A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals
•mHealth system can be effectively utilized for real-world data collection.•Physiological and psychological stress has an influence on pain.•Stress vulnerability is a significant predictor of pain thresholds. Mobile health systems integrating wearable devices are emerging as promising tools for regi...
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| Veröffentlicht in: | International journal of medical informatics (Shannon, Ireland) Ireland), 2024-04, Vol.184, p.105371-105371, Article 105371 |
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| container_title | International journal of medical informatics (Shannon, Ireland) |
| container_volume | 184 |
| creator | Gungormus, Dogukan Baran Garcia-Moreno, Francisco M. Bermudez-Edo, Maria Sánchez-Bermejo, Laura Garrido, José Luis Rodríguez-Fórtiz, María José Pérez-Mármol, José Manuel |
| description | •mHealth system can be effectively utilized for real-world data collection.•Physiological and psychological stress has an influence on pain.•Stress vulnerability is a significant predictor of pain thresholds.
Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored.
To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease.
A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions.
The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores.
The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies. |
| doi_str_mv | 10.1016/j.ijmedinf.2024.105371 |
| format | Article |
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Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored.
To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease.
A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions.
The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores.
The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.</description><identifier>ISSN: 1386-5056</identifier><identifier>EISSN: 1872-8243</identifier><identifier>DOI: 10.1016/j.ijmedinf.2024.105371</identifier><identifier>PMID: 38335744</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Aged ; Chronic Disease ; Chronic pain ; Chronic Pain - diagnosis ; Computer-based systems ; Cross-Sectional Studies ; Humans ; Mobile health systems ; Rheumatic Diseases ; Semi-automatic systems ; Stress ; Telemedicine ; Wearable devices ; Wearable Electronic Devices</subject><ispartof>International journal of medical informatics (Shannon, Ireland), 2024-04, Vol.184, p.105371-105371, Article 105371</ispartof><rights>2024 The Author(s)</rights><rights>Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c363t-829a55d8007c335a9fba327ac0b6f6c3319546c2b785d3ce1b332d70035e04803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ijmedinf.2024.105371$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38335744$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gungormus, Dogukan Baran</creatorcontrib><creatorcontrib>Garcia-Moreno, Francisco M.</creatorcontrib><creatorcontrib>Bermudez-Edo, Maria</creatorcontrib><creatorcontrib>Sánchez-Bermejo, Laura</creatorcontrib><creatorcontrib>Garrido, José Luis</creatorcontrib><creatorcontrib>Rodríguez-Fórtiz, María José</creatorcontrib><creatorcontrib>Pérez-Mármol, José Manuel</creatorcontrib><title>A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals</title><title>International journal of medical informatics (Shannon, Ireland)</title><addtitle>Int J Med Inform</addtitle><description>•mHealth system can be effectively utilized for real-world data collection.•Physiological and psychological stress has an influence on pain.•Stress vulnerability is a significant predictor of pain thresholds.
Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored.
To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease.
A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions.
The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores.
The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.</description><subject>Aged</subject><subject>Chronic Disease</subject><subject>Chronic pain</subject><subject>Chronic Pain - diagnosis</subject><subject>Computer-based systems</subject><subject>Cross-Sectional Studies</subject><subject>Humans</subject><subject>Mobile health systems</subject><subject>Rheumatic Diseases</subject><subject>Semi-automatic systems</subject><subject>Stress</subject><subject>Telemedicine</subject><subject>Wearable devices</subject><subject>Wearable Electronic Devices</subject><issn>1386-5056</issn><issn>1872-8243</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkLlOxDAQhi0E4n4F5JImi484TjoQ4pKQaKC2HHsCs8qx2M6ifXu8WqClmuub6yfkgrMFZ7y6Wi5wOYDHsVsIJsqcVFLzPXLMay2KWpRyP_uyrgrFVHVETmJcMsY1U-UhOZK1lEqX5TGZb2iEAQs7p2mwCR0dHsH26YPGTUww0Dni-E6_wAbb9kA9rNFBpN0UKHoYE3abLbCyOBYBepvA5yDYARKESHGk0HsI_Sa7HtfoZ9vHM3LQZQPnP_aUvN3fvd4-Fs8vD0-3N8-Fk5VM-Y3GKuVrxrTLB9uma60U2jrWVl2VU7xRZeVEq2vlpQPeSim8ZkwqYGXN5Cm53M1dhelzhpjMgNFB39sRpjka0YiyaWqtmoxWO9SFKcYAnVkFHGzYGM7MVnKzNL-Sm63kZid5brz42TG3ufzX9qtxBq53AORP1wjBRIcwujwqgEvGT_jfjm9TCpcf</recordid><startdate>202404</startdate><enddate>202404</enddate><creator>Gungormus, Dogukan Baran</creator><creator>Garcia-Moreno, Francisco M.