Extensive Review of Persuasive System Design Categories and Principles: Behavioral Obesity Interventions.


Journal

Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056

Informations de publication

Date de publication:
05 Jun 2020
Historique:
received: 06 09 2019
accepted: 21 05 2020
entrez: 6 6 2020
pubmed: 6 6 2020
medline: 7 4 2021
Statut: epublish

Résumé

In this extensive review of behavioral digital obesity interventions, we reviewed randomized control trials aimed at weight loss or maintaining weight loss and identifying persuasive categories and principles that drive these interventions. The following databases were searched for long-term obesity interventions: Medline, PsycINFO, Academic Search Complete, CINAHL and Scopus. The inclusion criteria included the following search terms: obesity, overweight, weight reduction, weight loss, obesity management, and diet control. Additional criteria included randomized control trial, ≥ 6 months intervention, ≥ 100 participants and must include persuasive technology. Forty-six publications were in the final review. Primary task support was the most frequently utilized persuasive system design (PSD) category and self-monitoring was the most utilized PSD principle. Behavioral obesity interventions that utilized PSD with a behavior change theory more frequently produced statistically significant weight loss findings. Persuasive technology and PSD in digital health play a significant role in the management and improvement of obesity especially when aligned with behavior change theories. Understanding which PSD categories and principles work best for behavioral obesity interventions is critical and future interventions might be more effective if they were based on these specific PSD categories and principles.

Identifiants

pubmed: 32500161
doi: 10.1007/s10916-020-01591-w
pii: 10.1007/s10916-020-01591-w
doi:

Types de publication

Journal Article Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

128

Auteurs

Scott Sittig (S)

School of Computing, University of South Alabama, Mobile, AL, 36688, USA. sittig@southalabama.edu.

Aleise McGowan (A)

School of Computing, University of South Alabama, Mobile, AL, 36688, USA.

Sriram Iyengar (S)

Department of Medicine, University of Arizona, Tucson, AZ, 85721, USA.

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Classifications MeSH