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Okamoto, Lin, Hiromatsu, and Goto: Changes in nocturnal-skin temperature and sleep parameters following endurance training sessions in female long-distance runners

Abstract

The present study investigated nocturnal skin temperature and sleep parameters after endurance training sessions in female long-distance runners. Eleven female long-distance runners were monitored for 7 consecutive days. Proximal skin temperature was measured every 5 min using a button-type thermometer (Hal-Share, SUN·WISE Co., Ltd., Osaka, Japan) attached to the groin area. Sleep parameters, including sleep efficiency, total sleep time, sleep latency, and wake time after sleep onset (WASO), were assessed using actigraphy (wGT3X-BT, ActiGraph LLC, Pensacola, FL, USA). Data were compared between two conditions: a day involving strenuous endurance training (Training Day; a total of 130 min comprising high-intensity running from 10:00-10:40 A.M., and jogging from 5:00-5:30 A.M. and 3:30-4:30 P.M.) and a day without training (Rest Day). Skin temperature during sleep was significantly higher on the Training Day compared with the Rest Day at 10 and 20 min after bedtime (p < 0.05), but significantly lower 60 min after bedtime (p < 0.05). Between 90 and 130 min after bedtime, skin temperature was significantly elevated compared with the value at sleep onset (p < 0.05). Total sleep time was significantly longer on the Training Day than on the Rest Day (447.6 ± 60.5 min vs. 360.2 ± 45.3 min; p < 0.05); sleep efficiency, sleep latency, and WASO showed no significant differences between the conditions. In conclusion, multiple endurance training sessions (three sessions per day) altered nocturnal skin temperature patterns during the initial 130 min of sleep and increased total sleep time in female long-distance runners.

INTRODUCTION

Endurance athletes engaged in prolonged exercise and high training volumes are at increased risk of developing accumulated fatigue, which can result in a sustained decline in performance known as overtraining syndrome (OT) [1]. In addition to excessive training, factors such as inadequate sleep, psychological stress, and poor nutritional status are involved with increasing importance of daily monitoring. However, many assessments rely on single time-point measures, even though physiological variables (e.g., body temperature) present diurnal rhythms [2]; reliance on assessments at fixed time points may fail to detect abnormalities or disturbances in these rhythms. Thus, continuous monitoring of diurnal variations using non-invasive techniques, such as measurements of skin temperature, heart rate, and autonomic nervous system activity, may be more suitable for application in real-world training environments.
Core temperature follows a circadian rhythm; it is typically lower in the morning and higher in the evening [3]. These fluctuations are closely associated with sleep quality [4], mental health status [5,6], and nutritional condition [7]. Sleep plays an essential role in facilitating recovery [8]. However, high-intensity training has been shown to increase wake time after sleep onset, possibly due to elevated nocturnal core temperature and sympathetic nervous system activity [9]. Although core temperature rhythms can influence the secretion of sleep-related hormones, including cortisol and melatonin [10,11], the impact of daytime training on nocturnal core temperature and sleep quality in endurance athletes remains insufficiently explored.
In a study simulating trail-running exercise (48 min total, comprising a 3-min warm-up followed by five 9-min exercise blocks) conducted at night (9:00 P.M.), core temperature remained elevated during the initial 3 h after bedtime, resulting in disrupted sleep architecture in well-trained athletes [12]. Although core temperature assessment is considered the gold standard, it typically requires rectal thermometry, which limits its practicality. In contrast, recent advances in wearable sensor technology have enabled continuous, non-invasive monitoring of skin temperature, providing valuable data on circadian dynamics. A recent study demonstrated the potential for assessing circadian rhythms through 24-h monitoring of skin temperature fluctuations [13].
The present study investigated nocturnal skin temperature rhythms and sleep parameters after multiple sessions of endurance training in female long-distance runners.

METHODS

Participants

Eleven female long-distance runners participated in the present study. Their mean ± standard deviation age, height, body weight, and body mass index were 19.6 ± 1.0 years, 158.6 ± 3.9 cm, 47.9 ± 2.8 kg, and 19.1 ± 1.2 kg/m2, respectively. The sample size was calculated using G-power (G*power version 3.1.9.7; Heinrich-Heine University, Dusseldorf, Germany). The results indicated that the minimum sample size was 10-12 (expected effect size for comparison between two conditions = 0.9-1.0, α = 0.05, power [1-β] = 0.8). Therefore, we recruited 11 participants. All participants were informed of the study procedures and potential risks prior to enrollment; they provided written informed consent. The study was approved by the Ethics Committee for Human Experiments of Ritsumeikan University (BKC-LSMH-2024-008) and was conducted in accordance with the principles of the Declaration of Helsinki.

