Evidence map›Paper›PMID 30202395›Full record

ArticleAmerican journal of lifestyle medicine

Diabetes Diagnosis and Screen-Based Sedentary Behavior Among US Adults.

Ruopeng An, Yan Yang

Abstract read
In one paragraph

Article in American journal of lifestyle medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

2 authors.

Ruopeng AnDepartment of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois.
Yan YangDepartment of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Champaign, Illinois.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes threatens a patient's health and quality of life, whereas disease diagnosis itself could potentially serve as a teachable moment for initiating behavior change. This study assessed diabetes diagnosis as a possible teachable moment for screen-based sedentary behavior among US adults. The nationally representative sample (n = 3690) came from the 2005-2006 and 2011-2012 National Health and Nutrition Examination Survey. Self-reported hours spent on screen-based sedentary behavior (television/video watching, computer/digital device use) were measured by the Global Physical Activity Questionnaire. Diabetes/prediabetes was identified by fasting plasma glucose and the glycated hemoglobin test. Logistic regressions were conducted to examine the relationship between diabetes diagnosis and screen-based sedentary behavior, adjusting for individual characteristics and sampling design. Compared with those with undiagnosed diabetes, the adjusted values for prevalence of daily television/video watching ≥2 hours (77.45% vs 65.14%), computer/digital device use ≥1 hour (43.20% vs 36.52%), and total screen time (ie, television/video watching plus computer/digital device use) ≥3 hours (66.75% vs 45.78%) were all noticeably higher among adults with diagnosed diabetes, although only the difference in the prevalence of daily total screen time was significant at P < .05. No evidence was found regarding diabetes diagnosis as a teachable moment in reducing screen-based sedentary behavior in US adults.

Indexed as

computer usediabetes diagnosissedentary behaviorteachable momenttelevision watching

Identifiers

PMID30202395
PMCPMC6124970

What OpenQuestion 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 OpenQuestion graph.