An initial exploration of fatigue as a predictor of quality of life in transplant athletes competing at national and international events

ABSTRACT Supporting organ transplant recipients’ quality of life after surgery continues to be of interest to health researchers and applied practitioners. However, literature and guidance on the factors linked to quality of life in transplant recipient athletes remains underreported. This study aimed to identify significant predictors of quality of life in an international sample of organ transplant recipient athletes (N = 99, Mage = 53 ± 14). Adopting a cross-sectional design, we collected the study data during the 2019 World Transplant Games which consisted of demographic items, health, and physical activity-related measures (i.e., task and ego orientation, fatigue severity, assessment of physical activity, physical activity enjoyment). Predictor variables were summarised into three categories: demographic factors, sport-related factors, and levels of fatigue with physical and mental quality of life functioning as the outcome variables. Hierarchical regression analyses exposed fatigue to exert a significant negative influence on both mental and physical quality of life perceptions explaining 49% and 64% of the variance in these variables respectively. Routine measurement and monitoring of transplant athletes’ level of fatigue in sport settings are recommended due to the negative bearing on quality of life that may be a potential barrier to sport participation and enjoyment.


Introduction
Quality of life (QoL) refers to an individual's overall well-being in daily life, reflecting subjective views on physical and mental health as well as experiences with environmental and social norms (Salehi et al., 2015).Physical health involves bodily pain, functioning, and work limitations, while mental health includes vitality, social functioning, and well-being affected by the impact of health conditions on social activities (Jang et al., 2019).According to the World Health Organization (WHO), QoL perceptions are shaped by an individual's judgement of their life circumstances, which is punctuated by culture and value systems (WHO, 1996).Therefore, perceptions of QoL encompass an individual's evaluation of their physical and mental health, considering their level of independence, social standing, and overall living conditions.
Improving QoL remains a central focus in various disciplines like social sciences, medicine, and health promotion, particularly in studies concerning chronic illness patient groups such as organ transplant recipients (Tarabeih et al., 2020;Wicks et al., 2014).Relevant study findings indicate that, while QoL may improve following organ transplant surgery (Burra & Ferrarese, 2019;Burra et al., 2007), transplant recipients continue to suffer enormous post-operative physical, emotional, and social issues.This is due, among other things, to adhering to strict medication regimens for the rest of one's life, lingering side effects from immunosuppressive drugs, adaptation/maintenance of new organ(s), and uncertainty about the future (Schulz & Kroencke, 2015;Yang et al., 2021).Indeed, McKay et al. (2021) recognised that the COVID-19 pandemic worsened QoL for solid organ transplant recipients noting feelings of reduced coping abilities, heightened uncertainty, and increased risk perceptions due to healthcare access restrictions, sporadic infections, and rising mortality rates.During the recent pandemic, organ transplant athletes, categorised as part of the "highly vulnerable" population, were anticipated to face similar challenges and additional precautions, beyond national restrictions, to safeguard themselves from infections (UK Government, 2020).Despite the return of sport events and competition schedules for transplant athletes to prepandemic levels in many regions worldwide, it is essential for researchers to persist in identifying and reporting factors affecting the QoL of transplant recipients in diverse contexts.
The literature on organ transplant recipients has associated various socio-demographic factors with their QoL.For example, a comprehensive study of French national kidney transplant recipients (N = 1424) revealed that health-related QoL declines notably with older age, lower socioeconomic status, and limited formal education (Mouelhi et al., 2018).Similar patterns were observed in prior French research, which identified notably lower health-related QoL scores among female renal transplant recipients who were unemployed, had lower education levels, lived alone, and had other infectious diseases (Gentile et al., 2013).Equivocal findings on the impact time since transplantation has on QoL are also reported in the literature with some studies suggesting no differential effect on health related QoL in kidney transplant recipients (Mazzoni et al., 2014), while other studies exposed positive links between time since transplantation and improved levels of QoL in cardiac recipients (Salyer et al., 2003).One potential explanation for some of the inconsistent findings is that the type of organ received influences the recipients' health-related difficulties and subsequent outlook on life.Wicks et al. (2014) support this conception by demonstrating that lung transplant recipients have poorer QoL than heart, liver, kidney, and pancreas transplant recipients.Tarabeih et al. (2020) comparison study of lung, kidney, heart, and liver recipients yielded similar results, indicating that the type of organ received may also influence recipients' postsurgery concerns and fears.
