Risk for low energy availability, disordered eating and sleep disturbance among female football players
Article information
Abstract
[Purpose]
Low energy availability (LEA) and mental health issues are prevalent among female athletes and can have adverse effects on health and sports performance. This study aimed to assess energy availability and, mental health status (depression, anxiety, sleep deprivation, drug misuse, alcohol misuse, and disordered eating) among female football players. Despite the availability of validated tools for the triads and REDs, few studies have focused on the prevalence of LEA and mental health in female football players. Furthermore, limited information is available on LEA and its consequences in India.
[Methods]
Professional female football players (n=25) aged 19-30 years were recruited using incidental sampling. LEA was estimated based on energy availability [energy intake – activity energy expenditure] and LEA female questionnaire (LEAF-Q). Sports mental health assessment tool-1 (SMHAT-1) was used to assess the mental health status. Descriptive statistics and chi square test were used to test the hypotheses.
[Results]
According to the factorial method and LEAF-Q, 24% and 12% of players had LEA, respectively. LEA symptoms, such as decreased gastrointestinal and menstrual function, were observed in 16% of participants, while 8% had a history of injury. 44% of the participants exhibited poor mental health. Sleep disturbance (12%) and disordered eating (44%), were among the most common mental health issues. No significant difference was found between LEA, disordered eating, and sleep disturbance.
[Conclusion]
Female football players were more likely to develop LEA and mental health disorders, such as disordered eating and sleep disturbance. LEA was not linked with disordered eating or sleep disturbance. Early detection of LEA and effective intervention enhances the health and performance of female football players.
INTRODUCTION
Energy availability is the amount of energy required to maintain the basic physiological functions by subtracting the energy intake from the exercise energy expenditure [1]. The mathematical formula for EA (EA = EI-EEE/FFM), which identifies the amount of energy that the body can contribute to functions associated with health, well-being, and performance, is well-established in sports science [2]. EA is important for athletes because it influences not only their athletic performance, but also their overall health and well-being. Positive EA occurs when an athlete’s energy intake (EI) is greater than their exercise energy expenditure (EEE), which is essential for maintaining optimal health and athletic performance [3].
A continuous five-day reduction in EA (< 30 kcal/kg/fat free mass [FFM]) affected the normal physiological functioning of female athletes [4]. A short- and long-term LEA contribute both directly and indirectly to the development of menstrual irregularities and compromised bone health [3,5-7]. LEA exposure to problematic relative energy deficiency in sports (REDs), which may cause alterations in metabolic rate, protein synthesis, menstrual and immune function, cardiovascular, bone, mental health, and various indices of physical performance [5-7].
In sports, LEA is a common problem, especially for athletes who are under constant pressure to maintain low body weight and high performance levels. Female athletes participating in individual sports, such as endurance, aesthetic, or weight category sports, and team sports, such as football, are more susceptible to LEA [3,5,6,8-10]. Athletes in these sports may practice extreme dieting techniques to reach a particular body weight or body composition, such as calorie restriction, intense exercise [11], and purging. Substantial emotional issues, such as fury, confusion, cognitive restraint, and stress have all been connected to short-, medium, and longterm LEA, as well as sleep disturbances [2]. LEA may affect women’s reproductive systems, delay menarche, and cause irregular menstrual cycles [2]. A recent study found a relationship between mood disturbances, menstrual dysfunction, and LEA in a New Zealand female football team [12].
Female athletes with LEA are at risk of major health problems. LEA increases the risk of bone injury and other bone-health-related issues [13]. Rapid weight loss throughout the growth stage negatively affects body composition and bone structure. LEA can impair the absorption and utilization of key nutrients, such as iron, thereby increasing the risk of anemia [14]. Additionally, it can reduce muscle power, endurance, and strength [15].
Participants in competitive sports are more likely to develop disordered eating or eating disorders due to vigorous exercise activity, thereby increasing the likelihood of LEA [6,16,17]. Research on LEA has mainly focused on adolescents or young adults who participate in different sports. Our understanding of the prevalence of LEA risk and its influence across different age groups is limited [18].
