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Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a multicenter cross-sectional study
Multimorbidity is increasing globally, emphasizing the need for effective self-management strategies. The Cumulative Complexity Model (CuCoM) offers a unique perspective on understanding self-management based on workload and capacity. This study aims to validate the CuCoM in multimorbid patients and...
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Published in: | Annals of medicine (Helsinki) 2025-12, Vol.57 (1), p.2451195 |
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description | Multimorbidity is increasing globally, emphasizing the need for effective self-management strategies. The Cumulative Complexity Model (CuCoM) offers a unique perspective on understanding self-management based on workload and capacity. This study aims to validate the CuCoM in multimorbid patients and identify tailored predictors of self-management.
This multicenter cross-sectional survey recruited 1920 multimorbid patients in five primary health centres and four hospitals in China. The questionnaire assessed workload (drug intake, doctor visits and follow-up, disruption in life, and health problems), capacity (social, environmental, financial, physical, and psychological), and self-management. Data were analyzed using latent profile analysis, chi-square, multivariate linear regression, and network analysis.
d Patients were classified into four profiles: low workload-low capacity (10.2%), high workload-low capacity (7.5%), low workload-high capacity (64.6%), and high workload-high capacity (17.7%). Patients with low workload and high capacity exhibited better self-management (β = 0.271,
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This multicenter cross-sectional survey recruited 1920 multimorbid patients in five primary health centres and four hospitals in China. The questionnaire assessed workload (drug intake, doctor visits and follow-up, disruption in life, and health problems), capacity (social, environmental, financial, physical, and psychological), and self-management. Data were analyzed using latent profile analysis, chi-square, multivariate linear regression, and network analysis.
d Patients were classified into four profiles: low workload-low capacity (10.2%), high workload-low capacity (7.5%), low workload-high capacity (64.6%), and high workload-high capacity (17.7%). Patients with low workload and high capacity exhibited better self-management (β = 0.271,
< 0.001), while those with high workload and low capacity exhibited poorer self-management (β=-0.187,
< 0.001). Social capacity was the strongest predictor for all profiles. Environmental capacity ranked second for 'high workload-high capacity' (R² = 3.26) and 'low workload-low capacity' (R² = 5.32) profiles. Financial capacity followed for the 'low workload-high capacity' profile (R² = 5.40), while psychological capacity was key in the 'high workload-low capacity' profile (R² = 6.40). In the network analysis, socioeconomic factors exhibited the central nodes (
< 0.05).
Personalized interventions designed to increase capacity and reduce workload are essential for improving self-management in multimorbid patients. Upstream policies promoting health equity are also crucial for better self-management outcomes.</description><identifier>ISSN: 0785-3890</identifier><identifier>ISSN: 1365-2060</identifier><identifier>EISSN: 1365-2060</identifier><identifier>DOI: 10.1080/07853890.2025.2451195</identifier><identifier>PMID: 39823193</identifier><language>eng</language><publisher>England: Taylor & Francis</publisher><subject>Adult ; Aged ; capacity ; China - epidemiology ; Cross-Sectional Studies ; cumulative complexity model ; Female ; Humans ; Male ; Middle Aged ; Multimorbidity ; Primary Care ; Self-Management ; Surveys and Questionnaires ; Workload ; Workload - statistics & numerical data</subject><ispartof>Annals of medicine (Helsinki), 2025-12, Vol.57 (1), p.2451195</ispartof><rights>2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2025 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2247-3c7ed73ccf9693d4d77666e9fe2fb08effcb45a5d633d51142a290f0bc3c4ca23</cites><orcidid>0000-0002-6744-5771 ; 0000-0001-5656-2262 ; 0000-0001-6165-046X ; 0000-0001-5906-631X ; 0000-0001-5753-5530 ; 0000-0003-4488-5624 ; 0000-0001-6947-3330</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11749107/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11749107/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39823193$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Binyu</creatorcontrib><creatorcontrib>Fu, Yujia</creatorcontrib><creatorcontrib>Wu, Jingjie</creatorcontrib><creatorcontrib>Xue, Erxu</creatorcontrib><creatorcontrib>Lai, Chuyang</creatorcontrib><creatorcontrib>Chen, Dandan</creatorcontrib><creatorcontrib>Wu, Qiwei</creatorcontrib><creatorcontrib>Yu, Jianing</creatorcontrib><creatorcontrib>Wu, Qiaoyu</creatorcontrib><creatorcontrib>Ye, Zhihong</creatorcontrib><creatorcontrib>Shao, Jing</creatorcontrib><title>Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a multicenter cross-sectional study</title><title>Annals of medicine (Helsinki)</title><addtitle>Ann Med</addtitle><description>Multimorbidity is increasing globally, emphasizing the need for effective self-management strategies. The Cumulative Complexity Model (CuCoM) offers a unique perspective on understanding self-management based on workload and capacity. This study aims to validate the CuCoM in multimorbid patients and identify tailored predictors of self-management.
