Match day energy intakes and estimated energy expenditure of female cricket players in the domestic setting

Match day energy intakes and estimated energy expenditure of female cricket players in the domestic setting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Match day energy intakes and estimated energy expenditure of female cricket players in the domestic setting Dillan Francis Potts, Sarah Chantler, Deborah R Smith, Stephanie Roe, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7517625/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Physical and nutritional behaviours among female cricketers have yet to be explored. Therefore, the purpose of the present study was to estimate energy intake (EI), total energy expenditure (TEE), macronutrient and fluid intake in elite and highly trained female cricketers over 24-hour periods across 50-over home fixtures. Methods: Ten elite and highly trained female cricketers (age 24.1 ± 4.9 years; body mass 73.8 ± 7.8 kg; stature 171.3 ± 3.2 cm) were observed over three 50-over home matches. Body composition was measured using dual X-ray absorptiometry. EI was assessed through a combination of food photography and weighing of food intake. Resting metabolic rate was measured using indirect calorimetry, physical activity was monitored using accelerometry, and thermic effect of food was estimated; therefore, TEE was inferred. Results: The mean daily EI, TEE and resulting energy balance (EB) were 2558 ± 622 kcal·day -1 , 4326 ± 525 kcal·day -1 and -1769 ± 799 kcal·day -1 , respectively. Mean carbohydrate, protein, fat and fluid intake was 4.06 ± 1.40 g·kg -1 day -1 , 1.63 ± 0.58 g·kg -1 day -1 , 1.21 ± 0.40 g·kg -1 day -1 and 3525 ± 1223 ml·day -1 , respectively. Conclusion: Overall, this study observed elite and highly trained female cricketers to lack responsiveness of intake to match day demands leading to large energy deficits. From a practical perspective, data suggests that practitioners should focus on educational strategies for fuelling match play in accordance with each player’s role. Women’s cricket energy balance sports nutrition Figures Figure 1 Figure 2 Figure 3 Figure 4 HIGHLIGHTS On match days, elite and highly trained female cricket players have high energy requirements (4326 ± 525 kcal ·day -1 ) and have been observed to be in a negative energy balance of -1769 ± 799 kcal·day -1 . Consequently, players are recommended to align intake in conjunction with individual match demands. Macronutrient and fluid intakes were 4.06 ± 1.40 g·kg -1 day -1 (carbohydrates), 1.63 ± 0.58 g·kg -1 day -1 (protein), 1.21 ± 0.40 g·kg -1 day -1 (fat), 1.4 ± 2.1 unit·day -1 (alcohol) and 3525 ± 1223 ml·day -1 of fluids, respectively. The present study should be used as a stepping stone to explore energy balance in women’s cricket. Future research is needed to investigate the intake and different roles and positions across teams. INTRODUCTION Women’s cricket is an intermittent sport which can be played in a variety of formats such as 100 balls (The Hundred), 20-over (120 balls, known as T20), or 50-over (300 ball, known as one day matches) matches. Typically, 50-over matches last 6–8 hours causing significant physical stress and energy expenditure (EE) (Mujika & Burke, 2010 ; McDonald, 2018 ). Throughout a match, demands can vary drastically due to positions within the team and the variability of fielding demands (Petersen et al., 2010 ). Match day (MD) demands in men’s 50-over cricket have shown low intensity movements such as walking or being static to be dominant (96%) (Duffield & Drinkwater, 2008 ). However, high intensity movements such as repeated sprint bouts still occur through running between the crease, bowling and fielding actions (Rudkin & O’Donoghue, 2008 ). Male data may be inappropriate to inform female players’ support as differences in boundary size, stature and professional training programmes (Stuelcken et al., 2007 ; ICC, 2023 ; Sims et al., 2023 ; Brazier et al., 2024 ; ECB, 2024), meaning the physical load in female 50-over cricket is unknown. There is no data on the demands of women’s cricket, and due to the unique nature of the sport, comparisons between other sports may be unpragmatic. Athletes should strive to meet nutritional recommendations on MDs to aid optimal fuelling prior to and during matches and then promote recovery post-match (Thomas et al., 2016 ). Previous research exploring 50-over EE has only been within university and recreational male cricketers which found inconsistent findings, ranging from 243 kcal·h − 1 to 664 ± 0.4 kcal·h − 1 expended (Christie et al., 2008 ; Pote & Christie, 2014 ; McDonald, 2018 ). The range may be explained by differences in level of performance and methodologies, where McDonald, ( 2018 ) used in-game data rather than simulations (Christie et al., 2008 ; Pote & Christie, 2014 ). Indeed, the observed sex differences in physical demands (such as distance covered) and characteristics (such as stature and fat-free mass (FFM)), plus field dimensions, would likely impact EE, which are found in other sports such as basketball and golf (Stuelcken et al., 2007 ; Petersen et al., 2009 ; Zunzer et al., 2013 ; Garcia-Byrne et al., 2020 ; Altavilla et al., 2023 ). As such, research is warranted to measure the intake and expenditure among female cricket players during MDs. Without guidance from female specific research, female cricketers are at heightened risk of under fuelling, performance detriments, poor recovery, and injury. With the growing number of fixtures in the women’s game each season, these risks are even more pertinent (Beck et al., 2015 ; Thomas et al., 2016 ). Although EI and EE have been explored in men (Christie et al., 2008 ; Pote & Christie, 2014 ; McDonald, 2018 ), to the authors’ knowledge, no research has explored EI and EE among female cricket players nor full 24-hour observations on MDs. Therefore, the aims of this study were to 1) quantify energy, macronutrient, and fluid intake during 50-over home matches, 2) estimate EE over a full 24-hour period of 50-over home matches, and 3) allow for EB to be quantified on MDs. MATERIALS AND METHODS Participants Elite, and highly trained female cricketers (tier 3 and 4; McKay et al., 2022 ) were recruited from a cricket club competing in the Rachael Heyhoe Flint Trophy, which is a 50 over match competition. For conciseness, participants will be referred to as experienced female cricketers. A total of 13 players consented to participate in the study, but only 10 played in 2 or more of the three home matches to be included in the analysis. Participant characteristics are summarised in Table 1 . Prior to recruitment, ethical approval was granted by the university ethics committee no.109736. Each participant signed an informed consent form after written and verbal explanation of the study. Prior to data collection, participants completed a self-reported menstrual cycle questionnaire (MCQ). The questionnaire was completed to inform concerns of menstrual health, as previous research indicates menstrual status may influence EE, EI and concerns of low energy availability (LEA) can be high in female athletes (Logue et al., 2020 ; Tucker et al., 2024 ). The MCQ identified 8 participants to be naturally menstruating and 2 were using contraceptive pills. All participants were instructed to maintain their typical dietary and physical activity behaviours during MDs. Table 1 Participant Characteristics of Experienced Female Cricketers. Players (N = 10) Age (years) 24.1 ± 4.9 Stature (cm) 171.3 ± 3.2 Body mass (kg) 73.8 ± 7.8 FFM (kg) 51.8 ± 7.0 FM (kg) 20.8 ± 3.9 FM (%) 26.8 ± 5.2 Total Body BMD (g/cm 2 ) 1.4 ± 0.1 Total Body Z-Score 2.3 ± 0.9 Note. FFM, fat free mass; FM, fat mass; BMD, bone mineral density. Values are represented as mean ± SD. Research Design An observational study design was adopted during the first three home matches of the 2023 competitive season, which were played within three weeks of each other. The EI and EE data was collected across the 24 hours of each 50-over home MDs. Body composition, RMR and familiarisation of the EI and TEE methods were complete within a 1-month period, prior to the start of the competitive season. See Fig. 1 for full data collection period. Body Composition Stature (cm) and body mass (kg) were measured, using International Standards for Anthropometric Kinathropometry techniques (Norton, 2018 ), to the nearest 1mm and 1g respectively, via a calibrated height stadiometer (Seca Alpha Stand, Birmingham, UK) and portable weighing scales (Seca Alpha, model 770, Birmingham, UK). Participants wore minimal clothing and were barefoot. All anthropometric and body composition measures were performed by the same researcher to reduce variation in values (Madden and Smith, 2016 ). Body composition outcomes were estimated using DXA (GE Lunar iDXA, GE Healthcare, Madison, WI), under standardised conditions (overnight fast prior to the scan, drank 500ml of water within 2 hours before the scan, and refrained from intense physical activity in the previous 24 hours) (Rodriguez-Sanchez & Galloway, 2015 ), the week prior to the in-season period. Percentage coefficient of variation (%CV) has been shown to equal 0.6, 0.3 and 2.5 for bone mineral density (BMD), FFM and fat mass (FM) respectively (Bilsborough et al., 2014 ). The primary outcome used from the DXA scans was FFM, which was used for estimating EA and to inform future research of the physical characteristics of experienced female cricketers (McKay et al., 2022 ). Energy Expenditure TEE was inferred by combining resting metabolic rate (RMR) measured using indirect calorimetry, exercise energy expenditure (EEE) measured using accelerometry and estimated thermic effect of food (TEF) as described below. Resting Metabolic Rate Participants’ RMR was measured using indirect calorimetry following best practice guidelines (Compher et al., 2006 ), as previously described by Wilson et al. ( 2024 ). All participants had a coefficient of variation of ≤ 10% ventilatory oxygen (VO 2 ) and carbon dioxide (VCO 2 ) and respiratory quotient (RQ) ≤ 5% within a single 5-minute interval (%CV: VO 2 : 7.86 ± 4.10; VO 2 : 8.65 ± 5.27; RQ: 3.69 ± 1.56), respectively (Compher et al., 2006 ). Data was averaged every 30 seconds to remove artefacts and exported to Microsoft Excel (2019, Seattle, USA). RMR was computed using the Weir ( 1949 ) equation. Exercise Energy Expenditure Participants EEE was assessed using a wearable tri-axial Acti-heart 5 BT device combined with a single-lead electrocardiogram (ECG) unit (CamNtech, Cambridgeshire, UK). Combined heart rate (HR) and accelerometry has previously been shown as a valid estimate of TEE compared