**Associations between Activity Pacing, Fatigue, and Physical Activity in Adults with Multiple Sclerosis: A Cross Sectional Study**

Received: 13 April 2020; Accepted: 9 June 2020; Published: 15 June 2020

**Abstract:** Fatigue is common in people with multiple sclerosis (MS). Activity pacing is a behavioral way to cope with fatigue and limited energy resources. However, little is known about how people with MS naturally pace activities to manage their fatigue and optimize daily activities. This study explored how activity pacing relates to fatigue and physical activity in people with MS. Participants were 80 individuals (60 females, 20 males) with a diagnosis of MS. The participants filled in questionnaires on their activity pacing, fatigue, physical activity, and health-related quality of life, 3–6 weeks before discharge from rehabilitation. The relationships between the variables were examined using hierarchical regression. After controlling for demographics, health-related quality of life, and perceived risk of overactivity, no associations were found between activity pacing and fatigue (β = 0.20; t = 1.43, *p* = 0.16) or between activity pacing and physical activity (β = −0.24; t = −1.61, *p* = 0.12). The lack of significant associations between activity pacing and fatigue or physical activity suggests that without interventions, there appears to be no clear strategy amongst people with MS to manage fatigue and improve physical activity. People with MS may benefit from interventions to manage fatigue and optimize engagement in physical activity.

**Keywords:** activity pacing; multiple sclerosis; perceived risk of overactivity; perceived fatigue; health-related quality of life; rehabilitation

#### **1. Introduction**

Symptoms of fatigue are among the most frequently reported and strongest predictors of functional disability in people with multiple sclerosis (MS) [1–3]. The experience of fatigue and perceived fatigability (changes in the sensations that regulates effort and endurance) draws behavioral adaptations, such as limiting the engagement in activities resulting in underactivity, or a lifestyle characterized by periods of overactivity followed by long extensive rest periods [4–7]. However, both underactivity and overactivity are linked with disability [8].

Despite growing efforts to manage fatigue through exercise interventions in people with MS, studies investigating the effect of exercise interventions report a high number of dropouts, and identified

that participants struggle to continue engaging in physical activity post-intervention [9,10]. This warrants the need to explore ways to enable long-term adoption of a physically active lifestyle.

Activity pacing is a self-management strategy that can help alter often-occurring inefficient activity patterns (underactivity and overactivity) and stimulate long-term engagement in an active lifestyle [11]. It involves dividing one's daily activities into smaller, manageable pieces to manage fatigue, and to maintain a steady activity pace, whilst reducing relapses [12,13]. However, current literature on how people naturally pace activities in daily life is limited and inconclusive [11–16]; some studies show that activity pacing is associated with higher levels of fatigue and lower physical activity [16,17], while others show the opposite or no association [8,18], and no clear strategies are available in rehabilitation treatment to optimize activity pacing to improve engagement in physical activity [14]. Similarly, quality of life has been proposed to impact activity pacing [8].

It is notable that most of the above studies aimed to explore issues in a range of chronic disabling conditions and did not focus on MS specifically. Thus while findings from these studies [8,13–18] contribute to our understanding of activity pacing, their broader focus with regards to multiple health behaviors and mixed populations may have resulted in failure to elicit key issues specific to engagement in physical activity for people with MS. Currently no study has explored people with MS in a natural approach to activity pacing, and its relations to fatigue and physical activity.

Understanding these associations can help guide and tailor rehabilitation treatment efforts for people with MS and promote an active lifestyle in this population. The aim of this study was to examine reported engagement in pacing and how it relates to fatigue and physical activity in people with MS just before discharge from rehabilitation, controlling for demographics, health-related quality of life, and perceived risk of overactivity. Based on the expectation that activity pacing would be an adaptive strategy to manage fatigue and optimize daily activities [14,17], we hypothesized that reported engagement in pacing would be associated with a decrease in fatigue and an increase in physical activity.

#### **2. Materials and Methods**

#### *2.1. Design*

This study was part of a multicenter longitudinal study (Rehabilitation, Sports, and Active lifestyle; ReSpAct) to evaluate the nationwide implementation of an active lifestyle program (Rehabilitation, Sports, and Exercise; RSE) among people with a wide range of chronic diseases and/or physical disabilities in Dutch rehabilitation [19,20]. Participants received either inpatient or outpatient rehabilitation at rehabilitation centers and departments of rehabilitation in hospitals because of MS. The current study uses a cross-sectional design based on baseline measurement (3–6 weeks before discharge from rehabilitation) of activity pacing behaviors, fatigue, physical activity, and health-related quality of life of people with MS, selected from the ReSpAct dataset. The study procedures were approved by the ethics committee of the Center for Human Movement Sciences of the University Medical Center Groningen, University of Groningen (reference: ECB/2013.02.28\_1) and at participating institutions.

#### *2.2. Participants*

Participants were recruited upon referral to the participating rehabilitation institutions across the Netherlands. Potential participants received information on study rationale and procedures, had questions answered, and were checked for the inclusion criteria. Participants were included in this study if they were 18 years and older, had a diagnosis of MS, had received rehabilitation care or treatment based on medicine consultation within one of the participating rehabilitation institutions, and participated in the 'RSE' program. Participants were excluded from the study if they were not able to complete the questionnaires, even with help, or participated in another physical activity stimulation program. Eligible participants who volunteered signed an informed consent form.

#### *2.3. Procedure*

Enrolled participants were assessed through a standardized baseline measurement, which consisted of filling out a set of questionnaires on paper or digitally [19,21–23]. As part of the full questionnaire and producer, first, participants indicated which physical activities they perform in the context of the rehabilitation treatment and on their own initiative by filling out an adapted version of the short questionnaire to assess health enhancing physical activity (SQUASH) [21]. Secondly, participants filled out short questionnaires on their perceived engagement in pacing, risk of overactivity, and fatigue [19,22]. Lastly, participants filled out a questionnaire on their health-related quality of life [23].

#### *2.4. Measures*

#### 2.4.1. Primary Measures

Fatigue severity was measured using the Fatigue Severity Scale (FSS) [22], a valid and reliable questionnaire to determine the impact of fatigue in people with MS [24]. The participants scored the nine items of the questionnaire on a scale of 1–7 (1, completely disagree; 7 completely agree). Mean fatigue score based on an average of the nine items was used. The mean fatigue score ranges from 1 to 7. A mean FSS score ≥4 was adopted as the cut-off for clinically significant fatigue [25].

Physical activity was assessed using an adapted version of the Short Questionnaire to Assess Health-Enhancing Physical Activity [21]. The questionnaire is a self-reported recall measure to assess daily physical activity based on an average week in the past month. The original questionnaire has demonstrated good test–retest reliability and internal consistency and moderate concurrent validity in ordering participants according to their level of physical activity [21,26,27]. Some minor changes were made to make the SQUASH applicable for people with a chronic disease or physical disability. Specifically, within the domains 'commuting activities', 'leisure-time', and 'sports activities', the items 'wheelchair riding' and 'hand cycling' were added. Also, 'tennis' was modified as '(wheelchair) tennis'. Total minutes of physical activity per week was calculated by multiplying frequency (days/week) and duration (minutes/day) for each activity.

Reported engagement in pacing was assessed with the 'engagement in pacing' subscale of the Activity Pacing and Risk of Overactivity Questionnaire [19]. This questionnaire was developed for use in the ReSpAct study [19]. The engagement in pacing subscale reflected reported engagement in pacing within daily routines and was the primary outcome in the current study. Participants scored the five items of the subscale on a scale of 1–5 (1, never; 2, rarely; 3, sometimes; 4, often; 5, very often). The mean subscale score ranged from 1 to 5, with higher score indicating high engagement in pacing.

Appendix A shows the preliminary validation metrics of the questionnaire. In summary, the sampling adequacy tested with the Kaiser–Myer–Olkin (KMO) and the Bartlett's test of sphericity showed that the questionnaire had a KMO of 0.722, and Bartlett's test was significant (*p* < 0.05), supporting a principal component analysis (PCA). Results of the PCA showed that there were two factors with an Eigen value >1.00, therefore based on the Kaiser's criterion two components were chosen. Factor loadings were used to assign the items to the two components. The two components explained 60.50% of the total variance and there was a negative correlation between the two components of −0.115.

#### 2.4.2. Background Measures and Confounders

Background demographics included age, gender, and body mass index, which was calculated from self-reported body mass and height (body mass (kg)/height2 (m2 )).

To assess health-related quality of life, the RAND-12-Item Health Survey (RAND-12) [23] was used. RAND-12 assesses seven health domains; general health, physical functioning, role limitations due to physical health problem bodily pain, role limitations due to emotional problems, vitality/mental health, and social functioning. The RAND-12 was scored using the recommended scoring algorithm for calculating general health [28], a composite score of person's health-related quality of life. Scores ranged from 18 to 62. A high score indicated better health-related quality of life. The RAND-12 has been proven to be a valid and reliable measure of health-related quality of life [29].

The 'risk of overactivity' subscale of the Activity Pacing and Risk of Overactivity Questionnaire [19] was used to measure perceived risk of overactivity within daily routines. Participants scored the two items of the subscale on a scale of 1–5 (1, never; 2, rarely; 3, sometimes; 4, often; 5, very often). The mean score ranged from 1 to 5, with higher score indicating high perceived risk of overactivity.

#### *2.5. Data Analysis*

Data were analyzed using IBM Statistical Package for the Social Sciences version 23.0 [30]. Based on descriptive statistics and visual inspection of frequency distributions, data were normally distributed. All values were reported using descriptive statistics of means, standard deviations, and interquartile ranges to summarize characteristics of participants. To ensure there was no multicollinearity, bivariate Pearson correlations were conducted to examine basic between-person associations among demographic and study variables, prior to testing the study hypotheses (variables were not highly correlated with each other, *r* < 0.8).

Hierarchical linear regression was used to test the study hypotheses. This statistical approach was optimal for adjustment for confounders, as we wanted to determine whether there were relationships between engagement in pacing and fatigue, and between engagement in pacing and physical activity after controlling for demographics, health-related quality of life, and perceived risk of overactivity.

To examine how engagement in pacing was related to fatigue and physical activity, two hierarchical regression analyses were conducted with fatigue or physical activity as the dependent variable, and engagement in pacing as the independent variable. Age, gender, body mass index, health-related quality of life, and perceived risk of overactivity were confounders.

These demographics and confounders were included in the models based on the fact that they are general demographic variables of interest in studies on physical activity behaviour and fatigue experience, and on known associations with perceived fatigability and physical activity behaviour [18,31]. We chose to analyze our data using these models based on the literature and our expectation that activity pacing may be a positive strategy to manage fatigue and optimize daily activities [14,15].

In both models, at the first step, gender, age, and body mass index were entered. At the second step, health-related quality of life and perceived risk of overactivity were entered, and at the third step engagement in pacing was entered. In both models, the variance inflation factors (VIFs) were examined for multicollinearity.

#### **3. Results**

Of the 89 participants included in the study, nine participants had incomplete data and were therefore excluded from the analysis. Characteristics of the sample (*N* = 80) are shown in Table 1. Of the sample, 75% were female *(n* = 60) and the mean age was 44 ± 11 years. The majority of the sample *(n* = 73, 91.3%) were scored as having clinically significant fatigue on the FSS (FSS score > 4). We found that 85.61% (*n* = 69) of the participants lived independently and 33.6% (*n* = 69) had a university education. The sample was, on average, overweight according the World Health Organization standards (body mass index ≥ 25.0 kg/m2 ).

Bivariate Pearson correlations (Table 2) showed that the variables were not strongly correlated with each other, providing support for the decision to include them into the primary analyses. Fatigue and health-related quality of life had the highest modest correlation (*r* = −0.41). The next modest correlations were between engagement in pacing and health-related quality of life (*r* = −0.27), and between engagement in pacing and fatigue (*r* = 0.27). These were followed by the correlations between engagement in pacing and physical activity (*r* = −0.25), and between engagement in pacing and age (*r* = 0.24). All other bivariate correlations were of modest magnitude (*r* ≤ ±0.22).

**Table 1.** Demographics of Participants (*N* = 80).

\* Interquartile range of the 25th percentile and the 75th percentile. a Completed university or higher.

**Table 2.** Bivariate Pearson correlations of all variables in the hierarchical linear regression models.

\* Correlation is significant at the 0.05 level. \*\* Correlation is significant at the 0.01 level.

#### *3.1. Primary Analyses*

#### 3.1.1. Relationship between Engagement in Pacing and Fatigue

Results of the relationship between engagement in pacing and fatigue, controlling for demographics and confounders (Table 3), showed no association between engagement in pacing and fatigue (β = 0.198; *t* = 1.43, *p* = 0.16). Among the confounders, health-related quality of life was negatively related to fatigue (β = −0.341; *t* = −2.57, *p* = 0.03).