This multicenter cross-sectional survey recruited 1920 multimorbid patients in five primary health centres and four hospitals in China. The questionnaire assessed workload (drug intake, doctor visits and follow-up, disruption in life, and health problems), capacity (social, environmental, financial, physical, and psychological), and self-management. Data were analyzed using latent profile analysis, chi-square, multivariate linear regression, and network analysis.
d Patients were classified into four profiles: low workload-low capacity (10.2%), high workload-low capacity (7.5%), low workload-high capacity (64.6%), and high workload-high capacity (17.7%). Patients with low workload and high capacity exhibited better self-management (β = 0.271,
< 0.001), while those with high workload and low capacity exhibited poorer self-management (β=-0.187,
< 0.001). Social capacity was the strongest predictor for all profiles. Environmental capacity ranked second for 'high workload-high capacity' (R² = 3.26) and 'low workload-low capacity' (R² = 5.32) profiles. Financial capacity followed for the 'low workload-high capacity' profile (R² = 5.40), while psychological capacity was key in the 'high workload-low capacity' profile (R² = 6.40). In the network analysis, socioeconomic factors exhibited the central nodes (
< 0.05).
Personalized interventions designed to increase capacity and reduce workload are essential for improving self-management in multimorbid patients. Upstream policies promoting health equity are also crucial for better self-management outcomes.</description><subject>Adult</subject><subject>Aged</subject><subject>capacity</subject><subject>China - epidemiology</subject><subject>Cross-Sectional Studies</subject><subject>cumulative complexity model</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multimorbidity</subject><subject>Primary Care</subject><subject>Self-Management</subject><subject>Surveys and Questionnaires</subject><subject>Workload</subject><subject>Workload - statistics & numerical data</subject><issn>0785-3890</issn><issn>1365-2060</issn><issn>1365-2060</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVUsuO0zAUjRCIKQOfAPKSTYqfccwGoRGPkUZiA2Jp3dg3rYckLnYK9C_4ZJy2M2JWls495_g-TlW9ZHTNaEvfUN0q0Rq65pSrNZeKMaMeVSsmGlVz2tDH1Wrh1AvponqW8y2llGtGn1YXwrRcMCNW1d_vMf0YIvjawQ5cmA8kjB0MMDnMBCZP5i2GVMBSnUmcSMahr0eYYIMjTjNxcdwN-OeonMgO5lDQTH6HeUvG_TCHMaYu-FJ_S-CEuMLARFyKOdcZ3RziBAPJ894fnldPehgyvji_l9W3jx--Xn2ub758ur56f1M7zqWuhdPotXCuN40RXnqtm6ZB0yPvO9pi37tOKlC-EcKX3UgO3NCedk446YCLy-r65Osj3NpdCiOkg40Q7BGIaWMhlVYHtAqMNkIKjo5LL6TRvdSaKQ9eUaaweL07ee323Yh-GS_B8MD0YWUKW7uJvyxjWhpGdXF4fXZI8ece82zHkB0O5Q4Y99kKppqWlrOyQlUn6nF9Cfv7fxi1SzTsXTTsEg17jkbRvfq_yXvVXRbEPytZuO8</recordid><startdate>202512</startdate><enddate>202512</enddate><creator>Zhao, Binyu</creator><creator>Fu, Yujia</creator><creator>Wu, Jingjie</creator><creator>Xue, Erxu</creator><creator>Lai, Chuyang</creator><creator>Chen, Dandan</creator><creator>Wu, Qiwei</creator><creator>Yu, Jianing</creator><creator>Wu, Qiaoyu</creator><creator>Ye, Zhihong</creator><creator>Shao, Jing</creator><general>Taylor & Francis</general><general>Taylor & Francis Group</general><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><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6744-5771</orcidid><orcidid>https://orcid.org/0000-0001-5656-2262</orcidid><orcidid>https://orcid.org/0000-0001-6165-046X</orcidid><orcidid>https://orcid.org/0000-0001-5906-631X</orcidid><orcidid>https://orcid.org/0000-0001-5753-5530</orcidid><orcidid>https://orcid.org/0000-0003-4488-5624</orcidid><orcidid>https://orcid.org/0000-0001-6947-3330</orcidid></search><sort><creationdate>202512</creationdate><title>Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a multicenter cross-sectional study</title><author>Zhao, Binyu ; Fu, Yujia ; Wu, Jingjie ; Xue, Erxu ; Lai, Chuyang ; Chen, Dandan ; Wu, Qiwei ; Yu, Jianing ; Wu, Qiaoyu ; Ye, Zhihong ; Shao, Jing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2247-3c7ed73ccf9693d4d77666e9fe2fb08effcb45a5d633d51142a290f0bc3c4ca23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Adult</topic><topic>Aged</topic><topic>capacity</topic><topic>China - epidemiology</topic><topic>Cross-Sectional Studies</topic><topic>cumulative complexity model</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Multimorbidity</topic><topic>Primary