The effects of injury, contextual match factors and training load upon psychological wellbeing in English Premier League soccer players via season-long tracking

ABSTRACT

The study aimed to track psychological wellbeing (PWB) across two consecutive soccer seasons examining the effects of injury, illness, training load (TL) and contextual match factors (playing status, match selection and individual win rate). Furthermore, examine PWB prior to injury or illness event. Thirty-two English Premier League (EPL) soccer players completed the “Warwick-Edinburgh Mental Wellbeing Scale” every two weeks. No differences were found for group averaged PWB across the seasons (52.2 ± 0.3 vs. 51.8 ± 1.1) (p > 0.05). Previous 7-day TL measured using GPS (session duration, total distance, explosive distance, low-intensity distance, high-speed distance (HSD) and sprint distance (SD)) were not related to current PWB (p > 0.05). Yet, previous 14-day HSD (r (385) = −0.095) and 21-day SD (r (385) = 0.100) were related to current PWB (p < 0.05). Only 100% (vs. 0%) win rate in the previous 14-days to the questionnaire revealed a higher current PWB score (52.7 ± 4.7 vs. 50.9 ± 5.6 (p < 0.05)). PWB did not differ prior to an injury or illness event, when players were injured or had low contextual match factors at time of questionnaire or previous match, and the previous 7-days (p > 0.05). In conclusion, PWB fluctuations across the season are associated with prior TL and multiple negative results. But prior PWB was not linked to injury or illness events. Implications for prioritising interventions to improve PWB during periods of chronic high intensity TLs and losing streaks, monitoring PWB, and use in injury and illness prediction are discussed.

Highlights

• Psychological wellbeing responses, as measured by the “Warwick-Edinburgh Mental Wellbeing Scale” did not change significantly at a group level between the phases of the two seasons.

• Prior training load was associated with wellbeing scores, specifically previous 14-day high-speed distance and 21-day sprint distance.

• Psychological wellbeing scores were only affected by win/loss rate in the previous 14-days.

• These findings highlight the importance of timely interventions to improve wellbeing in periods of negative results, and the recommendation of longitudinally monitoring wellbeing.

KEYWORDS:

• Football
• stressors
• hedonic wellbeing
• Eudaimonic wellbeing
• monitoring

Introduction

Recent research in professional soccer has indicated the prevalence of Mental Health (MH) symptoms and disorders are pertinent and potentially greater than alternative sports (Gouttebarge, Frings-Dresen, et al., 2015; Gouttebarge, Aoki, et al., 2015; Junge & Feddermann-Dermont, 2016; Kilic et al., 2018). Within 262 soccer players, 37% reported symptoms of common MH disorders over a 12-month period (Kilic et al., 2018). Additionally, within 607 male soccer players, 9% reported alcohol misuse, 38% anxiety and depression, and 58% adverse nutrition (Gouttebarge, Aoki, et al., 2015). The prevalence may also vary upon age (Kuettel, Durand-Bush & Larsen, 2021), as higher rates of depression (15 vs. 6.6%) and lower psychological wellbeing (PWB) (48 vs. 52) in youth vs. senior male soccer players have been reported (Abbott et al., 2019; Grimson et al., 2021).

Sporting and non-sporting risk factors such as negative life events, performance difficulty, media scrutiny and injury, are just a few challenges faced by elite athletes which could impact MH and PWB (Purcell et al., 2019; Rice et al., 2016). Moreover, English Premier League (EPL) soccer players are no exception, given their exposure to excessive training loads (TL), fixture congestion, contextual match factors and sleep deprivation because of travel and evening matches, which could also impact MH and PWB (Abbott et al., 2019; Carling et al., 2015; Rice et al., 2016). These stressors could also fluctuate across seasons or career phases, causing potential for periodised vulnerability to poor MH and PWB (Hughes & Leavey, 2012). Lower PWB in EPL soccer players may occur in the “late” vs. “early” stages of a season (Grimson et al., 2021). Likely, due to sport rather than non-sport related stressors (e.g. injury), often singled out as having the greatest influence on MH and PWB (Schinke et al., 2017).

