Digital Ecological Momentary Assessment Data for a Social Prescribing Yoga Intervention

A UK-based feasibility study collected intensive longitudinal data from 13 participants in East London during a four-week online chair-based yoga programme. The dataset integrates smartwatch-derived physiological indicators (stress, sleep, heart rate) with twice-daily self-reported wellbeing assessments using an adapted Short Warwick-Edinburgh Mental Wellbeing Scale. The data was created by Ian Tucker and published on figshare in 2026 under a CC-BY-4.0 license.

Use Cases

• Analyzing within- and between-person variability in wellbeing trajectories based on repeated self-report measures.
• Exploring associations between physiological stress markers and self-reported wellbeing based on integrated wearable and EMA data.
• Investigating methodological challenges like device synchronisation and questionnaire adherence in digital health evaluations.
• Developing models for intensive longitudinal data analysis in a community-based health intervention context.

Strengths

• Integrates two distinct data modalities: wearable-derived physiological data and self-reported wellbeing assessments.
• Provides a methodological foundation for real-time evaluation, as stated in the study conclusion.
• Includes qualitative findings on participant acceptability and engagement factors.

Limitations

• Small participant cohort of 13 individuals, with 11 included in quantitative analyses.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is described as a feasibility study and was not designed to assess intervention effectiveness.

Provenance

Source

Ian Tucker via figshare.

Collection Method

Mixed-methods feasibility study involving smartwatch data collection and twice-daily digital questionnaires.

Time Range

Data collected during a four-week intervention period.

Freshness

Last updated 2026-05-22 06:10:56; freshness should be verified.

Geography

East London, United Kingdom.