4.2. Future Research
ADVERSE EVENTS
Future interventions should use a less complex and longer-term study design to systematically demonstrate which individualization mechanisms lead to greater effectiveness of mHealth interventions in terms of distress coping. The focus of future mHealth interventions in the healthcare setting should be as low-threshold access as possible, including push notifications and ideally an on-site project coordinator who can provide technical support, establish accountability, and remind participants to follow procedures. To reduce the dropout rate, future studies could offer incentives, simplify the study process, improve face-to-face communication, monitor and address adverse events, and foster a more positive research culture.Furthermore, machine learning elements could improve future mHealth interventions by providing personalized and data-driven solutions for health problems. Machine learning algorithms can analyze large amounts of data from various sources, such as electronic health records, wearable devices, and mobile apps, to identify patterns and make predictions about a patient’s health status. This information could then be used to further provide targeted and individualized interventions and improve health outcomes. In addition, machine learning mechanisms could be used in future studies to explain the high dropout rate of this study using available data on technology satisfaction, dropout reasons, and app usage behavior.