Evidence map›Paper›PMID 25451897›Full record

Trial reportDiabetes research and clinical practice2014

Perceptions and experiences of using automated bolus advisors amongst people with type 1 diabetes: a longitudinal qualitative investigation.

J Lawton, J Kirkham, D Rankin, K Barnard, C L Cooper, C Taylor, S Heller, J Elliott, REPOSE Group

Abstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Diabetes research and clinical practice, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Influences on Technology Use and Efficacy in Type 1 Diabetes.Journal of diabetes science and technology · 2016
    Review
  10. Review
  11. 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.

J LawtonCentre for Population Health Sciences, University of Edinburgh, Edinburgh, UK. Electronic address: j.lawton@ed.ac.uk.
J KirkhamCentre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
D RankinCentre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
K BarnardHuman Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
C L CooperClinical Trials Research Unit, University of Sheffield, Sheffield, UK.
C TaylorThe Sheffield Diabetes and Endocrine Centre, Northern General Hospital, Sheffield, UK.
S HellerAcademic Unit of Diabetes, Endocrinology and Metabolism, University of Sheffield, Sheffield, UK.
J ElliottAcademic Unit of Diabetes, Endocrinology and Metabolism, University of Sheffield, Sheffield, UK.
REPOSE Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsWe explored people's reasons for, and experiences of, using bolus advisors to determine insulin doses; and, their likes/dislikes of this technology. SUBJECTS AND

methods42 people with type 1 diabetes who had received instruction in use of bolus advisors during a structured education course were interviewed post-course and 6 months later. Data were analysed thematically.

resultsParticipants who considered themselves to have poor mathematical skills highlighted a gratitude for, and heavy reliance on, advisors. Others liked and chose to use advisors because they saved time and effort calculating doses and/or had a data storage facility. Follow-up interviews highlighted that, by virtue of no longer calculating their doses, participants could become deskilled and increasingly dependent on advisors. Some forgot what their mealtime ratios were; others reported a misperception that, because they were pre-programmed during courses, these parameters never needed changing. Use of data storage facilities could hinder effective review of blood glucose data and some participants reported an adverse impact on glycaemic control. DISCUSSION: While participants liked and perceived benefits to using advisors, there may be unintended consequences to giving people access to this technology. To promote effective use, on-going input and education from trained health professionals may be necessary.

Indexed as

Drug Dosage CalculationsMedication AdherencePerceptionAdultAlgorithmsBlood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1FemaleFollow-Up StudiesGlycated HemoglobinHumansHyperglycemiaHypoglycemiaHypoglycemic AgentsInsulinBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanHypoglycemic AgentsInsulinAutomated bolus advisorPatient experienceQualitative methodsTechnologyType 1 diabetes

Identifiers

PMID25451897
PMCPMC4270460

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.