| Literature DB >> 35845770 |
Mohamed Romdhani1,2, Achraf Ammar3,4, Khaled Trabelsi1,5, Hamdi Chtourou1,2, Jacopo A Vitale6, Liwa Masmoudi1, Mathieu Nédélec7, Dale E Rae8, Ramzi A Al Horani9, Helmi Ben Saad10, Nicola Bragazzi11,12, Gürhan Dönmez13, Ismail Dergaa14, Tarak Driss15, Abdulaziz Farooq16, Omar Hammouda15,17, Nesrine Harroum18, Bahar Hassanmirzaei19,20, Karim Khalladi16, Syrine Khemila2,21, Leonardo Jose Mataruna-Dos-Santos22,23, Imen Moussa-Chamari24, Iñigo Mujika25,26, Hussein Muñoz Helú27, Amin Norouzi Fashkhami28, Laisa Liane Paineiras-Domingos29,30, Mehrshad Rahbari Khaneghah28, Yoshitomo Saita31, Maher Souabni15, Nizar Souissi2,21, Jad Adrian Washif32, Johanna Weber33,34, Piotr Zmijewski35, Lee Taylor36,37, Sergio Garbarino38,39, Karim Chamari16.
Abstract
Objective: Disrupted sleep and training behaviors in athletes have been reported during the COVID-19 pandemic. We aimed at investigating the combined effects of Ramadan observance and COVID-19 related lockdown in Muslim athletes.Entities:
Keywords: confinement; pandemic; religious fasting; sleep-wake pattern; training load
Year: 2022 PMID: 35845770 PMCID: PMC9283087 DOI: 10.3389/fnut.2022.925092
Source DB: PubMed Journal: Front Nutr ISSN: 2296-861X
Description of the different sections of the survey.*
| Section | Description | Items |
| Section 1 | Explanation of the study | Invited volunteers to confirm eligibility |
| Section 2 | Demographic information | Sex |
| Section 3 | Training questions | Preferred TOD to train |
| Section 4 | Pittsburgh sleep quality index (PSQI) | Bedtime |
| Section 5 | Napping questions | Nap timing |
| Section 6 | Nutrition- and health-related questions | Body mass |
| Section 7 | Ramadan questions | Sleep quality and training volume during Ramadan and lockdown compared to lockdown outside of Ramadan |
*The full survey questions are provided as
Demographic characteristics of the overall sample, Muslim and non-Muslim athletes.
| Overall sample ( | Muslim athletes ( | Non-Muslim athletes ( | ||
| Sex | Male | 54% | 61% | 48% |
| Female | 45% | 38% | 51% | |
| Not declared | 1% | 1% | 1% | |
| Age | Mean | 25.1 | 25.1 | 25.1 |
| Range | 18–61 | 18–52 | 18–61 | |
| ≤25 | 67% | 58% | 74% | |
| >25 | 33% | 42% | 26% | |
| Sport discipline | N° disciplines | 56 | 44 | 56 |
| Team | 63% | 51% | 72% | |
| Individual | 37% | 49% | 28% | |
| Level of competition | Elite | 37% | 41% | 34% |
| Non-elite | 63% | 59% | 66% | |
| Geographic location | N° countries | 49 | 44 | 46 |
| Asia | 57% | 59% | 55% | |
| Europe | 14% | 16% | 12% | |
| America | 18% | 1% | 31% | |
| Africa | 11% | 24% | 2% | |
| Australia | 0.1% | 0.1% | 0.1% |
FIGURE 1Difference between Muslim (n = 1,681) and non-Muslim (n = 2,230) athletes during- compared to pre-lockdown in (A) PSQI (the Pittsburgh Sleep Quality Index) and (B) ISI (Insomnia Severity Index). * means significant within group effect of the lockdown at p < 0.001. # means difference between groups during-lockdown at p < 0.001. Significance is assessed by mixed model ANOVA and the Bonferroni post-hoc test.
Changes in sleep, training, and eating behaviors from pre- to during-lockdown in Muslim athletes.
