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Sensors and Materials, Vol. 34, No. 8 (2022) 2929–2954
MYU Tokyo
S & M 3015

Analysis and Visualization of Relationship between Stress
and Care Activities toward Reduction in Caregiver Workload†
Atsushi Miyaji,1 Yuta Kimata,1 Tomokazu Matsui,1
Manato Fujimoto,2* and Keiichi Yasumoto1
1Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
2Osaka Metropolitan University, Osaka, Osaka 558-8585, Japan(Received May 18, 2022; accepted July 15, 2022)

Keywords: caregiver, stress level, objective stress, subjective stress

In Japan, the demand for nursing homes is increasing owing to the rapid aging of the
population, and the shortage of caregivers has become a serious problem. This problem has beenrecognized as a social issue because it increases the workload per caregiver. In response, wehave been developing a platform that can easily collect data on nursing care activities to reduce
the workload on the basis of the idea that the mental state (i.e., stress) of caregivers, whichchanges during care activities, might markedly affect their work efficiency. The objective of thisstudy was to obtain new knowledge for reducing the workload of caregivers by visualizing andanalyzing their stress. Specifically, we asked caregivers to wear the device, from which we
obtained the objective stress indicators of R–R interval (RRI) and low frequency (LF)/highfrequency (HF) ratio. We also obtained subjective stress indicators from questionnaires
administered before the start of the experiment, before work, during breaks, and after work. To
confirm that the stress level of caregivers depends on their care activity, work shift, and workday,
we conducted an empirical experiment in an actual nursing home. We distributed devices andapplications to five caregivers, obtained measurement data for a total of 28 days, and analyzed
and visualized the changes in psychological states related to care activities, work shift, andworkdays. As a result, we were able to collect data on the psychological state for 5 to 15 days per
caregiver. Furthermore, the analysis of the data showed that, even though objective stressindicators and subjective evaluations did not necessarily coincide, stress tended to increase withspecific care activities, work shift, and workdays. In addition, the subjective and objective stressindicators may change depending on the personality of the caregivers themselves.

1.

Introduction

The aging of the population is a major policy issue in Japan. As of October 2020, 28.8% of
the population were aged 65 and above, the highest level among developed countries, as reportedby the Cabinet Office.(2) Under these circumstances, the demand for nursing homes has been
increasing in recent years, in which the presence of caregivers is essential. On the other hand, if*Corresponding author: e-mail: manato@omu.ac.jp†The part of this paper has already been presented at AWASNʼ21.(1)https://doi.org/10.18494/SAM3972

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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the population continues to age, the supply of long-term care services may not be able to keep up
with the shortage of workers. According to a survey released by the Ministry of Health, Labour,and Welfare in 2015, there will be a shortage of approximately 377000 nursing care workers by
2025. The shortage of caregivers is expected to sharply increase the workload per caregiver,which is a serious problem in nursing homes, impacting on their safety and quality. Therefore, toreduce the workload of caregivers, services are required to make caregiving tasks more efficient.
We have been attempting to solve the above problems with information and communication
technology (ICT)(3,4) We consider that changes in caregivers’ mental state (i.e., stress) might
have a significant impact on their workload. Therefore, this study focuses on changes in stresscaused by caregiving work with the aim of improving caregiving work efficiency. If suchchanges in stress can be estimated, they could be applied to an objective review of the caregivers
themselves, a review of care work plans, and the promotion of activities to refresh caregivers.
Many methods have been proposed to measure stress, such as using questionnaires.(5,6)
However, these methods place a high burden on respondents owing to the large number ofquestions. Thus, it is desirable to be able to analyze and estimate data from biological indicators.
Studies on estimating stress utilizing biological data have been conducted in many situations,such as in offices and daily life.(7,8) Although these studies can estimate stress on a daily basis,
they do not focus on specific activities. Also, these methods based on biological indicators do not
focus on the level of stress that the participants felt on each task.
In this paper, we focus on caregiver stress and aim to obtain new knowledge to reduce theworkload of caregivers by visualizing and analyzing changes in their stress indicators related tocare activities. As a first step, we conducted a measurement experiment using a heart-rate sensor
and questionnaires on caregivers at a nursing home in order to confirm the relationship between
care activity and stress among caregivers.(1) The results of the analysis showed that stress tended
to increase with specific care activities, confirming several relationships between care activities
and stress. In addition, from the interview at the nursing home, we received an opinion thatchanges in stress may be caused by differences in work shift (day shift, night shift, and so on),in the number of patients cared for, and in the personalities of the individual caregivers. On thebasis of this opinion, we reviewed the data measurement method and aimed to obtain further
findings on changes in care activities and stress states. To accomplish this, we conducted anexperiment for 28 days with five caregivers wearing a heart-rate sensor in cooperation with
Senior Villa Jure Hashimoto, a residential fee-based nursing home in Hashimoto, Wakayama,
Japan. Caregivers are asked to wear a heart-rate sensor, which measured objective stressindicators such as R–R interval (RRI) and low frequency (LF)/high frequency (HF) ratio. In
addition, questionnaires were administered before the start of the experiment, before work,
during breaks, and after work to measure the caregivers’ subjective stress indicators. The
analysis of the collected data showed that, even though objective stress indicators and subjective
stress indicators did not necessarily coincide, stress tended to increase with specific care
activities, work shift, and workdays. Also, we found that the subjective and objective stressindicators may change depending on the personality of the caregivers themselves.

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The rest of this paper is organized as follows. The existing work related to our research isprovided in Sect. 2. Section 3 explains objective and subjective stress indicators to measure
biological data associated with stress. Section 4 describes a system for measuring caregivers’
stress at work by collecting biological indicators. Section 5 describes the experiment conductedin an actual nursing home. Finally, Sect. 6 concludes this paper.

2.

