The dynamic relationship between emotional and physical states: an observational study of personal health records

Abstract

Objectives
Recently, there has been increasing interest in preventing and managing diseases both inside and outside medical institutions, and these concerns have supported the development of the individual Personal Health Record (PHR). Thus, the current study created a mobile platform called “Mind Mirror” to evaluate psychological and physical conditions and investigated whether PHRs would be a useful tool for assessment of the dynamic relationship between the emotional and physical conditions of an individual.

Methods
Mind Mirror was used to collect 30 days of observational data about emotional valence and the physical states of pain and fatigue from 20 healthy participants, and these data were used to analyze the dynamic relationship between emotional and physical conditions. Additionally, based on the cross-correlations between these three parameters, a multilevel multivariate regression model (mixed linear model [MLM]) was implemented.

Results
The strongest cross-correlation between emotional and physical conditions was at lag 0, which implies that emotion and body condition changed concurrently. In the MLM, emotional valence was negatively associated with fatigue (β =−0.233, P<0.001), fatigue was positively associated with pain (β =0.250, P<0.001), and pain was positively associated with fatigue (β =0.398, P<0.001).

Conclusion
Our study showed that emotional valence and one’s physical condition negatively influenced one another, while fatigue and pain positively affected each other. These findings suggest that the mind and body interact instantaneously, in addition to providing a possible solution for the recording and management of health using a PHR on a daily basis.