to doubly labelled water (DLW) (0.74 correlation coefficients) (Santos et al., 2014 ). Each device was individually calibrated prior to data collection using an 8-minute step test, which is a built-in function of the Acti-heart software version 4.0.92 (Cambridge Neurotechnology Ltd., Papworth, UK). Maximal and average HR was measured through the ECG unit. During data collection, the monitor was attached using two standard ECG electrodes with one being positioned over the xiphoid process and the other below the left pectoral and between the fifth and sixth rib (Heydenreich et al., 2019 ). Participants were given verbal, visual and written instruction on how to position the Acti-heart 5 devices for home use. The participants were required to wear the Acti-heart 5 devices for the 24-hour period of each MD. All three components of EE were considered through analysing the data from the accelerometers using ActiLife software (Version 5.0.5, CamNtech, Cambridgeshire, United Kingdom) but was only used to determine EEE. The researcher kept note of the start and end of each innings across all MDs of data collection to calculate EEE accurately. The acti-heart 5 devices estimated EEE for the warm-up, duration of each innings and for additional exercise/cool downs post-match. These were added together to equal EEE across each match (Jagim et al., 2019 ). The TEF using EI data was calculated as 2.5, 7, and 27.5% of their intake for lipids, carbohydrate and protein respectively, from previously validated values (Russell & Pennock, 2011 ). Energy for the TEF was combined with RMR and EE from the Acti-heart devices to infer TEE. Energy Intake On MDs, a combination of methods was used to collect dietary intake. These methods were short, prospective and over non-consecutive MDs to reduce player burden and minimise misreporting (Thomas et al., 2016 ; Roberts et al., 2023 ). All participants were briefed and made familiar with EI methods during the familiarisation visit as well as during a familiarisation match ahead of home match data collection. When participants were off site (prior to and after the match) they were asked to follow the ‘Snap-n-Send’ method as described by Costello et al. ( 2017 ). If the picture was deemed unsuitable for accurate analysis, the participant was immediately contacted for clarification. During matches (on site) all food and fluid was weighed and photographed by the researchers, using an individual visual identifier for each player (Aleto Scale, FM299 and iPad Air, Apple A1474, USA). The catering chefs gave access to weighed portions and recipes of food prior to MDs to facilitate data collection and analysis. Assessment of EI, through Snap-n-Send and weighed measures appears to be both effective and previously validated (Costello et al., 2017 ; McDonald, 2018 ). Each participant was also provided with a labelled fluid bottle which they were asked to drink from and not share with others. Fluid intake was captured by weighing each bottle prior to and after each innings (Salter, 1066, China). Once all food and fluid intake for the full 24 hours of MD were logged, energy and nutrient intake was calculated using Nutritics Ltd. (2023, Dublin, Ireland). The blind inter-reliability was undertaken for dietary analysis by the primary investigator and a second Sport and Exercise Nutrition Registrant (SENR) accredited nutritionist (96.8% agreement of EI). Fluids in the results included water, alcohol and water from food. These methods were repeated for each of the 24 hours of home matches included in the 3-week data collection period. Energy Balance and Energy Availability Energy balance was calculated by subtracting estimated total energy intake (TEI) from TEE. Additionally, to better inform energy status, EA was measured. Energy availability is defined as TEI minus EEE relative to FFM (TEI – EEE ÷ FFM) (Loucks et al. 2011 ), which was calculated from each MD. GPS and Physical Activity During match play, participants’ external training load was determined using Catapult S5 GPS technology (Catapult Sports, Leeds, United Kingdom), which was worn in a customised vest, positioned between the scapulae (Catapult S5 Vest, Leeds, United Kingdom). Duration, and distance data were collected at 10Hz which has shown to be a valid and reliable tool for monitoring movements and activity of various intensities in team sports athletes (Anderson et al., 2015 ; Giménez et al., 2019 ). All devices were activated 30 minutes prior to data collection (Maddison & Ni Mhurchu, 2009 ). Statistical Analysis All data is expressed as means ± standard deviation (SD) and were normally distributed. Data was analysed using statistical software (SPSS version 27, IBM, USA), and significance was set at p < 0.05. Paired t–tests were performed to compare EI and TEE on each MD, and repeated measures analysis of variance (ANOVA) was used to test differences in parametric variables of EI, TEE, EB and EA between days. On the MD3 one active player withdrew from the study therefore their data was included in MD1 and MD2 but not MD3. Three participants’ Acti-heart devices failed to record TEE and EEE on MD1, but the remaining 7 participants are included in MD1 data. Missing data, due to equipment malfunctions were categorised as missing completely at random (MCAR). By simply excluding the MCAR data, the reduced sample size could reduce precision and power within our statistics (Austin et al., 2021 ). Therefore, multiple imputation was used by completing a missing value analysis. A separate-variance t-test was completed on missing values > 5%. An expectation-maximisation regression analysis was completed which estimated missing data using maximal likelihood. Overall, this method reduces the loss of power, violation of the intent to treat principle, and increases justification of the causal inferences (Li et al., 2015 ). Repeated measures ANOVA was also conducted on all macronutrients (g·day − 1 , g·kg − 1 day − 1 and % of TEI) between MDs. Mauchly’s test of sphericity was used, and the Greenhouse-Geisser correction was implemented if the assumption of sphericity was violated (P < 0.05). All other variables such as overs played, playing time, distances covered, HR, EEE and TEF are displayed descriptively. RESULTS Participant Characteristics Participant characteristics are presented in Table 1 . During data collection all active participants did not report any restricted diets, food preferences or intolerances which could alter macronutrient distribution or intake. Match Characteristics All MDs failed to complete the full 50-overs in any of the innings. Both MD1 and MD3 were disrupted by weather conditions, while in MD2 all players were dismissed within 25 overs of play (Table 2 ). Table 2 Match Characteristics 50-Over Matches. MD1 MD2 MD3 All MDs Total Overs Played 69.3 37 80 62.1 ± 22.4 1st Innings Overs 36.0 18.1 44.2 32.5 ± 13.3 2nd Innings Overs 33.3 18.5 35.4 29.1 ± 9.2 Playing Time (mins (hours)) 315 (5.25) 192 (3.22) 359 (5.98) 289 ± 87 (4.82 ± 1.43) Batting Innings Distance (m) 2507 ± 1523 2511 ± 950 2400 ± 2270 2473 ± 1581 Bowling & Fielding Innings Distance (m) 5718 ± 1333 6150 ± 1264 10,054 ± 2093 7307 ± 1563 Total Distance (m) 8225 ± 2198 8661 ± 1764 12,454 ± 5588 9780 ± 3183 Average Heart Rate (beats·min) 81 ± 9 75 ± 5 83 ± 11 80 ± 8 Max Heart Rate (beats·min) 184 ± 7 187 ± 8 181 ± 10 184 ± 8 Weather Wet/Raining Dry/Sunny Wet/Raining - Note. Values are represented as mean ± SD. ***Insert Table 2 here*** Energy Intake, Expenditure, Balance and Availability Mean EI for MD1, 2 and 3 was 2565 ± 782 kcal·day − 1 , 2573 ± 799 kcal·day − 1 and 2535 ± 511 kcal·day − 1 , respectively. Mean TEF for MD1, 2 and 3 was 236 ± 75 kcal·day − 1 , 240 ± 86 kcal·day − 1 , and 224 ± 46 kcal·day − 1 , respectively. Mean TEE for MD1, 2 and 3 was 4285 ± 545 kcal·day − 1 , 4001 ± 798 kcal·day − 1 and 4908 ± 969 kcal·day − 1 , respectively. Figure 2 illustrates mean EI and TEE across MDs. Mean EB, EEE and EA are presented in Table 3 . There was a significant difference in TEE (P = 0.007), EB (P = 0.016) and EA (P = 0.008) between MDs. However, no significant difference was found between MDs for EI (P = 0.897). Table 3 Energy Balance, Exercise Energy Expenditure and Energy Availability across 50-over Match Days within Experienced Female Cricketers. Variable MD1 (n = 10) MD2 (n = 10) MD3 (n = 9) All MDs Energy EEE kcal·day − 1 2217 ± 376 2189 ± 761 3189 ± 822 2560 ± 497 MJ·day − 1 9.28 ± 1.57 9.16 ± 3.18 13.34 ± 3.44 10.71 ± 2.08 kcal·kgFFM·day − 1 44.04 ± 12.08 41.97 ± 13.39 61.70 ± 14.54 49.60 ± 8.17 kcal.h − 1 422 ± 72 680 ± 236 533 ± 137 545 ± 101 Balance kcal·day − 1 -1509 ± 801 # -1428 ± 962* # -2370 ± 1356* # -1769 ± 799 # MJ·day − 1 -6.31 ± 3.35 − 5.97 ± 4.03 -9.92 ± 5.67 -7.40 ± 3.34 kcal·kgFFM·day − 1 -29.14 ± 15.47 -27.59 ± 18.58 -45.78 ± 26.20 -34.17 ± 15.44 Availability Kcal·kgFFM − 1 ·day − 1 24.85 ± 7.17 33.70 ± 19.93* 15.62 ± 21.34* 24.72 ± 13.00 RMR Baseline kcal·day − 1 1573 ± 192 Note. MD, match day; MJ, megajoules; FFM, fat free mass; EEE, exercise energy expenditure; RMR, resting metabolic rate; TEF, thermic effect of food. All values represented as Mean ± SD. Significant difference at *P < 0.05 with Repeated measured ANOVA; #P < 0.05 with T-test. The mean TEE across MD2 was significantly lower than MD3 (P = 0.002) as presented in Fig. 2 . However, no significant difference in TEE across MD1 and MD2, and MD1 and MD3 were found (P = 1.000 and P = 0.067, respectively). Mean EA showed MD2 to be significantly lower than MD3 (P = 0.048). However, no significant difference between MD1 and MD2 (P = 0.638), nor MD1 and MD3 (P = 0.124) were found. Figure 2 demonstrates the mean EI across MDs was lower than expenditure (P < 0.001). EI was lower than TEE on MD1 (P < 0.001), MD2 (P = 0.001) and MD3 (P < 0.001) in all players, respectively. ***Insert Table 3 here*** Macronutrient Intake Mean carbohydrate intake for MD1, 2 and 3 was 304 ± 107 g·day − 1 , 288 ± 118 g·day − 1 and 299 ± 81 g·day − 1 , respectively. Mean protein intake for MD1, 2 and 3 was 117 ± 40 g·day − 1 , 126 ± 44 g·day − 1 , and 113 ± 27 g·day − 1 , respectively. Mean fat intake across MD1, 2 and 3 was 92 ± 31 g·day − 1 , 90 ± 28 g·day − 1 and 82 ± 25 g·day − 1 , respectively. Mean alcohol intake for MD1, 2 and 3 was 0.9 ± 1.8 unit·day − 1 , 2.3 ± 2.4 unit·day − 1 and 0.9 ± 2.0 unit·day − 1 , respectively. Mean fluids intake across MD1, 2 and 3 was 3811 ± 1189 ml·day − 1 , 3116 ± 1087 ml·day − 1 and 3648 ± 1393 ml·day − 1 , respectively. Figure 3 expressed mean macronutrient intakes relative to BM. There was no significant difference in macronutrient intake of g·day − 1 , g·kg − 1 day − 1 or % of TEI between all matches (P > 