Related WorkIn this section, we introduce existing studies on stress estimation using questionnaires anddevices, and then describe some related studies conducted in the nursing care field. Finally, weclarify the position of our study with respect to the existing studies.
2.1

Stress estimation by questionnaire

Since questionnaires can easily collect and quantitatively evaluate individual stress, various
questionnaires have been developed in Japan and abroad in recent years. These questionnaires
encourage the confirmation and self-control of one’s own stress. The Ministry of Health, Labour,and Welfare developed the Brief Occupational Stress Questionnaire in an attempt to prevent
mental disorders among workers. This questionnaire can be used to regularly assess stressconditions to reinforce the primary prevention of mental health problems among workers. Stresshas also been suggested to cause a decline in the quality of life (QoL). The World HealthOrganization (WHO) has developed questionnaires such as WHOQOL-100(5) and SF-36(6) toquantitatively assess QoL. However, these questionnaires have a very large number of questions(54 questions for the Brief Occupational Stress Questionnaire, 100 questions for WHOQOL-100,
and 36 questions for SF-36), and if the questionnaire is administered every day, the respondents
will be overburdened. Also, these questionnaires are conducted sporadically, meaning thatchanges in stress conditions cannot be captured in a timely manner.
2.2

Stress estimation by devices

With the improvement of sensing and analysis technologies, studies have been conducted invarious situations and environments to estimate and analyze stress by collecting biometric
information using devices. Fukuda et al.(7) constructed a machine learning model to predict
three levels of mood (depression, positivity, and anxiety) obtained from the Depression andAnxiety Mood Scale (DAMS) questionnaire at the time of waking for office workers in acompany using sleep data acquired from a wearable device. Garcia-Ceja et al.(8) classified
subjects’ stress into three levels using accelerometer data from smartphones and the Oldenburg
Burnout Inventory,(9) which was conducted three times a day. The sensors in these studies werein the subjects’ smartphones and also in wearable devices and fitness trackers, and their range ofapplications is expanding.
Heart-rate variability, another type of biometric information, can be measured non-invasively
with small sensors and wearable devices, and is used to analyze mental stress. Matsui et al.(10)2932

Sensors and Materials, Vol. 34, No. 8 (2022)

visualized differences in stress by collecting psychological indicators related to activities ofdaily living (ADLs) to understand the QoL of residents during daily life in their homes. Cinaz etal.(11) classified mental workload levels in an office scenario by modeling heart-rate data frommobile electrocardiogram (ECG) loggers and by subjective assessment using the NASA-TLX
workload index. Muaremi et al.(12) built a machine learning model to estimate daily and longterm stress for individuals using voice, physical activity, and communication data acquired fromsmartphones during the day and heart-rate variability acquired from a chest-belt heart-rate
sensor during nighttime sleep. Adler et al.(13) built a system to find indicators of stress resilience(adaptability to stress) using step count, sleep, heart rate, and ecological momentary assessments
collected from wearable devices. They also proposed a method of predicting future stressresilience from the collected data. Thus, relating heart-rate variability to daily activity may leadto the identification of stressful behaviors.
Some studies have also analyzed the relationship between personality and stress by
incorporating indicators related to individual personality.(14,15) For instance, Kallio et al.(15)assessed employee stress and productivity from environmental data from schools and offices
using machine learning, and evaluated the relationship with personality traits using the Big Five
personality traits. The results revealed that people with extroverted personalities are more likely
to be stressed by poor environments and are more sensitive to environmental factors whenstressed.
2.3

Research in nursing care field

With the improvement of ICT and devices, there has been much research in recent years on
utilizing these technologies in the nursing care field. In particular, the creation of nursing carerecords is considered to be the most time-consuming task in nursing care work.(16,17) For thisreason, much effort is being made to reduce the burden of nursing care record creation. Morita etal.(3) proposed a semi-automatic system for estimating the location of elderly people on the basisof the received signal strength index (RSSI) emitted from a BLE beacon and creating a record ofcaregiving activities by arranging the location data in time series. Wada et al.(4) developed amobile memo system that can easily record detailed items (e.g., diet and excretion status)
associated with caregiving behavior, in addition to time-series information on caregiving
behavior. Inoue et al.(18) developed an application system that simultaneously collects sensor
data from smartphones owned by caregivers and caregiver behavior labels recorded bycaregivers. The system is expected to automatically generate nursing care records throughbehavior recognition and significantly shorten the time required to create nursing care records.
Studies have also been conducted on monitoring elderly people. Postawka and Rudy(19)developed a system that recognizes posture and behavior based on skeletal coordinates obtained
from Kinect, and identifies dangerous activity. Abdulbaqi et al.(20) developed a system thatwirelessly measures biometric information, such as the body temperature and pulse rate of apatient, and sends an SMS to the patient when any parameter reaches a value of concern.

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2.4

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Position of our study

Most of the studies on stress estimation mentioned in Sects. 2.1–2.3 focus mainly on theworkplace and daily life. Although using the methods in these studies, stress can be estimated at
specific times, they have not yet been able to estimate stress according to the activity, which is
the objective of this study. In this paper, we propose a method that enables us to collect data
related to stress from caregivers working in an actual nursing home using a heart-rate sensor andto visualize and analyze in detail changes in stress during caregivers’ work and the relationshipbetween care activity and stress. The method used in this study may be applied to an objective
review of the caregivers themselves, a review of care work plans, and the promotion of strategies
to reduce the stress of caregivers. Thus, in Sect. 3, we first describe methods of measuring
biological indicators associated with stress used in our study. Then, in Sect. 4, we describe asystem for measuring caregivers’ stress at work by collecting biological indicators.

3.