Full text

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ORIGINAL RESEARCH
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Open Access Full Text Article
http://dx.doi.org/10.2147/NDT.S120995
The dynamic relationship between emotional and
physical states: an observational study of personal
health records
Ye-Seul Lee1
Won-Mo Jung1
Hyunchul Jang2
Sanghyun Kim2
Sun-Yong Chung3
Younbyoung Chae1
1Acupuncture and Meridian Science
Research Center, College of Korean
Medicine, Kyung Hee University,
Seoul, 2Mibyeong Research Center,
Korean Institute of Oriental
Medicine, Daejeon, 3Department of
Neuropsychiatry, College of Korean
Medicine, Kyung Hee University, Seoul,
Republic of Korea
Objectives: Recently, there has been increasing interest in preventing and managing diseases
both inside and outside medical institutions, and these concerns have supported the development
of the individual Personal Health Record (PHR). Thus, the current study created a mobile plat-
form called “Mind Mirror” to evaluate psychological and physical conditions and investigated
whether PHRs would be a useful tool for assessment of the dynamic relationship between the
emotional and physical conditions of an individual.
Methods: Mind Mirror was used to collect 30 days of observational data about emotional
valence and the physical states of pain and fatigue from 20 healthy participants, and these data
were used to analyze the dynamic relationship between emotional and physical conditions.
Additionally, based on the cross-correlations between these three parameters, a multilevel
multivariate regression model (mixed linear model [MLM]) was implemented.
Results: The strongest cross-correlation between emotional and physical conditions was at
lag 0, which implies that emotion and body condition changed concurrently. In the MLM,
emotional valence was negatively associated with fatigue (β =-0.233, P,0.001), fatigue was
positively associated with pain (β =0.250, P,0.001), and pain was positively associated with
fatigue (β =0.398, P,0.001).
Conclusion: Our study showed that emotional valence and one’s physical condition negatively
influenced one another, while fatigue and pain positively affected each other. These findings
suggest that the mind and body interact instantaneously, in addition to providing a possible
solution for the recording and management of health using a PHR on a daily basis.
Keywords: emotion, fatigue, pain, personal health record (PHR), time-series analysis
Introduction
Mobile health, which is the use of mobile communication and computing technologies
for medicine and public health, is rapidly expanding.1,2 A personal health record (PHR)
is defined as a health record created using mobile computing technologies in which
health information and personal health data are maintained by the patient.3 Models of
PHR vary across a large range. One PHR model utilizes patient-generated data about
health and lifestyle that are recorded using a personal computer or Web application
and that help address specific health concerns.4,5 Because a PHR records health-related
data generated by a patient, it is not only a repository of that patient’s data but also
a tool that facilitates interactions between the medical provider and patient via the
provision of related health information.6–8 Additionally, because technologies are
designed to streamline the diagnosis and treatment processes, and big data analytics
offer novel perspectives regarding the contribution of health data to health care, PHRs
Correspondence: Younbyoung Chae
Acupuncture and Meridian Science
Research Center, College of Korean
Medicine, Kyung Hee University,
1 Hoegi-dong, Dongdaemun-gu, Seoul
130-701, Republic of Korea
Tel +82 2 961 2208
Fax +82 2 963 2175
Email ybchae@khu.ac.kr
Journal name: Neuropsychiatric Disease and Treatment
Article Designation: Original Research
Year: 2017
Volume: 13
Running head verso: Lee et al
Running head recto: The dynamic relationship between emotional and physical states
DOI: http://dx.doi.org/10.2147/NDT.S120995
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provide the opportunity for a greater degree of interaction
when managing health in one’s daily life.9
Several recent studies have used PHRs to examine patient
health and activity levels or to assess the management of
patients through interventions outside medical institutions.
Druss et al10 evaluated the effects of electronic PHRs on the
quality of medical care in 170 patients with serious mental
illnesses who were treated at a community medical center
and found that the use of PHRs significantly improved the
quality of medical care and increased the use of medical
services by patients. Espie et al11 created a mobile health care
application for the management of sleep disorders, which
involved the development and psychometric validation of a
brief scale (the Sleep Condition Indicator [SCI]), to evaluate
sleep disorders in everyday clinical practice. These authors
found that web-based cognitive behavioral therapy had a
positive effect on the treatment of insomnia.12 Park et al9
developed a PHR based on teen-specific needs to promote
better self-awareness and chronic disease management and
determined that chronic psychological or physical states
require constant attention because the symptoms can fluctuate
spontaneously over time. Moreover, although patients often
focus on immediate problems, the use of PHRs may provide
a tool that can gain the attention of individuals and remind
them of these problems when not immediately apparent.9
Taken together, these findings suggest that PHRs constitute
a useful tool to record the range of, and changes in, physical
and psychological health states outside hospitals.
A growing body of research has called attention to the
influence of emotion on health and the possible management
of the relationship between these two variables. The instan-
tiation of an affective state directly involves alterations in
multiple physiological systems of the body, which leads to
physiological responses that can directly influence physical
health depending on the nature, frequency, and time course
of the emotional state.13 Physiological responses are meant
to be adaptive in the short term but can lead to maladap-
tive outcomes in the long term if not correctly regulated.14
Furthermore, recent evidence has sufficiently demonstrated
the importance of comorbid relationships among emotional,
psychological, and physical symptoms.15–20 For example, a
worldwide survey using a national representative sample
identified an association between chronic pain and mental
disorders.21–23 Similarly, there is a growing consensus that
negative emotions influence the development of cardiovascu-
lar diseases24 and that chronic digestive disorders are closely
linked with a variety of psychological disorders, including
depression.25,26 Taken together, these studies provide a
clear indication that chronic physical symptoms are best
understood in the context of psychological factors. In this
respect, the use of PHRs could provide a strategy for improv-
ing medical care for patients with comorbid psychological
and physical illnesses.
In the current study, a mobile application called “Mind
Mirror” was developed as a PHR to evaluate the daily affec-
tive states and physical conditions of individual patients.
Using participant-generated health records, detailed infor-
mation on health was collected on a daily basis through
the participants’ engagement and self-monitoring. This
mobile platform was developed in order to collect health
records while maximizing the patient’s self-assessment
in terms of paying attention to their physical condition
and feelings throughout the day. A well-known example
of self-assessment of daily experiences is the day recon-
struction method (DRM), in which a participant reviews
daily affective experiences and the subjective feelings.27,28
This was a 30-day observational study that aimed to char-
acterize the dynamic relationship between the emotional
state of patients and their physical states, including pain
and fatigue, and to determine whether self-recorded tools
such as PHRs would be useful for the assessment of the
relationship between an individual’s emotional and physi-
cal conditions.
Methods
Participants
All participants were recruited via an online advertisement
posted on the Internet. The inclusion criteria for the cur-
rent study were as follows: participants were required to
1) be between 20 and 40 years of age, 2) have no history
of any neuropsychological disorder or acute or chronic
pain disorder, and 3) not be taking any type of medication.
Written informed consent was obtained prior to participation.
This study was conducted in accordance with the guidelines
issued by the human subjects committee and approved by
the institutional review board of Kyung Hee University in
Seoul, Republic of Korea.
Study design and procedures
The primary goals for the Mind Mirror mobile application
were to assess the daily emotional and physical changes of
patients and to provide a platform from which large-scale
data could be collected and analyzed. A novel user interface
format was applied to measure the affective and physical
states of a participant every 2 hours from 9:00 AM to 9:00 PM
for 30 days. The natural starting point of research on the
dynamic nature of these symptoms lies in the analysis of
symptoms measured over different time points. The analysis

