Table 3 GLM Model Results

From: A Novel method for quantifying fluctuations in wearable derived daily cardiovascular parameters across the menstrual cycle

Ā 

GLM: RHRamp model

GLM: RMSSDamp model

Predictors

Estimates

CI

p

Estimates

CI

p

Age

āˆ’ 0.04***

āˆ’ 0.05ā€“āˆ’ 0.04

<0.001

āˆ’ 0.09***

āˆ’ 0.11ā€“āˆ’ 0.07

<0.001

RHR ( < 54.3 BPM)

1.54***

0.88āˆ’ 2.20

<0.001

Ā Ā Ā 

RHR (54.3 BPM - 59.3 BPM)

1.57***

1.12āˆ’ 2.01

<0.001

Ā Ā Ā 

RHR (59.3 BPM - 64.4 BPM)

2.76***

1.28āˆ’ 4.24

<0.001

Ā Ā Ā 

RHR ( > 64.4 BPM)

0.65*

0.03āˆ’ 1.26

0.040

Ā Ā Ā 

BMI

0.00

āˆ’ 0.01āˆ’ 0.01

0.433

0.03*

0.00–0.06

0.026

RMSSD ( < 40.4 ms)

Ā Ā Ā 

3.96***

2.83–5.08

<0.001

RMSSD (40.4 ms - 54.1 ms)

Ā Ā Ā 

6.20***

5.07āˆ’ 7.32

<0.001

RMSSD (54.1 ms - 73.3 ms)

Ā Ā Ā 

7.04***

4.35āˆ’ 9.73

<0.001

RMSSD ( > 73.3 ms)

Ā Ā Ā 

2.63*

0.50āˆ’ 4.76

0.016

Observations

9968

Ā Ā 

9968

Ā Ā 

R2

0.040

Ā Ā 

0.062

Ā Ā 
  1. *p < 0.05, **p < 0.01, ***p < 0.001.
  2. Estimates, Confidence Intervals (CI), test statistics, and p-values for the predictors in the RHRamp (n = 9968) and RMSSDamp (n = 9968) GLM models. This model illustrates the relationship between age, BMI, and Baseline RHR or RMSSD on cardiovascular amplitude throughout the menstrual cycle in naturally cycling individuals.