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Chronobiology International
The Journal of Biological and Medical Rhythm Research
ISSN: 0742-0528 (Print) 1525-6073 (Online) Journal homepage: http://www.tandfonline.com/loi/icbi20
Seasonal effects on the sleep–wake cycle, the
rest–activity rhythm and quality of life for
Japanese and Thai older people
Yu Kume, Sachiko Makabe, Naruemol Singha-Dong, Patama Vajamun,
Hataichanok Apikomonkon & Jiranan Griffiths
To cite this article: Yu Kume, Sachiko Makabe, Naruemol Singha-Dong, Patama Vajamun,
Hataichanok Apikomonkon & Jiranan Griffiths (2017): Seasonal effects on the sleep–wake cycle,
the rest–activity rhythm and quality of life for Japanese and Thai older people, Chronobiology
International, DOI: 10.1080/07420528.2017.1372468
To link to this article: http://dx.doi.org/10.1080/07420528.2017.1372468
Published online: 24 Oct 2017.
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Date: 25 October 2017, At: 14:23
CHRONOBIOLOGY INTERNATIONAL
https://doi.org/10.1080/07420528.2017.1372468
Seasonal effects on the sleep–wake cycle, the rest–activity rhythm and quality of
life for Japanese and Thai older people
Yu Kume a, Sachiko Makabeb, Naruemol Singha-Dongc, Patama Vajamunc, Hataichanok Apikomonkond,
and Jiranan Griffithsd
Department of Occupational Therapy, Akita University, Graduate School of Health Sciences, Akita, Japan; bDepartment of Clinical Nursing,
Akita University, Graduate School of Health Sciences, Akita, Japan; cInstitute of Nursing, Suranaree University of Technology, Nakhon
Ratchasima, Thailand; dDepartment of Occupational Therapy, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai,
Thailand
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a
ABSTRACT
ARTICLE HISTORY
Background: The sleep-wake cycle and the rest–activity rhythm are known to change with aging, and
such changes have been implicated in higher levels of depression as well as an increased incidence of
dementia. However, information supporting seasonal changes in the sleep–wake cycle, the rest–activity
rhythm and quality of life in older community-dwelling people remains insufficient. The aim of the
present study was to prospectively investigate seasonal effects on the sleep–wake cycle, the rest–activity
rhythm and quality of life among older people living in areas of Japan or Thailand with different climate
classifications.
Method: The survey was conducted from March 2016 to May 2017, and 109 participants were recruited
from Japan and Thailand: 47 older people living in Akita prefecture, Japan, and 62 older people living in
Chiang Mai or Nakhon Ratchasima, Thailand. According to the Köppen–Geiger classification of Asian
climates comprising tropical, desert, steppe, temperate and subarctic climates, Akita prefecture, which is
located in northern Japan, is classified as a humid subarctic climate, while the Thai study areas are
classified as tropical savanna. To monitor parameters of the sleep–wake cycle during nighttime (e.g. total
sleep time, sleep latency, sleep efficiency, awaking time and frequency of sleep interruptions) and to
calculate parameters of the rest–activity rhythm over the 24 h profile (e.g., interdaily stability, intradaily
variability, relative amplitude, mean of least active 5 h period and mean of most active 10 h period), all
the participants from both countries wore an Actiwatch 2 device on their nondominant wrist continuously for 7 days during each local season. The World Health Organization Quality of Life
Questionnaire-BREF (WHOQOL-BREF) was also assessed during each local season.
Results: The final sample size was 37 older people living in Akita prefecture, Japan, and 44 older people
living in Thailand; these subjects completed the data collections during each local season. The dropout
rates were 21% in Japan and 29% in Thailand. The results for the Japanese subjects showed a
significantly shorter sleep time with higher levels of activity during the nighttime on summer
(p < 0.001) and a fragmented rest–activity rhythm over the 24 h profile on winter (p < 0.001). The
older Thai participants exhibited a poor state of night sleeping year-round, and a significant relationship
was observed between seasonal variations in motor activity and the social domain of WHOQOL-BREF for
each Thai season (|r| = 0.4, p < 0.01).
Conclusion: These findings provide new and important information regarding seasonal effects on the
sleep–wake cycle, the rest–activity rhythm and quality of life in older community-dwelling people
living in two different Asian climates. Consequently, clinical preventions targeting such seasonal
variations might be useful for improving the quality of life of older Japanese and Thai individuals.
Received 18 June 2017
Revised 22 August 2017
Accepted 23 August 2017
Introduction
The incidence of sleep disorders is higher among
older people than among younger adults (AncoliIsrael 2009; Cooke and Ancoli-Israel 2011), and
sleep disorders have been implicated in higher levels
of depression and poorer cognitive function, in lifestyle and social environment factors, and in an
KEYWORDS
Rest–activity rhythm;
sleep–wake cycle; seasonal
effect; quality of life;
actigraphy
increased incidence of dementia (Cross et al. 2015;
Kume et al. 2016; Luik et al. 2013). External stimuli,
including light exposure and physical and social
activities, have substantial effects on the sleep–wake
cycle in older people (Grandin et al. 2006;
Gumenyuk et al. 2012; Mauvieux et al. 2007). One
such environmental factor is the inexorable passage
of the seasons. Seasonal changes are a considerable
CONTACT Kume Yu
kume.yuu@hs.akita-u.ac.jp
Graduate School of Medicine, Doctorial Course in Health Sciences, Department of Occupational
Therapy, Akita University, Assistant Professor, Ph. D., Hondo 1-1-1 Akita, JAPAN 010-8543.
