Music performance anxiety and the Italian sport psychology S.F.E.R.A. model: An explorative study on 77 professional musicians

Abstract

Music performance anxiety (MPA) affects most musicians and is commonly listed among the triggering factors of several pathologies, such as depression, overuse syndrome, choking under pressure and focal dystonia. The study aimed at investigating the relationship between the S.F.E.R.A. model theory (SFERA), a multidimensional model for enhancing sport performances, and MPA in professional musicians. The model allows to analyze performances by monitoring five constructs representing underlying cognitive and metacognitive factors that influence performance, highlighting individual strengths and improvement areas. The results confirmed that severe MPA symptoms are a common issue among musicians (47%), while 36% of the sample experienced musculoskeletal pain related to playing their musical instrument in the 2 months preceding the study. Moreover, musicians who suffered from practice-related pain reported higher levels of MPA. The SFERA factors were strongly and negatively correlated with MPA scores, suggesting that improving the SFERA factors might be helpful in reducing MPA: musicians with high MPA showed significantly lower SFERA scores. Moreover, Energy was the strongest predictor of MPA scores among the five SFERA factors, suggesting that musicians with high anxiety levels present difficulties on emotional regulation during the performance and struggle to focus on their performances while ignoring dysfunctional thoughts. The study indicates that SFERA scores can predict MPA and that sport psychology programs applied to the music field may also be useful protective factors for musicians’ psychophysiological health, especially in music academies and universities.

Keywords

music performance anxiety sport psychology SFERA model prevention protocols musician’s health

Musicians have been often addressed as small-muscle athletes (Horvath, 2003; Palac & Grimshaw, 2006; Paull & Harrison, 1997) as they mostly focus on the execution of accurate and perfectly timed small movements. Dick et al. (2013) identified several similarities between musicians and athletes: they both train and practice daily and they often perform in front of an audience. During their careers, musicians and athletes often compete with their colleagues (Antonini Philippe, Kosirnik, Ortuño, & Biasutti, 2021): they participate in numerous competitions or auditions and they undergo frequent periods of “off-season”, when their professional activity is momentarily reduced. Both groups sometimes perform even in the presence of pain, presenting a high risk of substance abuse and of developing professional injuries that can abruptly end their careers (Dick et al., 2013). Just as in sports, the relationship between musicians and their teachers is crucial for ensuring their well-being and professional accomplishments (Antonini Philippe et al., 2020).

However, it should also be noted that there are several differences between athletes and musicians. For instance, the definition of excellent performance is different as the criteria for assessing performance quality are way more subjective in the music domain than in sports (Habe et al., 2019). Musicians tend to avoid seeking professional help, preferring advice given by their peers and teachers, possibly due to the limited access to psychological support and training in their professional music career (Pecen et al., 2016). Differences were also identified in the way musicians and athletes experience flow when performing (Habe et al., 2019). Musicians show greater self-criticism (Powers et al., 2009) and perceive more the somatic dimension of performance anxiety than athletes (Núñez et al., 2020).

Despite these differences, expertise and performance in music and sport seem to be determined by common psychological factors (Biasutti, 2017; Harmat et al., 2021; Martin, 2008; Preckel et al., 2020), as motivation, self-efficacy, flow, and performance anxiety.

In fact, Moyle (2012) demonstrated that different sport psychology theories and techniques can be successfully applied to the field of performing arts to improve performance through interventions aimed at goal setting, motivation, attention, self-talk and imagery. Moreover, these techniques can be useful in preventing the adoption of dysfunctional copying strategies, while reducing the risk of injuries or burnouts. Osborne et al. (2014) investigated the effects of performance psychology training from the sport field on music students: after a short intervention, participants reported significant reduction in music performance anxiety (MPA), greater confidence and concentration as well as improvements in performance preparation and resilience.

