» Articles » PMID: 37555829

Stability of Clustering of Lifestyle Risk Factors in the Dutch Adult Population and the Association with Mental Health

Overview
Specialty Public Health
Date 2023 Aug 9
PMID 37555829
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Lifestyle factors often co-occur in clusters. This study examines whether clusters of lifestyle risk factors, such as smoking, alcohol use, physical inactivity, poor diet, sexual risk behaviour, cannabis and other drug use, change over time in a representative sample of Dutch adults. Additionally, the association between mental health and self-reported depression of lifestyle clusters was examined.

Methods: Each year cross-sectional data of approximately 7500 individuals of 18 years and older from the annual Dutch Health Survey of 2014-2019 were used. Clusters were determined by a two-step cluster analysis. Furthermore, regression analyses determined the association between clusters of lifestyle risk factors and mental health.

Results: Results show six clusters composed of one, multiple or no lifestyle risk factors. The clusters remained relatively stable over time: in some clusters, the number of people slightly changed between 2014 and 2019. More specifically, clusters that increased in size were the cluster with no lifestyle risk factors and the cluster with multiple lifestyle risk factors. Furthermore, results show that clusters with none to a few lifestyle risk factors were associated with better mental health and a lower prevalence of self-reported depression compared with clusters with multiple lifestyle risk factors.

Conclusions: The clustering of lifestyle risk factors remained stable over time. People with multiple lifestyle risk factors had poorer mental health than those without risk factors. These findings may emphasize the need for intervention strategies targeting this subgroup with multiple lifestyle risk factors.

Citing Articles

Lifestyle trajectories in middle-aged adults and their relationship with health indicators.

Roca-Ventura A, Solana-Sanchez J, Cattaneo G, Tormos-Munoz J, Pascual-Leone A, Bartres-Faz D Front Public Health. 2024; 12:1412547.

PMID: 38903574 PMC: 11188459. DOI: 10.3389/fpubh.2024.1412547.

References
1.
Adjibade M, Lemogne C, Julia C, Hercberg S, Galan P, Assmann K . Prospective association between combined healthy lifestyles and risk of depressive symptoms in the French NutriNet-Santé cohort. J Affect Disord. 2018; 238:554-562. DOI: 10.1016/j.jad.2018.05.038. View

2.
Rumpf H, Meyer C, Hapke U, John U . Screening for mental health: validity of the MHI-5 using DSM-IV Axis I psychiatric disorders as gold standard. Psychiatry Res. 2002; 105(3):243-53. DOI: 10.1016/s0165-1781(01)00329-8. View

3.
Kaku D, Lowenstein D . Emergence of recreational drug abuse as a major risk factor for stroke in young adults. Ann Intern Med. 1990; 113(11):821-7. DOI: 10.7326/0003-4819-113-11-821. View

4.
Leech R, McNaughton S, Timperio A . The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act. 2014; 11:4. PMC: 3904164. DOI: 10.1186/1479-5868-11-4. View

5.
Askari M, Heshmati J, Shahinfar H, Tripathi N, Daneshzad E . Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int J Obes (Lond). 2020; 44(10):2080-2091. DOI: 10.1038/s41366-020-00650-z. View