0.05). Distance Covered Compared to Carbohydrate Intake Individual distance covered compared to carbohydrate intakes relative to BM across MDs is represented in Fig. 4 . DISCUSSION This study is the first to quantify energy, macronutrient and fluid intake and compare to TEE during 50-over matches in women’s cricket. Across all MDs there was consistently large energy deficits due to no significant changes in EI despite significant differences in TEE. The current findings indicate a lack of compensation and responsiveness to individual MD workloads leading to large deficits across MDs. These findings are the beginning of evidence-based guidance for female cricketers on MDs. Energy Expenditure and Match Characteristics Present findings of TEE were 4285 ± 545 kcal·day − 1 , 4001 ± 798 kcal·day − 1 and 4908 ± 969 kcal·day − 1 across MD1, 2 and 3. No previous literature has explored TEE within women’s cricket, and due to the stand-alone characteristics of the sport, comparisons are difficult. However, the expenditure found within the current study was unexpectedly high. For context, observations in other elite female team sports such as rugby union and netball found mean TEE of 3229 ± 545 kcal·day − 1 and 3217 ± 287 kcal·day − 1 across training and MDs, respectively (Wilson et al., 2024 ; Costello et al., 2024 ). These differences could be attributed to the unique duration of cricket (for example the mean duration of MDs 289 ± 87 mins), mixed with the variety of low and high intensity movements. However, present findings of total distance (9780 ± 3183 m) align to findings conducted by Brinkmans et al. ( 2024 ), who reported distances between 8283–10,114 m in elite female footballers and found lower TEE of 2882 ± 278 kcal·day − 1 using DLW. Mean HR within the present study was 80 ± 8 beats·min across the averaging 289 ± 87 mins of match play. Such low average HR may be surprising given EEE was 545 ± 101 kcal·h − 1 during match play. Comparatively McDonald, ( 2018 ), found an EEE of 243 ± 64 kcal·h − 1 within male cricketers. The unexpected findings of EEE and therefore TEE within the present study used the same methodologies as Costello et al. ( 2024 ), which found acti-heart with HR calibrations to underestimate TEE between − 90.8–917.8 kcal·day − 1 compared to DLW within elite female netballers. Additionally, Santos et al. ( 2014 ) found just 41% agreement in acti-heart with HR compared to DLW for EEE in elite basketballers. Therefore, repeated measures of EE within this population may be pragmatic to further validate these findings. Energy Balance The current study found on average all players to be in a negative EB across each MD. Resulting in a mean EB of -1769 ± 799 kcal·day − 1 . Despite differences in MD characteristics and EEE, EI was not compensated and remained consistent. Furthermore, all MDs did not complete a full 50-over innings which could cause greater EEE if the full 50-over innings were played. Since no previous research has investigated 24-hour EI or EE within any cricket players, no direct comparisons can be made. However, when comparing current findings to other female sports it is clear energy deficits are prevalent (Yli-Piipari, 2019 ; Dasa et al., 2023 ; Curtis et al., 2023 ). Dasa et al. ( 2023 ) found professional female footballers to have deficits of 644 kcal·day − 1 on average across rest, training and MDs. Similarly, Yli-Piipari, ( 2019 ) observed deficits of 998 kcal·day − 1 while using ActiGraph accelerometry on MDs in female football and tennis players. Both current and previous findings within female athletes suggests that TEE outweighs intake. Consequently, highlighting the need for nutritional support to increase adherence to energy demands. Since such large deficits were observed LEA could be a concern, however, prolonged periods of observation are required to establish acute or clinical effects on health and performance such as cognitive function and glycogen availability. Macronutrients On average, carbohydrate intake was 4.06 ± 1.40 g·kg -1 day -1 . There is no existing data for women’s cricket on macronutrient intake. However, based on other sports nutrition requirements the range may be 3–6 g·kg -1 day -1 on MDs (Burke et al., 2011; Berlin et al., 2023 ). While on average these players meet their carbohydrate requirements, EB is not met. Therefore, players with greater demands in field such as fast bowlers with higher quantities of repeated sprints and powerful bowling actions, and all-rounders who contribute both in field and batting, may require higher carbohydrate intake than wicketkeepers and spin bowlers (Petersen et al., 2009 ; Petersen et al., 2010 ). Practitioners would expect players with greater physical load during MDs to compensate through increasing carbohydrate intake. However, when comparing individual distance covered and relative carbohydrate intake there was a lack of responsiveness to individual demands. Consequently, due to the unpredictability of each MD, cricket players should adapt a responsive approach to each match, periodising intake based on their role within the team and performance outcomes of the match - an approach common in other team sports like soccer (Fernandes, 2020 ). As such, increased carbohydrate intake may delay fatigue, improve performance and help to meet energy demands (Baker et al., 2015 ; Williams & Rollo, 2015 ). Protein consumption is important to promote recovery; without adequate protein intake, protein breakdown may exceed synthesis increasing recovery time, muscle soreness and damage (Hausswirth & Le Meur, 2011 ; Moore, 2019 ). Whilst the timing of protein intake throughout the day (particularly around exercise) may need to be considered for maximising recovery and adaptation, this is largely dependent on whether total daily protein requirements are met (Jäger et al., 2017 ). On average, protein intake equalled 1.63 ± 0.58 g·kg -1 day -1 , which falls within the ACSM recommended values of 1.6–1.8 g·kg -1 day -1 (Thomas et al., 2016 ). However, players who experience greater repeated sprints and changes in direction (such as batters when running between wickets), may benefit from increasing protein intake above these ranges. High levels of repeated sprints within female athletes have shown to reduce sprint and jump performance for ≥ 48 hours (Keane et al., 2015 ). Since breaks between fixtures can be as little as 48 hours within the women’s game, performance detriments may be a concern without adequate protein intake. Fat consumption is important for cricketers as it provides energy for the predominantly low intensity movements that occur within a cricket match. Fat intake averaged 1.21 ± 0.40 g·kg − 1 day − 1 , which equals 31.49 ± 5.50% of TEI. Similar fat intake has been observed among elite female footballers with intakes of 1.4 ± 0.7 g·kg − 1 day − 1 and Australian footballers 33.2 ± 6.5% of TEI (Condo et al., 2019 ; Dasa et al., 2023 ). Recommended values of fat intake are between 20–35% of TEI, therefore fat intake was within these values across MDs. Overall, an increased EI is recommended, through the combination of all macronutrients, however fat % should remain within the recommended range. Strengths and Limitations A strength of the current study was the inclusion of DXA, as a valid and reliable method to assess FFM (Lee & Gallagher, 2008 ), which in turn allowed for a better prediction of EA (Bilsborough et al., 2014 ). Similarly, the gold standard to estimate RMR through indirect calorimetry was used, as predictive equations have shown both under- and over-estimation when compared to present methods (-115 ± 118 kcal – 213 ± 97 kcal) (Compher et al., 2006 ; Jagim et al., 2018 ). EI methods of snap-n-send provides less burden on participants compared to more traditional methods including diet diaries or recalls but remains cheap and easy to administer (Simpson et al., 2017 : Roberts et al., 2023 ). Snap-n-send has shown underestimation of EI from both inexperienced and experienced sports nutritionists (MD = -1.2 MJ, TEE = 20 17.8%; MD = -0.6 MJ, 14.3% respectively) (Stables et al., 2021 ). However, reference images were taken of each food item, and each item was weighed to provide further reliability. Inter-reliability was also performed on dietary analysis to reduce further variability. The shortened time frames of snap-n-send, through use of non-consecutive days has also shown to reduce misreporting (Costello et al., 2017 ; Roberts et al., 2023 ). Since the present study is the first within female cricket it is not without limitations. Firstly, data collection was limited to a small sample within one team. Therefore, the results do not necessarily represent all female cricket clubs nor those within the same league. The small sample and differences in battering and bowling orders, meant positional comparisons were not analysed. However, it does give an insight into the intake and expenditure of female cricketers on MDs. As previously mentioned, estimations of EEE and therefore TEE through accelerometry may need repeating in a larger cohort to inform potential overestimations and positional differences. However, due to the movement patterns and padding within cricket, accelerometry was a viable option as there were no signalling or skin contact issues and DLW was financially unattainable. Despite the above limitations, the current study aims to be the catalyst for future work in an otherwise underrepresented cohort and provides a novel and robust insight into the EI and TEE of experienced female cricketers across 50-over home fixtures. CONCLUSION This study is the first to investigate EI and EE within female cricket players on 50-over MDs. The findings indicate that experienced female cricket players have high energy requirements on MDs and are at high risk of a negative EB. While this data is likely to change overtime due to improving professionalism and player standards, this study is the starting point for insight into the women’s game. Future research should aim to explore a larger cohort with positional differences to enhance understanding of individual MD strategies and interpretation of the present findings. Moreover, investigating the EB on rest and training days to understand potential compensatory food intake around MDs could be insightful too. Declarations CONFLICT OF INTEREST STATEMENT The authors declare that the manuscript submitted is not published elsewhere. The authors declare that they have no conflicts of interest. Author Contribution DFP, SC, DRS and MAB contributed to the study conception and design; DFP, SC, DRS, MAB, RB, JD-J, SR, MA and MA observation and data collection; LC contributed to blind inter-reliability within energy intake data; The manuscript was drafted by DFP ; all authors read, reviewed and edited. All authors approved the final version of the manuscript. References Anderson, L., Orme, P., Di Michele, R., Close, L.G., Morgans, R., Drust, B. and Morton, J.P. