Frontiers in medicine · 2026
Association between IoT-driven dynamic pulmonary function monitoring and quality of life in community-dwelling patients with chronic obstructive pulmonary disease: a 2-year longitudinal study.
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Zhao Mingming, Zheng Haixia, Yang Liangfeng, Yang Minmin, Lin Yu, Kong Miaomiao, Wang Nan, Hang Yanping, Zhao Yuguo
Аннотация
OBJECTIVE: To characterize dynamic changes in Internet of Things (IoT)-driven pulmonary function and examine their impact and predictive value on quality of life (QoL) in patients with chronic obstructive pulmonary disease (COPD).
METHODS: Using multistage stratified cluster random sampling, 3,000 residents with COPD high-risk factors were screened between January 2023 and January 2024 in this single-arm observational cohort study, and 360 patients who fulfilled the contemporaneous GOLD diagnostic criteria for COPD and had complete longitudinal monitoring data were included in the final analysis. All participants underwent 2-year IoT-based dynamic monitoring of forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC, peak expiratory flow (PEF), and maximal voluntary ventilation (MVV), and rates of change were calculated. Quality of life (QoL) was assessed using the WHOQOL-BREF questionnaire, and participants were stratified into good QoL (n = 184) and poor QoL (n = 176) groups according to the total score. Univariate analysis, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed.
RESULTS: After 2 years, physical/environmental scores increased, while psychological/social relationship and total QoL scores decreased (P < 0.001). FVC, FEV1, FEV1/FVC, PEF, and MVV improved from baseline (P < 0.001). Smoking status, cumulative smoking load, occupational dust/harmful gas exposure, and biomass fuel use were independently associated with poorer QoL, whereas rates of change in FVC, FEV1, and MVV were independently associated with better QoL (P < 0.05). The combined model (AUC = 0.834) outperformed behavioral factor model and dynamic physiological indicator model (P < 0.05).
CONCLUSION: Dynamic pulmonary function changes obtained via IoT, particularly rates of change in FVC, FEV1, and MVV, are independently associated with QoL in COPD. Combining these indicators with behavioral and exposure factors was associated with improved predictive accuracy for poor QoL, supporting risk stratification and individualized management.
METHODS: Using multistage stratified cluster random sampling, 3,000 residents with COPD high-risk factors were screened between January 2023 and January 2024 in this single-arm observational cohort study, and 360 patients who fulfilled the contemporaneous GOLD diagnostic criteria for COPD and had complete longitudinal monitoring data were included in the final analysis. All participants underwent 2-year IoT-based dynamic monitoring of forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC, peak expiratory flow (PEF), and maximal voluntary ventilation (MVV), and rates of change were calculated. Quality of life (QoL) was assessed using the WHOQOL-BREF questionnaire, and participants were stratified into good QoL (n = 184) and poor QoL (n = 176) groups according to the total score. Univariate analysis, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed.
RESULTS: After 2 years, physical/environmental scores increased, while psychological/social relationship and total QoL scores decreased (P < 0.001). FVC, FEV1, FEV1/FVC, PEF, and MVV improved from baseline (P < 0.001). Smoking status, cumulative smoking load, occupational dust/harmful gas exposure, and biomass fuel use were independently associated with poorer QoL, whereas rates of change in FVC, FEV1, and MVV were independently associated with better QoL (P < 0.05). The combined model (AUC = 0.834) outperformed behavioral factor model and dynamic physiological indicator model (P < 0.05).
CONCLUSION: Dynamic pulmonary function changes obtained via IoT, particularly rates of change in FVC, FEV1, and MVV, are independently associated with QoL in COPD. Combining these indicators with behavioral and exposure factors was associated with improved predictive accuracy for poor QoL, supporting risk stratification and individualized management.