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Comparing office-based and ambulatory blood pressure monitoring in clinical trials

Journal of Human Hypertension volume 19, pages 77–82 (2005)Cite this article

Abstract

Ambulatory blood pressure monitoring (ABPM) is commonly used in clinical trials. Yet, its ability to detect blood pressure (BP) change in comparison to multiple office-based measurements has received limited attention. We recorded ambulatory and five daily pairs of random zero (RZ) BPs pre- and post-intervention on 321 adult participants in the multicentre Dietary Approaches to Stop Hypertension trial. Treatment effect estimates measured by ambulatory monitoring were similar to those measured by RZ and did not differ significantly for waking vs 24-h ambulatory measurements. For systolic BP, the standard deviations of change in mean 24-h ambulatory BP (8.0 mmHg among hypertensives and 6.0 mmHg among nonhypertensives) were comparable to or lower than the corresponding standard deviations of change in RZ-BP based on five daily readings (8.9 and 5.9 mmHg). The standard deviations of change for mean waking ambulatory BP (8.7 and 6.7 mmHg) were comparable to those obtained using three to four daily RZ readings. Results for diastolic BP were qualitatively similar. Ambulatory monitoring was more efficient (ie, a smaller sample size could detect a given BP change) than three to four sets of daily RZ readings and required fewer clinic visits. The average of 33 ambulatory BP readings during the waking hours had an efficiency comparable to that from the mean of four daily pairs of RZ-BPs. Participants readily accepted the ABPM devices, and their use requires less staff training. ABPM provides a useful alternative to RZ-BP measurements in clinical trials.

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Acknowledgements

We wish to thank the DASH participants for their contribution to this study, and the DASH staff and the members of the DASH Data Safety and Monitoring Board for all their hard work on behalf of the study. This work was supported by cooperative agreements #HL50968, HL50972, HL50977, HL50981, HL50982, HL02642, RR02635, and RR00722 from the National Heart, Lung, and Blood Institute, the Office of Research on Minority Health, and the National Center for Research Resources of the National Institutes of Health.

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Authors and Affiliations

  1. Kaiser Permanente Center for Health Research, Portland, OR, USA

    W M Vollmer

  2. Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA

    L J Appel

  3. Duke Hypertension Center and Sarah W. Stedman Center for Nutritional Studies, Duke University Medical Center, Durham, NC, USA

    L P Svetkey

  4. Boston University, Boston, MA, USA

    T J Moore

  5. Kaiser Permanente Center for Health Research, Honolulu, HI, USA

    T M Vogt

  6. Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

    P R Conlin

  7. Division of Epidemiology and Clinical Applications, NHLBI, NIH, Rockville, MD, USA

    M Proschan

  8. Pennington Biomedical Research Center, Baton Rouge, LA, USA

    D Harsha

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  1. W M Vollmer
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  2. L J Appel
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  3. L P Svetkey
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  4. T J Moore
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  5. T M Vogt
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  6. P R Conlin
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  7. M Proschan
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Correspondence to W M Vollmer.

Appendix A

Appendix A

This appendix describes the procedure used to compare the various intervention effect size estimates in Table 1.

Let OM1 and OM2 be two correlated outcome measures (eg, RZ-BP and mean 24-h ambulatory blood pressure), and assume interest is focused on the effect of the DASH dietary pattern. For OM1, the effect of the DASH diet, net of control, can be expressed as (), where is the observed mean difference in OM1 from baseline to end-of-study for participants eating the DASH diet, and is the comparable difference for those eating the control diet. In order to test whether the DASH diet effects measured by OM1 and OM2 are equivalent, we compute

which can be rewritten as

Once in this form, δ can be seen to be the simple difference in two means, X̄ and Ȳ, where X1, …, Xn1 and Y1, …, Yn2 are n1+n2 mutually independent individual paired differences defined by Xi=(ΔOM1DASH,i−ΔOM2DASH,i) and Yi=(ΔOM1cntl,i−ΔOM2cntl,i). If sx2 and sy2 are the estimated standard deviations of the X's and Y's, then

will have an approximate Student's t-distribution for sufficiently large n1 and n2, and can thus be used to test the null hypothesis that δ=0.

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Vollmer, W., Appel, L., Svetkey, L. et al. Comparing office-based and ambulatory blood pressure monitoring in clinical trials. J Hum Hypertens 19, 77–82 (2005). https://doi.org/10.1038/sj.jhh.1001772

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