Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal...

Area of Science:

• Digital health
• Mental health technology
• Biomarker discovery

Background:

• Depression is a growing global health concern, necessitating scalable prevention strategies beyond treatment.
• Subclinical depression requires focused attention for effective public health interventions.
• Digital biomarkers offer a promising avenue for early detection and intervention in depression.

Purpose of the Study:

• Develop digital biomarkers for subclinical depression symptoms.
• Create digital biomarkers to assess the severity of subclinical depression.
• Evaluate a digital intervention's efficacy in reducing subclinical depression symptoms and severity.

Main Methods:

• A digital intervention (BEDDA) including a conversational agent and breathing exercises was administered daily for up to 45 days.
• Data collected included self-reports on mood, anhedonia, depression, anxiety, stress, voice, and breathing patterns.
• Smartwatch data (heart rate, heart-rate variability) were collected from 25% of participants for physiological analysis.

Main Results:

• Digital voice and breathing biomarkers show potential for unobtrusive assessment of depression.
• Physiological data from smartwatches can complement self-report measures.
• The digital intervention demonstrated potential in reducing symptoms of subclinical depression.

Conclusions:

• Digital biomarkers derived from voice and breathing can enhance depression diagnosis and prevention.
• These biomarkers offer a complementary or alternative assessment to traditional self-reports.
• The study supports the efficacy of standalone digital health interventions for depression prevention.