Athletes and musicians are exposed to the risk of developing a wide range of physical and psychological injuries, sport psychologists systematically research this topic aiming to provide the former with specific assistance as well as prevention and rehabilitation protocols. However, the culture of prevention in musicians is still inadequate and rarely proper assistance is offered in music academies and professional environments (Chan & Ackermann, 2014): for instance, Kenny and Ackermann (2015) investigated injuries in a sample of 377 professional musicians and found that 84% had experienced performance-impairing pain and 50% reported pain at the time of investigation. The high prevalence of playing-related injuries is probably due to the relatively young age of the music medicine field: in fact, the interest in sport science originated at the end of the 19th century and the very first experiments investigating the use of psychology for enhancing athletes’ performance took place around 1940. Few years later, various sports psychology organizations began to develop protocols for improving performance and preventing injuries (Kornspan, 2012). In the music research field instead, it was only in 1989 when the “Performing Arts Medicine Association” was established by Alice G. Brandfonbrener and the first issue of the “Journal of Medical Problems of Performing Artists” was published (Lederman, 2014).

Risk factors in musicians

The recent literature shows that 93% of professional musicians experience musculoskeletal injuries at some point in their career due to their intensive training (Árnason et al., 2014; Kok et al., 2016). Several playing-related injuries can occur during musicians’ professional career: for instance, Arezes et al. (2013) showed that musicians’ exposure to high sound pressure levels without the use of appropriate hearing protection can lead to hearing loss. Moreover, Bonde et al. (2018) showed that professional musicians have a higher incidence of mental disorders than the non-musician population: in particular, musicians seem more exposed to the risk of developing sleep disorders (Kapsetaki & Easmon, 2019; Nedelcut et al., 2018), drug and alcohol abuse (Lutchman & Liebenberg, 2017; Stormer et al., 2017), depression (Kenny & Ackermann, 2015; Vaag et al., 2016), burnouts (Thomson & Jaque, 2017) as well as MPA (Kenny & Ackermann, 2015; Nedelcut et al., 2018; Vaag et al., 2016). Furthermore, musician’s dystonia is a relatively common movement disorder, with a lifetime prevalence of about 1%–2%, which involves the deterioration of coordination and control of intensively trained voluntary movements. Recent literature suggests that genetic and environmental factors contribute to the onset of the disease (Schmidt & Altenmüller, 2019). For instance, Altenmüller and Jabusch (2009) showed that musicians affected by focal dystonia have greater perfectionism, anxiety and social phobia than their colleagues. Finally, Altenmüller et al. (2015) presented a heuristic model which aims at explaining the interaction between sensorimotor and psychological factors in triggering motor disorders in musicians: these include workload, social pressure, perfectionism, and anxiety.

MPA

MPA affects about 70% of musicians, in 15% to 20% of which a severe anxiety disorder can be diagnosed (Ackermann et al., 2014). Many outstanding musicians suffered from MPA, for example, Maria Callas, Enrico Caruso, Luciano Pavarotti, Arthur Rubenstein, and Sergei Rachmaninoff (Kenny, 2011).

Kenny (2010, p. 433) defines MPA as

the experience of marked and persistent anxious apprehension related to musical performance that has arisen through specific anxiety conditioning experiences and which is manifested through combinations of affective, cognitive, somatic and behavioral symptoms. It may occur in a range of performance settings but is usually more severe in settings involving high ego investment and evaluative threat. It may be focal (i.e., focused only on music performance), or occur comorbidly with other anxiety disorders, in particular social phobia. It affects musicians across the lifespan and is at least partially independent of years of training, practice and level of musical accomplishment. It may or may not impair the quality of the musical performance.

In every field of human performance, the right amount of physiological activation might be necessary to achieve excellent results. According to Yerkes-Dovson’s theory (Teigen, 1994), a specific amount of arousal is necessary for achieving optimal musical performances, an amount that depends on the personal characteristics of the performer and the activity considered (Kenny, 2011). However, the literature on emotions reports conflicting evidence for the role of anxiety in enhancing human performance, some individuals seem to seek the experience of anxiety while others try to avoid it as much as they can.

Despite a frequently found general preference for low levels of anxiety (Tamir et al., 2007; Tamir & Ford, 2012), individuals might aim at experiencing higher levels of anxiety and arousal in specific conditions which occur during a performance. For instance, Lane et al. (2011) showed that 15% of the runners considered in their study were convinced that anxiety facilitated their athletic performance. Their findings suggest that if the experience of anxiety is perceived as beneficial to performance quality, athletes aim at increasing it (Osborne et al., 2020; Tamir et al., 2015; Vercelli, 2019a). Anxious musicians often consider the quality of their performances rather inconsistent, “depending on the day” or with “nerves being outside of their control” (Ivaldi et al., 2001; Pecen et al., 2016). To cope with MPA, it is not uncommon among musicians to use arguable remedies and performance enhancing drugs such as alcohol or medications without prescription (Burin et al., 2019). In fact, Carey and Grant (2014) point out that this mechanism is often promoted by pedagogical traditions mostly based on the “teaching as we were taught” principle rather than inspired by empirical findings and modern methods for enhancing student’s performance while ensuring their health in the short and long terms (Pecen et al., 2016), giving rise to “Pedagogical Inertia” (Shulman, 2005).