**Table 3.** Hierarchical linear regression model showing the relationship between engagement in pacing and fatigue.

β, Standardized regression coefficients from the complete regression model accounting for all variables; B, unstandardized regression coefficients from the complete regression model accounting for all variables; df, degree of freedom; SE, standard error of B. Note: In this model, fatigue was the dependent variable, engagement in pacing was an independent variable, and the other variables were confounders.

#### 3.1.2. Relationship between Engagement in Pacing and Physical Activity

Results of the relationship between engagement in pacing and physical activity, controlling for demographics and confounders (Table 4), revealed no association between engagement in pacing and physical activity (β = −0.242; *t* = −1.61, *p* = 0.12). None of the demographics and confounders was related to physical activity (*p* ≥ 0.05).

**Table 4.** Hierarchical linear regression model showing the relationship between engagement in pacing and physical activity.

β, Standardized regression coefficients from the complete regression model accounting for all variables; B, unstandardized regression coefficients from the complete regression model accounting for all variables; df, degree of freedom; SE, standard error of B. Note: In this model, physical activity was the dependent variable, engagement in pacing was an independent variable, and the other variables were confounders.

For all analyses, the VIFs were low showing that there was no problem of multicollinearity (range: 1.04–1.30).

#### **4. Discussion**

This study explored relations of reported engagement in pacing with fatigue and physical activity, while controlling for demographics, health-related quality of life, and perceived risk of overactivity in adults with MS and found no associations between engagement in pacing and fatigue or and physical activity. These findings were similar to the findings of Murphy et al. [18] but did not support our hypothesis that engagement in pacing would be associated with low fatigue and high physical activity. Regarding the confounders, health-related quality of life was negatively related to fatigue. Descriptive statistics showed people with MS demonstrated clinically significant fatigue complaints, which was similar to studies evaluating fatigue in the MS population [32], high engagement in pacing and a high perceived risk of preventing overactivity. The total minutes of physical activity level reported by participants in our study is consistent with previous research involving people with MS [6,33]. The FSS score (5.43 ± 1.11) and percentage of participants reporting clinically significant fatigue (91.3%) in our study were comparable with those reported in other studies involving people with MS [1,34,35]. In their studies, Weiland et al. [34] and Hadgkiss et al. [35] reported median FSS score of 4.9 (IQR 3.2–6.1) with 65.6% of the sample reporting clinically significant fatigue. Similarly, Merkelbach et al. [1] reported a mean FSS score of 4.4 ± 1.6, with 58.75% of the sample reporting clinically significant fatigue.

Bivariate correlation analysis conducted prior to the primary analyses revealed a moderate negative association between fatigue and health-related quality of life, indicating high fatigue was associated with low health-related quality of life. Furthermore, there was a weak negative association between engagement in pacing and health-related quality of life, suggesting that high engagement in pacing was associated with low health-related quality life. Together, these findings suggest that without interventions, there appears to be no clear strategy for using physical activity to ameliorate fatigue symptoms and improve quality of life amongst people with MS. This underscores the need to explore the potential of guiding and advising people with MS regarding optimal pacing behaviour and to develop therapeutic interventions.

A possible explanation for the lack of associations between reported engagement in pacing and fatigue or physical activity after controlling for demographic and confounding variables, coupled with the clinically significant fatigue found in this study, may be multiplicity in persons' attitudes towards physical activity in relation to fatigue symptoms. People with MS who experience more disruption through fatigue in daily life may be consciously limiting their activities to prevent fatigue worsening, or exhibiting all-or-nothing behaviour, which is a lifestyle characterized by periods of overactivity (when feeling good) and as a consequence of that, feeling overtly fatigued afterwards, followed by long extensive rest periods to recover from residual symptoms or prevent symptoms re-occurring. For those consciously limiting their activities to prevent fatigue worsening, more engagement in pacing will

most likely result in less physical activity, while for those exhibiting a lifestyle characterized by periods of overactivity and prolonged inactivity, more active engagement in pacing will most likely result in more physical activity, and thus when both attitudes are present in the subject population no relations between activity pacing and physical activity may be found.

This further highlights the importance to explore the natural use of activity pacing in relation to what we know from literature to help guide treatment efforts for people with MS. Tailored advice and goal-directed interventions on how to approach activity effectively, such as guidance on optimal use of pacing, might be beneficial for people with MS. For example, people who avoid physical activity in anticipation to fatigue might score high on engagement in pacing but may need advice to engage more in physical activity, and could be provided with a graded consistent program of physical activity to increase their health, as well as be given information and strategies to help change their beliefs that "I should do less if I am tired" or "symptoms are always a sign that I am damaging myself." Similarly, people who have developed an all-or-nothing behaviour style might need advice to be more aware of anticipatory ways of engaging in pacing to develop a consistent pattern of paced activity and rest.

To our knowledge this is the first study to tap into the experiences of people with MS during their daily routines and explore the associations between engagement in pacing, fatigue, and physical activity. Adequate management of fatigue might be essential to improve health and wellbeing in people with MS, based on the findings of this study and previous literature that revealed most people with MS experience high levels of fatigue throughout the day [31]. Though the sample size in this study was substantial for this population (*N* = 80), it would be useful to replicate these analyses in a larger sample to obtain more precise estimates of the model parameters while controlling for confounders. Furthermore, the adapted SQUASH, and the Activity Pacing and Risk of Overactivity Questionnaire used in this study are recent and have undergone limited validity and sensitivity testing, which may have influenced the study findings. Currently, further studies on the validity of these measurements and usage for the current purposes are being conducted. Although self-report measures are more feasible in population studies, they are susceptible to biases as they involve recalling activities (over days, weeks, or months) that could lead to underreporting or overreporting. Using an objective device would allow to examine more macro levels of activity and is warranted in future study.

To optimize generalizability within the population of people living with MS, this study was conducted solely in people with MS. Generalizability to other populations might therefore be limited, as findings may vary per condition [14]. Unfortunately, there was a lack of information on participants' MS type and MS disability in this study, which limits the ability to draw firm conclusions. These variables could influence the study findings. Lastly, the weak bivariate correlations between reported engagement in pacing and fatigue, and between reported engagement in pacing and physical activity found may have accounted for the lack of associations after controlling for demographics, health-related quality of life, and perceived risk of overactivity. It is worth noting that although participants received rehabilitation treatment as part of the larger multicenter study, a structured activity pacing program was not included and we do not think this has influenced the findings of this study. Future studies should further explore how engagement in pacing and perceived risk of overactivity relate to performance of activities of daily living, to allow for firm conclusions and help advice people with MS on how to engage in an active lifestyle. Additionally, exploratory studies on how activity pacing behaviour might affect physical activity, fatigue, and health-related quality of life over a longer period of time are warranted. Such studies should explore higher versus lower fatigue group in terms of clinical fatigue cut-off point (FSS > 4) or a median split, to help better understand associations.

#### **5. Conclusions**

This study examined the relationships between reported engagement in pacing and fatigue and physical activity in people with MS, while controlling for demographics, perceived risk of overactivity, and health-related quality of life. No associations were found between reported engagement in pacing and fatigue, or between reported engagement in pacing and physical activity. We found that

low health-related quality of life was associated with high fatigue. People with MS might benefit from targeted interventions to better manage their fatigue and improve their health and wellbeing. Ascertaining engagement in pacing may be important to help tailor advice on optimal pacing behaviour for people with MS. There is a need to explore the potential of guiding and advising people with MS on activity pacing and develop therapeutic interventions.

**Author Contributions:** Conceptualization, U.S.A. and F.J.H.; Methodology, U.S.A., F.H., and B.L.S.; Validation, F.J.H., F.H., L.H.V.v.d.W., and R.D.; Formal Analysis, U.S.A.; Investigation, U.S.A., F.H., and B.L.S.; Resources, F.J.H., L.H.V.v.d.W., and R.D.; Data Curation, F.H. and B.L.S.; Writing—Original Draft Preparation, U.S.A. and F.J.H.; Writing—Review and Editing, U.S.A., F.J.H., F.H., B.L.S., L.H.V.v.d.W., and R.D.; Supervision, F.J.H., L.H.V.v.d.W., and R.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank all participants for their contribution to the ReSpAct study. Furthermore, we would like to thank the following organizations for their support in the ReSpAct study: Adelante zorggroep, Bethesda Ziekenhuis, De Trappenberg, De Vogellanden, Maasstad Ziekenhuis, Medisch Centrum Alkmaar, Militair Revalidatiecentrum Aardenburg, Revalidatiecentrum Leijpark, Revalidatiecentrum Reade, Revalidatie Friesland, Revant, Rijnlands Revalidatiecentrum, RMC Groot Klimmendaal, Scheper Ziekenhuis, Sint Maartenskliniek, Sophia Revalidatie, Tolbrug Revalidatie, ViaReva, and Stichting Onbeperkt Sportief.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Factor loadings of the seven items of the Activity Pacing and Risk of Overactivity Questionnaire using Principal Component Analysis with oblique rotation.

Factor 1: Engagement in pacing; Factor 2: Perceived risk of overactivity. \* Loadings that can be explicitly assigned to a single factor (factor loading >0.40).

#### **References**

*J. Funct. Morphol. Kinesiol.* **2020**, *5*, 43

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Adjuvant Therapy Reduces Fat Mass Loss during Exercise Prescription in Breast Cancer Survivors**

**Gabriele Mascherini \* , Benedetta Tosi, Chiara Giannelli, Elena Ermini, Leonardo Osti and Giorgio Galanti**

Dipartimento di Medicina Sperimentale e Clinica, Università degli Studi di Firenze, 50134 Firenze, Italy<EMAIL_ADDRESS>(B.T<EMAIL_ADDRESS>(C.G<EMAIL_ADDRESS>(E.E<EMAIL_ADDRESS>(L.O<EMAIL_ADDRESS>(G.G.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-3396895925

Received: 26 May 2020; Accepted: 11 July 2020; Published: 15 July 2020

**Abstract:** Improvements in cancer care over the years have increased the numbers of cancer survivors. Therefore, quality of life, fat mass management and physical activity are growing areas of interest in these people. After the surgical removal of a breast cancer, adjuvant therapy remains anyway a common strategy. The aim of this study was to assess how adjuvant therapy can affect the effectiveness of an unsupervised exercise program. Forty-two women were enrolled (52.0 ± 10.1 years). Assessments performed at baseline and after six months of exercise prescription were body composition, health-related quality of life, aerobic capacity by Six-Minute Walk Test, limbs strength by hand grip and chair test and flexibility by sit and reach. Statistical analyses were conducted by ANOVA tests and multiple regression. Improvements in body composition, physical fitness and quality of life (physical functioning, general health, social functioning and mental health items) were found. The percentage change in fat mass has been associated with adjuvant cancer therapy (intercept = −0.016; b = 8.629; *p* < 0.05). An unsupervised exercise prescription program improves body composition, physical fitness and health-related quality of life in breast cancer survivors.

**Keywords:** physical activity; oncology; unsupervised exercise; lifestyle; exercise prescription

#### **1. Introduction**

Breast cancer is the most commonly diagnosed cancer in women around the world, the second cause of cancer death in female population of developed countries (198,000 deaths in 2012) and the first cause of cancer death in Italian women (12,274 deaths in 2015) [1].

The importance of lifestyle in the etiopathogenesis of this disease is well-demonstrated [2]. After a cancer diagnosis, patients report a feeling of fatigue that can result from the side effects of the treatment or from the cancer itself. This promotes an increase in physical inactivity, which increases the likelihood of incurring overweight and obesity [3]. The excess weight condition is associated with a low-grade systemic inflammation that promotes the development of insulin resistance, atherosclerosis and tumor growth, even in cancer survivors. This may explain the association between cancer and cardiovascular / metabolic diseases [4]. The impact of comorbidities on all-causes mortality in breast cancer survivors is remarkable [5].

Women with a diagnosis of breast cancer may experience disease and treatment-related adverse physiological and psychosocial effects at short and long-term [6]. After surgery, adjuvant therapy in the form of hormone therapy, chemotherapy or target molecular therapy is generally considered [1]. These therapeutic choices seem to promote short-term body composition changes by increasing body water and long-term in terms of increasing fat mass [7].

In order to reduce chronic inflammation, fat mass reduction is one of the most important outcome of any exercise prescription program, as it can contribute to decrease recurrence risk and to increase disease-free and overall survival [8]. Exercise training in breast cancer survivors can maintain or improve VO2peak, significantly improved lean body mass, upper and lower body strength [9].

Cancer can also negatively affect in terms of health-related quality of life (HRQoL) and psychosocial and physical function [6]. It is well known that physical activity has small-to-moderate beneficial effects on HRQoL, as well as on emotional or perceived physical and social function, anxiety, cardiorespiratory fitness [6,9] of breast cancers survivors during and after adjuvant treatment. HRQoL, whose improvements are considered a prognostic indicator of overall survival in cancer patients, normally worsens after cancer diagnosis and during cancer treatments [10,11].

Physical activity interventions may help to improve prognosis and may alleviate the adverse effects of adjuvant therapy in terms of body composition and HRQoL. Home-based exercise program demonstrated both a short and long-term effectiveness in physical function and body composition parameters [12,13].

