Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns:...

Area of Science:

• Digital health interventions
• Mental health technology
• Behavioral science

Background:

• Stressful work environments contribute to mental health issues like depression.
• Traditional mental health care access is limited by time and resources.
• Mobile health (mHealth) offers real-time interventions adaptable to user data.

Purpose of the Study:

• Investigate optimal timing for mHealth interventions in stressful work environments.
• Target mood, activity, and sleep behaviors for improved mental health.
• Analyze intervention effectiveness based on prior user states.

Main Methods:

• Conducted a 6-month micro-randomized trial with 1565 medical interns.
• Assigned weekly push notifications (mood, activity, sleep, or control).
• Collected daily mood, step count, and sleep data; assessed moderation by prior week's data.

Main Results:

• Previous week's low mood improved effectiveness of mood interventions.
• Previous week's low activity/sleep enhanced effectiveness of respective interventions.
• Intervention effects were positive when user state was low, negative when high.

Conclusions:

• Individual state significantly impacts mHealth intervention receptivity.
• Personalized timing of interventions is key to maximizing efficacy.
• Future research should focus on state-contingent mHealth delivery.