Professional team sport athletes regularly undertake long-haul transmeridian travel for competition, training camps, and/or preseason tours. Together with financial considerations, these demanding schedules dictate travel itineraries and consequently the proximity of arrival before the commencement of competition. Although optimal readiness to perform is required in the days after arrival, limited evidence exists describing the effect of an episode of long-haul travel on team sport–specific physical performance (1,22). Consequently, inferences from an understanding of chronobiology obtained through stringently controlled laboratory studies are used to inform field-based travel strategies. For example, on the basis of the premise that it is easier to delay than advance circadian rhythms, in theory, jet lag symptoms should be worse when traveling east compared with west (34). Although this has implications for athlete preparedness after travel, there is currently no information on the posttravel timeline of recovery of physical performance for team sports or a comparison of this timeline between east and west travel.
The interrelated effects of both jet lag and travel fatigue are present after long-haul transmeridian travel, with increased daytime fatigue, reduced motivation, and sleep disruption commonly observed (2,14,37). Upon arrival, endogenous circadian rhythms in body temperature and melatonin are misaligned with external cues at the destination (8,23,25). Until these rhythms align with the new time zone, sleep, fatigue, motivation, and ensuing physical performance may be negatively affected (22,25). Moreover, a change in the rhythm of a morning nadir and late afternoon peak in physical performance may occur, with both physical and mental performance capacities reduced outside of this circadian peak “window” (22). In addition, conditions encountered during travel, such as the uncomfortable seating arrangements, noise levels, and timing of stopovers, may also disrupt sleep and exacerbate mood states (6,11,14). Although these demands may contribute to performance reductions in combination with circadian misalignment, data on the recovery timeline of and the time of day influence on sports-specific performance after an acute episode of long-haul travel remain limited.
Changes in the grip strength have consistently been reported after a long-haul transmeridian travel in both athletic (23,25) and nonathletic (8) populations. Specifically, a change in the performance rhythm of a morning nadir and a late afternoon peak (local time) occurs and grip strength is reduced outside of its circadian peak window (8,23,25). Reduced countermovement jump (CMJ) performance has also been observed after 24-h travel east across eight time zones in national team skeleton athletes (5). However, although the use of grip strength and jump tests as performance measures may assist with the logistics of testing athletes around travel and competition, they have limited ecological relevance to physical performance in team sports (22). As a consequence, most current travel advice is based on stringently controlled laboratory-based research, which to date has had poor translation to high-performance athletes in the field (1,33). For example, the rate of adaptation to a new time zone is estimated as half a day per hour of the time difference west and one day per hour of the time difference east (9,26). Although these rates of adaptation have implications for team sport physical performance, the timeline of performance recovery after travel in either direction is yet to be elucidated (9,22,26).
As a result of this limited understanding of the performance recovery timeline after long-haul transmeridian travel, many practitioners currently rely on personal experience or anecdotal evidence for travel schedules and training prescription after travel. Thus, the aim of the present study was to compare the timeline of recovery of sleep, subjective jet lag, fatigue, and physical performance measures related to team sports after east and west long-haul transmeridian travel. It was hypothesized that physical performance, sleep, jet lag, and fatigue would be worse after travel, and that east travel would cause greater disruption to these responses compared with west travel.
Thirty students from a local university (Sydney, Australia) were screened through a general health questionnaire and an interview with a member of the research team for inclusion in the study. Eleven were excluded on the basis of the following criteria: female, current smoker, sleep disorder, sleep medication use or illness, extreme morning or evening diurnal preference, insufficient fitness level (unable to obtain level 16 on the Yo-Yo intermittent recovery level 1 [YYIR1] test), and shiftwork or transmeridian travel across more than two time zones undertaken in the month before data collection. Of the 19 available participants, 10 healthy, physically trained men were recruited to participate (mean ± SD: age, 20.6 ± 2.7 yr; height, 179.1 ± 5.4 cm; and body mass, 77.05 ± 7.83 kg). All participants had recent training history in a range of athletic events, including running, football, rugby, and soccer, and were involved in some form of physical (aerobic and strength) training at least 2–3 times per week. Before the commencement of the study, participants were informed of any associated risks and provided verbal and written informed consent. The study was approved by the Anti-Doping Lab Qatar Institutional Ethics Review Board (IRB No. EXT2014000003).
