Table 2.

The respondent characteristics that had the greatest influence in predicting the use of wearable health care devices.

Predictors		Prediction of the use of a wearable health care device in the last 12 months
		Adjusted odds ratioa		95% CI		P value
Age (years)b
	35-49		0.79		0.54-1.16			.22
	50-64		0.57		0.37-0.87			<.001
	65-74		0.46		0.28-0.76			<.001
	≥75		0.47		0.24-0.89			.01
Genderc: Female		1.26		0.96-1.65			.01
Educationd
	High school graduate		0.48		0.14-1.62			.14
	Some college		1.06		0.30-3.69			.04
	At least a college graduate		1.04		0.31-3.51			.05
Race/ethnicitye
	African American		1.48		0.89-3.81			.09
	Hispanic		1.24		0.88-3.06			.12
	White		1.65		0.97-2.79			.05
	Other		1.29		0.42-4.01			.65
Marital statusf: married		1.02		0.68-1.54			.91
Household income ($ US)g
	20,000 to <35,000		0.80		0.41-1.57			.51
	35,000 to <50,000		1.82		0.84-3.97			.12
	50,000 to <75,000		1.49		0.82-2.68			.18
	≥75,000		2.60		1.39-4.86			<.001
General health		1.17		0.98-1.39			.01
Frequency of provider visits		0.97		0.89-1.05			.38
Weight perception		1.16		1.06-1.27			<.001
Presence of chronic conditions		0.91		0.63-1.31			.61
Attitude towards exercise		1.23		1.06-1.43			<.001
Technology self-efficacy		1.33		1.21-1.46			<.001

^aAdjusted odds ratios and 95% CIs generated from multivariate logistic regression. Model accounts for replicate weights.

^bReference: 18-34 years.

^cReference: male.

^dReference: less than high school.

^eReference: Asian.

^fReference: nonmarried.

^gReference: <20,000.