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7517625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513026709,"identity":"a783c0c1-c356-43ea-8e0f-e91f6043f5c5","order_by":0,"name":"Dillan Francis Potts","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Dillan","middleName":"Francis","lastName":"Potts","suffix":""},{"id":513026710,"identity":"3ba61ec7-563d-4089-b324-42adefcf15b8","order_by":1,"name":"Sarah Chantler","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Chantler","suffix":""},{"id":513026711,"identity":"57349de1-2b01-4252-b3d6-3e17890b0123","order_by":2,"name":"Deborah R Smith","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"R","lastName":"Smith","suffix":""},{"id":513026712,"identity":"efb5ff1d-e563-40ba-9b57-edbee73386c8","order_by":3,"name":"Stephanie Roe","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Roe","suffix":""},{"id":513026713,"identity":"93262256-d039-46e7-8b8d-fbe2d6b52944","order_by":4,"name":"Marina Alexander","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Alexander","suffix":""},{"id":513026714,"identity":"9f72117b-c3f1-4867-b2c2-143546e1fe54","order_by":5,"name":"Robert J Naughton","email":"","orcid":"","institution":"England and Wales Cricket Board","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"J","lastName":"Naughton","suffix":""},{"id":513026715,"identity":"081dcc9b-0b84-4082-80e1-29e581dfd901","order_by":6,"name":"Joshua Darrall-Jones","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Darrall-Jones","suffix":""},{"id":513026716,"identity":"cbbe5d78-aaac-4434-97b6-cd74eff1b4c3","order_by":7,"name":"Lucy Chesson","email":"","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Chesson","suffix":""},{"id":513026717,"identity":"0a491b6a-b4e1-4c1c-add2-c07424a18508","order_by":8,"name":"Matthew Anderson","email":"","orcid":"","institution":"Yorkshire Country Cricket Club","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Anderson","suffix":""},{"id":513026718,"identity":"92e68d8d-45a3-4c46-a3f3-6bb8dbc9e783","order_by":9,"name":"Meghan A Brown","email":"data:image/png;base64,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","orcid":"","institution":"Leeds Beckett University","correspondingAuthor":true,"prefix":"","firstName":"Meghan","middleName":"A","lastName":"Brown","suffix":""}],"badges":[],"createdAt":"2025-09-02 12:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7517625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7517625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91087208,"identity":"7ac1fedd-085c-4656-aebc-2cad54d8da8a","added_by":"auto","created_at":"2025-09-11 12:32:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142322,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the Research Design and Procedure.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7517625/v1/4b8a0d2010a8953b77804a06.png"},{"id":91087200,"identity":"09ed1d34-cda0-4b5d-a749-6da16a576531","added_by":"auto","created_at":"2025-09-11 12:32:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29219,"visible":true,"origin":"","legend":"\u003cp\u003eTotal match day energy intake compared to energy expenditure. Significant difference across match days *P\u0026lt;0.05 with Repeated measured ANOVA; #P\u0026lt;0.05 with T-test.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7517625/v1/3854ec98a4afa9036b67cb87.png"},{"id":91087201,"identity":"132384b7-bc9a-494a-883a-3f254f8ade4a","added_by":"auto","created_at":"2025-09-11 12:32:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24629,"visible":true,"origin":"","legend":"\u003cp\u003eTotal macronutrient intake expressed relative to body mass.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7517625/v1/5c5a3d3991a560c125fa299f.png"},{"id":91087212,"identity":"7f9f5556-655b-41f0-94a6-143de7e3030d","added_by":"auto","created_at":"2025-09-11 12:32:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60652,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual carbohydrate intake compared to distance covered across each match day.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7517625/v1/55e691054f175417d36db5df.png"},{"id":109366471,"identity":"3489b330-9b86-4d3b-96a0-d6069259eb5b","added_by":"auto","created_at":"2026-05-16 10:10:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550325,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7517625/v1/d663254e-c975-44f2-b1ad-25745933be74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMatch day energy intakes and estimated energy expenditure of female cricket players in the domestic setting\u003c/p\u003e","fulltext":[{"header":"HIGHLIGHTS ","content":"\u003cul\u003e\n \u003cli\u003eOn match days, elite and highly trained female cricket players have high energy requirements (4326 ± 525 kcal ·day\u003csup\u003e-1\u003c/sup\u003e) and have been observed to be in a negative energy balance of -1769 ± 799 kcal·day\u003csup\u003e-1\u003c/sup\u003e. Consequently, players are recommended to align intake in conjunction with individual match demands.\u003c/li\u003e\n \u003cli\u003eMacronutrient and fluid intakes were 4.06 ± 1.40 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(carbohydrates), 1.63 ± 0.58 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(protein), 1.21 ± 0.40 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(fat), 1.4 ± 2.1 unit·day\u003csup\u003e-1\u0026nbsp;\u003c/sup\u003e(alcohol) and 3525 ± 1223 ml·day\u003csup\u003e-1\u003c/sup\u003e of fluids, respectively.\u003c/li\u003e\n \u003cli\u003eThe present study should be used as a stepping stone to explore energy balance in women’s cricket. Future research is needed to investigate the intake and different roles and positions across teams.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eWomen\u0026rsquo;s cricket is an intermittent sport which can be played in a variety of formats such as 100 balls (The Hundred), 20-over (120 balls, known as T20), or 50-over (300 ball, known as one day matches) matches. Typically, 50-over matches last 6\u0026ndash;8 hours causing significant physical stress and energy expenditure (EE) (Mujika \u0026amp; Burke, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; McDonald, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Throughout a match, demands can vary drastically due to positions within the team and the variability of fielding demands (Petersen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Match day (MD) demands in men\u0026rsquo;s 50-over cricket have shown low intensity movements such as walking or being static to be dominant (96%) (Duffield \u0026amp; Drinkwater, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, high intensity movements such as repeated sprint bouts still occur through running between the crease, bowling and fielding actions (Rudkin \u0026amp; O\u0026rsquo;Donoghue, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Male data may be inappropriate to inform female players\u0026rsquo; support as differences in boundary size, stature and professional training programmes (Stuelcken et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; ICC, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sims et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brazier et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; ECB, 2024), meaning the physical load in female 50-over cricket is unknown. There is no data on the demands of women\u0026rsquo;s cricket, and due to the unique nature of the sport, comparisons between other sports may be unpragmatic.\u003c/p\u003e\u003cp\u003eAthletes should strive to meet nutritional recommendations on MDs to aid optimal fuelling prior to and during matches and then promote recovery post-match (Thomas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous research exploring 50-over EE has only been within university and recreational male cricketers which found inconsistent findings, ranging from 243 kcal\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 664\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 kcal\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e expended (Christie et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pote \u0026amp; Christie, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McDonald, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The range may be explained by differences in level of performance and methodologies, where McDonald, (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used in-game data rather than simulations (Christie et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pote \u0026amp; Christie, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Indeed, the observed sex differences in physical demands (such as distance covered) and characteristics (such as stature and fat-free mass (FFM)), plus field dimensions, would likely impact EE, which are found in other sports such as basketball and golf (Stuelcken et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zunzer et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Garcia-Byrne et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Altavilla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As such, research is warranted to measure the intake and expenditure among female cricket players during MDs. Without guidance from female specific research, female cricketers are at heightened risk of under fuelling, performance detriments, poor recovery, and injury. With the growing number of fixtures in the women\u0026rsquo;s game each season, these risks are even more pertinent (Beck et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough EI and EE have been explored in men (Christie et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pote \u0026amp; Christie, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McDonald, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), to the authors\u0026rsquo; knowledge, no research has explored EI and EE among female cricket players nor full 24-hour observations on MDs. Therefore, the aims of this study were to 1) quantify energy, macronutrient, and fluid intake during 50-over home matches, 2) estimate EE over a full 24-hour period of 50-over home matches, and 3) allow for EB to be quantified on MDs.