Evidence map›Paper›PMID 29394930›Full record

ArticleBMC nephrology2018

Evaluating the prevalence and opportunity for technology use in chronic kidney disease patients: a cross-sectional study.

Ann Bonner, Kerri Gillespie, Katrina L Campbell, Katina Corones-Watkins, Bronwyn Hayes, Barbara Harvie, Jaimon T Kelly, Kathryn Havas

Abstract readEvaluation Study
In one paragraph

Article in BMC nephrology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

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

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Shared Decision-Making for a Dialysis Modality.Kidney international reports · 2022
    Review
  17. Article
  18. Article
  19. Kidney transplant recipient perspectives on telehealth during the COVID-19 pandemic.Transplant international : official journal of the European Society for Organ Transplantation · 2021
    Article
  20. 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

8 authors.

Ann BonnerSchool of Nursing, Queensland University of Technology, Brisbane, Australia. ann.bonner@qut.edu.au.ORCID 0000-0001-9920-6743
Kerri GillespieSchool of Nursing, Queensland University of Technology, Brisbane, Australia.
Katrina L CampbellRenal Service, Princess Alexandra Hospital, Woolloongabba, Australia.
Katina Corones-WatkinsSchool of Nursing and Midwifery, Griffith University, Gold Coast, Australia.
Bronwyn HayesRenal Unit, Cairns Hospital, Cairns, Australia.
Barbara HarvieRenal Unit, Queanbeyan Hospital, Queanbeyan, Australia.
Jaimon T KellyFaculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia.
Kathryn HavasSchool of Nursing, Queensland University of Technology, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) is increasing worldwide and early education to improve adherence to self-management is a key strategy to slow CKD progression. The use of the internet and mobile phone technologies (mHealth) to support patients is considered an effective tool in many other chronic disease populations. While a number of mHealth platforms for CKD exist, few studies have investigated if and how this population use technology to engage in self-management.

methodsUsing a cross-sectional design across five health districts in Queensland (Australia), a 38-item self-report survey was distributed to adults with CKD attending outpatient clinics or dialysis units to measure current use and type of engagement with mHealth, perceived barriers to use, and opportunities to support CKD self-management. Odds ratio (OR) were calculated to identify associations between demographic characteristic and mHealth use.

resultsOf the 708 participants surveyed, the majority had computer access (89.2%) and owned a mobile phone (83.5%). The most likely users of the internet were those aged ≤ 60 years (OR: 7.35, 95% confidence interval [CI]: 4.25-12.75, p < 0.001), employed (OR: 7.67, 95% CI: 2.58-22.78, p < 0.001), from non-indigenous background (OR: 6.98, 95% CI: 3.50-13.93, p < 0.001), or having completed higher levels of education (OR: 3.69, CI: 2.38-5.73, p < 0.001). Those using a mobile phone for complex communication were also younger (OR: 6.01, 95% CI: 3.55-10.19, p < 0.001), more educated (OR: 1.99, 95% CI: 1.29-3.18, p < 0.01), or from non-indigenous background (OR: 3.22, 95% CI: 1.58-6.55, p < 0.001). Overall, less than 25% were aware of websites to obtain information about renal healthcare. The mHealth technologies most preferred for communication with their renal healthcare teams were by telephone (56.5%), internet (50%), email (48.3%) and text messages (46%).

conclusionIn the CKD cohort, younger patients are more likely than older patients to use mHealth intensively and interactively although all patients' technology literacy ought to be thoroughly assessed by renal teams before implementing in practice. Further research testing mHealth interventions to improve self-management in a range of patient cohorts is warranted.

Indexed as

AdolescentAdultAgedAustraliaCell PhoneCross-Sectional StudiesFemaleHumansInternetMaleMiddle AgedPrevalenceRenal Insufficiency, ChronicSelf CareTelemedicineYoung AdultChronic kidney diseaseE-healthEnd stage kidney diseaseInternetmHealthMobile phoneSmart phoneTelehealth

Identifiers

PMID29394930
PMCPMC5797344

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.