eSano - An eHealth Platform for Internet- and Mobile-based Interventions

The BrEasT cancer afTER-CARE (BETTER-CARE) programme to improve breast cancer follow-up: design and feasibility study results of a cluster-randomised complex intervention trial

BACKGROUND

The risk of breast cancer patients for long-term side effects of therapy such as neurotoxicity and cardiotoxicity as well as late effects regarding comorbidities varies from individual to individual. Personalised follow-up care concepts that are tailored to individual needs and the risk of recurrences, side effects and late effects are lacking in routine care in Germany.

METHODS

We describe the methodology of BETTER-CARE, a parallel-arm cluster-randomised controlled trial conducted at 15 intervention and 15 control centres, aiming to recruit 1140 patients, and the results of the pilot phase. The needs- and risk-adapted complex intervention, based on existing development frameworks, includes a multidisciplinary network and digital platforms for symptom and need documentation and just-in-time adaptive interventions. The control group comprises usual care according to clinical guidelines. The primary outcome is health-related quality of life (EORTC QLQ-C30 global health), and secondary outcomes include treatment adherence.

RESULTS

The 2-month pilot phase comprising 16 patients in one intervention and one control pilot centre demonstrated the feasibility of the BETTER-CARE approach.

DISCUSSION

BETTER-CARE is a feasible intervention and study concept, investigating individualised needs- and risk-adapted breast cancer follow-up care in Germany. If successful, the approach could be implemented in German routine care.

TRIAL REGISTRATION

German Clinical Trial Register DRKS00028840. Registered on April 2022.

Engagement analysis of a persuasive-design-optimized eHealth intervention through machine learning

The challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-based eHealth intervention for procrastination, using a dataset from a randomized controlled trial of 233 university students. Various machine learning models, including Decision Tree, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines, were employed to predict patterns of user engagement. The study adopted a two-phase analytical approach. In the first phase, all features of the dataset were included, revealing 'total_minutes'-the total time participants spent on the intervention and the eHealth platform-as the most significant predictor of engagement. This finding emphasizes the intuitive notion that early time spent on the platform and the intervention is a strong indicator of later user engagement. However, to gain a deeper understanding of engagement beyond this predominant metric, the second phase of the analysis excluded 'total_minutes'. This approach allowed for the exploration of the roles and interdependencies of other engagement indicators, such as 'number_intervention_answersheets'-the number of completed lessons, 'logins_first_4_weeks'-login frequency, and 'number_diary_answersheets'-the number of completed diaries. The results from this phase highlighted the multifaceted nature of engagement, showing that while 'total_minutes' is strongly correlated with engagement, indicating that more engaged participants tend to spend more time on the intervention, the comprehensive engagement profile also depends on additional aspects like lesson completions and frequency of platform interactions.

Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study

The proliferation of online eHealth has made it much easier for users to access healthcare services and interventions from the comfort of their own homes. This study looks at how well one such platform-eSano-performs in terms of user experience when delivering mindfulness interventions. In order to assess usability and user experience, several tools such as eye-tracking technology, think-aloud sessions, a system usability scale questionnaire, an application questionnaire, and post-experiment interviews were employed. Participants were evaluated while they accessed the first module of the mindfulness intervention provided by eSano to measure their interaction with the app, and their level of engagement, and to obtain feedback on both the intervention and its overall usability. The results revealed that although users generally rated their experience with the app positively in terms of overall satisfaction, according to data collected through the system usability scale questionnaire, participants rated the first module of the mindfulness intervention as below average. Additionally, eye-tracking data showed that some users skipped long text blocks in favor of answering questions quickly while others spent more than half their time reading them. Henceforth, recommendations were put forward to improve both the usability and persuasiveness of the app-such as incorporating shorter text blocks and more engaging interactive elements-in order to raise adherence rates. Overall findings from this study provide valuable insights into how users interact with the eSano's participant app which can be used as guidelines for the future development of more effective and user-friendly platforms. Moreover, considering these potential improvements will help foster more positive experiences that promote regular engagement with these types of apps; taking into account emotional states and needs that vary across different age groups and abilities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12652-023-04635-4.