Participants completed a minimum of two familiarization sessions with all experimental measures and procedures. Performance data were collected at 0900 h and 1700 h local time (deemed ecologically relevant to training and competition) on four consecutive days in the 2 wk before outbound travel as a baseline (BASE), and the first 4 d after long-haul transmeridian travel from Australia to Qatar (WEST; Fig. 1). After a 4-d wash-out, data were collected at the same time of day, 2 d before (PRE-EAST) and on the first 4 d after return travel from Qatar to Australia (EAST). In addition, sleep was measured throughout the aforementioned data collection periods and perceptual data were collected immediately before all performance testing sessions. Participants abstained from caffeine, alcohol, and additional strenuous activity for 24 h before and during each data collection period. Considering the outside daytime temperatures in Qatar (~30°C–35°C) at the time of data collection (September/October) and the potential influence of heat acclimation on performance, participants' heat exposure was minimized to less than 1-h incidental passive heat exposure per day. Food and fluid intake was documented throughout in a food diary, with participants instructed to replicate their intake during BASE as closely as possible during WEST and EAST. Participants were provided with a standardized 1.5–2.0 g·kg−1 body mass of carbohydrate, including 600 mL of fluid (Gatorade™; PepsiCo, Chicago, IL) immediately after all performance testing sessions. Before all performance testing, urine specific gravity (USG) was assessed (Digital Refractometer, Atago, WA) to determine hydration status from a midstream urine sample.
The departure and arrival times for WEST (outbound travel) were 1930 h Australian Eastern Standard Time (AEST) and 0855 h the next day Arabia Standard Time (AST [AEST −8 h]). In total, there were three flights, with 21 h of travel (17 h of total flight duration, 4 h of total transit time) west across eight time zones:
- Sydney, Australia (1930 h local time) to Melbourne, Australia (2105 h)
- Melbourne, Australia (2240 h) to Abu Dhabi, United Arab Emirates (0645 h + 1 d)
- Abu Dhabi, United Arab Emirates (0855 h) to Doha, Qatar (0855 h)
The departure and arrival times for EAST (return travel) were 1825 h AST and 2220 h the next day AEST (AST +8 h). In total, there were three flights, with 21 h of travel (17 h of total flight duration, 4 h of total transit time) east across eight time zones:
- Doha, Qatar (1825 h local time) to Abu Dhabi, United Arab Emirates (2020 h)
- Abu Dhabi, United Arab Emirates (2220 h) to Perth, Australia (1350 h + 1 d)
- Perth, Australia (1600 h) to Sydney, Australia (2220 h)
Participants traveled in economy class for all flights and were given no instructions by the research team regarding behaviors during travel (e.g., when to eat and sleep, how much fluid to consume, and how often they should walk around the cabin). To match as closely as possible the duration (h) between arrival and initial testing in EAST and WEST, because of the differences in local arrival times, none of the measures outlined below were collected on day 1 at 0900 h in EAST.
All performance testing sessions in both locations were performed indoors on a basketball court in a temperate, air-conditioned environment (~20°C–25°C), with the same equipment and research team. A warm-up consisting of 5 min of standardized submaximal running followed by 10 min of general whole-body movements was completed at the beginning of all performance testing sessions (32). Participants performed a CMJ test (32) at both 0900 and 1700 h, along with a 20-m sprint test and the YYIR1 test (20) at 1700 h only. These tests are commonly used to assess the physical performance levels of team sport athletes, and participants were blinded from their results in all performance tests until the end of the study.
Jump height was measured using a linear position transducer (GymAware; Kinetic Performance Technologies, Canberra, ACT, Australia) sampling at 50 Hz, which was placed between the participant's feet on the floor and attached to a belt secured around the waist. The linear transducer transmitted displacement-time data to a hand-held unit, which was subsequently analyzed using commercially available software (GymAware; Kinetic Performance Technologies). Participants performed two sets of three maximal CMJ values using a self-selected depth, with a minimum of 3-min recovery between each set. Peak power and force and jump height were determined for each jump, and after the removal of outliers (>3 SD values away from the mean), an average of the six jumps was calculated and used for analyses.
Three maximal 20-m sprints were performed with a minimum of 3-min recovery between each. Splits were measured at 5 and 20 m using a single-beam infrared timing gate system (Microgate, Bolanzo, Italy). The fastest 5- and 20-m sprint times were included in ensuing analyses. YYIR1 performance was determined by total distance covered at the point of volitional exhaustion, which has been identified as a valid and reliable measure of team sport physical performance capacity (20). A modified version of the YYIR1 was used with feedback regarding the level removed from the test audio mp3. Given the removal of audio feedback, a stopwatch was used to record the time elapsed at volitional exhaustion for each participant, which was later converted to total distance covered.