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The dynamic relationship between emotional and physical states
of time series data requires detailed records that reflect subtle
changes in the emotional and physical conditions of individu-
als over time within a single day.
In the current study, participants completed the entries
of their daily emotional and physical states using the Mind
Mirror software; they were instructed to complete the data
input into the application at night prior to going to bed. Data
regarding emotional valence, fatigue, and pain were entered
by the participants over 30 days at seven time points each
day between 9:00 AM (09:00) and 9:00 PM (21:00). Of the
eligible participants (n=21), one participant was unable to
finish the study due to loss of mobile phone; thus, the final
data analyses in the current study included 20 participants
(eight females, mean age =24.7 years).
Development of software for Mind Mirror
For the current study, an iPhone Operating System (iOS)
software application was developed using Xcode 6.4 and
the programming language “Swift”; this application required
iOS 8.4 Software Development Kit (SDK) or higher. Explicit
methods were used to record the subjective ratings of the
participants regarding their physical conditions and subjec-
tive valences of daily emotion. The explicit method collects
data on emotions and physical conditions that the participant
cognitively feels by asking direct questions or presenting
straightforward tasks regarding their daily condition.29 Upon
turning on the application, a title page with a logo, as well
as a message that reads “Start new record”, appears. Once
the participant has clicked on this message, the application
begins its initial steps. The participants entered this applica-
tion once a day (Figure 1A).
Measurements of emotional and physical
states using PHRs
Measurement of emotional states
This application applied two main methods to measure
explicit emotions or feelings that were presented as tasks
for the participants to complete on a daily basis. The first
measure was designed to quantitatively visualize positive
or negative affective states. On the screen, three auxiliary
lines with the numbers 5, 0, and -5 next to each line were
provided. The words “Pleasant” and “Unpleasant” were
placed beside the numbers 5 and -5 to indicate positive and
negative emotional valences, respectively. Circles that could
be dragged were located on the middle line, and time points
shown in units of 2 hours from 9:00 AM to 9:00 PM were
visible. The participants moved the circles up and down on
a vertical plane within the range of -5 to 5 to express their
emotional states (Figure 1B).
The second measure depicted six basic emotions as
described by Ekman: anger, disgust, fear, joy, sadness, and
surprise.13 The emotions were presented on each vertex of a
hexagon, and the relative intensities of the six distinct emo-
tions were defined as the circumplex model. Moreover, 10
small circles, which were depicted as drops of water, were
presented at the center of the hexagon, and a single drop of
water moved to attach to the corresponding emotion when
the user touched any of the six emotions a single time. Each
participant was asked to touch the emotions in order to dis-
tribute the 10 drops of water as a description of their overall
emotional state for the day; it was not necessary to use all
10 drops of water (Figure 1C). The data from this measure
were collected from the participants as well, but not used
for the study analysis.
Measurement of physical states
The first step in determining a participant’s physical state
involved asking questions about the weather during each day
using simple pictures. After choosing the weather, the partici-
pants were asked to complete their assessment of the length
and quality of the previous night’s sleep and to evaluate their
digestive function throughout the day. The last step involved
assessing one chronic condition that the participant wanted to
monitor for 30 days, which was reported prior to starting the
study; the personalized chronic symptoms ranged from eye
irritation to dizziness. To assess the chronic symptom, each
participant moved a button across a bar that was labeled at
each end to indicate the “Worst” and “Best” conditions. This
system applied a variation of the visual analog scale (VAS)
in which performances in computer-based and Web-based
research have been validated.30 While categorical scales reach
an ordinal-scale level, the VAS extends the precision and
discrimination of daily reports (Figure 1D). The data from
this measure were collected from the healthy participants
and not used for the study analysis.
Two main methods were applied to measure physical
conditions according to time. Like the emotional tasks, these
were presented as tasks for the participants to complete on
a daily basis and quantitatively described the positive or
negative physical states of each participant. The first task
involved assessing one’s overall pain and the second task
involved assessing one’s overall fatigue throughout the
day. On the screen, three auxiliary lines with the numbers
10 and zero next to the top and bottom lines, respectively,
were provided. The words “Extremely severe” and “None”
were placed besides the numbers 10 and zero to indicate the
intensities of pain and fatigue. Circles that could be dragged
were located on the middle line and time points shown in

