BACKGROUND
Therapeutic inertia, poor medication adherence, and lack of patient engagement are very common with contemporary hypertension management and these are attributable mainly to lack of precise tailoring to individual needs. Expanding mHealth and home monitoring results in rapid accumulation of continuously growing series of patient-specific data and enables personalized interventions at very low cost.
OBJECTIVE
The study aims to identify useful individual characteristics from series of patient-specific home measurements and implications for personalized hypertension management.
METHODS
The study extracted, from an existing database, series of patient-specific data about systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse and pulse blood pressure (PBP) monitored at home using electronic tonometer. Scatter plots were used to visualize the series of home-measurements, for all and each patients, along time difference from the first to the current measurement (TDF), time difference from the previous to the current measurement (TDP), hour of measurement, and ambient temperature when blood measurement took place, respectively. Then the scatter plots were manually examined for meaningful characteristics.
RESULTS
Great variations existed between individual patient and all patients as a group and from patient to patient, in terms of: average value, range, trend, cyclic change of SBP, DBP, pulse and PBP along all the influencing variables examined; and frequency, regularity and trend of monitoring behavior. The individual characteristics derived provide a variety of useful clues for personalized interventions, e.g., a patient: with high enough (say180mmHg) or increasing BP may merit intensified therapeutic and behavioral measures; with great variations in SBP/DBP may need help finding and removing causes that lead to the elevated measures; with high BP plus large and irregular TDP may lack adequate awareness of his/her hypertension.
CONCLUSIONS
Expansion of home blood monitoring results in rapid accumulation of continuously growing series of patient-specific data. These data have great potential for characterizing and tailoring hypertension management.