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Data Collection III

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Updated: Jan 12, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

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Published on: June 20, 2025

Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment.

H A LE¹, Rithika Lakshminarayanan², Jixin Li²

¹Khoury College of Computer Sciences, Northeastern University, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

|November 3, 2025

Summary

This summary is machine-generated.

Audio-micro-experience sampling (audio-μEMA) allows for open-ended, temporally dense self-reports via speech input. This novel method achieved a 67.7% response rate in a 14-day feasibility study, showing high participant engagement.

Keywords:

Audio Ecological Momentary Assessment Experience Sampling Microinteraction Physical Activity Measurement Ubiquitous and Wearable Computing

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Area of Science:

Human-Computer Interaction
Behavioral Data Collection
Wearable Technology

Background:

Micro-experience sampling (μEMA) on smartwatches enables high-temporal-density self-reports but is limited to multiple-choice answers due to screen size.
Open-ended responses are valuable for capturing nuanced behavioral data but are challenging with current micro-interaction methods.

Purpose of the Study:

To introduce and evaluate audio-μEMA, a novel speech-input-based micro-interaction self-report method.
To assess the usability and feasibility of audio-μEMA for temporally dense, open-ended behavioral data collection in a free-living setting.

Main Methods:

A one-hour usability study followed by a 6- to 21-day within-subject feasibility study.
Participants used audio-μEMA for self-reporting physical activities and postures every 2 to 5 minutes.
Qualitative exploration of usability and factors influencing response rates.

Main Results:

Participants were highly engaged despite frequent interruptions (12-20 times/hour).
An average response rate of 67.7% was achieved for audio-μEMA over a period of up to 14 days.
Identified factors impacting feasibility, including implementation, methodological, and participant challenges.

Conclusions:

Audio-μEMA is a feasible and engaging method for temporally dense, open-ended self-reports.
The findings provide insights into deploying audio-μEMA for real-time activity recognition systems.
This method offers a promising alternative for capturing rich behavioral data in ecological settings.