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Gender Differences in Sleep and Mental Health in Elite Athletes

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Study 1 (00:00 – 03:29)

Cognitive-resilient traits may be important for protecting against poor sleep and mental health, especially during chronic stress. Mr Luis Mascaro, and colleagues, investigated the associations of cognitive fitness and gender to sleep and mental health, and the interaction between cognitive fitness and gender on sleep and mental health, in competitive athletes during the COVID-19 pandemic.

Mascaro, L., Drummond, S. P., Leota, J., Boardman, J. M., Hoffman, D., Rajaratnam, S. M., Aidman, E., & Facer-Childs, E. R. (2023). Cognitive fitness modulates gender differences in sleep and mental health among competitive athletes under chronic stress. Frontiers in Physiology, 14, 1118822. https://doi.org/10.3389/fphys.2023.1118822

Study 2 (03:29 – 10:48)

Elite athletes may be at risk of poor mental health for a number of reasons. Mr Luis Mascaro, and colleagues, investigated sex differences in sleep, circadian rhythms, and mental health, as well as the moderating role of sex in the prediction of mental health, among male professional and female semi‐professional elite athletes.

Mascaro, L., Leota, J., Hoffman, D., Rajaratnam, S. M., Drummond, S. P., & Facer‐Childs, E. R. (2024). Disruptions to sleep and circadian rhythms are associated with poorer athlete mental health in female, but not male, elite Australian Rules footballers. Journal of sleep research, e14186. https://doi.org/10.1111/jsr.14186

Image courtesy of interviewee. June 18, 2024

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The study aimed to investigate gender differences in sleep and mental health among competitive athletes during the COVID-19 pandemic. Researchers focused on the potential impact of resilient personality traits, termed cognitive fitness, on these differences. They collected data through questionnaires from athletes, primarily in Melbourne during lockdowns in May 2020, measuring mental health symptoms such as depression, anxiety, and stress, alongside sleep patterns.

Findings indicated that women athletes reported significantly higher levels of mental health issues compared to men, particularly under chronic stress. However, no notable gender differences were found in sleep quality after controlling for cognitive fitness. Women also exhibited lower self-control and higher intolerance of uncertainty. Cognitive fitness was linked to better mental health outcomes but not directly to sleep.

The study emphasized the importance of supporting women athletes during stressful periods and suggested that training cognitive traits like self-control could be beneficial. It highlighted a need for more comprehensive research into sleep, circadian rhythms, and their relationship to mental health, especially concerning female athletes, who are often at greater risk of mental health issues compared to their male counterparts.

Looking forward, the researchers suggested monitoring sleep and circadian health as vital indicators of mental wellness as women's sports evolve towards professional status. The study underscored the need for investment and research in women's sports to ensure the mental well-being of female athletes.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.105 --> 00:01.845
The aims of this study were

00:01.945 --> 00:04.245
to explore gender differences in the sleep

00:04.245 --> 00:06.245
and mental health of competitive athletes

00:06.505 --> 00:08.485
during the COVID-19 pandemic.

00:09.065 --> 00:11.685
Um, and we were specifically interested in wondering whether

00:11.705 --> 00:15.645
gender differences in resilient personality traits, which is

00:15.645 --> 00:17.125
what we class as cognitive fitness,

00:17.575 --> 00:18.845
might explain these gender

00:18.845 --> 00:20.645
differences in sleep and mental health.

00:21.225 --> 00:25.125
For this study, we administered questionnaires to athletes.

00:25.465 --> 00:27.285
Um, we advertised through social media,

00:27.425 --> 00:31.085
but also through various sports industry contacts that,

00:31.225 --> 00:33.805
the co-authors and other investigators had,

00:33.805 --> 00:34.925
to recruit these athletes.

00:35.745 --> 00:38.085
And our main outcomes asked about sleep

00:38.205 --> 00:40.885
and mental health symptoms over the past month.

