Modelling mood states in athletic performance

Cockerill, Ian M.; Nevill, Alan M.; Lyons, Noel

doi:10.1080/02640419108729881

Cited by 39 publications

(28 citation statements)

References 6 publications

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“…Cockerill et al (1991) used multiple linear regression to identify which of Morgan's (1985) Pro® le of Mood States (POMS) were best able to predict the cross-country race times of 81 runners competing in the 1990 British Students' Cross-country Championships. When all six POMS factors were entered into Minitab' s `BREG' multiple regression routine as possible predictors of run time, the best subset of mood factors was found to be:…”

Section: Discussionmentioning

confidence: 99%

“…The best of these methods, backward elimination, begins with a full or saturated model and the least important variables can then be eliminated sequentially (based on the size of the tstatistic for dropping the variable from the model). When stepwise regression using backward elimination was applied to the cross-country running results of Cockerill et al (1991), the same solution as that chosen from the Minitab output (equation 1) was obtained. Note that when both standard and forward stepwise regression methods were used to predict the athletes' run times, only the factor `Tension' was selected.…”

Section: Discussionmentioning

confidence: 99%

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Selected issues in the design and analysis of sport performance research

Atkinson¹,

Nevill²

2001

Journal of Sports Sciences

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207

137

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The aim of this review is to discuss some issues in the design and statistical analysis of sport performance research, rather than to supply an authoritative `cookbook' of methods. In general, we try to communicate some possible solutions to the conundrum of how to maintain both internal and external validity, as well as optimize statistical power, in applied sport performance research. We start by arguing that some sport performance research has been overly concerned with physiological predictors of performance, at the expense of not providing a valid and reliable description of the exact nature of the task in question. We show how the in¯uence of certain factors on competitive performances can be described using linear or logistic regression. We discuss the choice of analysis for factorial repeated-measures designs, which is complicated by the assumption of `sphericity' in a univariate general linear model, and the relatively low statistical power of the multivariate approach when used with small samples. We consider a little-used and simpler technique known as `analysis of summary statistics' . In multi-group pre-and post-test designs, a useful technique can be to pair-match individuals on their performance scores in a counterbalanced fashion before the intervention or control has been introduced. Finally, we outline how con® dence intervals can help in making statements about the probability of the population diþ erence in performance exceeding the value designated as being worthwhile or not.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Selected issues in the design and analysis of sport performance research

Atkinson¹,

Nevill²

2001

Journal of Sports Sciences

Self Cite

207

137

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“…If all mood dimensions were conceptualised as potentially facilitative or debilitative of performance (i.e., the ES for Anger becomes +0.70), an overall ES of 0.42 would result from the above example. The rationale for investigating mood dimensions separately rather than collectively is strengthened by evidence that successful athletes report higher Anger scores than unsuccessful athletes in karate (McGowan & Miller, 1989;McGowan, Miller, & Henschen, 1990;Terry & Slade, 1995); and higher Tension and Anger scores in cross country running (Cockerill, Nevill & Lyons, 1991).…”

