title: Factors influencing white-coat effect.
abstract: BACKGROUND
  The transient blood pressure (BP) rise during clinical visits is usually referred to as white-coat effect (WCE). The aim of the present study was to investigate factors that may influence the WCE.
  
  
  METHODS
  A total of 2004 subjects underwent office BP measurements and 24-h ambulatory BP monitoring (ABPM) on the same day. The WCE was estimated as the difference between office and average daytime ambulatory BP (ABP). According to the office and daytime BP values, the study population was divided into normotensives (NTs), white-coat hypertensives (WCHs), masked hypertensives (MHTs), and sustained hypertensives (SHTs). Statistical analyses were performed using one-way analysis of variance and multiple linear regression models.
  
  
  RESULTS
  The mean systolic and diastolic WCE was 9 +/- 16 and 7 +/- 12 mm Hg, respectively. In the entire group of patients, multiple linear regression models revealed independent determinants of systolic WCE in the following rank order: office systolic BP (SBP) (beta = 0.727; P < 0.001), female gender (beta = 0.166; P < 0.001), daytime SBP variability (beta = 0.128; P < 0.001), age (beta = 0.039, P = 0.020), and smoking (beta = 0.031, P = 0.048). A 1.0 mm Hg increase in daytime SBP variability correlated with an increment of 0.589 mm Hg (95% confidence intervals, 0.437-0.741) in the systolic WCE. The regression analyses for diastolic WCE revealed the same factors as independent determinants. A 1.0 mm Hg increase in daytime diastolic BP (DBP) variability was independently associated with an increment of 0.418 mm Hg (95% confidence intervals, 0.121-0.715) in the diastolic WCE.
  
  
  CONCLUSIONS
  Factors such as gender, age, smoking, office BPV and daytime BPV may exert an important influence on the magnitude of the WCE.
authors.affiliation: Department of Clinical Therapeutics, Alexandra Hospital, University of Athens, Athens, Greece. stathismanios@yahoo.gr
authors.forename: Efstathios D
authors.initials: ED
authors.lastname: Manios
authors.forename: Eleni A
authors.initials: EA
authors.lastname: Koroboki
authors.forename: Georgios K
authors.initials: GK
authors.lastname: Tsivgoulis
authors.forename: Konstantinos M
authors.initials: KM
authors.lastname: Spengos
authors.forename: Ioanna K
authors.initials: IK
authors.lastname: Spiliopoulou
authors.forename: Fiona G
authors.initials: FG
authors.lastname: Brodie
authors.forename: Konstantinos N
authors.initials: KN
authors.lastname: Vemmos
authors.forename: Nikolaos A
authors.initials: NA
authors.lastname: Zakopoulos
mesh_terms: D000328:Adult; D000368:Aged; D001794:Blood Pressure / Q000502:physiology*; D018660:Blood Pressure Monitoring, Ambulatory; D002940:Circadian Rhythm / Q000502:physiology*; D005260:Female; D006801:Humans; D006973:Hypertension / Q000453:epidemiology* / Q000503:physiopathology*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010824:Physicians' Offices / Q000706:statistics & numerical data*; D015995:Prevalence; D011379:Prognosis; D012307:Risk Factors; D017678:Sex Distribution; D012907:Smoking / Q000453:epidemiology