This study aimed to:

#### **2. Materials and Methods**

#### *2.1. Subjects*

The data of this observational study were collected from September 2015 to September 2017. The Breast Unit of the Careggi University Hospital selected and enrolled patients. After, they began the exercise prescription program at the Sports Medicine Center of the same University Hospital.

#### 2.1.1. Inclusion Criteria

The patients included in this study met the following inclusion criteria: (1) female, (2) from 21 to 65 years old, (3) physiological or pharmacological induced menopausal (4) history of surgery for breast cancer, (5) no participation in other training programs or no regular attendance at health clubs.

#### 2.1.2. Exclusion Criteria

The participants were excluded from the study if they said they were physically unable to participate in the treatment protocol or if changes in their physical activity behavior were contraindicated. The participants were also excluded if they were taking antipsychotic medication or undergoing any weight-loss strategy.

The participants were required to provide their written consent prior to their inclusion in the study as well as a letter of approval to participate from their oncologist and sports physician. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration.

#### *2.2. Procedures*

The program at Sport Medicine Center consisted in:

#### 2.2.1. Medical History and Cardiac Evaluation

All patients were evaluated before starting the program to exclude any contraindication and thus provide eligibility to physical exercise. They underwent assessment by health questionnaire (to exclude any family history for chronic or metabolic diseases, anticancer therapies, comorbidity and any symptoms), physical examination, ECG at rest and 2-dimensional echocardiography to exclude chemotherapy-induced cardiotoxicity.

#### 2.2.2. Lifestyle Assessment

Lifestyle was assessed at the beginning of the program to evaluate the spontaneous physical activity [14]. An accelerometer (armband model MF-SW, display model DD100, SenseWear®, BodyMedia®, Pittsburgh, Pennsylvania, USA) on the non-dominant arm of the patients to be kept for one week. The parameters provided by the specific software were:

#### 2.2.3. Body-Composition Analysis

The same researcher evaluated body composition, measuring anthropometric parameters, skinfold for subcutaneous adipose tissue and bio impedance for fat-free mass [15].

Measures of weight were approximated to the nearest 0.1 kg, those of height to the nearest 0.5 cm (Seca GmbH &Co., Hamburg, Germany), BMI was then calculated (kg/m2 ). Waist, hip, operated and not operated arm (in case of bilateral surgery, non-dominant arm values were considered as "operated") circumferences were also measured using a measuring tape (Holtain Limited, Crosswell, UK, 1.5 m flexible tape). Waist–hip ratio was calculated [16].

Triceps, biceps, subscapular and supra-iliac skinfolds were measured by calipers (Holtain, Limited Tanner/Whitehouse skinfold caliper, Crosswell, UK) and sum (mm) of the four skinfold sites was calculated [17].

Bio impedance analysis (BIA 101 Sport edition, Akern, Florence, Italy) provided the values of resistance (Rz) and reactance (Xc) [18]: from these two the phase angle (PA), the amounts of total body water (TBW), extracellular water (ECW), intracellular water (ICW), fat free mass (FFM), body cell mass (BCM), muscle mass (MM) and fat mass (FM) were obtained.

#### 2.2.4. Health-Related Physical Fitness Parameters

The six minute walk test (6MWT) assessed cardiovascular fitness, because most daily life activities are performed at submaximal levels of exertion, this test may better reflect the functional exercise level for daily physical activities [19]. The parameters recorded during 6MWT were distance covered (6 MWD), peak heart rate with a heart rate monitor, systolic and diastolic blood pressure at rest and at the end of the test and self-perception of effort (CR10) [20].

Muscle fitness evaluation were performed with easily executable and reproducible tests in an outpatient setting such as sit & reach for flexibility [21], the hand grip test to estimate the overall static strength of the upper limbs with both arms [22] and the chair test to assess the strength of the lower limbs [23].

#### 2.2.5. Health-Related Quality of Life Assessment

Health-related quality of life was assessed by administering the SF-36 questionnaire. This is a validated tool that measures eight health concepts [24]: physical functioning (PF), role limitations due to physical health problems (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role limitations due to personal or emotional problems (RE) and mental health (MH) perceptions. Scores for each domain range from 0 to 100, with a higher score defining a more favorable health state [25].

#### 2.2.6. Exercise Prescription

Exercise program were prescribed at the end of the first visit following American College of Sports Medicine guidelines [26]. The program did not include supervised exercise. The combination of duration and weekly session aerobic training (such as walking, cycling or jogging) were established starting from thirty minutes five times per week (150 min per week) while intensity were establish in terms of heart rate and perceived of effort based on 6MWT. In addition, a target of number daily step were provided. At the end of each aerobic exercise sessions, flexibility exercise have been recommended. resistance training has been suggested twice per week with 8 exercises involving the main muscle groups, performed for 3 sets with 10 repetitions. The exercises were chosen based on the possibility of being performed safely at home (such as bodyweight squat and glute bridge for the lower limbs, lateral raise and biceps curl for the upper limbs). At the end of each visit, the prescribed exercise program was described. Furthermore, for resistance exercise, a demonstration was performed by qualified personnel in physical exercise followed by repetition by the patient as a learning test. Exercise program were individually updated every follow-up visit following the results of the assessments.

#### *2.3. Statistical Analysis*

The data were expressed as mean ± standard deviation. The Shapiro–Wilk test was used to assess the normal distribution of variables. one-way ANOVA was performed to evaluate the variations of the body composition and fitness parameters between baseline and final study values (T0-T5). The Cohen's d effect size (ES) was calculated to determine the magnitude of effect. ES was assessed using the following criteria: small < 0.20, medium < 0.50 and large < 0.80.

The answers to the SF-36 questions were recorded and recalibrated to obtain a raw score that was then converted into the correspondent percentage score. A paired Student's t-test was used to establish differences between baseline and six months SF-36 parameters.

A multiple linear regression was used to assess the relationship between the percentage change in fat mass during the program calculate as [(∆ T5-T0 FM/FM T0)·100)] and three potential predictors as: (1) adjuvant cancer therapy, (2) fat mass at baseline and (3) age at baseline.

One-way ANOVA and the Bonferroni's test for multiple comparison were used to establish the potential differences among the different kinds of adjuvant therapy in term of change of fat mass. Therapy was considered with four subgroups: hormone therapy only, chemotherapy and/or target therapy, any combination of therapies, no therapy. The data were analyzed using SPSS-IBM 20 (SPSS, Inc., Chicago, IL, USA). The statistical significance threshold was set at a *p*-value = 0.05.

#### **3. Results**

A group of 42 women (age 52.0 ± 10.1 years) were considered eligible for the study and were then enrolled. All the patients had been diagnosed with a stage IIIC or inferior breast cancer and started the program after the surgery: 48% had undergone unilateral mastectomy with lymphadenectomy, 19% unilateral quadrantectomy with lymphadenectomy, 19% unilateral quadrantectomy, 12% bilateral mastectomy with bilateral lymphadenectomy, 2% bilateral mastectomy with unilateral lymphadenectomy.

Specific neoplastic therapy was: 15 took hormone therapy (tamoxifen and aromatase inhibitors), 9 underwent chemotherapy and/or target therapy (anthracycline and trastuzumab), 9 combined hormone therapy with chemotherapy and/or target therapy, and 9 did not undergo any adjuvant cancer therapy.

#### *3.1. Lifestyle Assessment*

The results of lifestyle assessment were:

#### *3.2. Body Composition Analysis*

Baseline anthropometric parameters assessment defined an overweight sample (BMI T0 = 27.3 ± 4.20 kg/m2 ) and 30% was obese (BMI ≥ 30.0 kg/m2 ; Table 1).

**Table 1.** Anthropometrics and skinfold parameters during 6 months follow-up.

During the six months of follow-up the patients lost weight progressively, getting close to overweight threshold (BMI T5 = 26.1 ± 3.87 kg/m2 ), others anthropometric parameters decreased, in particular waist circumference dropped below the cardio metabolic risk threshold. Skinfold thickness data and bio impedance analysis showed that weight loss is principally imputable to a fat mass loss and secondarily to extracellular water loss (Table 1).

On the contrary, body cellular mass and intracellular water did not show any significant change. The amount of total body water and fat free mass reduced is attributable to extracellular mass loss (Table 2).

**Table 2.** Bio-impedance parameters during 6 months follow-up.

Legend: PA—phase angle; TBW—total body water; ECW—extra cellular water; ICW—intra cellular water; FFM—fat free mass; BCM—body cellular mass; FM—fat mass.

#### *3.3. Health-Related Physical Fitness Parameters*

All the physical fitness parameters improved progressively. Moreover, the values at rest of systolic (SBP), diastolic (DBP) blood pressures and mean arterial pressure (MAP) decreased significantly (Table 3).

**Table 3.** Physical fitness parameters related to health during 6 months follow-up.

Legend: HR—heart rate; SBP—systolic blood pressure; DBP—diastolic blood pressure; MAP—mean arterial pressure.

#### *3.4. Health-Related Quality of Life Assessment*

As far as the health-related quality of life is concerned, all the eight health concepts measured by the SF-36 questionnaire were improved: those found in PF, GH, SF and MH scales registered a significant improvement, and those found in BP, VT and RE scales were, anyhow, very close to significance threshold (Table 4).

**Table 4.** Results of health-related quality of life parameters measured by social functioning (SF)-36 questionnaire.

Legend: PF—physical functioning; RP—role limitations due to physical health problems; BP—bodily pain; GH—general health; VT—vitality; SF—social functioning; RE—role limitations due to personal or emotional problems; MH—mental health perceptions.

#### *3.5. Relationship between Adjuvant Cancer Therapy and Changes in Fat Mass*

The association between percentage change in fat mass and the three potential predictors were (R2 = 0.52; MSE = 5.73): adjuvant cancer therapy coefficients = 8.63, *p* < 0.05; fat mass at baseline coefficients = −0.32, *p* = 0.36; age at baseline coefficients = 0.18, p = 0.26. In particular, this association between change in fat mass and adjuvant cancer therapy one-way ANOVA shows these differences existed between no therapy subgroup and any other subgroup (F = 5.12, *p* = 0.018, ES = 0.68), but no differences existed among these last subgroups of therapy:

Therefore, adjuvant cancer therapy was negatively associated with fat mass loss observed between baseline and six months.

#### **4. Discussion**

This study confirmed the effectiveness in terms of body composition and physical fitness of unsupervised exercise program in breast cancer survivor [12,13].

After the surgical removal of a breast cancer, adjuvant therapy is a common strategy. However, higher breast cancer risk with hormone replacement therapy is particularly evident among lean women, in postmenopausal women who are not taking exogenous hormones; general obesity is a significant predictor for breast cancer recurrence. Moreover, increased plasma cholesterol leads to accelerated tumor formation and exacerbates their aggressiveness [6].

The sample of the present study shows the anthropometric and lifestyle parameters in line with other studies already present [27]; these characteristics do not appear to guarantee a healthy level of cardiorespiratory fitness. Therefore, these patients should carry out a regular exercise program in order to ensure an improvement in health-related physical fitness parameters [28].

The therapeutic efficacy of the physical exercise is now consistent and demonstrated by systematic reviews and meta-analyses in the context of secondary and tertiary prevention of breast cancer [29,30]. Physical activity in breast cancer survivors may be more effective at modifying serum IGF-1 levels in women who are not taking tamoxifen [31], on the insulin pathway may be more pronounced for obese or sedentary women [32]. A marginal effect of physical activity in terms of decreasing circulating levels of biomarkers of inflammation in particular (CRP) [33] and in circulating levels of markers of cell-mediated immunity [34]. In this context, unsupervised training strategy could be an option in a physical exercise therapy perspective.

One of the aims of the study was to evaluate the influence of adjuvant therapy on the effectiveness of an exercise program on fat loss and how different therapeutic choices can have a different effect.  However, the difference in efficacy between the different therapeutic strategies and the comparison of efficacy with those who do not perform any adjuvant therapy is not specified. The results of multiple regression in this study confirm the effectiveness of the exercise in reducing fat mass during adjuvant therapy without however any difference between the various therapeutic choices. However, those who did not have an adjuvant treatment regimen reported a greater reduction in fat mass than those who had an adjuvant therapy in place.

Health-related quality of life results are consistent with those shown in other studies where SF-36 questionnaire was used [36]. SF-36 is indeed a questionnaire that can be applied to many different clinical situations and that measures health concepts (particularly RE and MH) that can be influenced by a large number of factors (including changes in cancer therapies, that can signify changes in side effects associated with them). Anyway, the documented correlation between the other six scales and physical health perception, together with the improvements observed in body composition and physical fitness, justify the attribution to the program of at least a part of the improvements that is proportional to the correlation coefficient of each scale. Indeed, the lack of significance for RP, BP, VT and RE scales is almost surely due to the difference in statistical power that characterizes the eight scales: with a larger sample, significance would be obtained. Regarding RE and MH scales instead, it is only possible to consider that women have on average improved: this cannot be random, supporting the thesis that the program is capable of improving these two health concepts as well. It is important to notice that a major score in BP, RP and RE scales corresponds, respectively to minor pain, less limitation due to physical health problems and less limitation due to personal or emotional problems.