00:41.545 --> 00:45.005
And this was during May in 2020, with most

00:45.005 --> 00:47.985
of the athletes coming from Melbourne, Australia,

00:47.985 --> 00:49.465
during which we were under lockdowns.

00:50.175 --> 00:52.635
Um, so for the mental health measure,

00:52.855 --> 00:56.275
it was a validated measure, assessing depression, anxiety,

00:56.655 --> 00:58.835
and stress, and to measure sleep.

00:58.935 --> 01:02.115
We asked brief one item questions about total sleep

01:02.475 --> 01:04.115
duration, their sleep timing,

01:04.655 --> 01:06.555
and their time taken to fall asleep.

01:07.685 --> 01:11.505
To measure cognitive fitness, we use trait-based measures,

01:11.505 --> 01:13.505
which asked about how athletes typically are,

01:14.045 --> 01:17.345
and they measured these measures measured self-control,

01:17.815 --> 01:20.585
intolerance of uncertainty and impulsivity.

01:20.885 --> 01:23.425
So all the athletes completed all these measured measures.

01:23.425 --> 01:24.505
And then we analyzed the data.

01:24.685 --> 01:27.825
We found that after controlling for their cognitive fitness,

01:28.035 --> 01:31.625
those resilient based, personality traits, we found

01:31.625 --> 01:34.425
that the women athletes reported higher symptoms

01:34.565 --> 01:36.225
of depression, anxiety,

01:36.605 --> 01:38.905
and stress relative to the men athletes

01:38.905 --> 01:41.225
during this time under chronic stress in the

01:41.225 --> 01:42.265
COVID-19 pandemic.

01:42.715 --> 01:44.345
After controlling for cognitive fitness,

01:44.345 --> 01:45.745
however, there were no,

01:45.745 --> 01:47.265
gender differences in their sleep.

01:48.555 --> 01:51.535
Um, women athletes also reported lower levels

01:51.595 --> 01:52.895
of self-control

01:53.395 --> 01:55.335
and higher levels of intolerance

01:55.335 --> 01:57.575
of uncertainty relative to men.

01:58.895 --> 02:00.675
So then we also found

02:00.675 --> 02:02.635
that cognitive fitness predicted mental health,

02:02.815 --> 02:05.235
but not sleep in a range of ways such

02:05.235 --> 02:07.195
that generally more optimal levels

02:07.215 --> 02:10.075
of cognitive fitness were associated with lower levels

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of self-reported poor mental health overall.

02:13.255 --> 02:14.995
We found that during this time,

02:15.085 --> 02:17.555
women athletes were reporting poor mental health.

02:17.775 --> 02:21.035
Um, so this is during the pandemic relative to men athletes.

02:21.655 --> 02:24.115
So while we believe that mental health should be a focus

02:24.175 --> 02:25.675
for all athletes all the time,

02:26.255 --> 02:28.275
our findings do highlight the importance

02:28.275 --> 02:31.075
of protecting women athletes, especially during times

02:31.575 --> 02:33.715
of sustained or longer term stress.

02:34.135 --> 02:36.795
And while the pandemic was one example for athletes,

02:36.795 --> 02:39.315
you could find other examples may include long-term

02:39.495 --> 02:41.195
injury or retirement.

02:41.535 --> 02:43.875
So once again, we found that we should be protecting

02:43.875 --> 02:45.475
or keeping an eye on women athletes

02:45.475 --> 02:47.755
during these particularly stressful times.

02:49.015 --> 02:51.675
Um, the associations we found between cognitive fitness

02:51.815 --> 02:54.035
and mental health also showed that coaches

02:54.175 --> 02:56.795
or sporting clubs could consider targeting

02:56.795 --> 02:58.915
or training traits such as self-control

02:58.915 --> 03:00.445
And the tolerance of uncertainty.

