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. 2025 Apr 29;35(2):e70080. doi: 10.1111/jsr.70080

The Association Between Circadian Disruption in Core Body Temperature Rhythm and Post‐Chemotherapy Sleep Disturbances in Breast Cancer Survivors

Joon Sung Shin 1,2, Sanghyup Jung 2, Geun Hui Won 3, Sun Hyung Lee 1,2, Jaehyun Kim 4, Saim Jung 5, Chan‐Woo Yeom 6, Kwang‐Min Lee 7, Kyung‐Lak Son 8, Jang‐il Kim 9, Sook Young Jeon 10, Han‐Byoel Lee 9,11,12, Bong‐Jin Hahm 1,2,13,✉
PMCID: PMC13003306  PMID: 40296750

ABSTRACT

Sleep disturbance is a common symptom experienced by approximately 70% of breast cancer survivors and persists after the conclusion of chemotherapy. This study aimed to quantify the circadian disruption of the core body temperature (CBT) rhythm and its correlation with sleep disturbance following chemotherapy. In a sample of 25 breast cancer survivors, CBT was measured using an ingestible thermometer pill to calculate the relative amplitude (RA) prior to chemotherapy. The Pittsburgh Sleep Quality Index (PSQI) was employed to assess global sleep quality at three time points: prior to the commencement of chemotherapy, at 1 month (T1) and 9 months (T2) following the conclusion of chemotherapy. Subjective and objective sleep latency (SLs and SLo) were measured using the PSQI and actigraphy, respectively. The lower RA group exhibited higher intradaily variability of CBT and a higher average temperature of the lowest 5‐h period in comparison to the higher RA group. The lower RA group exhibited lower global sleep quality than the higher RA group at both time points (T1 and T2). SLs in the lower RA group were significantly longer compared to the higher RA group at both time points. Furthermore, the discrepancy in SL (∆SLs–SLo) demonstrated a notable between‐group difference at both time points. The findings of this study suggest that breast cancer survivors with pronounced circadian disruption of the CBT rhythm prior to chemotherapy are more prone to sleep disturbances following chemotherapy. Additionally, prolonged SLs may be a contributing factor to their poor sleep quality.

Trial Registration: clinicaltrials.gov: NCT04364347

Keywords: actigraphy, breast cancer, circadian disruption, core body temperature, sleep disturbance, sleep latency

1. Introduction

Breast cancer is the most common cancer in women (Bray et al. 2024), and sleep disturbance is a common symptom experienced by 60% of breast cancer survivors (Sanford et al. 2013). Poor sleep quality in breast cancer survivors is linked to poor quality of life and is associated with a range of physical and mental health outcomes, including survival rate (Palesh et al. 2014; Sanford et al. 2013).

A substantial proportion of breast cancer survivors, ranging from 33% to 78%, continue to experience sleep disturbances after completion of cancer treatment (Wu et al. 2024). It is thus imperative to undertake further research in order to ascertain the factors associated with sleep disturbance following treatment, with a view to identifying patients at high risk and establishing a basis for preventive approaches.

There is a recognised association between circadian rhythm disruption and sleep disturbance (Amidi and Wu 2022). Additionally, there have been reports indicating that pre‐treatment circadian rhythm disruption is associated with sleep disturbance in cancer patients (Ancoli‐Israel et al. 2006; Berger et al. 2007).

Core body temperature (CBT) is a circadian rhythm that is regulated by the thermoregulatory centre in the preoptic area (Refinetti 2020). The CBT rhythm is reflective of the circadian rhythm of the individual, as it is synchronised by rhythmic input from the suprachiasmatic nuclei, the central circadian clock (Coiffard et al. 2021). A body of research has demonstrated that the reduced amplitude of the CBT rhythm is associated with a blunted metabolite rhythm of circadian clock genes, suggesting a molecular level relationship between the CBT rhythm and circadian disturbances (Windred et al. 2024).

The CBT rhythm is also known to be associated with the initiation and maintenance of sleep. A decline in CBT and an increase in peripheral heat loss have been demonstrated to be associated with sleep latency (SL) (Iijima et al. 2024; Rogers et al. 2007). It has been reported that aberrant nocturnal CBT is associated with prolonged SL (Brown et al. 2017). Moreover, evidence has been presented that demonstrates a reduction in the amplitude of the CBT rhythm in patients diagnosed with circadian rhythm sleep–wake disorders, thereby suggesting a correlation between the amplitude of the CBT rhythm and sleep disturbances (Narita et al. 2011).