Importantly, poor MH has been associated with injury risk in soccer players (Ivarsson et al., 2013; Watson et al., 2017). Therefore, just as training is monitored and balanced with adequate recovery to manage physical injuries, so too must the psychological demands with strategies supporting MH (Kuettel & Larsen, 2019). Subjective and objective monitoring tools are widely utilised to assess physical wellbeing and manage physical injuries yet omit monitoring tools to manage psychological demands and subsequent injury and illness risk (Heidari et al., 2019). Questionnaires are simple, low cost and time efficient and could encourage help-seeking behaviour in athletes (Bird et al., 2018; Halson, 2014; Souter et al., 2018). Nevertheless, previous research within elite sport has focused more on the presence of MH symptoms than PWB and utilised diagnostic questionnaires (e.g, GAD-7 and CED-S).

The notion, athletes are healthy without a clinical disorder is over simplistic (Henriksen et al., 2020). Therefore, the negative conceptualisation of MH, as the absence of mental illness (e.g. depression and anxiety) has shifted to encompass positive MH aspects and the functioning and flourishing of individuals (Kuettel, Pedersen, , et al., 2021; Schinke et al., 2017; Tennant et al., 2007). Subsequently, understanding levels of happiness and pleasure (hedonic wellbeing) and extent to which a person is functioning fully (Eudaimonic wellbeing), is important in athletes (Nicholls et al., 2020; Ryan & Deci, 2001). Recent research utilised the “Warwick-Edinburgh Mental Wellbeing Scale” (WEMWBS) (Stewart-Brown et al., 2009) to assess potential MH disturbances in professional male soccer players (Abbott et al., 2019; Grimson et al., 2021; Kuettal et al., 2021). The WEMWBS covers both the Hedonic & Eudaimonic aspect of wellbeing which represent the MH component of the Keye's “Two continua model” (Keyes, 2007) and is correlated with MH symptoms (e.g. anxiety and depression) (Kuettal et al., 2021). Therefore, an understanding of how sport-related stressors (e.g. injury, win rate, match selection) in EPL soccer players impact PWB could inform practitioners of when to periodise interventions to optimise subsequent performance, wellbeing and injury risk (Donohue et al., 2018; Poucher et al., 2021; Purcell et al., 2019).

Previous research regarding contextual factors such as age, gender, playing position, injury and symptoms of depression and anxiety in elite senior male and female soccer players remains equivocal (Junge & Feddermann-Dermont, 2016; Junge & Prinz, 2018). Limitations could be cross-sectional research designs, whereby MH symptoms were captured at one-time point. Additionally, a longitudinal approach may be more favourable as players can act as their own control, and fluctuations to PWB can be captured. Only one previous study has investigate PWB in academy soccer players (Abbott et al., 2019), and reported injury and or de match-selection, accounted for 50% of the variability within PWB (Abbott et al., 2019).

Given particular risk factors may vary across career phases (Purcell et al., 2019) and the potential for age related differences in PWB (Abbott et al., 2019; Kuettal et al., 2021), knowledge on how sport-related stressors such as injury and match (de)selection in EPL senior players impacts PWB requires investigation.

Therefore, instead of focusing on the mental illness (symptoms of depression and anxiety), the current study aimed to examine and enhance the understanding of the impact injury and contextual match factors (individual win rate, match selection and playing status) and TL upon PWB (e.g. positive wellbeing). Moreover, the potential relationship between PWB and subsequent injury and illness occurrence was explored. Based upon previous literature, that injury and match deselection in U23 soccer players resulted in lower PWB, it was hypothesised that similar findings would be reported. It was also hypothesised that PWB would be lower prior to an injury.

Methods

Participants

Thirty-two first-team professional male soccer players from an EPL club were invited to participate in this study (height: 183.7 ± 8.8 cm; weight: 80.8 ± 8.3 kg; age: 26.6 ± 4.0 years). All participants were classified as elite athletes (Swann et al., 2015), and competed in the 2019–2020 and/or 2020–2021 seasons, comprised of routine training sessions and matches. Full approval was received from the local ethics review board and participants provided informed written consent for access to their routinely collected anonymous data.

Procedure

Participants completed a questionnaire to assess PWB across the different phases of the two seasons (Figure 1). This was completed on a bi-weekly basis between 9:00∼9:30 am on the second day following a match. This day was selected as it was considered the optimum time to reduce the impact of the preceding or following match. The questionnaire was optional and administered by the club doctor to complete in a confidential manner. Any issues raised could be identified to the individual and necessary interventions could be put in place. Questionnaire data was then anonymised and connected to the remaining data through participant ID numbers before being given to the researcher. Session duration and external workload calculated by 10 Hz Global Positioning System (GPS) units (Vector, Catapult Innovations, Melbourne, Australia) (worn during training session and matches) were recorded. Data were downloaded using Catapult Openfield Cloud Software for analysis (Catapult Cloud Version 2.0.1, Catapult Innovations, Melbourne, Australia). Specific variables collected and respective definitions are displayed in Table 1. An injury was defined as any injury that resulted in time loss from training or matches (Fuller et al., 2006). An illness was defined as a player self-reporting cold symptoms on the daily wellness questionnaire or that resulted in time lost from training or matches. Match selection was defined as participants that were available being named in the match day squad (11 players, 7 substitutes).