Moreover, musical education often takes place through individual one-to-one lessons, implying a dyadic didactic relationship which is fundamental for the person all a musical development of the students (Rink et al., 2017), the quality of this relationship can influence not only the effectiveness of teaching but also students’ well-being and the development of meta-cognitive abilities (Antonini Philippe et al., 2020; Schiavio et al., 2021). As a result, in cases of distress or physical pain musicians often seek help from their peers or teachers who they trust and respect rather than from health professionals (Pecen et al., 2016).

Proper psychological support should instead take place in form of psychotherapeutic treatments as well as psycho-educational programs, given their effectiveness in managing performance anxiety (Diaz, 2018; Okan & Usta, 2021). For instance, a study by Diaz (2018) found that musicians who practiced mindfulness for 6 months and meditated at least on a weekly basis significantly reduced their level of MPA. Mindfulness-based training protocols for musicians produced excellent results not only in reducing performance anxiety but also in improving other key factors related to their psychological well-being and musical performance, these protocols were effective in improving concentration and practice efficiency while reducing anxiety (Ornoy & Cohen, 2021). At the same time, musicians improved their interpersonal relationships and showed greater self-esteem and self-confidence (Steyn et al., 2016). Further studies have observed how breathing techniques (Güsewell, 2010) and yoga (Khalsa et al., 2009) can also improve performance anxiety management in musicians: body-oriented practices, such as the Alexander or Feldenkrais techniques, can improve the management of MPA through different physical exercises (Mumm et al., 2020). Brooker (2018) demonstrated the effectiveness of Eyes Movement Desensitization and Reprocessing (EMDR) therapy, and clinical hypnosis in the context of music education, showing that through these interventions musicians significantly decreased their MPA and improved the quality of their performances. Moreover, cognitive behavioral treatments were effective in treating anxiety disorders such as MPA (Mumm et al., 2020).

S.F.E.R.A

According to its authors, entering the S.F.E.R.A (SFERA, Vercelli & Gambarino, 2008), meaning “sphere” in Italian, of maximum performance means achieving a mental state that leads to the most functional mental representation of a performance (Vercelli & Gambarino, 2008). The SFERA model was developed in 2006 at the Operational Unit of Sport Psychology SUISM in Turin, Italy. It is nowadays a reference for CONI (Italian National Olympic Committee) and Juventus Football Club in Turin: the model was intended as a multidimensional assessment tool to guide athletes in their performances by optimizing their mental and physical resources and to support them throughout their professional career.

Sport psychology models, as the SFERA model, are frameworks representing “a context for assessing and interpreting psychological variables that affect performance and provides a model for facilitating Performance Readiness, a key to sustained performance excellence” (Aoyagi et al., 2017, p. 8).

In fact, SFERA is a multifactorial model that allows immediate quantitative analysis of a performance. It is used to highlight the psychological areas that need to be improved through training and interventions, with the aim at achieving the maximum potential of the performer (Ferrari & Vercelli, 2019). Specifically, after an initial psycho-educational phase regarding the constructs underlying each SFERA factor, athletes are guided through a post-performance analysis based on self-assessment. In the subsequent training phases, athletes, with the support of psychologists and technical coaches, analyze their strengths and weaknesses based on the SFERA factors and plan interventions tailored to their needs and the context of the performance. The strength of the model lies in the fact that it allows a real time monitoring of performances (see Figure 1).

Figure 1.

Example of SFERA Chart.

Furthermore, it allows trainers and psychologists to quickly identify psychological deficiencies, to improve them and to help athletes to reach their maximum potential. SFERA can be also used to measure the effectiveness of interventions based on sports psychology protocols and management techniques (Harmison, 2011).

