Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare

Area of Science:

• Ubiquitous computing
• Digital mental health
• Clinical informatics

Background:

• Passive sensing technologies offer potential for objective mental health measurement.
• Current passive sensing detection tools exhibit poor generalization across diverse patient populations.
• Understanding these generalization challenges is crucial for advancing digital mental health tools.

Purpose of the Study:

• To review the challenges of passive sensing for mental health detection.
• To explore how passive sensing can be adapted to support clinical decision-making.
• To identify research directions for integrating passive sensing into mental healthcare.

Main Methods:

• Narrative literature review on passive sensing in mental health.
• Qualitative analysis of findings from two studies involving 41 mental health clinicians.
• Exploration of clinician perspectives on passive sensing data interpretation and utility.

Main Results:

• Passive sensing detection tools lack generalizability in heterogeneous samples.
• Clinicians highlighted the need for contextual information to interpret passive sensing data.
• Passive sensing can reveal patient problems and inform treatment targets when integrated with clinical context.

Conclusions:

• Actionable sensing, focusing on supporting clinical actions rather than just detection, is a more promising research direction.
• Future research should focus on the intersection of actionable sensing and mental healthcare.
• Aligning ubiquitous computing research with clinical needs is essential for effective digital mental health interventions.