Furthermore, there is a documented discrepancy between subjective and objective sleep parameters in cancer patients, with the presence of subjective sleep disturbances correlating with circadian rhythm disruptions (Donzella et al. 2024; Oxana Palesh et al. 2017). In light of prior research indicating a link between subjective time perception and CBT rhythm, it is plausible to hypothesise that these discrepancies are associated with alterations in CBT rhythm. However, to the best of our knowledge, no study has yet analysed the potential association between the rhythm of CBT and sleep disturbances in cancer patients.

The aim of this study was to assess the circadian disruption of CBT in terms of relative amplitude and to determine its association with global sleep quality. In particular, sleep disturbance was analysed by focusing on sleep latency, a sleep variable known to be associated with disturbance in CBT.

2. Materials and Method

2.1. Participants

This study was a secondary analysis of a prospective observational study designed to evaluate the impact of chemotherapy on circadian rhythm (title: “Chemotherapy‐induced circadian rhythm disruption”). Participants were recruited for this study from a tertiary general hospital in Seoul, Republic of Korea, between December 2019 and January 2023. The following criteria were used to identify participants eligible for the study: (1) a diagnosis of breast cancer and subsequent initiation of chemotherapy, (2) premenopausal status, and (3) stage I–III invasive breast adenocarcinoma. The following criteria were used to determine exclusion from the study: (1) A diagnosis of any other cancer within the past 5 years, (2) Eastern Cooperative Oncology Group performance status > 1, (3) Other physical conditions that may impact survival, (4) Other physical conditions that may impact sleep or mood, (5) A current or past history of psychiatric disorders, including depressive, anxiety, and sleep disorders, for a period lasting at least 1 month, (6) Current use of psychotropic medication, (7) Night work or shift work in the past 6 months, or (8) Travel by air across a minimum of three meridians over the past 6 months (Shin et al. 2024). After initial screening, a total of 39 individuals provided informed consent, of which 25 participants completed the baseline assessment (Figure S1).

2.2. Study Design and Settings

Participants were admitted to the clinical trial unit for an overnight stay, a week or two in advance of the initiation of chemotherapy (T0, Figure S1). At the time of admission, a range of socio‐demographic and clinical characteristics were evaluated using self‐report questionnaires. During this admission, CBT was assessed, and to confirm that the groups according to circadian disruption of CBT were maintained, a repeat assessment of core body temperature was conducted after the completion of chemotherapy (T1).

Sleep disturbances were evaluated at baseline (T0) and subsequently at two post‐treatment time points, namely, 1 month (T1) and 9 months (T2) following the completion of chemotherapy. This was done using the Pittsburgh Sleep Quality Index (PSQI) and actigraphy with a sleep diary. The evaluation window was set at ±2 weeks from the scheduled date of the assessment.

This study was conducted in accordance with the ethical standards set forth in the 2013 revised Declaration of Helsinki and the ICH‐GCP guidelines. The study was registered with the Clinical Research Registry (www.clinicaltrials.gov) with the objective of ensuring the disclosure of pertinent study information (ID: NCT04364347). Prior to the commencement of the study, the protocol was approved by the Institutional Review Board of Seoul National University Hospital (No. 1903–052‐1017) and written informed consent was obtained from all participants. To compensate participants for their time and travel expenses, they were offered an incentive payment of 360 USD for each admission and 72 USD for each visit.

2.3. Measures

2.3.1. Core Body Temperature Rhythm

The core body temperature (CBT) rhythm was assessed over a 24‐h period using the CorTemp system (HQ Inc. Palmetto, FL, USA) (Sparling et al. 1993) (Figure S2). The measurement range of the ingestible thermometer pill is 30°C–45°C, with a measurement error of ±0.1°C and a collection interval of 30 s. Prior to the commencement of the study, all thermometer pills were calibrated, and participants were instructed to ingest the thermometer pill at least 1 h before the initiation of the measurement.

The participants were instructed to adhere to a consistent sleep–wake cycle. To minimise light exposure, all lighting was extinguished during sleep, and light levels were maintained below 30 lx using a light meter during the blood draws that were part of the study. They were required to record the time of consumption of any beverages or food during the measurement period and to notify the researcher before passing a stool. In the event of the thermometer pill being eliminated from the body within 24 h, the time of elimination was confirmed by visual inspection. A detailed account of the data pre‐processing methodology employed for analysis can be found elsewhere (Shin et al. 2024).