| Variable | Lockdown | Z | Δ % (SD) |
| MD | 95% CI | ||
|
| ||||||||
| Pre | During | |||||||
| PSQI score (a.u.) | 4.3 ± 2.4 | 6.2 ± 3.1 | 22.1 | 83.4 (151.3) | 0.001 | 0.68 | 1.9 | 1.7–2.1 |
| ISI score (a.u.) | 5.5 ± 4.7 | 8.6 ± 6.6 | 20.9 | 149 (338.4) | 0.001 | 0.54 | 3.1 | 2.9–3.4 |
| Preferred training TOD (hh: mm) | 14:39 ± 4:37 | 15:25 ± 4:33 | 7.6 | 11.2 (40.7) | 0.001 | 0.19 | 0:45 | 0:31–0:59 |
| Training sessions (N°⋅week–1) | 4.9 ± 2.4 | 3.1 ± 2.4 | 22.9 | –29.5 (50.1) | 0.001 | 0.75 | –1.8 | –1.9 to –1.7 |
| Bedtime (hh: mm) | 23:12 ± 1:58 | 01:08 ± 1:12 | 30.4 | 7.7 (7.5) | 0.001 | 1.16 | 1:56 | 1:45–2:07 |
| Wake time (hh: mm) | 7:45 ± 1:47 | 10:55 ± 2:35 | 31.9 | 47.7 (41.9) | 0.001 | 1.48 | 3:10 | 3:04–1:16 |
| Mid-sleep time (hh: mm) | 3:30 ± 1:15 | 5:59 ± 2:03 | 33 | 88.4 (96.7) | 0.001 | 1.51 | 2:29 | 2:19–2:38 |
| Total sleep time (min) | 458 ± 71 | 515 ± 90 | 22.1 | 14.2 (21.9) | 0.001 | 0.71 | 57 | 53–62 |
| Time in bed (min) | 517 ± 100 | 617 ± 120 | 27.6 | 21.5 (25.7) | 0.001 | 0.91 | 100 | 93–105 |
| Sleep efficiency (%) | 90.1 ± 11.9 | 85.7 ± 13.8 | 16.9 | –5.5 (8.5) | 0.001 | 0.34 | –4.4 | –6 to –2.7 |
| Sleep onset latency (min) | 20.6 ± 16.4 | 36.5 ± 30.1 | 24.7 | 102 (131.2) | 0.001 | 0.65 | 15.9 | 14.6–17.2 |
| Nap frequency (N°⋅week–1) | 1.7 ± 1.6 | 2.1 ± 1.8 | 6.9 | 21.1 (9.5) | 0.001 | 0.24 | 0.36 | 0.26–0.46 |
| Nap duration (min) | 14.1 ± 20.9 | 21.2 ± 26.2 | 9.8 | 50.3 (25.4) | 0.001 | 0.29 | 7.1 | 5.7–8.5 |
| Nap timing (hh: mm) | 14:26 ± 1:41 | 14:44 ± 1:43 | 5.4 | 11.4 (37.5) | 0.001 | 0.14 | 0:18 | 0:13–0:23 |
| Eat after midnight (a.u.) | 0.6 ± 1.1 | 1.2 ± 1.3 | 19.3 | 108 (213) | 0.001 | 0.49 | 0.61 | 0.46–0.76 |
| Body mass (kg) | 70.4 ± 14.4 | 72 ± 15.4 | 14.5 | 2.2 (5.7) | 0.001 | 0.11 | 1.56 | 1.36–1.75 |
| Meals (N°⋅day–1) | 2.9 ± 1.2 | 3.2 ± 1.5 | 6.7 | 24.3 (78.1) | 0.001 | 0.22 | 0.23 | 0.15–0.31 |
| Caffeinated beverages (N°⋅day–1) | 1.7 ± 1.3 | 1.9 ± 1.5 | 8.9 | 37.4 (100.1) | 0.001 | 0.14 | 0.27 | 0.21–0.34 |
Wilcoxon signed rank test was used to compare variables measured pre- and during-lockdown. a.u., arbitrary unit; d, Cohen’s effect size; h, hour; ISI, Insomnia Severity Index; kg, Kilogram; MD, mean difference; min, minutes; N°, number; p, probability; PSQI, Pittsburgh Sleep Quality Index; SD, standard deviation; TOD, time-of-day; 95% CI, 95% Confidence interval;
FIGURE 2The bold arrows present the direct effect (multiple regression) and the light arrows present the indirect effects (mediation) of different independent variables on the dependent variable. The increase in PSQI (Pittsburgh Sleep Quality Index) score from pre- to during-lockdown (i.e., Δ%) was directly associated to the longer SOL (sleep onset latency) and daytime nap duration, later bedtime and reversely associated to TST (total sleep time). The magnitude of the association between independent and dependent variable is expressed as the semi-partial correlation coefficient squared (i.e., in percentages; the unique contribution of each independent variable within the model). The association between later bedtime and higher PSQI scores during-lockdown was mediated by the increased late night meals and longer SOL. Also, the increased late night meals and longer daytime nap duration mediated the relationship between longer SOL and higher PSQI scores. The magnitude of the mediation effect is expressed as percentages according to the formula (indirect effect/total effect × 100).
FIGURE 3The bold arrows present the direct effect (multiple regression) and the light arrows present the indirect effects (mediation) of different independent variables on the dependent variable. The increase in ISI (Insomnia Severity Index) score from pre- to during-lockdown (i.e., Δ%) was directly associated to the longer SOL (sleep onset latency) and daytime nap duration, and later bedtime and preferred time of day to train. The magnitude of the association between independent and dependent variable is expressed as the semi-partial correlation coefficient squared (i.e., in percentages; the unique contribution of each independent variable within the model). The association between later bedtime and higher ISI scores during-lockdown was mediated by the later preferred time of day to train and daytime nap timing, lower training frequency and longer SOL. Also, the increased late night meals and lower training frequency mediated the relationship between longer SOL and higher ISI scores. The magnitude of the mediation effect is expressed as percentages according to the formula (indirect effect/total effect × 100).
FIGURE 4Muslim athletes were asked to subjectively rate their sleep quality (Much worse, Worse, Neutral, Better, and Much better) and their training volume (Much lower, Lower, Neutral, Higher, and Much higher) during-lockdown and Ramadan compared to lockdown outside of Ramadan.