Unraveling psychosocial mechanisms underlying exercise habit in young and middle-aged adults with metabolic syndrome: A PLS-SEM study

Young and middle-aged adults with metabolic syndrome (MetS) often struggle to maintain exercise habit despite adequate health literacy. This cross-sectional study of 211 Chinese adults applied partial least squares structural equation modeling (PLS-SEM) and multi-group analysis to examine psychosocial determinants, including social support, illness perception, exercise self-efficacy, autonomous (i.e. intrinsic) motivation, and physical activity neighborhood environment. Exercise self-efficacy and autonomous motivation showed positive associations with exercise habit, while negative illness perceptions showed negative associations. Social support showed indirect associations with exercise habit through self-efficacy and autonomous motivation, and neighborhood environment showed a mixed pattern, with a direct negative association and an indirect positive association through self-efficacy. Subgroup analyses indicated employed and healthier individuals had stronger self-efficacy-habit links, whereas unemployed or comorbid individuals had stronger social support-habit links. These findings illustrate associations among psychological and contextual factors, offering insights into potential pathways for sustainable exercise engagement.

Authors

Institutions

Publication Details

Journal
Journal of Health Psychology
Published
2026-05-11
DOI
https://doi.org/10.1177/13591053261445724
Primary Topic
Physical Activity and Health
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unraveling psychosocial mechanisms underlying exercise habit in young and middle-aged adults with metabolic syndrome: A PLS-SEM study

Hui Zhu, Siqi Cao, Yanyan Lu, Na Na et al.
Journal of Health Psychology
Physical Activity and Health
article

Unraveling psychosocial mechanisms underlying exercise habit in young and middle-aged adults with metabolic syndrome: A PLS-SEM study

Hui Zhu, Siqi Cao, Yanyan Lu, Na Na, Bo Hu, Yijun Xu, Xiuli Zhu
article en

Abstract

Young and middle-aged adults with metabolic syndrome (MetS) often struggle to maintain exercise habit despite adequate health literacy. This cross-sectional study of 211 Chinese adults applied partial least squares structural equation modeling (PLS-SEM) and multi-group analysis to examine psychosocial determinants, including social support, illness perception, exercise self-efficacy, autonomous (i.e. intrinsic) motivation, and physical activity neighborhood environment. Exercise self-efficacy and autonomous motivation showed positive associations with exercise habit, while negative illness perceptions showed negative associations. Social support showed indirect associations with exercise habit through self-efficacy and autonomous motivation, and neighborhood environment showed a mixed pattern, with a direct negative association and an indirect positive association through self-efficacy. Subgroup analyses indicated employed and healthier individuals had stronger self-efficacy-habit links, whereas unemployed or comorbid individuals had stronger social support-habit links. These findings illustrate associations among psychological and contextual factors, offering insights into potential pathways for sustainable exercise engagement.

Journal of Health Psychology
Qingdao University (CN), Affiliated Hospital of Qingdao University (CN), Qingdao Municipal Hospital (CN)
Natural Science Foundation of Shandong Province
Quality Education
Openalex Percentile: Top 7%
Physical Activity and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.