Evidence map›Paper›PMID 33767336›Full record

ArticleNPJ digital medicine2021

Social media language of healthcare super-utilizers.

Sharath Chandra Guntuku, Elissa V Klinger, Haley J McCalpin, Lyle H Ungar, David A Asch, Raina M Merchant

Abstract read
In one paragraph

Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
  2. Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data.American heart journal plus : cardiology research and practice · 2026
    Article
  3. Article
  4. Article
  5. Article
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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

6 authors.

Sharath Chandra GuntukuPenn Medicine Center for Digital Health, Philadelphia, PA, USA. sharathg@upenn.edu.ORCID http://orcid.org/0000-0002-2929-0035
Elissa V KlingerPenn Medicine Center for Digital Health, Philadelphia, PA, USA.
Haley J McCalpinPenn Medicine Center for Digital Health, Philadelphia, PA, USA.
Lyle H UngarPenn Medicine Center for Digital Health, Philadelphia, PA, USA.
David A AschPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Raina M MerchantPenn Medicine Center for Digital Health, Philadelphia, PA, USA.

Funding

Digital Phenotyping and Cardiovascular HealthR01HL141844 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MERCHANT, RAINA · 2019 to 2022
$3.1M
NHLBI NIH HHS R01 HL141844Robert Wood Johnson Foundation (RWJF) 72695
6 · The paper itself

Abstract

An understanding of healthcare super-utilizers' online behaviors could better identify experiences to inform interventions. In this retrospective case-control study, we analyzed patients' social media posts to better understand their day-to-day behaviors and emotions expressed online. Patients included those receiving care in an urban academic emergency department who consented to share access to their historical Facebook posts and electronic health records. Super-utilizers were defined as patients with more than six visits to the Emergency Department (ED) in a year. We compared posts by super-utilizers with a matched group using propensity scoring based on age, gender and Charlson comorbidity index. Super-utilizers were more likely to post about confusion and negativity (D = .65, 95% CI-[.38, .95]), self-reflection (D = .63 [.35, .91]), avoidance (D = .62 [.34, .90]), swearing (D = .52 [.24, .79]), sleep (D = .60 [.32, .88]), seeking help and attention (D = .61 [.33, .89]), psychosomatic symptoms, (D = .49 [.22, .77]), self-agency (D = .56 [.29, .85]), anger (D = .51, [.24, .79]), stress (D = .46, [.19, .73]), and lonely expressions (D = .44, [.17, .71]). Insights from this study can potentially supplement offline community care services with online social support interventions considering the high engagement of super-utilizers on social media.

Identifiers

PMID33767336
PMCPMC7994843

What OpenQuestion holds

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Registered trials

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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.