© 2017 Taylor & Francis Group, LLC
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2
Y. KUME ET AL.
factor to manage the health of older adults throughout the year, and some investigators have proposed a
correlation between seasonal variations and sleep
duration (Yetish et al. 2015), the quality of the
sleep–wake cycle (Honma et al. 1992; OkamotoMizuno and Tsuzuki 2010) and changes in physical
activity in older people (Goodwin et al. 2001).
Since sleep disorders in older people have been
investigated using actigraphy, the use of actigraphy is
suitable for large population-based studies or
long-term epidemiological surveys and also allows
the evaluation of two different rhythms: the
sleep–wake cycle from applying algorithms on
rest–activity data (Ancoli-Israel et al., 2003) and the
rest–activity rhythm by nonparametric analysis
(Luik et al. 2013). In contrast, polysomnography
examinations are typically performed over the
course of one night while the subject sleeps in a
laboratory (Jean-Louis et al. 2001; Kushida et al.
2001). Interestingly, in a nonparametric analysis
using actigraphy data obtained over multiple days,
Luik et al. (2013) have reported that a more fragmented distribution of activity and inactivity over
24 h periods was a characteristic of the rest–activity
rhythm in older persons and was significantly correlated with the severity of depressive symptoms. In a
previous report, we showed that the fragmentation
of the rest–activity rhythm by nonparametric analysis over multiple days (e.g. for 7 days) can be characterized for older people with dementia in a nursing
home (Kume et al. 2016). In addition, a nonparametric method developed by Van Someren et al.
(1999) has a high sensitivity for the detection of
changes in the rest–activity rhythm based on motor
activity in older adults (Huang et al. 2002) and is
currently being promoted as a research methodology
for a wide range of diseases including Alzheimer
disease (Harper et al. 2004), schizophrenia (Berle
et al., 2010) and brain stroke (Goncalves et al.
2014). However, as few studies have examined seasonal changes in the rest–activity rhythm in older
people, the seasonal effects on the rest–activity
rhythm and quality of life (QOL) in older people
are not fully understood. Thus, we hypothesized
that the summer and winter seasons in Japan and
the rainy season in Thailand might have characteristic effects on measurements of the sleep–wake
cycle, the rest–activity rhythm and QOL.
The objective of the present study was to investigate seasonal changes in the sleep–wake cycle, the
rest–activity rhythm and QOL of older people
prospectively over the course of an entire year. The
seasonal effects on the sleep–wake cycle, the
rest–activity rhythm and QOL were examined prospectively in older volunteers living in Northern
Japan and Thailand, which were identified as areas
representative of humid subarctic and tropical
savanna climates, respectively, according to the
Köppen–Geiger climate classification (Kottek et al.
2006; Rubel and Kottek 2010). The confirmation of
international differences in the sleep–wake cycle, the
rest–activity rhythm and QOL among older people
living in Japan and Thailand could potentially suggest educational or rehabilitative targets specific to
different Asian environments (e.g. differences in air
temperature, humidity and snowfall depth).
Method
Participants
For participants in the present study, sample size
calculations were based on the G*Power (Faul et al.
2009, 2007). According to the G*Power for repeated
analysis of variance measurements, a sample of 36
participants per group to detect a clinically relevant
effect with α = 0.05, power = 95% and effect size = 0.25
was estimated in Japan, and the sample size in
Thailand was 43 participants per group. To allow for
a dropout of approximately 25%, the sample size was
47 people in Akita prefecture, Japan and 62 people in
Thailand which include 32 people in Chiang Mai
province and 30 people in Nakhon Ratchasima
Province, Thailand. For each local area, the inclusion
criteria for participants were as follow: (1) a fully
independent level of activities of daily living, (2) fairly
healthy without any organic central nervous system
disorder, substance-related disorder or mental retardation, (3) no use of a cane or a wheelchair for
transportation because of physical impairments and
(4) no use benzodiazepine for sleep disturbances
throughout the survey’s period. This survey was
approved by the ethics committee of Faculty of
Medicine, Akita University (approval number1316)
and was performed in accordance with the
Declaration of Helsinki II.
CHRONOBIOLOGY INTERNATIONAL
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Procedure
The survey was conducted from March in 2016 to
May in 2017. The data collections were performed
through four seasons in Akita prefecture, Japan (e.g.
spring, summer, autumn and winter), or through
three seasons (e.g. rainy, winter and summer) in
Chiang Mai and Nakhon Ratchasima, Thailand.