As shown in the literature, many sport psychology instruments can be effective in enhancing performance in different domains (i.e., Moyle, 2012; Osborne et al., 2014): similarly, the SFERA model was developed mainly in the sports performance context and later generalized to other performances fields (Caputo et al., 2023; Emanuel & Cortese, 2020; Vercelli, 2019b).

The acronym S.F.E.R.A represents five fundamental psychological factors influencing a performance: Synchrony, Force, Energy, Rhythm, and Activation (Vercelli, 2019a).

More specifically, synchrony is the ability to be concentrated and focused on the activity performed (Vercelli & Gambarino, 2008): lack of synchrony represents a dissociation between mind and body, loosing contact with the present moment.

Force are sets of physical, technical, and mental resources and strengths aimed at achieving optimal performances that one is aware of possessing (Ferrari & Vercelli, 2019, Vercelli, 2019b). Thus, the force factor measures the extent to which athletes can recognize and control their own strengths: this component is closely related to the concept of self-efficacy theorized by Bandura (1997).

Energy refers to the proper regulation of mental and physical energies used during the performance: specifically, it is connected to the ability of self-regulating and managing emotional states to be able to elicit the appropriate amount of arousal during the performance. The right amount of energy and arousal allows athletes to perform demanding movements and to enter the mental state of flow (Nakamura & Csikszentmihalyi, 2014) while avoiding fatigue or boredom. Therefore, an optimal performance is achieved when individuals are highly focused and feel comfortable with a demanding activity they are performing (Vercelli & Gambarino, 2008). For instance, in sports competitions, athletes may often experience situations in which their energy is “taken away” from the performance. This factor represents the ability of channeling and regulating physical and mental resources toward the performed task, thus avoiding useless distractions and multitasking, which has proved to be rather inefficient in terms of energy consumption (Vercelli, 2019b).

Rhythm refers to metacognitive awareness strategies of planning, self-monitoring, evaluation (Jonker et al., 2010) and leadership (Mango et al., 2019), through which athletes perceive the feeling of control (Williams, 2015). These metacognitive components are essential also for successfully preparing for competitions: for example, the ability of efficiently scheduling practice sessions, considering the right amount of breaks and warm-up/cool down phases. The construct of rhythm refers to the athlete’s awareness of their own rhythms and needs, both physiological and mental, for example allowing the implementation of appropriate strategies to recover from intensive training routines (Vercelli, 2019a).

Activation represents the commitment and passion that drive individuals during their professional activities, enhancing their motivation and supporting them in successfully overcoming their limits (Caputo et al., 2023; Vercelli & Gambarino, 2008). This factor is strongly related to the personality traits such as openness to experience and intrinsic motivation (Williams, 2015).

Aim of the study

This study aims at investigating the use of SFERA in a sample of professional musicians. Moreover, it explored the relationship between MPA and scores from the SFERA model. As secondary objective of the study, we investigated the incidence of severe anxiety symptomatology and musculoskeletal pain as well as the use of strategies and techniques aimed at reducing MPA.

Method

Design

The study collected data from a sample of professional musicians who filled out an online questionnaire. This included the SFERA questionnaire as well as quantitative measures of MPA, playing-related pain, and coping strategies for MPA. The Central Ethics Committee at the Leibniz University Hannover approved the present study.

Participants

Participation was open to all classical musicians with a formal degree in music, regardless of their instrumental specialization: 77 professional musicians with an average age of 32.14 years (SD = 10.04) took part in the study. Participants were 50.6% males and 49.4% females. They were recruited from Italian music academies and were individually sent emails of invitation to participate in the online questionnaire: the recruiting process was assisted by “De Sono,” an Italian non-profit association offering financial assistance and support for talented local and international musicians. Participants took part in the study on a voluntary basis, without receiving any compensation.

Materials

The SFERA questionnaire for musicians was developed by contextualizing its original version for athletes (Vercelli & Gambarino, 2008) to the musical domain. This was achieved by applying minor edits to the original items, preserving their original meaning: for instance, the item “When I work, I am bored” was rewritten as “When I play, I am bored.” The music-adapted version of SFERA was administered in Italian and consisted of 40 items in total. All items were scored on a 5-point Likert scale of agreement, ranging from 1 “completely disagree” to 5 “completely agree.”

