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Fatigue: Biomedicine, Health &
Behavior
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Fatigue and circadian activity rhythms
in breast cancer patients before and
after chemotherapy: a controlled study
Lianqi Liu a , Michelle Rissling a b , Ariel Neikrug a b , Lavinia
Fiorentino a c , Loki Natarajan c d , Michelle Faierman a , Georgia
Robins Sadler c e , Joel E. Dimsdale a b c , Paul J. Mills a b c ,
Barbara A. Parker c f & Sonia Ancoli-Israel a b c
a Department of Psychiatry, University of California, San Diego, La
Jolla, CA, USA
b SDSU/UCSD Joint Doctoral Program in Clinical Psychology, San
Diego, CA, USA
c UCSD Moores Cancer Center, La Jolla, CA, USA
d Department of Family and Preventive Medicine, University of
California, San Diego, La Jolla, CA, USA
e Department of Surgery, University of California, San Diego, La
Jolla, CA, USA
f Department of Medicine, University of California, San Diego, La
Jolla, CA, USA
To cite this article: Lianqi Liu , Michelle Rissling , Ariel Neikrug , Lavinia Fiorentino , Loki
Natarajan , Michelle Faierman , Georgia Robins Sadler , Joel E. Dimsdale , Paul J. Mills , Barbara A.
Parker & Sonia Ancoli-Israel (2013): Fatigue and circadian activity rhythms in breast cancer patients
before and after chemotherapy: a controlled study, Fatigue: Biomedicine, Health & Behavior, 1:1-2,
12-26
To link to this article: http://dx.doi.org/10.1080/21641846.2012.741782
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Fatigue and circadian activity rhythms in breast cancer patients
before and after chemotherapy: a controlled study
Lianqi Liu
a
, Michelle Rissling
a,b
, Ariel Neikrug
a,b
, Lavinia Fiorentino
a,c
,
Loki Natarajan
c,d
, Michelle Faierman
a
, Georgia Robins Sadler
c,e
, Joel
E. Dimsdale
a,b,c
, Paul J. Mills
a,b,c
, Barbara A. Parker
c,f
and Sonia Ancoli-Israel
a,b,c,f,
*
a
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA;
b
SDSU/
UCSD Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA;
c
UCSD Moores
Cancer Center, La Jolla, CA, USA;
d
Department of Family and Preventive Medicine, University
of California, San Diego, La Jolla, CA, USA;
e
Department of Surgery, University of California,
San Diego, La Jolla, CA, USA;
f
Department of Medicine, University of California, San Diego,
La Jolla, CA, USA
(Received 5 August 2012; final version received 17 October 2012)
Background: Breast cancer (BC) patients often experience cancer-related fatigue
(CRF) before, during, and after their chemotherapy. Circadian rhythms are
24-hour cycles of behavior and physiology that are generated by internal
pacemakers and entrained by zeitgebers (e.g., light). A few studies have
suggested a relationship between fatigue and circadian rhythms in some clinical
populations. Methods: One hundred and forty-eight women diagnosed with
stage I–III breast cancer and scheduled to receive at least four cycles of adjuvant
or neoadjuvant chemotherapy, and 61 controls (cancer-free healthy women)
participated in this study. Data were collected before (Baseline) and after four
cycles of chemotherapy (Cycle-4). Fatigue was assessed with the Short Form of
Multidimensional Fatigue Symptom Inventory (MFSI–SF); circadian activity
rhythm (CAR) was recorded with wrist actigraphy (six parameters included:
amplitude, acrophase, mesor, up-mesor, down-mesor and F-statistic). A mixed
model analysis was used to examine changes in fatigue and CAR parameters
compared to controls, and to examine the longitudinal relationship between
fatigue and CAR parameters in BC patients. Results: More severe CRF (total
and subscale scores) and disrupted CAR (amplitude, mesor and F-statistic) were
observed in BC patients compared to controls at both Baseline and Cycle-4 (all
p’s < 0.05); BC patients also experienced more fatigue and decreased amplitude
and mesor, as well as delayed up-mesor time at Cycle-4 compared to Baseline
(all p’s < 0.05). The increased total MFSI–SF scores were significantly associated
with decreased amplitude, mesor and F-statistic (all p’s < 0.006). Conclusion:
CRF exists and CAR is disrupted even before the start of chemotherapy. The
significant relationship between CRF and CAR indicate possible underlying
connections. Re-entraining the disturbed CAR using effective interventions such
as bright light therapy might also improve CRF.