India is ranked 11 among Asian nations and 60 in the FIFA women’s world football rankings. According to FIFA rankings, India’s women national team is one of the strongest in the world [19]. Female participation in football has recently substantially grown as its popularity and competitiveness continue to increase. It has become crucial to understand and address the various aspects of female athletes’ well-being and performance. To improve football players’ performance, continuous scientific evaluation, intervention, and monitoring are required. Despite the availability of validated tools for the triads and REDs few studies have focused on the prevalence of LEA and mental health in female football players. Furthermore, limited information is available on LEA and its consequences in India. Therefore, the present study aimed to assess body composition, EA, and mental health status, including depression, anxiety, sleep deprivation, drug misuse, alcohol misuse, and disordered eating; and the association between mental health status and EA in professional female football players.
METHODS
Study participants
The study involved 25 female professional football players aged 19–30 years in the pre-competition phase, who were recruited using incidental sampling. The inclusion criteria were healthy players who participated in state-level competitions (performance level-5; training hours/week: >17; sessions/week: >5; years of training: >6) [20], absence of musculoskeletal injuries, willingness to participate, and signed consent. The exclusion criteria were pregnancy, polycystic ovary syndrome, acute illness or injury, and use of medication for illness during the study. This study was approved by the institutional ethics committee of Manav Rachna International Institute of Research and Studies, Haryana, India (Ref.No.MRIIRS/FAHS/DEC/2022-23/N&D/M002) and conducted in accordance with the Helsinki and STROBE guidelines. The players were informed about the tests and written consent was obtained.
Study design
The players were restricted from engaging in any physical activity for the preceding 24 h, required to maintain their regular eating and sleeping schedules, and asked to arrive at the lab after an 8–12 hour fast on the study day. Upon arrival at the laboratory, height, weight, and segmental body composition were recorded. In a well-fed state, data on nutritional consumption, exercise logs, and mental health were collected.
Anthropometry and body composition
Height was measured using a stadiometer, and the measurements were made to the nearest 0.1 cm following the International Society for the Advancement of Kinanthropometry (ISAK) guidelines [21]. Body weight and segmental body composition were assessed using a body composition analyzer (bioelectrical impedance analysis method [BIA], In-body, 270, Mumbai, India), although this is not the gold standard for body composition measurements [22]. Participants wore Lycra swimwear and spandex, all accessories were removed, and the thoracic gas volume was estimated using the manufacturer’s default values. Segmental body composition parameters, such as skeletal muscle mass, fat free mass (FFM), body fat mass, percent body fat, visceral fat area, total body water, extracellular water intracellular water, protein, and minerals were recorded.
Total energy expenditure (TEE)
The present study estimated basal metabolic rate (BMR), activity energy expenditure (AEE), and non-exercise activity thermogenesis (NEAT) to arrive at TEE [23].
BMR
The BMR was predicted using the Cunningham equation: 500+22 × FFM [24].
AEE and NEAT
AEE was estimated by considering the energy expended during physical activity, whereas NEAT was estimated by considering non-exercise activities using a 7-day activity record. They were calculated by multiplying the metabolic equivalent (MET) [The work-to-rest metabolic rate ratio. One MET is defined as 1 kcal/kg/hour and is about similar to the energy cost of sitting quietly] values of each activity by the time spent performing the activity in minutes and using the correction factor for FFM [25].
EA
EA is the energy from food that is still available for bodily systems to perform the best after exercise-related energy expenditure is considered2. EA is expressed as kcal/kg FFM/day and calculated as follows:
EA = {dietary EI (kcal) - EEE (kcal)}/FFM (kg)/day] [2].
EA<30 kcal/kg/FFM was considered low, 30–45 kcal/kg/FFM was considered moderate, and >45 kcal/kg/FFM was considered optimal [17].
Energy, macronutrient, and micronutrient intake
Food intake patterns were assessed using a 7-day diet history. Furthermore, the information was converted to calories, macronutrients, and micronutrients using the Indian Food Composition Table [26]. Total fluid intake was measured by considering the water from food, metabolic water, and fluids consumed per day [27].
LEA in females questionnaire (LEAF-Q)
The LEAF-Q is a validated tool for assessing LEA in female athletes [28]. It consists of 22 questions in four sections, including injury history, gastrointestinal function, menstrual function, and contraceptive use. The scoring system assigns points based on responses, with scores of 7 or lower indicating “not at risk “ and 8 or higher indicating” at risk.”
