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. 2021 Feb 5;31(2):125–131. doi: 10.2188/jea.JE20190140

Table 2. Sex-specific changes in systolic and diastolic blood pressures with time by the frequency of laughter in everyday life.

  Men Women

  Frequency of laughter Frequency of laughter

  Almost everyday (reference) 1 to 5 days per week 1 to 3 days a month or
almost never
Almost everyday (reference) 1 to 5 days per week 1 to 3 days a month or
almost never
Number 318 164 72 653 190 44

    β (95% CI) P value β (95% CI) P value   β (95% CI) P value β (95% CI) P value

Systolic blood pressure                    
Baseline difference 0 0.75(−1.9, 3.9) 0.62 −2.38(−6.5, 1.7) 0.26 0 1.10(−1.4, 3.6) 0.39 0.64(−4.2, 5.4) 0.79
Time-dependent difference** 0 −0.34(−1.1, 0.3) 0.34 0.96(−0.2, 1.8) 0.05* 0 −0.11(−0.7, 0.5) 0.74 0.23(−1.0, 1.5) 0.72
Mean value for 2010, (SD) 133.2(1.0) 133.1(1.3)   130.7(2.0)   124.4(0.7) 124.9(1.2)   124.4(2.5)  
Mean value for 2014, (SD) 133.0(1.1) 131.4(1.4)   134.1(2.2)   125.0(0.7) 124.9(1.2)   126.9(3.0)  

Diastolic blood pressure                    
Baseline difference 0 −0.16(−1.9, 2.0) 0.87 −2.09(−4.8, 0.7) 0.14 0 0.86(−0.9, 2.6) 0.34 −0.06(−4.0, 2.7) 0.72
Time-dependent difference** 0 −0.07(−0.5, 0.4) 0.74 0.72(0.1, 1.2) 0.02* 0 −0.31(−0.7, 0.1) 0.11 −0.07(−0.8, 0.7) 0.86
Mean value for 2010, (SD) 77.0(0.6) 77.4(0.8)   75.9(1.2)   72.8(0.4) 73.4(0.8)   72.1(1.5)  
Mean value for 2014, (SD) 76.6(0.6) 76.8(0.8)   78.9(1.3)   73.7(0.5) 72.7(0.8)   71.8(1.9)  

CI, confidence interval.

*P value of interaction with time.

**Time × the frequency of laughter.

Adjusted for age, baseline occupational status (managerial, professional, manual labor, sales and services, self-employed, farming, housewife or unemployed), antihypertensive medication use from 2010 to 2014, presence of mental stress at baseline, depressed in mood and declining interest at baseline, current smoking, drinking status and BMI from 2010 to 2014.

The P-value of interaction with time is based on the assessment comparing between reference category (“almost every day”) and other categories by using the linear mixed-effect models.