Keywords: breast cancer; fatigue; circadian activity rhythm; chemotherapy
© 2013 IACFS/ME
*Corresponding author. Email: sancoliisrael@ucsd.edu
Fatigue: Biomedicine, Health & Behavior, 2013
Vol. 1, Nos. 1–2, 12–26, http://dx.doi.org/10.1080/21641846.2012.741782
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Introduction
Studies show that breast cancer (BC) patients often experience fatigue before, during,
and after their chemotherapy.[1–6] Cancer-related fatigue (CRF) interferes with
patients’daily life and impacts their quality of life,[7–10] and is often an important
cause of discontinuation of their treatment.[11] CRF is also associated with significant
disability and increased utilization of health care resources.[12] The causes of CRF are
not clear but are believed to be multifactorial, as CRF is highly correlated to physiologi-
cal factors (e.g., pain, anemia, neuroendocrine changes, altered energy metabolism,
inflammation), psychological factors (e.g., stress, depression, anxiety), socio-cultural
factors (e.g., education, cognitive and behavioral response), and chronobiological
factors (e.g., sleep and circadian rhythms).[6,12,13–15] Ancoli-Israel et al. hypoth-
esized that in BC patients, CRF might be associated with decreased exposure to
bright light and desynchronized activity-related circadian rhythms.[13] Our data, col-
lected in BC patients, indeed found a significant correlation with more fatigue associ-
ated with less bright light exposure.[16]
Circadian rhythms are 24-hour cycles of behavior and physiology that are generated
by one or more internal pacemakers and exist in all mammals. In humans, the supra-
chiasmatic nucleus (SCN) in the hypothalamus serves as the central neural pacemaker.
Humans possess a circadian rhythm of approximately 24 hours. Examples of circadian
rhythms include alternations of hormone secretion, body temperature, and sleep–wake
cycles. The 24-hour circadian rhythms are entrained by a number of environmental
stimuli (zeitgebers), such as the timing of activity, exercise, social interaction, and
the sleep–wake schedule, etc., but the predominant entraining agent is light, including
daylight and artificial light.[17,18]
A few studies have suggested a relationship between fatigue and circadian rhythms
in different types of clinical populations, while some other studies failed to identify a
relationship. Attarian et al. [19] found a significant correlation between fatigue and dis-
rupted sleep cycles in multiple sclerosis patients. Shibui et al. published a case report on
periodic fatigue due to desynchronization in a patient with non-24-hour sleep–wake
syndrome.[20] In cancer patients, CRF has been associated with a few circadian activity
rhythm (CAR) parameters, such as mesor (a circadian rhythm-adjusted mean of the
daily activity), acrophase (timing of the peak of the activity rhythm), amplitude
(peak of the activity), and disruptions of the 24-hour autocorrelation coefficient
during chemotherapy and radiotherapy.[21–23] Bower et al. [24] found that in BC sur-
vivors those with persistent fatigue showed a significantly more flattered cortisol slope
than those without. In two studies [25,26] focusing on the correlation between CAR and
quality of life in patients with metastatic colorectal cancer, the desynchronized rest–
activity rhythms prior to chemotherapy were associated with more fatigue, poor
quality of life and shorter survival; however, the relationships were only examined
cross-sectionally. On the other hand, two other studies found no association between
fatigue and the 24-hour autocorrelation coefficient in either BC survivors or other
cancer patients.[27,28]
Most of these reports explored the relationship between fatigue and circadian
rhythms during therapy or in cancer survivors and none were compared to non-
cancer controls in longitudinal studies. Therefore, in this study, utilizing a group of
innovative actigraphy-based CAR parameters,[29] we examined both CRF and CAR
before and after four cycles of chemotherapy in a group of women with BC, and com-
pared their results with those of cancer-free women. We hypothesized that women with
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BC would experience more fatigue and disrupted CAR than controls, and that there
would be a relationship between CRF and CAR.
Methods
Participants
One hundred and forty-eight women with newly diagnosed stage I–III BC receiving at
least four cycles of adjuvant or neoadjuvant chemotherapy participated in this study.