Sleep patterns were monitored using self-report diaries and wrist activity monitors (Actiwatch-64; Philips Respironics, Bend, OR). According to previously described methods, data from the sleep diaries and activity monitors were used to determine when participants were awake and asleep (29). All time records were scored as wake unless (i) the sleep diary indicated that the participant was lying down attempting to sleep and (ii) the activity counts from the monitor were sufficiently low to indicate that the participant was immobile (i.e., where the weighted activity count for an epoch fell below the defined threshold). In this study, sensitivity was set at medium, which corresponds to a threshold activity count of 40 (35). When these two conditions were satisfied simultaneously, time was scored as sleep. This scoring process was conducted using a Philips Respironics' Actiwatch algorithm, which has been used to quantify sleep/wake behavior in airline pilots and long-haul truck drivers (7,20). The following variables were derived from the sleep diary and activity monitor data;
- Time in bed (h:min): the period between going to bed and getting up
- Sleep onset (hh:mm): the time at which a participant first fell asleep after going to bed
- Sleep offset (hh:mm): the time at which a participant last woke before getting up
- Sleep duration (h:min): the amount of time spent in bed asleep
- Sleep efficiency (%): sleep duration expressed as a percentage of time in bed
- Daytime nap duration (min): the amount of time spent in bed asleep during a daytime nap
- Cumulative sleep duration (h:min): the sum of the sleep obtained at night and any sleep obtained the following day during a daytime nap(s)
NB: To isolate the effect of long-haul travel on nighttime sleep, participants were instructed to avoid daytime napping throughout the study. Analysis of the sleep diaries and activity monitors indicated, however, that some participants were unable to avoid daytime napping.
Unless stated otherwise, all perceptual measures were entered into a Microsoft Excel spreadsheet on a laptop computer immediately before the physical performance testing. Each participant sat at a separate desk, had no access to previous responses, used an individual laptop, and wore noise canceling headphones to minimize external distractions and peer influence on their responses (4).
The Liverpool John Moore's University jet lag questionnaire was used to assess participants' subjective ratings of jet lag (37). The specific times of assessment for each subscale were as follows: 1) jet lag (one question), 0900 and 1700 h; 2) sleep (five questions), 0900 h only; 3) fatigue (one question), 0900 and 1700 h; 4) diet (three questions), 1700 h only; 5) mental performance and mood (three questions), 1700 h only; and 6) bowel movement (two questions), 1700 h only. Following a method previously outlined (33), data with a “negative outcome” were given a positive score and data with a “positive outcome” were given a negative score, before being pooled for summation into five categories: (a) jet lag, (b) sleep, (c) function (c.1. fatigue + c.2. mental performance and mood), (d) diet, and (e) bowel movement. A greater overall value indicates worse symptoms (i.e., a positive number indicates a worse than “normal” response and a negative number indicates a better than “normal” response). At both 0900 and 1700 h, participants' general level of motivation was assessed on a Likert scale from 0 (“none”) to 4 (“very motivated”) in 0.5-point increments. Lastly, approximately 30 min after the completion of the physical performance tests (1700 h only), a session rating of perceived exertion (sRPE) (10) and physical feeling (18) were obtained from participants.
Unless specified, data are presented as mean ± SD. Normality of the observed data was assessed using quantile–quantile (Q–Q) plots and was deemed plausible in all instances, with data presented as mean ± SD. Initially, comparisons between BASE day 1 and PRE-EAST day 1 were made for all variables using linear-mixed models and standardized ES (Cohen's d) analysis. Because no significant (P < 0.05) differences were observed and all ES values (Cohen's d) were small (d < 0.40), raw change in all performance variables on each of the 4 d after WEST and EAST were calculated from the corresponding day at BASE (i.e., day 1 EAST was compared with day 1 BASE). As such, PRE-EAST measures were used as an internal control to ensure no training or fatigue effect had occurred before return travel. Raw values on each of the 4 d at BASE, WEST, and EAST were used for all sleep and perceptual variables. Differences between condition (travel direction) and interactions between condition and time (day and time of day) for the raw change in performance variables and raw values for sleep and perceptual variables were analyzed using linear-mixed models. This type of analysis is preferred because it (i) allows for missing data, (ii) can accurately model different covariate structures for repeated-measures data, and (iii) can model between-subject variability (36). The most appropriate model for each variable was chosen using the smallest Akaike information criterion in accordance with the principal of parsimony. Where significance was obtained (P < 0.05), least significant difference post hoc tests were used. All statistical analyses were performed using SPSS (Version 21; IBM, Armonk, NY). Furthermore, standardized ES analysis was used to interpret the magnitude of differences between conditions and over time. Because of the substantial amount of statistical analyses performed, only large ES values (d > 0.90) are reported.
To aid clarity, results are structured within each ensuing paragraph as follows: (a) differences between travel direction (BASE, WEST, and EAST), (b) interactions between travel direction and day (1–4), and (c) interactions between time of day (0900 and 1700 h) with travel direction and day. Of note, 0900 and 1700 h local time would have corresponded to approximately 1700 and 0100 h body clock time on day 1 after WEST and 0100 and 0900 h body clock time after EAST (Fig. 1).
Results for CMJ performance variables are presented in Table 1. A significant main effect of travel direction was observed for peak force (F1,84 = 3.94, P = 0.04), with a greater mean reduction across the 4 d in EAST compared with WEST (P = 0.03). Furthermore, to be outlined in the following section, significant interactions between travel direction, day, and time of day were evident for both peak power (F5,86 = 4.59, P = 0.001) and peak force (F5,83 = 4.70, P = 0.001).