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eElite, and highly trained female cricketers (tier 3 and 4; McKay et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were recruited from a cricket club competing in the Rachael Heyhoe Flint Trophy, which is a 50 over match competition. For conciseness, participants will be referred to as experienced female cricketers. A total of 13 players consented to participate in the study, but only 10 played in 2 or more of the three home matches to be included in the analysis. Participant characteristics are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Prior to recruitment, ethical approval was granted by the university ethics committee no.109736. Each participant signed an informed consent form after written and verbal explanation of the study. Prior to data collection, participants completed a self-reported menstrual cycle questionnaire (MCQ). The questionnaire was completed to inform concerns of menstrual health, as previous research indicates menstrual status may influence EE, EI and concerns of low energy availability (LEA) can be high in female athletes (Logue et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tucker et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The MCQ identified 8 participants to be naturally menstruating and 2 were using contraceptive pills. All participants were instructed to maintain their typical dietary and physical activity behaviours during MDs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Characteristics of Experienced Female Cricketers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlayers (N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStature (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFFM (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFM (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFM (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Body BMD (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Body Z-Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNote. FFM, fat free mass; FM, fat mass; BMD, bone mineral density. Values are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Design\u003c/h3\u003e\n\u003cp\u003eAn observational study design was adopted during the first three home matches of the 2023 competitive season, which were played within three weeks of each other. The EI and EE data was collected across the 24 hours of each 50-over home MDs. Body composition, RMR and familiarisation of the EI and TEE methods were complete within a 1-month period, prior to the start of the competitive season. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for full data collection period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eBody Composition\u003c/h3\u003e\n\u003cp\u003eStature (cm) and body mass (kg) were measured, using International Standards for Anthropometric Kinathropometry techniques (Norton, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), to the nearest 1mm and 1g respectively, via a calibrated height stadiometer (Seca Alpha Stand, Birmingham, UK) and portable weighing scales (Seca Alpha, model 770, Birmingham, UK). Participants wore minimal clothing and were barefoot. All anthropometric and body composition measures were performed by the same researcher to reduce variation in values (Madden and Smith, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBody composition outcomes were estimated using DXA (GE Lunar iDXA, GE Healthcare, Madison, WI), under standardised conditions (overnight fast prior to the scan, drank 500ml of water within 2 hours before the scan, and refrained from intense physical activity in the previous 24 hours) (Rodriguez-Sanchez \u0026amp; Galloway, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the week prior to the in-season period. Percentage coefficient of variation (%CV) has been shown to equal 0.6, 0.3 and 2.5 for bone mineral density (BMD), FFM and fat mass (FM) respectively (Bilsborough et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The primary outcome used from the DXA scans was FFM, which was used for estimating EA and to inform future research of the physical characteristics of experienced female cricketers (McKay et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEnergy Expenditure\u003c/h3\u003e\n\u003cp\u003eTEE was inferred by combining resting metabolic rate (RMR) measured using indirect calorimetry, exercise energy expenditure (EEE) measured using accelerometry and estimated thermic effect of food (TEF) as described below.\u003c/p\u003e\n\u003ch3\u003eResting Metabolic Rate\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; RMR was measured using indirect calorimetry following best practice guidelines (Compher et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), as previously described by Wilson et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All participants had a coefficient of variation of \u0026le;\u0026thinsp;10% ventilatory oxygen (VO\u003csub\u003e2\u003c/sub\u003e) and carbon dioxide (VCO\u003csub\u003e2\u003c/sub\u003e) and respiratory quotient (RQ)\u0026thinsp;\u0026le;\u0026thinsp;5% within a single 5-minute interval (%CV: VO\u003csub\u003e2\u003c/sub\u003e: 7.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10; VO\u003csub\u003e2\u003c/sub\u003e: 8.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27; RQ: 3.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56), respectively (Compher et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Data was averaged every 30 seconds to remove artefacts and exported to Microsoft Excel (2019, Seattle, USA). RMR was computed using the Weir (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1949\u003c/span\u003e) equation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eExercise Energy Expenditure\u003c/h2\u003e\u003cp\u003eParticipants EEE was assessed using a wearable tri-axial Acti-heart 5 BT device combined with a single-lead electrocardiogram (ECG) unit (CamNtech, Cambridgeshire, UK). Combined heart rate (HR) and accelerometry has previously been shown as a valid estimate of TEE compared to doubly labelled water (DLW) (0.74 correlation coefficients) (Santos et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Each device was individually calibrated prior to data collection using an 8-minute step test, which is a built-in function of the Acti-heart software version 4.0.92 (Cambridge Neurotechnology Ltd., Papworth, UK). Maximal and average HR was measured through the ECG unit. During data collection, the monitor was attached using two standard ECG electrodes with one being positioned over the xiphoid process and the other below the left pectoral and between the fifth and sixth rib (Heydenreich et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Participants were given verbal, visual and written instruction on how to position the Acti-heart 5 devices for home use. The participants were required to wear the Acti-heart 5 devices for the 24-hour period of each MD. All three components of EE were considered through analysing the data from the accelerometers using ActiLife software (Version 5.0.5, CamNtech, Cambridgeshire, United Kingdom) but was only used to determine EEE. The researcher kept note of the start and end of each innings across all MDs of data collection to calculate EEE accurately. The acti-heart 5 devices estimated EEE for the warm-up, duration of each innings and for additional exercise/cool downs post-match. These were added together to equal EEE across each match (Jagim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The TEF using EI data was calculated as 2.5, 7, and 27.5% of their intake for lipids, carbohydrate and protein respectively, from previously validated values (Russell \u0026amp; Pennock, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Energy for the TEF was combined with RMR and EE from the Acti-heart devices to infer TEE.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnergy Intake\u003c/h3\u003e\n\u003cp\u003eOn MDs, a combination of methods was used to collect dietary intake. These methods were short, prospective and over non-consecutive MDs to reduce player burden and minimise misreporting (Thomas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roberts et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All participants were briefed and made familiar with EI methods during the familiarisation visit as well as during a familiarisation match ahead of home match data collection.\u003c/p\u003e\u003cp\u003eWhen participants were off site (prior to and after the match) they were asked to follow the \u0026lsquo;Snap-n-Send\u0026rsquo; method as described by Costello et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). If the picture was deemed unsuitable for accurate analysis, the participant was immediately contacted for clarification.\u003c/p\u003e\u003cp\u003eDuring matches (on site) all food and fluid was weighed and photographed by the researchers, using an individual visual identifier for each player (Aleto Scale, FM299 and iPad Air, Apple A1474, USA). The catering chefs gave access to weighed portions and recipes of food prior to MDs to facilitate data collection and analysis. Assessment of EI, through Snap-n-Send and weighed measures appears to be both effective and previously validated (Costello et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McDonald, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each participant was also provided with a labelled fluid bottle which they were asked to drink from and not share with others. Fluid intake was captured by weighing each bottle prior to and after each innings (Salter, 1066, China).\u003c/p\u003e\u003cp\u003eOnce all food and fluid intake for the full 24 hours of MD were logged, energy and nutrient intake was calculated using Nutritics Ltd. (2023, Dublin, Ireland). The blind inter-reliability was undertaken for dietary analysis by the primary investigator and a second Sport and Exercise Nutrition Registrant (SENR) accredited nutritionist (96.8% agreement of EI). Fluids in the results included water, alcohol and water from food. These methods were repeated for each of the 24 hours of home matches included in the 3-week data collection period.\u003c/p\u003e\n\u003ch3\u003eEnergy Balance and Energy Availability\u003c/h3\u003e\n\u003cp\u003eEnergy balance was calculated by subtracting estimated total energy intake (TEI) from TEE. Additionally, to better inform energy status, EA was measured. Energy availability is defined as TEI minus EEE relative to FFM (TEI \u0026ndash; EEE\u0026thinsp;\u0026divide;\u0026thinsp;FFM) (Loucks et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which was calculated from each MD.