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414
Lee et al
units of 2 hours from 9:00 AM to 9:00 PM were visible. The
participants moved the circles up and down on a vertical
plane within the range of 10–0 to express their physical states
throughout the day; the same procedure was applied for both
pain and fatigue (Figure 1E and F).
Data processing and analysis
Because this study focused on the dynamic relationship
between emotional and physical states, records with a more
frequent temporal resolution (units of 2 hours) were analyzed;
thus, a time series analysis of the data of 20 participants was
conducted to determine the correlations between the emotional
and physical states. The collected data of the participants dur-
ing the 30-day study were placed in a property list (.plist) file
format that was converted to a comma separated values (.csv)
file for the data analyses. There were three different categories
of time series data – emotional valence, pain, and fatigue –
which illustrated the experiences and emotional states of
the participants on a daily basis. The 30-day data from
20 participants had average missing assessments of 1.7 days.
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Figure 1 Structure of the “Mind Mirror” mobile application.
Notes: (A) Starting page for the Mind Mirror mobile application; clicking on “Start new record” changes the application screen to the next gure. (B) Explicit measurement
of Emotion I. The blue circle in the middle line is moved by the participant to express the level of emotional valence, which has a range from -5 to 5 and is measured every
2 hours from 9:00 AM to 9:00 PM. (C) Explicit measurement of Emotion II. The hexagon represents a circumplex model of six distinct emotions according to Ekman:
happiness, anger, fear, disgust, surprise, and sadness. A single click on one of the six emotions moves the blue circle at the center to the clicked emotion. The participant can
divide 10 blue circles into different emotions to illustrate the type and strength of the emotions that he or she felt during the day. (D) Basic health information of a patient
throughout the day. The participant was asked to choose the weather of the day by clicking on one of the various weather icons. They were then asked to note the length
and quality of sleep the previous night by moving a button within a bar that ranged from “Worst” to “Best”. Similarly, the participants rated digestive function throughout the
day. Chronic symptoms were identied prior to the trial; moving the button within a bar that ranged from “None” to “Extremely severe” changed the severity of the daily
symptoms over 30 days, which the patient could track. (E) Measurement of pain. The blue circle in the bottom line was moved by the participant to express the level of pain
during the day. (F) Measurement of fatigue. The blue circle in the middle line was moved by the participant to express the level of fatigue during the day.