BC patients came from two studies which had identical protocols and data collection
began before the start of chemotherapy and spanned at least four cycles of treatment:
the first study focused on fatigue, sleep, and circadian rhythms (Study 1; N= 79),
and the second focused on chemotherapy-related cognitive impairments (Study 2;
N= 69). Sixty-one cancer-free women who were age- and socio-economic status
matched to BC patients in Study 2 also participated in this study as healthy controls
(see Figure 1 for the screening and enrollment processes).
Pregnant women, those undergoing bone marrow transplants, and those with meta-
static breast cancer, with confounding underlying medical illnesses, with significant
pre-existing anemia or with other physical or psychological impairments, were
excluded from both studies. The University of California Committee on Protection
of Human Subjects and the UCSD Moores Cancer Center’s Protocol Review and
Monitoring Committee approved both studies, and an informed consent was obtained
from each woman at the beginning of her participation.
Figure 1. Screening and enrollment flowchart.
14 L. Liu et al.
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Measures
Fatigue
Fatigue was assessed using the Short Form of Multidimensional Fatigue Symptom
Inventory (MFSI–SF).[30] The MFSI–SF is a 30-item questionnaire, which is com-
prised of five subscales: General Fatigue, Physical Fatigue, Emotional Fatigue,
Mental Fatigue, and Vigor. Each subscale includes six items and each item is rated
on a five-point scale indicating how true the statement was during the last week
(0 = not at all, 4 = extremely). The sum of the General, Physical, Emotional, and
Mental subscale scores minus the Vigor subscale score generates a total score. The
range of possible scores for each subscale is 0–24, and the range for total score is
from –24 to 96, with higher scores indicating severer fatigue, except for the Vigor sub-
scale, where larger scores indicate less fatigue. The MFSI–SF has been validated and is
a reliable tool for assessing the full spectrum of cancer-related fatigue symptoms in both
clinical and research applications.[31]
Circadian activity rhythm (CAR)
CAR was analyzed by fitting each subject’s activity data to a five-parameter extended
cosine model.[29] The activity data were measured with two types of actigraphs: the
Actillume-II (Ambulatory Monitoring, Inc., Ardsley, New York) for Study 1 and Acti-
watch-Light (Philips Respironics Mini Mitter, Bend, OR) for Study 2. The Actillume-II
contains a microprocessor, 32K RAM memory, and associated circuitry, and uses a
piezoelectric linear accelerometer (sensitivity <0.003 g-force) with a sampling rate of
20 Hz to measure and record wrist movement. The Actiwatch-Light is similar in size
to a watch (37 × 29 × 9 mm; weight 17 grams). It uses a piezoelectric linear acceler-
ometer (sensitivity <0.01 g-force) with a sampling rate of 32 Hz to measure and
record wrist movement. A one-minute epoch setting was used for both actigraphy
devices. The epoch-by-epoch activity data were downloaded to a desktop computer
for the further CAR analysis. To establish equivalency between the two devices, a vali-
dation study in eight volunteers was conducted with both devices worn concurrently on
the same wrist for 72-hours. The Actillume-II-derived SUMACT (summary activity)
count and the Actiwatch-Light-derived activity count data were highly correlated
(both r>0.85), and therefore deemed equivalent for the purpose of this study.
This five-parameter extended cosine model is an anti-logistic transformation of the
standard cosine curve, and it allows for estimation of parameters describing the shape of
the 24-hour rest/activity rhythm.[29] The following six CAR parameters were gener-
ated from the model in this study: the amplitude (peak or maximum of the curve,
with a lower amplitude suggesting a less rhythmic rhythm); the acrophase (time of
day of the peak activity, with a later time suggesting more phase-delay); the mesor
(half-way between minimum and maximum of the curve, a circadian rhythm-adjusted
mean of the daily activity); the time of day that the subject switched from low activity to
high activity (i.e., the time of day that activity increased from below the mesor to above
the mesor, called “up-mesor,”a later time suggesting that the subject became active
later in the day); the time of day where the subject switched from high activity to
low activity (i.e., the time of day that activity decreased from above the mesor to
below the mesor, called “down-mesor,”a later time suggesting that the subject
settled down later in the evening); and the F-statistic (the “goodness of fit”of the
fitted function with a larger value indicating more robust rhythms). In brief, datasets
Fatigue: Biomedicine, Health & Behavior 15
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that were most rhythmic or had the strongest rhythms yielded the largest F-statistic and
the smallest standard error.