</creator><creator>Bermudez-Edo, Maria</creator><creator>Sánchez-Bermejo, Laura</creator><creator>Garrido, José Luis</creator><creator>Rodríguez-Fórtiz, María José</creator><creator>Pérez-Mármol, José Manuel</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202404</creationdate><title>A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals</title><author>Gungormus, Dogukan Baran ; Garcia-Moreno, Francisco M. ; Bermudez-Edo, Maria ; Sánchez-Bermejo, Laura ; Garrido, José Luis ; Rodríguez-Fórtiz, María José ; Pérez-Mármol, José Manuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-829a55d8007c335a9fba327ac0b6f6c3319546c2b785d3ce1b332d70035e04803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aged</topic><topic>Chronic Disease</topic><topic>Chronic pain</topic><topic>Chronic Pain - diagnosis</topic><topic>Computer-based systems</topic><topic>Cross-Sectional Studies</topic><topic>Humans</topic><topic>Mobile health systems</topic><topic>Rheumatic Diseases</topic><topic>Semi-automatic systems</topic><topic>Stress</topic><topic>Telemedicine</topic><topic>Wearable devices</topic><topic>Wearable Electronic Devices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gungormus, Dogukan Baran</creatorcontrib><creatorcontrib>Garcia-Moreno, Francisco M.</creatorcontrib><creatorcontrib>Bermudez-Edo, Maria</creatorcontrib><creatorcontrib>Sánchez-Bermejo, Laura</creatorcontrib><creatorcontrib>Garrido, José Luis</creatorcontrib><creatorcontrib>Rodríguez-Fórtiz, María José</creatorcontrib><creatorcontrib>Pérez-Mármol, José Manuel</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of medical informatics (Shannon, Ireland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gungormus, Dogukan Baran</au><au>Garcia-Moreno, Francisco M.</au><au>Bermudez-Edo, Maria</au><au>Sánchez-Bermejo, Laura</au><au>Garrido, José Luis</au><au>Rodríguez-Fórtiz, María José</au><au>Pérez-Mármol, José Manuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals</atitle><jtitle>International journal of medical informatics (Shannon, Ireland)</jtitle><addtitle>Int J Med Inform</addtitle><date>2024-04</date><risdate>2024</risdate><volume>184</volume><spage>105371</spage><epage>105371</epage><pages>105371-105371</pages><artnum>105371</artnum><issn>1386-5056</issn><eissn>1872-8243</eissn><abstract>•mHealth system can be effectively utilized for real-world data collection.•Physiological and psychological stress has an influence on pain.•Stress vulnerability is a significant predictor of pain thresholds.
Mobile health systems integrating wearable devices are emerging as promising tools for registering pain-related factors. However, their application in populations with chronic conditions has been underexplored.
To design a semi-automatic mobile health system with wearable devices for evaluating the potential predictive relationship of pain qualities and thresholds with heart rate variability, skin conductance, perceived stress, and stress vulnerability in individuals with preclinical chronic pain conditions such as suspected rheumatic disease.
A multicenter, observational, cross-sectional study was conducted with 67 elderly participants. Predicted variables were pain qualities and pain thresholds, assessed with the McGill Pain Questionnaire and a pressure algometer, respectively. Predictor variables were heart rate variability, skin conductance, perceived stress, and stress vulnerability. Multiple linear regression analyses were conducted to examine the influence of the predictor variables on the pain dimensions.
The multiple linear regression analysis revealed that the predictor variables significantly accounted for 27% of the variability in the affective domain, 14% in the miscellaneous domain, 15% in the total pain rating index, 10% in the number of words chosen, 14% in the present pain intensity, and 16% in the Visual Analog Scale scores.
The study found significant predictive values of heart rate variability, skin conductance, perceived stress, and stress vulnerability in relation to pain qualities and thresholds in the elderly population with suspected rheumatic disease. The comprehensive integration of physiological and psychological stress measures into pain assessment of elderly individuals with preclinical chronic pain conditions could be promising for developing new preventive strategies.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>38335744</pmid><doi>10.1016/j.ijmedinf.2024.105371</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Aged Chronic Disease Chronic pain Chronic Pain - diagnosis Computer-based systems Cross-Sectional Studies Humans Mobile health systems Rheumatic Diseases Semi-automatic systems Stress Telemedicine Wearable devices Wearable Electronic Devices |
| title | A semi-automatic mHealth system using wearable devices for identifying pain-related parameters in elderly individuals |
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