AM testing (0900 h local time, ~1700 h WEST, ~0100 h EAST)
A greater reduction in peak power was detected in WEST compared with EAST on day 2 (P > 0.05, d > 0.90).
Compared with day 1, a significantly greater reduction in peak force was observed on days 2 and 4 in WEST (P < 0.05).
PM testing (1700 h local time, ~0100 h WEST, ~0900 h EAST)
Although no significant differences were detected between travel directions, large ES indicated that the increase in peak power and peak force on day 1 in WEST was different from the reduction observed in EAST (P > 0.05, d > 0.90).
The increase in peak power on day 4 was significantly different from the reduction on days 2 and 3 in WEST and EAST (P < 0.05). The increase in peak force on day 1 was significantly different from the reduction on days 2 and 3 in WEST (P < 0.05), and the reduction in peak force on day 3 was significantly different from all other days in WEST and days 1 and 4 in EAST (P < 0.05).
On day 3, the reduction in peak force at 1700 h was significantly greater than the reduction at 0900 h in WEST (P < 0.05). On day 4, the increase in peak power at 1700 h was significantly different from the reduction at 0900 h in both WEST and EAST, and the increase in peak force at 1700 h was significantly different from the reduction at 0900 h in WEST (P < 0.05).
Twenty-meter sprint and YYIR1 results are presented in Figure 2. A significant interaction between travel direction and day was observed for 20-m sprint (F6,49 = 3.46, P = 0.006) and YYIR1 (F6,39 = 5.96, P < 0.001) performance, respectively.
PM testing (1700 h local time, ~0100 h WEST, ~0900 h EAST)
A significantly slower 20-m sprint time was detected on day 2 in EAST compared with WEST (P = 0.03). On day 1, the reduction in YYIR1 distance in EAST was significantly different from the increase in WEST (P < 0.001). Large ES indicated a greater reduction in YYIR1 distance in EAST compared with WEST on day 2 (d = 1.06). Conversely, the increase in YYIR1 distance on day 4 in EAST was greater than that in WEST (d = 1.00).
Five-meter sprint time was significantly slower on days 1 and 2 compared with day 4 in EAST (P < 0.05), with large ES also suggesting that it was slower on day 3 (d = 1.25). Similarly, 20-m sprint time was significantly slower on days 1 and 2 compared with days 3 and 4 in EAST (P < 0.05). The reduction in YYIR1 distance on days 1 and 2 in EAST was significantly different from the increase on days 3 and 4 (P < 0.05), whereas the increase in YYIR1 distance on day 1 was significantly greater than that on days 2 and 4 in WEST (P < 0.05).
Results for all sleep variables are presented in Table 2. A significant main effect of travel direction was observed for time in bed (F2,71 = 15.57, P < 0.001), sleep onset (F2,70 = 36.69, P < 0.001), sleep offset (F2,67 = 4.75, P = 0.012), and sleep duration (F2,70 = 13.03, P < 0.001). A significant interaction between travel direction and day was detected for sleep onset (F11,49 = 4.00, P < 0.001), sleep offset (F11,41 = 5.78, P < 0.001), and sleep duration (F11,48 = 2.11, P = 0.037).
Mean sleep onset and mean offset were significantly later and mean time in bed and mean sleep duration were significantly reduced across the 4 d in EAST compared with BASE and WEST (P < 0.05). Sleep onset and sleep offset were significantly later and time in bed and sleep duration were significantly reduced on the day of arrival in EAST compared with WEST (P < 0.05). Similarly, sleep onset was significantly later and time in bed and sleep duration were significantly reduced on days 1 and 2 in EAST compared with BASE and WEST (P < 0.05). Sleep onset was also significantly later on day 4 in EAST compared with BASE and WEST (P < 0.05). Large ES suggested that sleep offset was later on days 2 and 3 in WEST compared with BASE, and sleep efficiency was reduced on day 1 in WEST and EAST compared with BASE (d > 0.90).
In WEST, sleep onset was significantly earlier and time in bed and sleep duration were significantly greater on the day of arrival compared with all other days (P < 0.05). Similarly, time in bed and sleep duration were significantly greater on the day of arrival compared with days 1 and 2 in EAST (P < 0.05). Moreover, sleep onset was significantly earlier on day 3 compared with day of arrival and day 4, sleep offset was significantly later on day of arrival compared with all other days, and time in bed and sleep duration were significantly greater on day 4 compared with days 1 and 2 in EAST (P < 0.05). Sleep onset and offset were significantly later on day 4 compared with all other days in BASE (P < 0.05).