While there are varying QoL perceptions among different organ transplant recipient groups, engaging in vigorous physical activity and exercise post-surgery is highly recommended to enhance QoL.This practice significantly contributes to overall physical functioning and fosters social and emotional wellbeing, positively impacting mental health (Gustaw et al., 2017;Neale et al., 2017).Notably, physically active transplant recipients exhibit higher QoL scores across most aspects compared to their inactive counterparts (Painter et al., 1997).Wu et al. (2008) supported these findings, indicating that cardiac transplant recipients who followed a regular exercise routine alongside standard healthcare experienced enhanced perceived QoL compared to those who solely received standard healthcare.Similarly, studies on kidney transplant recipients showed improved QoL perceptions comparable to healthy general population samples after engaging in increased physical activity (Nielens et al., 2001;Mazzoni et al., 2014).However, despite clinicians advocating for regular exercise after transplant surgery for health benefits, along with many transplant recipients participating in sports voluntarily as a form of self-care and gratitude towards donors (Wiltshire et al., 2021), literature emphasises the need for tailored guidance to minimise negative impacts on recipients' health and performance (Wright et al., 2019).This cautionary recommendation is based on insights from therapy team members, including physiotherapists and sport therapists, regarding the management of transplant athletes during transplant games (Wright et al., 2019).More specifically, these authors discovered that professionals assisting transplant recipient athletes tend to favour general medical guidance over insights directly provided by the athletes themselves.This overlooks crucial details such as the effects of medications on denervation during training and recovery, increased risks of infections, and possible damage to bones and tissues due to various sports-related activities like training, competition, sports massage, manipulations, and taping (Wright et al., 2019).
Nonetheless, transplant recipients encounter challenges adhering to regular exercise due to decreased aerobic capacity, exercise tolerance, mobility loss, muscle weakness, and fatigue (Didsbury et al., 2013;Lin et al., 2017).Fatigue, in particular, is a significant barrier to sports participation and performance among recipients (van Adrichem et al., 2016).It's also linked to reduced QoL (Anderson & Ferrans, 1997;Knobbe et al., 2021).However, understanding fatigue's impact on transplant athletes' QoL perceptions remains limited (Wright et al., 2019).This lack of understanding reflects the inadequacy of current post-transplant exercise guidance for professionals working with transplant athletes (Hames et al., 2022).Thus, athletes often resort to generic training advice that may not suit their specific needs, potentially compromising the health benefits, fitness, enjoyment, performance, and overall well-being in sports.In this respect, it is worth mentioning that enjoyment and well-being are key psychological components of QoL and are primary motivators for post-operative sport participation among transplant recipients (Hames et al., 2022;Jooste et al., 2020).Despite these challenges, the number of transplant recipients engaging in sports continues to rise due to advancements in medical care (Wright et al., 2019;Wiltshire et al., 2021).
Therefore, understanding the factors that impact transplant athletes' QoL is crucial to improve post-operative guidance and support services.Consequently, this study seeks to assess how demographic factors (such as age, gender, transplant type, employment, education, and time since transplant) and sportsrelated elements (like primary sport, physical activity levels, motivation, and enjoyment), as well as fatigue, impact the QoL of transplant athletes.We believe that the findings resulting from this study could guide tailored post-operative support services and mitigate factors affecting QoL during sports participation.

Design
A cross-sectional survey design was used to examine common predictors of health related QoL within transplant athletes (physical & mental domains).Following a thorough fine combing of extant literature on organ transplant recipients, we have identified several variables to investigate as possible predictors of QoL within transplant recipient athletes.For the purpose of this study, the predictor variables of interest were broadly grouped across three categories: (i) Demographical Information -this included age, sex, transplant type, employment status, education, and time since transplant; (ii) Sport-Related Factors -comprising of primary sport played, levels of physical activity, motivation, and physical activity enjoyment in addition to; (iii) A measure of fatigue severity.The outcome variables measured were mental QoL (MQoL) and physical QoL (PQoL).