Figure 1. Group average mental wellbeing scores across the 2019–2020 and 2020–2021 season.

Table 1. Absolute GPS parameters, previous 7-day, 14-day and 21-day training load (mean ± SD).

Training load	Previous 7-day	Previous 14-day	Previous 21-day
Session duration (min)	242 ± 68	492 ± 85	700 ± 147
Total distance (m)	20,909 ± 6156	42,960 ± 9970	63,106 ± 13,809
Low-intensity distance (m)	19,888 ± 5851	40,881 ± 9489	62,004 ± 13,577
Explosive distance (m)	1345 ± 437	2716 ± 677	3988 ± 967
High-speed distance (m)	831 ± 327	1690 ± 556*	2539 ± 807
Sprint distance (m)	190 ± 129	388 ± 220	551 ± 277*

Note: Low intensity distance (0–5.5 m/s), Explosive Distance (accelerating and decelerating > 2 m/s²), high-speed distance (5.5–7 m/s) and sprint distance (>7 m/s). *Reveals a statistical significance.

PWB questionnaire

PWB was assessed using the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) (Stewart-Brown et al., 2009). The WEMWBS has been utilised to monitor mental wellbeing in athletic populations (Abbott et al., 2019; Nicholls et al., 2020). The WEMWBS had excellent levels of reliability within our sample, with a Cronbach alpha of 0.90. The questionnaire is comprised of a 14-item self-report scale that assesses positive thoughts and feelings. Responses are made relative to the previous two weeks. Each statement is scored on a 1–5 Likert Scale (1 = “none of the time”, 5 = “all of the time”). A global score ranging between 14 and 70 is then calculated by adding up item scores. The higher the score, the higher the level of PWB.

Data analysis

All data analysis was performed with SPSS (SPSS Version 26.0). Normal distribution was checked and considered normally distributed if the Shapiro–Wilk test was p > 0.05. A one-way repeated measures analysis of variance (ANOVA) was utilised to assess changes to PWB across the different phases of the two seasons outlined in Figure 1. Pearson's correlations were utilised to determine the relationship between PWB and cumulative previous 7, 14 and 21-day workload for each GPS parameter. Multiple paired t-tests were utilised to assess differences in PWB scores, when 100 vs. 0% Injured vs. Not Injured, Ill vs. Not Ill, Selected vs. Unselected, Played vs. Didn't Play and Win vs. Loss, at the time of the questionnaire (TOQ), one- and two-weeks prior to the questionnaire. Furthermore, utilised to assess differences in PWB scores, one and two weeks prior to an Illness and Injury Occurrence vs. Non-Occurrence. Cohen's d effect sizes were used to determine the strength of the differences obtained in the t-test (0.1 = small, 0.3 = medium and 0.5 = large) (Cohen, 1988). A multivariate regression was utilised to examine the effects of each previous 7-day workload variable and contextual match variable presented in Table 2, upon PWB (dependent variable). Independent variables for each player were calculated and entered into the regression. Statistical significance was determined at p <0.05.

Table 2. The contextual match factors across the two seasons (mean ± SD).

Contextual match factor	Previous game/time of questionnaire	Previous 7-days to questionnaire	Previous 14-days to questionnaire
Injury (time out injured %)	8.1 ± 10.6	7.9 ± 11.2	7.4 ± 11.1
Illness (% of time with an illness)	1.4 ± 3.1	1.4 ± 2.0	1.3 ± 1.8
Match selection (% of matches selected for)	76.0 ± 2 6.4	75.1 ± 25.5	74.9 ± 24.8
Individual win rate (% of games won)	25.0 ± 17.3	23.9 ± 13.3	25.5 ± 8.5
Playing status (% of games started or subbed)	76.6 ± 23.2	72.7 ± 22.9	70.1 ± 21.6

Results

PWB across the two seasons

Average PWB scores during the two seasons were 52.2 ± 0.3 and 51.8 ± 0.5 respectively and ranged between 50.6 and 53.0. A one-way repeated measures ANOVA revealed no main effect for time (f_(9,81) = 0.630, p = 0.768, η²_partial= 0.034), and therefore no changes in PWB between the phases of the season (Figure 1).

