Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to understand how obesity risk varies according to multiple lifestyle behavior recommendations
Authors' conclusion
Using a novel statistical approach, this study shows that additive effects of multiple lifestyle behaviours predominantly explain differences in BMI and obesity risk. Meeting more public health lifestyle recommendations is important in mitigating obesity risk.
Study context
- Population
- Men and women from the UK Biobank; fruit and vegetable intake, physical activity, sleep duration, alcohol intake (meeting vs not meeting guidelines) and smoking status (previous, current, never)139,540 men · 125,455 women
- Exposure
- Strata defined by combinations of adherence to five lifestyle behaviour recommendations
- Outcome
- BMI and obesity status (obesity vs normal weight); VPC, PCV, predicted BMI and obesity risk
Methodological detailsMAIHDA (linear and binary logistic multilevel models), 48 strata of five lifestyle behaviours
- MAIHDA (linear and binary logistic multilevel models)
- 48 strata of five lifestyle behaviours
Main finding
As reported in the abstract
Obesity risk across strata
Model 1 (null) Variance Partition Coefficient (VPC), males
7%
Model 1 (null) VPC, females
5%
Model 3 VPC, males
0.5%
Model 3 VPC, females
0.1%
Observational data: associations, not effects.
Further findings
- Results were similar for BMI in the linear models
Original publication
Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to understand how obesity risk varies according to multiple lifestyle behavior recommendations. Int J Obes (Lond). Published 7 January 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →