How individuals change during internet-based interventions for depression: A randomized controlled trial comparing...

Area of Science:

• Psychiatry
• Digital Health
• Psychotherapy Research

Background:

• Internet-based interventions (IBI) for depression effectively reduce symptoms.
• Patterns of symptom change during standardized versus individualized IBI are not well understood.
• Identifying patient subgroups with distinct symptom trajectories is crucial for optimizing IBI effectiveness.

Purpose of the Study:

• To investigate and compare symptom change patterns in standardized versus individualized cognitive-behavioral Internet-based interventions for depression.
• To identify distinct subgroups of individuals based on their symptom trajectories during IBI.
• To explore client characteristics associated with different improvement patterns.

Main Methods:

• A randomized controlled trial involving 1,089 mildly to moderately depressed individuals.
• Comparison of standardized versus individualized module-wise feedback within a 6-week cognitive-behavioral IBI.
• Assessment of depressive symptoms (PHQ-9) at baseline and before each module.
• Growth mixture modeling to identify symptom change trajectories and associated baseline variables.

Main Results:

• No significant differences in symptom change patterns were observed between standardized and individualized IBI conditions.
• Two distinct subgroups emerged: immediate improvers (62.5%) and delayed improvers (37.5%).
• Immediate improvers showed rapid symptom reduction, while delayed improvers had stable symptoms initially and smaller overall improvement, associated with higher treatment expectations, current major depressive disorder, and lower social support.

Conclusions:

• Both standardized and individualized feedback in IBI for depression result in comparable symptom improvement trajectories.
• Strategies like expectation management and enhancing social support may benefit individuals at risk for delayed improvement in IBI.
• Understanding symptom change patterns can inform personalized treatment approaches within digital mental health interventions.