PWB and TL

Previous 7, 14 and 21-day workloads are presented in Table 1. Previous 14-day HSD (r (385) = −0.095, p = 0.039) and previous 21-day SD (r (385) = 0.100, p = 0.030) revealed small correlations with PWB. All other workload variables were not related to PWB scores (p > 0.05).

PWB and contextual match factors

A multiple linear regression revealed no contextual factors or TL, in respect to the previous match/TOQ outlined in Table 2 predicted PWB (p > 0.05).

The influence of contextual match factors upon PWB, at the TOQ, or in relation to the previous 7 and 14 days are displayed in Figure 2. No difference in PWB was revealed when winning (52.5 ± 5.8) vs. losing (51.9 ± 5.5) the previous match (t₍₂₅₎ = 1.103, p = 0.281, d = 0.22), or when 100% winning (51.7 ± 6.0) vs. losing in the previous week (51.6 ± 5.5) (t₍₂₄₎ = 0.144, p = 0.887, d = 0.03). Higher PWB was revealed when 100% winning (52.7 ± 4.7) vs. losing (50.9 ± 5.6) in the previous two weeks (t₍₂₇₎ = 2.945, p = 0.007, d = 0.57).

Figure 2. The influence of contextual match factors upon MW, at the time of questionnaire, and previous 7 and 14 days. *Indicates statistical significance. Graph (a) demonstrates MW effect upon injury status. Graph (b) demonstrates MW effect upon playing status. Graph (c) demonstrates MW effect upon win rate. Graph (d) demonstrates MW effect upon match selection.

No difference in PWB was revealed when playing (52.0 ± 4.6) vs. not playing (51.6 ± 5.5) the previous match (t₍₂₁₎= 0.606, p = 0.551, d = 0.13). Additionally, when 100% playing (52. 1 ± 4.5) vs. not playing (51.8 ± 5.6) the previous week (t₍₂₁₎ = 0.570, p = 0.575, d = 0.12), or when 100% playing (50.9 ± 4.0) vs. not playing (51.8 ± 5.5) the previous two weeks (t₍₁₁₎ = −0.901, p = 0.387, d = 0.27).

No difference in PWB was revealed when selected (52.7 ± 4.9) vs. unselected (51.8 ± 4.1) for the previous match (t₍₂₁₎ = 1.944, p = 0.065, d = 0.42). Additionally, when 100% selected (52.8 ± 4.8) vs. unselected (51.8 ± 4.1) the previous week (t₍₂₁₎ = 1.958, p = 0.064, d = 0.43), or when 100% selected (53.1 ± 4.8) vs. unselected (51.9 ± 4.6) for the previous two weeks (t₍₁₆₎ = 1.709, p = 0.107, d = 0.43).

No difference in PWB was revealed when injured (50.6 ± 4.5) vs. not injured (52.1 ± 4.1) at the TOQ (t₍₁₆₎ = 1.551, p = 0.140, d = 0.38). Additionally, when 100% injured (51.2 ± 4.8) vs. not injured (52.6 ± 3.4) the previous week (t₍₁₄₎ = 1.079, p = 0.300, d = 0.29), or when 100% injured (51.5 ± 4.5) vs. not injured (53.3 ± 2.5) the previous two weeks (t₍₁₂₎ = 1.241, p = 0.241, d = 0.36). No difference in PWB was revealed when ill (48.9 ± 7.1) vs. not ill (49.8 ± 7.1) at the TOQ (t₍₆₎ = 0.996, p = 0.358, d = 0.41).

PWB prior to an injury and illness

No differences were demonstrated in PWB the week prior to an injury (50.3 ± 7.2) vs. non-injury (50.7 ± 5.9) (t₍₁₂₎ = 0.380, p = 0.710, d = 0.11), or two weeks prior to an injury (50.3 ± 7.1) vs. non-injury (51.2 ± 5.9) (t₍₁₈₎= 0.935, p = 0.363, d = 0.21). No difference in PWB occurred the week prior to an illness (50.9 ± 6.2) vs. non-illness (51.5 ± 4.9) (t₍₁₁₎ = 0.850, p = 0.413, d = 0.25), or two weeks prior to an illness (50.8 ± 7.3) vs. non-illness (50.9 ± 5.7) (t₍₁₉₎ = 0.105, p = 0.917, d = 0.02).

