| dc.description.abstract | Predicting hypertension among individuals using historical blood pressure (BP)
readings has been the focus of recent studies in Artificial Intelligence in health. This has been
occasioned by the growing prevalence of hypertension in the general population as well as
individuals’ desire to manage their health. The BP data used for predictions is collected during
clinical visits and does not capture fluctuations in between clinic visits. Additionally, current
prediction approaches rely solely on the BP readings without regard for the individual’s
lifestyle and activities, which are known to affect individuals’ BP. This study developed a
system comprising of (i) a smartwatch with a Photolethysmograph (PPG) heart rate sensor for
detecting the BP and (ii) a mobile phone application for receiving the BP readings and to
collect data on participants age, weight, height and other health conditions. This system was
piloted for regular collection of BP and related activity data for use in monitoring and
prediction of an individual’s BP. Activities at the time of BP reading (sleep, exercise, chores)
were also recorded. An alert was sent to the participant if the BP reading was abnormal. The
pilot unearthed the following challenges: inability of the smartwatch to take readings on darkskinned
persons, the short time interval (30 mins) duration for data collection caused
inconveniences, missing of readings during device charging, lack of complete integration
between smartwatch and mobile application for the automatic transmission and recording of
readings, inability to take readings in some locations due to security concerns for devices,
inability to take readings at night because the smartwatch required light to function, and cases
of forgetfulness by the participants in wearing smartwatch and/or entering the data.
Recommendations from pilot include (i) an increase in the time interval to four hourly (ii)
automate the process of taking and recording BP reading (iii) identify a smart watch that uses
both PPG and ECG, and (iv) explore ways to for those not so literate to use the application. | en_US |