Methods of Measuring Biological Indicators Associated with Stress

In this section, we describe methods of measuring biological indicators associated withstress. Specifically, we explain the objective and subjective stress indicators used to measure themental state of caregivers during care activity.
3.1

Objective stress indicators

We first describe the objective stress indicators used in our study. Heart-rate variability is atypical biological indicator for measuring objective stress. It is calculated from the periodicallyfluctuating heartbeat interval, and various methods of analyzing heart-rate variability exist,
focusing on the time and frequency domains. The RRI calculated by time-domain analysis
represents the interval of the R wave, the characteristic wave of the heartbeat. The standard
deviation of N–N intervals (SDNN), calculated from the standard deviation of the RRI, is anindex of parasympathetic activity, and a higher value indicates a more relaxed state. As shown in
Fig. 1, the Lorenz plot,(21) which plots the RRI on a 2D orthogonal graph and represents
fluctuations in heart rate, is also used as an indicator for estimating stress. Here, the RRI at apoint is plotted on the x-axis and the RRI one beat later on the y-axis, and all the plotted points
are projected onto axes corresponding to the y = x and y = −x axes. After plotting, the standard

Fig. 1.

(Color online) Lorenz plot.

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deviation of the distance from the origin on the y = x axis is defined as σ x and that on the y = −xaxis is defined as σ−x. The distribution of the Lorenz plot is taken as an ellipse whose area S isgiven by
S = π × σ x× σ−x.

(1)

S represents the fluctuation of the heartbeat and indicates whether the parasympathetic
nervous system is dominant. S tends to be small during stress and large during relaxation. This
indicator is also used in various situations to estimate stress.
The LF/HF ratio, calculated from the frequency conversion of heart-rate variability into a
power spectrum, is also used to determine the state of stress. The frequency-converted heart-rate
variability is divided into an LF component representing the low frequency range (0.04 to 0.15
Hz) and an HF component representing the high frequency range (0.15 to 0.40 Hz). The LF/HF
ratio is the power ratio of the LF and HF components. During relaxation, the parasympathetic
nervous system is activated, and both the LF and HF components increase, but during stress, the
LF component increases, while the HF component decreases. Therefore, the LF/HF ratio ishigher during stress than during relaxation and can therefore be used to determine the stressstate.
3.2

Subjective stress indicators

Next, we describe the subjective stress indicators used in our study. Some questionnaires areused to assess subjective human stress. This section describes the questionnaires utilized tomeasure workers’ stress state and mental workload.
Depression and Anxiety Mood Scale (DAMS)(22)
DAMS is a questionnaire designed to measure three types of mood: positive, depressed, andanxious. The respondents are asked to choose, on a seven-point scale, the degree to which they
fit the nine adjectives: pleasant, happy, lively, anxious, worried, anxious, downcast, gloomy, anddislikeable.
Utrecht Work Engagement Scale (UWES)(23)
UWES is a survey instrument based on the concept of “work engagement” proposed by
Schaufeli and Salanova. There are three versions: 17-item, nine-item (shortened), and three-item(ultrashortened) versions.
Recovery Experience Questionnaire (REQ)(24)
REQ is a questionnaire about behaviors to restore the psychological state that deteriorates ina stressful environment to its original level. Respondents are asked to answer questions about
how they spend their time after the end of the workday. REQ was found to be positively
correlated with work engagement.
NASA-TLX(25)The NASA Task Load Index (NASA-TLX) is used to analyze the factors of mental load
during work and consists of six evaluation items: 1) mental demand, 2) physical demand, 3)
temporal demand, 4) own performance, 5) effort, and 6) frustration.

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On the basis of these four questionnaires, we developed the questionnaire on work used inour study. The detailed contents of our questionnaire are described in Sect. 5.3.

4.

System for Collecting Objective Stress of Caregivers

In this section, we describe a system developed for estimating caregivers’ stress at work bycollecting biological indicators related to stress to achieve the research objective described in
Sect. 2.
4.1

System architecture

Figure 2 shows the system configuration. The system collects data using the heart-rate sensor
and records care activity labels by a smartphone application. In this system, a device that canmeasure heart-rate variability is worn to collect objective stress indicators and estimate stress. A
Union Tool Co. WHS-3 chest-mounted heart-rate sensor was chosen because it is easy to wear
and does not interfere with caregivers’ work activities. This sensor calculates RRI, LF, and HFfrom heart-rate data collected at a sampling period of 1 ms and can store the data in a Bluetooth
device such as an iPod touch.
4.2

How to record labels for care activities

In this section, we explain how labels are recorded for care activities. To accurately record thecare activity of caregivers, a care activity recording application for a mobile device is used torecord the care activity time during work. In order not to disturb the caregiving work, theapplication can be operated by simply pressing the icon of the target care activity to record thestart/end time. Figure 3 shows an example of an application screen. We implemented theapplication on the iPod touch, which is easy to carry around during work, after consulting withcaregivers. The application displays the participant’s ID, the care activity currently being
performed, and a button representing each care activity. When the button of the care activity istapped, the button turns yellow and switches to the care activity during work. Otherwise, thebutton remains white to indicate that no activity is in progress. With the implemented
application, the caregiver simply presses the button for the next care activity to be performed,

Fig. 2.

(Color online) System configuration.

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Fig. 3.

(Color online) Care activity label recording application.

and the application can be easily used without the need for complicated operations. Also, thisapplication can be used to generate time-series data for care activity labels. Because caregivers
generally cannot perform more than one type of care activity at the same time, our application
does not currently support multilabeling.
4.3

Stress measurement by care activity

The collected stress data and activity labels are used to output the stress value for each care
activity. Figure 4 shows the method used to output the stress value for each care activity. The
RRI and LF/HF ratio are calculated from the heart rate sensor and used as the caregiver’s
biological data at that time. From the stress data and care activity labels, the stress value for eachtime period when the care activity was being performed is extracted. After that, the average
stress value is calculated to output the stress value for each care activity.

5.