Across the 4 d, a total of two naps were recorded in BASE and EAST, and eight naps in WEST. Mean nap duration across the 4 d was 35 ± 11, 59 ± 11, and 73 ± 4 min for BASE, WEST, and EAST, respectively. However, the interaction between travel direction and day for cumulative sleep duration was not different from the aforementioned results for sleep duration.
Results for jet lag and function ratings are presented in Figure 3. A significant main effect of travel direction was observed for jet lag (F2,147 = 45.29, P < 0.001), function (F2,185 = 15.18, P < 0.001), and diet (F2,190 = 7.06, P = 0.001). Specifically, mean jet lag and function ratings across the 4 d were significantly worse in EAST compared with BASE and WEST, and WEST compared with BASE (P < 0.05). Conversely, mean diet ratings were significantly worse in WEST (3.3 ± 2.5 AU) compared with BASE (2.2 ± 2.2 AU) and EAST (1.9 ± 2.1 AU; P < 0.01).
AM testing (0900 h local time, ~1700 h WEST, ~0100 h EAST)
Jet lag was significantly worse in WEST compared with BASE on days 1 and 2 (P < 0.05), EAST compared with BASE on days 2–4 (P < 0.05), and EAST compared with WEST on day 3 (P < 0.05). Large ES indicated that jet lag was still elevated on day 4 in EAST and WEST compared with BASE (d > 0.90). Function ratings were significantly worse on day 2 in EAST compared with BASE and WEST (P < 0.05). Large ES indicated that sleep ratings were worse on days 1 and 4 in BASE (3.7 ± 4.1 and 3.3 ± 4.8 AU) compared with WEST (0.7 ± 3.0 and 0.9 ± 3.2 AU, d > 0.90), on day 4 in BASE compared with EAST (0.8 ± 3.7 AU, d = 1.11), and on day 2 in EAST (4.8 ± 5.1 AU) compared with WEST (2.0 ± 2.5 AU, d = 1.06).
Jet lag ratings were significantly lower on day 4 compared with days 1 and 2 in EAST (P < 0.05). Large ES also suggested that jet lag was lower on day 4 compared with days 1 and 2 in WEST (P > 0.05; d > 0.90). Sleep ratings were significantly better on day 4 compared with 2 (P = 0.02) in EAST.
PM testing (1700 h local time, ~0100 h WEST, ~0900 h EAST)
Jet lag was significantly worse in WEST compared with BASE on days 1 and 2 (P < 0.05) and EAST compared with BASE and WEST on days 1–3 (P < 0.05). Large ES indicated that jet lag was still elevated on day 4 in EAST and WEST compared with BASE (d > 0.90). Function ratings were significantly worse on day 1 in EAST compared with BASE and WEST, on day 2 in EAST compared with BASE, and on day 3 in EAST and WEST compared with BASE (P < 0.05). Large ES also indicated that function was worse on day 2 in WEST compared with BASE (d = 1.32). Diet ratings were significantly worse on days 1 and 2 in WEST (4.3 ± 3.2 and 4.0 ± 2.8) compared with EAST (2.2 ± 2.2 and 2.0 ± 1.7) and BASE (2.5 ± 1.5 and 2.2 ± 2.6; P < 0.05), and bowel movement ratings were significantly worse on day 1 in EAST compared with BASE (1.6 ± 2.5 vs 0.2 ± 0.3 AU, P = 0.01).
Jet lag ratings were significantly lower on days 3 and 4 compared with day 1 in EAST (P < 0.05). Large ES also suggested that jet lag was lower on day 4 compared with days 1 and 2 in WEST (P > 0.05; d > 0.90). Function ratings were significantly better on day 4 compared with all other days in EAST (P < 0.01), and on day 3 compared with day 1 in WEST (P = 0.03). Bowel movement ratings were significantly better on day 3 (0.2 ± 0.5 AU) and day 4 (0.2 ± 0.6 AU) compared with day 1 (1.6 ± 2.5 AU) in EAST (P < 0.05). Lastly, diet ratings were significantly better on days 3 and 4 (2.5 ± 2.2 and 2.2 ± 2.4 AU) compared with days 1 and 2 (4.3 ± 3.2 and 4.0 ± 2.8 AU) in WEST (P < 0.01).
On day 4, function ratings were significantly better at 1700 h compared with 0900 h in EAST (P < 0.01).
Motivation, RPE, and physical feeling
Results for RPE and physical feeling are presented in Figure 2, with results for motivation presented in Figure 3. A significant main effect of travel direction was identified for motivation (F2,190 = 34.12, P < 0.001), with mean motivation across the 4 d significantly reduced in EAST compared with BASE and WEST, and WEST compared with BASE (P < 0.05). A significant main effect of travel direction was evident for physical feeling (F2,70 = 3.97, P = 0.023), with mean ratings across the 4 d significantly worse in EAST compared with BASE (P = 0.007).
