
associations between deep sleep and training load only exist in
poor sleepers. If that is the case, these results hold important
practical implications for coaches and athlete support staff as
athletes who struggle with sleep disturbances may be at higher
risk of experiencing poor recovery or generally are in need for
more or longer recovery than athletes who sleep well.
Limitations
The results of the present study should be interpreted with several
limitations in mind. First, while retaining the question structure,
we adapted the questions from the original WQ (McLean et al.,
2010) to fit the purpose of this study. Moreover, despite its
frequent use for the monitoring of athletes’ well-being, the
validity and reliability of the WQ have not been formally assessed.
Further, the levels of mental strain in the current study are
relatively low (mean 3.5 ±SD 1.6). The reasons for the mild
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mental strain may be due to the high prevalence of good sleepers
or that athletes generally cope well with their situation. Therefore,
the results based on the mental strain scale should be interpreted
with these limitations in mind. Second, we only find a small
proportion of explained variance in the analysis of reciprocal
associations between sleep, mental strain, and training load (aim
1). This is likely due to the fact that a plethora of different
variables may have influenced the associations at play. Such
variables may include coping styles, personality traits, regularity
of sleep/wake patterns, chronotype, or relationship with coaches.
In the present study, these variables are unaccounted for. In
addition, the current sample includes only 9 poor sleepers,
and the rest of the sample are good sleepers. Therefore, the
statistical analyses exploring whether the reciprocal associations
between sleep, mental strain, and training load are moderated
by sleeper category (good vs. poor) should be interpreted with
caution. Last, although the device used for sleep monitoring
used in the current study performed well in the validation
study against PSG, the relationship of the Somnofy-derived sleep
parameters and PSG data is not perfect (Toften et al., 2020).
Hence, some measurement error regarding the sleep variables
should be acknowledged.
CONCLUSION
The present study provides novel results on how variations
in mental strain and training load influence sleep patterns
in junior endurance athletes and vice versa. Mental strain is
inversely associated with subsequent TST, REM sleep, and SE,
and training load is inversely associated with subsequent REM
sleep. As many competitive athletes face mentally and physically
straining sport-related situations, these results hold potentially
important implications for informing the future development
of specific sleep and mental health–related interventions for
this group. Further, we show that increased sleep durations and
increased sleep stages (light, deep, and REM) are associated with
subsequent decrease in athletes’ training load. These findings
indicate an increased need for recovery and subsequent self-
regulatory reduction of training loads by the athletes. Currently,
there is consensus regarding neither how much sleep competitive
athletes should obtain nor their optimal sleep stage distribution.
Specifying competitive athletes’ individual sleep needs is crucial
for optimization of athletes’ recovery. Last, in athletes subjectively
categorized as poor sleepers, increases in SOL are associated
with increases in subsequent mental strain. In the same
group, increases in deep sleep are associated with decreases in
subsequent training load. The present study is the first to identify
the moderating effects of subjective sleep quality, which are
especially relevant for the practice field. We suggest that coaches
should pay close attention to athletes’ subjective experience of
sleep as this person-level characteristic may be key in identifying
athletes at risk for poor recovery.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation, to any
qualified researcher.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Regional Committee for Medical and Health
Research Ethics in Central Norway. Written informed consent
from the participants’ legal guardian/next of kin was not required
to participate in this study in accordance with the national
legislation and the institutional requirements.
AUTHOR CONTRIBUTIONS
MH and FM designed and conceptualized the study. ØS and
SP contributed in this process. FM facilitated contact with the
schools involved in this study. MH collected the data and wrote
the manuscript. FM and ØS supervised the data collection. CK
supervised the statistical approach. MH and CK carried out the
analyses. All authors were involved in the interpretation of the
statistical analyses, editing of the manuscript, and approved the
final version of the manuscript.
FUNDING
This study was funded by the Centre for Elite Sports
Research, Department of Neuromedicine and Movement
Science, Norwegian University of Science and Technology,
Trondheim, Norway.
ACKNOWLEDGMENTS
Athletes’ participation is deeply appreciated. We thank research
assistants Emilie F.W. Raanes and Maja Olsen for help with data
collection. We acknowledge the financial support of the Centre
for Elite Sports Research.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
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Copyright © 2020 Hrozanova, Klöckner, Sandbakk, Pallesen and Moen. This is an
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