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415
The dynamic relationship between emotional and physical states
An interpolation process was applied to complete the data
for time points between 11:00 PM and 7:00 AM when the
participants were assumed to be asleep. Using this prepro-
cessing procedure, five time points for a single night, or
150 time points in total, were filled in to complete the data
for the 30-day study period. To fill in the empty time points,
the function “na.interp” in the “forecast” package of R (http://
www.R-project.org), which assumes a linear relationship,
was used to complete the data set.
Following the preprocessing procedure, three sets of
time series data for each of the 20 participants were available
for data analysis. A logarithmic transformation was applied
to each of the emotional, pain, and fatigue series to ensure
the normality and homogeneity of variance of the residuals.
Cross-correlations between the emotional valence and pain,
emotional valence and fatigue, as well as fatigue and pain
time series were analyzed by participant, and the correlation
coefficients were averaged to visualize the correlation analysis.
R software (version 3.2.3, “Wooden Christmas Tree”) and
R Studio (version 0.99.892) were used for the data analyses.
Mixed linear model (MLM)
To investigate the possible relationships between emotional
valence and physical states using the data of the 20 partici-
pants, a regression model was applied with consideration
given for the random effects of the participants. When select-
ing a regression model to determine whether there were asso-
ciations among emotion, fatigue, and pain, it was assumed
that there were concurrent dynamics between emotion and
the body based on cross-correlation results. Accordingly,
a mixed model for multilevel data with a combination of
between-subject and within-subject factors was used.
While some methods for time series analyses account
for random effects in multilevel data,31 many other methods
examine the concurrent changes between multiple time series
without lagged effects. Thus, a single model was selected to
analyze the three series to determine whether any associations
existed. The multilevel regression model, or the MLM,32,33
allows for the estimation of hierarchically structured lon-
gitudinal data on the individual and group levels. For the
current data, a mixed model with participants as the random
effect without temporal dislocation was applied because
cross-correlation analyses of the series unanimously showed
the highest correlation at lag 0. Based on the regression
beta coefficients, the dynamic structure between emotional
valence, pain, and fatigue was visualized in a network; the
green line indicates a positive relationship, whereas the red
line indicates a negative relationship.
Results
Correlations between emotional and
physical states
Following the data preprocessing, an augmented Dickey–
Fuller test analyzing the three time series with no lagged
differences indicated that the logarithmic emotion series,
pain series, and fatigue series were each stationary time
series. Thus, no temporal dislocation was required to meet
the stationarity requirement for the regression analysis.
The correlation analysis revealed that there were negative
correlations between emotional valence and fatigue, as well
as emotional valence and pain, while pain and fatigue were
positively correlated; all three relationships showed the
highest correlations at lag 0. Figure 2 depicts the plots of
the mean cross-correlation values among the participants
between the emotional valence and fatigue, emotional
valence and pain, and pain and fatigue series. The cross-
correlation was the strongest at lag 0; at lag 0, the mean of
the participants’ cross-correlation coefficient (ccf) values
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Figure 2 Group-level correlogram of correlation coefcients according to the time lags among emotion, fatigue, and pain.
Notes: Cross-correlation analyses of the time series of emotion, fatigue, and pain conducted by participant; the 20 acquired correlation coefcients in each set (emotion
and fatigue, emotion and pain, as well as pain and fatigue) were averaged among the 20 participants and shown in the correlogram. The threshold was ±0.96, which is the
threshold level for cross-correlations with 15 time points. (A) Group-level cross-correlation analysis of emotion and fatigue by time. (B) Group-level cross-correlation
analysis of emotion and pain by time. (C) Group-level cross-correlation analysis of pain and fatigue by time.