Discussion

This study aimed to examine the influence of injury, contextual match factors and TL upon PWB in EPL soccer players, and PWB levels prior to an injury or illness. In contrary to both hypotheses, the results suggest only previous two-week win rate significantly influenced PWB, and previous 14-day HSD and 21-day SD were related to PWB. Moreover, PWB was no different prior to an injury or illness.

PWB and contextual match factors

The current study revealed PWB was unaffected by the proceeding match result, which has been reported previously in academy soccer players (Abbott et al., 2019). Both studies administered questionnaires on a specific matchday, therefore sufficient recovery from the match could have prevented interference with PWB. Nevertheless, higher PWB was evident with an 100% vs. 0% two-week win rate, suggesting multiple results could impact PWB. In speculation, player contracts are “success” dependant, reflecting results and performance, exacerbating incentives to win. The WEMWBS assesses competence, autonomy and positive relationships (Giles et al., 2020). Therefore, negative results could deteriorate athletes perceived competency, relationships and therefore PWB . Nevertheless, the current study also suggests contextual factors indicative of competency, such as playing status and match selection, did not influence PWB, in agreement with research in senior players (Junge & Prinz, 2018). Yet, in academy players, 10% variability in PWB were related to match selection (Abbott et al., 2019). It is plausible, senior players attribute not playing and de-selection to factors, such tactics and player rotation, due to fixture congestion, and physical demands of senior football. Younger athletes however if unselected, may experience heightened anxiety trying to impress key stakeholders to earn professional contracts (Abbott et al., 2019). Age could be a predictor of anxiety symptoms in youth female athletes (Junge & Prinz, 2018). Moreover, depression symptoms were significantly greater in U21s vs. senior footballers (Junge & Feddermann-Dermont, 2016). Given the relationship between depression and anxiety, it is likely youth athletes are more vulnerable to poor MH. However, regarding PWB this may not always suffice as younger athletes have reported higher PWB and lower stress scores than their senior counterparts (Belz et al., 2018; Kuettall et al., 2021).

Current findings also suggest the negative trend in PWB during an EPL season (Grimson et al., 2021) could be result dependant rather than playing or selection status. Given lower PWB could attenuate performance and increase risk of injury/illness (Ivarsson et al., 2013; Reardon et al., 2019; Watson et al., 2017), without timely interventions during poor results, such consequences could remain. Nevertheless, a 3-point change in PWB is “clinically meaningful” (Maheswaran et al., 2012), and a 1-point higher PWB score was identified when winning. Therefore poor results are unlikely to have clinical implications.

In agreement with the current study, PWB could not be explained by previous 7-day TL (Abbott et al., 2019). Suggesting, the physical demands of EPL football, may also not be sensitive enough to predict PWB. However, current findings revealed associations between PWB and 14-day HSD (r = −0.095) and 21-day SD (r = 0.100). The weak associations with TL are like those reported between PWB and in-season active mins (Grimson et al., 2021) and could reflect a high n number. Alternatively, the tendency to accrue a higher SD and HSD during match play, and training respectively, could mean higher HSD accrued in unselected players. Therefore, associations between SD, HSD and PWB could be confounded by match selection, reported to influence PWB (Abbott et al., 2019). A moderate effect size of match selection upon PWB was also reported in the current study.

The current study revealed no difference in PWB when injured or not. Gouttebarge, Frings-Dresen, et al. (2015) reported no difference in anxiety and depression symptoms in current/former soccer players. In contrast, U23 soccer players reported lower PWB (Abbott et al., 2019) and higher anxiety and depression symptoms in Swiss senior and U21 soccer players (Junge & Feddermann-Dermont, 2016). Moreover, associations were reported between severe injuries and distress (r = 0.15), and anxiety (r = 0.13) (Gouttebarge, Aoki, et al., 2015). Discrepancies could exist due to injury severity and age. Studies reporting attenuated PWB with injuries, examined severe injuries resulting in over a week absence from sport (Abbott et al., 2019; Gouttebarge, Frings-Dresen, et al., 2015). In contrary, this study examined any time-loss injury, but revealed a moderate effect size when injured vs. not injured over two weeks. Together, prolonged absence from sport may attenuate PWB.