Experiments

In this section, we describe our experiment conducted in an actual nursing home. We first
explain the experimental objective and outline. Then, we describe care activities targeted in thisexperiment and the questionnaire used in our experiment. Finally, we describe the experimental
results.
5.1

Objective and outline

To confirm that the proposed system can record stress during work and that caregivers’ stressvalues change depending on their care activity, work shift, and workday, we carried out theexperiment at a nursing home after obtaining approval from the Ethics Review Committee ofNara Institute of Science and Technology (Approval Number: 2018-I-31-2). As shown in Fig. 5,
this experiment was conducted on five caregivers at Senior Villa Jure Hashimoto, a residential

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Fig. 4.

Fig. 5.

2937(Color online) Method used to output stress value.

(Color online) Experiment environment (Senior Villa Jure Hashimoto).

fee-based nursing home in Hashimoto, Wakayama, Japan. The experiment was conducted from
June 28, 2021 to July 8, 2021 and from July 19, 2021 to August 4, 2021, with a total of 28 days ofdata collection. Caregivers were asked to wear a WHS-3 heart-rate sensor on their chests before
work and to perform their usual duties. In addition, to measure subjective stress, they were asked
to answer a questionnaire three times a day: before the start of work, during a break, and after
work.
5.2

Target care activities

Table 1 shows the 14 care activities considered in this experiment and their definitions, where
the activities were selected after consultation with the nursing home administrator. In addition tothese activities, “Rest” and “Other” buttons were added to the caregiving record application,
allowing users to select from a total of 16 types of activity.

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Table 1
Target care activities.
Activity
Meal Assistance
Meal Preparation and Serving
Dismantling and Washing Dishes
Toilet Assistance
Changing Diapers
Bed AssistanceBathing Assistance
Movement AssistanceBed Sheet Changing
Cleaning
Laundering
Dementia Support
Drug Management
Writing Care Record

5.3

Definition
Supporting the elderly in eating
Preparation before meals
Clean up after meals
Assist elderly people when they defecate
Change diapers of the elderly
Raise the elderly out of bed or put them to bed
Assist the elderly to take a bath
Assist the elderly with transferring from one place to another
Changing bed sheets
Cleaning the nursing home
Washing clothes
Dealing with the elderly with dementia
Manage medications taken by the elderly
Create a care record of the elderly

Questionnaires

To collect caregivers’ subjective stress, the questionnaire was conducted three times a day:before the start of work, during a break, and after work. In a preliminary questionnaireadministered before the start of the experiment, caregivers were asked about their gender andyears of experience as a caregiver to ascertain their abilities and experience. Furthermore, todetermine whether the personality of the caregivers affects their stress, we used the Big Fivequestionnaire to assess personality. This questionnaire consists of 60 questions that classify the
personality of individuals into five traits: neuroticism, extroversion, openness, cooperativeness,and sincerity, each with 12 questions. All items were answered on a 7-point scale from 0 to 6.
Table 2 shows the content of the questionnaire administered three times a day during the
workday, where caregivers were asked to respond by considering their mood and motivation atthat time. All items were answered on a 7-point scale from 0 to 6. The work questionnaire wasbased on DAMS, UWES, REQ, and NASA-TLX described in Sect. 3.2 and is shown in Table 3.
In this questionnaire, we categorized the nine adjectives of the DAMS into three moods, i.e.,
positive, depressed, and anxious. The three adjectives belonging to each mood were answered on
a 7-point scale from 0 to 6, and then the final rating was made by summing those numbers. Also,
the UWES scores were evaluated by rating each of the three questions on a scale of 0 to 6 andthen summing those numbers. Similarly, the REQ was evaluated by answering each of the fourquestions designed within each item (Psychological detachment, Relaxation, Mastery, andControl) on a 7-point scale from 0 to 6 and then summing those numbers. The NASA-TLX wasevaluated on a 7-point scale from 0 to 6 for each question.
The DAMS and UWES are answered three times a day, the REQ is answered before work
because it relates to how the participants spent their time after their previous shift, and theNASA-TLX is answered after work because it evaluates the entire workday. In the questionnaireadministered after the work, the caregivers also responded to the stress by care activity toevaluate the degree of stress they felt for each care activity they engaged in during the day’swork.

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Table 2
Workday questionnaire content.
Question Item
Number of Questions
DAMS
9
UWES
3
REQ
16
NASA-TLX
6
Stress Assessment by
14
Care Activity

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Before Work○○○

During Break○○After Work○○○○

Table 3Work questionnaire used in the experiment.
Item
Description
Positive Mood (Vigorous: 0–6, Happy: 0–6, Enjoy: 0–6)
Depressed Mood (Gloomy: 0–6, Disagreeable: 0–6, Dull: 0–6)
DAMS: Understanding the moodAnxious Mood (Worried: 0–6, Anxious: 0–6, Afraid: 0–6)
Energy at work, psychological resilience: Do you feel energizedwhen you are working? (0–6)
Enthusiasm for work: Do you feel enthusiastic about your work?
UWES: Understanding work motivation(0–6)
Focus and immersion in the work: Feeling overwhelmed by work?
(0–6)
Forget about work (0–6)
Don’t think about work at all (0–6)
REQ: Psychological detachment
Keep your distance from work (0–6)
Take a break from the burdens of work (0–6)
Relax and unwind (0–6)
Do something relaxing (0–6)
REQ: Relaxation
Spend time to relax (0–6)
Spend time on leisure time (0–6)
Learn something new (0–6)
Find something intellectually challenging (0–6)
REQ: Mastery
Try something challenging (0–6)
Do something that expands your horizons (0–6)
I think I can decide for myself what to do (0–6)
I decide my own schedule (0–6)
REQ: Control
I decide how I spend my time (0–6)
I can get things done the way I want them done (0–6)
Mental Demand: How much mental and perceptual activity was
required (0–6)
Physical Demand: How much physical activity was required(0–6)
Temporal Demand: How much time pressure you felt on the
progress of the task, the speed of progress, and the timing of theNASA-TLX: Identification of workload factors
task (0–6)
Performance: How well do you think you are doing in setting up
the task to accomplish your goal? (0–6)
Effort: How hard did you work on the required tasks? (0–6)
Frustration: How frustrated, stressed, satisfied, or relaxed did you
feel while doing the task? (0–6)
All items are answered on a 7-point scale from 0 to 6.