Mental health status
Mental health status was assessed using the Sports Mental Health Assessment Tool-1 (SMHAT-1), developed by the International Olympic Committee (IOC) [29]. This tool is helpful for assessing athletes’ mental health and revealing whether they are currently experiencing mental health issues or are at risk of doing so. This tool assesses the degree of depression, anxiety, sleep deprivation, drug and alcohol misuse, and disordered eating; and comprised has three steps. Step 1, triage tool (Athletes form-1), is used to assess athletes’ psychological strain. A score above 16 indicates a potential risk for mental health symptoms and disorders, and further screening is recommended. Step 2, screening tool (Athletes form-2), is used to assess various mental health problems, such as, anxiety, depression, sleep disturbance, alcohol and drug misuse, and ED. A score > 10 indicates the prevalence of anxiety and depression, > 8 indicates sleep disturbance, > 4 indicates alcohol misuse in men, >3 indicates alcohol misuse in women, > 2 indicates drug misuse, and >4 indicates ED. If the scores are high in more than one screening, step 3a is recommended for brief intervention and monitoring; based on the severity of the condition, clinical assessment and management (step-3b) is recommended.
Statistical analysis
Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 27.0, IBM, Armonk, NY, USA). Descriptive statistics, such as means, standard deviations, and frequency distributions were applied to all parameters. The Shapiro–Wilk test was used to determine the normality of the data. The data related to nutrient intake were abnormal; therefore, the chi-square test was applied to determine the association between LEA and nutrient intake. The association between LEAF-Q scores, body composition, and mental status was identified using one-way analysis of variance. Significance was set at p<0.05.
RESULTS
The study involved 25 state level female football players divided into three groups based on their EA: LEA, moderate energy availability (MEA), and optimal energy availability (OEA). No dropouts occurred until the end of the study period.
Anthropometry and body composition
The anthropometry and body composition of the players are presented in Table 1. No significant differences were found in age, height, weight, body mass index (BMI), skeletal muscle mass, total body water, FFM, protein, minerals, or waist-to-hip ratio among the three EA groups. However, there was a statistically significant difference in body fat percentage (p<0.05) among the three groups. Compared with the MEA and OEA groups, the LEA group had a considerably lower body fat percentage. Therefore, LEA may affect body fat percentage.
Anthropometry and body composition of female football players.
Prevalence of LEA
The prevalence of LEA based on the LEAF-Q in professional female football players (n=25) is shown in Figure 1, with 12% of female athletes experiencing LEA. Among LEA symptoms, 16% of athletes experienced impaired gastrointestinal and menstrual function, and 8% had an injury history. The participants reported not using contraceptives or other hormonal pills, transdermal patches, and injectable or intrauterine devices. The data showed that the LEA symptoms gastrointestinal, menstrual function and injury are independent of one another, with a prevalence of 12% based on LEAF-Q. According to the findings based on the factorial approach (EI and EEE), 24% of players had LEA, 36% had MEA, and 40% had OEA (Figure 2).
Prevalence of LEA and its symptoms in female football players based on LEAF-Q.
LEA: Low Energy Availability; LEAF-Q: Low Energy Availability in Females-Questionnaire.
Prevalence of LEA in female football players based on calculated EA.
LEA: Low Energy Availability; EA: Energy Availability, MEA: Moderate Energy Availability, OEA: Optimum Energy Availability.
EI, TEE, and EA
According to the findings, there was no significant difference between the groups with varying degrees of energy availability in BMR, AEE, and total daily energy expenditure (TDEE) (Table 2). However, the OEA group had the highest total daily EI (TDEI) and the LEA group had the lowest, with significant differences in TDEI across the groups (p<0.05). Additionally, there were substantial differences in EA between the OEA group and the other two groups. The results showed that a low TDEI can lead to LEA.
TDEI, TDEE and EA in female football players.
