Evidence map›Paper›PMID 31633755›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Emotion sharing in remote patient monitoring of patients with chronic kidney disease.

Robin Huang, Na Liu, Mary Ann Nicdao, Mary Mikaheal, Tanya Baldacchino, Annabelle Albeos, Kathy Petoumenos, Kamal Sud, Jinman Kim

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
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  4. Consumer- and patient-oriented informatics innovation: continuing the legacy of Warner V. Slack.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
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

9 authors at 3 institutions in 1 country.

Robin HuangSchool of Computer Science, The University of Sydney, Camperdown, Australia.
Na LiuSchool of Computer Science, The University of Sydney, Camperdown, Australia.
Mary Ann NicdaoHome Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia.
Mary MikahealHome Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia.
Tanya BaldacchinoHome Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia.
Annabelle AlbeosHome Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia.
Kathy PetoumenosThe Kirby Institute, UNSW Sydney, Sydney, NSW, Australia.
Kamal SudHome Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia.
Jinman KimSchool of Computer Science, The University of Sydney, Camperdown, Australia.
Blacktown & Mount Druitt Hospital · AUThe University of Sydney · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. MATERIALS AND

methodsA custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients' text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients' emotions (emoticon and sentiment) and TTD.

resultsUsage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13-5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22-2; P = .003) when compared to positive sentiments. DISCUSSION: The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts.

conclusionPatients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.

Indexed as

Computer GraphicsEmotionsMobile ApplicationsTelemedicineAdultAgedFemaleHemodialysis, HomeHumansMaleMiddle AgedMonitoring, PhysiologicRenal Insufficiency, ChronicUser-Computer Interfacedata collectionemoticon sharingmHealthmobile healthremote monitoringsentiment analysis

Identifiers

PMID31633755
PMCPMC7647270
OpenAlexW2982228070

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.