AM testing (0900 h local time, ~1700 h WEST, ~0100 h EAST)
Motivation was significantly reduced on day 1 in WEST compared with BASE (P = 0.04), day 2 in EAST compared with BASE and WEST (P < 0.05), and days 3 and 4 in EAST compared with BASE (P < 0.05).
Motivation was significantly lower on day 3 compared with day 2 in WEST (P < 0.05).
PM testing (1700 h local time, ~0100 h WEST, ~0900 h EAST)
Motivation was significantly reduced on days 1 and 2 in EAST compared with BASE and WEST (P < 0.05), and day 3 in EAST compared with BASE (P < 0.05). Large ES also suggested that motivation was reduced on day 3 in EAST compared with WEST (d = 1.08).
Motivation was significantly lower on day 3 compared with day 1 in WEST (P = 0.04), and significantly better on day 4 compared with days 1–3 in EAST (P < 0.05). Physical feeling was significantly worse on days 1 and 2 in EAST compared with BASE (P < 0.05), and large ES indicated worse physical feeling in EAST on day 2 compared with WEST (d = 0.97) and on day 3 compared with BASE and WEST (d > 0.90). RPE was significantly higher on day 4 compared with day 1 at BASE (P = 0.04), with large ES suggesting that it was also higher on day 3 compared with day 1 (d = 1.05). Physical feeling was significantly better on day 4 compared with day 1 in EAST (P = 0.01), with large ES indicating that it was better on day 4 compared with all other days (d > 0.90).
Motivation was significantly lower at 1700 h compared with 0900 h on day 2 in WEST (P = 0.03).
Across the 4 d, mean USG was significantly lower in WEST (1.012 ± 0.006) compared with BASE (1.016 ± 0.007) and EAST (1.019 ± 0.007; P < 0.05), and BASE compared with EAST (P < 0.05). USG was significantly lower in WEST (1.014 ± 0.007) and EAST (1.014 ± 0.007) compared with BASE (1.019 ± 0.006) on day 1 (P < 0.05), BASE (1.015 ± 0.008 and 1.015 ± 0.007) and WEST (1.013 ± 0.006 and 1.011 ± 0.005) compared with EAST (1.020 ± 0.006 and 1.021 ± 0.007) on days 2 and 3 (P < 0.05), and WEST (1.018 ± 0.004) compared with BASE (1.017 ± 0.008) and EAST (1.018 ± 0.008) on day 4 (P < 0.05). USG was significantly greater on day 1 compared with days 2 and 3 in BASE (P < 0.05), but significantly reduced on day 1 compared with days 2 and 3 in EAST (P < 0.05).
The present study investigated the effects of 21-h air travel across eight time zones, both west and east, on sleep, subjective jet lag, fatigue and motivation, and physical performance related to team sports. Results indicated that west travel had negligible effects on sleep, fatigue, and intermittent sprint performance. Conversely, sleep quantity and intermittent sprint performance were reduced up to day 2, alongside increased perceived fatigue and reduced motivation up to day 4 after east travel. Irrespective of travel direction, reduced maximal sprint and CMJ performance were evident up to days 3 and 4 after travel, respectively. As such, the present study indicates performance deficits in lower-body power are evident up to 96 h after travel in either direction. Although within the initial 72 h, sleep, fatigue, and both maximal and intermittent sprint performance are disturbed to a greater extent after east travel. Accordingly, practitioners should be aware that 72 h after long-haul travel may be required before the restoration of optimal readiness to train or compete.
Training and competition can occur within 24–48 h after long-haul travel for elite team sport athletes. A novel finding of the present study was therefore the reduction of intermittent sprint performance on days 1 and 2 after east but not west travel. This is despite the time of day (1700 h local time) corresponding to more “unfavorable” body clock time in west compared with east travel (~0100 h vs ~0900 h on day 1). Reduced and unchanged intermittent sprint performance has previously been reported on day 1 after 24 h of simulated travel, with sleep duration on the night of arrival the main difference between the two studies (13,14). Thus, reduced sleep duration observed in the first three nights after east travel could explain the reduction in YYIR1 performance in the present study. Comparable to previous simulated (13,14) and actual travel studies (15,37), subjective ratings of fatigue and motivation were exacerbated after travel in the present study, particularly east. In part, these altered subjective responses as a consequence of either circadian misalignment, sleep disruption, and/or the demands of travel could also explain the reduction of intermittent sprint performance (14,30). Although east and west travel had similar flight schedules and durations, the change in time zones meant a difference in local arrival time. This could also have contributed to the intermittent sprint performance reduction after east but not west travel, because there was a greater duration (~15 h) between arrival time and initial performance testing in west compared with east. Other detrimental consequences of travel that could affect performance are the low cabin humidity and potential risk of hypohydration (17). However, although USG across the 4 d was lower after west travel compared with baseline and east travel, results indicated that participants were adequately hydrated (USG <1.020) across all data collection periods (24). In summary, although west long-haul transmeridian travel with a morning arrival seems to induce negligible changes in intermittent sprint capacity, east travel with an evening arrival should be of concern to athletes and practitioners.
