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Hagum CN, Shalfawi SAI. The Factorial Validity of the Norwegian Version of the Multicomponent Training Distress Scale (MTDS-N). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E7603. [PMID: 33086587 PMCID: PMC7590227 DOI: 10.3390/ijerph17207603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND Athlete self-report measures (ASRM) are methods of athlete monitoring, which have gained considerable popularity in recent years. The Multicomponent Training Distress Scale (MTDS), consisting of 22 items, is a promising self-report measure to assess training distress among athletes. The present study aimed to investigate the factorial validity of the Norwegian version of MTDS (MTDS-N) among student-athletes (n = 632) attending the optional program subject "Top-Level Sports" in upper secondary schools in Norway. METHODS A confirmatory factor analysis (CFA) was conducted to assess the six-factor model proposed by Main and Grove (2009). McDonald's omega (ω) along with confidence intervals (CIs) were used to estimate scale reliability. After examining the fit of the CFA model in the total sample, covariates were included to investigate group differences in latent variables of MTDS-N, resulting in the multiple indicators multiple causes (MIMIC) model. Further, direct paths between the covariates and the factor indicators were included in an extended MIMIC model to investigate whether responses to items differed between groups, resulting in differential item functioning (DIF). RESULTS When modification indices (MIs) were taken into consideration, the alternative CFA model revealed that MTDS-N is an acceptable psychometric tool with a good fit index. The factors in MTDS-N all constituted high scale reliability with McDonald's ω ranging from 0.725-0.862. The results indicated statistically significant group differences in factor scores for gender, type of sport, hours of training per week, school program, and school level. Further, results showed that DIF occurred in 13 of the MTDS-N items. The student-athletes' reports of training distress were moderate. CONCLUSION The MTDS-N may be suitable for use in a Norwegian population to assess student-athletes' training distress in a reliable manner. The indications of group effects suggest that caution should be used if one is interested in making group comparisons when the MTDS-N is used among student-athletes in Norway until further research is conducted.
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Affiliation(s)
- Cathrine Nyhus Hagum
- Department of Education and Sports Science, University of Stavanger, 4036 Stavanger, Norway;
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A 2-year longitudinal follow-up of performance characteristics in Chinese male elite youth athletes from swimming and racket sports. PLoS One 2020; 15:e0239155. [PMID: 33044967 PMCID: PMC7549762 DOI: 10.1371/journal.pone.0239155] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022] Open
Abstract
Training in elite sport aims at the optimization of the athletic performance, and to control the athletes`progress in physiological, anthropometrical and motor performance prerequisites. However, in most sports, the value of longitudinal testing is unclear. This study evaluates the longitudinal development and the influence of intense training over 2-years on specific physiological performance prerequisites, as well as certain body dimensions and motor abilities in elite youth athletes. Recruited between 11-13 years of age at Shanghai Elite Sport school, the sample of student-athletes (N = 21) was categorized as the swimming group (10 athletes), and the racket sports group (11 players: 7 table tennis and 4 badminton players). The performance monitoring took place over two years between September 2016 and September 2018 and included 5 test waves. In all the test waves, the athletes were assessed by means of three physiological measurements (vital capacity, hemoglobin concentration, heart rate at rest), three anthropometric parameters (body height, body weight, chest girth), and two motor tests (back strength, complex reaction speed). Seven out of eight diagnostic methods exhibit medium to high validity to discriminate between the different levels of performance development in the two sports groups. The investigated development of the performance characteristics is attributed partly to the inherited athletic disposition as well as to the different sport-specific training regimens of the two sports groups.