The measurements in Japan were carried out in
spring from March to May in 2016, in summer
from June to August in 2016, in autumn from
September to November in 2016 and in winter
from December in 2016 to February in 2017. In
parallel, the Thai side performed the measurements
in rainy season from the middle of May to the middle
of October in 2016, in winter season from the middle
of October in 2016 to the middle of February in 2017
and in summer season from the middle of February
to the middle of May in 2017.
As the background of each local region collected
data in this study, Akita prefecture is located in the
northern Japan (AKITA, latitude 39°43.0´ N and
longitude 140°5.9´ E), classified a subarctic zone
humid climate (Kottek et al. 2006; Rubel and Kottek
2010). On the other hand, Thailand belongs to a
savanna climate based on the Köppen–Geiger climate
classification (Kottek et al. 2006; Rubel and Kottek
2010), located in the tropical area between latitude 5°
37´ N to 20°27´ N and longitude 97°22´ E to 105°37´
E. From the meteorological point of view, the climate
of Thailand is divided into three seasons as follows:
rainy or southwest monsoon season from mid-May to
mid-October, winter or northeast monsoon season
from mid-October to mid-February, and summer or
pre-monsoon season from mid-February to mid-May
(The Thai Meteorological Department 2017). Table 1
lists the meteorological data during the survey’s period reported by the Japan Meteorological Agency
(2017) and the Thai Meteorological Department
(2017).
After obtaining informed consent from all the participants, the demographic data was firstly collected,
including age, gender, body mass index (BMI), the
duration of education, timed up and go test (TUG),
mini mental state examination (MMSE), single residence, occupation, alcohol intake and smoking.
Actiwatch 2 (Philips Respironics, Inc., Murrysville,
PA, USA) worn on the participants’ nondominant
wrists for continuous seven days without removal
3
Table 1. The meteorological data during the survey’s period in
Japan and Thailand.
Akita Seasonal mean of air
temperature (°C)
Seasonal mean of
precipitation (mm)
Seasonal mean of
relative humidity (%)
Seasonal mean of
snowfall depth (cm)
Seasonal total sunshine
duration (h)
Chiang Mai
Spring Summer Autumn Winter
10.8
23.2
14.2
1.9
130.3
158.8
152.3
162.7
69.0
77.0
75.7
79.3
9.7
0
1.7
89.7
123.7
54.8
186.4
Seasonal mean of air
temperature (°C)
Seasonal mean of
precipitation (mm)
Nakhon
Seasonal mean of air
Ratchasima temperature (°C)
Seasonal mean of
precipitation (mm)
197.1
Rainy Winter Summer
27.7 24.8
29.3
177.0
34.7
26.6
28.5
26.3
29.3
207.5
11.0
154.7
The meteorological data in Akita prefecture, Japan, was reported by
the Japan Meteorological Agency in May, 2017.
The meteorological data in Chiang Mai province (Northern region) and
Nakhon Ratchasima province (Northeast region), Thailand, was
reported by the Thai Meteorological Department in May 2017.
The Thai Meteorological Department has not reported the seasonal
relative humidity (%) in 2016 and 2017. The reference data of
seasonal relative humidity (%) based on 1981 to 2010 period was
81% in the rainy, 74% in the winter and 63% in the summer of the
Northern region, and 80% in the rainy, 69% in the winter and 66% in
the summer of the Northeast region.
was used to collect objective measurements of the
sleep–wake cycle and the rest–activity rhythm for
each local season. The WHO Quality of Life-BREF
(WHOQOL-BREF) (WHO, 1996) was also used to
collect subjective measurements of the QOL during
each local season.
Outcome
Actiwatch 2 was used to estimate the sleep–wake cycle
throughout the night with the rest–activity rhythm
over the 24 h profile. The sleep–wake cycle during
the night was analyzed using Actiware version 6.09
(Philips Respironics, Inc., Murrysville, PA, USA),
including the total sleep time (min), the sleep latency
(min), the sleep efficiency (percent), the awake time
(min) and the frequency of a wake status (time)
throughout the night. The nonparametric analysis of
the rest–activity rhythm was performed using the data
of activity counts (AC) for Actiwatch 2. Variables of
the nonparametric method include interdaily stability
(IS), intradaily variability (IV) and relative amplitude
4
Y. KUME ET AL.
(RA) (Van Someren et al. 1999; Witting et al. 1990).
Recently, Goncalves et al. (2014) have reported an
update of nonparametric methods. However, since a
few reports are available on the updated methods, the
present study applied original methods of Van
Someren et al. (1999). The IS, which provides information about the rest–activity rhythm synchronization to supposedly stable environmental stimuli, is
calculated from the mean 24 h profile as the following
formula:
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IS ¼
Pp
xh xÞ2
h¼1 ð
P
p ni¼1 ðxi xÞ2
n
The IV yields information about the fragmentation
in the rest–activity rhythm over the 24 h profile,
calculated from hourly raw data. The fragmentation
estimated by IV may be derived from a change in
the rest–activity rhythm, such as a presence of a
diurnal nap or a nocturnal awakening and a failure
to maintain levels of an adequate activity over the
24 h profile. The formula for IV is as follows:
P
n ni¼2 ðxi xi1 Þ2
IV ¼
P
ðn 1Þ ni¼1 ðxi xÞ2
In equation of IS or IV, n is the total number of data,
p is the number of data entries per day, xh is the
hourly average, x is the average of all the data and xi
represents each hour of raw data. The IS ranges from
0 for Gaussian noise, which consisted of normal
distribution noise, to 1 for a perfect synchronization.
