Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Data Collection III

Data Collection III

The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Exploring Collaboration Breakdowns Between Provider Teams and Patients in Post-Surgery Care.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2026

Same author

Semantically enriching personal mobility data using OpenStreetMap: a case study using smartphone users' frequently visited places.

Journal of location based services·2026

Same author

Challenges and Strategies of Managing Mental Health Medication in Real-World Practices: Interview Study Among University Students.

JMIR human factors·2026

Same authorSame journal

The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies·2026

Same author

Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.

Annotation of real-world data for artificial intelligence systems : 9th international workshop, ARDUOUS 2025, Bologna Italy, October 25-26, 2025, proceedings. International Workshop on Annotation of Real-World Data for Artificial Intell...·2026

Same author

Within- and Between-Individual Compliance in Mobile Health: Joint Modeling Approach to Nonrandom Missingness in an Intensive Longitudinal Observational Study.

JMIR mHealth and uHealth·2025

Search research articles

Related Experiment Video

Updated: Jan 12, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

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

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

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

Related Experiment Videos

Last Updated: Jan 12, 2026

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption

Published on: June 20, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

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.