Marathon Training: Gender and Age Aspects

Reed, Jennifer L.; Gibbs, Jenna C.

doi:10.1007/978-3-319-29728-6_7

Cited by 5 publications

(7 citation statements)

References 157 publications

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“…The main findings showed that some of the variables analyzed, namely, sex and age, influence the reasons for participation by amateur athletes, while marital status did not evidence any such association. As previous research shows, age and sex have been analyzed in a binomial way, in order to ascertain whether sex–age makes a difference, how far it extends, or in which direction these differences exist in marathon race participants ( Reed and Gibbs, 2016 ; Nikolaidis et al, 2018 ). One of the most analyzed variables has been athletes’ sex when trying to understand endurance athletes’ reasons for practicing their sport.…”

Section: Discussionmentioning

confidence: 99%

“…Our results suggest that sporting event organizers, health promotion specialists, and coaches should consider how female runners evidence statistically greater motivation than men in personal and social dimensions such as psychological coping, while men are more motivated with result-oriented dimensions such as personal goal achievement, competition, and recognition, in line with previous research ( Waśkiewicz et al, 2019b ). It is thus understood from these results that women may gain more psychological benefits from running than men ( Reed and Gibbs, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…MOMS, as its name “Motivations of Marathoners Scale” suggests, was created specifically in order to measure marathon athletes’ reasons for participation, and therefore, athletes’ motivations have been analyzed in many marathons from within different social contexts, e.g., in Greece ( Nikolaidis et al, 2019 ), Poland ( Malchrowicz-Mośko et al, 2019 ; Waśkiewicz et al, 2019a ), Spain ( Ruiz-Juan and Zarauz Sancho, 2014 ; Parra-Camacho et al, 2019 ), and especially in the United States ( Ogles and Masters, 2003 ; Rundio et al, 2014 ; Hanson et al, 2015 ; Buning and Walker, 2016 ), the context in which the scale was initially created. The variables associated with different reasons for participating have also been diverse, with the following being the most analyzed variables: runners’ sex ( Waśkowski, 2011 ; Summers et al, 2016 ; Malchrowicz-Mośko and Rozmiarek, 2018 ; Malchrowicz-Mośko and Poczta, 2019 ) and runners’ age ( Ogles and Masters, 2003 ; Reed and Gibbs, 2016 ; Poczta et al, 2018 ; Nikolaidis et al, 2019 ). This constitutes an attempt to describe whether the reasons for a person taking part in an endurance event depend on athletes’ sex and age.…”

Section: Introductionmentioning

confidence: 99%

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Do Sex, Age, and Marital Status Influence the Motivations of Amateur Marathon Runners? The Poznan Marathon Case Study

et al. 2020

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The purpose of this research was to describe reasons for participating in a marathon and their association with marital status, age, and sex. Four hundred and ninety-three runners in total, 144 of whom were women and 349 of whom were men, completed the Polish version of the Motivations of Marathoners Scale (MOMS), containing nine dimensions, which was released through an online survey at the Poznan Marathon in Poland (2019). Athletes’ age (ranges 19–25, 26–35, 36–50, 51–70 years) and marital status (single, married, divorced) were asked of the participants. The results showed that statistical significant associations were found between athletes’ motivational aspects and athletes’ sex and age. To this end, three MOMS dimensions were statistically associated with athletes’ sex, a further three dimensions were different age-wise, and, lastly, marital status did not show differences in any of the dimensions. Moreover, statistical differences were not found in the multivariate analysis comparing marital status, sex, and age range according to MOMS. Recreational runners’ reasons for participating in a marathon are different depending on certain sociodemographic variables; therefore, these characteristics should be considered when addressing different athletes in order to provide them with the most suitable information for taking part in such events.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Do Sex, Age, and Marital Status Influence the Motivations of Amateur Marathon Runners? The Poznan Marathon Case Study

et al. 2020

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“…There are many studies on the typology of runners and the sociodemographic profiles of participants in mass running events-half-marathons, marathons, ultra-marathons, triathlons or ultra-triathlons-and their motivational structures. Running motivations have already been analysed for variables such as age, gender and place of residence [1][2][3][4][5][6][7][8][9][10][11][12][13]. investigated age-related motivations in half-marathon participation.…”

Section: Introductionmentioning

confidence: 99%

Do Years of Running Experience Influence the Motivations of Amateur Marathon Athletes?