Reductions in CMJ performance were evident up to day 4 after both east and west travel in the present study. The timeline of recovery was different from the other measures of physical performance, with the greatest differences after both east and west travel compared with baseline on day 3, instead of day 1. Interestingly, the return of CMJ performance to baseline level coincided with greater motivation on day 4 at 1700 h compared with 0900 h local time after both east and west travel. In national team skeleton athletes, CMJ height was reduced after similar travel demands, but only on day 1 where testing occurred at ~0100 h body clock time (5). If circadian misalignment was the expected cause of CMJ performance impairments in the present study, (1) a change in the performance rhythm of a morning nadir and a late afternoon peak would have occurred, (2) east travel would have likely had a greater effect on performance compared with west travel, and (3) performance would have been reduced outside of its circadian peak window (22). Although testing after west travel at 0900 h (particularly day 1 after arrival) corresponded with the time of peak performance (~1700 h body clock time), there were negligible differences in CMJ performance between testing times (0900 h vs 1700 h) and travel directions (east vs west). The absence of these differences could be due to the nullification of circadian influence from a thorough warm-up (32,34), fatigue from travel, sleep disruption and/or previous exercise (34), or sensitivity of the CMJ test. Hence, although results from the present study suggest a 96-h recovery timeline for explosive lower-body power after both east and west long-haul travels, the time of day (local or body clock) does not seem to be influential.
Reduced 5- and 20-m sprint performance was also observed up to day 3 after both east and west travel. Similar to intermittent sprint performance, a greater reduction in 20-m sprint performance was detected on day 2 after east compared with west travel. This is again despite the time of day (1700 h local time) corresponding to a more unfavorable body clock time after west travel (~0100 h on day 1) and suggests that the time of day influence on performance posttravel may not be as integral as previously purported (25,27). In the same aforementioned group of skeleton athletes, no change in 30-m sprint performance was observed after similar travel demands (3), which was attributed to the competitive training environment and/or the athlete's high levels of motivation. Given the differences between testing and competition environments, motivational issues may explain the performance decrements noted in the present study (3,28,34). Regardless, compared with intermittent sprint capacity, the greater levels of neuromuscular fatigue, as indicated by reductions in both CMJ and 20-m sprint performance, are of concern for athletes after both east and west long-haul travel.
On the basis of an understanding of chronobiology, delayed sleep onset and early waking are anticipated after long-haul east and west travel, respectively (9,34,39). More prolonged sleep disruption after east travel is also expected, because circadian rhythms adjust quicker to a delay than an advance (9,34,39). To date, however, limited data on the effect of long-haul transmeridian travel on sleep patterns in passengers exist. Hence, a novel finding of the present study was that although there was no effect of west travel on posttravel sleep patterns, sleep duration was reduced for 3 d after east travel because of a later sleep onset and reduced time in bed. Conversely, in academics traveling to conferences, reduced sleep quantity and quality, and earlier sleep onset and waking were reported for 3 d after east travel, whereas delayed sleep onset and waking were reported for 5 d after west travel, with no changes in sleep quantity or quality (31). Considering that the delayed waking after west travel was attributed to the specific cohorts' lack of commitments (31), the anticipated early waking in west could have been masked in the present study by morning testing requirements, akin to training or competition demands. An average of only 6.5 h of sleep per night was obtained at baseline, which is recognized as below the recommended 7–9 h (19). Thus, a greater effect of travel, particularly west, may have been observed in individuals with better sleep habits. Subtle yet important differences in sleep architecture may also have been detected through polysomnography, which was unavailable in the present study (2,35). Lastly, because napping is a frequently reported symptom after long-haul transmeridian travel, it could also be argued that different results may have been observed in the present study if the participants were not instructed to avoid napping.
A common occurrence after long-haul travel overnight is that on the night of arrival, the homeostatic factor of sleep regulation overrides the circadian factor and sleep quantity and/or quality increases (12,16,34). This is likely due to sleep disruption during travel, as a result of the flight schedule (e.g., enforced waking by a stopover) and/or an uncomfortable sleeping position (9,13,14). Compared with all subsequent days, increased sleep duration was evident on the night of arrival after west travel due to an earlier sleep onset and increased time in bed. Although it cannot be determined whether this was due to reduced sleep duration during travel, limitations exist for accurately assessing sleep during travel via actigraphy and/or self-report diaries. Conversely, sleep duration was reduced on the day of arrival after east travel due to an evening arrival (2220 h local time), which is typical due to an advance in time zones and has previously been reported to disrupt sleep in professional football players (11,15). Results from the present study suggest that a combination of travel demands, particularly the flight schedule and circadian misalignment, disrupted sleep after east travel.
