This paper is in the following e-collection/theme issue:

Fitness Trackers and Smart Pedometers/Accelerometers (684) Evaluation and Research Methodology for mHealth (251) Research Instruments, Questionnaires, and Tools (764) mHealth for Data Collection and Research (1223) Wearable Devices and Sensors (1177) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1010) Instruments and Questionnaires for Physical Activity and Lifestyle (122)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64323, first published .
Determining Cluster-Specific Differences in the Number of Days Required to Reliably Predict Habitual Physical Activity: Intraclass Correlation Resampling Analysis

Determining Cluster-Specific Differences in the Number of Days Required to Reliably Predict Habitual Physical Activity: Intraclass Correlation Resampling Analysis

Determining Cluster-Specific Differences in the Number of Days Required to Reliably Predict Habitual Physical Activity: Intraclass Correlation Resampling Analysis

Authors of this article:

Conor Jordan Murphy1, 2 Author Orcid Image ;   Gabriel M Jouan1, 3 Author Orcid Image ;   Katrin Y Friðgeirsdóttir1, 2 Author Orcid Image ;   Anna Sigridur Islind1, 3 Author Orcid Image ;   Jose M Saavedra1, 2 Author Orcid Image ;   María Óskarsdóttir3 Author Orcid Image ;   Erna Sif Arnardóttir1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Conor Jordan Murphy   1, 2 , PhD ;   Gabriel M Jouan   1, 3 , PhD ;   Katrin Y Friðgeirsdóttir   1, 2 , PhD ;   Anna Sigridur Islind   1, 3 , PhD ;   Jose M Saavedra   1, 2 , PhD ;   María Óskarsdóttir   3 , PhD ;   Erna Sif Arnardóttir   1 , PhD

1 School of Technology, Reykjavik University Sleep Institute, Reykjavík University, Reykjavik, Capital Region, Iceland

2 Sports Science Department, Physical Activity, Physical Education, Sport and Health (PAPESH) Research Centre, School of Social Sciences, Reykjavík University, Reykjavik, Capital Region, Iceland

3 Department of Computer Science, Reykjavík University, Reykjavik, Capital Region, Iceland

Corresponding Author:

  • Conor Jordan Murphy, PhD
  • School of Technology, Reykjavik University Sleep Institute
  • Reykjavík University
  • Menntavegur 1
  • Reykjavik, Capital Region 101
  • Iceland
  • Phone: 354 599 6200
  • Email: conorm@ru.is