Participants
Data collection initially began at the start of the 2019 World Transplant Games held in Newcastle-Gateshead in August and ended in December 2019.Opportunity sampling was employed through the following recruitment strategies: hand out of study participation invitations containing a weblink to an online survey (generated in Qualtrics) to athletes during the games, through the use of paper-based study questionnaires, social media advertisements (promoted through the WTG Hashtags) containing a weblink to our to an online survey, and through contact with sport team leaders to request that they distribute our electronic study participation invitation to their fellow members.Furthermore, a snowball-sampling strategy was also undertaken whereby participants of the research were requested to forward/share the study link with other transplant athletes in their contact list.
The inclusion criteria for taking part in the study required eligible participants to be an organ transplant athlete who is 18-years or older and have taken part in a transplant game either at a national or international level.For this study, athletes were considered as someone who has competed at national or international transplant games event.Participants were also required to have a good command of the English language to accurately respond to the survey items that was presented in English.Initially, a sample of 135 participants (Paper-Based: N = 14; Online-Based: N = 121) were recruited.Following the removal of incomplete surveys, a final sample of 99 participants (Mage = 53 ± 14) who had provided data on all the questionnaires were retained for data analysis (see Table 1).It is reasoned that this attrition rate was due to challenges with online data collection during the games, as paperbased data collection is easier for participants to return to whilst competing.

Demographic items
Demographic items prompted participants to report their age, sex, nationality, sport(s) played, highest level of sport pretransplant, current employment status, highest level of education, type of transplant(s) received, time since last transplant, and whether or not the participant was currently competition within the 2019 WTG.

Rapid assessment of physical activity questionnaire (RAPA)
The RAPA questionnaire (Topolski et al., 2006) contains 7 statements on increasing levels of physical activity from "I rarely or never do any physical activity" to "I do 20 minutes or more a day of vigorous physical activities, 3 or more days a week".
Participants are required to answer each statement (Yes/No) to provide an indication of their current levels of physical activity.Participants who indicate greater weekly engagement in physical activity are assigned a higher final score -ranging from 1 (low engagement) to 7 (high engagement).The RAPA questionnaire has previously been employed for assessing physical activity levels within transplant recipient samples (Wickerson et al., 2021).

Physical Activity Enjoyment Scale (PACES)
A revised version of PACES (Kendzierski & DeCarlo, 1991;Motl et al., 2001) containing 16 items was employed to measure enjoyment in physical activity.Participants were asked to rate each item on a 7-point Likert scale, ranging "totally disagree" to "totally agree".Each item began with the statement "when I am physically active" and finished with a statement such as "I enjoy it" or "I feel bored".Participant responses are totalled to give a score out of 112, with higher scores indicating higher enjoyment of physical activity.PACES has been shown to have a high internal consistency, with a Cronbach's alpha of 0.89 (Kendzierski & DeCarlo, 1991).

Short Form Health Survey (SF-36)
The SF-36 (Ware & Sherbourne, 1992) is a widely used healthrelated QoL measure covering eight aspects of mental (energy/ fatigue, social functioning, emotional well-being, and emotional role limitations) and physical (physical functioning, physical role limitations, bodily pain, general health perceptions) health.Example items from the SF-36 include: "During the past 4 weeks, have you had any problems with your work or other regular daily activities as a result of your physical health?","How much bodily pain have you experienced during the past 4 weeks?".These questions, along with others in the SF-36 are rated on several sets of unique Likert-scale responses to comprehensively capture an individual's perceptions and experiences across various dimensions of health.Each sub-scale has been shown to demonstrate good internal consistency, with Cronbach's alpha ranging from 0.76 to 0.90 (Jenkinson et al., 1994).
Example items on the SMS-2 include "because people around me reward me when I do" and "because it gives me pleasure to learn more about my sport".Participants are required to answer these statements through a 7-point Likert scale ranging from "not at all" to "very true" to indicate why they participate in sport activities.Overall scores for each of the six sub-scales are taken as an average of responses for each corresponding subscale items.The six sub-scales of the SMS-2 have been shown to have acceptable internal consistency, with Cronbach's alpha values ranging from 0.76 to 0.86 (Pelletier et al., 2013).

