title: [Value of a predictive model of ambulatory blood pressure integrating physical activity].
abstract: OBJECTIVE
  To determine how much of the variations of blood pressure during a 24 hour period could be accounted for by a change in activity and establish a predictive model.
  
  
  METHODS
  Twenty three healthy subjects (mean age 25 +/- 2 years) were studied. The BP, heart rate (HR), and time of measure (T) were recorded by ambulatory BP monitoring using Spacelabs (4 measures per hour). At each measure the subject noted in a diary the degree of activity on a six level semi-quantitative scale.
  
  
  METHODS
  A model was constructed using an analysis of covariance. Different parameters were added in succession to reach a model of the type P: P0 + A + beta + (HR-HR0) + H, were P = predicted systolic pressure, P0 = mean systolic BP over the 24 hours. A variation in systolic BP for activity level, beta = the slope of the regression between systolic BP and HR during activity A, and HR0 the mean HR during this activity.
  
  
  RESULTS
  1) In order to test the model, the values measured in one subject were compared to the predicted values from the model in 22 others. The procedure was then repeated for the other subjects. This common model predicted 41 +/- 21% of fluctuations in BP of the subject analysed with a range of 0 to 66%. 2) In order to refine the individual model two subjects were explored 7 times over 24 h of non consecutive days. The measures of the last recording were compared to the predicted values from the application of the model to the six preceding recordings. The model then predicted 81% and 66% of the BP values of the test day. The mean of the 24 hour individual difference over a one hour period between the measures and its predicted value by the model was 0.13 +/- 4.8 mmHg, and -0.75 +/- 7.7 mmHg.
  
  
  CONCLUSIONS
  This study expresses in a quantitative fashion the importance of the level of activity in the evaluation of the level of ambulatory BP. The introduction of this method of quantification and analysis seems logical in therapeutic trial. The difference in the predictions by the model for some subjects poses the problem of uniform coding of activities and that of the recognition of other events such as stress and dreaming in sleep.
authors.affiliation: Service de médecine interne-cardiologie, CHU Michallon, Grenoble.
authors.forename: J P
authors.initials: JP
authors.lastname: Siché
authors.forename: F
authors.initials: F
authors.lastname: Marques
authors.forename: S
authors.initials: S
authors.lastname: Charbonnier
authors.forename: J P
authors.initials: JP
authors.lastname: Baguet
authors.forename: B
authors.initials: B
authors.lastname: Diourte
authors.forename: J M
authors.initials: JM
authors.lastname: Mallion
mesh_terms: D000328:Adult; D001794:Blood Pressure / Q000502:physiology; D018660:Blood Pressure Monitoring, Ambulatory; D002940:Circadian Rhythm; D015444:Exercise / Q000502:physiology; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D008954:Models, Biological; D011237:Predictive Value of Tests