 Aerobic and resistance exercises prescription is capable of improving body composition, physical fitness and health-related quality of life of breast cancer survivors. American College of Sport Medicine's guidelines [26] provide rough indications about intensity, duration and frequency of aerobic, resistance and flexibility exercises, highlighting the importance of considering a large number of factors (age, exercise endurance, drugs taken, cancer stage) to prescribe exercise safely and effectively. Anyhow, the ultimate goal of any exercise prescription program for cancer survivors is to induce long-term modifications in patients' lifestyle, in order to reduce recurrence risk, cancer mortality and all causes of mortality [38]. From this perspective, unsupervised exercise programs can ensure good adherence in cancer survivors [39], even in those undergoing adjuvant treatments.

The present study shows strengths. First, all the subjects belonged to the same Breast Unit, therefore they undergone to surgery from the same surgeon group. Second, the sample receive the exercise program from the same Sport Medicine Center by the same specialist, therefore the methodology was standardized.

The study has limitations. The first limit is in the observational nature of an outpatient exercise prescription program; therefore, the intervention of the researcher was limited, and some adjustments were not possible. Second, body composition changes did not take into account the energy intake; therefore, the results have a reduced generalizability.

Larger samples are needed to confirm these results and future research directions could be the association between adjuvant therapies and the loss of fat mass assessed also with lipid blood values in addition to subcutaneous fat.

#### **5. Conclusions**

The results shown in this study demonstrate that an unsupervised exercised prescription program produces mid-term improvements in body composition, physical fitness and health-related quality of life of breast cancer survivors.  Longer-term follow-up studies are needed to establish the real capacity of this training strategy to induce long-term lifestyle changes.

**Author Contributions:** Conceptualization, G.M. and G.G.; methodology, L.O.; validation, G.G., L.O. and G.M.; formal analysis, L.O. and G.M.; investigation, E.E.; data curation, G.M. and L.O.; writing—original draft G.M., C.G. and B.T.; project administration, C.G. and B.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Review* **Adapted Physical Activity to Ensure the Physical and Psychological Well-Being of COVID-19 Patients**

**Grazia Maugeri 1 and Giuseppe Musumeci 1,2,3,\***

**Abstract:** The novel coronavirus disease 2019 (COVID-19) has been responsible for a global pandemic involving massive increases in the daily numbers of cases and deaths. Due to the emergency caused by the pandemic, huge efforts have been made to develop COVID-19 vaccines, the first of which were released in December 2020. Effective vaccines for COVID-19 are needed to protect the population, especially healthcare professionals and fragile individuals, such as older people or chronic-diseaseaffected patients. Physical exercise training generally has health benefits and assists in the prevention of several chronic diseases. Moreover, physical activity improves mental health by reducing anxiety, depression, and negative mood and improving self-esteem. Therefore, the present review aims to provide a detailed view of the literature, presenting updated evidence on the beneficial effects of adapted physical activity, based on personalized and tailor-made exercise, in preventing, treating, and counteracting the consequences of COVID-19.

**Keywords:** COVID-19; prevention; physical activity; inactivity; home-based exercise; mental health; psychological well-being

#### **1. Introduction**

The social effects caused by the global spread and pandemic of SARS-CoV-2 is having unimaginable consequences that the world has never faced in past decades. After the WHO declared SARS-CoV-2 a health emergency, the world responded quickly to "flatten the curve" or limit the spread of the virus by banning travel and closing non-essential businesses and educational institutions, as well stopping all kinds of large gatherings. In this first phase of the pandemic, around half of the world's population was under full or partial lockdown to limit the spread of the deadly virus [1]. The unprecedented restrictions prompted by the raging SARS CoV-2 pandemic halted a wide variety of economic activities throughout the world. Day by day, the need for essential healthcare equipment increased in parallel with the increase of infected patients and death tolls. More than 100 countries closed their borders and worldwide air travel demand plummeted just after the announcement of the pandemic by the WHO. This severely impacted the world's supply chain and international trade [2]. Economists agreed that there would be an enormous negative impact on global economic development due to COVID-19, which would possibly plunge the world economy into a deep recession [3]. Therefore, after this initial lockdown period, a second phase started, involving the partial reopening of the economy. However, increasing cases and limited numbers of intensive care beds have caused public health authorities in Europe to re-impose temporary lockdowns.

Global health authorities have been informed by epidemic and infectious disease specialists, who have faced the present health emergency by taking cues from past epidemics. However, we now know that the story of COVID-19 cannot be compared to past

**Citation:** Maugeri, G.; Musumeci, G. Adapted Physical Activity to Ensure the Physical and Psychological Well-Being of COVID-19 Patients. *J. Funct. Morphol. Kinesiol.* **2021**, *6*, 13. https://doi.org/10.3390/jfmk6010013

Received: 25 November 2020 Accepted: 27 January 2021 Published: 29 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

epidemics. Besides the high direct mortality for such a contagious acute disease, COVID-19 has placed extreme pressure on healthcare systems, altering access to health services of patients living with other pathologies, such as non-communicable diseases (NCDs). Moreover, such NCDs (e.g., diabetes mellitus, hypertension, cerebrovascular disease, coronary artery disease, chronic obstructive pulmonary disease) have been shown to predict poor prognosis in patients with COVID-19. SARS-CoV-2 and NCDs are clustering within social groups according to patterns of socioeconomic inequality that are deeply embedded in our societies. Limiting the harm caused by SARS-CoV-2 will demand that far greater attention is paid to NCDs and socioeconomic inequality than has previously been done. The aggregation of these diseases against a background of social and economic disparity aggravates the adverse effects of each separate disease. COVID-19 is not a pandemic or simple comorbidity—it is a syndemic [4]. Addressing COVID-19 means addressing hypertension, obesity, malnutrition, diabetes, cardiovascular and chronic respiratory diseases, cancer, and psychological and neurodegenerative disorders. Paying more attention to NCDs and socioeconomic inequality should be a strategic plan for both rich and poor nations in order to limit the harm caused by SARS-CoV-2.

The worldwide spread of SARS-CoV-2 infection has caused governments of various countries to take swift and unprecedented protective measures, including placing cities in lockdown and closing places where large gatherings would occur. Quarantine has radically changed the daily habits of the entire population, requiring people to practice "social distancing". For example, in Italy, the Italian Ministry of Education (MIUR) decided to invest huge resources in special desks to promote higher interpersonal distance in classrooms to avoid the risk of infection [5]. Although such strategies have contained the COVID-19 outbreak, the prolonged self-isolation has deeply affected active lifestyles, leading healthy individuals and athletes to states of physical inactivity, with related consequences of hypomobility and inactivity-associated disorders, such as a reduction in maximal oxygen consumption (VO2max), endurance capacity, loss of muscle strength and mass, overweight, and decrease joint lubrification [6–9]. Just a few days of a sedentary lifestyle are sufficient to induce fiber denervation, insulin resistance, and low-grade systemic inflammation [10].

The positive effects of regular physical activity on general health are well known in the field of modern medicine. Physical activity counteracts cardiovascular vulnerability, inflammation, muscle atrophy, bone and cartilage loss or degeneration, and the reduction of aerobic capacity [11,12]. Physical exercise is also closely related to cognitive function and neurodegenerative disorders by inducing cellular and molecular processes underlying neurogenesis and synaptogenesis cascade, which enhance learning, memory, and brain plasticity [13,14]. These effects are extremely important, especially in light of new evidence showing brain damage among the consequences of COVID-19, including delirium, stroke, and brain inflammation. Moreover, adapted physical activity ameliorates one's self-esteem and provides a sense of well-being by reducing the development of mental disorders [15]. In light of this evidence, the purpose of this narrative mini-review is to summarize the beneficial effects of the adapted physical activity performed before-, during-, and postinfection of COVID-19. To this end, four databases were used: PubMed, Scopus, Web of Science, and Google Scholar. The last search was conducted on 30 December 2020. The following keywords and combinations thereof were used: "exercise", "physical activity", "adapted physical activity", "physical exercise", "SARS-CoV-2", "SARS CoV-2 pandemic", "COVID-19 pandemic". The initial study selection was performed via title and abstract screening. Duplicates were removed. The full texts of the selected articles were carefully read and analyzed in order to extract the appropriate data from each text.

#### **2. The Beneficial Effects of Physical Activity before COVID-19 Infection**

The development of COVID-19 is strictly linked to the interaction between SARS-CoV-2 and the host's immune system. The virus affects the response of the immune system, leading to leukopenia with high levels of pro-inflammatory mediators. Several studies have shown that in mild cases of COVID-19, macrophages of pulmonary tissue are able to

counteract SARS-CoV-2 and the innate and adaptive immune responses are able to fight viral replication. In contrast, severe cases of COVID-19 provoke a storm of pro-inflammatory cytokines and a lymphopenia state [16]. This "hyper inflammation" is characterized by aberrant pathogenic T cells and inflammatory monocytes, which are rapidly activated and produce a large number of cytokines, thus inducing the inflammatory storm [16]. Moreover, some COVID-19-affected patients have developed acute demyelinating encephalomyelitis (ADEM), without showing respiratory symptoms [17,18], and in some cases Guillan-Barrè syndrome has been diagnosed, characterized by nerve damage [19]. The SARS-CoV-2 virus is usually not present in patients' cerebrospinal brain fluid. Therefore, it is possible to speculate that brain inflammation is caused by the immune system and that the neurological complications of COVID-19 might be provoked by the deregulated immune response rather than the virus itself [20]. The role of physical exercise in improving immune response has largely been demonstrated. Moderate and adapted physical activity increases the anti-inflammatory cytokines, immunoglobulins, and immune cells in circulation, as well as the anti-pathogenic activity of macrophages [21]. In this way, physical exercise may cause reductions of the burden of pathogen and the abnormal inflammatory cells that damage the lungs [22]. Interestingly, COVID-19-affected patients have reported higher levels of cytokines, such as TNF-α, IFN-γ, IL-1β, and IL-6, as compared to healthy subjects [23,24]. Moreover, the expression levels of these pro-inflammatory cytokines to be appeared directly related to the severity of the patient's condition, confirming that the activation of the inflammatory process is linked to disease severity [23,24].

The implementation of the physical activity program could play a key role in counteracting the imbalance in antiviral immunity, protecting the individual against inflammation induced by COVID-19. According to WHO recommendations, all adults should undertake 150–300 min of moderate-intensity, 75–150 min of vigorous-intensity physical activity, or some equivalent combination of moderate-intensity and vigorous-intensity aerobic physical activity per week. Among children and adolescents, an average of 60 min/day of moderate– vigorous-intensity aerobic physical activity across the week provides health benefits. These guidelines recommend regular muscle-strengthening activity for all age groups [25]. The adapted physical activity comprises an exercise program designed in a personalized way in order to adapt to the physiological characteristics and the state of health of each subject. Its beneficial role in low-grade chronic inflammation was demonstrated in both the periphery and in the brain [26]. During its practices, stress hormones and microglia proliferation are decreased. Moreover, physical activity attenuates the release of proinflammatory cytokines through the modulation of anti-inflammatory cytokines, such as IL-1Ra, IL-6, and IL-10, as well as cytokine inhibitors, such as cortisol, prostaglandin E2, and soluble receptors against TNF and IL-2 [26]. Considering that adapted physical activity has shown several benefits for most chronic diseases and microbial infections with preventive and therapeutic effects, in the pre-infection phase, it may represent an important tool to prevent COVID-19 infection [27]. Furthermore, several pieces of evidence supported the direct relationship between exercise and psychological well-being. Individuals who practice regular physical activity ameliorate one's self-esteem and provide a sense of well-being, leading to reduced depressive and anxiety symptoms [28,29]. This plethora of positive effects is due to the involvement of the hypothalamic–pituitary–adrenal (HPA) axis and the endogenous opioid system, both of which are implied in anxiety, stress, depression, and emotional responses [30,31]. In addition, regular exercise promotes the release of several trophic factors, including brain-derived neurotrophic factor (BDNF), which exerts a positive role in both anxiety and depressive disorders [32]. Quarantine and physical isolation measures may have had long-lasting and wide-ranging negative psychosocial impacts, which may have been amplified by a reduction in physical activity levels. Several works have demonstrated the negative impacts of decreased physical activity on psychological well-being [33–36]. In particular, a decrease in the amount of physical activity is associated with higher levels of perceived stress and anxiety [33]. A study performed on older adults showed that those who met the global recommendations on physical activeness had higher levels of resilience

and lower levels of depressive symptoms [36]. The promotion of resilience during the COVID-19 pandemic is a crucial aspect for patients, considering that it is linked to positive emotions in stressful situations, locus of control, self-efficacy, optimism, and better quality of life (physical and psychological) [35]. In particular, Lesser and Nienhuis [33] reported that individuals who were more physically active showed greater mental health scores, whereas inactive subjects before the COVID-19 pandemic who became more active during the lockdown exhibited lower levels of anxiety. Moreover, a cross-national study between Germany, Italy, Russia, and Spain, showed that individuals with depression symptoms are at risk of developing a worse psychological condition during the current Covid-19 pandemic; instead, physical activity counteract such negative effects [37]. Interestingly, the profoundly negative impacts on psychological health and well-being in the population seem to be higher in females and young adults [32].

