Evidence map›Paper›PMID 36222806›Full record

ArticleJMIR human factors2022

Behavior Change App for Self-management of Gestational Diabetes: Design and Evaluation of Desirable Features.

Mikko Kytö, Saila Koivusalo, Antti Ruonala, Lisbeth Strömberg, Heli Tuomonen, Seppo Heinonen, Giulio Jacucci

Registry-linked trialAbstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03941652 (Usability Study of the Sensors and the eMOM GDM Application eHealth in Treatment of Gestational Diabetes - eMOM GDM -Study), which is not on this map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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.

NCT03941652 active not recruitingnot on this map

Usability Study of the Sensors and the eMOM GDM Application eHealth in Treatment of Gestational Diabetes - eMOM GDM -Study

TypeobservationalSponsorHelsinki University Central HospitalRan2019 to 2026Enrolled54ConditionsGestational Diabetes
3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines 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

7 authors.

Mikko KytöHelsinki University Hospital IT Management, Helsinki University Hospital, Helsinki, Finland.ORCID https://orcid.org/0000-0002-4936-3502
Saila KoivusaloDepartment of Gynecology and Obstetrics, Turku University Hospital, Turku, Finland.ORCID https://orcid.org/0000-0002-9482-9826
Antti RuonalaDepartment of Computer Science, University of Helsinki, Helsinki, Finland.ORCID https://orcid.org/0000-0003-2385-4861
Lisbeth StrömbergDepartment of Computer Science, University of Helsinki, Helsinki, Finland.ORCID https://orcid.org/0000-0002-3407-8186
Heli TuomonenDepartment of Computer Science, University of Helsinki, Helsinki, Finland.ORCID https://orcid.org/0000-0002-3204-0205
Seppo HeinonenDepartment of Gynecology and Obstetrics, Turku University Hospital, Turku, Finland.ORCID https://orcid.org/0000-0001-5949-0874
Giulio JacucciDepartment of Computer Science, University of Helsinki, Helsinki, Finland.ORCID https://orcid.org/0000-0002-9185-7928

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGestational diabetes (GDM) has considerable and increasing health effects as it raises both the mother's and the offspring's risk for short- and long-term health problems. GDM can usually be treated with a healthier lifestyle, such as appropriate dietary modifications and sufficient physical activity. Although telemedicine interventions providing weekly or more frequent feedback from health care professionals have shown the potential to improve glycemic control among women with GDM, apps without extensive input from health care professionals are limited and have not been shown to be effective. Different features in personalization and support have been proposed to increase the efficacy of GDM apps, but the knowledge of how these features should be designed is lacking.

objectiveThe aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature.

methodsWe designed an interactive GDM prototype app that provided example implementations of desirable features, such as providing automatic and personalized suggestions and social support through the app. Women with GDM explored the prototype and provided feedback in semistructured interviews.

resultsWe identified that (1) self-tracking data in GDM apps should be extended with written feedback, (2) habits and goals should be highly customizable to be useful, (3) the app should have different functions to provide social support, and (4) health care professionals should be notified through the app if something unusual occurs. In addition, we found 2 additional themes. First, basic functionalities that are fast to learn by women with GDM who have recently received the diagnosis should be provided, but there should also be deeper features to maintain interest for women with GDM at a later stage of pregnancy. Second, as women with GDM may have feelings of guilt, the app should have a tolerance for and a supporting approach to unfavorable behavior.

conclusionsThe feedback on the GDM prototype app supported the need for desirable features and provided new insights into how these features should be incorporated into GDM apps. We expect that following the proposed designs and feedback will increase the efficacy of GDM self-management apps.

trial registrationClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.

Indexed as

behavior changedigital healtheHealthfeaturesgestational diabetesmobile apppersonalized health careself-managementself-trackingtelehealth

Identifiers

PMID36222806
PMCPMC9607927

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Registered trials

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.