The SFERA questionnaire is composed of one main scale and five subscales of 8 items each. In the present sample, SFERA’s main scale showed excellent internal consistency, α = .93, while its subscales achieved acceptable Cronbach’s alpha values:

Synchrony (α = .77), item example: “I only think about what I am doing.”

Force (α = .79), item example: “I recognize my qualities.”

Energy (α = 67), item example: “I can manage my emotions regardless of what is happening.”

Rhythm (α = .78), item example: “I can coordinate my schedule.”

Activation (α = .77), item example: “When I play, I have fun.”

The Kenny Music Performance Anxiety Inventory (K-MPAI, Kenny, 2011) is a well-established questionnaire investigating MPA (Antonini Philippe, Kosirnik, Klumb, et al., 2021; Kenny, 2015; Williamon, 2009). The instrument is composed of 40 items scored on a 7-point Likert scale of agreement ranging from 0 “strongly disagree” to 6 “strongly agree.” The questionnaire investigates MPA according to Kenny’s definition (Kenny, 2011) and based on Barlow’s theory of emotions (Barlow, 2000, 2004). The instrument has been extensively validated across different languages: this study used the certified Italian translation of the questionnaire (Antonini Philippe et al., 2023; Kenny, 2017). In this study, K-MPAI showed excellent internal consistency, α = .93. Moreover, the questionnaire is composed of eight subscales measuring different facets of MPA which are here reported together with examples of their items and Cronbach’s alpha values:

Proximal somatic anxiety and worry about performance (α = .88), 11 items, that is, “Prior to, or during a performance, I experience dry mouth.”

Negative cognitions (α = .83), eight items, that is, “Thinking about the evaluation I may get interferes with my performance.”

Psychological vulnerability (α = .79), eight items, that is, “I often feel that life has not much to offer me.”

Parental empathy (α = .71), four items, that is, “My parents were mostly responsive to my needs.”

Memory (α = .96), two items, that is, “When performing without sheet music, my memory is reliable.”

Generational transmission of anxiety (α = .81), three items, that is, “Excessive worrying is a characteristic of my family.”

Anxious apprehension (α = .71), three items, that is, “I never know before a concert whether I will perform well.”

Biological vulnerability (α = not applicable), consisting of only one item “From early in my music studies, I remember being anxious about performing.”

The coping strategies inventory included a list of strategies and techniques often used by musicians to cope with performance anxiety, taken from Burin et al. (2019). The list was supplemented by other techniques mentioned in the recently published manual “Fear of performing in musicians: a cognitive-behavioral treatment guide” by Mumm et al. (2020). Participants were asked to indicate which strategies they commonly employed to cope with MPA at the time of the investigation (multiple choices were allowed).

Procedure

Participants provided information about their musical background and filled out the music-adapted version of the SFERA questionnaire, the K-MPAI questionnaire and the coping strategies inventory through a dedicated online platform. The total completion time was approximately 15 min.

Data analyses

Correlations were run to investigate the relationship between SFERA and K-MPAI scores, including in the analysis their main scales as well as their subscales. A one-way analysis of variance (ANOVA) was performed to identify significant differences in SFERA scores in relation to severe anxiety symptomatology. This analysis was conducted by splitting the sample into two groups, applying the cut-off value of 105 points on the K-MPAI main scale suggested by the recent literature (Ackermann et al., 2014; M. S. Osborne et al., 2020). Thus, participants with K-MPAI scores lower than 105 were assigned to the low anxiety group while the remaining musicians were grouped into the high anxiety group. Finally, linear mixed effects regressions models with random intercepts per gender were run to assess which SFERA subscale was the best predictor of MPA. All analyses were performed using the statistical software RStudio (RStudio Team, 2021) and the R package lme4.

Results

Descriptive statistics

In the present sample, the most frequently used techniques and strategies aimed at reducing MPA were performance simulations (81%), increasing the amount of practice (66%) and deep breathing (64%). The least employed techniques were instead the use of alcohol (3%), hypnosis (2%), consulting a medical doctor (2%) or a psychiatrist (2%) as well as antidepressants drugs (2%). Moreover, 36% of the sample experienced musculoskeletal pain related to playing their musical instrument in the 2 months preceding the investigation. Further information is reported in Supplemental Appendix 1.