#### **3. Adapted Physical Activity Program during COVID-19 Infection**

Considering the clinical characteristics of COVID-19, infected patients, who are compelled to rest in bed, are not able to perform normal activities of daily life or perform regular physical activity. Nevertheless, considering the multiple positive effects caused by exercise, adapted physical activity in all phases of recovery of patients (Figure 1) represents an important strategy to attenuate the decline in cognition function and to improve physical and psychological well-being in individuals affected by COVID-19. When treating patients—and given the intensive medical management involved for some COVID-19 patients, including prolonged protective lung ventilation, immobility, sedation, and treatment with neuromuscular blocking agents—in the acute phase it is possible to adopt only passive types of exercise performed by physiotherapists or kinesiologists, such as whole-body vibration (WBV) exercise and passive range-of-motion (pROM) exercises. In the post-acute phase, however, physiotherapists or kinesiologists can organize bed-based exercise programs (e.g., flexion and extension of the limbs and trunk) and assist patients to mobilize independently to stand-up and perform normal daily functions according to the Barthel index, such as washing, eating, and so on. Other adapted physical activities, comprising passive, active-assisted, active, or resisted joint range-of-motion exercises, are fundamental to restore and improve respiratory and cardiocirculatory functions, joint integrity, rangeof-motion, muscle strength, and mental condition. During the day, hospitalized patients should perform the exercises alone by following a guided self-assessment for people with an acquired disability, which should be administered by physiotherapists or kinesiologists. This progressive approach as well as the known effects of general well-being can help keep patients busy and attenuate feelings of depression due to complete immobility. Regaining self-mobility can result in the patient acquiring better self-esteem and a strong response to depression.

Pneumonia, a severe complication of the virus, has been shown to induce cognitive decline due to sustained hypoxia [38,39]. Moreover, pneumonia patients were found to possess high levels of pro-inflammatory cytokines, leading to neuroinflammation and neurodegeneration [40]. Therefore, the positive influence of physical activity on cognitive performance is fundamental to accelerate the subsequent full recovery of COVID-19 patients. In fact, different studies have demonstrated that physical exercise enhances the neuronal activity and hippocampal neurogenesis essential for cognition [41,42]. Furthermore, adapted physical activity for COVID-19 patients represents a key psychological support. Exercise stimulates the cholinergic, dopaminergic, and serotonergic systems, enhancing mood by reducing depression, anxiety, and panic attacks [6,34]. In light of these positive effects on mental and psychological health, adapted or tailor-made exercises in COVID-19-affected individuals should be considered.

[45].

**Figure 1.** Health benefits from regular adapted physical activity in COVID-19-affected patients. **Figure 1.** Health benefits from regular adapted physical activity in COVID-19-affected patients.

#### Pneumonia, a severe complication of the virus, has been shown to induce cognitive decline due to sustained hypoxia [38,39]. Moreover, pneumonia patients were found to **4. Adapted Physical Activity Program Post COVID-19 Infection**

possess high levels of pro-inflammatory cytokines, leading to neuroinflammation and neurodegeneration [40]. Therefore, the positive influence of physical activity on cognitive performance is fundamental to accelerate the subsequent full recovery of COVID-19 patients. In fact, different studies have demonstrated that physical exercise enhances the neuronal activity and hippocampal neurogenesis essential for cognition [41,42]. Furthermore, adapted physical activity for COVID-19 patients represents a key psychological support. Exercise stimulates the cholinergic, dopaminergic, and serotonergic systems, enhancing mood by reducing depression, anxiety, and panic attacks [6,34]. In light of these positive effects on mental and psychological health, adapted or tailor-made exercises in COVID-19-affected individuals should be considered. **4. Adapted Physical Activity Program Post COVID-19 Infection** Once completely well, to maintain mental and physical well-being, it is vital for infected individuals to gradually resume physical activity and exercise with an adapted or tailor-made home-based exercise program administered by a sport scientist. The goal is to return to pre-infection levels of fitness. In this phase, the effects of physical activity on the brain trigger systemic influences on the entire body. Moreover, exercise promotes the release of endorphins, which enhance psychological well-being, favoring faster recovery and a return to normal life. Beyond conventional exercises to improve physical Once completely well, to maintain mental and physical well-being, it is vital for infected individuals to gradually resume physical activity and exercise with an adapted or tailor-made home-based exercise program administered by a sport scientist. The goal is to return to pre-infection levels of fitness. In this phase, the effects of physical activity on the brain trigger systemic influences on the entire body. Moreover, exercise promotes the release of endorphins, which enhance psychological well-being, favoring faster recovery and a return to normal life. Beyond conventional exercises to improve physical conditions, different activities are recommended to enhance psychological well-being, such as listening to music, reading or listening to a book, watching TV, playing cards, table games, and the utilization of "exergames" (i.e., active video games). These activities allow patients to keep busy, reducing depression. In particular, the use of exergames can positively affect motivation and self-efficacy by inducing physical activity practice [43]. Exergames use action and motion sensors, which allow the patient to be physically active, simulating several sport types, such as cycling, running, walking, rowing, and swimming. Moreover, the patient can also play exergames with a partner, favoring interaction and communication between them [44]. Another potentially beneficial activity during the patient recovery could be yoga. The practice of this discipline promotes endogenous melatonin secretion, positively affecting sleep quality, anxiety, and depressive disorders [45].

conditions, different activities are recommended to enhance psychological well-being, such as listening to music, reading or listening to a book, watching TV, playing cards, table games, and the utilization of "exergames" (i.e., active video games). These activities allow patients to keep busy, reducing depression. In particular, the use of exergames can positively affect motivation and self-efficacy by inducing physical activity practice [43]. Exergames use action and motion sensors, which allow the patient to be physically active, simulating several sport types, such as cycling, running, walking, rowing, and swimming. Moreover, the patient can also play exergames with a partner, favoring interaction and communication between them [44]. Another potentially beneficial activity during the patient recovery could be yoga. The practice of this discipline promotes endogenous melatonin secretion, positively affecting sleep quality, anxiety, and depressive disorders To allow the complete recovery of individuals, an interesting approach with several therapeutic benefits is Nordic walking. This activity is typically carried out in "healthy" environments, such as mountain, sea, and countryside settings, and is suitable for people of all ages. Nordic walking is useful for adapted motor re-education, especially for COVID-19 patients who have developed respiratory, metabolic, cardiovascular, and walking problems. Through the use of a specific pair of poles, Nordic walking engages the upper body muscles, and relative to normal walking would increase the overall energy expenditure [46]. Furthermore, since poles are held in both hands, the knees and joints are subjected to less stress; therefore, Nordic walking might be recommended in degenerative cartilage disorders, such as osteoarthritis [47], as it improves motor function and strength [48]. Interestingly, Nordic walking e-poles developed by Gabel, the Italian leading manufacturer in this area, are able to acquire the primary parameters that characterize the proper movement technique, providing feedback regarding the patient's performance and assisted walking. Nordic walking strengthens cognitive function, attention, and executive functions by positively affecting patient quality-of-life [49]. It is noteworthy that Nordic walking also exerts a positive effect on an individual's psychological well-being. Compared to normal walking, a

previous study showed that this discipline elicited significant psychological improvements, ameliorating depression and sleep disturbance [50].

#### **5. Conclusions**

The SARS-CoV-2 virus represents the major societal challenge, with important repercussions for people's mental and physical health. The beneficial effects of physical exercise in improving quality of life and well-being have been extensively documented. An adapted physical activity program may represent an important factor to prevent COVID-19 infection, as well as a useful complementary tool to improve the physical and psychological outcomes of COVID-19-affected patients. A suitable exercise program may strengthen the respiratory system, providing immune protection in the long term and reducing treatment costs. Furthermore, in the post-infection phase, an adapted or tailor-made home-based exercise program ensures a faster return to pre-infection fitness by enhancing self-esteem and resilience to stress and reducing anxiety and depression.

**Funding:** This work was supported by the University Research Project Grant (PIACERI Found— NATURE-OA—2020–2022), Department of Biomedical and Biotechnological Sciences (BIOMETEC), University of Catania, Italy.

**Acknowledgments:** Our thoughts go out to all the victims of this pandemic and their families.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Article* **Prevalence of Low Energy Availability in Collegiate Women Soccer Athletes**

**Meghan K. Magee 1,2, Brittanie L. Lockard 2,3, Hannah A. Zabriskie 4 , Alexis Q. Schaefer 5 , Joel A. Luedke 5 , Jacob L. Erickson 6 , Margaret T. Jones 1,2,\* and Andrew R. Jagim 2,5,6**

Received: 17 November 2020; Accepted: 16 December 2020; Published: 18 December 2020

**Abstract:** (1) **Background:** Limited information exists on the prevalence of low energy availability (LEA) in collegiate team sports. The purpose of this study was to examine the prevalence of LEA in collegiate women soccer players. (2) **Methods:** Collegiate women soccer athletes (*n* = 18, height: 1.67 ± 0.05 m; body mass: 65.3 ± 7.9 kg; body fat %: 24.9 ± 5.6%) had their body composition and sport nutrition knowledge assessed in the pre-season. Energy availability was assessed mid-season using a 4-day dietary log and activity energy expenditure values from a team-based monitoring system. A validated screening tool was used to screen for LEA. (3) **Results:** The screening tool classified 56.3% of athletes as at risk of LEA (<30 kcal/kg of FFM); however, the actual dietary intake identified 67% as LEA. Athletes identified as non-LEA consumed significantly more absolute (*p* = 0.040) and relative (*p* = 0.004) energy than LEA athletes. (4) **Conclusions:** There was a high prevalence of LEA among collegiate women soccer athletes. Although previously validated in women endurance athletes, the LEA screening tool was not effective in identifying those at risk of LEA in this sample of athletes.

**Keywords:** energy availability; relative energy deficiency in sport; sports nutrition; women soccer athletes; nutrition knowledge; LEAF-Q

#### **1. Introduction**

Athletes have specific dietary requirements in order to meet training demands and optimize performance [1]. Typically, athletes have higher activity levels; greater lean body mass; and require higher amounts of energy, protein, and carbohydrates per day compared to non-athletes. However, previous research has indicated that athletes often do not meet the nutritional guidelines specific to their level of training, with women athletes tending to exhibit dietary deficiencies more frequently than male athletes [2–12]. Insufficient energy intake may predispose an athlete to low energy availability (LEA), which is thought to be a primary contributor to a complex condition characterized by a multifactorial state of physiological dysfunction referred to as Relative Energy Deficiency in Sport (RED-S) syndrome [13]. The International Olympic Committee published a consensus statement on RED-S [13] which described RED-S as similar to the well-known Female Athlete Triad paradigm, but encompassing a broader definition to include a spectrum of physiological dysfunction attributable

to chronic energy deficiency. This deficiency may result from insufficient energy intake, excessive energy expenditure from training, or a combination of both. Further, RED-S is commonly associated with reductions in performance, with a concomitant increased risk of health complications including, but not limited to, impairments in metabolism, menstrual function, bone health, immunity, protein synthesis, reproduction abilities, and cardiovascular health [13].

A common strategy to determine an athlete's risk of energy deficiency is to assess energy availability level (kcal/kg FFM/day), which is calculated by subtracting the activity energy expenditure from energy intake. Calculated energy availability values are commonly interpreted as follows: low < 30 kcal/kg FFM/day; moderate 30–45 kcal/kg FFM/day; optimal > 45 kcal/kg FFM/day (Ref. [14]). Energy availability serves as a means to quantify the residual energy available to support an athlete's physiological functions, and, if below a certain threshold, may be a primary causative factor contributing to RED-S over time [13]. Further, a higher prevalence of LEA is reported in women athletes, as indicated in previous studies [15–18]. Although valuable, the calculation of energy availability is laborious and can be challenging when working with a large number of athletes. Therefore, screening tools such as the Low Energy Availability in Females Questionnaire (LEAF-Q) [19] have been developed and previously validated in a cohort of women endurance athletes and dancers, but not collegiate team sport athletes. Certain body composition parameters, densitometry metrics, hematological markers, and metabolic tests have also been used to screen for those at risk of RED-S with variable degrees of efficacy [20–22]. A better understanding of the utility of these screening tools across multiple populations would help practitioners to identify athletes at risk of LEA.