03:00.875 --> 03:02.165
With the hope that,

03:03.005 --> 03:05.565
training these traits could help protect athletes from poor

03:05.565 --> 03:08.805
mental health in the future, we do hope

03:08.805 --> 03:12.205
that future research should track the progress of,

03:12.605 --> 03:14.245
outcomes such as sleep, mental health during

03:14.965 --> 03:17.405
repeated intervals, during a chronic stressor.

03:17.665 --> 03:18.965
So not just at one time point.

03:19.505 --> 03:21.885
And if we can actually start to see some data as

03:21.885 --> 03:23.525
to whether testing,

03:23.765 --> 03:26.445
training cognitive fitness might improve mental health,

03:26.445 --> 03:27.845
that would be a logical future.

03:27.845 --> 03:31.065
Directions from our research, athletes have a range

03:31.065 --> 03:33.465
of specific risk factors to poor mental health.

03:34.115 --> 03:35.945
These can include stigma

03:36.125 --> 03:38.625
or hesitancy to seek support,

03:38.675 --> 03:40.545
sustaining injuries which restrict

03:40.545 --> 03:41.905
their capacity to compete.

03:42.205 --> 03:44.745
Um, very high training loads during the week

03:44.765 --> 03:47.105
and for a long period of time,

03:47.165 --> 03:49.785
having worries about whether they'll be selected to perform,

03:49.885 --> 03:51.505
to compete in the team on the weekend,

03:52.135 --> 03:56.585
worries about their contract length, media scrutiny,

03:57.165 --> 03:58.705
travel and time away from home,

03:59.045 --> 04:01.865
and also worries about how they'll perform on game day.

04:02.455 --> 04:06.025
However, we were interested in investigating deeper into the

04:06.025 --> 04:07.425
sleep and circadian timing

04:07.925 --> 04:09.425
as factors as to poor mental health.

04:10.185 --> 04:12.425
'cause research has found that sleep does relate

04:12.425 --> 04:13.665
to mental health in athletes.

04:14.245 --> 04:16.665
Um, and sleep also happens to correlate with a lot

04:16.665 --> 04:18.025
of risk factors to mental health.

04:18.455 --> 04:21.465
However, the role of the circadian system,

04:21.635 --> 04:24.705
which is our internal 24 hour biological rhythm,

04:25.075 --> 04:26.145
which governs sleep,

04:26.565 --> 04:29.385
has not been measured objectively using biology,

04:30.165 --> 04:31.625
in athletes.

04:31.805 --> 04:34.025
And that's what we were excited

04:34.025 --> 04:35.105
to measure for the first time.

04:36.205 --> 04:39.785
We also, the research also sites that female athletes,

04:40.285 --> 04:41.585
are at greater risk

04:41.585 --> 04:43.665
of poor mental health relative to male athletes.

04:43.815 --> 04:46.625
However, we do need more research to examine the factors

04:46.625 --> 04:49.625
that might contribute to the sex differences,

04:49.655 --> 04:51.945
with our research postulating that sleep

04:51.965 --> 04:55.105
and circadian timing might be one of those factors

04:55.665 --> 04:56.985
contributing to this sex difference.

04:58.205 --> 05:00.705
So then embark on our research with elite male

05:00.765 --> 05:02.905
and female Australian rules footballers.

05:03.325 --> 05:05.665
We also face the current state of players.

05:05.685 --> 05:08.585
It is at the moment that elite A FLW

05:09.385 --> 05:12.505
athletes are paid less, have less contract security,

05:12.995 --> 05:14.545
often train in the evening,

05:15.045 --> 05:17.985
and ultimately have fewer access to supports relative

05:17.985 --> 05:19.225
to the male a FL players.

05:19.685 --> 05:21.305
So while both groups are elite,

05:21.565 --> 05:24.465
the female athletes compete semi-professionally while the

05:24.465 --> 05:26.345
male athletes compete professionally.

05:26.845 --> 05:29.625
So we, although we're, we weren't able to investigate

05:29.625 --> 05:34.145
that specifically, we do use the paper to speculate as to

05:34.145 --> 05:36.385
how that might have contributed to findings as well.