Procedure
Participants were referred by oncologists in the San Diego area, and were mainly from
the UCSD Moores Cancer Center. After consent forms were signed, medical records
were abstracted for medical history and current medication use. Data reported in this
study were collected after diagnosis but before the start of chemotherapy (Baseline),
and at the end of the fourth cycle of treatment (Cycle-4).
At both data collection time points, women completed a set of questionnaires,
including MFSI–SF, wore an actigraphy for three consecutive 24-hour periods (i.e.,
72 hours), and completed a sleep log used for editing actigraphy data. For each
woman, actigraphy was recorded on the same days of the week at each time point.
The day chosen was based on the day of the chemotherapy administration. While the
ideal recording time for an actigraph is generally one week, due to potential subject
burden, the minimum of three days suggested by the AASM practice parameters for
actigraphy [32] was used in both studies.
Cancer-free controls were age and education matched to the cancer patients in
Study 2. Potential cancer-free controls were friends referred by the cancer patient or
were recruited independently by study personnel. Once consented, controls were given
the same questionnaires and wore actigraphs at the same two time points as the patients.
Data analysis
Descriptive statistics (mean, standard deviation and standard errors) were calculated for
all outcomes at both time points. T-test and Chi-square test were used to examine the
differences between the two samples of BC patients in terms of demographic and
disease/treatment characteristics. Analysis of covariance (ANOVA) was performed
between the patients and controls groups in fatigue and CAR measurements while con-
trolling for BMI and Study (1 or 2).
A mixed model analysis [33] was used to examine changes in fatigue and CAR par-
ameters from Baseline to Cycle-4 in BC patients comparing with controls, and to
examine the longitudinal relationship between fatigue and CAR parameters for BC
patients only. This modeling approach accounts for correlations in repeated measures
within a subject, and also allows for partially missing data (i.e., subjects with
missing data at some, but not all, time-points can be included in the model). A
random intercept was included in each mixed model to account for subject-specific
effects. In the mixed models examining the changes of fatigue or CAR over time,
the chemotherapy week (time), case-control status (group), and the time*group inter-
action were modeled as fixed effects. Since a few CAR parameters (amplitude,
mesor, and F-statistic) were significantly different between the two studies, the Study
(1 or 2) variable was also adjusted in these mixed models.
In those mixed models examining the longitudinal relationship between fatigue
(response) and CAR parameters (predictor), total MFSI–SF scores were the dependent
variable, and CAR parameters (amplitude, acrophase, mesor, up-mesor, down-mesor,
or F-statistic) were the independent variable. In addition to a random intercept, the coef-
ficient of the CAR variable was included as a random effect, thereby allowing for
subject-specific slope terms for CAR parameter in the model. These mixed models
16 L. Liu et al.
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were adjusted for BMI, chemotherapy week (time) and Study (1 or 2). Adjusted
regression coefficients (ß-value) with standard errors and associated p-values are
presented.
All analyses were performed using version 9.2 of SAS (SAS Institute Inc. 2008).
Given that there were seven outcomes of interest (total MFSI–SF score and six CAR
variables; the analyses of the five subscales of MFSI–SF were secondary), a Bonferroni
correction would yield p< 0.0071 as statistically significant. However, we present all
p-values in Table 2 and note which tests meet a Bonferroni adjustment.
Results
Study 1 data were collected between 2000 and 2005, and as was recommended at that
time, women in Study 1 all received three-week chemotherapy cycles. Study 2 data
were collected between 2005 and 2010, at which point the recommended treatment
regimen started to change to a two-week cycle; therefore 37 (63%) women in Study
2 received a two-week cycle regimen and 22 (37%) received a three-week cycle
regimen of chemotherapy. Since there were no significant differences between those
with two-week and those with three-week regimens in any measurements (demo-
graphics, disease/treatment, questionnaires, and CAR parameters) (all p’s > 0.05), the
different length of treatment cycles was not considered as a confounder in this study.