Experiment Overview

Skin temperature and sleep parameters were monitored over 7 consecutive days during the competitive season (December 2023) while participants resided in their dormitory. Data were compared between two conditions: a day with intensified training (Training Day) and a day without training (Rest Day). On the Training Day, participants completed an interval running session (10:00-10:40 A.M.) in addition to jogging sessions in the early morning (5:00-5:30 A.M.) and late afternoon (3:30-4:30 P.M.), totaling 130 min of exercise. The training schedule with multiple endurance training sessions consisting of both prolonged running session and an interval running session is general among competitive long-distance runners. On the Rest Day, no structured physical activity (e.g., running or resistance training) was performed, and participants were free to follow their individual routines.
To reflect real-life conditions, meal times, bathing, and sleep schedules were not standardized. However, participants recorded the timing of these daily activities, along with their training log and the details of meals and beverages consumed, using daily activity sheets. Although breakfast and dinner were standardized across all participants in the dormitory, each athlete recorded the amount of rice consumed. Lunch was not standardized and was self-selected; participants documented their meals through photographs and written records.

Skin Temperature Monitoring

Skin temperature was continuously measured at 5-min intervals using a button-type thermometer (Hal-Share, SUN·WISE Co., Ltd., Osaka, Japan) affixed to the groin area with adhesive tape. The groin area was selected for measurement because it was considered less susceptible to ambient temperature even when wearing a running shirt. Body (skin) temperature varies depending on the anatomical site. We selected the groin region for skin temperature measurement, since it has been reported to be less affected by ambient conditions [14]. The device was worn continuously for the entire 7-day measurement period. Participants were instructed to replace the adhesive tape as needed to prevent skin irritation. Thermometers were applied the day before the start of the measurement period. To minimize the influence of ambient winter temperatures (approximately 7°C), only skin temperature data collected during sleep were included in the analysis. Sleep periods were identified based on the sleep times recorded by each participant.

Sleep Parameters

Sleep quality and duration were assessed using wrist actigraphy (wGT3X-BT, ActiGraph LLC, Pensacola, FL, USA) worn on the non-dominant wrist. Participants wore the actigraphy device beginning 30 min prior to their self-reported bedtime and removed it upon waking the next morning. Raw actigraphy data were downloaded using the manufacturer’s software, ActiLife (version 6.13.5; Acti-Graph LLC). Data were stored in 60-s epochs and analyzed in conjunction with the participants’ daily activity records in ActiLife software.
The following sleep parameters were extracted and analyzed: total sleep time, sleep efficiency, sleep latency, and wake time after sleep onset (WASO).

Statistical Analyses

All data were presented as mean ± standard deviation. Statistical analyses were performed using SPSS software (version 28.0; IBM Corp., Armonk, NY, USA). For skin temperature time-series data, a two-way repeated-measures analysis of variance was used to assess interaction effects (condition × time) and main effects. When a significant interaction or main effect was observed, a post hoc Tukey’s test was conducted. For other variables, paired t-tests were used to compare mean values between conditions. Effect size was calculated by Cohen’s d for a paired t-test and partial eta squared (η²) for a two-way repeated-measures analysis of variance. A p-value of < 0.05 was considered statistically significant for all analyses.

RESULTS

Skin Temperature

Table 1 presents the mean, maximum, minimum, and delta (Δ) skin temperatures during sleep and the first 180 min (3 h) after bedtime on the Training Day and Rest Day. No significant differences were observed in any of these variables between the two conditions (p > 0.05). However, Δskin temperature during the first 180 min after bedtime was significantly lower on the Training Day (p < 0.05).
Figure 1 illustrates the fluctuations in skin temperature during the first 180 min (3 h) after bedtime. This time frame was selected based on previous evidence indicating that multiple endurance training sessions can influence body temperature regulation during the initial 3 h of sleep [12]. A significant main effect of time (p < 0.05, η2 = 0.591) and a significant interaction between time and condition (p < 0.05, η2 = 0.194) were detected. When comparing individual time points between the two conditions, skin temperature was significantly higher on the Training Day than on the Rest Day at 10 and 20 min after bedtime (p < 0.05). Conversely, at 60 min after bedtime, skin temperature was significantly lower on the Training Day (p < 0.05). Additionally, skin temperature was significantly elevated relative to bedtime values between 90 and 130 min after bedtime (p < 0.05).

