Optimizing Intervention Components of a Preventive Stress Management mHealth Intervention for Health Care Workers: Experimental Factorial Study

Background: Work stress is a prevalent risk factor for mental health problems, such as burnout and depression. Health care workers, especially during the COVID-19 pandemic, face high levels of work stress that make them a vulnerable population in need of support. Digital interventions are a promising way to combat this issue, offering the possibility of scalable programs that are easily accessible. While a wide range of stress management techniques can be incorporated into digital interventions, applying the multiphase optimization strategy enables systematic evaluation of what specific content most contributes to preventing the negative effects of work stress. Objective: The primary aim of this research was to identify which digital intervention components and combinations of components are most effective at preventing symptoms of stress-related health problems. These insights are valuable to inform future intervention development for optimizing intervention design. Methods: This study tested 5 digital intervention components aimed at improving stress management among workers. Engagement and Demands components allow participants to self-reflect on their work engagement and work challenges, while the Control component aids a more action-oriented process considering job crafting and detachment strategies. The Journaling component encourages a deeper reflection, and the Psychoeducation component provides evidence-based strategies for managing stress. In an experimental factorial study, Swedish health care workers (n=297) tested different versions of the intervention containing all possible combinations of these 5 components. Stress-related health outcomes, such as burnout, anxiety, and depression, were measured using questionnaires immediately before, immediately after, and 1 month after the end of the intervention. Results: The most promising intervention effects were observed when the Demands and Control components were present together in the intervention. Including these components led to an increase in social support (β=0.68; P<.001) and job crafting (β=0.41; P=.06) during the intervention, as well as a decrease in symptoms of emotional exhaustion (β=-0.50; P=.005), burnout (β=-0.54; P=.004), and anxiety (β=-0.44; P=.04) after the intervention. Notably, including one of the components without the other made outcomes worse than including neither of these 2 components. Furthermore, mindfulness after the intervention increased when both the Engagement and Demands components (β=0.72; P=.001) were included as well as when the Journaling and Psychoeducation components were included (β=0.46; P=.04). Conclusions: Results indicate that components aiding self-insight should be integrated with components providing actionable advice to achieve optimal intervention effects. Results from this optimization study may inform the development of preventive digital stress management interventions to be tested in future randomized controlled trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.