Malchrowicz-Mośko

Gravelle

Dąbrowska

et al. 2020

IJERPH

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The aim of the study was to investigate if years of running experience influence the motivations of marathon athletes. An empirical study was conducted during the last (20th) PKO Poznan Marathon, one of the largest and most popular mass running events in Poland, which was held in Poznan (Poland) in October 2019. A total of 493 marathon runners (29% of whom were female, and 71% of whom were male) took part in the cross-sectional study, which used the diagnostic survey method. The questionnaire employed the division of motives from the motivation of marathoners scale (MOMS) by Masters et al., adapted to the Polish language by Dybala. Running motivations have already been analysed for variables such as age, gender and place of residence, but there is a research gap regarding existing research, as the relationship between motivations and running experience has not yet been studied. One-way analysis of variance for independent samples was used to verify statistical hypotheses. Prior to making the relevant calculations, the assumption of homogeneity of variance was checked via Levene’s test. Variances were assessed with an F-test, and if they were unequal, Welch’s correction was applied. Eta squared (η2) was used as a measure of effect size. The calculations carried out showed that running experience was not a statistically significant factor in the motivations of runners taking part in a marathon.

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“…Among master runners, defined as runners aged 40 years and older, V Ȯ2peak is an important predictor of distance running performance (Wiswell et al, 2000). As endurance athletes age beyond their mid-twenties, their V Ȯ2peak decreases (Reed & Gibbs, 2016). Race times in long-distance events, i.e.…”

Section: Introductionmentioning

confidence: 99%

Predictors of long-distance race performance in master runners

Lee¹,

Snyder²,

Lundstrom³

2020

jhse

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Peak aerobic power (V Ȯ2peak) and parameters related to training are associated with long-distance running performance in master athletes. Running economy (RE) predicts performance in younger runners, but its relationship to racing ability in older athletes is unclear. Allometrically scaled RE (alloV Ȯ2; ml kg -0.66 min -1 ), energy cost (EC; kcal kg -1 km -1 ), and percent of V Ȯ2peak (%V Ȯ2peak) required in a submaximal bout represent RE more accurately than V Ȯ2 does. The VDOT score, estimating V Ȯ2peak and RE, can be used to compare races of different distances. Purpose: To determine predictors of temperature-converted VDOT in master runners training for a long-distance race (10-26.2 mi). Methods: Twenty-three master runners (age 57±9 years; eight females) performed treadmill marathon-intensity-effort (MIE) and V Ȯ2peak tests within four weeks of their goal race. The MIE occurred at 88% of predicted maximum heart rate, which corresponds to estimated marathon intensity. Participants completed online training-history surveys. Forward stepwise multiple linear regression was used to find key predictors of VDOT. The alpha level for significance was .05. Results: Converted VDOT was significantly associated with 3-year peak weekly training distance (3YP) (r = 0.454, p = .039), V Ȯ2peak (r = 0.845, p = .000), alloV Ȯ2 (r = 0.623, p = .005), and EC (r = -0.528, p = .018). The bestfitting model included V Ȯ2peak and 3YP (r = 0.898). Conclusion: Physiological and training factors are related to race performance in master runners. The best predictors of VDOT are V Ȯ2peak and 3YP. Training to enhance these variables may improve distance-running performance in masters.

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Marathon Training: Gender and Age Aspects

Cited by 5 publications

References 157 publications

Do Sex, Age, and Marital Status Influence the Motivations of Amateur Marathon Runners? The Poznan Marathon Case Study

Do Sex, Age, and Marital Status Influence the Motivations of Amateur Marathon Runners? The Poznan Marathon Case Study

Do Years of Running Experience Influence the Motivations of Amateur Marathon Athletes?

Predictors of long-distance race performance in master runners

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