Macro and micronutrient intake pattern
The macro-and micronutrient intake patterns of the female football players are shown in Table 3. The intake of available carbohydrates was significantly lower in the LEA group than in the MEA and OEA groups (p<0.05). Similarly, protein and fat intakes were higher in the OEA and MEA groups than in the LEA group owing to the higher intake of nutrients. A similar trend was observed for available carbohydrates (g/kg/BW) and fat (g/kg/BW), but not for protein (g/kg/BW). There were no discernible variations in the intakes of beta-carotene, vitamin C, calcium, or iron between the groups. However, total folate and zinc intake were significantly higher (p<0.05) in the MEA and OEA groups than in the LEA group are shown in Table 3.
Macro and micronutrients intake pattern.
Fluid intake pattern
Data on fluid intake patterns are shown in Table 4. There were no significant differences among the three groups for daily total fluid intake or fluid intake per kg/BW. However, a significant difference was observed in the amount of fluid consumed from food and metabolism (p<0.05). The LEA group’s water intake from foods and metabolism was significantly lower than that of the MEA and OEA groups due to poor food intake, and therefore macronutrients.
Fluid intake pattern.
Mental health status
We found that 44% of players were experiencing symptoms related to poor mental health. Among them, 44% were had disordered eating and 12% had sleep disturbances (Figure 3).
Mental health status of female football players.
SMHAT: Sports Mental Health Assessment Tool -1.
The association between mental health status and LEA was analyzed using the chi-square test. There were no significant differences among the EA groups in self-reported LEA symptoms (LEAF-Q), sleep disturbances, or disordered eating (Table 5)
Association between LEA and mental health status.
DISCUSSION
To the best of our knowledge, this is the first study to estimate the body composition, prevalence of LEA, and its association with mental health status of professional female football players in India.
A statistically significant difference in body fat percentage was observed between the EA groups. The low body fat percentage in LEA could be due to poor energy availability in female athletes [5,28]. Energy shortages may cause stored fat to be used for energy, leading to a decreased body fat percentage. However, a low body fat percentage may have negative health effects, such as hormonal imbalances and poor bone health [5]. EA may influence body composition more than the overall body size of female football players.
The widespread prevalence of LEA in female athletes is caused by their tendency to consume less energy than expected. The present study found that 24% of athletes were experiencing LEA based on the calculated EA and 12% based on the LEAF-Q. In previous studies, LEA was found in 30% of female Australian football players [30] and 45% of female leisure exercisers [31].
Among LEA symptoms, impaired gastrointestinal and menstrual function, and 8% had an injury history. Female athletes who participate in high-intensity sports or exercise are more likely to have irregular menstrual cycles and amenorrhea, which increases the risk of injuries, stress fractures, and lower bone mineral density3. Female athletes also menstrual disorders and use contraceptives [32,33].
A cross-sectional study of Australian Olympic athletes revealed that there may be a relationship between LEA risk and self-reported injury, illness, and gastrointestinal disturbances [34]. Determining the risk of LEA in female athletes is challenging because of the variability in menstrual dysfunction and presence of LEA [34,35]. LEAF-Q has been validated in runners, but not in football players; however, it has been previously used in football players [29,36,37]. Furthermore, due to the limited scope of LEAF-Q and inability to study the causes of a persistent energy shortage, it is challenging to pinpoint the probable source of the LEA [28]. To identify underlying reasons, the IOC has also recommended that screening for DE/ED be conducted in conjunction with LEA screening [5,6].
According to the findings, there was no significiant difference between the groups with varying degrees of EA in BMR, AEE, and TDEE, suggesting that these variables were steady and resistant to variations in energy supply. Another study observed no significant changes in BMR and AEE between groups when they examined the energy expenditure of individuals with different BMIs [38]. Similarly, a Larson– Meyer investigation of the energy expenditure of athletes with various training loads revealed no statistically significant differences in TDEE [39].
The OEA group had the highest TDEI and the LEA group had the lowest, with significant differences in TDEI across the groups (p<0.05). Additionally, there were substantial differences in the EA between the groups, with the OEA group having the highest EA and the LEA group having the lowest. Melin et al. [35] found 20% LEA (30 kcal/kg/LBM) [Lean body mass], 37% MEA (30–45 kcal/kg/LBM), and 43% OEA (45 kcal/kg/LBM) in elite female endurance athletes (n=40).30 In recent findings, LEA was assigned to participants for both EI- and energy expenditure-driven reasons.