Caregivers in the experimental facility have the following four types of shift work (early,
regular, night, and on-duty), so the type of shift work for the day is selected in the pre-workquestionnaire.

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•
•
•
•

Sensors and Materials, Vol. 34, No. 8 (2022)

Early: 7:00–16:00
Regular: 8:30–17:30
Night: 16:30–9:00 (of which 5:30–7:00 is for resting)On-duty: 17:30–9:00 (of which 21:30–5:30 is for resting)

5.4

Results

5.4.1 Summary of data
Table 4 shows the attributes of the five participants (one male and four female) obtained fromthe preliminary questionnaire conducted before the start of the experiment. Three participants
had more than 10 years of experience in nursing care work, indicating that a relatively large
number of participants had been in the field for a long time. Figure 6 shows the personality
characteristics of the participants obtained from the Big Five questionnaire. “Neuroticism” and“openness” differed greatly among the participants, whereas there was no significant differencein “cooperativeness”. Because caregivers have many opportunities to interact with residents andwith other caregivers, the ability to care for and empathize with others is an importantcharacteristic, and the results showed little difference among caregivers.
Table 4
Participant attributes.
Participant ID

GenderAge

ID_01

Female

50’s

ID_02

Male

30’s

ID_03

Female

30’s

ID_04

Female

50’s

ID_05

Female50’s

Fig. 6.

(Color online) Personality characteristics of participants.

Years of experiencein nursing care work
More than 10 years,
less than 15 years
More than 5 years,
less than 10 years
More than 10 years,
less than 15 years
Less than 3 years
More than 10 years,
less than 15 years

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Although the biological indicators, questionnaires, and care activity data were collected for a
total of 28 days, there were cases in which the devices were faulty or inadequately attached, anddata were not collected in some cases. Similarly, in the questionnaire, there were many cases inwhich the participants forgot to answer the questionnaire when they were busy with their work
because they needed to answer it while performing their usual duties. Table 5 shows the final
number of data collected. In the questionnaire, the number of responses was the highest before
work and lowest after work. In some cases, the answers to the questionnaire were always
constant, and in other cases, the number of recorded care activity labels was small and theparticipants forgot to record them.
The following are the results of the questionnaire responses of the five caregivers. Figure 7
shows the average stress assessed for each care activity. The average stress was relatively highfor dementia support, bathing assistance, bed assistance, and drug management, which requiredetailed confirmation, and low for housework, such as bed sheet changing, cleaning, andlaundry, which do not require contact with elderly people. When we focused on care activities
related to bed care, we found that the average stress was higher for bed assistance than for bed
sheet changing. This is because bed assistance requires more physical strength than bed sheetchanging, as bed assistance involves supporting the elderly so that they do not fall over,according to a staff member at the facility who was interviewed after the experiment. In
addition, when we focused on care activities related to eating, the results showed that the meal
Table 5
Number of data obtained in experiment.
Collected Data
WHS-3
Before Work
Questionnaire
During BreakAfter Work
Care Activity

Fig. 7.

Total acquisition days
57
74
65
56
63

(Color online) Average stress assessment by care activity (all participants).

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assistance was more stressful than meal preparation and serving and collecting and washingdishes because the meal assistance was performed while paying attention to the elderly person.
This clearly shows that there is a difference in stress even among activities related to the same
domain.
Figure 8 shows the average of the DAMS results by item, work shift, and response timing.
The positive mood index tended to be lower than the depression and negative mood indexes. In
terms of work shift, night shift workers had lower positive moods than those working during theday, such as early shift workers and regular shift workers. Conversely, the people doing theregular shift tended to have relatively low depression and negative mood scores, and a highpositive mood score. Regarding the timing of the response, the positive mood score increased
with the passage of work hours. Figure 9 shows the mean UWES results by work shift andresponse timing. Although there is no significant change with the timing of the response, the

(a)

(b)

(c)
Fig. 8.

(Color online) Average of DAMS results (all participants). (a) Item, (b) work shift, and (c) response timing.

(a)Fig. 9.

(b)(Color online) Average of UWES results (all participants). (a) Work shift and (b) responding timing.

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average UWES result is higher for regular work than for other types of work. Figure 10 shows
the mean REQ values by item and work shift. The “Psychological distance” score is lower thanthe other scores, suggesting that the respondents do not sufficiently switch off from their jobs
when they are not at work. Figure 11 shows the mean values for each of the NASA-TLX items
and for each type of work. The work accomplishment level was generally low. While mental
demand and physical demand were high for daytime work, such as early arrival and regular
work, time pressure and dissatisfaction were high for late-night work, such as night shift and onduty shift.
Section 5.4.2 and the subsequent sections present the results of the analysis of WHS-3 dataobtained from the device. In this study, five participants attached WHS-3 and collected data, but
owing to device malfunctions and insufficient attachment, three participants did not havesufficient data for analysis. Therefore, we focused on the two participants for whom sufficient
data could be obtained to see if the individual objective indicators were consistent with thesubjective indicators.
Table 6 shows the total implementation time for each care activity. The total implementation
time was calculated from the time-series data of the nursing care activity labels recorded by ourapplication. ID_01 had a longer time for collecting and washing dishes and for changing diapers,
while ID_02 had a longer time for writing care records and changing diapers. Figure 12 shows

(a)

(b)

Fig. 10. (Color online) Average of REQ results (all participants). (a) Item and (b) work shift.

(a)

(b)

Fig. 11. (Color online) Average of NASA-TLX results (all participants). (a) Item and (b) work shift.