\u003c/p\u003e\u003cp\u003eGPS and Physical Activity\u003c/p\u003e\u003cp\u003eDuring match play, participants\u0026rsquo; external training load was determined using Catapult S5 GPS technology (Catapult Sports, Leeds, United Kingdom), which was worn in a customised vest, positioned between the scapulae (Catapult S5 Vest, Leeds, United Kingdom). Duration, and distance data were collected at 10Hz which has shown to be a valid and reliable tool for monitoring movements and activity of various intensities in team sports athletes (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gim\u0026eacute;nez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All devices were activated 30 minutes prior to data collection (Maddison \u0026amp; Ni Mhurchu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll data is expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and were normally distributed. Data was analysed using statistical software (SPSS version 27, IBM, USA), and significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Paired t\u0026ndash;tests were performed to compare EI and TEE on each MD, and repeated measures analysis of variance (ANOVA) was used to test differences in parametric variables of EI, TEE, EB and EA between days. On the MD3 one active player withdrew from the study therefore their data was included in MD1 and MD2 but not MD3. Three participants\u0026rsquo; Acti-heart devices failed to record TEE and EEE on MD1, but the remaining 7 participants are included in MD1 data. Missing data, due to equipment malfunctions were categorised as missing completely at random (MCAR). By simply excluding the MCAR data, the reduced sample size could reduce precision and power within our statistics (Austin et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, multiple imputation was used by completing a missing value analysis. A separate-variance t-test was completed on missing values\u0026thinsp;\u0026gt;\u0026thinsp;5%. An expectation-maximisation regression analysis was completed which estimated missing data using maximal likelihood. Overall, this method reduces the loss of power, violation of the intent to treat principle, and increases justification of the causal inferences (Li et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Repeated measures ANOVA was also conducted on all macronutrients (g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and % of TEI) between MDs. Mauchly\u0026rsquo;s test of sphericity was used, and the Greenhouse-Geisser correction was implemented if the assumption of sphericity was violated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All other variables such as overs played, playing time, distances covered, HR, EEE and TEF are displayed descriptively.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eParticipant characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During data collection all active participants did not report any restricted diets, food preferences or intolerances which could alter macronutrient distribution or intake.\u003c/p\u003e\u003cp\u003eMatch Characteristics\u003c/p\u003e\u003cp\u003eAll MDs failed to complete the full 50-overs in any of the innings. Both MD1 and MD3 were disrupted by weather conditions, while in MD2 all players were dismissed within 25 overs of play (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMatch Characteristics 50-Over Matches.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMD1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMD2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMD3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAll MDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Overs Played\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st Innings Overs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2nd Innings Overs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlaying Time (mins (hours))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e315 (5.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e359 (5.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e289\u0026thinsp;\u0026plusmn;\u0026thinsp;87\u003c/p\u003e\u003cp\u003e(4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBatting Innings Distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2507\u0026thinsp;\u0026plusmn;\u0026thinsp;1523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2511\u0026thinsp;\u0026plusmn;\u0026thinsp;950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2400\u0026thinsp;\u0026plusmn;\u0026thinsp;2270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2473\u0026thinsp;\u0026plusmn;\u0026thinsp;1581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBowling \u0026amp; Fielding Innings Distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5718\u0026thinsp;\u0026plusmn;\u0026thinsp;1333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6150\u0026thinsp;\u0026plusmn;\u0026thinsp;1264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,054\u0026thinsp;\u0026plusmn;\u0026thinsp;2093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7307\u0026thinsp;\u0026plusmn;\u0026thinsp;1563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Distance (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8225\u0026thinsp;\u0026plusmn;\u0026thinsp;2198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8661\u0026thinsp;\u0026plusmn;\u0026thinsp;1764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12,454\u0026thinsp;\u0026plusmn;\u0026thinsp;5588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9780\u0026thinsp;\u0026plusmn;\u0026thinsp;3183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Heart Rate (beats\u0026middot;min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax Heart Rate (beats\u0026middot;min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeather\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWet/Raining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDry/Sunny\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWet/Raining\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote. Values are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e***Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere***\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEnergy Intake, Expenditure, Balance and Availability\u003c/h2\u003e\u003cp\u003eMean EI for MD1, 2 and 3 was 2565\u0026thinsp;\u0026plusmn;\u0026thinsp;782 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2573\u0026thinsp;\u0026plusmn;\u0026thinsp;799 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2535\u0026thinsp;\u0026plusmn;\u0026thinsp;511 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean TEF for MD1, 2 and 3 was 236\u0026thinsp;\u0026plusmn;\u0026thinsp;75 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 240\u0026thinsp;\u0026plusmn;\u0026thinsp;86 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 224\u0026thinsp;\u0026plusmn;\u0026thinsp;46 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean TEE for MD1, 2 and 3 was 4285\u0026thinsp;\u0026plusmn;\u0026thinsp;545 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 4001\u0026thinsp;\u0026plusmn;\u0026thinsp;798 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 4908\u0026thinsp;\u0026plusmn;\u0026thinsp;969 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates mean EI and TEE across MDs. Mean EB, EEE and EA are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a significant difference in TEE (P\u0026thinsp;=\u0026thinsp;0.007), EB (P\u0026thinsp;=\u0026thinsp;0.016) and EA (P\u0026thinsp;=\u0026thinsp;0.008) between MDs. However, no significant difference was found between MDs for EI (P\u0026thinsp;=\u0026thinsp;0.897).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnergy Balance, Exercise Energy Expenditure and Energy Availability across 50-over Match Days within Experienced Female Cricketers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMD1 (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMD2 (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMD3 (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAll MDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eEnergy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2217\u0026thinsp;\u0026plusmn;\u0026thinsp;376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2189\u0026thinsp;\u0026plusmn;\u0026thinsp;761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3189\u0026thinsp;\u0026plusmn;\u0026thinsp;822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2560\u0026thinsp;\u0026plusmn;\u0026thinsp;497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMJ\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal\u0026middot;kgFFM\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.97\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.70\u0026thinsp;\u0026plusmn;\u0026thinsp;14.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e422\u0026thinsp;\u0026plusmn;\u0026thinsp;72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e680\u0026thinsp;\u0026plusmn;\u0026thinsp;236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e533\u0026thinsp;\u0026plusmn;\u0026thinsp;137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e545\u0026thinsp;\u0026plusmn;\u0026thinsp;101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBalance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1509\u0026thinsp;\u0026plusmn;\u0026thinsp;801\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1428\u0026thinsp;\u0026plusmn;\u0026thinsp;962*\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2370\u0026thinsp;\u0026plusmn;\u0026thinsp;1356*\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1769\u0026thinsp;\u0026plusmn;\u0026thinsp;799\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMJ\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal\u0026middot;kgFFM\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-29.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-27.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-45.78\u0026thinsp;\u0026plusmn;\u0026thinsp;26.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-34.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKcal\u0026middot;kgFFM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.70\u0026thinsp;\u0026plusmn;\u0026thinsp;19.93*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.62\u0026thinsp;\u0026plusmn;\u0026thinsp;21.34*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMR Baseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ekcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e1573\u0026thinsp;\u0026plusmn;\u0026thinsp;192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote. MD, match day; MJ, megajoules; FFM, fat free mass; EEE, exercise energy expenditure; RMR, resting metabolic rate; TEF, thermic effect of food. All values represented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Significant difference at *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with Repeated measured ANOVA; #P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with T-test.