The IV measure ranges from 0 to 2, whose higher
value indicates a more fragmented rhythm. The RA
was calculated using the data of the most active 10 h
period (M10) (i.e. the level of activity during the
daytime) and the least active 5 h period (L5) (i.e.
the level of activity during the nighttime) obtained
from the average 24 h profile. The RA measure varies
between 0 for a lower amplitude and 1 for a higher
amplitude.
RA ¼
M10 L5
M10 þ L5
For an analysis of the WHOQOL-BREF, four
domains (e.g. physical, psychological, social relations
and environment) were then scored and transformed
to the 0–100 scale using the follow formula (The
World Health Organization (WHO) 1996):
Transformed Scale ¼
ðActual raw score Lowest possible raw scoreÞ 100
Possible raw score range
Statistical analysis
As the statistical analysis, the Friedman test was
applied to compare the median of measurements of
the sleep–wake cycle, the rest–activity rhythm and
the QOL among seasons in Japan and in Thailand
using the adjusted p value of a multiple comparison
test for post-hoc tests. After a difference (Δ) between
maximum and minimum values of each parameter
of the rest–activity rhythm throughout Japanese or
Thai seasons was calculated, the Mann–Whitney test
was carried out to compare seasonal variations
between countries. Further, the Spearman correlation analysis was applied to analyze the relationship
between the seasonal variations of the rest–activity
rhythm and QOL measurements for each season in
Japan or Thailand. SPSS version 21.0 for Windows
(SPSS Inc., Chicago, IL, USA) was used for the
analysis, and the level of significance was set at
p = 0.05.
Results
The final sample size completed data collections by
Actiwatch 2 through all seasons was 37 older people
in Akita prefecture, Japan, and 20 older people in
Chiang Mai and 24 older people in Nakhon
Ratchasima, Thailand. Age, gender and BMI were
not significantly different between Japanese and Thai
groups, but the duration of education (Japanese/
Thai: 12.0 ± 2.4 years/7.1 ± 4.3 years), TUG
(6.9 ± 1.4 s/12.8 ± 2.9 s) and MMSE (27.1 ± 2.1
scores/22.2 ± 4.7 scores) were significantly different
between the groups (p < 0.0001) (see Table 2).
As shown in Table 3, Japanese participants had a
significant difference of total sleep time throughout
the night (χ2(3) = 37.2, p < 0.001), the fragmented
rhythm over the 24 h profile as the IV indexed
(χ2(3) = 14.2, p = 0.003), the relative amplitude
between rest and active status as indexed according
to the RA (χ2(3) = 19.4, p < 0.001) and the L5 values
(χ2(3) = 25.8, p < 0.001) among the seasons.
CHRONOBIOLOGY INTERNATIONAL
Table 2. Demographics of participants in Japan and Thailand
(mean ± SD).
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N
Age (years)
Gender (% female)
Height (m)
Weight (kg)
BMI (kg/m2)
TUG (s)
MMSE (score)
Education (years)
Single residence (% yes)
Occupation (% yes)
Alcohol intake (% yes)
Smoking (% yes)
Japan
37
74.3 ± 6.3
59.5%
1.56 ± 0.08
60.1 ± 7.5
24.8 ± 3.0
6.9 ± 1.4
27.1 ± 2.1
12.0 ± 2.4
21.6%
13.5%
56.8%
8.1%
Thailand
t
p Value
44
72.9 ± 6.2
1.0
0.34
75.0%
0.14
1.54 ± 0.08
0.8
0.42
55.7 ± 10.0
2.2
0.03
23.3 ± 4.0
2.0
0.05
12.8 ± 2.9 −11.7 <0.0001
22.2 ± 4.7
6.2 <0.0001
7.1 ± 4.3
6.5 <0.0001
0%
0.001
54.5%
0.002
6.8%
<0.0001
2.3%
0.33
Age, height, weight, BMI, TUG, MMSE and education show the p value
of the unpaired t test between groups.
Gender, single residence, occupation, alcohol intake and smoking show
the p value of the χ2 test.
BMI: body mass index; TUG: timed up and go test; MMSE: mini mental
state examination.
According to a multiple comparison test, the summer
season had significantly the shortest sleep times and
the highest L5 values in all of Japanese seasons, and
the winter season had significant higher IV values
than the other seasons. Additionally, the psychological domain of the WHOQOL-BREF differed significantly among the seasons with having no significance
according a multiple comparison test (see Table 4).