In order to evaluate the disruption of the circadian rhythm of the CBT rhythm, both nonparametric analysis and cosinor analysis were employed, in accordance with the methodology utilised in a previous study to analyse circadian disruption of CBT (Lax et al. 2012). Nonparametric analyses yielded the relative amplitude (RA) and intradaily variability (IV) of the CBT rhythm. The RA is indicative of the robustness of the circadian rhythm, with a higher RA indicating a higher temperature during the diurnal period and a relatively lower temperature during the nocturnal period. For the calculation of the RA, it is necessary to assess the temperature of the highest 10‐h period (M10) and the lowest 5‐h period (L5). The RA was then derived as the difference between M10 and L5, divided by the sum of these values. The IV represents circadian rhythm fragmentation. In light of the potential impact of missing values on the results of nonparametric analyses, 14 participants with more than 30 min of consecutive missing values and less than 24 h of data were excluded from the nonparametric analysis.

The mesor (midline estimating statistic of rhythm), amplitude, and CBTmin (the time at CBT minimum) were quantified through the application of cosinor analysis (Cornelissen 2014). For the purposes of this analysis, five participants with missing values spanning a period of more than two consecutive hours were excluded.

2.3.2. Pittsburgh Sleep Quality Index

The validated Korean version of the PSQI was administered to assess global sleep quality over the past 4 weeks (Buysse et al. 1989; Shin and Kim 2020). The PSQI has been demonstrated to exhibit satisfactory levels of internal consistency, test–retest reliability, and validity in cancer patients (Beck et al. 2004). According to the scoring manual, a global score (range 0–21) was derived, with higher scores indicating more severe sleep disturbances. In breast cancer survivors, a cut‐off of 8 was previously identified to discriminate between good and poor sleep (Chen et al. 2018). In the present study, the reliability of the measurement was assessed by calculating Cronbach's alpha, which yielded a value of 0.50.

In addition to the global score, the item from the PSQI, “During the past month, how long (in minutes) has it usually taken you to fall asleep each night?” was employed to obtain a subjective measurement of sleep latency (SLs) (Arditte Hall et al. 2023).

2.3.3. Actigraphy for Sleep Analyses

To quantify sleep disturbances with objective sleep variables, the Actigraph GT3X+ (ActiGraph LLC, Pensacola, FL, USA), a device whose validity was previously assessed for polysomnography, was employed (Slater et al. 2015). Participants were instructed to wear the ActiGraph GT3X+ on their non‐dominant wrist for a period of seven consecutive days and to record their sleep patterns in a sleep diary. The data were collected at a frequency of 80 Hz, converted to 60‐s epochs, and analysed using the ActiLife version 6.9.2 software (Pensacola, FL, USA). Any periods of non‐wear as documented in the sleep diary, in addition to any periods identified as non‐wear time by the modified Choi algorithm, were excluded from the subsequent analysis (Choi et al. 2011).

The Cole‐Kripke algorithm was utilised to ascertain the sleep and wake states of the participants, with the aid of the ActiLife software (Cole et al. 1992). The time in bed (TIB) was calculated using a combination method of sleep diary and actigraphy. This entailed confirming or adjusting the time in and out of bed, as recorded on the sleep diary, based on the actigraphy data. Sleep onset was defined as the time point marking the commencement of the first 10 consecutive minutes of immobility following the act of getting into bed, with no more than 1 min of mobility occurring within that interval (Fekedulegn et al. 2020). The interval between the time of getting into bed and the onset of sleep, as determined by ActiLife software, was defined as SL. Additionally, other sleep variables, including total sleep time (TST), sleep efficiency (SE), and wakefulness after sleep onset (WASO), were also assessed. The SE was calculated as the ratio of TST to the TIB. The aforementioned sleep variables were calculated and averaged for each night and for each participant.

2.3.4. Discrepancy Between Subjective and Objective Sleep Latency

To gain further insight into the sleep disturbances observed in the lower RA group, a comparison was made between subjective SL (SLs) and objective SL (SLo), as previous studies have indicated SL as a sleep variable associated with circadian disturbances in CBT rhythms (Brown et al. 2017; Seol et al. 2023). The discrepancy between subjective and objective measurement of SL (∆SLs—SLo) was determined by subtracting the SLo obtained through actigraphy with sleep diary from the SLs derived from the PSQI item (Yoon et al. 2022).

