HOURLY ENGAGEMENT PATTERNS IDENTIFICATION WITH A WEARABLE BLOOD PRESSURE MONITOR

Objective: High engagement in self-monitoring of blood pressure (BP) is associated with improved BP control. Wearable devices that measure BP using photoplethysmographic (PPG) signals allow cuffless, continuous BP monitoring, potentially increasing user engagement. Despite ongoing efforts, little is known about user engagement on an hour-by-hour basis. This is the first large-scale population-based study to investigate hourly engagement patterns with a wearable BP monitor.Design and method: Users voluntarily purchased and wore a validated, CE-marked, cuffless wrist BP monitor (Aktiia SA, Neuchâtel, Switzerland). All users provided permission to use anonymous or aggregated data for research through the commercial agreement of usage for the Aktiia monitor. Every time the user accesses the smartphone application – i.e., synchronisation – the data from the bracelet are transferred via Bluetooth and forwarded to Aktiia’s cloud server, where they are stored. The smartphone application also displays the BP values measured by the Aktiia monitor and can be checked at any time by the user. The number of hourly App accesses for four weeks (01-28 September 2023) were retrospectively analysed (641,896 total synchronisations, median [IQR]: 24 [9-52] synchronisations per user). The hourly count was establish using a 1-hour centred rolling window with a 5-minute step. Results: Data from 15,740 European residents (57.9±11.1 years, 21.8% female, BMI 27.9±4.9 kg/m2) were included in the investigation. Figure 1 illustrates the number of synchronisations by hour per user, superimposed by day of the week, highlighting changes in the attention windows according to each day of the week. A clear weekly pattern of interest was noticed from Sunday evening until Friday morning, with reduced interest in self-monitoring between Friday evening until Sunday morning. A noticeable lag of approximately 1-h on weekend mornings – suggesting users wake up later during weekends. Conclusions: This study identified distinct hourly engagement patterns, revealing specific times when users typically check their BP values. By translating these patterns into actionable strategies for personalized healthcare delivery, we can enhance user engagement and improve BP control. This approach paves the way for creating targeted, data-driven interventions that have the potential to substantially improve hypertension management.