Sleep Parameters

Table 2 presents the sleep parameters, including sleep efficiency, total sleep time, sleep latency, and WASO, for both the Training Day and Rest Day. Total sleep time was significantly longer on the Training Day than on the Rest Day (p < 0.05). However, no significant differences were found in sleep efficiency, sleep latency, or WASO between the two conditions (p > 0.05).

DISCUSSION

The present study investigated the effects of multiple endurance training sessions (three sessions per day, totaling 130 min) on nocturnal skin temperature and sleep parameters in female long-distance runners. Multiple endurance training sessions did not affect the mean, maximum, minimum, or delta of nocturnal skin temperature during whole sleep. In contrast, the delta skin temperature during the first 3 h after bedtime was significantly lower on the Training Day than Rest Day. Also, a significant difference was observed in the temporal profile of skin temperature during the first 3 h after bedtime between the Training Day and Rest Day. Moreover, total sleep time was significantly longer after the Training Day. However, sleep efficiency, sleep latency, and WASO did not differ significantly between conditions; thus, the increased total sleep time may reflect greater homeostatic sleep pressure (increased sleep quantity) rather than improved sleep quality. These findings suggest that nocturnal skin temperature fluctuations reflect increased physiological stress induced by multiple endurance training sessions in female athletes. To our knowledge, this is the first study to report nocturnal proximal skin temperature fluctuations and sleep quality after multiple endurance training sessions in this population.
Continuous 24-h monitoring of core body temperature and melatonin levels is considered the gold standard for assessing circadian rhythms [15]. However, the invasiveness of rectal thermometry, high cost of ingestible sensors, and logistical challenges of collecting multiple blood samples limit the feasibility of such methods in field settings. Therefore, in the present study, we focused on proximal skin temperature during the initial 3 h of sleep, given the previously established inverse relationship between core temperature and proximal skin temperature at night [16-18]. As expected, skin temperature rapidly increased after bedtime in both conditions, consistent with previous studies [16,17]. This transient rise in skin temperature reflects enhanced peripheral heat dissipation, which facilitates a decline in core body temperature—a process considered essential for initiation of sleep [16,19,20].
The present study was conducted under free-living conditions without strict regulation of sleep timing, to reflect the natural lifestyle of endurance athletes. Given that the study took place during winter (average ambient temperature approximately 7°C), skin temperature data collected during the day were likely influenced by environmental temperature. Therefore, our analyses focused on the 3-h period after bedtime, during which a consistent increase in skin temperature was observed within the first 20 min, indicating the promotion of heat loss at sleep onset. Previous studies have suggested that such thermoregulatory changes at bedtime contribute to reduced sleep latency [16]; however, no significant difference in sleep latency was observed between conditions in the present study. Further research involving continuous monitoring of core temperature and simultaneous measurement of both proximal and distal skin temperatures (e.g., distal-proximal skin temperature gradient) under controlled environmental conditions would help clarify the thermoregulatory mechanisms that influence sleep after intensive training.
Currently, evidence regarding the effects of multiple daily endurance training sessions on nocturnal thermoregulation and sleep parameters in athletes remains limited. Aloulou et al. reported that a 48-min simulated trail run performed at night (9:00 P.M.) increased core temperature during the first 3 h of sleep and reduced total sleep time in well-trained athletes [12]. In contrast, our findings demonstrated an increase in total sleep time after the Training Day. This discrepancy may be explained by environmental factors, such as the lower ambient temperature during outdoor winter training in the present study, which could have mitigated the post-exercise elevation in body temperature. Additionally, the timing of the final exercise session (starting from 3:30 P.M.) in the present study was earlier than that of the nighttime session utilized by Aloulou et al. [12], possibly reducing the impact on nocturnal physiological responses.
Thomas et al. found that total sleep time was significantly increased after both high-intensity interval running and low-intensity steady-state running performed in the evening (6:00-7:00 P.M.), relative to a no-exercise condition [21]. They attributed this effect to elevated energy expenditure and/or enhanced peripheral blood flow. A similar mechanism may explain the prolonged sleep duration observed in our participants after endurance training, although the present study did not assess energy expenditure or skin blood flow, precluding definitive conclusions regarding their contributions.
The present study had several limitations. First, we were unable to control individual factors that may influence nocturnal skin temperature and sleep parameters, including the timing of bathing [22,23], menstrual cycle phase [17,24,25], seasonal environmental variations [26,27], and external factors during sleep (e.g., clothing, bedding). In particular, menstrual cycle phase and the presence or absence of menstruation may have affected body temperature fluctuations with increased inter-individual variabilities. Although we were not able to clarify the impact of menstruation for nocturnal skin temperature change due to limited numbers of submits, further determination would be valuable. Also, external factors (e.g., sleep environment) were not strictly controlled among participants, but these conditions did not differ within participants across the two conditions. Therefore, the impact of different sleep environment would be minor. Second, core body temperature a critical component of thermoregulatory assessment was not measured. Recent studies have demonstrated the potential of continuous monitoring of distal skin temperature (e.g., at the wrist) for health-related applications, including predicting changes in heat dissipation and circadian rhythmicity [7,28-30]. Therefore, future studies should incorporate simultaneous monitoring of proximal and distal skin temperatures to better characterize thermoregulatory responses. In addition, skin temperature was measured at a single proximal site (groin), which may not fully represent whole body thermoregulatory responses. Fourth, the comparison between “Training Day” and “Rest Day” may be excessive. Further determination of the impact of different training volume would be required. Finally, although skin temperature was continuously recorded over a 24-h period, only nighttime data were analyzed. Given that data collection occurred in December (winter) in Japan, outdoor training sessions during the day likely influenced daytime skin temperatures due to ambient conditions. Thus, further studies are warranted to evaluate circadian rhythms using continuous skin temperature monitoring under both controlled and free-living conditions in highly trained athletes.
In conclusion, multiple endurance training sessions (three sessions per day) altered the time course of nocturnal proximal skin temperature during the initial 3 h after bedtime and were associated with increased total sleep time in female long-distance runners. These findings provide novel insights into the thermophysiological and sleep-related responses to endurance exercise in elite female athletes.

