Evidence map›Paper›PMID 29535079›Full record

ReviewJMIR human factors2018

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review.

Marion Waite, Clare Martin, Rachel Franklin, David Duce, Rachel Harrison

Open access · goldAbstract readReview
In one paragraph

Review in JMIR human factors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 19 citations in OpenAlex.

  1. Pooled it
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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

5 authors at 2 institutions in 1 country.

Marion WaiteFaculty of Health and Life Sciences, Oxford Brookes University, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-9527-9475
Clare MartinFaculty of Technology, Design & Engineering, Oxford Brookes University, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-3147-3746
Rachel FranklinOxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-8245-5644
David DuceFaculty of Technology, Design & Engineering, Oxford Brookes University, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-5745-0745
Rachel HarrisonFaculty of Technology, Design & Engineering, Oxford Brookes University, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-0636-7546
Oxford Brookes University · GBUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople with type 1 diabetes (T1D) undertake self-management to prevent short and long-term complications. Advanced technology potentially supports such activities but requires consideration of psychological and behavioral constructs and usability issues. Economic factors and health care provider capacity influence access and uptake of advanced technology. Previous reviews have focused upon clinical outcomes or were descriptive or have synthesized studies on adults with those on children and young people where human factors are different.

objectiveThis review described and examined the relationship between human factors and adherence with technology for data logging processes in adults with T1D.

methodsA systematic literature search was undertaken by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality appraisal was undertaken and data were abstracted and categorized into the themes that underpinned the human factor constructs that were examined.

resultsA total of 18 studies were included. A total of 6 constructs emerged from the data analysis: the relationship between adherence to data logging and measurable outcomes; satisfaction with the transition to advanced technology for self-management; use of advanced technology and time spent on diabetes-related activities; strategies to mediate the complexities of diabetes and the use of advanced technology; cognition in the wild; and meanings, views, and perspectives from the users of technology.

conclusionsIncreased treatment satisfaction was found on transition from traditional to advanced technology use-insulin pump and continuous glucose monitoring (CGM); the most significant factor was when blood glucose levels were consistently <7.00 mmol/L (P ≤.01). Participants spent considerable time on their diabetes self-care. Logging of data was positively correlated with increasing age when using an app that provided meaningful feedback (regression coefficient=55.8 recordings/year; P ≤.01). There were benefits of CGM for older people in mediating complexities and fears of hypoglycemia with significant differences in well-being (P ≤.001). Qualitative studies explored the contextual use and uptake of technology. The results suggested frustrations with CGM, continuous subcutaneous insulin infusion, calibration of devices, and alarms. Furthermore implications for "body image" and the way in which "significant others" impacted on the behavior and attitude of the individual toward technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts, which has implications for the way in which future technologies should be designed. Quantitative studies were limited by small sample sizes, making it difficult to generalize findings to other contexts. This was further limited by a sample that was predominantly white, well-controlled, and engaged with self-care. The use of critical appraisal frameworks demonstrated where research into human factors and data logging processes of individuals could be improved. This included engaging people in the design of the technology, especially hard-to-reach or marginalized groups.

Indexed as

adherenceadultbarrierclosed loop systemscompliancecontinuous glucose monitoringreminder systemself-careself-managementSensor-augmented pump therapyT1Dtechnologytelehealthtelemedicinetype 1 diabetes mellitususability

Identifiers

PMID29535079
PMCPMC5871738
OpenAlexW2789277806

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.