Table 5 lists results of the sleep–wake cycle
throughout the night and the rest–activity rhythm
over the 24 h profile in Thailand. Thai participants
had a significance on total sleep time (χ2(2) = 6.7,
p = 0.04). According to a multiple comparison test,
the summer season had a shorter sleep time during
the night than the winter season in Thailand. Table 6
also shows that the psychological domain of the QOL
5
measurement had a significant difference among Thai
seasons with no differences regarding a multiple comparison test.
Figure 1 illustrates a box plot of the comparison of
the Δ rest–activity rhythm parameters between Japan
and Thailand, and included a significant difference for
the nonparametric unpaired t test. The Mann–
Whitney test showed that a variation of fragmentation
(e.g. ΔIV) in Japanese participants was significantly
higher than that in Thailand (p < 0.05), whereas ΔL5
in Japanese individuals was significantly lower than
that in Thailand (p < 0.05). Table 7 displays the
Spearman correlation analysis of the Δ rest–activity
rhythm parameters (e.g. ΔIS, ΔIV, ΔRA, ΔL5 and
ΔM10) and the QOL measurements for each season
in Japan and Thailand. In univariate analysis, the ΔRA
in Japanese participants was correlated with physical
domain in the spring (r = 0.35, p < 0.05), social
domain in the summer (r = 0.35, p < 0.05) and
environmental domain in the winter (r = 0.41,
p < 0.05) and the ΔRA in Thai side was correlated
with psychological domain in the winter (r = −0.40,
p < 0.05). Additionally, the ΔM10 in Thai side was
significantly correlated with social domain of all seasons (rainy; r = −0.42, p < 0.01, winter; r = −0.45,
p < 0.01, summer; r = −0.41, p < 0.01), having a
significant correlation with psychological (r = −0.40,
p < 0.05) and environmental domain (r = −0.32,
p < 0.05) in the rainy.
Discussion
In this survey, we examined seasonal effects on the
sleep–wake cycle, the rest–activity rhythm and QOL of
Table 3. The comparison of the sleep–wake cycle throughout the night and the rest–activity rhythm over the 24 h profile in the
participants (N = 37) among Japanese four seasons.
Spring
Summer
Autumn
Winter
Median
Quartile
Median
Quartile
Median
Quartile
Median
Quartile
Total sleep Time (min)
434.4
79.1**
371.4
65.5** †† ‡‡‡
414.4
97.8†† §
435.2
122.5‡‡‡ §
Sleep latency (min)
10.8
21.8
11.7
15.8
7.2
18.1
10.7
18.5
Sleep efficiency (%)
83.9
9.9
83.0
11.8
83.9
7.8
84.0
9.9
Awaking time (min)
54.0
35.6
53.3
41.6
50.0
36.5
57.0
37.2
Awaking frequency (time)
24.8
16.2
24.2
14.4
23.3
14.2
25.8
14.5
IS
0.61
0.14
0.59
0.15
0.60
0.12
0.61
0.13
0.87
0.33‡‡
0.98
0.31* † ‡‡
IV
0.87
0.31*
0.89
0.18†
RA
0.91
0.07**
0.85
0.08**
0.91
0.08
0.90
0.09
852.9
718.8‡
925.4
636.3††
L5 (counts)
796.6
633.9*** 1242.2
626.9*** †† ‡
M10 (counts)
18215.7 8007.1
17143.7 8472.6
19058.0 7272.3
17605.0 10256.3
χ2 (3)
37.2
4.8
3.45
1.1
3.1
1.3
14.2
19.4
25.8
1.5
p Value
<0.001
0.19
0.33
0.78
0.38
0.72
0.003
<0.001
<0.001
0.67
*p < 0.05, **p < 0.01, ***p < 0.001; †p < 0.05, ††p < 0.01; ‡p < 0.05, ‡‡p < 0.01, ‡‡‡p < 0.001; §p < 0.05; the adjusted p value of a multiple
comparison test for post-hoc tests.
IS: interdaily stability; IV: intradaily variability; RA: relative amplitude; L5: the least active 5 h period obtained from the average 24 h profile; M10: the
most active 10 h period obtained from the average 24 h profile.
Table 3 indicates χ2 (degree of freedom) and p value of the Friedman test among seasons.
6
Y. KUME ET AL.
Table 4. The comparison of WHOQOL-BREF measurements in the participants among four Japanese four seasons.
Spring
Physical domain (score: 0–100)
Psychological domain (score: 0–100)
Social domain (score: 0–100)
Environmental domain (score: 0–100)
n
35
37
36
35
Median
67.9
62.5
66.7
59.4
Summer
Quartile
14.3
16.6
14.6
15.7
Median
67.9
54.2
66.7
59.4
Autumn
Quartile
17.9
12.5
8.4
18.7
Median
64.3
54.2
66.7
56.3
Winter
Quartile
14.3
20.9
8.4
15.6
Median
64.3
58.3
66.7
56.3
Quartile
17.9
16.7
8.4
15.6
χ2 (3)
4.6
9.2
2.3
5.4
p Value
0.20
0.03
0.51
0.15
Table 4 indicates χ2 (degree of freedom) and p value of the Friedman test among seasons.
