Healthy lifestyle behaviors among individuals with chronic obstructive pulmonary disease in urban and rural communities in China: a large community-based epidemiological study

Yan, Ruohua; Wang, Yang; Jiang, Bo; Li, Wei

doi:10.2147/copd.s144978

Cited by 18 publications

(16 citation statements)

References 30 publications

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“…COPD is a highly prevalent lung disease characterized by persistent airflow limitation due to a mixture of obstructive bronchiolitis and emphysema. The morbidity and mortality of COPD are high and continue to increase [ 2 ], such that COPD is projected to become the third-leading cause of death worldwide by 2030.…”

Section: Introductionmentioning

confidence: 99%

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Wu¹,

Li²,

Huang³

et al. 2021

JMIR Mhealth Uhealth

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Background The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Objective The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days. Methods This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality–sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model. Results The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality–sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved. Conclusions Using wearable devices, home air quality–sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.

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Section: Introductionmentioning

confidence: 99%

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Wu¹,

Li²,

Huang³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Information on demographics, socioeconomic status, lifestyle behaviour, medical history and living condition was collected using interview‐based questionnaires 11,12 . Height and weight were measured by standard instruments without hats or shoes, and wearing minimal clothing.…”

Section: Methodsmentioning

confidence: 99%

“…Information on demographics, socioeconomic status, lifestyle behaviour, medical history and living condition was collected using interview-based questionnaires. 11,12 Height and weight were measured by standard instruments without hats or shoes, and wearing minimal clothing. Forced expiratory volume in the first second (FEV 1 ) and forced vital capacity (FVC) were recorded using a portable spirometer without spirographs (MicroGP, MicroMedical, Chatham, IL, USA), following the American Thoracic Society 2005 guidelines.…”

Section: Data Sourcesmentioning

confidence: 99%

Regional differences in the contribution of smoking, dietary and cooking behaviours to airflow obstruction in China: A population‐based case–control study

et al. 2022

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Background and objective The major contributing risk factors to airflow obstruction (AO) in China remain largely unknown. We examined the environmental and lifestyle risk factors of unrecognized AO in the baseline of a population‐based cohort drawn from 115 urban and rural communities across 12 provinces in China. Methods Amongst 46,285 adults recruited from 2005 to 2009, 3686 were identified with AO on spirometry (defined by the ratio of forced expiratory volume in the first second to forced vital capacity <0.7) and without known chronic lung disease. These cases were age‐ and sex‐matched to 11,129 controls with normal spirometry and no chronic lung disease from the same community. Conditional multivariable adjusted OR and population attributable fraction (PAF) were calculated for each identified risk factor and their combined effect. Results Compared to controls, smoking initiation age <20 years (OR 1.22 [95% CI 1.01–1.48]), smoking duration ≥40 years (OR 1.82 [1.50–2.22]), low vegetables (OR 1.86 [1.67–2.07]) and fruits (OR 1.14 [1.02–1.29]) intake, cooking with biomass fuels (OR 2.54 [2.32–2.78]) and poor kitchen ventilation (OR 1.37 [1.19–1.58]) were significantly associated with elevated risks of unrecognized AO. The combined effect of these lifestyle factors significantly elevated the odds by 25 fold (18.6–34.3). The addition of prior tuberculosis and low socioeconomic status further increased the odds to 40.1 (28.2–57.0) and the PAF to 66.7% (51.1–78.1). Conclusion Smoking, unhealthy diet, biomass cooking fuels and low socioeconomic status are strongly associated with AO. Addressing these risk factors could substantially reduce the burden of AO in China.

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“…For the subjects, we provide a standardized epidemiological questionnaire (Yan, Wang, Bo, & Li, 2017) to collect demographic and personal data. After sorting the collected information, we select some variables for statistical analysis, including: age, gender, smoking status.…”

Section: Demographic and Clinical Datamentioning

confidence: 99%

TERT gene polymorphisms are associated with chronic obstructive pulmonary disease risk in the Chinese Li population

Ding

et al. 2019

Molec Gen & Gen Med

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Background Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide and is characterized by a partially reversible airflow limitation. Currently, many studies put forward that COPD is associated with both genetic and environmental factors. It has been reported that germline mutations in telomerase are risk factors for COPD susceptibility. In this study, we validated the association between TERT polymorphisms and COPD risk with a case–control study in the Chinese Li population. Methods A total of 279 COPD patients and 290 control individuals were recruited. We identified five single nucleotide polymorphisms (SNPs) in TERT that were associated with COPD. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated in logistic regression models after adjusting for age and gender to assess the association. Results In the genetic model analysis, we found the “C/T‐T/T” genotype of rs10069690 in TERT was associated with an increased COPD risk in the dominant model (p = 0.046); the rs2853677 in TERT was significantly associated with increased COPD risk based on the codominant model (“A/G” genotype, p = 0.033), dominant model (A/G‐G/G genotype, p = 0.0091), and log‐additive model (p = 0.023). The rs2853676 in TERT could increase the risk of COPD in the dominant model (“C/T‐T/T” genotype, p = 0.026) and in the Log‐additive model (p = 0.022). Conclusion Our data shed new light on the association between TERT SNPs and COPD susceptibility in the Chinese Li population.

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Healthy lifestyle behaviors among individuals with chronic obstructive pulmonary disease in urban and rural communities in China: a large community-based epidemiological study

Cited by 18 publications

References 30 publications

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study

Regional differences in the contribution of smoking, dietary and cooking behaviours to airflow obstruction in China: A population‐based case–control study

TERT gene polymorphisms are associated with chronic obstructive pulmonary disease risk in the Chinese Li population

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