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Lee et al
were as follows: Emotion–Fatigue =-0.289 (P,0.001);
Emotion–Pain = -0.208 (P=0.007); and Pain–Fatigue =0.321,
(P,0.001).
Multilevel regression model of the
emotional and physical states
There was no heterogeneity in the random slopes of the
between-subjects factor. Table 1 summarizes the dynamic
interactions among the three series (β), as well as the Akaike
information criterion (AIC), Bayesian information criterion
(BIC), and log likelihood of each model. The MLM revealed
that fatigue was negatively associated with emotional valence
(β =-0.233, P,0.001) and positively associated with pain
(β =0.398, P,0.001). Additionally, pain was significantly
associated with fatigue (β =0.250, P,0.001) and emotional
valence was significantly more negative as fatigue increased
(β =-0.150, P,0.001). Emotional valence also exhibited a
negative change as pain increased (β =-0.022, P=0.063) and
pain increased as emotion changed negatively (β =-0.021,
P=0.063), but these results were not statistically significant.
The dynamic network structure between emotional valence,
pain, and fatigue is presented in Figure 3.
Discussion
The current study developed a PHR called Mind Mirror
for the recording of daily emotions, pain, and fatigue. By
using explicit methods to retrospectively record changes in
emotional valence, pain, and fatigue every 2 hours on a daily
basis, this mobile platform enabled the collection of a set
of individualized time series data that illustrated emotional
and physical changes in individual patients. The analyses
revealed that there were dynamic relationships between
daily emotional and physical states in healthy participants.
At lag 0, emotion and fatigue were negatively correlated,
while fatigue and pain were positively correlated. In the
dynamic structural network produced by the MLM, the body,
or the physical states of pain and fatigue, instantly interacted
with emotional valence. Additionally, the overall physical
states of fatigue and pain seemed to positively interact with
each other, which may have interactively facilitated either the
improvement or worsening of a condition. The DRM is one
way of self-assessment on the daily affective experiences.27,28
While self-assessments of the recent affective experiences
such as DRM have been studied to contribute to subjective
well-being, our study focused on not only the emotional states
but also the participant’s physical symptoms that may affect
their daily experiences. By doing so, this mobile platform
aimed to provide a tool for assessing daily situations, as well
as a daily record of psychological and physical states.
The current findings suggest that emotional valence
and fatigue directly influence one another and that emo-
tional valence and pain influence one another through the
Table 1 Mixed generalized linear regression model of the multiple time series for emotional valence, fatigue, and pain
Model 1 Model 2 Model 3
(Pain ← Emotion + Fatigue) (Fatigue ← Emotion + Pain) (Emotion ← Pain + Fatigue)
βSE P-value βSE P-value βSE P-value
Emotion -0.021 0.011 0.063 -0.233 0.014 ,0.001 – – –
Fatigue 0.250 0.009 ,0.001 – – – -0.150 0.009 ,0.001
Pain – – – 0.398 0.014 ,0.001 -0.022 0.012 0.063
Log-likelihood -15,408.1 -15,536.2 -17,162.2
AIC 30,830.2 31,086.3 34,338.5
BIC 30,878.7 31,134.8 34,387.0
Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; SE, standard error.
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Figure 3 Dynamic network among emotional valence, fatigue, and pain according to
a multilevel regression analysis.
Notes: The green arrow indicates a positive relationship, and the red arrow
indicates a negative relationship. A solid line indicates a signicant association, and a
dotted line indicates a nonsignicant relationship.