Fatigue Severity Scale (FSS)
The FSS (Krupp et al., 1989) is 9 item questionnaire which asks participants questions on the impact of fatigue upon their daily lives on a 7-point Likert scale "strongly disagree" to "strongly agree" over the past week.Example statements include "fatigue causes frequent problems for me" and "exercise brings on my fatigue".An average is taken of participant's responses across the nine questions, with higher scores indicating greater levels of fatigue.The FSS has been shown to have high internal consistency, with a Cronbach's alpha of 0.94 (Kleinman et al., 2001).

Procedure
The study received full ethical approval from the Health and Life Sciences ethics committee at Northumbria University (Ref no: 17650).Participants either accessed the survey through an online link or were administered with paper copies by a member of the research team during the time of the 2019 WTG event.Participants were informed of the study aims before providing informed consent.Participants firstly filled out the biographic questionnaire to provide details of their demographical characteristics and transplant-related history.
Participants were then asked to complete the seven psychometric assessments.The order which these questionnaires were presented to participants was randomised to prevent order effects from occurring.Participants were then debriefed and given the chance to enquiry about the research.

Treatment of data
Data was transferred to and analysed using SPSS version 26.Firstly, expectation maximisation was used to calculate values for missing data within the data set (Zhang et al., 2014).Cronbach's alpha was then calculated for each questionnaire, with alphas computed upon the subscales for the questionnaires that included them.This highlighted that the amotivation, external motivation and introjected motivation subscales of the SMS-2 had alpha of less than .50 and therefore these were excluded from the main analysis due to not reliably measuring the intended constructs.
Descriptive statistics (M, SD and frequencies) were calculated for the components of the SF-36, FSS and remaining predictor and outcome variables (see Table 2).Two hierarchical linear regressions were conducted to assess potential predictors for both MQoL and PQoL.To assess which predictors to include in either model, tests of difference were conducted for categorical variables upon MQoL and PQoL.For continuous variables, correlations were run with both outcome variables.Each potential predictor variable was assessed for skewness and potential outliers, with nonparametric equivalent tests used for any variables that showed significant skew.Variables showing a significant effect upon, or correlations with, MQoL or PQoL were then included within the regression model for that outcome variable.Assumptions of a hierarchical regression were then tested for both models, with no violations identified.The regressions were then carried out with demographic variables included within block 1, sport-related variables included within block 2, and fatigue added in block 3.

PQoL
The final regression model (block 3) revealed to be significant and accounted for 64% of the variance in PQoL (M = 79.40,SD = 17.31), (R 2 = 0.64, F(3,95) = 55.22,p < 0.01).Fatigue (M = 3.16, SD = 1.48) was found to be a significant negative predictor of PQoL, with standardised beta weights showing this predictor to have the strongest association to the outcome variable (ß = −.76,p < 0.01).The inclusion of fatigue into the model significantly increased the model fit (ΔR 2 = 0.46, p < 0.01) and reduced the association between physical activity enjoyment (M = 98.16,SD = 13.14) and PQoL (see Table 3), suggesting fatigue explains a unique amount of the variance of the PQoL in this model.