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Simim MADM, Souza HDS, Cardoso Filho CA, Gianoni RLDS, Bezerra RR, Affonso HDO, Amadio AC, D’Almeida V, Serrão JC, Claudino JG. Sleep quality monitoring in individual sports athletes: parameters and definitions by systematic review. Sleep Sci 2020; 13:267-285. [PMID: 33564374 PMCID: PMC7856669 DOI: 10.5935/1984-0063.20200032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 06/22/2020] [Indexed: 12/26/2022] Open
Abstract
In the present review, we identify which instruments and parameters are used for sleep quality monitoring in individual sport athletes and which definitions were used for sleep quality parameters in this literature field. Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews. The initial search returned 3316 articles. After the removal of duplicate articles, eligibility assessment, 75 studies were included in this systematic review. Our main findings were that the most widely used measurement instruments were Actigraphy (25%), Rating Likert Scales (16%) and Sleep Diary (13%). On sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Effciency = 11% and Sleep Latency = 9%), the main point is that there are different definitions for the same parameters in many cases reported in the literature. We conclude that the most widely used instruments for monitoring sleep quality were Actigraphy, Likert scales and Sleep diary. Moreover, the definitions of sleep parameters are inconsistent in the literature, hindering the understanding of the sleep-sport performance relationship.
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Affiliation(s)
- Mário Antônio de Moura Simim
- Federal University of Ceará, Institute of Physical Education and Sports - Fortaleza - Ceará - Brazil
- Federal University of Ceará, Master Program in Physioterapy and Functioning - Fortaleza - Ceará - Brazil
| | - Helton de Sá Souza
- Universidade Federal de São Paulo, Departamento de Psicobiologia - São Paulo -Brazil
- Centro Universitário de Volta Redonda - UniFOA, Curso de Educação Física - Volta Redonda - Rio de Janeiro - Brazil
| | | | - Rodrigo Luiz da Silva Gianoni
- Paulista University - UNIP
- LOAD CONTROL, Research and Development Department - Contagem - Minas Gerais - Brazil
- Peruíbe College - FPbe - UNISEPE
| | | | - Helvio de Oliveira Affonso
- Appto Physiology, Laboratory of Exercise, Nutrition and Sports Training, Espirito Santo - Vitoria - Espírito Santo - Brazil
- Vila Velha University, Pharmaceutical Sciences Graduate Program - Vila Velha - Espírito Santo - Brazil
| | - Alberto Carlos Amadio
- Universidade de São Paulo, School of Physical Education and Sport - Laboratory of Biomechanics- Brazil
| | - Vânia D’Almeida
- Universidade Federal de São Paulo, Departamento de Psicobiologia - São Paulo -Brazil
| | - Júlio Cerca Serrão
- Universidade de São Paulo, School of Physical Education and Sport - Laboratory of Biomechanics- Brazil
| | - João Gustavo Claudino
- Universidade de São Paulo, School of Physical Education and Sport - Laboratory of Biomechanics- Brazil
- LOAD CONTROL, Research and Development Department - Contagem - Minas Gerais - Brazil
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Feijen S, Tate A, Kuppens K, Barry LA, Struyf F. Monitoring the swimmer's training load: A narrative review of monitoring strategies applied in research. Scand J Med Sci Sports 2020; 30:2037-2043. [PMID: 32767794 DOI: 10.1111/sms.13798] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/09/2020] [Accepted: 08/03/2020] [Indexed: 12/12/2022]
Abstract
The high incidence of injury during swim training and the increasing demands of the sports make monitoring of the swimmer's training load a key concept requiring further investigation. Research has previously introduced numerous methods for the purposes of monitoring the swimmer's training load, but a narrative review discussing the strengths and limitations of each method is lacking. Consequently, this narrative review aims to summarize the monitoring strategies that have been applied in research on competitive swimmers. This knowledge can assist professionals in the field in choosing which method is appropriate in their particular setting. The results from this study showed that external training load was predominantly obtained through real-life observation of the swimmers' training volume. However, research has investigated a number of internal load monitoring tools, including blood lactate, training heart rate, and perceived effort of training. To date, blood lactate markers are still considered most accurate and especially recommended at higher levels of competitive swimming or for those at greater risk of injury. Further, mood state profiling has been suggested as an early indicator of overtraining and may be applied at the lower competitive levels of swimming. Professionals in the field should consider the individual, the aim of the current training phase, and additional logistical issues when determining the appropriate monitoring strategy in their setting.
