Evidence map›Paper›PMID 42516706›Full record

ArticleBMJ public health2026

Exploring demographic-behavioural profiles in a large cohort of Japanese adults: a latent class analysis approach for 137 357 individuals.

Takahiro Miki, Naoki Onozato, Yosuke Fujisawa, Masashi Kanai, Takashi Hamatani, Kakeru Tateyama, Takuya Toda, Tsutomu Sugawara, Yuta Hagiwara

Abstract read
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Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Takahiro MikiPREVENT Inc, Nagoya, Aichi, Japan.ORCID https://orcid.org/0000-0002-0648-2675
Naoki OnozatoSumitomo Life Insurance Company, Tokyo, Japan.
Yosuke FujisawaSumitomo Life Insurance Company, Tokyo, Japan.
Masashi KanaiPREVENT Inc, Nagoya, Aichi, Japan.
Takashi HamataniSumitomo Life Insurance Company, Tokyo, Japan.
Kakeru TateyamaSumitomo Life Insurance Company, Tokyo, Japan.
Takuya TodaPREVENT Inc, Nagoya, Aichi, Japan.
Tsutomu SugawaraPREVENT Inc, Nagoya, Aichi, Japan.
Yuta HagiwaraPREVENT Inc, Nagoya, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Understanding health behaviour patterns is critical to addressing public health challenges. In Japan, lifestyle factors such as smoking, physical inactivity, poor diet and insufficient sleep significantly contribute to non-communicable diseases (NCDs). Identifying how these behaviours cluster within populations offers valuable insights for effective prevention and intervention strategies. Latent class analysis (LCA) enables the identification of behavioural patterns to inform tailored public health initiatives. Purpose: To identify and characterise demographic-behavioural profiles among Japanese adults using LCA. Method: Data from 137 357 individuals participating in a health promotion programme in Japan (1 January 2022 to 31 December 2022) were analysed. Participants aged 20 years and older with complete records for all relevant health behaviour variables were included. Variables included age, gender, smoking status, alcohol consumption, sleep duration, physical activity, sedentary behaviour and salt intake. LCA was conducted to identify behaviour clusters, with the optimal number of classes determined using Bayesian and Akaike information criteria. Results: From a cohort of 137 357 individuals, nine clusters were identified: (1) middle-aged women with short sleep duration (20.8%), (2) middle-aged men with elevated health behaviour risks (8.4%), (3) young adults with short sleep duration (18.4%), (4) healthy middle-aged men (2.6%), (5) sedentary middle-aged men with short sleep duration (25.3%), (6) typical middle-aged women (5.2%), (7) physically active young men with poor dietary habits (2.5%), (8) unhealthy middle-aged men with inconsistent behaviours (8.5%) and (9) older adults with moderate health concerns (8.4%). Conclusion: LCA revealed diverse health behaviour patterns, emphasising the need for tailored health interventions targeting specific subgroups to mitigate NCD risks.

Indexed as

classificationEpidemiologyPreventive MedicinePublic Health

Identifiers

PMID42516706
PMCPMC13405014

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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.