Common trends among athletes regarding dietary practices indicate a misunderstanding of energy and macronutrient requirements and the role of certain dietary supplements [23], which likely contributes to the dietary deficiencies and occurrence of LEA commonly seen in athletes [24]. However, inconsistencies exist as to how the nutrition knowledge of athletes has been assessed in the past [23]. Therefore, in an effort to create a more standardized method of assessing nutrition knowledge, Trakman et al. [25,26] developed an abridged nutrition knowledge questionnaire that is specific to the dietary requirements of athletes. In theory, a higher level of nutrition knowledge may positively influence an athlete's dietary behaviors, as previous research in athletes has indicated that those with greater sport nutrition knowledge scores were more likely to self-report healthier dietary practices [27]. Further, previous sport nutrition education interventions have led to marked improvements in nutrition knowledge [4], quality of diet [4,28], body composition, and performance [28] over the course of a season. However, the potential relationship between nutrition knowledge and LEA in athletes has yet to be fully elucidated.

In the United States, Division III athletes represent ~39% of the National Collegiate Athletic Association, which includes more than 440 Division III women's soccer programs. Limited data exist in regard to LEA prevalence in collegiate team sport athletes, specifically in women's soccer. Likewise, the effectiveness of screening tools, such as the LEAF-Q, in identifying those at risk of LEA has yet to be determined, or how nutrition knowledge may influence energy availability in collegiate team sport athletes. Therefore, the purpose of the current study was to examine the prevalence of LEA among a convenience sample of Division III collegiate women soccer athletes and to examine the utility of the LEAF-Q as a tool to screen for those at risk of LEA. A secondary aim was to examine the relationship between nutrition knowledge, energy availability, and dietary intake.

#### **2. Materials and Methods**

#### *2.1. Experimental Design*

The current observational study began prior to the start of the fall academic term and soccer season, when athletes completed a body composition assessment and an electronic online survey previously developed to assess sport nutrition knowledge. At the soccer season's midpoint, athletes completed a different online electronic survey to screen for LEA. At this time, athletes also completed a 4-day

monitoring period to assess energy availability. Players were asked to record dietary intake and were equipped with a team-based heart rate monitoring system with on-board inertial sensors to assess activity energy expenditure throughout each practice and match during the 4-day monitoring period.

#### *2.2. Subjects*

Eighteen National Collegiate Athletic Association (NCAA) Division III women soccer athletes participated in the current study. The participant demographic data are presented in Table 1. All players who were medically cleared were invited to participate in this study. Procedures were approved by the University's Institutional Review Board for the Protection of Human Subjects (IRB #19-AJ-707). The study was conducted according to the Declaration of Helsinki guidelines. Written consent was obtained from all the subjects prior to data collection.

**Table 1.** Summary of subject demographics (*n* = 18).

Values represented as mean ± standard deviation.

#### *2.3. Procedures*

Upon arrival to the laboratory, height and body mass were recorded to the nearest 0.01 cm and 0.02 kg, respectively, using a stadiometer (Detecto, Webb City, MO, USA) and digital scale (BOD POD model 2000A; BOD POD; Cosmed USA, Concord, CA, USA), with each subject barefoot. Body composition variables (i.e., percent body fat, fat-free mass, and fat mass) were assessed using air displacement plethysmography (BOD POD model 2000A; BOD POD; Cosmed USA, Concord, CA, USA) according to standard operating procedures. The thoracic gas volume was estimated.

#### 2.3.1. Energy Availability

Athletes were asked to record dietary intake using an online commercially available nutrition analysis program (MyFitnessPal, Under Armour, Baltimore, MD, USA). Prior to this period, they were educated on methods to estimate portion sizes and provided with informational packets and instructional videos to promote accurate self-reporting. Daily energy and macronutrient intakes were averaged across the 4-day monitoring period. Absolute energy and macronutrient intakes (kcal/day or g/day) were recorded and were also made relative to body weight (kcal/kg/day or g/kg/day) to allow for comparison between individuals. Activity energy expenditure was assessed using wearable monitoring devices (Polar TeamProTM Polar Electro, Oy, Finland) and calculated using proprietary algorithms. Energy availability was then calculated by subtracting the activity energy expenditure from energy intake and expressed as kcals per kilogram of FFM. A threshold of <30 kcal/kg of FFM was used to classify players as having LEA [14].

#### 2.3.2. Low Energy Availability in Females Questionnaire

The LEAF-Q, originally developed as a paper survey, was converted into an online electronic format for ease of distribution and scoring. The 25-item questionnaire asks a series of questions pertaining to prior injury history, gastrointestinal issues, menstrual cycle patterns, and contraception use. A score of ≥8 would classify the athlete as having LEA. It has previously been shown to have an acceptable degree of sensitivity (78%) and specificity (90%) in women athletes and a Cronbach's alpha ≥ 0.71 [19].

#### 2.3.3. Abridged Sport Nutrition Knowledge Questionnaire

The Abridged Sport Nutrition Knowledge Questionnaire (ANSKQ) consists of 37 items that assess general (*n* = 17) and sport (*n* = 20) nutrition knowledge and has previously been shown to be a valid and reliable questionnaire and has a PerSepIndex = 0.80 [29]. The scores from the ANSKQ were automatically calculated and categorized using the knowledge scoring system of poor (0–49%), average (50–65%), good (66–75%), and excellent (75–100%) from previously published methods [26].

#### **3. Statistical Analysis**

A sensitivity and specificity analysis was completed to examine the ability of the LEAF-Q to identify those at risk of LEA. Tests of normality were conducted, and it was found that Shapiro–Wilk was violated for average energy availability (AEA) for non-LEA athletes (*p* = 0.044) and for average energy intake (AEI) for LEA athletes. Thus, differences in AEA and AEI were assessed using the Mann–Whitney U test. Differences in other variables of energy intake between athletes having LEA and athletes without LEA were analyzed using independent samples *t*-tests. Data were considered statistically significant when the probability of a type I error was 0.05 or less. Pearson correlation coefficients were used to examine the relationships between EA, LEAF-Q scores, ASNKQ scores, BF %, FFM, fat mass, body mass, and body mass index. The following criteria were used for interpreting the correlation coefficients: very weak: <0.20; weak: 0.20–0.39; moderate: 0.40–0.59; strong: 0.60–0.79; and very strong: >0.80 [30]. Cohen's d, utilizing pooled standard deviations, was used to assess the effect sizes for differences in the energy intake variables. The effect sizes were interpreted using the following criteria: 0.2 = trivial; 0.2–0.6 = small; 0.7–1.2 = moderate; 1.3–2.0 = large; and >2.0 = very large. All the data were analyzed using the Statistical Package for the Social Sciences (SPSS, Version 25.0; IBM Corp., Armonk, NY, USA).

#### **4. Results**

A total of 66.7% percent of athletes (*n* = 12) presented with LEA (23.0 ± 5.7 kcals/kg FFM) versus non-LEA (*n* = 6; 36.4 ± 7.3 kcals/kg FFM). The LEAF-Q survey tool only identified 56.3% of athletes as at risk of LEA. The sensitivity and specificity analysis yielded a true positive rate of 40.0% and a true negative rate of 16.7% when using the LEAF-Q as a screening tool for LEA.

Table 2 provides a summary of the dietary intake of all athletes. In comparison to athletes with LEA, athletes without LEA consumed more relative (*p* = 0.004) energy than those with LEA. Athletes without LEA also consumed higher amounts of carbohydrates (absolute: *p* = 0.029; relative: *p* = 0.003) and relative fat (*p* = 0.013) than LEA athletes.  Additionally, there were no differences in the absolute energy intake between the two groups.

The mean score from the ASNKQ indicated that 44.7% of the questions were answered correctly. When analyzing the difference in scores between the athletes with and without LEA, the athletes with LEA scored lower compared to the athletes without LEA (40.9 ± 10.4% vs. 52.4 ± 9.8%; *p* = 0.040; ES = 1.14). Moderate inverse relationships were observed between the mean energy availability values and body mass (r = −0.503) and FFM (r = −0.520), and between the ASNKQ scores and fat mass (r = −0.508), as presented in Table 3.

**Table 2.** A summary of average daily energy and macronutrient intake by energy status.

Values represented as mean ± standard deviation. \* *p* < 0.05, \*\* *p* < 0.01.

**Table 3.** Relationships between body composition parameters, energy availability, and sport nutrition knowledge.

\* Correlation is significant at the 0.05 level (2-tailed). \*\* Correlation is significant at the 0.01 level (2-tailed). BF% = body fat percentage; FFM = fat-free mass; FM = fat mass; EA = energy availability; LEAFQ = low energy availability in females questionnaire; ASNKQ = abridged sport nutrition knowledge questionnaire.

#### **5. Discussion**

The purpose of the current study was to assess the prevalence of LEA among a cohort of collegiate women soccer athletes and to examine the utility of the LEAF-Q as a tool to screen for those at risk of LEA. A secondary aim was to assess the relationship between nutrition knowledge, energy availability, and energy intake. The main findings of the current study found the prevalence of LEA to be 67% among the current cohort of athletes when assessed directly through dietary analysis and activity energy expenditure. This prevalence rate is higher than previous findings reported in women soccer players at the NCAA Division I (26–33%) [31] and professional levels (23%) [32], as well as in collegiate volleyball (20%) [33] and elite endurance athletes (12–20%) [19]. However, prevalence rates of 40–60% have been observed in collegiate women endurance athletes [15,16], which are only slightly below those observed in the current study. Such similarity was unexpected, considering that, unlike soccer, success in endurance sports requires a high training volume and typically a smaller body type with minimal body fat, which predisposes one to LEA. Therefore, it would be expected that soccer athletes would be less likely to exhibit LEA compared to endurance athletes. The mean energy availability value observed in the current study was 27.5 ± 8.9 kcals/kg FFM/day, which is below the threshold of <30 kcals/kg FFM/day used to classify those with LEA [14], and below that previously reported in women soccer athletes at the professional level (35 kcals/kg FFM/day) [32]. Further, the aforementioned professional soccer athletes [32] had access to nutritional staff, compared to athletes in the current study whose institution did not staff a sport dietitian or nutritionist.

The high degree of variability in the LEA values across different team sports may be partially attributed to the limited access to sports nutrition education and provisional food (fueling stations), resources that may be more common at more competitive collegiate and elite levels. In fact, previous research has indicated that sport nutrition education interventions and access to a sports dietitian improve eating behaviors and nutritional knowledge in collegiate athletes [4,34,35]. However, some studies have shown [15,36] that, while a nutritional education intervention led to a measurable increase in nutrition knowledge, the prevalence of LEA did not change. Therefore, a combined approach of nutrition education, opportunities to practice dietary skills as well as behavior change therapy [37] may be necessary to help athletes minimize the risk of nutritional deficiencies [38].

The results from the current study indicate that the LEAF-Q was not an effective screening tool, as it only identified 56.3% of the soccer athletes as at risk of LEA, while the direct assessment of LEA identified 66.7% of the athletes as exhibiting LEA. Further, the sensitivity and specificity analysis yielded significantly lower values than those previously published when the tool was validated [19]. Similar findings have also been reported at the professional level [32]; therefore, the LEAF-Q may have limited utility as a screening instrument in women's soccer, at least when used as a standalone tool. The LEAF-Q has been used successfully in elite sprinters for identifying LEA in conjunction with additional primary indicators, including energy availability, the presence of amenorrhea, low bone mineral density, and hormone abnormalities [39]. Therefore, a comprehensive monitoring plan may be warranted in order to accurately screen athletes and those at risk or in need of further clinical evaluations.

A novel finding from the current study was that athletes with LEA scored lower (41% = poor) on the ANSKQ than athletes without LEA (52% = average) [26], suggesting that lower nutrition knowledge may increase the likelihood of insufficient energy intake, as athletes may have less of an understanding of how to meet the dietary requirements of their sport. Further, moderate differences between those with LEA and those without, as determined by effect size, were observed.  There were no additional relationships between nutrition knowledge, energy availability, and body composition values.

Low energy availability values are one of many metrics that may indicate dietary insufficiencies. The athletes in the current study had a daily mean energy intake of 30 kcals/kg/day, which is below the recommended energy intake of 40–60 kcals/kg for women team sport athletes [7,40] and likely contributed to the high prevalence of LEA observed. Similar findings have been reported in the Under−21 United States Women's National Soccer team, with an average daily energy intake of 34 kcals/kg/day reported [8]. Additionally, the daily carbohydrate intake of 3.7 g/kg per day observed in the current study was below the recommended 5–12 g/kg of bodyweight for soccer players [41]. This finding is consistent with other studies in women's soccer, which have frequently noted inadequate carbohydrate intakes ranging from 3.3 to 5.0 g/kg/day [8–12,32]. In the current study, suboptimal energy and macronutrient intakes were more common in athletes with LEA (Table 2). The inability of collegiate athletes to meet nutritional recommendations for their respective sport is not uncommon, as it has been previously reported in both men and women athletes participating in football [42], lacrosse [7], swimming [6], basketball [2], gymnastics [2], and volleyball [33]. While nutritional recommendations have been established for women soccer athletes, the results of the current study demonstrate a continued need for sport nutrition education interventions to be part of regular team activities. Moreover, the high prevalence of LEA is concerning and supports the need to identify individuals at risk of LEA, particularly at lower levels of competition, who may not have access to sport dietitians or other nutrition-centric resources. The early detection of LEA and the implementation of appropriate interventions may reduce the risk of conditions such as RED-S. Therefore, there remains a need for accurate and efficient screening tools to identify those at risk of LEA.