Notes

ACKNOWLEDGEMENTS

We would like to appreciate all participants who participated in the study.

CONFLICTS OF INTEREST

The authors declare that they have no conflict of interest.

Figure 1.

Skin temperature fluctuation during the first 3 hours of sleep after bedtime.

Values are mean ± SD. †; P < 0.05 between conditions. *; P < 0.05 vs. time point 0 min (bedtime).
pan-2026-0008f1.jpg
Table 1.
Skin temperature during sleep on training day and rest day
Training day Rest day P value (Effect size)
Mean (℃) 35.65 ± 0.46 35.76 ± 0.23 0.43 (d = 0.3)
 180 min after bedtime 35.60 ± 0.39 35.77 ± 0.39 0.15 (d = 0.5)
Maximum (℃) 36.49 ± 0.34 36.63 ± 0.38 0.35 (d = 0.3)
 180 min after bedtime 36.26 ± 0.45 36.53 ± 0.44 0.14 (d = 0.5)
Minimum (℃) 33.83 ± 1.09 34.01 ± 0.88 0.58 (d = 0.2)
 180 min after bedtime 34.40 ± 0.89 34.28 ± 0.85 0.45 (d = 0.3)
ΔTsk (℃) 2.67 ± 0.92 2.62 ± 0.90 0.87 (d = 0.1)
 180 min after bedtime 1.85 ± 0.75* 2.26 ± 0.74 0.03 (d = 0.9)

Values are mean ± SD. Skin temperature during sleep was extracted from the overall data based on the sleep time recorded by the participants. ΔTsk was calculated by subtracting minimum of Tsk from the maximum of Tsk.

* P < 0.05 vs. Rest day.

Table 2.
Sleep parameters on training day and rest day
Training day Rest day P value (Effect size)
Sleep efficiency (%) 91.1 ± 5.4 88.7 ± 7.8 0.71 (d = 0.3)
Total sleep time (min) 447.6 ± 60.5* 360.2 ± 45.3 < 0.05 (d = 0.4)
Sleep latency (min) 2.9 ± 5.7 8.2 ± 13.3 0.29 (d = 0.3)
WASO (min) 40.0 ± 25.9 36.9 ± 27.5 0.32 (d = 0.01)

Values are mean ± SD. WASO: Wake time after sleep onset.

* : P < 0.05 vs. Rest day.

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