Evidence map›Paper›PMID 39487401›Full record

ArticleBMC public health2024

Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES.

Zhiyuan Sun, Yunhao Yuan, Vahid Farrahi, Fabian Herold, Zhengwang Xia, Xuan Xiong, Zhiyuan Qiao, Yifan Shi, Yahui Yang, Kai Qi and 4 more

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Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Zhiyuan SunCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Yunhao YuanSchool of Information Engineering, Yangzhou University, Yangzhou, 225127, China.
Vahid FarrahiInstitute for Sport and Sport Science, TU Dortmund University, 44227, Dortmund, Germany.
Fabian HeroldResearch Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences Brandenburg, University of Potsdam, 14476, Potsdam, Germany.
Zhengwang XiaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Xuan XiongDepartment of Physical Education, Nanjing University, Nanjing, 210033, China.
Zhiyuan QiaoCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Yifan ShiCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Yahui YangCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Kai QiCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Yufei LiuDepartment of Sport, Gdansk University of Physical Education and Sport, Gdansk, 80-336, Poland.
Decheng XuCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China.
Liye ZouBody-Brain-Mind Laboratory, School of Psychology, Shenzhen University, Shenzhen, 518060, China. liyezou123@gmail.com.
Aiguo ChenCollege of Physical Education, Yangzhou University, Yangzhou, 225127, China. agchen@nsi.edu.cn.

Funding

Fok Ying Tong Education Foundation 141113National Social Science Fund of China 23ATY008
6 · The paper itself

Abstract

backgroundOverweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults.

methodsData were obtained from 46,057 adults in the China Health and Nutrition Survey (2004-2011) and the National Health and Nutrition Examination Survey (2007-2014). Basic demographic information, self-reported lifestyle factors, including physical activity, macronutrient intake, tobacco and alcohol consumption, and body weight status were collected. Three machine learning models, namely decision tree, random forest, and gradient-boosting decision tree, were employed to predict body weight status from lifestyle factors. The SHapley Additive exPlanation (SHAP) method was used to interpret the prediction results of the best-performing model by determining the contributions of specific lifestyle factors to the development of overweight and obesity in adults.

resultsThe performance of the gradient-boosting decision tree model outperformed the decision tree and random forest models. Analysis based on the SHAP method indicates that sedentary behavior, alcohol consumption, and protein intake were important lifestyle factors predicting the development of overweight and obesity in adults. The amount of alcohol consumption and time spent sedentary were the strongest predictors of overweight and obesity, respectively. Specifically, sedentary behavior exceeding 28-35 h/week, alcohol consumption of more than 7 cups/week, and protein intake exceeding 80 g/day increased the risk of being predicted as overweight and obese.

conclusionPooled evidence from two nationally representative studies suggests that recognizing demographic differences and emphasizing the relative importance of sedentary behavior, alcohol consumption, and protein intake are beneficial for managing body weight status in adults. The specific risk thresholds for lifestyle factors observed in this study can help inform and guide future research and public health actions.

Indexed as

Life StyleMachine LearningNutrition SurveysObesityOverweightAdultChinaDecision TreesFemaleHumansMaleMiddle AgedRisk FactorsYoung AdultAlcohol consumptionInterpretable machine learningLifestyleOverweight and obesityPhysical activityRelative importance

Identifiers

PMID39487401
PMCPMC11529325

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.