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Table 6
Implementation time for each care activity (min) (ID_01, ID_02).
Activity
ID_01
Meal Assistance
43.5
Meal Preparation and Serving
468.3
Dismantling and Washing Dishes
630.7
Toilet Assistance
83.8
Changing Diapers
523.3
Bed Assistance
0.0
Bathing Assistance
11.4
Movement Assistance
326.6
Bed Sheet Changing
60.0
Cleaning
231.5
Laundering
194.7
Dementia Support
1.8
Drug Management
185.3
Writing Care Record
98.8

(a)

ID_02
258.6
522.3
246.9
61.3
582.9
267.6
10.6
217.7
0.0
0.0
319.7
114.9
390.4
937.2

(b)

Fig. 12. (Color online) Average stress assessment by care activity (ID_01, ID_02). (a) ID_01 and (b) ID_02.

the average stress assessment by care activity. Dementia support was high for both caregivers,
while diaper changing and bed assistance were the most common activities for ID_01, and drug
management and writing care records were the most common activities for ID_02, both of whichwere individual tasks.
Figures 13 and 14 show the mean values of the DAMS by item, work shift, and timing of the
response for ID_01 and ID_02, respectively, indicating that the depressed mood score was lower
than the other mood scores for ID_01, but increased during night shift. The depression andnegative mood scores was highest before work and decreased after work. On the other hand, thepositive mood score was slightly higher than the other mood scores for ID_02, and the difference
between the depression and negative mood scores was especially large during on-duty work
shift. The positive mood score was consistently higher than the other mood scores, withdepression and negative mood scores decreasing during breaks.
Figures 15 and 16 show the mean values of work engagement by work shift and response
timing for ID_01 and ID_02, respectively. Although there was no significant change in UWES

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(a)

(b)

(c)
Fig. 13. (Color online) Average of DAMS results (ID_01). (a) Item, (b) work shift, and (c) responding timing.

(a)

(b)

(c)
Fig. 14. (Color online) Average of DAMS results (ID_02). (a) Item, (b) work shift, and (c) responding timing.

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(a)

(b)

Fig. 15. (Color online) Average of UWES results (ID_01). (a) Work shift and (b) responding timing.

(a)

(b)

Fig. 16. (Color online) Average of UWES results (ID_02). (a) Work shift and (b) responding timing.

score with the timing of the response, it was found that the scores for night shift work wereslightly lower for both ID_01 and ID_02.
Figures 17 and 18 show the average REQ results by item and work shift for ID_01 and ID_02,
respectively. Scores tended to be high for control and low for psychological distance for both
ID_01 and ID_02, and the score for relaxation was also high for ID_02. For ID_02, there was alarge difference between night shift work and on-duty shift work for psychological distance,
indicating that there is a difference in how the workers spend their time before the start of theworkday, since the rest time during work hours is also significantly different for the same latenight shift.
Figures 19 and 20 show the average NASA-TLX results by item and work shift for ID_01 andID_02, respectively. These effects may have contributed to the increases in depression andnegative mood scores in the DAMS and the decrease in UWES score. For ID_02, there were no
significant differences among the items. In terms of work shift, time pressure was higher for thenight shift and on-duty work shift. The same tendency was observed for ID_01, suggesting thatthe smaller number of caregivers during late-night work increases the time burden.
5.4.2 Analysis results by care activity
In this section, we analyze the care activities on the basis of the stress indicator data measured
from the WHS-3 device. Care activities that were not recorded in the caregiving record

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(a)

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(b)

Fig. 17. (Color online) Average of REQ results (ID_01). (a) Item and (b) work shift.

(a)

(b)

Fig. 18. (Color online) Average of REQ results (ID_02). (a) Item and (b) work shift.

(a)

(b)

Fig. 19. (Color online) Average of NASA-TLX results (ID_01). (a) Item and (b) work shift.

(a)

(b)

Fig. 20. (Color online) Average of NASA-TLX results (ID_02). (a) Item and (b) work shift.

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application were considered to have not been performed during the experimental period, andtheir results were set to 0. As for the Lorenz plot, since the outliers were widely distributed, thearea was calculated by using the interquartile range (IQR) of the RRI.
Figures 21–23 respectively show the SDNN, LF/HF ratio, and Lorenz plot area for eachparticipant by care activity. For ID_01, the SDNN is low, the Lorenz plot area is small and the

(a)

(b)

Fig. 21. (Color online) SDNN by care activity. (a) ID_01 and (b) ID_02.

(a)

(b)

Fig. 22. (Color online) LF_HF ratio by care activity. (a) ID_01 and (b) ID_02.

(a)

(b)

Fig. 23. (Color online) Lorentz plot area by care activity. (a) ID_01 and (b) ID_02.

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LF/HF ratio is high for bed sheet changing. For ID_02, the SDNN is low, the Lorenz plot area issmall, and the LF/HF ratio is high for bathing assistance. Since the SDNN and Lorenz plot area
respectively tend to be low and small during periods of stress and high and large during periodsof relaxation, and the LF/HF ratio tends to be high during periods of stress and low during
periods of relaxation, the above-mentioned activities may be relatively stressful. On the other
hand, the opposite tendency (high SDNN and large Lorenz plot area and low LF/HF ratio) was
observed for movement assistance for ID_01 and for writing care records for ID_02, suggestingthat these activities are not particularly stressful. In addition, for ID_02, the tendency to be in arelaxed state during rest was pronounced, whereas this tendency was not observed for ID_01.
5.4.3 Analysis results by work shift
Figures 24–26 respectively show the SDNN, LF/HF ratio, and Lorenz plot area by work shift
for each participant. Participant ID_01 was more likely to feel stress during daytime work suchas in the early shift and regular shift, while for ID_02, the SDNN and Lorenz plot area
respectively tended to be lower and smaller and the LF/HF ratio tended to be higher for the earlyshift and on-duty shift. On the other hand, the opposite trend was observed for both types ofnight shift, suggesting that the mental burden was small.