2.3.5. Self‐Reported Questionnaires for Circadian and Psychiatric Characteristics

The validated Korean version of the Morningness‐Eveningness Questionnaire (MEQ) (Horne and Ostberg 1976; Lee et al. 2014) and the Munich Chronotype Questionnaire (MCTQ) (Roenneberg et al. 2003; Suh et al. 2018) were employed to evaluate the circadian characteristics of the participants. The MEQ is comprised of 19 items, with a total score ranging from 16 to 86. Based on this score, respondents are classified as evening (16–41 points), intermediate (42–58 points), or morning (59–86 points) chronotypes.

The MCTQ assesses social jet lag, defined as the discrepancy between the midpoint of workday and holiday sleep periods. Social jet lag represents the desynchronisation of social and biological circadian rhythms. Additionally, the MSFsc (mid‐sleep on free days corrected for sleep debt on weekdays), which serves as a phase marker for the sleep–wake rhythm, is produced.

The Hospital Anxiety and Depression Scale (HADS) was employed to assess anxiety and depression (Oh et al. 1999; Zigmond and Snaith 1983). A cutoff score of 7 and 9 on the HADS was utilised to indicate significant anxiety and depression, respectively, in cancer patients (Annunziata et al. 2020).

2.4. Statistical Analyses

Prior to the analysis of the correlation between circadian rhythm disturbance, as assessed by the RA of CBT, and sleep disturbance following chemotherapy, the RA of CBT rhythm and sleep metrics at each designated time point were plotted in a scatter plot. This analysis did not reveal a significant linear relationship between the two metrics. Consequently, the participants were stratified into two distinct groups based on the median value of the RA of CBT rhythm: one group designated as the a “higher RA” group and a “lower RA” group. A comparison of the higher and lower RA groups was conducted with respect to demographic, clinical, and circadian characteristics. This was achieved through the utilisation of Fisher's exact test and the chi‐square test for the categorical variables, while continuous variables were evaluated for normality using the Shapiro–Wilk test. Thereafter, these variables were analysed using either the Student's t‐test or the Mann–Whitney U‐test.

In order to compare sleep disturbances between the groups according to circadian disruption, the sleep variables were presented as the mean and standard deviation for each time point and were analysed using a linear mixed effects model. Interaction terms for time point and group were included in the model to determine whether differences in sleep variables between the two groups varied as a function of time point. This model incorporated fixed effects to account for the influence of age (a continuous variable) and chemotherapy type (a categorical variable, comprising adjuvant and neoadjuvant treatments), given that younger age and recent surgery are known risk factors for sleep disturbances in breast cancer survivors (Sonia Ancoli‐Israel 2015; Oxana Palesh et al. 2013). In cases where the interaction was considered statistically significant, a comparison test between groups at each time point was performed as a post hoc analysis. For each model, the normality of the residuals was assessed and the assumptions of normality were found to be met using both histograms and normal Q‐Q plots. The power analysis of the mean differences between the RA groups yielded a power of 86.98%, 96.50%, and 67.80% for PSQI, SLo, and SLs, respectively.

All statistical analyses were conducted using IBM SPSS Statistics, version 29.0 (IBM Corp., Armonk, NY, USA). A p‐value less than 0.05 (two‐tailed) was considered statistically significant for all analyses. The Bonferroni correction, with the threshold for statistical significance set at a p‐value less than 0.05/number of analyses, was used to adjust for multiple comparisons.

3. Results

3.1. Demographic and Clinical Characteristics of the Participants

Of the 39 individuals who provided informed consent for the study, the RA of CBT in 25 participants was calculated (Table 1). The mean follow‐up duration for the higher RA and lower RA groups was 315.85 ± 160.39 and 272.90 ± 163.07 days, respectively (Mann–Whitney U‐test p = 0.341). The mean age of all participants was 42.16 years. A higher proportion of the lower RA group received neoadjuvant chemotherapy (p = 0.011). With regard to baseline psychobehavioural characteristics, no statistically significant differences were observed between the RA groups in chronotype, social jet lag, anxiety, and depression.

TABLE 1.

Baseline characteristics of participants based on the relative amplitude of their core body temperature.

