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Validation of a wrist-type home nocturnal blood pressure monitor in the sitting and supine position according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-9601T.

Mitsuo KuwabaraKanako HaradaYukiko HishikiTakayoshi OhkuboKazuomi KarioYutaka Imai
Published in: Journal of clinical hypertension (Greenwich, Conn.) (2020)
This study aimed to validate the accuracy of the Omron HEM-9601T, an automatic wrist-type device for self-blood pressure (BP) measurement with a timer function for automatic measurement of nocturnal BP, in the sitting position according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation/International Organization for Standardization (ANSI/AAMI/ISO) 81060-2:2013 guidelines, and to assess its performance in the supine position by applying the same protocol as conducted in the sitting position. The mean differences between the reference BPs and HEM-9601T readings were 1.2 ± 6.9/1.1 ± 5.5 mmHg, 2.2 ± 6.5/1.8 ± 5.7 mmHg, 0.1 ± 6.6/1.5 ± 6.2 mmHg, and -0.8 ± 7.2/0.5 ± 6.4 mmHg for systolic BP/diastolic BP for criterion 1 in the sitting position, supine with sideways palm position, supine with upward palm position, and supine with downward palm position, respectively. In addition, the mean differences and their standard deviations for systolic BP and diastolic BP calculated according to criterion 2 in the ANSI/AAMI/ISO 81060-2:2013 guidelines were acceptable in all four positions. In conclusion, the Omron HEM-9601T fulfilled the validation criteria of the ANSI/AAMI/ISO81060-2:2013 guidelines when used in the sitting position with the wrist at heart level, and its accuracy in the supine position was acceptable and roughly equivalent to that in the sitting position. The wrist-type home BP monitor could be a more suitable tool for repeated nocturnal BP measurements at home than upper-arm devices, and could improve the reliability of diagnosis and management of nocturnal hypertension.
Keyphrases
  • blood pressure
  • hypertensive patients
  • left ventricular
  • obstructive sleep apnea
  • heart rate
  • machine learning
  • sleep apnea
  • deep learning
  • atrial fibrillation
  • neural network
  • weight loss