(a)

(b)

Fig. 24. (Color online) SDNN by work shift. (a) ID_01 and (b) ID_02.

(a)

(b)

Fig. 25. (Color online) LF/HF ratio by work shift. (a) ID_01 and (b) ID_02.

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(a)

(b)

Fig. 26. (Color online) Lorentz plot area by work shift. (a) ID_01 and (b) ID_02.

5.4.4 Analysis results by work day
Figures 27–29 respectively show the SDNN, LF/HF ratio, and Lorenz plot area by workday
for each participant. For ID_01, the LF/HF ratio was high and the Lorenz plot area was small on
July 2, 6, and 7, suggesting the possibility of stress. For ID_02, the same tendency was observedon July 4, 26, and 28, suggesting that ID_02 experienced stress. The work shift on the above
days was either early shift or regular shift, and the work was performed during the daytime.
Therefore, it is considered that the mental burden of daytime work fluctuates greatly dependingon the day.
5.5

Discussion

5.5.1 Relationship between personality and stress indicators
In the previous section, we analyzed two caregivers. In Fig. 6, we focus on their personality
traits. ID_01 tended to be high in conscientiousness and low in openness. People with highconscientiousness tend to be more self-controlled and focused but cautious.(26) ID_01 had ahigher value for the REQ item of control (being able to decide what to do in one’s leisure time)than for the other REQ items. Moreover, the value for the NASA-TAX item of effort (how hardpeople work on tasks) was higher for ID_01. On the other hand, ID_02 tended to have highvalues for extraversion and openness and low values for neuroticism. People with highextraversion tend to be cheerful and positive and those with low neuroticism tend to have low
anxiety and depression. The positive mood score in the DAMS was higher than the depression or
negative mood score for ID_02. These characteristics may have affected the results of theDAMS. In addition, the fact that both participants felt little mental stress when they took a break
may also have affected their personality traits. The above results suggest that the subjective andobjective stress indicators vary depending on the personality.
5.5.2 Comparison of subjective and objective stress indicators
The results of the analysis of objective stress indicators by care activity showed that ID_01
may have experienced stress during bed sheet changing and ID_02 may have experienced stressSensors and Materials, Vol. 34, No. 8 (2022)

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(b)

Fig. 27. (Color online) SDNN by workday. (a) ID_01 and (b) ID_02.

(a)

(b)

Fig. 28. (Color online) LF_HF ratio by workday. (a) ID_01 and (b) ID_02.

(a)

(b)

Fig. 29. (Color online) Lorentz plot area by workday. (a) ID_01 and (b) ID_02.

during bathing assistance. However, these results did not clearly appear to be mentally stressful
tasks in the subjective stress evaluation results. Conversely, the objective stress indicators did
not indicate that the caregivers experienced stress even when they indicated care activities thatwere relatively stressful in the questionnaire. The results of the analysis of the objective stressindicators by work shift showed that ID_01 may have experienced stress in the early shift andregular shift, while ID_02 may have experienced stress in the early shift and on-duty shift. The

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results of DAMS and UWES did not change significantly for these types of work. This indicatesthat care activities and work shift that tend to be stressful on the basis of objective stressindicators do not necessarily coincide with the results of subjective stress indicators. This may
be due to the inaccurate recording of care activity labels and the effects of external environmental
factors. Therefore, while subjective stress can be used as a guideline for estimating objective
stress related to care activities, it is not an absolute measure. One of the factors affecting theresults is the movement of the caregiver. The WHS-3 heart-rate sensor used in this study is also
equipped with an acceleration sensor. The acceleration data showed no significant changes in thecare activity or work shift. In the future, we plan to analyze the effects of human movement andheart-rate variability on the basis of step count data and other data from wearable devices suchas Fitbit.
5.5.3 Stress caused by recording activity and answering a questionnaire
We think that the application we implemented for recording care activity is highly convenient,
as mentioned in Sect. 4.2. However, when the care activity to be performed is short, the caregiverneeds to switch to the next care activity immediately, which causes cases where the caregiverforgets to input the next activity. When such cases occur frequently, we think that it may be
stressful for the caregiver to operate the application to record the care activity. Thus, we willplan to consider addressing this problem by combining the care activity record platform using
BLE beacons, which have been used in previous studies, with our application implemented inthis study. By utilizing the care activity record platform, we can reduce the need to manually
record care activity records, which will help prevent recording errors and reduce stress. Also, inthe questionnaire, some participants forgot to answer on several occasions. This is thought to be
due to the fact that the participants were so busy and stressed out that they did not have time toanswer the questionnaires. In this case, data could not be obtained at times of high stresscompared to those at times of low stress, and the data obtained could be biased. For this reason,
we think that appropriate notification of questionnaire responses even during busy times andquestionnaire design that allows simple responses are necessary; for example, we will plan toconsider incorporating a questionnaire function into our application used to record the care
activity implemented in this study.

6.

