Sports medicine (Auckland, N.Z.) · 2025
Impact of an Overload Period on Heart Rate Variability, Sleep Quality, Motivation, and Performance in High-level Swimmers: Use of Explainable Artificial Intelligence (XAI) to Assess Training Load Variations.
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Carvalho Diogo D, Goethel Márcio Fagundes, Erblang Mégane, Vilas-Boas João Paulo, Pyne David B, Fernandes Ricardo J, Lopes Philippe
Аннотация
BACKGROUND: Understanding the impact of training sessions on physiological, psychological, and immunological responses is crucial for adequate training periodization and preventing negative influences on health, training, and performance.
OBJECTIVES: To characterize the responses of heart rate variability (HRV), sleep time and quality, motivation, dry-land strength, and swimming performance to an overload period of three consecutive 7-day cycles (cycles 1, 2, and 3) with different training intensity and volume dynamics. Secondly, to test the capability of HRV to assess daily variation in training loads on the basis of explainable artificial intelligence (XAI) models.
METHODS: A total of 14 high-level swimmers (4 males and 10 females, aged 17.5 ± 1.5 years) were monitored via an orthostatic test, Hooper index, sleep questionnaires, and rating of perceived exertion (RPE) of each training session. The self-reported and prescribed training loads were compared. At the beginning of each cycle and at the end of cycle 3, swimmers completed anthropometric testing, countermovement jumps, hand-grip strength tests, and a 5 × 200 m incremental protocol.
RESULTS: High-level swimmers accurately perceived their daily training loads. However, differences between the training and RPE loads emerged on weekends, indicating that physiological and psychological loads have different influences and should be considered simultaneously when characterizing training loads. The overload period was characterized by an increase in both training (27%) and RPE (20%) loads without eliciting a negative effect on sleep quantity and quality. During the overload period, supine (F2.18 = 3.448, η2 = 0.28; p = 0.05) and standing (F2.18 = 3.809, η2 = 0.30; p = 0.04) mean heart rate (HR) increased and supine log root mean square of the successive differences (LnRMSSD; F2.18 = 4.379, η2 = 0.33; p = 0.028) and maximal blood lactate (F3.27 = 3.441, η2 = 0.28; p = 0.03) decreased during and after cycle 3 (respectively). Dry-land and swimming performances were maintained, indicating that the autonomic nervous system appears to be more sensitive (XAI models r2 = 0.91 and 0.9) to changes in acute/short-term training load.
CONCLUSIONS: HRV indices, particularly supine RMSSD and mean HR, were the most sensitive markers of training load variation, while sleep, strength and power, and swimming performance remained stable. HRV can be employed as a practical tool for monitoring training responses and managing training loads in competitive swimmers.
OBJECTIVES: To characterize the responses of heart rate variability (HRV), sleep time and quality, motivation, dry-land strength, and swimming performance to an overload period of three consecutive 7-day cycles (cycles 1, 2, and 3) with different training intensity and volume dynamics. Secondly, to test the capability of HRV to assess daily variation in training loads on the basis of explainable artificial intelligence (XAI) models.
METHODS: A total of 14 high-level swimmers (4 males and 10 females, aged 17.5 ± 1.5 years) were monitored via an orthostatic test, Hooper index, sleep questionnaires, and rating of perceived exertion (RPE) of each training session. The self-reported and prescribed training loads were compared. At the beginning of each cycle and at the end of cycle 3, swimmers completed anthropometric testing, countermovement jumps, hand-grip strength tests, and a 5 × 200 m incremental protocol.
RESULTS: High-level swimmers accurately perceived their daily training loads. However, differences between the training and RPE loads emerged on weekends, indicating that physiological and psychological loads have different influences and should be considered simultaneously when characterizing training loads. The overload period was characterized by an increase in both training (27%) and RPE (20%) loads without eliciting a negative effect on sleep quantity and quality. During the overload period, supine (F2.18 = 3.448, η2 = 0.28; p = 0.05) and standing (F2.18 = 3.809, η2 = 0.30; p = 0.04) mean heart rate (HR) increased and supine log root mean square of the successive differences (LnRMSSD; F2.18 = 4.379, η2 = 0.33; p = 0.028) and maximal blood lactate (F3.27 = 3.441, η2 = 0.28; p = 0.03) decreased during and after cycle 3 (respectively). Dry-land and swimming performances were maintained, indicating that the autonomic nervous system appears to be more sensitive (XAI models r2 = 0.91 and 0.9) to changes in acute/short-term training load.
CONCLUSIONS: HRV indices, particularly supine RMSSD and mean HR, were the most sensitive markers of training load variation, while sleep, strength and power, and swimming performance remained stable. HRV can be employed as a practical tool for monitoring training responses and managing training loads in competitive swimmers.