Table 5. The comparison of the sleep–wake cycle throughout the night and the rest–activity rhythm over the 24 h profile in the
participants (N = 44) among Thai three seasons.
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Rainy
Total sleep time (min)
Sleep latency (min)
Sleep efficiency (%)
Awaking time (min)
Awaking frequency (time)
IS
IV
RA
L5 (counts)
M10 (counts)
Median
371.5
10.3
82.2
50.5
22.8
0.60
0.81
0.85
1459.6
18135.8
Winter
Quartile
76.8
12.9
7.9
40.5
11.8
0.18
0.32
0.13
1099.4
6849.6
Median
388.2
9.6
82.2
62.8
26.8
0.60
0.78
0.88
1273.1
19410.3
Summer
Quartile
104.6*
11.9
11.7
60.9
10.4
0.21
0.23
0.09
838.9
8032.8
Median
351.4
11.0
82.2
59.7
25.5
0.66
0.78
0.88
1462.2
21960.4
Quartile
62.5*
10.2
9.7
34.4
10.2
0.15
0.27
0.10
1206.0
11706.1
χ2 (2)
6.7
0.1
1.0
4.9
2.2
0.7
0.2
3.3
2.0
5.1
p Value
0.04
0.98
0.62
0.09
0.33
0.72
0.89
0.20
0.38
0.08
*p < 0.05; the adjusted p value of a multiple comparison test for post-hoc tests.
IS: interdaily stability; IV: intradaily variability; RA: relative amplitude; L5: the least active 5 h period obtained from the average 24 h profile.
Table 5 indicates χ2 (degree of freedom) and p value of the Friedman test among seasons.
Table 6. The comparison of WHOQOL-BREF measurements in the participants among Thai three seasons.
Rainy
Physical domain (score: 0–100)
Psychological domain (score: 0–100)
Social domain (score: 0–100)
Environmental domain (score: 0–100)
n
40
40
40
40
Median
58.9
54.2
58.3
61.0
Quartile
14.3
12.5
31.2
14.8
Winter
Median
53.6
54.2
66.7
56.3
Quartile
10.7
8.3
25.0
12.5
Summer
Median
55.4
58.3
66.7
57.9
Quartile
10.7
8.3
25.0
12.5
χ2 (2)
6.0
6.4
0.8
4.7
p Value
0.05
0.04
0.67
0.09
Table 6 indicates χ2 (degree of freedom) and p value of the Friedman test among seasons.
older adults living in specific areas of Japan and
Thailand. The results of this study indicated that
Japanese older people had significantly shorter total
sleep times and higher L5 values over the 24 h profile
during the summer and a more fragmented rest–
activity rhythm over the 24 h profile, as shown by
higher IV values, during the winter, compared with
other seasons in Japan. In contrast, the Thai participants hardly showed any differences in the sleep–wake
cycle or the rest–activity rhythm parameters according
to season, with a significant difference in total sleep
time only observed between winter and summer.
At first, the results regarding nighttime sleep during summer and winter agreed with those of
Okamoto-Mizuno and Tsuzuki’s report (2010), since
the older subjects had a significantly shorter total sleep
time (mean ± SE: 350.8 ± 15.7 min) in summer than
in fall (403.2 ± 16.4 min) or winter (426.5 ± 14.2 min),
with a sleep time of under the mean of 7 h a night
(Ancoli-Israel 2009). However, no differences on
sleep efficiency were observed among seasons in the
results showed in Table 3, and this result disagreed
with Okamoto-Mizuno’s report that sleep efficiency
significantly decreased in the summer (81.4 ± 2.9%)
compared with in the fall (90.2 ± 1.2%) or winter
(91.6 ± 1.3%) (Okamoto-Mizuno and Tsuzuki
2010). In addition, our results for sleep efficiency
suggest relatively low values throughout the seasons
compared with the 85% cut-off value for poor sleepers
(Lacks and Morin 1992). We believe that among older
subjects living in Northern Japan the total sleep time
during the night might be shorter during summer
than during other seasons.
Our results of the rest–activity rhythm also support
the hypothesis that older people are easy to have a
fragmented rest–activity rhythm over the 24 h profile
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CHRONOBIOLOGY INTERNATIONAL
7
Figure 1. The comparison of a difference (Δ) of the rest–activity rhythm parameters between Japan and Thailand.
during winter in Japan. For the participants who were
monitored throughout the Japanese seasons, the fragmentation score during winter (e.g. median IV ±
interquartile range (IQR): 0.98 ± 0.31) was under the
1.10 cut-off value for IV (Kodama et al. 2016).
Although few reports are available regarding seasonal
characteristics examined using nonparametric methods, one possibility is that the fragmented rest–activity
rhythm over the 24 h profile might be higher during
the winter, as possibly supported by the Nakanojo
study in which older adults exhibited relatively low
step counts and physical activity amounts under or
over 3 metabolic equivalents (METs) as measured
using an accelerometer during winter, with values
peaking in spring or autumn (Yasunaga et al. 2008).
