. Author manuscript; available in PMC: 2020 Nov 1.

Published in final edited form as: Psychosom Med. 2019 Nov-Dec;81(9):791–798. doi: 10.1097/PSY.0000000000000750

Table 3.

Hierarchical regression model predicting temporal changes in emotional exhaustion [EE] N = 167)

Model 2
	b	se	β	p	b	se	β	p
Age	.02	.01	.23	.002	.02	.01	.16	.041
Sex	−.14	.18	−.06	.43	−.17	.18	−.07	.35
BMI	−.02	.02	−.07	.37	−.03	.02	−.09	.20
Alcohol consumption	−.35	.25	−.11	.16	−.41	.24	−.14	.095
Smoking	.44	.25	.13	.077	.50	.24	.14	.040
Caffeine consumption	−.28	.34	−.06	.41	−.26	.33	−.06	.44
PHQ-9	.05	.02	.21	.048	.04	.02	.18	.073
EE _T1	−.45	.08	−.58	<.001	−.44	.08	−.57	<.001
HRV					−.47	.15	−.23	.002
R²				.26^**
ΔR²								.05^*

Note. BMI = body mass index; EE _T1 = Maslach Burnout Inventory – General Survey, emotional exhaustion sub-dimension at T1 biomarker sampling; PHQ-9 = Patient Health Questionnaire, depression sum-score; HRV = vagal-mediated heart rate variability (e.g. root mean square of successive difference between heart beats [RMSSD]).

p <.05;

^**

p <.001