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The dynamic relationship between emotional and physical states
mediating symptom of fatigue, even in healthy individuals.
These findings agree with those of previous studies that
identified a relationship between negative emotions and
physical states.20–24,35–38 For example, there are strong genetic
links between chronic widespread musculoskeletal pain and
fatigue as well as between chronic widespread pain and
depression.34 Other studies have identified a close relation-
ship between emotional valence and one’s physical condi-
tion. Accordingly, chronic pain is associated with mental
disorders, and negative emotions are known to influence
the development of cardiovascular diseases.21,22,24,35 Pain
disorders without a definite cause, including fibromyalgia
(in which the most common symptoms are pain, fatigue,
and depression), affect patients in such a way that treatments
help to relieve symptoms but not to eliminate the cause of
the disorders.34,36 Diseases such as cancer are associated
with a variety of emotional symptoms, including anxiety
and depression, as well as physical symptoms, including
pain and fatigue.20,37 On the other hand, although there is
evidence that mood influences pain disorders, the influence
of a negative emotional state on one’s physical state has also
been shown to be either selective, general, or unclear.23,38 A
30-day observational study demonstrated that emotions affect
healthy individuals and that this influence is instantly evident
even in the absence of awareness of a patient.24
In this respect, PHRs may provide a possible strategy
for improving medical care for patients with comorbid
psychological and physical illnesses.39 Thus, the findings
of the current study can be applied to health management
by medical professionals and institutions for the purpose
of analyzing daily emotions that can lead to diseases as
well as for predicting possible changes in daily emotions.
Participant-generated health data, such as those produced by
the current study, hold potential for the self-monitoring and
daily measurement of health conditions, which will aid in
the investigation of possible relationships between an indi-
vidual’s emotions and chronic physical symptoms outside the
hospital. In a more general sense, PHRs point to the promise
of health technologies for managing health and preventing
the occurrence or worsening of various disorders among
members of the general population who have access to mobile
technology. Moreover, a variety of diverse information
can be obtained using this format, depending on the target
population, including inpatients within medical facilities,
outpatients who make regular visits, and healthy individuals
who are yet to receive medical checkups. Reduced gaps in
health-related information may aid in the diagnoses of indi-
viduals who do not have a clear cause for their symptoms.
As these technologies are developed and distributed in the
near future, it will be essential to ensure that they are avail-
able for and tested in patients with psychological and/or
psychosomatic symptoms.
The current study has several limitations that must be
noted. This study found marked and direct, but not signifi-
cant, relationships between pain and emotion, which may
be interpreted from two different perspectives. First, this
study was conducted with a limited number of participants
and, thus, further investigations are needed to compare these
findings with those of other studies in order to establish
generalizability with other age groups and health settings
with a larger population. Second, this study was conducted
using only a subset of participants with regular emotional
and physical health statuses and, thus, they may have had
different relationships between their emotional and physical
states relative to chronic disease patients. For instance, mental
disorders, including depression, are known to be comorbid
with chronic pain.40 Thus, further research is needed to
examine the benefits of this application and other types of
novel technologies in different patient groups. Furthermore,
the mobile application-based PHR used in the current study
required that the participants enter all data and access their
records using smartphones, which may have limited the
population eligible for this intervention. This data collection
procedure has also limited data quality control. Third, the
retrospective assessment of the pain, fatigue, and emotional
valence by the participants, as indicated in our study, may
introduce recall bias. While the strategy to use this mobile
platform minimized any missing values during the trial,
the recall bias may have consequences for the data quality.
One way of avoiding this problem, as used by other mobile
platforms, is to prompt the participants several times during
the day. Finally, the current analyses did not include data
collected from the circumplex model of categorical emotion
or other items, including sleep or other chronic symptoms,
because it was focused on the temporal dynamics of the
relationships between the emotional and physical states of
patients. It would be interesting to investigate the interplay
between different types of emotions and physical symptoms
in the future. Future studies in which a large sample size
and the analysis of a combination of other items are utilized
when an application-based PHR is widely used worldwide
should be conducted.
Conclusion
The current 30-day observational study examined rela-
tionships among pain, fatigue, and emotional valence and

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418
Lee et al
provided evidence that the physical condition and emotional
state of healthy participants are interrelated. Positive changes
in emotional valence were associated with improvements in
physical condition via decreases in pain and fatigue, while
negative changes in emotional valence were associated with
the aggravation of pain and fatigue. By measuring differences
in daily emotions, the current study provided basic informa-
tion about both emotional and physical health in daily life
using a mobile platform that recorded emotional and physical
changes throughout the day on a daily basis. These findings
also suggest that further data collection and analyses will
contribute to the ability to predict an individual’s emotional
and physical health conditions, which would aid in the man-
agement of an individual’s health on a daily basis.
Acknowledgments
This research was supported by a grant-in-aid from the Korea
Institute of Oriental Medicine (grant number K15511). The
funders had no role in study design, data collection and analy-
sis, decision to publish, or preparation of the manuscript.
Disclosure
The authors report no conflicts of interest in this work.
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