Conclusions

In this paper, we focused on the psychological state of caregivers and aimed to obtain new
knowledge to reduce their workload. To this end, we conducted a data collection experiment fora total of 28 days at a nursing home to investigate whether caregivers’ psychological states areaffected by their care activity, work style, workday, and personality. In the experiment, objective
stress indicators, such as RRI and LF/HF ratio, obtained from the device and subjective stressindicators obtained from questionnaires were measured. Although some caregivers did not wear
the device sufficiently or forgot to answer the questionnaire, we were able to collect data on the
biological state of each caregiver for approximately 5 to 15 days. Furthermore, we analyzed andSensors and Materials, Vol. 34, No. 8 (2022)

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visualized objective and subjective stress indicators from the biological data of the twoparticipants with the largest amount of data. As a result, although the objective indicators andsubjective assessments did not necessarily coincide, we found that stress tended to increase forsome specific care activities, work shifts, and workdays. It was also suggested that differences in
the personalities of the caregivers themselves led to differences in the subjective and objective
stress indicators.
To conduct a more general evaluation in the future, measurement experiments using a larger
number of participants over a longer period at multiple nursing care facilities are needed. Also,
there were some cases of nonresponse due to the large number of items in the questionnaire.
Therefore, to increase the amount of data in the future, it is important to review the questionnaire
and the targeted caregiving activities. Regarding devices for collecting stress indicator data,
heart-rate sensors have the disadvantages of being time-consuming to wear and of being
physically uncomfortable. Thus, we intend to examine whether it is possible to measure changes
in mental and physical states using only wearable devices that are relatively easy to wear.
Furthermore, we need to investigate the degree of data loss due to participants’ mistakes, such as
forgetting to push a button or answer the questionnaire, to improve our study. Finally, we plan toincrease the amount of data through multiple collection experiments, construct a stressestimation model using these data as feature values, and clarify the relationship between the care
activity and stress in more detail.

Acknowledgments
This study was supported in part by Innovation Platform for Society 5.0 and the Japan
Society for the Promotion of Science, Grant-in-Aid for Scientific Research number JP20H04177.
We would like to thank Ms. Inoue, the manager of Senior Villa Jure Hashimoto, for the very
helpful guidance in this work as well as for allowing Senior Villa Jure Hashimoto to be used inthis research.

References
1 A. Miyaji, T. Matsui, Z. Zhang, H. Choi, M. Fujimoto, and K. Yasumoto: 50th Int. Conf. Parallel Processing
Workshop (ICPPW) (ACM, 2021) 1–8.
2 Cabinet Office Japan: Annual Report on the Ageing Society (Summary) FY2021 (2021).
3 T. Morita, K. Taki, M. Fujimoto, H. Suwa, Y. Arakawa, and K. Yasumoto: J. Sens. 2018 (2018) 1.
4 H. Wada, Z. Zhang, M. Fujimoto, Y. Arakawa, and K. Yasumoto: 13th Int. Symp. Medical Information andCommunication Technology (ISMICT) (IEEE, 2019) 1–6.
5 The Whoqol Group: Social Sci. & Med. 46 (1998) 1569.
6 J. E. Ware Jr.: Spine 25 (2000) 3130.
7 S. Fukuda, Y. Matsuda, Y. Arakawa, K. Yasumoto, and Y. Tani: WristSense 2020: 6th Workshop on Sensing
Systems and Applications Using Wrist Worn Smart Devices (WristSense 2020).
8 E. Garcia-Ceja, V. Osmani, and O. Mayora. IEEE J. Biomed. Health Inf. 20 (2015) 1053.
9 E. Demerouti and A. B. Bakker: The Oldenburg Burnout Inventory: Handbook of Stress and Burnout in HealthCare (2008) pp. 65–78.
10 T. Matsui, K. Onishi, S. Misaki, H. Suwa, M. Fujimoto, T. Mizumoto, W. Sasaki, A. Kimura, K. Maruyama,
and K. Yasumoto: Advanced Information Networking and Applications Proc. 35th Int. Conf. Advanced
Information Networking and Applications 225 (Springer, AINA, 2021) 731–744.

2954

Sensors and Materials, Vol. 34, No. 8 (2022)

11 B. Cinaz, B. Arnrich, R. L. Marca, and G. Tröster: Personal Ubiquitous Comput. 17 (2013) 229.
12 A. Muaremi, B. Arnrich, and G. Tröster: BioNanoScience 3 (2013) 172.
13 D. A. Adler, V. W.-S. Tseng, G. Qi, J. Scarpa, S. Sen, and T. Choudhury: Proc. ACM on Interactive, Mobile,
Wearable and Ubiquitous Technologies 5 (2021) 1–32.
14 S. Shiffman, A. A. Stone, and M. R. Hufford: Annu. Rev. Clin. Psychol. 4 (2008) 1.
15 J. Kallio, E. Vildjiounaite, J. Koivusaari, P. Räsänen, H. Similä, V. Kyllönen, S. Muuraiskangas, J. Ronkainen,
J. Rehu, and K. Vehmas: Build. Environ. 175 (2020) 106787.
16 H. Miwa, T. Fukuhara, and T. Nishimura: Adv. Human Side Serv. Eng. 312 (2012) 3.
17 S. Inoue, N. Ueda, Y. Nohara, and N. Nakashima: Proc. 2015 ACM Int. Joint Conf. Pervasive and Ubiquitous
Computing (2015) 1269–1280.
18 S. Inoue, P. Lago, T. Hossain, T. Mairittha, and N. Mairitth: Proc. ACM on Interactive, Mobile, Wearable andUbiquitous Technologies 3 (2019) 1–24.
19 A. Postawka and J. Rudy. Comput. Sci. 19 (2018) 257.
20 A. S. Abdulbaqi, A. J. Obaid, and S. A. H. Alazawi: Int. J. Online Biomed. Eng. 17 (2021) 70.
21 Y. Matsumoto, N. Mori, R. Mitajiri, and Z. Jiang: J. Life Support Eng. 22 (2010) 105.
22 L. A. Clark: The Anxiety and Depressive Disorders: Descriptive Psychopathology and Differential Diagnosis(1989).
23 W. Schaufeli and M. Salanova: Manage. Social and Ethical Issues in Organ. 135 (2007) 177.
24 A. Shimazu, S. Sonnentag, K. Kubota, and N. Kawakami: J. Occup. Health 54 (2012) 196.
25 S. G. Hart: Nasa Task Load Index (tlx). Vol. 1.0; Computerized Version (1986).
26 R. A. Power and M. Pluess: Transl. Psychiatry 5 (2015) e604.