05:36.965 --> 05:39.505
We studied these elite Australian rules footballers

05:39.565 --> 05:41.185
who compete in the A FL.

05:41.185 --> 05:43.265
Those are the male players and the A FLW,

05:43.265 --> 05:45.265
those are the female, athletes.

05:45.685 --> 05:47.745
Um, we studied them during their preseason.

05:47.965 --> 05:50.785
So this was before the actual competitive season started.

05:51.055 --> 05:53.865
They were in a training period, so they were going

05:53.865 --> 05:55.425
to the club three to four times a week

05:55.425 --> 05:56.825
to train. Um, there was

05:56.825 --> 05:57.925
No travel during that time.

05:59.025 --> 06:02.525
And we, at first, when we first recruited them,

06:02.625 --> 06:05.645
we gave them a mental health measure using a very athlete

06:05.965 --> 06:08.045
specific mental health self-report questionnaire.

06:08.855 --> 06:12.405
After that, we had them wearing a wrist-worn device

06:12.785 --> 06:16.285
to measure their sleep quality, the sleep duration,

06:16.745 --> 06:20.685
the sleep timing, and the sleep regularity over a two week

06:20.685 --> 06:23.325
period using this objective wrist warrant device,

06:24.385 --> 06:26.165
we then very excitingly measured

06:26.165 --> 06:29.565
that biological circadian timing by measuring the timing

06:29.745 --> 06:33.405
of melatonin onset, melatonin being a hormone important

06:33.505 --> 06:35.725
for regulating sleep and circadian rhythms.

06:36.145 --> 06:38.285
And we measure that using saliva samples,

06:38.375 --> 06:42.485
which we collected in a specific evening under dim light

06:42.495 --> 06:43.605
controlled conditions.

06:44.035 --> 06:46.685
That was a very exciting, protocol

06:46.705 --> 06:48.325
to conducted with these athletes.

06:49.215 --> 06:50.635
We also gave them a questionnaire

06:50.635 --> 06:54.035
to measure their subjective preference to be more

06:54.035 --> 06:56.155
of a morning person or an evening person.

06:57.745 --> 07:01.965
And while we are interested in the circadian timing such

07:01.965 --> 07:03.125
as being more late

07:03.145 --> 07:05.445
or early, we were also interested in the

07:05.515 --> 07:06.765
circadian alignment.

07:07.305 --> 07:10.365
And that refers to the synchrony between the timing

07:10.425 --> 07:12.885
of actual behavior versus the timing

07:12.945 --> 07:14.965
of the internal biological timing.

07:15.705 --> 07:18.925
And we can measure that using phase angle,

07:19.055 --> 07:22.645
which is just the difference in time between melatonin onset

07:22.985 --> 07:24.765
and actual sleep onset.

07:25.105 --> 07:27.725
And you find generally, you find that people go

07:27.725 --> 07:29.605
to sleep approximately two hours

07:29.615 --> 07:31.965
after the melatonin starts to onset.

07:32.185 --> 07:33.445
But we wanted to actually get

07:33.445 --> 07:35.165
what those figures were for athletes.

07:36.105 --> 07:37.965
So we compared the male

07:38.025 --> 07:40.085
and female athletes on all these measures,

07:40.385 --> 07:42.645
and importantly tested whether the relationship

07:42.645 --> 07:46.045
between sleep and mental health or circadian timing

07:46.185 --> 07:49.685
and alignment and mental health was different depending on

07:49.685 --> 07:52.085
whether it was a male or female athlete.

07:52.345 --> 07:53.965
We found that the male, the female

07:54.485 --> 07:56.045
athletes reported poorer mental health

07:56.525 --> 07:57.605
relative to the male athletes.

07:57.795 --> 08:01.005
They also had later circadian timing in terms

08:01.005 --> 08:04.285
of a later melatonin onset self-reporting being more

08:04.285 --> 08:06.965
of an evening type and actually sleeping later.