Total (n = 25) High RA (n = 13) Low RA (n = 12) p a
Age 42.16 ± 5.87 41.85 ± 6.90 42.50 ± 4.80 0.787
BMI
≥ 23 14 (56.0) 6 (46.2) 8 (66.7) 0.302
Marital status 0.160
Unmarried 5 (20.0) 1 (7.7) 4 (33.3)
Married 20 (80.0) 12 (92.3) 8 (66.7)
Occupation status 0.411
Employed/self‐employed 9 (36.0) 6 (46.2) 3 (25.0)
Homemaker/Not currently working 16 (64.0) 7 (53.8) 9 (75.0)
Education 0.821
Up to high school 14 (56.0) 7 (53.8) 7 (53.8)
College graduate or higher 11 (44.0) 6 (46.2) 5 (41.7)
Receptor status
ER positive 22 (88.0) 13 (100.0) 9 (75.0) 0.096
PR positive 21 (84.0) 13 (100.0) 8 (66.7) 0.039*
HER2 positive 20 (80.0) 11 (84.6) 9 (75.0) 0.645
Disease stage 0.040*
I 2 (8.0) 2 (15.4) 0 (0.0)
II 19 (76.0) 11 (84.6) 8 (66.7)
III 4 (16.0) 0 (0.0) 4 (33.3)
Type of Chemotherapy 0.011*
Adjuvant 17 (68.0) 12 (92.3) 5 (41.7)
Neo‐adjuvant 8 (32.0) 1 (7.7) 7 (58.3)
Chemotherapy regimen 1.000
AC → D b 18 (72.0) 9 (69.2) 9 (75.0)
Others c 7 (28.0) 4 (30.8) 3 (25.0)
MEQ Chronotype 0.463
Morning type 8 (32.0) 5 (38.5) 3 (25.0)
Intermediate type 13 (52.0) 7 (53.8) 6 (50.0)
Evening type 4 (16.0) 1 (7.7) 3 (25.0)
MCTQ
Social jet lag (min) 36.60 ± 42.00 36.15 ± 37.09 37.08 ± 48.45 0.936
MSFsc (h) d 04:07 ± 1:16 03:58 ± 1:08 04:14 ± 1:24 0.648
HADS
Anxious 2 (8.0) 2 (15.4) 0 (0.0) 0.480
Depressed 7 (28.0) 2 (15.4) 5 (41.7) 0.202
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Note: Data are presented as mean ± SD or n (%).

Abbreviations: AC → D, doxorubicin/cyclophosphamide→docetaxel; BMI, body mass index; CBT, core body temperature; ER, oestrogen receptor; HADS, hospital anxiety and depression scale; HER2, human epidermal growth factor receptor 2; MCTQ, Munich chronotype questionnaire; MEQ, morningness‐eveningness questionnaire; MSFsc, mid‐sleep on free days corrected for sleep debt on weekdays; PR, progesterone receptor; RA, relative amplitude; SD, standard deviation.

a

Chi‐square test (BMI, education, disease stage, and MEQ chronotype), Fisher's exact test, Mann–Whitney U test (social jet lag), and student t‐test.

b

Doxorubicin 60 mg/m2 and cyclophosphamide 600 mg/m2 once every 3 weeks for 4 cycles, followed by docetaxel 300 mg/m2 once every 3 weeks for 4 cycles.

c

3 participants from the high RA of CBT group received AC, docetaxel/carboplatin/trastuzumab/pertuzumab, and cyclophosphamide/methotrexate/5‐fluorouracil, respectively. Two participants from the low RA of CBT group received weekly paclitaxel/carboplatin→doxorubicin/cyclophosphamide. Two participants from each group received docetaxel/cyclophosphamide.

d

Data were not available for 4 participants in the high RA group and 1 participant in the low RA group who used the alarm during weekends.

*

p < 0.05.

3.2. Circadian and Sleep Variables of the Participants

The mean duration of CBT data collection was 24.99 ± 1.62 and 25.62 ± 5.50 h for the higher RA and lower RA groups, respectively (Mann–Whitney U‐test p = 0.010). The participants with lower RA of CBT rhythm exhibited higher IV compared to those with higher RA (p < 0.001, Table 2). The L5 of the lower RA group was significantly higher in the lower RA group (p = 0.027), as evidenced by the raw data waveform of the two groups (Figure S2). The amplitude assessed with cosinor analysis was lower in the lower RA group compared to the higher RA group (p < 0.001). The baseline sleep variables, as measured with actigraphy and a sleep diary, were found to be not significantly different between the RA groups. A subsequent evaluation was conducted 1 month after the conclusion of chemotherapy (T1), and the results indicated that there were no significant differences between the higher RA and lower RA groups in terms of the IV, L5, and amplitude of CBT rhythm (Table S1).

TABLE 2.

Circadian and sleep variables in the participants at T0.