There were no significant differences between the two samples of BC patients in
terms of age, race, body mass index (BMI), education level, marital status, annual
household income, baseline menopausal status, cancer stage, surgery type, chemother-
apy regimen, and fatigue (all p’s > 0.05); the two samples were therefore merged
together in this study. There were also no significant differences between the merged
BC patients group and control group in age, race, education, marital status, income,
or baseline menopausal status; however, as BC patients’BMI was significantly
higher than that in controls (p= 0.04), BMI was then adjusted when comparing the
two groups. Detailed demographic and disease characteristics of all women are listed
in Table 1.
Fatigue
BC Patients experienced more fatigue than controls at both time points as well as more
fatigue at Cycle-4 than at Baseline. As shown in Figure 2, for BC patients, total MFSI–
SF scores increased significantly from Baseline to Cycle-4 (p< 0.0001), as well as the
scores of General (p< 0.0001), Physical (p= 0.0004), and Mental (p< 0.0001) sub-
scales; meanwhile, the Vigor subscale score decreased significantly from Baseline to
Cycle-4 (p= 0.03), but there were no significant changes for the Emotional subscale
scores (p= 0.2). For controls, there were no significant changes from Baseline to
Cycle-4 in any scores of MFSI–SF (all p’s > 0.1).
Compared to controls, at Baseline, BC patients reported significantly more total (p
= 0.001), Emotional (p< 0.0001), and Mental (p= 0.006) fatigue, and less Vigor (p=
0.05), but there were no significant differences in the General (p= 0.1) and Physical (p
= 0.07) subscales scores. At Cycle-4, BC patients reported significantly higher total and
General, Physical, Emotional, and Mental subscale scores and less Vigor subscale
scores of MFSI–SF (all p’s < 0.006).
When comparing the changes over time, there was a significant time*group inter-
action in MFSI–SF total (p= 0.0002), General (p< 0.0001), Physical (p= 0.02),
Fatigue: Biomedicine, Health & Behavior 17
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Mental (p= 0.0002), and Vigor (p= 0.04) scores, indicating more increases of fatigue
in BC patient than in controls.
Circadian activity rhythm (CAR)
BC Patients experienced more disrupted rhythms compared to controls at both time
points and had more disruption at Cycle-4 than at Baseline. The changes of the six
CAR parameters for BC patients vs. controls are shown in Figure 3. For BC patients,
Table 1. Demographic, disease and treatment characteristics of the participants (n= 148).
Variable Study 1 (n= 79) Study 2 (n= 69) Controls (n= 61)
Age (years)
Mean (SD) 50.7 (9.8) 51.3 (9.0) 52 (9.4)
Range 34–79 31–76 29–81
BMI (kg/m
2
)
Mean (SD) 28.8 (6.9) 27.5 (7.4) 26.0 (7.0)
Range 17.4–51.8 19.3–61.9 19.1–56.7
Race [n(%)]
Caucasian 60 (76.0) 61 (88.4) 52 (85.3)
Non-Caucasian 19 (24.0) 8 (11.6) 9 (14.7)
Education [n(%)]
Some college and below 40 (50.6) 32 (46.4) 22 (36.1)
Completed college and above 39 (46.4) 37 (53.6) 39 (63.9)
Marital status [n(%)]
Never married 8 (10.1) 3 (4.4) 7 (11.5)
Divorced/separated/widowed 15 (19.0) 19 (27.5) 12 (19.7)
Married 56 (70.9) 47 (68.1) 42 (68.8)
Household annual income [n(%)]
≤$30,000 11 (13.9) 11 (15.9) 2 (3.3)
> $30,000 58 (73.4) 49 (71.0) 50 (82.0)
Refused to answer 10 (12.7) 9 (13.1) 9 (14.7)
Baseline menopausal status [n(%)]
Pre-menopause 32 (42.7) 28 (40.6) 21 (35.0)
Peri-menopause 7 (9.3) 9 (13.0) 8 (13.3)
Post-menopause 21 (28.0) 27 (39.1) 22 (36.7)
Hysterectomy 4 (20.0) 5 (7.3) 9 (15.0)
Not available 4 0 1
Cancer stage [n(%)]
Stage I 22 (30.6) 15 (25.9) –
Stage II 36 (50.0) 24 (41.4)
Stage III 14 (19.4) 19 (32.8)
Not available 7 11
Surgery type
Lumpectomy 27 (37.5) 24 (41.4) –
Mastectomy 32 (44.4) 28 (48.3)
Double mastectomy 4 (5.6) 3 (5.2)
No surgery before Chemotherapy 9 (12.5) 3 (5.2)
Not available 7 11
Chemotherapy regimen [n(%)]
AC based 62 (86.1) 47 (78.3) –
Others 10 (13.9) 13 (21.7)
Not available 7 9
Note: AC = Doxorubicin + Cyclophosphamide.
