High blood pressure (BP) is considered as an important risk factor for cognitive impairment and dementia. BP variability (BPV) may contribute to cognitive function decline or even dementia regardless of BP level. This study aims to investigate whether BPV is an independent predictor for cognitive impairment or dementia. Literature searches were performed in MEDLINE, Embase, PsycINFO, CINAHL, and Web of Science to May 2021. Longitudinal studies that assessed the risk of dementia or cognitive impairment with BPV as the predictor was included. Meta-analysis and meta-regression were performed to evaluate the effect of BPV on the risk of dementia or cognitive impairment. A total of 5919 papers were identified, and 16 longitudinal studies were included, which had >7 million participants and a median age from 50.9 to 79.9 years and a median follow-up of around 4 years. Thirteen studies reported visit-to-visit BPV and concluded that systolic BPV increases the risk of dementia with a pooled hazard ratio of 1.11 (95% CI, 1.05–1.17), and increases the risk of cognitive impairment with a pooled hazard ratio of 1.10 (95% CI, 1.06–1.15). Visit-to-visit diastolic BPV also increased the risk of dementia and cognitive decline. A meta-regression revealed a linear relationship between higher BPV and risks of dementia and cognitive impairment. Similar findings were observed in the studies with day-to-day BPV. This study suggests that long-term BPV is an independent risk factor for cognitive impairment or dementia, so an intervention plan for reducing BPV can be a target for early prevention of dementia.

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Digital solutions for Blood Pressure Monitoring (or Telemonitoring) have sprouted in recent years. Innovative solutions are often connected to the Internet of Things (IoT), mobile health (mHealth) platform for instance. However, clinical validity, technology cost and cross-platform data integration remain the major barriers to the application of these solutions. In this paper, we present an IoT-based and AI-embedded Blood Pressure Telemonitoring (BPT) system, which facilitates home blood pressure monitoring for individuals. This system uses machine learning techniques to enable automatic digits recognition, with an F1 score of 98.5%; and the cloud-based portal developed for automated data synchronization and risk stratification. Positive feedbacks on trial implementation are received from three clinics. The overall system architecture, development of machine learning model in digit identification, and cloud-based telemonitoring are addressed in this paper, alongside the followed implications.