IntroductionCardiovascular diseases are the leading cause of death worldwide and are partly caused by modifiable risk factors. Cardiac rehabilitation addresses several of these modifiable risk factors, such as physical inactivity and reduced exercise capacity. However, despite its proven short-term merits, long-term adherence to healthy lifestyle changes is disappointing. With regards to exercise training, it has been shown that rehabilitation supplemented by a) home-based exercise training and b) supportive digital tools can improve adherence.MethodsIn our multi-center study (ClincalTrials.gov Identifier: NCT04458727), we analyzed the effect of supportive digital tools like digital diaries and/or wearables such as smart watches, activity trackers, etc. on exercise capacity during cardiac rehabilitation. Patients after completion of phase III out-patient cardiac rehabilitation, which included a 3 to 6-months lasting home-training phase, were recruited in five cardiac rehabilitation centers in Austria. Retrospective rehabilitation data were analyzed, and additional data were generated via patient questionnaires.Results107 patients who did not use supportive tools and 50 patients using supportive tools were recruited. Already prior to phase III rehabilitation, patients with supportive tools showed higher exercise capacity (Pmax = 186 ± 53 W) as compared to patients without supportive tools (142 ± 41 W, p < 0.001). Both groups improved their Pmax, significantly during phase III rehabilitation, and despite higher baseline Pmax of patients with supportive tools their Pmax improved significantly more (ΔPmax = 19 ± 18 W) than patients without supportive tools (ΔPmax = 9 ± 17 W, p < 0.005). However, after adjusting for baseline differences, the difference in ΔPmax did no longer reach statistical significance.DiscussionTherefore, our data did not support the hypothesis that the additional use of digital tools like digital diaries and/or wearables during home training leads to further improvement in Pmax during and after phase III cardiac rehabilitation. Further studies with larger sample size, follow-up examinations and a randomized, controlled design are required to assess merits of digital interventions during cardiac rehabilitation.

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identified. Objectives: This study investigated the effects of de-identification on classification. Methods: Two pseudonymisation and two classification algorithms were applied to clinical messages from a telehealth system. Divergence in classification compared to clear text classification was measured. Results: Overall, de-identification notably altered classification. The delicate classification algorithm was severely impacted, especially losses of sensitivity were noticeable. However, the simpler classification method was more robust and in combination with a more yielding pseudonymisation technique, had only a negligible impact on classification. Conclusion: The results indicate that de-identification can impact text classification and suggest, that considering de-identification during development of the classification methods could be beneficial.

Telehealth services for long-term monitoring of chronically ill patients are becoming more and more common, leading to huge amounts of data collected by patients and healthcare professionals each day. While most of these data are structured, some information, especially concerning the communication between the stakeholders, is typically stored as unstructured free-texts. This paper outlines the differences in analyzing free-texts from the heart failure telehealth network HerzMobil as compared to the diabetes telehealth network DiabMemory. A total of 3,739 free-text notes from HerzMobil and 228,109 notes from DiabMemory, both written in German, were analyzed. A pre-existing, regular expression based algorithm developed for heart failure free-texts was adapted to cover also the diabetes scenario. The resulting algorithm was validated with a subset of 200 notes that were annotated by three scientists, achieving an accuracy of 92.62%. When applying the algorithm to heart failure and diabetes texts, we found various similarities but also several differences concerning the content. As a consequence, specific requirements for the algorithm were identified.

Heart failure (HF) is one of the biggest concerns for health care systems in developed countries. To support the long-term treatment of HF patients, the Austrian Institute of Technology implemented a HF telehealth network called "HerzMobil". While most data within this network are stored in a structured format, health care professionals can also communicate via clinical notes in free text format. These notes are hardly ever analyzed automatically, even though a large number contains valuable information for the patient's treatment process. With currently more than 20,000 notes stored in the system, an automatic approach is beneficial to spare manual screening time. One important step in this process concerns the extraction of time references from the notes. This information could, for example, be used to match the time references with events from the same note. Therefore, two Python scripts were developed to: extract time references from the notes (Script A) and subsequently calculate the corresponding dates (Script B). Script A was compared to an already existing Python library and achieved superior results for all calculated key figures. The time calculation algorithm of Script B achieved an accuracy of 75.34%. These scripts could be implemented in the HerzMobil network to provide additional information for the treatment process and further improve the telehealth system.