On the basis of our data, we suggest that eBPM is a suitable alternative to AuscBPM in clinical trials and registration studies, and may carry specific advantages. Automatic data transfer of recorded values to electronic patient files may further minimize observer bias. Manufacturers should consider such findings for the development of professional devices.

Early combination therapy is increasingly recommended in hypertension management because of increased risk of adverse effects with high-dose monotherapy. However, this risk is not necessarily increased for high doses of angiotensin receptor blockers (ARB). ValTop study compared efficacy and safety of high vs. conventional dose of valsartan in hypertensive patients. ValTop was a controlled, randomized, double-blind trial. Of 6035 screened subjects, 4004 mild-to-moderate hypertensive patients (mean seated diastolic blood pressure (MSDBP) 90-109 mm Hg) started 4-week open-label treatment with valsartan 160 mg. Of them, 3776 were randomized to receive valsartan 160 mg (N¼1900) or 320 mg (N¼1876) o.d. for 4 weeks. In 28-week open-label extension study, all participating patients (N¼642) received valsartan 320 mg. Valsartan 160 mg reduced MSDBP by 10.0 mm Hg in the initial open-label phase. Further BP reductions in the double-blind phase were significantly (Po0.0001) greater in the 320 mg group than in the 160 mg group for MSDBP (1.6 ± 0.18 mm Hg vs. 0.5 ± 0.18 mm Hg) and mean seated systolic BP (3.3 ± 0.31 mm Hg vs. 0.7 ± 0.31 mm Hg). The size of the additional effect of the 320 mg dose on BP was similar in subjects controlled or not by the initial 160 mg dose. Adverse event (AE) rates were similar in both treatment groups, drug-related AEs occurring in o5% of subjects in each phase. High-dose valsartan is safe and effective in uncomplicated mild-to-moderate hypertension independently of the initial response to a moderate dose. High-dose ARB monotherapy may thus be a viable option in hypertension management.

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Exponential growth of health‐related data collected by digital tools is a reality within pharmaceutical and medical device research and development. Data generated through digital tools may be categorized as relevant to efficacy and/or safety. The enormity of these data requires the adoption of new approaches for processing and evaluation. Recognition of patterns within the safety data is vital for sponsors seeking regulatory approval for their new products. Nontraditional data sources may contain relevant safety information; early evaluation of these data will help to determine the product safety profile. Advanced technologies have allowed the development of digital tools to screen these data, which in some situations are classified as software as a medical devices and subject to clinical evaluation and post‐marketing surveillance. Artificial intelligence may help to reduce or even eliminate noise from within these data, allowing safety experts to focus on the most pertinent evidence. We propose a data typology and provide considerations on how to define adverse events within different types of data, even where no human reporter exists. Proposals are made for the automation of screening processes. We consider validation aspects to support solutions that are proven to produce reliable results, and to deliver trusted outputs to stakeholders.

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