08:07.185 --> 08:09.205
So there was a female athletes that had later

08:09.835 --> 08:12.525
circadian timing, which is quite in contrast

08:12.525 --> 08:14.165
to the literature, which typically found

08:14.165 --> 08:16.685
that female athletes, that females earlier

08:17.735 --> 08:20.625
when we analyzed sex moderations, we found that sleep

08:20.625 --> 08:22.185
and circadian disruption was related

08:22.185 --> 08:24.505
to mental health only in the female athletes,

08:25.045 --> 08:26.305
but not the male athletes.

08:26.765 --> 08:30.185
So specifically when the female athletes had low sleep

08:30.185 --> 08:34.745
quality, low sleep regularity, and extreme morning

08:34.805 --> 08:36.825
or evening type or a,

08:37.085 --> 08:40.345
or a large circadian misalignment, that's either going

08:40.345 --> 08:42.825
to bed too early relative to a circadian rhythm

08:43.165 --> 08:45.985
or too late relative to rhythm.

08:46.565 --> 08:50.305
Um, only for female athletes were these factors, related

08:50.405 --> 08:51.585
to poor mental health.

08:51.765 --> 08:54.825
But this was not the case in male athletes, and that's

08:54.825 --> 08:55.825
What we found. Our foundings

08:55.825 --> 08:57.245
did reveal that sleep

08:57.265 --> 08:59.925
and circadian interventions could actually,

09:00.055 --> 09:03.005
could potentially be a key to improving mental health

09:03.025 --> 09:04.045
for female athletes.

09:04.465 --> 09:07.245
And our research group at Monash University actually hopes

09:07.245 --> 09:08.685
to pilot such a study in the future.

09:08.745 --> 09:13.485
So watch this space. Um, our findings also might speak

09:13.605 --> 09:17.285
to the semi-professional of the A FLW as it stands such

09:17.285 --> 09:19.685
as the lower pay, the contract insecurity,

09:20.135 --> 09:22.645
later training times, and lower access to support.

09:23.425 --> 09:25.925
Um, specifically we were able to investigate one

09:25.925 --> 09:28.405
of these factors more specifically being the,

09:28.745 --> 09:31.725
the most likely the timing of training such

09:31.725 --> 09:35.165
that when we found our female athletes were training later,

09:35.505 --> 09:37.965
that was likely driving why their

09:38.485 --> 09:41.485
internal biology was also later compared

09:41.485 --> 09:44.365
to the male athletes who trained more so in the morning.

09:44.995 --> 09:46.925
However, we do need further research

09:46.925 --> 09:50.005
that thoroughly investigate how things like pay

09:50.105 --> 09:51.245
or contract security

09:51.345 --> 09:54.125
or access to supports might also be contributing

09:54.425 --> 09:58.045
to these results as currently, we only know on a broad level

09:58.155 --> 10:01.525
that a lot of, A FLW athletes experience those things.

10:01.585 --> 10:04.045
We don't quite have the data to put those except

10:04.045 --> 10:05.525
for well-known, pay gap,

10:05.585 --> 10:07.765
but we didn't get that specifically for our athletes.

10:08.745 --> 10:11.565
So finally, our findings do strengthen the calls

10:11.665 --> 10:13.085
to invest more in women's sport

10:13.225 --> 10:16.165
and to continue to conduct more research in female athletes,

10:16.165 --> 10:17.525
which we believe is very important

10:17.545 --> 10:18.885
and needs to continue to be done.

10:19.775 --> 10:22.725
Sleep and circadian rhythms could be valuable indicators

10:23.025 --> 10:24.085
of mental health risk

10:24.305 --> 10:27.005
and these outcomes should be continued to be monitored

10:27.005 --> 10:29.365
and athletes, especially on the pathway

10:29.915 --> 10:33.285
towards full-time professionalism as current,

10:33.995 --> 10:36.485
well reports would indicate the A FLW is hoping

10:36.505 --> 10:38.765
to be professional by about 2026.