As shown in Figure 1, the greater seasonal variation
(i.e. ΔIV) in IV values in Japanese participants also
suggests that the fragmented rest–activity rhythm
over the 24 h profile should be carefully observed in
Japanese older people during winter. Furthermore,
since the L5 values over the 24 h profile generally
mean the levels of activity during the nighttime, our
results suggest that the levels of activity during the
nighttime are higher during summer, and the higher
values of L5 on summer may be characterized in
Japanese older people living in a community.
Though a paucity of material is available on our findings, some studies have reported that an increase in L5
values may express a marked trait of the sleep–wake
cycle in older adults (Huang et al. 2002) and a state of
night sleeping with frequent sleep interruptions
(Goncalves et al. 2015). Thus, these findings suggest
the importance of monitoring the levels of activity
during nighttime as higher values of L5 were indicated
in Japanese older people, with parameters of the
sleep–wake cycle.
In disagreement with our hypothesis, only seasonal
variations in total sleep time were observed in the
8
Y. KUME ET AL.
Table 7. The correlation between seasonal variations (Δ) of the rest–activity rhythm parameters and WHOQOL-BREF measurements
among Japanese and Thai seasons.
Japan (N = 35)
Physical domain
Psychological domain
Social domain
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Environmental domain
Season
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
ΔIS
.033
.199
.143
.029
.092
−.174
−.004
−.061
−.057
−.071
−.075
−.278
−.049
−.150
−.083
−.012
ΔIV
−.036
.074
.156
.014
.151
−.020
.196
.134
.002
−.116
.124
−.049
.313
−.011
.113
.319
ΔRA
.353*
.184
.095
.316
.235
.152
.176
.138
.218
.346*
.240
.258
.294
.212
.212
.414*
Thailand (N = 40)
ΔL5
.195
−.072
.158
.120
.101
.098
.038
.064
.226
.159
.248
.160
.071
.022
.126
.183
ΔM10
.017
.132
.165
.123
.284
.278
.177
.255
−.115
−.013
−.012
−.126
.178
−.120
.119
.022
Season
Rainy
Winter
Summer
ΔIS
.017
−.067
−.085
ΔIV
−.102
.011
−.071
ΔRA
.178
−.118
−.148
ΔL5
.127
.185
−.013
ΔM10
−.023
.016
−.070
Rainy
Winter
Summer
−.085
−.114
−.136
−.027
−.033
−.162
.255
−.397*
−.247
.144
−.231
.033
−.361*
−.145
−.267
Rainy
Winter
Summer
−.020
−.304
−.272
−.260
−.197
−.299
.049
−.304
−.179
−.015
−.096
−.073
−.423**
−.447**
−.410**
Rainy
Winter
Summer
−.162
−.208
−.142
−.053
.066
.105
.089
−.257
−.183
.003
−.047
.074
−.321*
−.241
−.249
*p < 0.05, **p < 0.01; statistics represent Spearman r correlations for each domain of QOL measurements and a difference of the rest–activity
rhythm parameters.
ΔIS: a difference between maximum and minimum of interdaily stability values among seasons; ΔIV: a difference between maximum and minimum
of intradaily variability values among seasons; ΔRA: a difference between maximum and minimum of relative amplitude values among seasons;
ΔL5: a difference between maximum and minimum of the most active 10 h period’s values among seasons; ΔM10: a difference between maximum
and minimum of the least active 5 h period’s values among seasons.
Thai participants, including a shorter sleep time during summer (median ± IQR: 351.4 ± 62.5 min) than in
winter (388.2 ± 104.6 min). The total sleep times of
the Thai participants during all the seasons were also
under the cut-off value of a mean of 7 h per night
(Ancoli-Israel 2009), resulting in a relatively shorter
sleep time than that of the Japanese participants.
Furthermore, our findings for sleep efficiency in the
Thai participants agreed with the prevalence of
insomnia (Assantachai et al. 2011; Sukying et al.
2003) and the appearance of poor sleep quality
(Sunuttra et al. 2008) in a questionnaire survey for
older Thai population, with a low rate of sleep efficiency below the border range (e.g. under 85% cut-off
value) for insomnia sufferers reported by Lacks and
Morin (1992). However, no direct comparisons of
sleep–wake cycles in older Thai adults have been
made. Therefore, our findings suggest that older
Thai people experience relatively short sleep times
and low sleep efficiency year-round. Regarding the
results for the rest–activity rhythm parameters in Thai
participants, although seasonal variations were not
well documented in this study, the L5 values of the
Thai participants were relatively higher than those of
the Japanese subjects. The increase in L5 values in
Thai participants may express higher levels of activity
during the nighttime and a sleep state with frequent
sleep interruptions (Goncalves et al. 2015; Van
Someren et al. 1999). Considering the increasing evidence that a high movement-activity level during the
nighttime are apparent in older Thai adults, as shown
in Figure 1, a greater variation in L5 values across the
Thai seasons suggests that higher levels of activity
during the night can be characterized as a seasonal
effect in older Thai adults.
