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

behavior change digital health eHealth features gestational diabetes mobile app personalized health care self-management self-tracking telehealth

Journal

JMIR human factors
ISSN: 2292-9495
Titre abrégé: JMIR Hum Factors
Pays: Canada
ID NLM: 101666561

Informations de publication

Date de publication:
12 Oct 2022
Historique:
received: 08 02 2022
accepted: 10 09 2022
revised: 09 09 2022
entrez: 12 10 2022
pubmed: 13 10 2022
medline: 13 10 2022
Statut: epublish

Résumé

Gestational 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. The aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature. We 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. We 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. The 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. ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.

Sections du résumé

BACKGROUND BACKGROUND
Gestational 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.
OBJECTIVE OBJECTIVE
The aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature.
METHODS METHODS
We 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.
RESULTS RESULTS
We 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.
CONCLUSIONS CONCLUSIONS
The 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 REGISTRATION BACKGROUND
ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.

Identifiants

pubmed: 36222806
pii: v9i4e36987
doi: 10.2196/36987
pmc: PMC9607927
doi:

Banques de données

ClinicalTrials.gov
['NCT03941652']

Types de publication

Journal Article

Langues

eng

Pagination

e36987

Informations de copyright

©Mikko Kytö, Saila Koivusalo, Antti Ruonala, Lisbeth Strömberg, Heli Tuomonen, Seppo Heinonen, Giulio Jacucci. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 12.10.2022.

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Auteurs

Mikko Kytö (M)

Helsinki University Hospital IT Management, Helsinki University Hospital, Helsinki, Finland.
Department of Computer Science, University of Helsinki, Helsinki, Finland.

Saila Koivusalo (S)

Department of Gynecology and Obstetrics, Turku University Hospital, Turku, Finland.
Department of Gynecology and Obstetrics, University of Turku, Turku, Finland.
Department of Gynecology and Obstetrics, Helsinki University Hospital, Helsinki, Finland.
Department of Gynecology and Obstetrics, University of Helsinki, Helsinki, Finland.

Antti Ruonala (A)

Department of Computer Science, University of Helsinki, Helsinki, Finland.

Lisbeth Strömberg (L)

Department of Computer Science, University of Helsinki, Helsinki, Finland.

Heli Tuomonen (H)

Department of Computer Science, University of Helsinki, Helsinki, Finland.

Seppo Heinonen (S)

Department of Gynecology and Obstetrics, Turku University Hospital, Turku, Finland.
Department of Gynecology and Obstetrics, University of Turku, Turku, Finland.

Giulio Jacucci (G)

Department of Computer Science, University of Helsinki, Helsinki, Finland.

Classifications MeSH