Evidence map›Paper›PMID 33442583›Full record

ArticleDigital biomarkers2020

"It's Not as Simple as Just Looking at One Chart": A Qualitative Study Exploring Clinician's Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data.

Alison Keogh, William Johnston, Mitchell Ashton, Niladri Sett, Ronan Mullan, Seamas Donnelly, Jonas F Dorn, Francesc Calvo, Brian Mac Namee, Brian Caulfield

Open access · goldAbstract read
In one paragraph

Article in Digital biomarkers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Alison KeoghInsight Centre of Data Analytics, University College, Dublin, Ireland.
William JohnstonInsight Centre of Data Analytics, University College, Dublin, Ireland.
Mitchell AshtonInsight Centre of Data Analytics, University College, Dublin, Ireland.
Niladri SettInsight Centre of Data Analytics, University College, Dublin, Ireland.
Ronan MullanSchool of Medicine, Trinity College Dublin, Dublin, Ireland.
Seamas DonnellySchool of Medicine, Trinity College Dublin, Dublin, Ireland.
Jonas F DornData and Digital, Novartis, Basel, Switzerland.
Francesc CalvoData and Digital, Novartis, Basel, Switzerland.
Brian Mac NameeInsight Centre of Data Analytics, University College, Dublin, Ireland.
Brian CaulfieldInsight Centre of Data Analytics, University College, Dublin, Ireland.
University College Dublin · IENovartis (Switzerland) · CHTrinity College Dublin · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient's physical activity patterns.

objectiveTo explore clinicians' opinions on different visualisations of actigraphy data.

methodsFour visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis.

resultsThree major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret.

conclusionThe current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

Indexed as

ActigraphyPhysical activityVisualisationsWearable technology

Identifiers

PMID33442583
PMCPMC7768127
OpenAlexW3109088148

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.