Discussion
This exploratory study aimed to identify significant predictors of QoL in transplant recipient athletes, contributing to the psychological well-being literature in this population (Johnson et al., 2013).The results highlighted fatigue as the most influential predictor for both PQoL and MQoL.Fatigue accounted for variance previously attributed to employment and enjoyment of physical activity, underscoring its substantial role in influencing QoL among transplant athletes, which paves the way for further research into its complex effects.Firstly, with limited research in this area, it's sensible to compare our sample's demographics with the national and international transplant athlete samples included in Johnson et al. (2013) and Painter et al. (1997) research.In these previous studies, kidney transplant recipients formed the largest part of each sample being predominantly male (65.7%), and had a similar average time since transplantation, which is slightly longer in our study (Myears = 11.06 ± 8.69).Our sample is nearly a decade older with less variability in age (Mage = 53.12± 8.69) compared to the previous ones.A comparison with another WTG sample (Johnson et al., 2013) reveals similarities in nationality, sports involvement, and education, with a majority from the UK, participating in athletics, and holding undergraduate degrees.The Johnson sample also had more elite participants pre-transplantation than our sample, while our study included more participants engaged in competitive sports before their transplant.Painter's US national transplant athlete sample offers data on the eight SF-36 component scores.Comparing them, we see similar averages in physical and social functioning and emotional well-being.Our current sample scored higher in physical and emotional role functioning and pain but lower in energy and general health, with general health showing the largest difference between the two samples.Approximately 27.3% of participants in our sample scored four or higher on the Fatigue Severity Scale, a threshold indicating severe fatigue according to prior research (van den Berg-Emons et al., 2014).
Although research on athlete-specific samples is limited, severe fatigue remains crucial in post-treatment care for broader transplant populations (Almgren et al., 2021).In this regard, studies have linked severe fatigue to impaired daily functioning and Health-Related Quality of Life (HRQoL) in liver transplant recipients (Van Ginneken et al., 2010).Furthermore, it's been associated with rumination frequency and sleep quality among kidney transplant recipients (P.Zhang et al., 2023) and predictive of self-efficacy in heart transplant recipients (Almgren et al., 2021).With this said, our study aligns with prior research demonstrating a negative correlation between fatigue and QoL in various clinical groups (Anderson & Ferrans, 1997) extending this association to the transplant athlete population.More specifically, in our regression models, fatigue explains part of the variance previously attributed to factors like employment and enjoyment of physical activity.While both were significant in earlier models for PQoL, they lost significance after fatigue was included.However, it should be noted that the significance of physical activity enjoyment in the final model was marginally above the .05cut-off value.For MQoL, both physical activity enjoyment and fatigue emerged as significant predictors in the final regression model.Nonetheless, introducing fatigue halved the predictive power of physical activity enjoyment in the final model, indicating fatigue's substantial role in explaining the relationship between QoL and physical activity enjoyment.The association between fatigue and life satisfaction among liver transplant recipients has been previously explored (Lin et al., 2017), albeit not specifically regarding physical activity enjoyment.This implies that fatigue may impact various aspects of enjoyment among transplant recipients.For example, Lin et al. (2017) suggested that fatigue might lead to increased exhaustion, reduced motivation, and diminished social interest, which could explain its effect on multiple enjoyment domains within transplant populations.This may also clarify why fatigue seems to weaken the link between physical activity enjoyment and different aspects of QoL, as reduced motivation and increased exhaustion likely contribute to lower enjoyment of physical activities and selfreported QoL.Moreover, the connection between physical activity enjoyment and QoL aligns with prior research by Johnson et al. (2013), which associated this facet of enjoyment with psychological well-being among WTG athletes.Therefore, A primary strength of this study lies in the recruitment of a substantial number of competitive transplant athletes through diverse sampling methods, thereby achieving remarkable statistical power within a usually challenging-to-access population.Furthermore, this study pioneers the identification of fatigue as a predictor of QoL among transplant athletes.This novel discovery not only underscores the necessity for future investigations to account for fatigue when examining factors affecting QoL in transplant athletes but also extends its relevance to other transplant recipients interested in regular exercise or sports participation.However, one notable limitation of the present study is the exclusion of several variables from the regression models due to inadequate internal reliability.Three out of the six subscales assessed by the SMSII showed inadequate internal reliability.Consequently, these subscales were omitted from the primary analyses of the study.As a result, the predictive capacity of amotivation, external motivation, and introjected motivation concerning QoL remains unclear.The employed measure (SMSII) might not be entirely reliable within the transplant athlete sample, as per a recent survey where over half of the participants cited reasons for competing in transplant games that focused on improving fitness, community engagement, and the enjoyment of competition (Hames et al., 2022).Smaller proportions aimed for records or winning events, suggesting a potential mismatch between aspects of motivation and the measurement of motivation subscales in the SMSII.This presents an opportunity for future research to assess the SMSII's suitability within the transplant athlete population and conduct psychometric validation to verify the current findings.Further validation of the SMSII in this group will lead to the selection of appropriate questions for examining the predictive features of motivation and improving our understanding of how sports factors effect QoL in transplant athletes.A further limitation of this study was not collecting medication data from participants.Transplant recipients are known to have to adhere to strict medication regimens (Yang et al., 2021), with similar medication regimens being shown to influence levels of fatigue (Onate-Figuérez et al., 2023), future research should collect this data to understand how medication can impact this relationship between fatigue and QoL in transplant athletes.
When addressing the practical implications of these findings and how they may be used to further the treatment of transplant recipients, the authors propose using motivational interviewing (MI) post-transplant surgery to help manage fatigue and increase PQoL and MQoL.MI is a personalised, patient-centred approach to health behaviour modification that helps alleviate ambivalence about change and boost intrinsic motivation for change (Miller & Rollnick, 2012).This notion is supported by findings from a meta-analysis suggesting that MI can increase physical activity among those with chronic health conditions (O'Halloran et al., 2014).Other research also identifies MI to reduce fatigue in recipients with multiple sclerosis (Borji et al., 2018), and increase medical adherence in transplant recipients (Senft et al., 2018).Furthermore, pilot data suggests that MI could be used successfully in conjunction with appropriate transition to community training programmes to maintain physiological health improvements (such as VO2 max.) in kidney transplant recipients following a 12-week aerobic or resistance training programme (O'Connor et al., 2017).These studies lend credence to the idea of using MI to reduce levels of fatigue in this group, which, according to the current study's findings, could lead to improvements in MQoL and PQoL.
Given that transplant athletes have strong intrinsic motivation (Jooste et al., 2020), exploiting this quality may improve the efficacy of MI in relieving fatigue.Furthermore, using several MI approaches could improve cost-effectiveness and practicality.For example, performing MI sessions online could improve accessibility for transplant recipients by reducing the need for travel.According to research, online MI sessions produce equivalent results to in-person sessions when it comes to changing physical activity and weight (Bus et al., 2018).While group-delivered MI may be less expensive, combining it with individually delivered sessions appears to be more successful than relying simply on group sessions (Lundahl & Burke, 2009).This could provide an opportunity to build on the existing studies on the effectiveness of group-delivered MI and determine whether high intrinsic motivation levels affect MI's overall efficacy.
Moreover, these findings have consequences for those who work with transplant athletes (coaches, managers, physiotherapists, and psychological support providers).Overall, it is recommended that athletes' fatigue levels be measured on a regular basis to avoid it from interfering with continued physical exercise and enjoyment.As fatigue weakens the link between physical exercise enjoyment and QoL in both models, it emphasises the need of evaluating this relationship, particularly during major transplant sporting events like the WTG.Given that most athletes compete in such events for fun (Hames et al., 2022), increased awareness of fatigue among team staff is critical.This understanding can ensure that all athletes have a happy experience, ultimately enhancing both physical and mental quality of life (PQoL and MQoL).