18 L. Liu et al.
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Figure 2. Comparisons of total and subscale (General, Physical, Mental, Emotional, and
Vigor) scores of the Multidimensional Fatigue Symptom Inventory–Short Form (MFSI–SF)
between BC patients and controls over time (mean + SE).
Notes: Higher scores indicate more fatigue, except Vigor subscale, where a higher score
indicates less fatigue. Baseline = before the start of chemotherapy, Cycle-4 = at the end of
cycle 4 of chemotherapy. Compared to controls at the same time point: *p< 0.05, **p< 0.01,
***p< 0.0001; compared to Baseline for the same group:
†
p< 0.05,
††
p< 0.01,
†††
p< 0.0001.
Fatigue: Biomedicine, Health & Behavior 19
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from Baseline to Cycle-4, the amplitude (p= 0.005), mesor (p= 0.005), and F-statistic
(p= 0.03) decreased significantly, and the up-mesor time delayed significantly ( p=
0.04); for controls, there were no significant changes between the two time points in
any of the six parameters (all p’s > 0.3).
Compared to controls, BC patients showed significantly lower amplitude and
smaller F-statistic at Baseline (both p= 0.01), and lower amplitude (p= 0.0004),
lower mesor (p= 0.02), and smaller F-statistic ( p< 0.0001) at Cycle-4, suggesting dis-
rupted CAR. There were no significant differences between the two groups in acro-
phase, up-mesor, or down-mesor.
When comparing the changes over time, there was a significant time*group inter-
action in the amplitude (p= 0.05) and mesor (p= 0.03), indicating more disruptions
in CAR in BC patients than in controls.
Associations between fatigue and CAR in BC patients
For BC patients only, using total MFSI–SF score as the dependent variable and CAR
parameters as the independent variable, after adjusting for time and Study, mixed model
analyses revealed that the changes of total MFSI–SF scores were significantly associ-
ated with the changes of amplitude, mesor, and F-statistic (all p’s < 0.01). Specifically,
women with lower amplitude, smaller mesor, and smaller F-statistics experienced more
fatigue, suggesting that more fatigue was associated with more disrupted CAR (see
Table 2).
Discussion
This study confirmed that BC patients not only reported significantly more CRF than
cancer-free controls, both before the start of and after four cycles of chemotherapy,
but they also reported significantly more fatigue after compared to prior to treatment.
BC patients’CAR showed the same pattern as fatigue, with disrupted CAR present
prior to the start of chemotherapy and becoming worse after treatment. Furthermore,
increased fatigue was significantly associated with disrupted CAR.
Compared to cancer-free controls, BC patients reported significantly worse scores
in total and all five subscale scores of the MFSI–SF, both before and after chemother-
apy, except for the General and Physical scores at Baseline, where the difference
between the two groups was not significant. These results indicate that, before che-
motherapy, BC patients were already mentally and emotionally fatigued and experi-
enced lack of energy. Since the General subscale includes six questions asking about
the general feeling of fatigue, such as “I feel tired,”“I feel fatigued,”and “Iam
pooped,”which do not refer to any cancer-specified field of fatigue, this subscale
cannot discriminate BC patients from controls before the start of chemotherapy. As
the other scales distinguished the two groups, asking questions about general fatigue
in cancer patients may not be enough to identify CRF in this population. After four
cycles of chemotherapy, BC patients experienced more CRF in all five domains of
fatigue compared to controls, as well as experiencing more fatigue than at Baseline,
suggesting that chemotherapy is a significant contributor to fatigue in cancer patients.
When CAR was examined, BC patients demonstrated significantly lower amplitude
and smaller F-statistic than controls at both time points. BC patients’amplitude also
significantly decreased after four cycles of chemotherapy. In addition, BC patients’
up-mesor time was significantly delayed after chemotherapy. Lower amplitude
20 L. Liu et al.
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Figure 3. Comparisons of circadian activity rhythm measures (amplitude, acrophase, mesor,
up-mesor, down-mesor, and F-statistic) between BC patients and controls over time (mean +
SE).