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean TEE across MD2 was significantly lower than MD3 (P\u0026thinsp;=\u0026thinsp;0.002) as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, no significant difference in TEE across MD1 and MD2, and MD1 and MD3 were found (P\u0026thinsp;=\u0026thinsp;1.000 and P\u0026thinsp;=\u0026thinsp;0.067, respectively). Mean EA showed MD2 to be significantly lower than MD3 (P\u0026thinsp;=\u0026thinsp;0.048). However, no significant difference between MD1 and MD2 (P\u0026thinsp;=\u0026thinsp;0.638), nor MD1 and MD3 (P\u0026thinsp;=\u0026thinsp;0.124) were found.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the mean EI across MDs was lower than expenditure (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). EI was lower than TEE on MD1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MD2 (P\u0026thinsp;=\u0026thinsp;0.001) and MD3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in all players, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e***Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere***\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMacronutrient Intake\u003c/h2\u003e\u003cp\u003eMean carbohydrate intake for MD1, 2 and 3 was 304\u0026thinsp;\u0026plusmn;\u0026thinsp;107 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 288\u0026thinsp;\u0026plusmn;\u0026thinsp;118 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 299\u0026thinsp;\u0026plusmn;\u0026thinsp;81 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean protein intake for MD1, 2 and 3 was 117\u0026thinsp;\u0026plusmn;\u0026thinsp;40 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 126\u0026thinsp;\u0026plusmn;\u0026thinsp;44 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 113\u0026thinsp;\u0026plusmn;\u0026thinsp;27 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean fat intake across MD1, 2 and 3 was 92\u0026thinsp;\u0026plusmn;\u0026thinsp;31 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 90\u0026thinsp;\u0026plusmn;\u0026thinsp;28 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 82\u0026thinsp;\u0026plusmn;\u0026thinsp;25 g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean alcohol intake for MD1, 2 and 3 was 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 unit\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 unit\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 unit\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Mean fluids intake across MD1, 2 and 3 was 3811\u0026thinsp;\u0026plusmn;\u0026thinsp;1189 ml\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 3116\u0026thinsp;\u0026plusmn;\u0026thinsp;1087 ml\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 3648\u0026thinsp;\u0026plusmn;\u0026thinsp;1393 ml\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e expressed mean macronutrient intakes relative to BM. There was no significant difference in macronutrient intake of g\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e or % of TEI between all matches (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDistance Covered Compared to Carbohydrate Intake\u003c/p\u003e\u003cp\u003eIndividual distance covered compared to carbohydrate intakes relative to BM across MDs is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study is the first to quantify energy, macronutrient and fluid intake and compare to TEE during 50-over matches in women\u0026rsquo;s cricket. Across all MDs there was consistently large energy deficits due to no significant changes in EI despite significant differences in TEE. The current findings indicate a lack of compensation and responsiveness to individual MD workloads leading to large deficits across MDs. These findings are the beginning of evidence-based guidance for female cricketers on MDs.\u003c/p\u003e\u003cp\u003eEnergy Expenditure and Match Characteristics\u003c/p\u003e\u003cp\u003ePresent findings of TEE were 4285\u0026thinsp;\u0026plusmn;\u0026thinsp;545 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 4001\u0026thinsp;\u0026plusmn;\u0026thinsp;798 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 4908\u0026thinsp;\u0026plusmn;\u0026thinsp;969 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across MD1, 2 and 3. No previous literature has explored TEE within women\u0026rsquo;s cricket, and due to the stand-alone characteristics of the sport, comparisons are difficult. However, the expenditure found within the current study was unexpectedly high. For context, observations in other elite female team sports such as rugby union and netball found mean TEE of 3229\u0026thinsp;\u0026plusmn;\u0026thinsp;545 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 3217\u0026thinsp;\u0026plusmn;\u0026thinsp;287 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across training and MDs, respectively (Wilson et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Costello et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These differences could be attributed to the unique duration of cricket (for example the mean duration of MDs 289\u0026thinsp;\u0026plusmn;\u0026thinsp;87 mins), mixed with the variety of low and high intensity movements. However, present findings of total distance (9780\u0026thinsp;\u0026plusmn;\u0026thinsp;3183 m) align to findings conducted by Brinkmans et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who reported distances between 8283\u0026ndash;10,114 m in elite female footballers and found lower TEE of 2882\u0026thinsp;\u0026plusmn;\u0026thinsp;278 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e using DLW. Mean HR within the present study was 80\u0026thinsp;\u0026plusmn;\u0026thinsp;8 beats\u0026middot;min across the averaging 289\u0026thinsp;\u0026plusmn;\u0026thinsp;87 mins of match play. Such low average HR may be surprising given EEE was 545\u0026thinsp;\u0026plusmn;\u0026thinsp;101 kcal\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during match play. Comparatively McDonald, (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), found an EEE of 243\u0026thinsp;\u0026plusmn;\u0026thinsp;64 kcal\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e within male cricketers. The unexpected findings of EEE and therefore TEE within the present study used the same methodologies as Costello et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which found acti-heart with HR calibrations to underestimate TEE between \u0026minus;\u0026thinsp;90.8\u0026ndash;917.8 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to DLW within elite female netballers. Additionally, Santos et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found just 41% agreement in acti-heart with HR compared to DLW for EEE in elite basketballers. Therefore, repeated measures of EE within this population may be pragmatic to further validate these findings.\u003c/p\u003e\u003cp\u003eEnergy Balance\u003c/p\u003e\u003cp\u003eThe current study found on average all players to be in a negative EB across each MD. Resulting in a mean EB of -1769\u0026thinsp;\u0026plusmn;\u0026thinsp;799 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Despite differences in MD characteristics and EEE, EI was not compensated and remained consistent. Furthermore, all MDs did not complete a full 50-over innings which could cause greater EEE if the full 50-over innings were played. Since no previous research has investigated 24-hour EI or EE within any cricket players, no direct comparisons can be made. However, when comparing current findings to other female sports it is clear energy deficits are prevalent (Yli-Piipari, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dasa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Curtis et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Dasa et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found professional female footballers to have deficits of 644 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e on average across rest, training and MDs. Similarly, Yli-Piipari, (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed deficits of 998 kcal\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e while using ActiGraph accelerometry on MDs in female football and tennis players. Both current and previous findings within female athletes suggests that TEE outweighs intake. Consequently, highlighting the need for nutritional support to increase adherence to energy demands. Since such large deficits were observed LEA could be a concern, however, prolonged periods of observation are required to establish acute or clinical effects on health and performance such as cognitive function and glycogen availability.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMacronutrients\u003c/h2\u003e\u003cp\u003eOn average, carbohydrate intake was 4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e. There is no existing data for women\u0026rsquo;s cricket on macronutrient intake. However, based on other sports nutrition requirements the range may be 3\u0026ndash;6 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e on MDs (Burke et al., 2011; Berlin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While on average these players meet their carbohydrate requirements, EB is not met. Therefore, players with greater demands in field such as fast bowlers with higher quantities of repeated sprints and powerful bowling actions, and all-rounders who contribute both in field and batting, may require higher carbohydrate intake than wicketkeepers and spin bowlers (Petersen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Petersen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Practitioners would expect players with greater physical load during MDs to compensate through increasing carbohydrate intake. However, when comparing individual distance covered and relative carbohydrate intake there was a lack of responsiveness to individual demands. Consequently, due to the unpredictability of each MD, cricket players should adapt a responsive approach to each match, periodising intake based on their role within the team and performance outcomes of the match - an approach common in other team sports like soccer (Fernandes, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As such, increased carbohydrate intake may delay fatigue, improve performance and help to meet energy demands (Baker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Williams \u0026amp; Rollo, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProtein consumption is important to promote recovery; without adequate protein intake, protein breakdown may exceed synthesis increasing recovery time, muscle soreness and damage (Hausswirth \u0026amp; Le Meur, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Moore, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whilst the timing of protein intake throughout the day (particularly around exercise) may need to be considered for maximising recovery and adaptation, this is largely dependent on whether total daily protein requirements are met (J\u0026auml;ger et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). On average, protein intake equalled 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e, which falls within the ACSM recommended values of 1.6\u0026ndash;1.8 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e (Thomas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, players who experience greater repeated sprints and changes in direction (such as batters when running between wickets), may benefit from increasing protein intake above these ranges. High levels of repeated sprints within female athletes have shown to reduce sprint and jump performance for \u0026ge;\u0026thinsp;48 hours (Keane et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Since breaks between fixtures can be as little as 48 hours within the women\u0026rsquo;s game, performance detriments may be a concern without adequate protein intake.