Figure 2 represents the distribution of K-MPAI scores across participants: their mean MPA score was 101.46 (SD = 38.07), while 54.5% of the sample showed a K-MPAI value above 105, thus reporting severe MPA symptoms (Ackermann et al., 2014; M. S. Osborne et al., 2020). Moreover, female musicians (M = 111.16, SD = 30.63) reported significantly higher levels of anxiety than their male peers (M = 92.03, SD = 42.42), t(69.198) = 2.27, p = .03. Table 1 reports mean and standard deviations for the main scale as well as for the five subscales constituting the SFERA model.

Figure 2.

Frequency Histogram of K-MPAI Main Scale Scores.

Table 1.

Descriptive Statistics for the SFERA Main Scale and Its Subscales.

	SFERA^a	Synchrony	Strengths	Energy	Rhythm	Activation
Mean	132.44	26.35	24.64	24.61	27.48	29.35
SD	24.00	5.68	6.35	5.04	5.45	5.30

Note. N = 77.

SFERA main scale.

Correlational analysis

To investigate the relationship between K-MPAI and SFERA scores, correlations were run between main scales and subscales of the two measurement instruments, as shown in Table 2: SFERA main scale was significantly and negatively correlated with K-MPAI main scale, r = −.740, p < .001, and most of its subscales, with Pearson’s r values ranging between −.169 and −.723. These included all the sub-facets of MPA listed in Kenny’s framework (Kenny, 2011) with the only exception of parental empathy. The same correlation trends could also be found for most of the SFERA subscales, with Pearson’s r values ranging between −.058 and −.693. In summary, the correlation matrix reported in Table 2 indicates that participants with low SFERA scores exhibit higher levels of MPA.

Table 2.

Correlation Between K-MPAI and SFERA Scores.

	SFERA^a	Synchrony	Strengths	Energy	Rhythm	Activation
K-MPAI main scale	–0.740***	–0.527***	–0.694***	–0.746***	–0.618***	–0.611***
K-MPAI 1	–.626***	–.455***	–.574***	–.582***	–.509***	–.581***
K-MPAI 2	– 0.557***	– 0.316**	– 0.613***	– 0.597***	– 0.438***	– 0.429***
K-MPAI 3	–.723***	–.582***	–.619***	–.693***	–.620***	–.611***
K-MPAI 4	–.169	–.219	–.164	–.190	–.093	–.058
K-MPAI 5	–.255*	–.216	–.143	–.295**	–.309**	–.153
K-MPAI 6	–.310**	–.193	–.270*	–.434***	–.254*	–.198
K-MPAI 7	–.719***	–.453***	–.689***	–.657***	–.640***	–.662***
K-MPAI 8	–.346**	–.179	–.386***	–.349**	–.303**	–.269*

Note. N = 77. K-MPAI = Kenny Music Performance Anxiety Inventory; K-MPAI 1 = proximal somatic anxiety and worry about performance; K-MPAI 2 = negative cognitions; K-MPAI 3 = psychological vulnerability; K-MPAI 4 = parental empathy; K-MPAI 5 = memory; K-MPAI 6 = generational transmission of anxiety; K-MPAI 7 = anxious apprehension; K-MPAI 8 = biological vulnerability.

SFERA main scale.

Correlations are significant at p < .05, **correlations are significant at p < .01, ***correlations are significant at p < .001.

Subsequently, a one-way ANOVA was performed to investigate differences in SFERA scores between low anxiety and high anxiety groups (see “Data analyses” section): as shown in Figure 3, musicians with severe anxiety symptomatology (M = 119.26, SD = 18.25) scored lower on the SFERA main scale than their counterparts (M = 148.26, SD = 20.35), F(1, 75) = 43.37, p < .001. Furthermore, a one-way ANOVA showed that musicians who experienced musculoskeletal pain in the 2 months preceding the investigation, reported significantly higher anxiety scores on the K-MPAI main scale than their colleagues, F(1, 75) = 6.91, p = .01, as shown in Figure 4.

Figure 3.

SFERA Scores in High and Low Anxiety Groups.

Figure 4.

K-MPAI scores in participants who reported musculoskeletal pain.