Total (n = 25) High RA (n = 13) Low RA (n = 12) p a
Nonparametric variables of CBT
RA (×10−2) 7.56 ± 2.53 9.45 ± 2.05 5.51 ± 0.79 < 0.001*
IV 0.39 ± 0.22 0.29 ± 0.15 0.50 ± 0.24 < 0.001*
M10 (°C) 37.43 ± 0.18 37.47 ± 0.14 37.38 ± 0.22 0.240
L5 (°C) 36.87 ± 0.23 36.77 ± 0.22 36.97 ± 0.21 0.027*
Cosinor variables of CBT
Mesor 37.19 ± 0.19 37.19 ± 0.16 37.20 ± 0.22 0.886
Amplitude 0.29 ± 0.11 0.37 ± 0.09 0.21 ± 0.05 < 0.001*
CBTmin (h) 04:34 ± 3:39 04:56 ± 2:02 04:08 ± 4:55 0.592
Sleep variables based on actigraphy and sleep diary b
SL (min) 8.33 ± 5.40 6.65 ± 4.58 10.33 ± 5.85 0.114
TST (min) 442.04 ± 59.86 456.24 ± 64.98 424.99 ± 51.08 0.649
SE (%) 82.86 ± 5.54 83.37 ± 5.28 82.25 ± 6.07 0.231
WASO (min) 83.12 ± 31.08 85.02 ± 32.68 80.83 ± 30.62 0.761
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Note: Data are presented as mean ± SD.

Abbreviations: CBT, core body temperature; CBTmin, the time at CBT minimum; IV, intradaily variability; L5, average temperature of the lowest 5‐h period; M10, average temperature of the highest 10‐h period; RA, relative amplitude; SD, standard deviation; SE, sleep efficiency; SL, sleep latency; TST, total sleep time; WASO, wakefulness after sleep onset.

a

Mann‐Whitney U test (IV and amplitude) and Student t‐test.

b

The sleep variables of one and two participants from the high and low RA groups, respectively, were unavailable due to the failure to record a sleep diary.

*

p < 0.05.

3.3. Global Sleep Quality

The mean follow‐up duration for the sleep disturbances for the higher RA and lower RA groups was 253.69 ± 179.66 and 331.75 ± 151.58 days, respectively (Mann–Whitney U‐test p = 0.247). The PSQI global score demonstrated significant differences as a function of RA groups and time points (p = 0.043). The post hoc analyses indicated that the PSQI global score was estimated to be higher in the lower RA group compared to the higher RA group at T1 and T2 (Figure 1 and Table S2).

FIGURE 1.

FIGURE 1

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Global sleep disturbances across time points.

3.4. Subjective and Objective Measurement of Sleep Latency

The interaction analysis of time point and RA group on SLs yielded a statistically significant result (p = 0.009). Subsequent post hoc analysis revealed that the SLs were longer in the lower RA group compared to the higher RA group at both T1 and T2 (Figure 2 and Table S3). In contrast, the SLo demonstrated no variation with the function of time point and RA groups (p = 0.342). The SLo at T1, estimated from the linear mixed effects model, was 9.13 min and 7.90 min in the higher RA and lower RA groups, respectively. Using the underestimation rate of actigraphy compared to polysomnography (38%) in previous studies, the polysomnographic SLo was estimated to be 14.73 min and 12.74 min in the higher RA and lower RA groups, respectively (Slater et al. 2015).

FIGURE 2.

FIGURE 2

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Subjective and objective sleep latency across time points.

3.5. Discrepancy Between Subjective and Objective Sleep Latency

In light of the divergent trends observed in SLs and SLo between the two RA groups, further analyses were conducted to ascertain whether the discrepancy between SLs and SLo varies by group and time point. The interaction analysis demonstrated a statistically significant difference as a function of time point and RA group (p = 0.008, Figure 3). Post hoc analyses revealed that SLs was 66.46 min longer than SLo in the lower RA group at T1 (Table S4). In contrast, SLs was only 3.46 min longer than SLo at T1 in the higher RA group. A statistically significant between‐group difference in (∆SLs—SLo) was also observed at T2 (p = 0.004).

FIGURE 3.

FIGURE 3

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Discrepancy between subjective and objective measurements of sleep latency.

4. Discussion

The results of this study indicate that the group with lower RA of CBT rhythm, which indicates more pronounced circadian disruption prior to chemotherapy, exhibited more severe sleep disturbance at 1 month (T1) and 9 months (T2) post‐chemotherapy compared to the group with higher RA levels.

