ReviewJournal of activity, sedentary and sleep behaviors2025
Visualising accelerometer-based 24/7 human movement behaviour data: an umbrella review and framework development from the LABDA project.
Review in Journal of activity, sedentary and sleep behaviors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRegular Physical Activity (PA) is important for disease prevention and health promotion. PA has been assessed through surveys, questionnaires, and devices such as accelerometers. Alongside PA, Sedentary Behaviour (SB) and sleep are the main components of 24/7 movement behaviours, and their adequate measurement is important for assessing health outcomes. Many different metrics to summarise 24/7 movement behaviours are used; however, little attention has been paid to visualising these metrics. Data visualisation is likely to impact the way results are communicated and understood by different audiences. This study systematically reviews 24/7 movement behaviour metrics, presents an overview of their visualisations, and develops a framework to guide context-specific visualisation choices.
methodsAn umbrella review was conducted in February 2025 in Scopus and Web of Science. Included papers were reviews of any type, with any human population and study design, having at least one of the three 24/7 movement behaviours as exposure or outcome measured through accelerometers, and clearly reporting the outcome metrics. Data extraction and an adapted thematic data analysis were performed in April 2025. The overview of the visualisations used for the metrics identified in the review and thematic analysis was created through non-systematic web searches and use of Microsoft Copilot. Finally, a framework was created based on the sender-receiver model for effective communication.
resultsIn total, 93 reviews were included, with a total of 5667 articles reporting on 134 unique output metrics based on accelerometer data. The most common metrics were step counts and time spent in Moderate-to-Vigorous Physical Activity (MVPA). The non-systematic web searches showed that most researchers use bar charts, line graphs, or pie graphs to visualise 24/7 movement behaviour data, while Copilot input provided more options of visualisations. The resulting framework was the product of an iterative process aggregating the previous results, providing clear guidance for organising metrics and their corresponding visualisations.
conclusionsThis study structures and summarises types of visualisations of accelerometer-derived metrics to describe 24/7 human movement behaviour data. Future research is needed to apply the framework in practical contexts and investigate how the visualisations are perceived by different audiences.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.