Evidence map›Paper›PMID 36678199›Full record

ArticleNutrients2023

Trajectories of 24-Hour Physical Activity Distribution and Relationship with Dyslipidemia.

Xiaojing Wang, Yongjun Wang, Zechao Xu, Xiang Guo, Hongmei Mao, Tingting Liu, Weiyi Gong, Zhaolong Gong, Qin Zhuo

Abstract read
In one paragraph

Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

6 citing papers in PubMed.

  1. Article
  2. Accelerometer-measured weekly and diurnal moderate-to-vigorous physical activity patterns and incident osteoporosis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
  3. Observational
  4. Article
  5. Observational
  6. Article
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

9 authors.

Xiaojing WangKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Yongjun WangKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Zechao XuBeijing Chaoyang District Center for Disease Control and Prevention, Beijing 100050, China.
Xiang GuoBeijing Chaoyang District Center for Disease Control and Prevention, Beijing 100050, China.
Hongmei MaoKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Tingting LiuKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Weiyi GongKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Zhaolong GongKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Qin ZhuoKey Laboratory of Trace Element Nutrition of National Health Commission (NHC), National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China.

Funding

National Key R&D Program of China 2018YFC2000600
6 · The paper itself

Abstract

The association between physical activity (PA) and dyslipidemia is well known, but the relationship between a temporal pattern of PA and dyslipidemia remain unknown. Here, we aimed to identify the intensity and temporal patterns of PA clustered by the trajectory model and their relationship with dyslipidemia. The participants were 701 adults (305 males) aged 18−60 years undergoing continuous measurement of PA with Actigraph GT3X+ accelerometers for at least 3 days. A trajectory analysis was applied based on moderate-to-vigorous intensity physical activity (MVPA) accumulated values over every period per day. The association between PA and dyslipidemia was estimated using a logistic regression model. Four distinct PA trajectory groups in the population were identified (continued low, stable and moderate, late increasing, and early increasing). Specifically, the “moderate and stable group” was associated with a decreased rate of high TG (p < 0.05) and the “moderate and stable group” and “late increasing group” were associated with decreased rates of low HDL-C (p < 0.05). In conclusion, there were four activity trajectory groups in this population and the continued low PA trajectory was associated with a high prevalent rate of an abnormal lipid profile, and continued and moderate activity or late afternoon increasing activity might have lower HDL-C distribution.

Indexed as

DyslipidemiasExerciseAccelerometryAdultHumansMaledyslipidemiaphysical activitytemporal patterntrajectory analysis

Identifiers

PMID36678199
PMCPMC9860816

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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 OpenQuestion graph.