The theoretical rate of realignment of circadian rhythms after transmeridian travel is half a day per hour of the time difference west and one day per hour of the time difference east (26). It was therefore anticipated that subjective jet lag ratings would be more prominent and persistent after east compared with west travel. Although this hypothesis can be accepted, the attenuation of jet lag ratings was faster than expected for both west (2 instead of 3–4 d) and east travel (4 instead of 7 d), which is consistent with data from other field-based studies (8,15). In the only other study to investigate the effect of travel direction on jet lag ratings, more prominent and persistent symptoms were also identified after east travel across eight time zones compared with west travel across six time zones in elite gymnasts (23). Furthermore, it was hypothesized that subjective jet lag would be worse at 0900 h after west travel and 1700 h after east travel, because this would have corresponded to ~0100 h body clock time on the first day after arrival. However, this was not consistently observed, which similar to CMJ performance could be because the circadian influence is nullified by other factors in field-based studies, such as fatigue from travel, sleep disruption, and/or previous exercise (34). Indeed, jet lag ratings are strongly associated with the level of perceived fatigue assessed at the same time (37). For example, increased subjective jet lag ratings were unexpectedly reported by professional football players for 5 d after long-haul travel north across one time zone (14). Results suggested that this was due to sleep disruption induced by an early departure time and evening competition, together with fatigue after competition rather than circadian rhythm misalignment (14).
Increased fatigue along with reduced motivation and tolerance to exercise was identified up to day 3 after east compared with west travel. These differences align with the reduced sleep duration observed for the first three nights after east travel and with previous observations that sleep disruption itself can exacerbate subjective ratings of fatigue, motivation, and tolerance to exercise (30). Together with the effect of sleep disruption on perceived fatigue, prolonged inactivity and exposure to mild hypoxia during air travel may also contribute (6). However, because the duration of travel was similar for east and west, any differences in perceived fatigue are likely to be attributed to circadian rhythm misalignment and/or sleep disruption. Previous research suggests that individuals are unable to maintain performance in sustained or repeated exercise bouts after sleep loss because of difficulties in maintaining the motivation to perform at a high intensity (27,30). Therefore, the cascade of travel-induced changes observed after east travel (reduced sleep duration together with exacerbated perceived fatigue and motivation) may explain the reduction of intermittent sprint performance.
In the present study, the participant population was relatively homogenous from a training and physical performance perspective, chronotype was accounted for, and a statistical technique that models for interindividual variation was used. Despite this, a common theme was the large interindividual variation in results, which may have masked potential differences between east and west travel. For example, when mean group reductions in performance were observed after travel, there were some individuals who performed better and vice versa. This variation in travel-induced responses could be due to age, flexibility of sleeping habits, and previous travel experience (15,38). In addition, the variation in motivation may have been larger in the present participant population compared with professional athletes (3,28,34). Regardless, significant interindividual variation in the disruption of body temperature circadian rhythms to 24 h of west travel across 10 time zones has previously been reported, with 50%–69% phase delays, 20%–38% phase advances, and 11%–19% unchanged (8). Thus, further research investigating the cause of this variation and using more progressive statistics to assess changes at an individual rather than a group level is warranted.
In conclusion, the present study is the first to compare the effect of east and west long-haul travel on integrated measures related to physical performance for team sports. Results suggest a greater effect of east travel on sleep, subjective jet lag, fatigue and motivation, and maximal and intermittent sprint performance, particularly within the first 48–72 h after arrival. Although despite including “pre-east” measures as an internal control, it is recognized as a limitation of the present study that it was not a counterbalanced design. It is also acknowledged that actual team sport performance requires more than activity from a single muscle or group of muscles, for example, neural control, central decision making, and motivation (28). Nonetheless, the present study provides coaches and practitioners with novel information on the timeline of recovery specific to team sport physical performance after east and west long-haul travel. These data can help to inform decisions on travel schedules in relation to competition and when to return to full training after travel. For example, a practical recommendation from the present study would be that team sport athletes should aim to arrive a minimum of 96 h before competition after both east and west long-haul travel for optimal performance. However, because of their demanding schedules, this is not always feasible. Thus, future research developing interventions that have field-based efficacy for reducing the effect of long-haul transmeridian travel, particularly on sleep, is warranted.
The authors would like to thank the participants for their time and enthusiasm, and gratefully acknowledge the allocation of resources provided by the Aspire Zone Foundation, Aspetar Orthopaedic and Sports Medicine Hospital, and University of Technology Sydney in support of this project. The authors would also like to thank Adulaziz Farooq and Lauren Banting for their assistance with statistical analysis.
Declarations: No conflicts of interest are declared. The research was solely funded by the corresponding authors' institution. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The present study results do not constitute endorsement by the American College of Sports Medicine.