Section: Introductionmentioning

confidence: 99%

The profile of mood states and athletic performance: Two meta-analyses

Beedie

Terry

Lane

2000

Journal of Applied Sport Psychology

308

242

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The present study was a meta-analysis of 29 published studies that used the Profile of Mood States (POMS) to investigate relationships between mood and athletic achievement or between mood and performance outcome. Results showed that effect sizes (ESs) for level of achievement were minimal (n = 15, Weighted Mean ES = 0.10, SD = 0.07), a finding consistent with a previous meta-analysis by Rowley, Landers, Kyllo, and Etnier (1995).Larger effects were found for performance outcome (n = 17, Weighted Mean ES = 0.31, SD = 0.12). Effects were moderate for Vigor, Confusion, and Depression, small for Anger and Tension, and very small for Fatigue. All effects were in the direction predicted by Morgan's (1985) mental health model. Effects were larger in sports of short duration, in sports involving open skills, and where performance was judged using self-referenced criteria.Findings suggest that the POMS has utility in the prediction of performance outcome but not in the prediction of level of achievement.Key words: Meta-analysis, Mood, POMS, Moderating variables The Profile of Mood States and Athletic Performance: A Meta-analysis There is a strong intuitive and anecdotal association between mood states and sport performance. However, empirical support for mood-performance relationships has been equivocal, despite the fact that more than 250 published studies have examined mood responses in sport and exercise settings (LeUnes & Burger, 1998) Sport psychology researchers have relied almost exclusively upon the Profile of Mood States (POMS: McNair, Lorr & Droppleman, 1971) as the measure of mood when examining links with athletic performance. The use of the POMS in sport was pioneered by Morgan and his co-workers (e.g. Morgan 1974;Morgan & Johnson, 1978;Morgan & Pollock, 1977;Nagle, Morgan, Hellickson, Serfass, & Alexander, 1975) who demonstrated that, when compared to population norms, the mood profiles of athletes particularly at the elite level were characterized by above average Vigor scores and below average scores for Tension, Depression, Anger, Fatigue, and Confusion. Morgan termed such a pattern of mood responses an iceberg profile and proposed that it was reflective of positive mental health (Morgan, 1980(Morgan, , 1985.Of the many specific research questions addressed within the area of mood and sport, three of the most frequently investigated have been (a) can mood responses differentiate the athlete from the non-athlete, (b) can mood responses differentiate athletes of varying levels of achievement, and (c) can mood responses differentiate performance outcome among athletes of similar ability? In respect of the first research question, qualitative reviews of the extant literature by LeUnes, Haywood, and Daiss (1988), Renger (1993), and Vanden Auweele, De Cuyper, Van Mele, and Rzewnicki (1993) demonstrate clearly that athletes typically report iceberg profiles, which by definition vary from population norms derived largely from nonathletes. Further, recently published normative data based on the mood res...

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“…Cockerill, Nevill and Lyons (1991) [15] used a regression model to show that tension, depression and anger could collectively predict finish time among cross-country runners. This may be the reason that they show decreased performance and placed them in lower division.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Mood States among Cricket Players during Competitive Season

Harikrishnan¹,

Chittibabu

2014

IJPEFS

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Abstract:The aim of the study was to analyze the mood states of high and low level cricket players during competitive season. 260 cricket players those participated in the Virudhunagar district cricket league during the year 2012-13 were selected as subjects to achieve the study objectives. The subjects confined to this study were classified into two groups as high and low level cricket players on the basis of their competitive standards. The high level group comprising 130 players selected from A Division teams and low level group constitutes 130 players selected from B Division teams. The age of the subjects were ranged between 15 and 34 years. Profile of Mood States Questionnaire developed by McNair et al., (1971) was used in the present study. This questionnaire constitutes 65-items which measures five negative scales such as fatigue, depression, tension, anger, confusion and positive scale vigour. The two-way factorial ANOVA with repeated measures on the last factor showed that tension, depression, anger and confusion showed no difference during cricket season between high and low level cricket players. However, vigor and fatigue interaction is significant and Scheffé S post hoc test was applied. Vigor and fatigue showed significant difference between the groups at start and mid season. However, vigor showed significant difference between the groups at end and within low level cricket players. It is concluded that positive factor vigor showed high in high level cricket players than low level cricket players. The cricket season showed fatigue in the middle of the season. Keywords: POMS, cricket, players, high and lowInternational Journal of Physical Education, Fitness and Sports ISSN: 2277: 5447 | Vol.3.No.4 | December 2014 96 | P a g e IntroductionSport is generally considered a primarily physical endeavor, involving the marshaling of bodily resources to complete a variety of specialized, demanding physical tasks. Undeniably, physical attributes such as speed, strength, endurance, power, coordination, agility, flexibility, and resilience are richly rewarded in competitive sport. Recognizing the abundant physical component of sport performance, scientists have investigated biomechanical, physiological, nutritional, metabolic, epidemiological, biochemical, pharmacological, and medical aspects of sport. Applied practitioners in exercise physiology, physiotherapy, sport biomechanics, sports medicine, sports nutrition, strength and conditioning, and other disciplines have translated research findings into interventions designed to enhance the physical performance capabilities of the athletes.Despite the pronounced emphasis on physical matters in the sport sciences, it is widely accepted that sport performance is influenced not only by physical attributes, but also by psychological factors. Some athletes seem to have a mental edge over other athletes with comparable physical characteristics and training backgrounds. Some athletes perform better under pressure, implement strategies more effectively, tolerates disc...

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Modelling mood states in athletic performance

Cited by 39 publications

References 6 publications

Selected issues in the design and analysis of sport performance research

Selected issues in the design and analysis of sport performance research

The profile of mood states and athletic performance: Two meta-analyses

Analysis of Mood States among Cricket Players during Competitive Season

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