The current results demonstrate higher duration of injury prior to the questionnaire, revealed higher PWB, thus availability of psychological support could be pertinent. The current study, and Gouttebarge, Frings-Dresen, et al. (2015) studied senior vs. youth athletes (Abbott et al., 2019; Junge & Feddermann-Dermont, 2016). Senior soccer typically has increased staff: player ratios and greater financial burdens associated with injured players, emphasising quick return to sport. Absence from sport whilst injured could threaten athletic identity and decline PWB in youth athletes (Abbott et al., 2019).

PWB prior to an injury or illness

PWB was no different prior to an injury vs. non-injury. It could be the association between prior PWB and injury is complex and multifactorial. When predicting injury, daily hassles, trait anxiety and negative life events stress accounted for 24% of the variance (Ivarsson et al., 2013). Moreover, worse daily mood was an independent predictor of injury (Watson et al., 2017). Both studies revealed associated risk of injury with “daily” indications of MH status rather than “fortnightly” measured in the current study. Therefore only acute changes in mood could be sensitive enough to determine injury risk. Alternatively, the previous research examined youth and sub-elite soccer players. Highly skilled soccer players have highly adaptive coping mechanisms (Ivarsson et al., 2013), and therefore psychological risk factors upon injury might be dependent upon skill level. That said, the current study revealed a moderate effect size prior to an injury vs. non-injury, and therefore in isolation PWB might not predict injury but could be employed in a battery of tests to monitor injury risk. Given the limited feasibility of daily monitoring MH in professional sport, further research should investigate the usefulness of the WEMWBS.

PWB across the two seasons

No significant change in PWB across two seasons was identified. Athletes ascertain how to deal with sport-related stressors, potentially leading to effective emotional regulation in sport and alternative life domains (Pensgaard & Duda, 2003). Therefore, EPL soccer players PWB could be resistant to both sporting and non-sporting related stressors. Notably, within sporting organisations, increasing emphasis is on athlete MH and provision of support (Henriksen et al., 2020). It is now mandatory in most football clubs to employ a sports psychologist (Kuettal, Durand-Bush & Larsen, 2021) and adopt regular screening and interventions (Purcell et al., 2019). The ability of current participants to have access to full-time psychological support may encourage help-seeking behaviour (Gulliver et al., 2012). Given that languishing athletes generally receive lower social support perceive higher stress levels and rate their sporting environment as less autonomy supportive compared to flourishing athletes (Kuettel, Pedersen & Larsen, 2021), it is prudent that support could explain steady PWB overtime. Nevertheless, declining trends in PWB exist during a season (Grimson et al., 2021). When considering the influence of stressors, these may still influence PWB but not significantly. Moreover, PWB varies on an individual level (Grimson et al., 2021), and therefore may mask fluctuations to PWB examined at a group level. Further investigation is required to understand contextual match factors on an individual level. It should be noted, the stigma surrounding MH and one's willingness to provide information (which may interfere with factors such as team selection) could lead to underreporting (Bird et al., 2018). Nevertheless, questionnaires were administered confidentially and only made available to the medical team to facilitate genuine responses. From a team perspective susceptibility to poor performance and injury/illness risk, are augmented with poor PWB (Reardon et al., 2019; Watson et al., 2017) and less likely to occur from a PWB perspective, based on the current findings. Importantly throughout the study, PWB scores ranged from 50.6 and 53.0, higher than the general population norm and depression threshold (50.2; Health Survey England, 2016; 44.5 Bianca, 2012) respectively.

Limitations of the current investigation include that data collection occurred during the COVID-19 pandemic. Nevertheless, data analysis excluded the COVID-19 lockdown period. Additionally, PWB did not change significantly pre vs. post lockdown in previous research (Grimson et al., 2021). Moreover, the lockdown caused a build-up of fixtures and increased physical demands; however, this was still insufficient to reveal associations between PWB and TL Therefore, the authors believe the COVID-19 pandemic had little impact upon the current investigation. Further limitations include,, the WEMWBS fails to adopt an assessment of physical wellbeing (including physical health, sleep, financial, living and work circumstances) (Giles et al., 2020). Moreover, caution should be applied when attributing these findings to alternative populations.

Conclusion

This study demonstrates that match result is the most important investigated contextual match factor upon PWB. Therefore, interventions when multiple negative results could prevent decline in PWB. Elite athletes PWB may be influenced by cumulative previous 14-day and 21-day TL. Practically high-intensity load manipulation could help maintain positive PWB. Furthermore, the efficacy of the WEMWBS predicting illness/injury risk is unlikely.

Acknowledgements

The authors would like to thank all the participants who took part in this study.

Disclosure statement

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

Additional information

Funding

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