The intake of available carbohydrates was significantly lower in the LEA group than in the MEA and OEA groups (p<0.05). Similarly, protein and fat intake was higher in the OEA and MEA groups than in the LEA group owing to a higher nutrient intake. Low carbohydrate intake can independently lead to RED-S-related health outcomes, such as bone health, immune response, and iron biomarkers [40-44]. Athletes may consciously or unconsciously reduce their nutrient intake, which can increase the risk of LEA. Several underlying problems, risks, and complex etiologies may predispose patients to LEA, and thus, RED-S. There are no clearly established explanations for why some athletes restrict EI [18]. The possible explanation is that LEA can stem from changes in dietary habits triggered by body dissatisfaction, and the perception that achieving a lower body weight will lead to improved performance or societal pressures for individuals to conform to specific appearance standards [45].
There were no significant differences among the three groups for daily total fluid intake or fluids per kg/BW. However, significant differences were observed in the amount of fluid consumed from food and metabolism (p<0.05). The LEA group consumed significantly lower amounts of macronutrients and foods rich in moisture than the MEA and OEA groups. In a previous study, athletes’ total fluid intake increased by 31% when their EI was increased by 20% [46].
Female experience mental disorders, such as depression, anxiety, disordered eating, psychological distress, and substance misuse [47]. Female athletes facing various sports-related stressors, such as injuries, performance failure, retirement, and career dissatisfaction, are more prone to mental disorders. Psychological indicators of problematic LEA and REDs include mood disturbances, cognitive dietary restraints, a drive for thinness, reduced sleep quality, and perfectionist tendencies [2]. Further research is needed to understand the dynamics of mental health and disordered eating behaviors in athletes. Therefore, it is important to examine athletes’ encounters with stress and mental disorders during high-pressure periods, such as during preparation for competitive games.
The present study showed that 44% of players suffered from poor mental health-related symptoms. Among them, 44% had disordered eating and 12% had sleep disturbances. These results are consistent with data from a study on the prevalence of mental health disorders in Olympic athletes (n=311) in Australia [36]. A total of 42% (n=132) of participants were at risk for LEA, with illness and mental health disturbances. The development of eating disorders in female athletes can be influenced by a variety of factors, including sports-specific pressures; societal and cultural expectations; high levels of stress, anxiety, or depression [48]; peer pressure [49]; and individual psychological and emotional factors. Major psychological distress, such as rage, disorientation, cognitive restraint, and stress, have all been linked to sleep disruption [1].
According to the results, there were no significant differences in LEA, sleep disturbances, and disordered eating. These results are consistent with prevalence data on the prevalence of LEA with or without eating disorders in collegiate female athletes (n=121) [50]. The etiology of disordered eating/eating disorders is complicated and involves a number of causal biopsychosocial factors, including maturation, sex, genes, sleep disturbances, poor self-image and esteem, perfectionism, and peer pressure to maintain a thin appearance [51].
This study was limited to small sample of female athletes (Age:19-30yrs) and a specific geographical area, which could hinder the generalization of the results to a larger population. Energy expenditure was calculated by the factorial method and body composition was assessed using the BIA (In body, 270), which is not a gold standard method, but is an easy means of evaluation. Furthermore, gastrointestinal and menstrual disorders were not confirmed using clinical diagnoses.
LEA, sleep disturbances, and disordered eating were prevalent among female football players. The prevalence of LEA based on LEAF-Q was lower as compared to that based on EA (EI and EE). Athletes with LEA have lower body fat percentage compared to that of other EA groups. LEA symptoms, gastrointestinal and menstrual disorders, and injury were independent of each other. LEA was not associated with sleep disturbances or disordered eating.
Monitoring the energy consumption and EA can improve performance and prevent harmful health effects. A multi-model strategy that addresses the physical and psychological causes of LEA is necessary. Further research is needed to understand the interaction between these factors and take precautions to prevent LEA.
Acknowledgements
The authors would like to thank Sports Science Centre, Manav Rachna International Institute of Research and Studies, Faridabad for the support.
This study was approved by the Institutional Ethical Committee of Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India (Ref. No: MRIIRS/FAHS/DEC/2022-23/N&D/M002 dated 09.01.2023)
Data will be available on request due to privacy/ethical restrictions.