Finally, significant differences in QOL measurements across the seasons were observed for a psychological domain in the Japanese (see Table 4) and Thai
participants (see Table 6), although the results of a
multiple comparison test were not significant. Our
results partially support the view that the seasonal
environment (e.g. increasing or decreasing air temperatures and light exposures) is associated with
changes in mood (Palinkas et al. 2000) and physical
or psychological fatigue (Fujii et al. 2015). According
to the correlation analyses shown in Table 7, seasonal
variations in the rest–activity rhythm parameters
across Japanese or Thai seasons were significantly
associated with several domains of QOL measurements in the participants in each country. Most interestingly, the ΔM10 of the rest–activity rhythm
parameters was negatively correlated with a social
domain for each season among the Thai participants
(e.g. rainy: r = −0.42, p < 0.01; winter: r = −0.45,
p < 0.01; summer: r = −0.41, p < 0.01), indicating
seasonal variations in motor activity and motor
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CHRONOBIOLOGY INTERNATIONAL
capacity. To date, available information supports the
hypothesis that lower M10 values are likely to be
correlated with motor difficulty or a reduction in
exercise (Carvalho-Bos et al. 2007), while higher
M10 values are likely to be associated with better
QOL (Goncalves et al. 2015). These findings suggest
that seasonal variations in motor capacity (e.g. ΔM10)
are potentially associated with the quality of social
activities year-round in older Thai people.
In conclusion, this study provides new and important information concerning seasonal effects on the
sleep–wake cycle, the rest–activity rhythm and QOL
in older community-dwelling people living in Japan
or Thailand. Our findings suggest that for older people living in Japan short sleep times and higher levels
of activity during the nighttime on summer are notable among parameters of the sleep–wake cycle and the
rest–activity rhythm, with a fragmented rest–activity
rhythm over the 24 h profile observed during winter.
On the other hand, older Thai people should pay
attention to a state of night sleeping year-round as
well as the relationship between seasonal variations in
motor activity over the 24 h profile (e.g. ΔM10) and
QOL based on social activities. This information
obtained through nonparametric analyses of the
rest–activity rhythm should help older people to
understand their current statuses as they relate to
the seasonal rest–activity rhythm and QOL.
Professionals also need to provide individual advice
based on an individual’s lifestyle and should provide
educational or rehabilitative programs to improve the
rest–activity rhythm and psychological status.
Moreover, information on international differences
could help older people worldwide to become more
aware of the different environments existing in Asian
countries (e.g. before considering migration or a prolonged stay).
Limitations of study
The absence of detailed information on the amount
of daylight and on lifestyles might be a potential
limitation of this study since modern light exposure
patterns (e.g. exposure to natural and/or electric
light) can affect sleep and other daily rhythms in
physiology and behavior (Wright et al. 2013). The
sample size in this study was also determined based
on sample size estimations using the G*Power (Faul
et al. 2009, 2007), but the samples were somewhat
9
biased with a female preponderance observed in
both countries. In addition, the significant difference
in general cognitive function between the countries
might have been influenced by potential educational
differences. These limitations were considered when
interpreting the present study findings. Interestingly,
our results indicated that the physical function status
of the Japanese participants (mean TUG ± SD:
6.9 ± 1.4 s) was significantly higher than that of the
Thai participants (12.8 ± 2.9 s), whereas the M10
values (i.e. motor activity during daytime) for each
season in Japan were lower than those in Thailand.
The actimetry worn on the participants’ nondominant wrist to measure the rest–activity rhythm
detects all wrist movements and records the accumulated ACs at 1 min intervals. These conditions
raise the possibility that older Thai people perform
physical activities or jobs that involve more upperlimb motions than lower-limb motions compared
with older Japanese individuals. However, this
point remains a matter of speculation. Further
research taking lifestyle differences between countries into consideration is needed.
Acknowledgements
The authors were supported by the staffs at Akita University,
Chiang Mai University and Suranaree University of
Technology for their assistance in performing measurements.
Mr. Tanaka S and Ms. Tanyalak Y from Bangkok branch office
at HOKUTO bank arranged to firstly meet researchers among
three universities. We wish to thank all the participants from
Daisen area, Itagawa-Shimoabukawa area, Kamiiwakawa area
in Akita prefecture, Japan and all the participants in Chiang
Mai and Nakhon Ratchasima, Thailand. In addition, for the
present study, the user license of the WHOQOL-BREF was
approved by the World Health Organization (WHO).
Description of authors’ roles
All of the authors designed the present study together. We
also contributed to data collections, data interpretation, writing and revision of the report.
Funding
This research was supported by the Grant-in-Aid for
Scientific Research (C) (No. 26463444, 2014-2018) in Japan.
This sponsor played no role in the research other than funding the research.
10
Y. KUME ET AL.
ORCID
Yu Kume
http://orcid.org/0000-0003-3318-1082
Downloaded by [UAE University] at 14:23 25 October 2017
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