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Record W4417451844 · doi:10.2196/85853

Impact of the Population Medicine Multimorbidity Intervention in Xishui County (POPMIX) on People at High Risk for Chronic Obstructive Pulmonary Disease Who Experience Mental Health Symptoms: Protocol for the POPMIX-MH Cluster Randomized Controlled Trial

2025· article· en· W4417451844 on OpenAlexvenueno aff
Wenjin Chen, Shiyu Zhang, Ying-ping Wang, Zhoutao Zheng, Ke Huang, Xingyao Tang, Xunliang Tong, Lei Tang, Jingxia Zhao, Liu He, Lirui Jiao, Yuxing Luo, Qiande Lai, Qiushi Chen, Aditi Bunker, Sebastián Vollmer, Pascal Geldsetzer, Dean T. Jamison, Till Bärnighausen, Ting Yang, Simiao Chen, Chen Wang

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthIntervention (counseling)COPDRandomized controlled trialProtocol (science)MultimorbidityPopulationAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and mental health conditions represent intersecting public health challenges, especially in resource-limited rural China. Existing care models often neglect the psychosocial needs of populations at high risk for COPD, resulting in limited effectiveness of prevention and management strategies. This study evaluates an integrated intervention designed to improve both mental and physical health outcomes among high-COPD-risk individuals with mental health symptoms, using a population medicine framework. OBJECTIVE: This study aims to evaluate the effect of an integrated, population medicine-based multimorbidity intervention package among high-COPD-risk individuals with mental health symptoms in Xishui County, Guizhou Province, China. METHODS: We are conducting a 12-month, 2-arm cluster randomized controlled trial across 26 townships in Xishui County, Guizhou, China. A total of 44,000 residents aged ≥35 years were screened using the Chronic Obstructive Pulmonary Disease Screening Questionnaire, identifying 10,000 individuals at high risk of COPD. Among them, 3807 individuals with Warwick-Edinburgh Mental Well-Being Scale scores below 45 were enrolled as participants. Intervention components include digital cognitive behavioral therapy-based mental health support, community screening, chronic disease management, patient education, digital follow-up, and team-based care. The primary outcomes are depressive symptoms (9-item Patient Health Questionnaire), anxiety symptoms (7-item General Anxiety Disorder), and mental well-being (Warwick-Edinburgh Mental Well-Being Scale). Secondary outcomes are control of chronic diseases, physiological and functional indicators such as lung function, health-related quality of life, mental and behavioral health, health care utilization, knowledge of COPD and asthma, productivity loss, and care cascade indicators for chronic conditions. RESULTS: Data collection for the POPMIX-MH trial began in June 2024. Baseline, 3-month, and 6-month assessments have been completed, and the 12-month follow-up assessments are planned to be completed in March 2026. CONCLUSIONS: This study is the first to integrate psychological support, chronic disease management, and community-based screening into a single scalable intervention package targeting multimorbidity in China. It tests the feasibility of applying population medicine principles, emphasizing integrated, preventive, and population-level care, within primary care systems in low-resource settings. By targeting both mental and physical health, it redefines chronic care beyond traditional organ-specific approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT06458218; https://clinicaltrials.gov/ct2/show/NCT06458218. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/85853.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0280.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.541
Teacher spread0.450 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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