Care</topic><topic>Self-Management</topic><topic>Surveys and Questionnaires</topic><topic>Workload</topic><topic>Workload - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Binyu</creatorcontrib><creatorcontrib>Fu, Yujia</creatorcontrib><creatorcontrib>Wu, Jingjie</creatorcontrib><creatorcontrib>Xue, Erxu</creatorcontrib><creatorcontrib>Lai, Chuyang</creatorcontrib><creatorcontrib>Chen, Dandan</creatorcontrib><creatorcontrib>Wu, Qiwei</creatorcontrib><creatorcontrib>Yu, Jianing</creatorcontrib><creatorcontrib>Wu, Qiaoyu</creatorcontrib><creatorcontrib>Ye, Zhihong</creatorcontrib><creatorcontrib>Shao, Jing</creatorcontrib><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><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Annals of medicine (Helsinki)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Binyu</au><au>Fu, Yujia</au><au>Wu, Jingjie</au><au>Xue, Erxu</au><au>Lai, Chuyang</au><au>Chen, Dandan</au><au>Wu, Qiwei</au><au>Yu, Jianing</au><au>Wu, Qiaoyu</au><au>Ye, Zhihong</au><au>Shao, Jing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a multicenter cross-sectional study</atitle><jtitle>Annals of medicine (Helsinki)</jtitle><addtitle>Ann Med</addtitle><date>2025-12</date><risdate>2025</risdate><volume>57</volume><issue>1</issue><spage>2451195</spage><pages>2451195-</pages><issn>0785-3890</issn><issn>1365-2060</issn><eissn>1365-2060</eissn><abstract>Multimorbidity is increasing globally, emphasizing the need for effective self-management strategies. The Cumulative Complexity Model (CuCoM) offers a unique perspective on understanding self-management based on workload and capacity. This study aims to validate the CuCoM in multimorbid patients and identify tailored predictors of self-management.
This multicenter cross-sectional survey recruited 1920 multimorbid patients in five primary health centres and four hospitals in China. The questionnaire assessed workload (drug intake, doctor visits and follow-up, disruption in life, and health problems), capacity (social, environmental, financial, physical, and psychological), and self-management. Data were analyzed using latent profile analysis, chi-square, multivariate linear regression, and network analysis.
d Patients were classified into four profiles: low workload-low capacity (10.2%), high workload-low capacity (7.5%), low workload-high capacity (64.6%), and high workload-high capacity (17.7%). Patients with low workload and high capacity exhibited better self-management (β = 0.271,
< 0.001), while those with high workload and low capacity exhibited poorer self-management (β=-0.187,
< 0.001). Social capacity was the strongest predictor for all profiles. Environmental capacity ranked second for 'high workload-high capacity' (R² = 3.26) and 'low workload-low capacity' (R² = 5.32) profiles. Financial capacity followed for the 'low workload-high capacity' profile (R² = 5.40), while psychological capacity was key in the 'high workload-low capacity' profile (R² = 6.40). In the network analysis, socioeconomic factors exhibited the central nodes (
< 0.05).
Personalized interventions designed to increase capacity and reduce workload are essential for improving self-management in multimorbid patients. Upstream policies promoting health equity are also crucial for better self-management outcomes.</abstract><cop>England</cop><pub>Taylor & Francis</pub><pmid>39823193</pmid><doi>10.1080/07853890.2025.2451195</doi><orcidid>https://orcid.org/0000-0002-6744-5771</orcidid><orcidid>https://orcid.org/0000-0001-5656-2262</orcidid><orcidid>https://orcid.org/0000-0001-6165-046X</orcidid><orcidid>https://orcid.org/0000-0001-5906-631X</orcidid><orcidid>https://orcid.org/0000-0001-5753-5530</orcidid><orcidid>https://orcid.org/0000-0003-4488-5624</orcidid><orcidid>https://orcid.org/0000-0001-6947-3330</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Aged capacity China - epidemiology Cross-Sectional Studies cumulative complexity model Female Humans Male Middle Aged Multimorbidity Primary Care Self-Management Surveys and Questionnaires Workload Workload - statistics & numerical data |
title | Workload-capacity imbalances and their impact on self-management complexity in patients with multimorbidity: a multicenter cross-sectional study |
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