Notes: Smaller values of amplitude and F-statistic indicate disrupted circadian activity rhythm;
later times of acrophase, up-mesor, and down-mesor indicate delayed circadian activity rhythms.
Baseline = before the start of chemotherapy, Cycle-4 = at the end of cycle 4 of chemotherapy.
Compared to controls at the same time point: * p< 0.05, ** p< 0.01, *** p< 0.0001; compared
to Baseline for the same group:
†
p< 0.05,
††
p< 0.01,
†††
p< 0.0001.
Fatigue: Biomedicine, Health & Behavior 21
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generally indicates less movement during the day and/or more movement during the
night. Delayed up-mesor time plus stable down-mesor time suggest BC patients’
active time during the day decreased after chemotherapy. Together with the decreased
F-statistic, these data indicate that BC patients experienced disrupted CAR before the
start of chemotherapy, and this disrupted CAR became more disrupted after four cycles
of chemotherapy. These results confirm our previously reported findings which were
based on data from Study 1.[34]
Mixed model analysis revealed that BC patients experienced more increases of CRF
(total and four out of five subscale scores on MFSI–SF) and more disruptions of CAR
(amplitude, mesor) from Baseline to after four cycles of chemotherapy than controls,
and the increased CRF was significantly associated with disrupted CAR, i.e., the
increased fatigue was significantly associated with decreases in amplitude, mesor,
and F-statistic. This relationship suggests that more fatigue was predicted by more dis-
rupted CAR in women with BC undergoing chemotherapy, or vice versa. Although a
few studies have identified a relationship between fatigue and CAR before [21,25,26]
or during/after cancer treatments,[22–24] the current study is the first to report findings
of increased CRF and disturbed CAR in women with BC compared to cancer-free
women, and a significant relationship between CRF and CAR both before and after
chemotherapy.
Another strength of this study is that a group of innovative CAR parameters was
used. This group of CAR parameters was derived from an extended cosine model;
[29] the curve generated by this model has a more rectangular-like wave shape than
the regular cosine curve, and thus has a better fit to the pattern of daily activity
rhythm recorded by actigraphy, i.e., nearly flattened and steady activity levels during
both the day and night. These CAR parameters have been utilized in other studies of
BC which showed that CAR gets progressively more desynchronized during che-
motherapy [34] and that bright light treatment improved CAR.[35,36]
The current study, along with previous findings, suggests that there is a significant
relationship between CRF and circadian rhythms, especially CAR. CRF and circadian
rhythms may share some common potential mechanisms, or a cause-and-effect relation-
ship may exist. Sleep problems are common among cancer patients [37] and these sleep
Table 2. Mixed model results for BC patients with fatigue as the dependent variable and
circadian activity rhythm parameters as the independent variable.
Mixed model results
Fatigue Circadian activity rhythms Adj. ß-value Standard error p-value
Total MFSI–SF score Amplitude –11.834 3.033 0.0002
Acrophase 0.476 0.887 0.6
Mesor –14.257 5.244 0.006
Up-mesor 0.307 0.738 0.7
Down-mesor 0.308 0.696 0.7
F-statistic –0.00652 0.00225 0.005
Notes: Adjusted for BMI, time and Study. MFSI–SF = Multidimensional Fatigue Symptom Inventory–Short Form,
higher scores indicate more fatigue. The p-values in bold meet the Bonferroni adjustment of p< 0.0071. Amplitude
was the maximum activity of the day, acrophase was the time of the peak activity of the day, mesor was the mean
activity, up-mesor or down-mesor was the time when the activity rose from below the average to above the average,
or from above the average to below the average, and F-statistic measured the overall rhythmicity of circadian
activity –a smaller F-statistic indicates less robust or disrupted circadian activity rhythm.
22 L. Liu et al.
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problems are associated and may also share some underlining mechanisms with fatigue.
