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Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes. Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage). Results: 15% of the sample spent < 1 month, 27% spent 1-2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6-1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3-2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI (p < 0.001; MD: 1.25; 95% CI: 0.87-1.63) and ISI (p < 0.001; MD: 2.5; 95% CI: 1.72-3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month (p < 0.001; MD: 1.28; 95% CI: 0.26-2.3). Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athlet

Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes. Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage). Results: 15% of the sample spent < 1 month, 27% spent 1-2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6-1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3-2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI (p < 0.001; MD: 1.25; 95% CI: 0.87-1.63) and ISI (p < 0.001; MD: 2.5; 95% CI: 1.72-3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month (p < 0.001; MD: 1.28; 95% CI: 0.26-2.3). Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athlet

OBJECTIVE: In a convenience sample of athletes, we conducted a survey of COVID-19-mediated lockdown (termed 'lockdown' from this point forward) effects on: (i) circadian rhythms; (ii) sleep; (iii) eating; and (iv) training behaviors. METHODS: In total, 3911 athletes [mean age: 25.1 (range 18-61) years, 1764 female (45%), 2427 team-sport (63%) and 1442 elite (37%) athletes] from 49 countries completed a multilingual cross-sectional survey including the Pittsburgh Sleep Quality Index and Insomnia Severity Index questionnaires, alongside bespoke questions about napping, training, and nutrition behaviors. RESULTS: Pittsburgh Sleep Quality Index (4.3 ± 2.4 to 5.8 ± 3.1) and Insomnia Severity Index (4.8 ± 4.7 to 7.2 ± 6.4) scores increased from pre- to during lockdown (p < 0.001). Pittsburgh Sleep Quality Index was predominantly influenced by sleep-onset latency (p < 0.001; + 29.8%), sleep efficiency (p < 0.001; - 21.1%), and total sleep time (p < 0.001; - 20.1%), whilst Insomnia Severity Index was affected by sleep-onset latency (p < 0.001; + 21.4%), bedtime (p < 0.001; + 9.4%), and eating after midnight (p < 0.001; + 9.1%). During lockdown, athletes reported fewer training sessions per week (- 29.1%; d = 0.99). Athletes went to bed (+ 75 min; 5.4%; d = 1.14) and woke up (+ 150 min; 34.5%; d = 1.71) later during lockdown with an increased total sleep time (+ 48 min; 10.6%; d = 0.83). Lockdown-mediated circadian disruption had more deleterious effects on the sleep quality of individual-sport athletes compared with team-sport athletes (p < 0.001; d = 0.41), elite compared with non-elite athletes (p = 0.028; d = 0.44) and older compared with younger (p = 0.008; d = 0.46) athletes. CONCLUSIONS: These lockdown-induced behavioral changes reduced sleep quality and increased insomnia in athletes. Data-driven and evidence-based recommendations to counter these include, but are not limited to: (i) early outdoor training; (ii) regular meal scheduling (whilst avoiding meals prior to

Crushing consists of two types of fracture. One is the compressive fracture, which takes place near the loading points, fragments from which follow the Rosin-Rammlar's equation. The other is the tensile fracture, whose fragments follow the Gaudin-Meloy's equation. An equation is proposed for the cumulative fraction of the initial volume, corresponding to fragments of dimension up to x, with four coefficients, alpha, beta, gamma and delta. Test results indicate gamma has no relation with feed size; delta with work input; and that alpha and beta are directly proportional to the work energy input to produce the first breaking in the sphere specimen with the dimension as the feed. The efficiency of fragmentation is independent of the kind of rock, the feed size and energy., Crushing consists of two types of fracture. One is the compressive fracture, which takes place near the loading points, fragments from which follow the Rosin-Rammlar's equation. The other is the tensile fracture, whose fragments follow the Gaudin-Meloy's equation. An equation is proposed for the cumulative fraction of the initial volume, corresponding to fragments of dimension up to x, with four coefficients, alpha, beta, gamma and delta. Test results indicate gamma has no relation with feed size; delta with work input; and that alpha and beta are directly proportional to the work energy input to produce the first breaking in the sphere specimen with the dimension as the feed. The efficiency of fragmentation is independent of the kind of rock, the feed size and energy.