10:39.185 --> 10:42.325
And as they embark on that pathway, we should continue

10:42.325 --> 10:44.045
to monitor the sleep at cicada rhythms

10:44.045 --> 10:47.445
and then mental health to see if those start to change as

10:47.445 --> 10:49.565
that shift appears as well in that landscape.

"Examining Gender Differences in Sleep and Mental Health Among Competitive Athletes During the COVID-19 Pandemic"

00:00:00 - The aims of this study were to explore gender differences in the sleep and mental health of competitive athletes during the COVID-19 pandemic. and we were specifically interested in wondering whether gender differences in resilient personality traits, which is what we class as cognitive fitness, might explain these gender differences in sleep and mental health. For this study, we administered questionnaires to athletes. we advertised through social media, but also through various sports industry contacts that, the co-authors and other investigators had, to recruit these athletes. And our main outcomes asked about sleep and mental health symptoms over the past month.
00:00:45 - of the athletes coming from Melbourne, Australia, during which we were under lockdowns. so for the mental health measure, it was a validated measure, assessing depression, anxiety, and stress, and to measure sleep. We asked brief one item questions about total sleep duration, their sleep timing, and their time taken to fall asleep. To measure cognitive fitness, we use trait-based measures, which asked about how athletes typically are, and they measured these measures measured self-control, intolerance of uncertainty and impulsivity. So all the athletes completed all these measured measures.
00:01:24 - We found that after controlling for their cognitive fitness, those resilient based, personality traits, we found that the women athletes reported higher symptoms of depression, anxiety, and stress relative to the men athletes during this time under chronic stress in the COVID-19 pandemic. After controlling for cognitive fitness, however, there were no, gender differences in their sleep. women athletes also reported lower levels of self-control and higher levels of intolerance of uncertainty relative to men. So then we also found that cognitive fitness predicted mental health, but not sleep in a range of ways such
00:02:07 - of cognitive fitness were associated with lower levels of self-reported poor mental health overall. We found that during this time, women athletes were reporting poor mental health. so this is during the pandemic relative to men athletes. So while we believe that mental health should be a focus for all athletes all the time, our findings do highlight the importance of protecting women athletes, especially during times of sustained or longer term stress. And while the pandemic was one example for athletes, you could find other examples may include long-term injury or retirement. So once again, we found that we should be protecting or keeping an eye on women athletes
00:02:49 - the associations we found between cognitive fitness and mental health also showed that coaches or sporting clubs could consider targeting or training traits such as self-control And the tolerance of uncertainty. With the hope that, training these traits could help protect athletes from poor mental health in the future, we do hope that future research should track the progress of, outcomes such as sleep, mental health during repeated intervals, during a chronic stressor. So not just at one time point. And if we can actually start to see some data as to whether testing, training cognitive fitness might improve mental health,
00:03:27 - Directions from our research, athletes have a range of specific risk factors to poor mental health. These can include stigma or hesitancy to seek support, sustaining injuries which restrict their capacity to compete. very high training loads during the week and for a long period of time, having worries about whether they'll be selected to perform, to compete in the team on the weekend, worries about their contract length, media scrutiny, travel and time away from home, and also worries about how they'll perform on game day. However, we were interested in investigating deeper into the sleep and circadian timing

"Exploring the Link Between Circadian Rhythms, Sleep Quality, and Mental Health in Elite Male and Female Athletes"