The participants in the lower RA group exhibited a lower RA of CBT rhythm compared to the higher RA group, indicating a relatively blunted rhythm of CBT. The lower RA group also demonstrated a higher IV, suggesting a more fragmented rhythm and, consequently, a greater degree of circadian disruption in comparison to the higher RA group. Blunted CBT rhythm is associated with increased heart rate and reduced heart rate variability, which may contribute to sleep disturbance (Bigalke et al. 2023). Furthermore, L5 in the lower RA group exhibited a higher value compared to the higher RA group. Given that the CBT rhythm generally manifests a pattern of dipping before sleep and rising before wakefulness, this finding indicates elevated nocturnal CBT in the lower RA group compared to the higher RA group. This elevated nocturnal CBT is recognised as a potential contributor to poor sleep quality (Seol et al. 2023).

The baseline sleep variables based on actigraphy, including SE and TST, were comparable to or lower than those observed in previous studies conducted on breast cancer patients, while SL and WASO were longer than those reported in previous studies (Kreutz et al. 2021). Notably, the SE was below the 85% threshold, which is considered a poor sleep cut‐off in the general population, even prior to chemotherapy (Schutte‐Rodin et al. 2008).

In evaluating global sleep quality in the lower RA group, PSQI global scores at T1 and T2 exceeded 8, which is indicative of poor sleep in cancer patients, and approached 10, which is associated with clinical insomnia (Chen et al. 2018). The SLs observed in both RA groups were comparable to those reported in previous studies (Kreutz et al. 2021). However, the SLs in the lower RA group exhibited a notable increase following chemotherapy, resulting in a significantly longer duration than that observed in the higher RA group. Given that the normal range of SLs is less than 30 min, the increased SLs observed in the lower RA group after chemotherapy are of clinical significance (Miley‐Åkerstedt et al. 2018). Although few studies have investigated CBT rhythm from the perspective of RA, this finding is in accordance with the results of previous studies that have reported an association between a decline in CBT rhythm and sleep onset (Brown et al. 2017).

In contrast to SLs, which demonstrated variations across groups and time points, SLo did not exhibit notable differences. The difference between SLs and SLo, Δ SLs–SLo, was found to be significantly different across groups and time points. This measure has been designed to assess sleep state misperception (Maltezos et al. 2024). The term “sleep state misperception,” also known as “paradoxical insomnia,” is used to describe individuals who report subjective discomfort with their sleep quality despite objective sleep variables falling within normal limits (Maltezos et al. 2024). Sleep state misperception is a prevalent phenomenon amongst individuals with insomnia, yet it has also been documented in studies examining cancer patients (Dean et al. 2013). Of the objective sleep variables measured by actigraphy, TST, SE, and WASO demonstrate a relatively high degree of agreement with subjective sleep variables measured by PSQI (Kreutz et al. 2021). Conversely, SL is known to exhibit disparate subjective and objective measures (Kreutz et al. 2021). According to the findings of a preceding study, the mean value of Δ SLs‐SLo was 24.5 min when SL was measured by polysomnography and sleep log in healthy individuals (Trimmel et al. 2021). In contrast, the mean value at T1 in the lower RA group in the present study was significantly higher, at 66.56 min, indicating a higher level of sleep state misperception in the lower RA group.

The higher level of sleep state misperception may be a factor that explains the lower global sleep quality observed in the lower RA group. As reported in previous studies, subjective time perception has been demonstrated to be related to CBT rhythm (Aschoff 1998; Kuriyama et al. 2005). Iijima et al. (2024) demonstrated that a decrease in body heat loss associated with a fall in CBT was associated with an increase in SLs, whereas no such association was observed for SLo (Iijima et al. 2024).

In addition to sleep state misperception, there are several factors which may have contributed to the prominent Δ SLs‐SLo in the lower RA group. First, actigraphy relies on a sleep diary to measure SL, which can be subject to error in recording bedtime on a daily basis. Furthermore, actigraphy is prone to underestimating SL due to the intrinsic limitations of its measurement algorithm (Kreutz et al. 2021). The Actigraph GT3X+, which was utilised in this study, has been demonstrated to underestimate SL by 38% in comparison to polysomnography (Slater et al. 2015). Nevertheless, upon conversion of the actigraphic SLo to polysomnographic SLo with an underestimation rate, it still becomes evident that the SLs is considerably longer than the SLo. Second, the time periods covered by each assessment differed, as the PSQI is evaluated through retrospective recall of the 4 weeks preceding the measurement, whereas actigraphy is assessed prospectively 7 days following the measurement.

