The increasing number of digital solutions developed for use in clinical health care settings is accompanied by new challenges to develop and conduct clinical studies that include eHealth technologies. Clinical study implementation plans often disregard or underestimate the necessity of additional administrative and logistic tasks required at clinical sites as well as ethical aspects to test digital solutions. Experiences made in the run-up of an observational clinical feasibility study at three international clinical sites in the framework of the MyPal project (https://mypal-project.eu/) result in recommendations to avoid delays and barriers in the planning of such prospective studies in clinical and also palliative care for increased efficiency.

Emotion dysregulation is regarded as a driving mechanism for the development of mental health problems and psychopathology. The role of emotion regulation (ER) in the management of cancer distress and quality of life (QoL) has recently been recognized in psycho-oncology. The latest technological advances afford ways to assess ER, affective experiences and QoL in child, adolescent and young adult (CAYA) cancer patients through electronic patient-reported outcomes (ePRO) in their daily environment in real-time. Such tools facilitate ways to study the dynamics of affect and the flexibility of ER. However, technological advancement is not risk-free. We critically review the literature on ePRO in cancer existing models of ER in pediatric psycho-oncology and analyze strength, weaknesses, opportunities and threats of ePRO with a focus on CAYA cancer research and care. Supported by personal study-based experiences, this narrative review serves as a foundation to propose a novel methodological and metatheoretical framework based on: (a) an extended notion of ER, which includes its dynamic, adaptive and flexible nature and focuses on processes and conditions rather than fixed categorical strategies; (b) ePRO as a means to measure emotion regulation flexibility and affect dynamics; (c) identifying early warning signals for symptom change via ePRO and building forecasting models using dynamical systems theory.