Conclusion
In conclusion, recognising fatigue as a key determinant in the QoL of transplant athletes provides a critical direction for future research that could promote the development of treatments aimed at improving QoL in this population.Furthermore, our findings provide guidelines for personnel who work with transplant athletes, emphasising the significance of recognising and managing fatigue in order to improve both the enjoyment of physical activity and the overall QoL of the athletes they support.

Disclosure statement
No potential conflict of interest was reported by the author(s).

Funding
The author(s) reported there is no funding associated with the work featured in this article.

Table 1 .
Frequencies (N and %) for all demographic variables of the sample separated by transplant type.

Table 2 .
Descriptive statistics (M and SD) and frequencies (N and %) for all predictor and outcome variables as well as their sub-components, separated by transplant type.

Table 3 .
Standardised regression coefficients (with 95% confidence intervals) for each of the 3 blocks of the linear regression models of PQoL (α=.88), as well as descriptive statistics and internal consistency of each continuous predictor variable, N = 99.was a significant positive predictor of MQoL.Standardised beta weights showed fatigue to have the strongest association to the MQoL in the model.The inclusion of fatigue into the model significantly increased the model fit (ΔR 2 = 0.34, p < .01)andreduced the association between physical activity enjoyment and MQoL (see Table4), suggesting that fatigue also explains a unique amount of the variance of MQoL in this model too.

Table 4 .
Standardised regression coefficients (with 95% confidence intervals) for each of the 3 blocks of the linear regression model of MQoL (α=.88), as well as descriptive statistics and internal consistency of each continuous predictor variable, N = 99.