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Cited by 645

The DOI matches its Crossref record.

Psychological monitoring of overtraining and staleness.

Open access
Morgan, W P, Brown, D R, Raglin, J S, O'Connor, P J, Ellickson, K A·1987·British Journal of Sports Medicine
DOI 10.1136/bjsm.21.3.107
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Abstract

It is widely agreed that overtraining should be employed in order to achieve peak performance but it is also recognised that overtraining can actually produce decrements in performance. The challenge appears to be one of monitoring stress indicators in the athlete in order to titrate the training stimulus and prevent the onset of staleness. The present paper summarises a ten-year research effort in which the mood states of competitive swimmers have been monitored at intervals ranging from 2-4 weeks during individual seasons for the period 1975-1986. The training cycle has always involved the indoor season which extends from September to March and the athletes who served as subjects were 200 female and 200 male competitive swimmers. The results indicate that mood state disturbances increased in a dose-response manner as the training stimulus increased and that these mood disturbances fell to baseline levels with reduction of the training load. Whilst these results have been obtained in a realistic setting devoid of experimental manipulation, it is apparent that monitoring of mood state provides a potential method of preventing staleness.

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Metadata

Authors
Morgan, W. P., Brown, D. R., Raglin, J. S., O'Connor, P. J., & Ellickson, K. A.
Venue
British Journal of Sports Medicine
Publisher
BMJ
Published
1987-09
Volume
21
Issue
3
Pages
107-114
ISSN
  • 0306-3674 (ISSN-L)
  • 1473-0480
DOI
10.1136/bjsm.21.3.107
OpenAlex
W2115103375
Cited by
645
References
0
FWCI
7.98
Type
journal article
Language
en
Open access
bronze

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.