00:04:10 - 'cause research has found that sleep does relate to mental health in athletes. and sleep also happens to correlate with a lot of risk factors to mental health. However, the role of the circadian system, which is our internal 24 hour biological rhythm, which governs sleep, has not been measured objectively using biology, in athletes. And that's what we were excited to measure for the first time. We also, the research also sites that female athletes, are at greater risk of poor mental health relative to male athletes. However, we do need more research to examine the factors that might contribute to the sex differences,
00:04:51 - and circadian timing might be one of those factors contributing to this sex difference. So then embark on our research with elite male and female Australian rules footballers. We also face the current state of players. It is at the moment that elite A FLW athletes are paid less, have less contract security, often train in the evening, and ultimately have fewer access to supports relative to the male a FL players. So while both groups are elite, the female athletes compete semi-professionally while the male athletes compete professionally. So we, although we're, we weren't able to investigate that specifically, we do use the paper to speculate as to
00:05:36 - We studied these elite Australian rules footballers who compete in the A FL. Those are the male players and the A FLW, those are the female, athletes. we studied them during their preseason. So this was before the actual competitive season started. They were in a training period, so they were going to the club three to four times a week to train. there was No travel during that time. And we, at first, when we first recruited them, we gave them a mental health measure using a very athlete specific mental health self-report questionnaire. After that, we had them wearing a wrist-worn device to measure their sleep quality, the sleep duration,
00:06:20 - period using this objective wrist warrant device, we then very excitingly measured that biological circadian timing by measuring the timing of melatonin onset, melatonin being a hormone important for regulating sleep and circadian rhythms. And we measure that using saliva samples, which we collected in a specific evening under dim light controlled conditions. That was a very exciting, protocol to conducted with these athletes. We also gave them a questionnaire to measure their subjective preference to be more of a morning person or an evening person.
00:07:01 - as being more late or early, we were also interested in the circadian alignment. And that refers to the synchrony between the timing of actual behavior versus the timing of the internal biological timing. And we can measure that using phase angle, which is just the difference in time between melatonin onset and actual sleep onset. And you find generally, you find that people go to sleep approximately two hours after the melatonin starts to onset. But we wanted to actually get what those figures were for athletes. So we compared the male
00:07:40 - and importantly tested whether the relationship between sleep and mental health or circadian timing and alignment and mental health was different depending on whether it was a male or female athlete. We found that the male, the female athletes reported poorer mental health relative to the male athletes. They also had later circadian timing in terms of a later melatonin onset self-reporting being more of an evening type and actually sleeping later. So there was a female athletes that had later circadian timing, which is quite in contrast to the literature, which typically found that female athletes, that females earlier when we analyzed sex moderations, we found that sleep

"Exploring Sleep, Circadian Rhythms, and Mental Health in Female Athletes: Implications for Support and Future Research"

00:08:22 - to mental health only in the female athletes, but not the male athletes. So specifically when the female athletes had low sleep quality, low sleep regularity, and extreme morning or evening type or a, or a large circadian misalignment, that's either going to bed too early relative to a circadian rhythm or too late relative to rhythm. only for female athletes were these factors, related to poor mental health. But this was not the case in male athletes, and that's What we found. Our foundings did reveal that sleep and circadian interventions could actually, could potentially be a key to improving mental health
00:09:04 - And our research group at Monash University actually hopes to pilot such a study in the future. So watch this space. our findings also might speak to the semi-professional of the A FLW as it stands such as the lower pay, the contract insecurity, later training times, and lower access to support. specifically we were able to investigate one of these factors more specifically being the, the most likely the timing of training such that when we found our female athletes were training later, that was likely driving why their internal biology was also later compared to the male athletes who trained more so in the morning.
00:09:46 - that thoroughly investigate how things like pay or contract security or access to supports might also be contributing to these results as currently, we only know on a broad level that a lot of, A FLW athletes experience those things. We don't quite have the data to put those except for well-known, pay gap, but we didn't get that specifically for our athletes. So finally, our findings do strengthen the calls to invest more in women's sport and to continue to conduct more research in female athletes, which we believe is very important and needs to continue to be done. Sleep and circadian rhythms could be valuable indicators of mental health risk and these outcomes should be continued to be monitored
00:10:47 - towards full-time professionalism as current, well reports would indicate the A FLW is hoping to be professional by about 2026. And as they embark on that pathway, we should continue to monitor the sleep at cicada rhythms and then mental health to see if those start to change as that shift appears as well in that landscape.

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