[38] Our current and previously published data confirm that, in addition to disturbed
sleep both before and during chemotherapy in BC patients,[1,39,40] CAR is disrupted
before the start of chemotherapy and becomes more disrupted during treatment.[34]
The disruptions in circadian rhythms can affect sleep quality and thus disrupt a
variety of physiological mechanisms pertaining to fatigue.[13] It is possible therefore
that the circadian rhythms disruption plays a role in the psychological experience of
fatigue.[41] However, on the other hand, both CRF and CAR could be consequences
of cancer treatment. Chemotherapy has been reported to be a major cause of CRF,
and chemotherapy-induced side effects (nausea, dizziness, dyspnea, etc.), or changes
of daily routine of activity, might also contributed to alternations of CAR. Therefore,
as concluded in a recent review by Payne,[42] few studies have focused on the relation-
ship between fatigue and circadian rhythms, and the exact interacting mechanisms,
including possible mediators and neurotransmitter mechanisms, still remain
unknown. Further studies are needed to answer these questions.
Light therapy has been shown to be effective for the treatment of circadian-rhythms-
related disorders such as delayed and advanced sleep phase syndromes, jet lag syn-
drome, shift work syndrome, even fatigue symptoms, by synchronizing circadian
rhythms.[43–45] Preliminary data from our laboratory also showed that bright light
prevented fatigue and CAR from deteriorating in women with BC undergoing che-
motherapy.[36,46] Since light is the predominant entraining agent for circadian
rhythms, these findings support the possible cause-and-effect relationship between
circadian rhythms and fatigue, although studies with larger sample sizes and among
other clinical populations are needed to confirm the effect of light therapy on fatigue.
This study had some limitations. The data were collected only in women with stage
I to stage III BC, so conclusions cannot be extended to patients with other stages of BC
or with other types of cancer. Patients and controls were all volunteers and were not
randomly selected; thus there might have been some sample selection bias which
could limit the generalizability of the study.
Only Study 2 had one year post-chemotherapy follow-up data (not reported here),
so the long-term relationship between fatigue and CAR after completing the treatment
could not be determined by this study. Circadian rhythms were only measured using
actigraphy; other measurements of circadian rhythms, such as salivary melatonin and
core body temperature, also need to be studied regarding the relationships between
fatigue and circadian rhythms in cancer patients.
In summary, BC patients experienced more fatigue and disrupted CAR than
cancer-free controls both before and after four cycles of chemotherapy; this fatigue
and disruption of CAR became worse after four cycles compared to pre-chemotherapy
in BC patients. In addition, the increase of fatigue was significantly associated with
disruption in CAR. Fatigue may be caused by disturbed circadian rhythms, or vice
versa. Additional studies examining the effect of light therapy on fatigue among
cancer patients through the possible mechanism of re-entraining circadian rhythms
are warranted.
Acknowledgements
Supported by NCI CA112035, UL1RR031980 (CTRI), the UCSD Stein Institute for Research
on Aging and the Department of Veterans Affairs Center of Excellence for Stress and Mental
Health (CESAMH).
Fatigue: Biomedicine, Health & Behavior 23
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Notes on contributors
Lianqi Liu, M.D., is an Associate Project Scientist in the Department of Psychiatry, University
of California, San Diego.
Michelle Rissling is Postdoctoral Fellow Sierra-Pacific MIRECC, VA Palo Alto Health Care
System, Department of Psychiatry and Behavioral Sciences, Stanford University School of
Medicine.
Ariel Neikrug, M.A., is a graduate student in the SDSU/UCSD Joint Doctoral Program in
Clinical Psychology, San Diego, Ca.
Lavinia Fiorentino, Ph.D., is an Assistant Clinical Professor in the Department of Psychiatry,
University of California, San Diego and the UCSD Moores Cancer Center, La Jolla, California.
Loki Natarajan, Ph.D., is a Professor in the Department of Family and Preventive Medicine,
University of California, San Diego.
Michelle Faierman is a medical student at Ohio State University College of Medicine.
Georgia Robins Sadler, Ph.D., is a Professor in the Department of Surgery, University of
California, San Diego.
Joel E. Dimsdale, M.D. is Professor Emeritus at the Department of Psychiatry, University of
California, San Diego.
Paul J. Mills, Ph.D., is a Professor in the Department of Psychiatry, University of California,
San Diego.
Barbara A. Parker, M.D., is a Clinical Professor in the Department of Medicine, University of
California, San Diego.
Sonia Ancoli-Israel, Ph.D., is a Professor Emeritus of Psychiatry and Medicine, University of
California, San Diego.
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