This research aims to adopt a complex systems perspective to thoroughly examine the impact of online mindfulness interventions on changes in emotional distress, with a particular focus on the mechanisms of action. Through a daily diary study design, the study seeks to reveal the complexity and dynamic characteristics of emotional changes and the underlying mechanisms（i.e. distress tolerance， experiential avoidance， cognition flexibility, and life engagement） throughout the intervention process. This research will enrich the theoretical framework of online mental health intervention and provide empirical evidence for optimizing online intervention strategies.

Emotional distress refers to psychological discomfort related to emotions-particularly anxiety and depression-as well as the associated suffering and impairments in work and daily life. According to a Gallup survey, approximately one-third of the global population experiences emotional distress (Daly & Macchia, 2023), with depression, anxiety, and anger being the most prominent aspects. Moreover, the distress associated with worry and sadness has increased significantly from 2009 to 2021. Anxiety disorders and depressive disorders are among the most common mental illnesses worldwide (Kessler et al., 2009; World Health Organization, 2017), and depression is the leading cause of suicide (Ferrari et al., 2014). In China, the lifetime prevalence rates for anxiety disorders and depression are approximately 7.5% and 6.8%, respectively (Y. Huang et al., 2019), with only about 0.5% of patients with depression receiving adequate treatment (Lu et al., 2021). Both anxiety and depressive disorders severely restrict psychosocial functioning and adversely affect normal life. Traditional psychotherapy research has largely been limited to phenomenological descriptions, lacking in-depth exploration of the underlying mechanisms, which in turn constrains the optimization and effectiveness of interventions.

The theories and methods from the field of complex systems can help overcome these limitations by providing a deeper theoretical framework and analytical tools for studying the processes and mechanisms underlying psychotherapeutic effects. In recent years, network analysis and dynamic systems research on psychological interventions for anxiety and depressive disorders have gradually advanced. However, most current studies collect data only before and after the intervention, paying little attention to the dynamic changes during the intervention period and the long-term impact during follow-up. At the network element level, most studies focus on symptom networks, with few incorporating mechanistic variables into their models. Understanding the mechanisms of intervention effects is critical, and this requires including more mechanistic variables in the analysis framework. In conjunction with dynamic systems theory, it is possible to further explore state transitions and their early warning signals. Although early warning signals theoretically hold the potential to predict sudden changes, their practical sensitivity and specificity still need to be improved in order to provide more reliable information for clinical practice.

In summary, existing research suggests that complex network methods hold potential value in clinical practice, warranting further exploration. However, current findings are insufficient to fully and deeply reveal the specific processes underlying psychotherapeutic effects (Holmes et al., 2018). Moreover, the practical utility of complex network methods in clinical settings remains to be verified (Contreras et al., 2019; Schreuder et al., 2023). This is particularly true in the field of mindfulness interventions, where research in this area is still lacking. By adopting a complex systems perspective, incorporating intensive longitudinal measurements, and including mechanistic variables（i.e. distress tolerance， experiential avoidance， cognition flexibility, and life engagement）, this study aims to explore feedback loops with temporal dynamic characteristics. Such an approach will enable researchers to gain a more comprehensive and in-depth understanding of the complexity and dynamics involved in the alleviation of emotional distress such as anxiety and depression.

Inclusion Criteria:

• Subjects with scores greater than 21 on the Kessler Psychological Distress Scale.

Exclusion Criteria:

• Subjects who could not access the Internet.
• Subjects with insufficient Chinese ability.
• Subjects who have participated in mindfulness-based projects for more than 6 weeks before, and / or the current frequency of meditation practice is more than once a week.
• Patients with schizophrenia or psychotic affective disorder, current organic mental disorder, substance abuse disorder and generalized developmental disorder.
• Subjects at risk of suicide.