Designing daily-life research combining experience sampling method with parallel data

Area of Science:

• Digital health
• Ambulatory monitoring
• Multidisciplinary research

Background:

• Ambulatory monitoring is increasingly used in mental and somatic healthcare for real-world wellbeing and treatment tracking.
• Experience sampling method (ESM) collects subjective patient time-series data.
• Digital devices gather objective parallel data with high frequency and duration.

Purpose of the Study:

• To provide overarching guidelines for effectively combining subjective experience sampling and objective parallel data in ambulatory monitoring.
• To address the limitations of existing data-type-specific guidelines.

Main Methods:

• Integration of literature review and expert opinions to formulate guiding principles.
• Development of recommendations for study design, data collection, management, and analysis.

Main Results:

• Treating ESM and parallel data as a single holistic time series from the study design phase is crucial.
• Understanding data fluctuation patterns and volatility ensures compatibility.
• Data operationalization should enable answering research questions regarding temporal and disease severity resolution.

Conclusions:

• The formulated considerations provide structure and support for researchers in this evolving multidisciplinary field.
• Guidelines facilitate the effective combination of diverse data streams in ambulatory health monitoring.