Evidence map›Paper›PMID 37927815›Full record

ArticleSSM - population health2023

Identifying Clusters of Health Behaviors in a Japanese Working Population at Risk for Non-Communicable Diseases: A Latent Class Analysis of 12,168 Individuals.

Takahiro Miki, Kojiro Yamamoto, Masashi Kanai, Kento Takeyama, Maki Iwatake, Yuta Hagiwara

Open access · goldAbstract read
In one paragraph

Article in SSM - population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 21% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Takahiro MikiPREVENT Inc.
Kojiro YamamotoPREVENT Inc.
Masashi KanaiPREVENT Inc.
Kento TakeyamaPREVENT Inc.
Maki IwatakePREVENT Inc.
Yuta HagiwaraPREVENT Inc.
Kanazawa University · JPSaitama Prefectural University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Noncommunicable diseases (NCDs) have become a significant global problem. Health behaviors are associated with NCDs, and characterizing populations using a public health approach can help provide specific interventions according to their characteristics. This study aims to examine the formation of clusters of health behavior combinations in the Japanese working population at risk of NCDs, taking into account the influences of age and gender, using latent class analysis. Methods: Participants were individuals at risk for NCDs but had not previously been diagnosed with any. Latent class analysis (LCA) was used to study clustering based on basic characteristics and health behaviors. All statistical analyses were conducted using R (Version 4.0.4) and the "poLCA" package (Version 1.6.0). Results: This study included 12,168 participants. LCA compared models with one to six latent classes. The five-class model was determined to be the most appropriate based on Bayesian Information Criterion, Akaike Information Criterion, and G^2 values, as well as distinguishable cluster characteristics. Cluster 1: "having healthy lifestyles but disliking hospitals"; Cluster 2: "women with healthy lifestyle behaviors"; Cluster 3: "general population"; Cluster 4: "middle-aged group in need of lifestyle improvement"; Cluster 5: "a group receiving treatment for lifestyle-related diseases." Conclusions: This study reveals discernible health behavior patterns in a sample of the Japanese population using large real-world data, suggesting the effectiveness of distinct approaches when considering a population approach to public health.

Indexed as

ClusteringHealth behaviorsJapanese populationLatent class analysisNoncommunicable diseases

Identifiers

PMID37927815
PMCPMC10622680
OpenAlexW4387674813

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.