These findings provide a foundation for understanding the pathophysiology of sleep disturbance in cancer patients and may contribute to the identification of potential risk factors for sleep disturbance after chemotherapy. Moreover, should further studies substantiate the hypothesis that sleep state misperceptions contribute to poor sleep quality in cancer patients experiencing circadian disruption, the potential for cognitive behavioural therapy to enhance sleep quality in these patients would be a viable avenue for exploration (Trimmel et al. 2021).

The present study is subject to the following limitations: First, the median split was utilised as the independent variable, which constrained statistical power. The utilisation of median splits entails several drawbacks, including the potential for arbitrary data categorisation near the median, an elevated risk of Type 2 error, and limited generalisability (McClelland et al. 2015). In this study, scatter plots were generated for the RA of CBT and the dependent variables to illustrate the relationship between the two variables prior to statistical analysis, but no linear relationship was identified. Given the paucity of prior research to assume a relationship between the RA of CBT and SL, the median split was employed as a preliminary study, an approach for physiologic data which lacks reference value (Iacobucci et al. 2015). To enhance the generalisability of our findings, it is recommended that a more substantial sample size be employed to ascertain the reproducibility of the results with continuous variables. Second, while the CBT variables demonstrated comparability with those observed in preceding studies (Carpenter et al. 2004), the external validity of the present study was constrained by sample size and the infrequency of CBT measurement in breast cancer patients. Third, it is established that neuropathic pain, anxiety, and other factors can contribute to sleep disturbances following chemotherapy. However, due to the limited number of participants, it was challenging to adjust for these potential confounding variables. Fourth, the limited sample size precluded an evaluation of the impact of chemotherapy regimens on sleep patterns. Fifth, while no patients had initiated hormonal therapy at T1, by T2, 10 out of 12 were undergoing hormonal therapy, which may have impacted sleep. Given that insomnia resulting from hormonal therapies such as tamoxifen is also prevalent, this should be taken into account in subsequent studies (Palesh et al. 2013). Sixth, the secondary outcome variable, PSQI, exhibited low internal consistency. This may be attributable to the limited sample size, necessitating the validation of the findings through replication in a more substantial sample. Finally, the younger age of onset of breast cancer in the Asian population may limit the generalisability of the findings, given that sleep metrics vary by age (Bray et al. 2024).

5. Conclusion

To the best of our knowledge, this is the first study to analyse the relationship between circadian disruption of CBT rhythm and sleep disturbance in cancer patients. We also found that sleep state misperception was more pronounced in patients with more severe circadian disruption in CBT rhythm, as measured by objective and subjective SL. Further studies based on larger samples are needed to elucidate the physiological mechanisms by which circadian disruption affects sleep disturbance and sleep state misperception.

Author Contributions

Joon Sung Shin: writing – original draft, conceptualization, data curation, formal analysis, investigation. Sanghyup Jung: data curation, methodology, conceptualization, investigation. Geun Hui Won: data curation, methodology, conceptualization. Sun Hyung Lee: conceptualization. Jaehyun Kim: conceptualization. Saim Jung: conceptualization. Chan‐Woo Yeom: investigation, conceptualization, supervision. Kwang‐Min Lee: conceptualization, supervision. Kyung‐Lak Son: investigation, conceptualization, supervision. Jang‐il Kim: methodology, resources. Sook Young Jeon: methodology, resources. Han‐Byoel Lee: methodology, resources. Bong‐Jin Hahm: conceptualization, methodology, funding acquisition, supervision.

Ethics Statement

Approved by the Institutional Review Board of Seoul National University Hospital (No. 1903–052‐1017).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1.jsr70080‐sup‐0001‐supinfo.

JSR-35-e70080-s001.docx (115.8KB, docx)

Acknowledgements

We would like to express our sincere gratitude to the participants who devoted their time and provided invaluable assistance for this study.

Shin, J. S. , Jung S., Won G. H., et al. 2026. “The Association Between Circadian Disruption in Core Body Temperature Rhythm and Post‐Chemotherapy Sleep Disturbances in Breast Cancer Survivors.” Journal of Sleep Research 35, no. 2: e70080. 10.1111/jsr.70080.

Funding: This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2019R1A2C1003675).

Data Availability Statement

The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available due to the presence of restrictions pertaining to privacy or ethical considerations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1.jsr70080‐sup‐0001‐supinfo.

JSR-35-e70080-s001.docx (115.8KB, docx)

Data Availability Statement

The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available due to the presence of restrictions pertaining to privacy or ethical considerations.

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