Regression models

To assess the suitability of SFERA main scale and its five subscales in predicting K-MPAI scores, linear mixed effects models were performed, entering gender as random effect, to account for gender-related differences in anxiety traits. Thus, a first model was run specifying K-MPAI main scale as criterion and SFERA main scale as the only predictor, which had a significant and negative effect on anxiety scores, β = −1.152, t(74.147) = −9.681, p < .001. The model explained approximately 58% of variance in the dependent variable, conditional R² = .581, marginal R² = .520.

A second mixed effects model was performed entering the five SFERA subscales (Synchrony, Strengths, Energy, Rhythm, and Activation) as regressors, K-MPAI main scale as criterion and gender as random effect. As shown in Table 3, Energy was the strongest predictor of MPA scores, β = −3.175, t(69.892) = −3.088, p = .003, while the model explained approximately 60% of variance in the dependent variable, conditional R² = .605, marginal R² = .570.

Table 3.

Linear Mixed Effects Regression Model for SFERA Subscales.

Fixed effects	Estimate	Std. Error	df	t-value	p-value
(Intercept)	247.251	17.767	40.828	13.916	<.001***
Synchrony	–0.333	0.732	70.181	–0.455	.651
Strengths	–1.853	0.767	70.869	–2.418	.018*
Energy	–3.175	1.028	69.892	–3.088	.003**
Rhythm	–0.134	0.904	70.769	–0.148	.882
Activation	–0.322	0.985	70.377	–0.327	.744
Random effect	Variance	SD
gender (intercept)	52.560	7.251

Note. N = 77. The mixed effects regression model was run entering K-MPAI main scale as criterion, SFERA subscales as fixed effects and gender as random effect. K-MPAI = Kenny Music Performance Anxiety Inventory.

p < .05. **p < .01. ***p < .001.

Discussion

This study aimed at exploring the use of SFERA, a multidimensional instrument for measuring psychological dimensions of performances in professional musicians. Moreover, it explored the relationship between MPA and scores from the SFERA questionnaire. In addition, it measured the incidence of musculoskeletal pain as well as the use of coping strategies for MPA.

The internal consistency indices for the SFERA questionnaire were acceptable or higher (α > .66), confirming its usability in the domain of music (Tavakol & Dennick, 2011). As shown in Table 1, the predominant SFERA factor was Activation, which is strongly associated with the motivation, passion and enjoyment experienced during professional activities: this supports the idea that, despite the physical demands and stressors associated with the profession, performing music is in itself a comfort and pleasure for the artists (Gross & Musgrave, 2017). The weakest factors were instead Energy and Strengths, suggesting difficulties in recognizing mental and physical resources, as well as in managing emotions and arousal during performances. Moreover, musicians with low Energy and Strengths values might suffer from low self-efficacy and misjudge their own physical and technical abilities.

Strong negative correlations were identified between MPA and SFERA scores, suggesting that improving the SFERA factors might be helpful to reduce MPA. Moreover, strong negative correlations were found between SFERA factors and several K-MPAI subscales, especially proximal somatic anxiety, negative cognitions, psychological vulnerability, and anxious apprehension. Correlations with the remaining K-MPAI subscales parental empathy, memory, generational transmission of anxiety and biological vulnerability were weaker. These results are in line with the SFERA model theory: SFERA factors measure psychological aspects of the performers rather than external dimensions such as the support received and genetic predispositions. In addition, the weak correlation between SFERA factors and memory might be due to its marginal relevance to music performance quality, as orchestra and chamber musicians -which composed in part our sample- often perform by reading scores. Therefore, the quality of their performance does not depend on their memorization skills.

Furthermore, linear mixed effects regression models indicated that Energy was the strongest predictor of MPA scores among the five SFERA factors.

The Energy factor measures the optimization of physical and mental resources aimed at excelling (Vercelli, 2019a): low values of this factor indicate negative feeling and uneasiness during performances. Energy allows musicians to use emotions as allies, while poor control of the factor leads to an excess of emotionality: according to Vercelli (2019a), during a performance it is necessary to release the excess energy generated by emotions, personal concerns, and expectations to regain a sense of order, focusing on the main aspects of the performance while ignoring dysfunctional thoughts. Thus, musicians with low Energy scores should implement arousal management training and task-switching mindset, directing their attention to salient aspects of a performance avoiding mental overload. In summary, these findings suggest that performance psychology theories, such as SFERA, might be a useful instrument for preventing MPA: unstable performance quality can lead musicians to greater insecurity and psychological vulnerability, therefore increasing the levels of anxiety and emotional distress. Lack of balance between the SFERA factors might increase MPA and mental brooding. Thus, the SFERA questionnaire could be an excellent assessment tool for investigating areas of intervention with the aim at achieving maximum potential and musicians’ well-being.