\u003c/p\u003e\u003cp\u003eFat consumption is important for cricketers as it provides energy for the predominantly low intensity movements that occur within a cricket match. Fat intake averaged 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which equals 31.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50% of TEI. Similar fat intake has been observed among elite female footballers with intakes of 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eday\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and Australian footballers 33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5% of TEI (Condo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dasa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recommended values of fat intake are between 20\u0026ndash;35% of TEI, therefore fat intake was within these values across MDs. Overall, an increased EI is recommended, through the combination of all macronutrients, however fat % should remain within the recommended range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eA strength of the current study was the inclusion of DXA, as a valid and reliable method to assess FFM (Lee \u0026amp; Gallagher, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which in turn allowed for a better prediction of EA (Bilsborough et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, the gold standard to estimate RMR through indirect calorimetry was used, as predictive equations have shown both under- and over-estimation when compared to present methods (-115\u0026thinsp;\u0026plusmn;\u0026thinsp;118 kcal \u0026ndash; 213\u0026thinsp;\u0026plusmn;\u0026thinsp;97 kcal) (Compher et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jagim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). EI methods of snap-n-send provides less burden on participants compared to more traditional methods including diet diaries or recalls but remains cheap and easy to administer (Simpson et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e: Roberts et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Snap-n-send has shown underestimation of EI from both inexperienced and experienced sports nutritionists (MD = -1.2 MJ, TEE\u0026thinsp;=\u0026thinsp;20 17.8%; MD = -0.6 MJ, 14.3% respectively) (Stables et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, reference images were taken of each food item, and each item was weighed to provide further reliability. Inter-reliability was also performed on dietary analysis to reduce further variability. The shortened time frames of snap-n-send, through use of non-consecutive days has also shown to reduce misreporting (Costello et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roberts et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSince the present study is the first within female cricket it is not without limitations. Firstly, data collection was limited to a small sample within one team. Therefore, the results do not necessarily represent all female cricket clubs nor those within the same league. The small sample and differences in battering and bowling orders, meant positional comparisons were not analysed. However, it does give an insight into the intake and expenditure of female cricketers on MDs. As previously mentioned, estimations of EEE and therefore TEE through accelerometry may need repeating in a larger cohort to inform potential overestimations and positional differences. However, due to the movement patterns and padding within cricket, accelerometry was a viable option as there were no signalling or skin contact issues and DLW was financially unattainable. Despite the above limitations, the current study aims to be the catalyst for future work in an otherwise underrepresented cohort and provides a novel and robust insight into the EI and TEE of experienced female cricketers across 50-over home fixtures.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study is the first to investigate EI and EE within female cricket players on 50-over MDs. The findings indicate that experienced female cricket players have high energy requirements on MDs and are at high risk of a negative EB. While this data is likely to change overtime due to improving professionalism and player standards, this study is the starting point for insight into the women\u0026rsquo;s game. Future research should aim to explore a larger cohort with positional differences to enhance understanding of individual MD strategies and interpretation of the present findings. Moreover, investigating the EB on rest and training days to understand potential compensatory food intake around MDs could be insightful too.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e\u003cp\u003eThe authors declare that the manuscript submitted is not published elsewhere. The authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDFP, SC, DRS and MAB contributed to the study conception and design; DFP, SC, DRS, MAB, RB, JD-J, SR, MA and MA observation and data collection; LC contributed to blind inter-reliability within energy intake data; The manuscript was drafted by DFP ; all authors read, reviewed and edited. All authors approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, L., Orme, P., Di Michele, R., Close, L.G., Morgans, R., Drust, B. and Morton, J.P. (2015) Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English Premier League: Implications for carbohydrate periodisation, \u003cem\u003eJournal of Sports Sciences\u003c/em\u003e, 34(13), pp. 1250\u0026ndash;1259. \u003c/li\u003e\n\u003cli\u003eAltavilla, G., D\u0026rsquo;Isanto, T., Raiola, G. and D\u0026rsquo;Elia, F. (2023) Different explosive strength and physiological demands between male and female basketball teams, \u003cem\u003ePhysical Education Theory and Methodology\u003c/em\u003e, 23(2), pp. 271\u0026ndash;275. \u003c/li\u003e\n\u003cli\u003eAustin, P.C., White, I.R., Lee, D.S. and van Buuren, S. 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(2024) Energy expenditure of international female rugby union players during a major international tournament: A doubly labelled water study, \u003cem\u003eApplied Physiology, Nutrition, and Metabolism\u003c/em\u003e, 49(10), pp. 1340\u0026ndash;1352. \u003c/li\u003e\n\u003cli\u003eYli-Piipari, S. (2019) Energy expenditure and dietary intake of female collegiate tennis and soccer players during a competitive season, \u003cem\u003eKinesiology\u003c/em\u003e, 51(1), pp. 70\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eZunzer, S.C., von Duvillard, S.P., Tschakert, G., Mangus, B. and Hofmann, P. (2013) Energy expenditure and sex differences of golf playing, \u003cem\u003eJournal of Sports Sciences\u003c/em\u003e, 31(10), pp. 1045\u0026ndash;1053. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Women’s cricket, energy balance, sports nutrition","lastPublishedDoi":"10.21203/rs.3.rs-7517625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7517625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysical and nutritional behaviours among female cricketers have yet to be explored. Therefore, the purpose of the present study was to estimate energy intake (EI), total energy expenditure (TEE), macronutrient and fluid intake in elite and highly trained female cricketers over 24-hour periods across 50-over home fixtures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTen elite and highly trained female cricketers (age 24.1 ± 4.9 years; body mass 73.8 ± 7.8 kg; stature 171.3 ± 3.2 cm) were observed over three 50-over home matches. Body composition was measured using dual X-ray absorptiometry. EI was assessed through a combination of food photography and weighing of food intake. Resting metabolic rate was measured using indirect calorimetry, physical activity was monitored using accelerometry, and thermic effect of food was estimated; therefore, TEE was inferred.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean daily EI, TEE and resulting energy balance (EB) were 2558 ± 622 kcal·day\u003csup\u003e-1\u003c/sup\u003e, 4326 ± 525 kcal·day\u003csup\u003e-1\u003c/sup\u003e and -1769 ± 799 kcal·day\u003csup\u003e-1\u003c/sup\u003e, respectively. Mean carbohydrate, protein, fat and fluid intake was 4.06 ± 1.40 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e, 1.63 ± 0.58 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e, 1.21 ± 0.40 g·kg\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e and 3525 ± 1223 ml·day\u003csup\u003e-1\u003c/sup\u003e, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, this study observed elite and highly trained female cricketers to lack responsiveness of intake to match day demands leading to large energy deficits. From a practical perspective, data suggests that practitioners should focus on educational strategies for fuelling match play in accordance with each player’s role.\u003c/p\u003e","manuscriptTitle":"Match day energy intakes and estimated energy expenditure of female cricket players in the domestic setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 12:31:56","doi":"10.21203/rs.3.rs-7517625/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1c18bdb-f311-4b9f-bd7f-6858073e1d3d","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T10:09:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 12:31:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7517625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7517625","identity":"rs-7517625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}