Table 4.

Pooled multivariable logistic regression results predicting the likelihood of having sleep apnea using imputed data.

Predictor	Model 1 Univariate		Model 2a Adjusted (−BMI)		Model 3b Adjusted (+BMI)
	OR (95% CI)	P Value	aOR (95% CI)	P Value	aOR (95% CI)	P Value
Individual physical activity measuresc, MET-h/wk
Loge walking	.93 (.92, .95)	< .001	.96 (.94, .97)	< .001	.98 (.96, 1.00)	.045
Loge moderate-intensity activity	.99 (.98, 1.01)	.534	1.00 (.98, 1.02)	.872	1.00 (.98, 1.02)	.835
Loge vigorous-intensity activity	.89 (.88, .90)	< .001	.95 (.94, .97)	< .001	.98 (.97, 1.00)	.041
Combined physical activity measured, MET-h/wk
Loge total physical activity	.88 (.87, .90)	< .001	.94 (.92, .95)	< .001	.98 (.96, 1.00)	.005

aOR = adjusted odds ratio, BMI = body mass index, CI = confidence interval, MET = metabolic equivalent. ^aModel adjusted for sex, age, age², time spent sitting, depression, high blood pressure, high cholesterol, blood glucose, stroke, heart disease, diabetes mellitus, myocardial infarction, alcohol frequency, smoking status, ethnicity, working status, and education level; ^bmodels adjusted for BMI, BMI², sex, age, age², time spent sitting, depression, high blood pressure, high cholesterol, blood glucose, stroke, heart disease, diabetes mellitus, myocardial infarction, alcohol frequency, smoking status, ethnicity, working status, and education level; ^cmodels fit with all 3 physical activity measures as individual covariates; ^dmodels fit with one combined physical activity measure: (log_e walking + log_e moderate-intensity activity + log_e vigorous-intensity activity).