In addition, the analyses revealed that 36% of the sample suffered from musculoskeletal pain related to practicing their instrument in the 2 months preceding the experiment, namely in the first months of 2021, during the COVID-19 pandemic. this indicates a high incidence of musculoskeletal injuries in musicians even in presence of a drastic reduction of music-related activities. Musicians who experienced musculoskeletal pain related to practicing music reported higher levels of MPA. Severe MPA symptoms were reported by 54.5% of the sample (Ackermann et al., 2014; M. S. Osborne et al., 2020) and female musicians showed higher levels of MPA than their male peers, in line with previous findings (M. S. Osborne & Kenny, 2008; Ryan, 2004).

In the coping strategies inventory, “increase practice” was among the most used remedy to MPA: anxious musicians often increase the length of their practice sessions to improve performance quality and better manage anxiety, thus increasing the risk of injuries related to overuse and excessive perfectionism. Moreover, the response “seeking help from a psychologist or psychotherapist” occurred as frequently as “discuss about anxiety with a music teacher,” (see Supplemental Appendix 1): this suggests that musicians often distrust professional clinicians, in favor of more accessible but less reliable sources of knowledge (Carey & Grant, 2014), thus feeding the “Pedagogical Inertia” and promoting educational traditions based on “teaching as we were taught” principles (Pecen et al., 2016; Shulman, 2005). Interestingly, 17% of the participants used anxiolytic drugs to cope with MPA, in accordance with the recent literature (Burin et al., 2019; Engelke & Ewell, 2011; Farzam & Jan, 2022; Osório et al., 2017).

This study comes with several limitations, one of which is its small sample size. The K-MPAI cut-off value of 105 points for severe anxiety symptomatology, commonly used in empirical research, might not be generalizable across musicians from different cultural backgrounds (Paliaukiene et al., 2018; Wiedemann et al., 2022). Moreover, despite the promising results here reported, it would be advisable to create a novel measurement instrument specifically intended for monitoring psychological factors underlying performances in the domain of music. Thus, further studies could focus on investigating the relationship between the new questionnaire, musicians’ performance, and well-being in a longitudinal study design.

In conclusion, performance anxiety is a common problem among musicians which can affect the quality of their performance, can degenerate into severe anxiety disorders (Ackermann et al., 2014) and, in extreme cases, can bring professional careers to an end.

As pointed out by Pecen and collaborators (2016), traditional MPA prevention protocols mainly focus on reducing its symptomatology, without addressing the physiological, psychological, and social roots of these symptoms. Courses and prevention protocols for MPA offered by music academies and universities are rare and insufficient (Okan & Usta, 2021). This study explored the application of SFERA theory, a holistic model used by several Italian sport psychologists, in the music domain. SFERA, by monitoring different psychological factors, aims at optimizing performance, promotes healthy habits in musicians, and it can be used as a prevention tool for MPA. Several programs and protocols of intervention in sports psychology have been proved effective in enhancing the well-being of athletes: SFERA model could be used as assessment tool to identify areas of intervention useful for improving musicians’ performance and health.

For these reasons, this study confirms the potential of training programs inspired by the sport psychology literature for the management and improvement of the psychological factors underlying music performance within the curricula of music academies and universities not only to improve performance quality but also to safeguard musicians’ well-being.

Supplemental Material

sj-docx-1-pom-10.1177_03057356231198239 – Supplemental material for Music performance anxiety and the Italian sport psychology S.F.E.R.A. model: An explorative study on 77 professional musicians

Supplemental material, sj-docx-1-pom-10.1177_03057356231198239 for Music performance anxiety and the Italian sport psychology S.F.E.R.A. model: An explorative study on 77 professional musicians by Luca Mazzon, Edoardo Passarotto, Eckart Altenmüller and Giuseppe Vercelli in Psychology of Music

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Edoardo Passarotto

Supplemental material

Supplemental material for this article is available online.

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