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Affiliation(s)
- Stef Feijen
- Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Angela Tate
- Department of Physical Therapy, Arcadia University, Glenside, PA, USA
| | - Kevin Kuppens
- Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Lorna A Barry
- Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
| | - Filip Struyf
- Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Woods AL, Rice AJ, Garvican-Lewis LA, Wallett AM, Lundy B, Rogers MA, Welvaert M, Halson S, McKune A, Thompson KG. The effects of intensified training on resting metabolic rate (RMR), body composition and performance in trained cyclists. PLoS One 2018; 13:e0191644. [PMID: 29444097 PMCID: PMC5812577 DOI: 10.1371/journal.pone.0191644] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Accepted: 01/09/2018] [Indexed: 11/26/2022] Open
Abstract
Background Recent research has demonstrated decreases in resting metabolic rate (RMR), body composition and performance following a period of intensified training in elite athletes, however the underlying mechanisms of change remain unclear. Therefore, the aim of the present study was to investigate how an intensified training period, designed to elicit overreaching, affects RMR, body composition, and performance in trained endurance athletes, and to elucidate underlying mechanisms. Method Thirteen (n = 13) trained male cyclists completed a six-week training program consisting of a “Baseline” week (100% of regular training load), a “Build” week (~120% of Baseline load), two “Loading” weeks (~140, 150% of Baseline load, respectively) and two “Recovery” weeks (~80% of Baseline load). Training comprised of a combination of laboratory based interval sessions and on-road cycling. RMR, body composition, energy intake, appetite, heart rate variability (HRV), cycling performance, biochemical markers and mood responses were assessed at multiple time points throughout the six-week period. Data were analysed using a linear mixed modeling approach. Results The intensified training period elicited significant decreases in RMR (F(5,123.36) = 12.0947, p = <0.001), body mass (F(2,19.242) = 4.3362, p = 0.03), fat mass (F(2,20.35) = 56.2494, p = <0.001) and HRV (F(2,22.608) = 6.5212, p = 0.005); all of which improved following a period of recovery. A state of overreaching was induced, as identified by a reduction in anaerobic performance (F(5,121.87) = 8.2622, p = <0.001), aerobic performance (F(5,118.26) = 2.766, p = 0.02) and increase in total mood disturbance (F(5, 110.61) = 8.1159, p = <0.001). Conclusion Intensified training periods elicit greater energy demands in trained cyclists, which, if not sufficiently compensated with increased dietary intake, appears to provoke a cascade of metabolic, hormonal and neural responses in an attempt to restore homeostasis and conserve energy. The proactive monitoring of energy intake, power output, mood state, body mass and HRV during intensified training periods may alleviate fatigue and attenuate the observed decrease in RMR, providing more optimal conditions for a positive training adaptation.
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Affiliation(s)
- Amy L Woods
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia.,Department of Physiology, Australian Institute of Sport, Bruce ACT, Australia
| | - Anthony J Rice
- Department of Physiology, Australian Institute of Sport, Bruce ACT, Australia
| | - Laura A Garvican-Lewis
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia.,Department of Physiology, Australian Institute of Sport, Bruce ACT, Australia.,Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Alice M Wallett
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia.,Department of Physiology, Australian Institute of Sport, Bruce ACT, Australia
| | - Bronwen Lundy
- Department of Nutrition, Australian Institute of Sport, Bruce ACT, Australia
| | - Margot A Rogers
- Department of Nutrition, Australian Institute of Sport, Bruce ACT, Australia
| | - Marijke Welvaert
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia
| | - Shona Halson
- Department of Physiology, Australian Institute of Sport, Bruce ACT, Australia
| | - Andrew McKune
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia.,Discipline of Biokinetics, Exercise and Leisure Sciences, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Kevin G Thompson
- Research Institute for Sport and Exercise, University of Canberra, Bruce ACT, Australia
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