#### **6. Limitations**

A limitation of the current study is the use of self-reported dietary intake, which may be subject to underreporting by participants [43]. Therefore, there is a potential bias for an overestimation in the prevalence of LEA. Further, the 4-day monitoring period may not be an adequate reflection of the normal dietary habits and activity levels of the athletes throughout the entire season. Therefore, it is possible that, while the LEAF-Q did not accurately identify those at risk of LEA when compared to direct measures of energy availability, the LEAF-Q may be more reflective of long-term energy status, as the questions are directed at health history and symptoms common to RED-S, which may manifest over time. An additional limitation is the small sample size. The prevalence of LEA among the current cohort of women soccer athletes may not be representative of all women soccer athletes across all levels of collegiate competition.

#### **7. Conclusions**

The results indicate there may be a high prevalence of LEA among collegiate women soccer athletes. Since the LEAF-Q was not an effective tool in identifying those at risk of LEA, it is recommended that future research examine the utility of LEAF-Q for women athletes participating in team sports. Additionally, the nutrition knowledge of collegiate women soccer athletes was classified as poor-to-average, with the athletes failing to meet several nutritional recommendations for their respective level of training. Athletes with LEA tended to have a lower nutrition knowledge score compared to those without LEA. Therefore, the development of additional screening tools for LEA would be beneficial for the team sport population. Sport nutrition education interventions are recommended to help athletes understand their advanced dietary requirements and provide practical strategies to meet these dietary recommendations. This may help athletes consume adequate amounts of energy and avoid having LEA.

**Author Contributions:** Conceptualization, A.R.J., J.A.L. and J.L.E.; methodology, A.R.J., A.Q.S. and J.A.L.; software, A.R.J., A.Q.S. and J.A.L.; formal analysis, A.R.J. and H.A.Z.; investigation, A.R.J., A.Q.S. and J.A.L.; resources, A.R.J., J.A.L. and J.L.E.; data curation, A.R.J., H.A.Z. and A.Q.S.; writing—original draft preparation, A.R.J., M.K.M., B.L.L. and M.T.J.; writing—review and editing, M.K.M., B.L.L., H.A.Z., J.L.E., M.T.J. and A.R.J.; visualization, A.R.J.; supervision, A.Q.S., J.A.L., J.L.E. and A.R.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank all the women soccer players and coaching staff who participated in this project and supported the study procedures.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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### *Article* **E**ff**ects of Dehydration on Archery Performance, Subjective Feelings and Heart Rate during a Competition Simulation**

**Alexandros Savvides, Christoforos D. Giannaki , Angelos Vlahoyiannis , Pinelopi S. Stavrinou and George Aphamis \***

Department of Life and Health Sciences, University of Nicosia, 46 Makedonitisas Avenue, CY1700 Nicosia, Cyprus<EMAIL_ADDRESS>(A.S<EMAIL_ADDRESS>(C.D.G<EMAIL_ADDRESS>(A.V<EMAIL_ADDRESS>(P.S.S.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 17 July 2020; Accepted: 25 August 2020; Published: 27 August 2020

**Abstract:** This study aimed to investigate the effect of dehydration on archery performance, subjective feelings and heart rate response. Ten national level archers performed two archery competition simulations, once under euhydration (EUH) and once in a dehydrated state (DEH), induced by 24-h reduced fluid intake. Hydration status was verified prior to each trial by urine specific gravity (USG ≥ 1.025). Archery score was measured according to official archery regulations. Subjective feelings of thirst, fatigue and concentration were recorded on a visual analogue scale. Heart rate was continuously monitored during the trials. Archery performance was similar between trials (*p* = 0.155). During DEH trial (USG 1.032 ± 0.005), the athletes felt thirstier (*p* < 0.001), more fatigued (*p* = 0.041) and less able to concentrate (*p* = 0.016) compared with the EUH trial (USG 1.015 ± 0.004). Heart rate during DEH at baseline (85 ± 5 b·min−1 ) was higher (*p* = 0.021) compared with EUH (78 ± 6 b·min−1 ) and remained significantly higher during the latter stages of the DEH compared to EUH trial. In conclusion, archery performance over 72 arrows was not affected by dehydration, despite the induced psychological and physiological strain, revealed from decreased feeling of concentration, increased sensation of fatigue and increased heart rate during the DEH trial.

**Keywords:** hydration; urine specific gravity; athletes; heart rate; fatigue; alertness

#### **1. Introduction**

Hydration is of considerable interest for health, thermoregulation, as well as sports and athletic performance. A cascade of physiological responses even to a mild deficit in total body water hinders the body's ability to thermoregulate and maintain blood flow [1]. Decreased plasma volume leads to decreased blood flow to the exercising muscles and contributes further to impaired aerobic capacity [1,2] on various athletic disciplines such as cycling [2,3], long-distance running [4] and rowing [5]. Water losses and ensuing hyperthermia during exercise can also impair team sports or tennis performance [6] and muscular endurance [7] but not maximum strength or power [8].

Archery requires a repetitive motion with great precision, and therefore, its physiological demands are not similar to other predominately aerobic or anaerobic sports. During each arrow throw, one arm is used to hold (push) the bow in a steady position while the other arm pulls the bow string, with increasing muscle tremor in order to hold arrow-target alignment until the release of the arrow [9]. It has been estimated that the average force required to pull the bow is 20 kg for the men and 18 kg for the women. When this is multiplied by the number of arrow throws during a competition, one can get the total workload required by the exercising muscles in the shoulder area [10] and the trapezoid muscle [11]. This requires potentially a degree of muscular endurance over the duration of the competition, which could be affected by dehydration [7], especially under certain environmental conditions, as for example increased ambient temperature. Furthermore, archers must maintain a steady posture and body alignment with the target, in order to be as successful as possible. Even moderate intensity exercise combined with dehydration can lead to altered posture and increased muscle tremor, whereas euhydration allows for good muscle function and retention of postural control [12].

In archers, cardiovascular system also undergoes a specific stress during training [13] and competition [14]. Quality shooting for hours has been shown to challenge cardiovascular fitness and hand-eye coordination [15]. Increased ambient temperature could amplify the burden of inadequate hydration levels, which in turn may lead not only to reduced aerobic performance [1], but also to decreased brain volume [16] and altered brain function [17]. Dehydration can also negatively affect mood and vigilance [18,19], and increase tension, anxiety and fatigue [18], adding to the existing archers' competition stress.

So far, with regards to archery performance during competition, little is known about the effects of fluid restriction and dehydration on skill performance, cognitive function and especially hand-eye coordination, as well as subjective feelings of fatigue and concentration of archers. Notably, findings from a laboratory setting may differ from actual results in a real competition scenario, and thus, the primary aim of the present study was to investigate archery performance under a dehydration state, following 24 h of restricted fluid intake, during a simulated competition in a hot environment. Secondary aims were to monitor heart rate (HR) responses and subjective feelings related to dehydration (thirst, alertness, concentration, fatigue) during an archery competition simulation.

#### **2. Materials and Methods**

#### *2.1. Participants*

Ten national level archers (males *n* = 7, females *n* = 3; age: 22 ± 3 year, height 171 ± 9 cm, body mass 74.4 ± 11.9 kg) volunteered for this study. All participants were at the advanced level, training regularly 5–6 days per week, 2–3 h per day and competing for at least two years in Division A of the national championship. Any kind of history of major disease or medication was strictly considered as exclusion criterion. All athletes were informed for the purposes of the study and provided a written consent form. The study was approved by the Cyprus National Biothetics Committee on 5th July 2019 (Project ID: EEBK/EΠ/2018/13). All procedures were conducted according to the manual of the Declaration of Helsinki in 1964 and its later amendments.

#### *2.2. Study Design*

An overview of study design is depicted in Figure 1. Data collection took place during the competitive period at the Cyprus Archery Federation facilities. During this study, the temperature was 36−37 ◦C, and relative humidity was 81–83%. Participants visited the accredited archery area on two occasions, in counterbalanced order, once under euhydration (EUH) and once under a dehydrated state (DEH) induced by a 24-h controlled fluid intake. The two trials took place in the morning (warm up started at 8:20 a.m. and the last arrow throw was completed by 10:50 a.m.) 7 days apart. Upon arrival at the archery area, anthropometrics were measured and hydration levels were assessed. Participants underwent a warm-up, followed by the competition simulation. Heart rate was monitored through the trials. Self-perceived feelings were recorded at the start and end of each session.

**Figure 1.** Overview of study design. DEH: Dehydration; EUH: Euhydration; USG: Urine Specific Gravity.

#### **Figure 1.** Overview of study design. DEH: Dehydration; EUH: Euhydration; USG: Urine Specific Gravity. *2.3. Fluid Restriction Protocol*

*2.3. Fluid Restriction Protocol*  All participants received dietary instructions to follow during all the study protocol, in order to avoid any nutrient deficiency. Caffeinated beverages, nutritional supplements and alcohol were not permitted for 48 h prior to trials. During the last 24 h prior to the trials, the participants followed the same isoenergetic nutritional plan, with a macronutrient distribution of 20%, 55% and 25% for protein, carbohydrate and fat, respectively. The nutrition plan was analyzed with Nutrilog Software v 2.60 (Marans, France), and the water content of the diet was approximately 1.2 L (0.9 L as water content of foods and 0.3 L as a result of macronutrient oxidation). In the dehydration scenario, the archers were further provided with 250 mL fluids at five intervals (total amount of fluids 5 × 250 mL = 1.25 L) during the last 24 h before the competition simulation. In the euhydration scenario, fluid All participants received dietary instructions to follow during all the study protocol, in order to avoid any nutrient deficiency. Caffeinated beverages, nutritional supplements and alcohol were not permitted for 48 h prior to trials. During the last 24 h prior to the trials, the participants followed the same isoenergetic nutritional plan, with a macronutrient distribution of 20%, 55% and 25% for protein, carbohydrate and fat, respectively. The nutrition plan was analyzed with Nutrilog Software v 2.60 (Marans, France), and the water content of the diet was approximately 1.2 L (0.9 L as water content of foods and 0.3 L as a result of macronutrient oxidation). In the dehydration scenario, the archers were further provided with 250 mL fluids at five intervals (total amount of fluids 5 × 250 mL = 1.25 L) during the last 24 h before the competition simulation. In the euhydration scenario, fluid intake was also standardized, providing archers with 500 mL fluids at breakfast, lunch and dinner and 250 mL of fluids at six intervals across the day (total amount of fluids (3 × 500 mL) + (6 × 250 mL) = 3 L).

#### intake was also standardized, providing archers with 500 mL fluids at breakfast, lunch and dinner and 250 mL of fluids at six intervals across the day (total amount of fluids (3 × 500 mL) + (6 × 250 mL) *2.4. Archery Competition Simulation Protocol*

= 3 L). *2.4. Archery Competition Simulation Protocol*  The competition simulation protocol consisted of a standardized warm-up, followed by the main archery competition phase. During the warm-up, each participant occupied one shooting line and upon a signal from the referee the participants shot 2 sets of 6 arrows at a 5-m distance targets over a The competition simulation protocol consisted of a standardized warm-up, followed by the main archery competition phase. During the warm-up, each participant occupied one shooting line and upon a signal from the referee the participants shot 2 sets of 6 arrows at a 5-m distance targets over a 4-min period with a 3-min break between sets in order to collect the arrows. Then, the participants shot 2 rounds of 6 arrows at 70 m targets, again each round within a 4-min time frame, with 3 min break for arrow collection, and rested for 10 min before the main competition phase began.

4-min period with a 3-min break between sets in order to collect the arrows. Then, the participants shot 2 rounds of 6 arrows at 70 m targets, again each round within a 4-min time frame, with 3 min break for arrow collection, and rested for 10 min before the main competition phase began. During the competition phase, the archers assumed their position in their respective shooting lines and shot 6 rounds of 6 arrows. According to the regulations, 4 min were allowed for each round to be completed, followed by a 3-min break to collect arrows and write down the score for each participant. This was followed by a 10-min rest break, before continuing with another round of 6 arrows, until all 6 rounds were completed. The archery competition simulation was conducted During the competition phase, the archers assumed their position in their respective shooting lines and shot 6 rounds of 6 arrows. According to the regulations, 4 min were allowed for each round to be completed, followed by a 3-min break to collect arrows and write down the score for each participant. This was followed by a 10-min rest break, before continuing with another round of 6 arrows, until all 6 rounds were completed. The archery competition simulation was conducted according to the regulations of the International Archery Federation, at the Olympic distance of 70 m. All health and safety measures were taken according to the international competition rules. No food or fluid ingestion was allowed during the two main trials.

