The 13th International Conference on Mobile Computing and Ubiquitous Network (ICMU2021)

Statistical Analysis between Sleep Status andOccupational Health Indicators for Detecting
Depression Signs in Healthy Workers
Shuichi Fukuda∗ , Yuki Matsuda∗ , Yutaka Arakawa† , Keiichi Yasumoto∗∗ Nara Institute of Science and Technology, Nara, Japan, Email: {fukuda.shuichi.fs5, yukimat, yasumoto}@is.naist.jp.
† Kyushu University, Fukuoka, Japan, Email: arakawa@ait.kyushu-u.ac.jp.

Abstract—Occupational health issues such as depressions havebecome a serious problem in recent years. There is an urgent
necessity for the early preventive detection of depression rather
than a yearly survey. Toward detecting depression signs, thispaper statistically analyzes the relationship between occupational
health indicators collected by questionnaires and sleep status(sleeping condition and heart rate data) during bedtime measured
by a common wearable device.
Index Terms—Sleep, wearable device, occupational health,depression, DAMS.

I. I NTRODUCTION
To ascertain the mental condition of employees, questionnaires such as Depression and Anxiety Mood Scale (DAMS) [1]are commonly used. DAMS consists of three scales: depression, positive, and anxiety. However, these questionnaires
are qualitative, and also it is difﬁcult to measure a person’scondition constantly because of the time required to answer
the questionnaire.
Our study aims to realize the early detection of depression signs by using a common wearable device to monitor
and assess the daily mental condition of ofﬁce workers. In
this paper, we especially focus on various sleep status data
during bedtime, such as stages of sleeping condition, heart
rate variability (HRV), and analyze the relationship between
occupational health indicators and these data.
Existing research has reported the differences in sleep
structure, e.g., sleep stage duration, the ratio of each sleep
stage, between healthy people and patients with psychiatricdisorders [2], [3]. Nutt et al. conducted a statistical analysis of
REM sleep status (e.g., frequency, duration, time interval) via
polysomnography on patients with depression symptoms [4].
The results showed that patients with depressive symptoms
had increased REM sleep duration and increased REM density (frequency of rapid eye movements per REM period)
compared to the healthy group. However, it requires speciﬁc
equipment and facilities to measure sleep status, which is notsuitable for monitoring daily life. They mainly target subjects
with severe mental ill. To the best of our knowledge, there is no
study that focuses on detecting depression signs for preventive
medical care targeting healthy workers.
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II. A NALYSIS OF S LEEP S TATUS AND O CCUPATIONAL
H EALTH I NDICATORSThe dataset which has been collected from 60 ofﬁce workers
in four Japanese companies over 2–3 weeks by Tani et al. [5]is used for analysis in this paper. It includes data of occupational health indicators (DAMS) collected by questionnaires
and sleep status (sleeping conditions and heart rate data)
during bedtime measured by a common wearable device,
Fitbit Charge 3.
Fitbit can detect four stages of sleep (awake state, REM
sleep, light sleep, and deep sleep) with the sampling frequencyof 1 Hz. Also, the heart rate data can be measured every threeseconds. Based on these data, R-R Interval (RRI) is calculated
60
× 1000, and HRV iswith the formula, RRI = HeartRateData
calculated using those values. The sampling frequency of Fitbit
is insufﬁcient to calculate the index in a frequency-domain,
so only a time-domain HRV is used in this paper. Moreover,
we derived the area of Lorenz Plot (ALP) which has recently
attracted attention as an indicator for understanding a state ofa parasympathetic nervous system as well as HRV [6].
The purpose of this study is to detect the signs of daily
depression in healthy workers, hence, we investigate whether
there are differences in sleep status explained above between
healthy workers and slightly depressed workers. Here, we
divided subjects into two groups “high” and “low” by medianvalues of each DAMS score for statistical analysis.
The procedure of the statistical test is following. First, weevaluated whether the distribution is normal by a Shapiro-Wilk
test at a signiﬁcance level of 5% for each of two high and low
feature groups. Next, the difference between the high and lowgroups was statistically analyzed by using Mann-Whitney’s U
test (in case of a non-normal distribution), Welch’s t-test (ifa case of a normal distribution). A two-sided test is used andthe signiﬁcance level is set as 1%.
At ﬁrst, statistical analysis results with sleep status data areshown in Table I. p-values with bold text represent that featureshows a signiﬁcant difference. We found the duration, number,and ratio of the REM sleep stage have a signiﬁcant difference
between the high and low groups in the depression and anxiety
scales. The duration and the ratio of REM sleep stages among
the high depression group are less than those of the low

The 13th International Conference on Mobile Computing and Ubiquitous Network (ICMU2021)

