Format

Send to

Choose Destination
J Strength Cond Res. 2010 Jan;24(1):230-4. doi: 10.1519/JSC.0b013e3181bd452e.

Time series analysis: evaluating performance trends within resistance exercise sessions.

Author information

1
Musculoskeletal Biomechanics Research Laboratory, University of Southern California, Los Angeles, California, USA. Loren.Chiu@ualberta.ca

Abstract

Recent technical advances allow coaches, sport scientists, and researchers to take frequent performance measurements, both within a training session and across a period of training. These performance measurements should demonstrate a systematic pattern, based on physiologic responses and adaptations; however, variability in performance, resulting from both physiologic and psychological factors, may hamper interpretation of these measures. This report describes the moving-average method used in time series analysis to reduce variability and elucidate systematic patterns during high-power resistance exercise. Men (n = 10) power athletes performed 3 high-power resistance exercise sessions (75% 1 repetition maximum [RM]/5 repetitions x 12 sets; 85% 1RM/3 x 15; 95% 1RM/1 x 20). Average barbell power was recorded during the exercise sessions using three-dimensional motion analysis. High-power resistance exercise resulted in increases in performance early in the exercise sessions (p < 0.05) and decreases in performance after the mid-point in the exercise session (p < 0.05). Deviations in performance responses were observed, which obscured the systematic pattern. A 3-point moving average reduced the effects of the deviations, allowing a systematic pattern, consistent with known acute physiologic responses, to be identified. Acutely, exercise elicits changes in performance that should follow a systematic pattern. Determining the systematic pattern associated with various exercise and loading parameters can be utilized to optimize such parameters to maximize the training stimulus while minimizing fatigue. The use of time series analysis, specifically the moving average technique, reduces within-session variability allowing the systematic pattern to be determined.

PMID:
19996783
DOI:
10.1519/JSC.0b013e3181bd452e
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Wolters Kluwer
Loading ...
Support Center