#### according to the regulations of the International Archery Federation, at the Olympic distance of 70 m. All health and safety measures were taken according to the international competition rules. No *2.5. Anthropometrics*

food or fluid ingestion was allowed during the two main trials. Upon arrival at the archery area, body mass (kg) was measured (participants wearing shorts and t-shirt only) using a portable scale (Seca model 755, Hamburg, Germany). Height (cm) was measured with a standing stadiometer (Seca model 720, Hamburg Germany).

#### *2.6. Hydration Status Assessment*

In the morning of each experimental trial, before study procedures, participants provided a first-morning-urine sample in a 60-mL container. Urine specific gravity (USG) was measured upon arrival of the participants at the archery center using a urine refractometer (DIGIT 0−12, Medline Scientific Limited, UK), to record hydration status. Euhydration/hypohydration cut-off point was set at USG < 1.025 [20]. All remaining urine was disposed down the toilet immediately after. No biological samples were stored after the determination of urine specific gravity.

#### *2.7. Heart Rate Response*

After urine collection, participants were allowed to rest, sitting comfortably, while a heart rate monitor (Polar H7, Polar Electro Oy, Professorintie S, FI-90440, Kempele, Finland) was attached on the participants' chest in order to continuously monitor heart rate throughout the exercise trials. Heart rate during the trials was recorded at baseline, end of the rest break after throw 6 and at the end of each throw.

#### *2.8. Subjective Feelings Monitoring*

At baseline and at the end of each trial, participants completed a subjective feelings questionnaire [21]. Athletes self-rated their feelings on thirst, fatigue, alertness and ability to concentrate on a 0–10 visual analogue scale, where "0" was "not-at-all" and "10" was "very much".

#### *2.9. Statistical Analysis*

Statistical analysis was performed with IBM®SPSS® statistics for Windows, version 25.0 (IBM Corp, Armonk, NY, USA). Data are reported as mean ± standard deviation. Differences on archery performance (total score) were detected with a paired-samples *t*-test. The normality of data was assessed by the Kolmogorov–Smirnov test. All data were normally distributed, and comparisons on subjective feelings between trials and over time (baseline vs. end of archery competition) were made with a two-way Repeated Measures ANOVA (time point × trial). Heart rate response at rest and after arrow throw was also analyzed with two-way Repeated Measures ANOVA (time point × trial). Bonferroni post-hoc analysis was performed where necessary. Effect sizes were calculated using partial eta squared (η 2 ) interpreted as 0.01 for small, 0.06 for moderate and 0.14 for large. Statistical significance was set at *p* < 0.05. Statistical power analysis was performed using the G\*Power 3.1 power analysis software. Post hoc power analysis revealed that the sample size of the present study was adequate to provide statistical power of both heart rate and other main parameters of the study such as fatigue, concentration and thirst) with >90% power and with a significance level, α = 0.05.

#### **3. Results**

Measurements of USG at baseline showed that USG at the DEH trial was 1.032 ± 0.005, which was above the dehydration cut off level for all athletes, and it was also higher (*p* < 0.001) than USG at the EUH trial (1.015 ± 0.004). This confirms that the participants performed the archery competition under the desired hydration state at each trial.

Total archery score was not different between trials (EUH 550 ± 63 points vs. DEH 562 ± 59 points; (*p* = 0.155)). No significant correlation was found between archery performance and USG levels (Figure 2).

Subjective feelings analysis (Figure 3) showed different responses between conditions. Regarding thirst, there was a trial effect (F1,9 = 45.6, *p* < 0.001, η 2 = 0.836), a time effect (F1,9 = 56.5, *p* < 0.001, η 2 = 0.863) and a time × time interaction (F1,9 = 10.5, *p* = 0.010, η 2 = 0.538), as sensation of thirst was higher at baseline of the DEH trial compared with EUH (*p* = 0.003) and thirst increased over time during both trials (EUH *p* < 0.001; DEH *p* < 0.001), but the magnitude of change of thirst during the trial was different between conditions. Regarding fatigue, there was a trial effect (F1,9 = 5.7, *p* = 0.041, η 2 = 0.388) and a time effect (F1,9 = 21.8, *p* = 0.001, η 2 = 0.708). The sensation of fatigue was higher at baseline of the DEH trial compared with EUH (*p* = 0.016), and fatigue increased over time in both trials (EUH *p* = 0.050; DEH *p* = 0.003). Analysis on concentration did not show any trial effect (F1,9 = 1.6, *p* = 0.244, η 2 = 0.147), but there was a time effect (F1,9 = 32.7, *p* < 0.001, η 2 = 0.784) and a time × trial effect (F1,9 = 28.2, *p* < 0.001, η 2 = 0.758). Concentration scores were lower at baseline of the DEH trial compared with the EUH trial (*p* = 0.009), but by the end of the trial, concentration feeling was similar between trials (*p* = 0.320) as this feeling was stable during the DEH trial (*p* = 0.260) and decreased in the EUH trial (*p* < 0.001). Analysis of alertness showed that there was no trial (F1,9 = 1.7, *p* = 0.223, η 2 = 0.160), no time effect (F1,9 = 0.20, *p* = 0.660, η 2 = 0.023), and neither a time × trial interaction (F1,9 = 0.18, *p* = 0.678, η 2 = 0.020). *J. Funct. Morphol. Kinesiol.* **2020**, *5*, 67 5 of 10

**Figure 2.** Archery Performance in relation to Urine Specific Gravity (USG) values. Panel (**a**) depicts both archery scores for EUH trial (open squares) and DEH trial (dark squares). Panel (**b**) depicts the correlation between ∆USG (USGDEH-USGEUH) and ∆Archery score (Archery ScoreDEH–Archery ScoreEUH). No significant correlation was found between USG and Archery Performance. **Figure 2.** Archery Performance in relation to Urine Specific Gravity (USG) values. Panel (**a**) depicts both archery scores for EUH trial (open squares) and DEH trial (dark squares). Panel (**b**) depicts the correlation between ∆USG (USGDEH-USGEUH) and ∆Archery score (Archery ScoreDEH–Archery ScoreEUH). No significant correlation was found between USG and Archery Performance. *J. Funct. Morphol. Kinesiol.* **2020**, *5*, 67 6 of 10

a time effect (F13,117 = 136.3, *p* < 0.001, η2 = 0.931) as HR during the trial was above baseline in both conditions, and there was also a time × trial effect (F13,117 = 2.2, *p* = 0.006, η2 = 0.199). Post hoc analysis showed that HR at baseline was higher in the DEH trial (*p* = 0.003), but it was similar during the first six throws (*p* > 0.106). During the short resting period after throw 6, HR remained elevated in the **Figure 3.** Subjective feelings of (**a**) Thirst, (**b**) Concentration, (**c**) Fatigue and (**d**) Alertness, at baseline and at the end of both Hydration (EUH) and Dehydration (DEH) trials. \* Denotes statistically significant differences at the 0.05 level (2-tailed). **Figure 3.** Subjective feelings of (**a**) Thirst, (**b**) Concentration, (**c**) Fatigue and (**d**) Alertness, at baseline and at the end of both Hydration (EUH) and Dehydration (DEH) trials. \* Denotes statistically significant differences at the 0.05 level (2-tailed).

DEH trial compared with EUH trial (*p* = 0.005), and HR was significantly higher during the DEH trial at throws 7 (*p* = 0.041), 9 (*p* = 0.043), 10 (*p* = 0.016) and 11 (*p* = 0.034). The statistical difference between the two trials at throw 8 was *p* = 0.086 and at throw 12 *p* = 0.075, indicating a trend towards higher HR during the DEH trial. Heart rate response analysis (Figure 4) indicated a trial effect (F1,9 = 9.9, *p* = 0.012, η 2 = 0.523), and a time effect (F13,117 = 136.3, *p* < 0.001, η 2 = 0.931) as HR during the trial was above baseline in both conditions, and there was also a time × trial effect (F13,117 = 2.2, *p* = 0.006, η 2 = 0.199). Post hoc analysis showed that HR at baseline was higher in the DEH trial (*p* = 0.003), but it was similar during

**Figure 4.** Heart rate response to hydration (open circles) and dehydration (dark squares) state, during the archery competition simulation. \* Indicates difference between trials (*p* < 0.050). During exercise,

In the current study, it was shown that dehydration induced psychological and physiological strain but did not alter shooting performance of national level archers during a competition simulation in hot environment. Specifically, the results of the current study indicate that archery score was not affected by dehydration. Nevertheless, significant differences on HR, subjective feelings of

Α compelling amount of evidence suggests that overall fitness, core strength, handgrip, upper body strength and static balance are related to archery performance and high scores [22,23]. In the present study, archery performance was similar between dehydration and euhydration trials; thereby, any potential effect on muscle function was not observed. Previous studies demonstrated that dehydration may decrease upper body muscle power during a maximum intensity anaerobic test [24] and during a fatiguing isometric strength protocol of repeated efforts at 85% of maximum

heart rate was higher than baseline in both trials.

fatigue, and concentration were observed between conditions.

**4. Discussion**

the first six throws (*p* > 0.106). During the short resting period after throw 6, HR remained elevated in the DEH trial compared with EUH trial (*p* = 0.005), and HR was significantly higher during the DEH trial at throws 7 (*p* = 0.041), 9 (*p* = 0.043), 10 (*p* = 0.016) and 11 (*p* = 0.034). The statistical difference between the two trials at throw 8 was *p* = 0.086 and at throw 12 *p* = 0.075, indicating a trend towards higher HR during the DEH trial. **Figure 3.** Subjective feelings of (**a**) Thirst, (**b**) Concentration, (**c**) Fatigue and (**d**) Alertness, at baseline and at the end of both Hydration (EUH) and Dehydration (DEH) trials. \* Denotes statistically significant differences at the 0.05 level (2-tailed).

*J. Funct. Morphol. Kinesiol.* **2020**, *5*, 67 6 of 10

**Figure 4.** Heart rate response to hydration (open circles) and dehydration (dark squares) state, during the archery competition simulation. \* Indicates difference between trials (*p* < 0.050). During exercise, heart rate was higher than baseline in both trials. **Figure 4.** Heart rate response to hydration (open circles) and dehydration (dark squares) state, during the archery competition simulation. \* Indicates difference between trials (*p* < 0.050). During exercise, heart rate was higher than baseline in both trials.

#### **4. Discussion 4. Discussion**

In the current study, it was shown that dehydration induced psychological and physiological strain but did not alter shooting performance of national level archers during a competition simulation in hot environment. Specifically, the results of the current study indicate that archery score was not affected by dehydration. Nevertheless, significant differences on HR, subjective feelings of In the current study, it was shown that dehydration induced psychological and physiological strain but did not alter shooting performance of national level archers during a competition simulation in hot environment. Specifically, the results of the current study indicate that archery score was not affected by dehydration. Nevertheless, significant differences on HR, subjective feelings of fatigue, and concentration were observed between conditions.

fatigue, and concentration were observed between conditions. Α compelling amount of evidence suggests that overall fitness, core strength, handgrip, upper body strength and static balance are related to archery performance and high scores [22,23]. In the present study, archery performance was similar between dehydration and euhydration trials; thereby, any potential effect on muscle function was not observed. Previous studies demonstrated that dehydration may decrease upper body muscle power during a maximum intensity anaerobic test [24] and during a fatiguing isometric strength protocol of repeated efforts at 85% of maximum A compelling amount of evidence suggests that overall fitness, core strength, handgrip, upper body strength and static balance are related to archery performance and high scores [22,23]. In the present study, archery performance was similar between dehydration and euhydration trials; thereby, any potential effect on muscle function was not observed. Previous studies demonstrated that dehydration may decrease upper body muscle power during a maximum intensity anaerobic test [24] and during a fatiguing isometric strength protocol of repeated efforts at 85% of maximum voluntary contraction [7]. Nevertheless, the stress on the muscles during those tests was much higher than the effort during archery, and thus, it could be assumed that this is the reason why the participants in the present study were similarly successful during the two trials.

It has been proposed that the performance of experienced archers lies beyond their strength and fitness levels, to the mental domain, as the ability to concentrate is of utmost importance [25]. Dehydration may lead to impaired attention and motor coordination [26], decreased concentration [21,27,28] and increased sense of fatigue [21,28,29]. Additionally, dehydration has been linked to decreased sport-specific performance, such as decreased ball throwing accuracy in cricket [30] and basketball throw accuracy [31]. Mental fatigue has been shown to impair tennis serve and ground strokes accuracy [32]. The effects of dehydration may be more intense in hot and humid conditions due to a potentially increased thermal strain, which can affect the nervous system, cerebral blood flow and increase mental fatigue [33]. In this investigation, participants reported that they felt less able to concentrate and more fatigued at the dehydration baseline. Despite these relatively negative feelings, the overall archery score was not affected, even though the trials took place under conditions of high ambient temperature and humidity. Literature shows that experienced archers have higher emotional intelligence and emotional regulation;

thereby, under stressful conditions, they may respond with higher consistency in cognitive processes during shooting [34].