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Cited by 21Mental Health Research TopicsMobile Health and mHealth ApplicationsEmotion and Mood Recognition

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

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Lee, Ye-Seul, Jung, Won-Mo, Jang, Hyunchul, Kim, Sanghyun, Chung, Sun-Yong, Chae, Youngbyoung·2017·Neuropsychiatric Disease and Treatment
DOI 10.2147/ndt.s120995PMC PMC5308597
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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. Keywords: emotion, fatigue, pain, personal health record (PHR), time-series analysis

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  • Previous study has shown through a time series analysis that emotions and physical states are related in a long-term observation even in healthy volunteers.

    “Previous study has shown through a time series analysis that the emotions and physical states are related in a long-term observation even in healthy volunteers”

    From Understanding Mind‐Body Interaction from the Perspective of East Asian Medicine (2017) · cites this paper as [31]

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Research works printed but not linked: 29

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Cited by

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Authors
Lee, Y.-S., Jung, W.-M., Jang, H., Kim, S., Chung, S.-Y., & Chae, Y.
Affiliations
  • Kyung Hee University (KR)
  • Korea Institute of Oriental Medicine (KR)
Corresponding author
Chae, Youngbyoung
ORCID
Venue
Neuropsychiatric Disease and Treatment
Publisher
Informa UK Limited
Published
2017-02
Volume
Volume 13
Pages
411-419
ISSN
  • 1176-6328 (ISSN-L)
  • 1178-2021
DOI
10.2147/ndt.s120995
PMC
PMC5308597
OpenAlex
W2586705218
Cited by
21
References
8
Citations by year
2026: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 1 · 2019: 6 · 2018: 2 · 2017: 1
FWCI
3.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuropsychiatric Disease and Treatment (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
Funders
Korea Institute of Oriental Medicine (K15511)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
personal health records, emotional valence, fatigue, pain, mind-body problem, mobile health platform, time series analysis

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