Aims Health data captured by commercially available smart devices may represent meaningful patient‐reported outcome measures (PROMs) in heart failure (HF) patients. The purpose of this study was to test this hypothesis by evaluating the feasibility of a new telemonitoring concept for patients following initial HF hospitalization. Methods and results We designed a cardio patient monitoring platform (CPMP) that comprised mobile iOS‐based applications for patients' smartphone/smartwatch and the equivalent application on a physicians' tablet. It allowed for safe and continuous data transmission of self‐measured physiological parameters, activity data, and patient‐reported symptoms. In a prospective feasibility trial with 692 patient days from 10 patients hospitalized for newly diagnosed HF with reduced ejection fraction (mean left ventricular ejection fraction (LVEF) 26.5 ± 9.8%), we examined the CPMP during the first 2 months following discharge (69 ± 15 observation days per patient). The mean daily step count recorded by the mobile devices emerged as a promising new PROM. Its 14 day average increased over the study period (3612 ± 3311 steps/day at study inclusion and 7069 ± 5006 steps/day at end of study; P < 0.0001). It is unique for continuously reflecting real‐life activity and correlated significantly with traditional surrogate parameters of cardiac performance including LVEF ( r = 0.44; 95% CI 0.07–0.71; P = 0.0232), 6 min walk test ( r = 0.67; 95% CI 0.38–0.84; P = 0.0002), and scores in health‐related quality of life questionnaires. Conclusions We provide the first patient monitoring platform for HF patients that relies on commercially available iOS/watchOS‐based devices. Our study suggests it is ready for implementation as a tool for recording meaningful PROMs in future HF trials and telemonitoring.

In this opinion paper we provide an overview of some challenges concerning data provenance in biomedical research. We reflect current literature and depict some examples of existing implicit or explicit provenance aspects in some standard data types in translational research. Furthermore, we assess the need of further data provenance standardization in biomedical informatics. Basic data provenance should provide a recall about the origin of the data, transformation process steps, support replication and presentation of the data. Even though usable concepts for the documentation of data provenance can be found in other fields as early as 2005, the penetration rate in biomedical projects and in the biomedical literature is quite low. The awareness for the necessity of basic data provenance has to be raised, the education of data managers has to be further improved.

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