TABLE IS TATISTICAL ANALYSIS RESULT WITH SLEEP STATUS DATA
p-value of statistical test
Depression Positive Anxiety
0.277
0.013
0.033
Duration of whole sleep
deep sleep stages
0.001
0.046
0.002
light sleep stages
0.053
0.672
0.172
a
REM sleep stages
0.002
0.001
0.000aawake stages
0.506
0.973
0.810
deep sleep stages0.424a
0.104
0.013
Number oflight sleep stages
0.502
0.370
0.421
REM sleep stages
0.000
0.031
0.002
awake stages
0.019
0.204
0.051
Ratio ofdeep sleep stages
0.000a0.139a0.001alight sleep stages
0.000
0.022
0.000
REM sleep stages
0.000
0.024
0.000
awake stages
0.607
0.082
0.026
Ratio for one hour after sleep onset ofdeep sleep stages
0.000
0.056
0.000
light sleep stages
0.002
0.036
0.000
REM sleep stages
0.289
0.714
0.569
awake stages
0.000
0.288
0.671
Time untilﬁrst deep sleep
0.000
0.169
0.000
ﬁrst light sleep
0.615
0.007
0.489
ﬁrst REM sleep
0.033
0.805
0.042
ﬁrst awake
0.588
0.015
0.037
Features

TABLE IIS TATISTICAL ANALYSIS RESULT WITH HRV DATA DURING BEDTIME
Part

WSa

Stage

-Deep
FSb
REM

Deep

a Normal distribution by Shapiro-Wilk test (p > 0.05).

LSc

depression group, and the number of REM sleep stages is 1.3
lower on average. When focusing on data for one hour after
sleep onset, ratio of awake stages has signiﬁcant differences inthe depression and anxiety scales. Also, the time until falling
into ﬁrst deep and light sleep also has signiﬁcant differences inthe three DAMS scales. They suggest the situation that people
have trouble falling asleep might strengthen the tendencyof depression. This relationship between REM sleep anddepression is consistent with the report of Pesonena et al. [3].
Secondlly, statistical analysis results with sleep status data
are shown in Table II. p-values with bold text represent thatfeature shows a signiﬁcant difference. SDNN and RMSSDrepresent the standard deviation of the RRI, and the root meansquare of the successive RRI differences respectively. Thesefeatures indicate an intensity of a parasympathetic nervous
system, with higher values indicating a relaxed state. Notethat the “Part” column means the data range which is used
for calculation (see footnotes of Table II). We found statisticalsigniﬁcance for features of ALP, SDNN and RMSSD between
the high and low groups of the depression scale in all dataranges. Regarding anxiety scale, some of HRV data at the ﬁrst
deep sleep and last REM sleep are signiﬁcantly different.
The results suggest that sleep status during bedtime measured by Fitbit can be used as an indicator to assess thedepression sign, as in previous studies with costly devices.
However, we also found the difference of positive scale does
not give much effect to sleep status, except the duration of theREM sleep stage and time until falling into ﬁrst light sleep.
III. C ONCLUSION
In this paper, we have conducted the statistical testing ofdifference in sleep status during bedtime between high androw groups of each DAMS scale. The ﬁndings in this paper
show the possibility that signs of depression and anxiety can be

REMFeatures
Mean (HR)SD (HR)
Mean (RRI)
SDNN (RRI)RMSSD (RRI)
ALP
Mean (HR)SD (HR)
Mean (RRI)
SDNN (RRI)RMSSD (RRI)
ALP
Mean (HR)SD (HR)
Mean (RRI)
SDNN (RRI)RMSSD (RRI)
ALP
Mean (HR)SD (HR)
Mean (RRI)
SDNN (RRI)RMSSD (RRI)
ALP
Mean (HR)SD (HR)
Mean (RRI)
SDNN (RRI)RMSSD (RRI)
ALP

p-value of statistical test
Depression Positive Anxiety
0.028
0.354
0.1350.566
0.423
0.342
0.025
0.364
0.127
0.004
0.884
0.022
0.000
0.444
0.107
0.000
0.557
0.036
0.606
0.089
0.987
0.066
0.109
0.000
0.614
0.094
0.974
0.014
0.355
0.000
0.131
0.534
0.407
0.019
0.688
0.001
0.045
0.382
0.332
0.076
0.683
0.086
0.041
0.384
0.314
0.005
0.522
0.041
0.001
0.171
0.026
0.001
0.317
0.033
0.076
0.110
0.024
0.125
0.333
0.571
0.077
0.070d
0.025d
0.001
0.887
0.073
0.004
0.199
0.219
0.0010.654
0.084
0.011
0.535
0.045
0.019
0.700
0.452
0.003d
0.414d
0.041d
0.000
0.665
0.040
0.000
0.468
0.005
0.000
0.554
0.010

a HRV data for the whole sleep.
b HRV data for the ﬁrst sleep stage after falling asleep.
c HRV data for the last sleep stage before waking up.
d Normal distribution by Shapiro-Wilk test (p > 0.05).

detected using daily sleep status data measured by a common
wearable device. In future work, we will build a model for
anomaly detection of depression and anxiety situations, andevaluate it.
ACKNOWLEDGMENT
This work was partially supported by JSPS KAKENHI
Grant Number JP18H03233.
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